Medicine, Faculty of
DSpace
UBCV
Trappenberg, Kai
Master of Science - MSc
University of British Columbia
Huntington’s disease (HD) is a neurodegenerative disorder that affects numerous brain functions, yet how altered sensory processing contributes to behavioural and learning deficits remains poorly understood. Previous wide-field mesoscale imaging and electrophysiological recordings from anesthetized HD model mice revealed that sensory stimulation induced widespread, and prolonged cortical activity in more brain regions compared to wildtype (WT) littermates. Further imaging of awake mice produced similar results, suggesting that HD disrupts the sensory processing networks of visual stimuli. Building on these findings, this project aimed to investigate the behavioural relevance of sensory spread by designing a custom Raspberry Pi-controlled two-alternative forced choice (2AFC) rig. Through multiple iterations, a reliable system was developed that enabled head-fixed zQ175 knock-in HD mice and WT controls crossed with Thy1-GCaMP6s mice, to perform a visual discrimination task while mesoscale calcium imaging recorded activity across layer 2/3 of the cortex. Mice that successfully licked the reward spout displayed decreased global cortical activity before the reward presentation, with WT mice showing more spatially localized suppression than HD mice. HD mice exhibited exaggerated cortical responses to visual stimuli and prolonged motor-related activity during licking. However, both HD and WT mice struggled to consistently associate single and double flash stimuli with the correct motor response, highlighting the challenge of linking altered cortical dynamics to behaviour. While technical challenges and limited sample sizes prevented definitive genotype-based comparisons, the custom behavioural system lays the groundwork for future studies on how sensory processing deficits contribute to cognitive impairments in HD. This work provides an important step toward understanding the interplay between cortical circuit dysfunction and behavioural outcomes in HD, offering a novel platform to investigate early sensory learning impairments.
Investigating the Neural Basis ofSensory Learning in a Mouse Model ofHuntington’s DiseaseInsights from Mesoscale MicroscopybyKai TrappenbergB.Sc., Dalhousie University, 2022A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Neuroscience)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April, 2025© Kai Trappenberg 2025The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the thesis entitled:Investigating the Neural Basis of Sensory Learning in a Mouse Model of Huntington’sDisease: Insights from Mesoscale Microscopysubmitted by Kai Trappenberg in partial fulfillment of the requirements forthe degree of Master of Sciencein NeurosscienceExamining Committee:Supervisor: Dr. Lynn Raymond, Professor, Psychiatry, University of British ColumbiaSupervisory Committee Member: Dr. Kurt Haas, Professor, Cellular and Physiological Sci-ences, University of British ColumbiaDr. Manu Madhav, Assistant Professor, Biomedical Engineering, University of British ColumbiaAdditional Supervisory Committee Members:Supervisory Committee Member: Dr. Stan Floresco, Professor, Psychology, University ofBritish ColumbiaiiAbstractHuntington’s disease (HD) is a neurodegenerative disorder that affects numerous brain func-tions, yet how altered sensory processing contributes to behavioural and learning deficitsremains poorly understood. Previous wide-field mesoscale imaging and electrophysiologi-cal recordings from anesthetized HD model mice revealed that sensory stimulation inducedwidespread, and prolonged cortical activity in more brain regions compared to wildtype(WT) littermates. Further imaging of awake mice produced similar results, suggesting thatHD disrupts the sensory processing networks of visual stimuli. Building on these findings,this project aimed to investigate the behavioural relevance of sensory spread by designing acustom Raspberry Pi-controlled two-alternative forced choice (2AFC) rig. Through multipleiterations, a reliable system was developed that enabled head-fixed zQ175 knock-in HD miceand WT controls crossed with Thy1-GCaMP6s mice, to perform a visual discrimination taskwhile mesoscale calcium imaging recorded activity across layer 2/3 of the cortex. Mice thatsuccessfully licked the reward spout displayed decreased global cortical activity before thereward presentation, with WT mice showing more spatially localized suppression than HDmice. HD mice exhibited exaggerated cortical responses to visual stimuli and prolongedmotor-related activity during licking. However, both HD and WT mice struggled to consis-tently associate single and double flash stimuli with the correct motor response, highlightingthe challenge of linking altered cortical dynamics to behaviour. While technical challengesand limited sample sizes prevented definitive genotype-based comparisons, the custom be-havioural system lays the groundwork for future studies on how sensory processing deficitscontribute to cognitive impairments in HD. This work provides an important step towardunderstanding the interplay between cortical circuit dysfunction and behavioural outcomesin HD, offering a novel platform to investigate early sensory learning impairments.iiLay SummaryHuntington’s disease (HD) is a genetic brain disorder that affects movement, thinking,and learning. This study explored how HD affects the brain’s ability to process sensoryinformation. Mice were trained to recognize flashes of light and lick a water spout fora reward using a custom-built experimental setup. During the task, brain activity wasmonitored using imaging techniques that track large-scale brain signals. HD mice showedstronger and longer-lasting brain activity in response to light flashes compared to healthymice. Although the exploratory and technical development nature limited the scope ofthe study when comparing diseased to healthy mice, the custom-built system provides avaluable tool for future research into how sensory processing is affected in HD, offeringinsight into early changes in the disease.iiiPrefaceAll experiments in this thesis were carried out in accordance with the Canadian Council forAnimal Care guidelines and approved by the University of British Columbia animal carecommittee (protocols A23-0083 and A21-0276).Generative AI in the form of ChatGPT was used to debug code and improve sentence claritythroughout this project.ivTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiList of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Huntington’s Disease (HD) . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 Somatic expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.2 Neuroanatomy of Huntington’s disease . . . . . . . . . . . . . . . . 31.1.3 Sensorimotor deficits in patients . . . . . . . . . . . . . . . . . . . . 51.1.4 Sensorimotor integration in mice . . . . . . . . . . . . . . . . . . . . 71.1.5 Mouse models of HD . . . . . . . . . . . . . . . . . . . . . . . . . . 71.2 Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.2.1 Sensory learning and associative learning . . . . . . . . . . . . . . . 101.2.2 Discrimination learning . . . . . . . . . . . . . . . . . . . . . . . . . 101.3 Mesoscale imaging and cortical dynamics . . . . . . . . . . . . . . . . . . . 111.4 Research context and objectives . . . . . . . . . . . . . . . . . . . . . . . . 131.4.1 Sensory spread . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131.4.2 Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.1 Animal model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2 Cranial window surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3 Behavioural paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3.1 Mouse habituation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3.2 Behavioural stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.4 Rig parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21vTable of Contents2.4.1 Custom parts: 3D prints, drivers, and headbars . . . . . . . . . . . 222.4.2 Raspberry Pi components, optics and lights . . . . . . . . . . . . . 222.4.3 Thorlabs components and rig assembly . . . . . . . . . . . . . . . . 242.5 Imaging setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.6 Custom scripts and pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . 252.6.1 Preprocessing step . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.6.2 Multi-day heatmaps and brain panels . . . . . . . . . . . . . . . . . 263 Technical Development and Behavioural Testing . . . . . . . . . . . . . . 273.1 Version 1.0 RPi-controlled Two-Alternative Forced Choice Task Rig (RPI-2AFC-Rig v1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.1.1 Behavioural testing of RPI-2AFC-Rig v1 (mouse cohort 1) . . . . . 303.2 Version 2.0: Motor adjustments (RPI-2AFC-Rig v2) . . . . . . . . . . . . 313.2.1 Behavioural testing of RPI-2AFC-Rig v2 (mouse cohort 2) . . . . . 323.3 Version 3.0: Major overhaul (RPI-2AFC-Rig v3) . . . . . . . . . . . . . . . 323.3.1 New light . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.3.2 RPI breadboard changes . . . . . . . . . . . . . . . . . . . . . . . . 343.3.3 Behavioural testing of RPI-2AFC-Rig v3: New stage, extended stim-ulus, and sidebias correction (mouse cohort 3.1 and cohort 3.2) . . . 363.4 Version 4.0: Tone start cue, and new headbars/cranial windows (RPI-2AFC-Rig v4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.4.1 Behavioural testing of RPI-2AFC-Rig v4 (mouse cohort 4): Paradigmchanges and stimulus adjustments. . . . . . . . . . . . . . . . . . . 403.5 Concluding insights: Final refinements and optimizations of the RPI-2AFC-Rig . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 Mesoscale Calcium Dynamics in Cortical Layer 2/3 During Behaviour 444.1 Primary visual cortex activation requires visual stimuli of duration greaterthan 30-ms (cohort 3.2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.2 Cortical activity differences between successful lick and no-lick trials . . . . 474.2.1 Successful lick trials elicit a large motor cortex response compared tono-lick trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484.2.2 Increased cortical activity preceding spout movement in no-lick versussuccessful lick condition . . . . . . . . . . . . . . . . . . . . . . . . . 494.3 Genotypic differences between WT and HD mice . . . . . . . . . . . . . . . 524.3.1 Visual responses in HD have a higher peak compared to WT . . . 534.3.2 Sustained motor cortex activity following spout presentation in HDmice compared to gradual decrease in WT . . . . . . . . . . . . . . 534.4 Exploratory insights into cortical dynamics in WT and HD mice . . . . . . 545 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565.1 Results discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565.1.1 Technical refinements and their impact on data quality . . . . . . . 565.1.2 Sensory processing and visual cortex activation . . . . . . . . . . . 575.1.3 Motor cortex activity and task engagement . . . . . . . . . . . . . . 58viTable of Contents5.1.4 Pre-stimulus activity and attentional engagement . . . . . . . . . . 585.2 Addressing the research question . . . . . . . . . . . . . . . . . . . . . . . . 595.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595.3.1 Sex differences and small sample size . . . . . . . . . . . . . . . . . 595.3.2 Cortical imaging limitations . . . . . . . . . . . . . . . . . . . . . . 605.3.3 Stimulus artifact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 615.4 Ultimate goal and rig changes . . . . . . . . . . . . . . . . . . . . . . . . . 625.4.1 Ultimate goal for the project . . . . . . . . . . . . . . . . . . . . . . 625.4.2 Future rig changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625.5 Partially complete future projects . . . . . . . . . . . . . . . . . . . . . . . 635.5.1 Machine learning to deconvolute mesoscale brain activity patterns . 635.5.2 Two-photon visual cortex retinotopic mapping . . . . . . . . . . . 645.5.3 Motion energy and cortical signal comparison . . . . . . . . . . . . 655.5.4 Spatiotemporal analysis . . . . . . . . . . . . . . . . . . . . . . . . . 655.6 Future projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675.7 Concluding remarks and implications . . . . . . . . . . . . . . . . . . . . . 68Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68AppendixA Methods: Mouse cranial window surgery SOP and driver schematic . 87B Supplementary Analysis Figures . . . . . . . . . . . . . . . . . . . . . . . . 90viiList of Figures1.1 Sensory spread phenotype . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.1 1st Iteration of behavioural stages . . . . . . . . . . . . . . . . . . . . . . . 192.2 1st Iteration of behavioural stage 3 . . . . . . . . . . . . . . . . . . . . . . . 212.3 Version 1.0 of the RPi controlled rig . . . . . . . . . . . . . . . . . . . . . . 243.1 Version 1.0 Building progression . . . . . . . . . . . . . . . . . . . . . . . . 283.2 Verion 1.0 RPi breadboard circuit diagram . . . . . . . . . . . . . . . . . . 293.3 Version 1.0, Cohort 1 performing stage 1 behavioural stage . . . . . . . . . 303.4 Version 2.0 RPi rig design, and cohort 2 performing behavioural stages . . . 313.5 Version 3.0 RPi rig design and 2nd iteration of behavioural stages . . . . . 333.6 Version 3.0 RPi breadboard circuit circuit diagram . . . . . . . . . . . . . . 353.7 Version 3.0, Cohort 3.1 performing behavioural stages. . . . . . . . . . . . . 373.8 Version 3.0, Cohort 3.2 performing behavioural stages . . . . . . . . . . . . 383.9 Version 4.0 New cranial windows, 3rd iteration of behavioural stages . . . . 403.10 Version 4.0 RPi rig design, and cohort 4 performing behavioural stages . . . 424.1 30ms light stimuli vs 60ms light stimuli (Rig 3.0, Cohort 3.2) . . . . . . . . 464.2 Statistical analysis of peak activity post-light-stimuli (Rig 3.0, Cohort 3.2) . 474.3 Brain panels of all single flash conditions of Cohort 4 (120ms light stimuli) . 484.4 Allen brain atlas regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.5 Brain region activity timecourse heatmaps across the trial (Rig 4.0, Cohort 4) 504.6 Brain region segmented timecourse traces (Rig 4.0, Cohort 4) . . . . . . . . 514.7 Brain region peak activity analysis comparing lick vs no-lick (Rig 4.0, Cohort4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524.8 Brain region peak activity analysis comparing WT and HD (Rig 4.0, Cohort4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545.1 Motion energy artifacts from behavioural camera (Rig 4.0, Cohort 4) . . . . 66A.1 Custom dual-power LED driver schematic . . . . . . . . . . . . . . . . . . . 89B.1 Double flash brain panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91B.2 Left hemisphere, single flash heatmaps . . . . . . . . . . . . . . . . . . . . . 91B.3 Left hemisphere, double flash heatmaps of brain region activity . . . . . . . 92B.4 Right hemisphere, double flash heatmaps of brain region activity . . . . . . 93B.5 All conditions, single flash peak analysis . . . . . . . . . . . . . . . . . . . . 94B.6 All conditions, double flash peak analysis . . . . . . . . . . . . . . . . . . . 95viiiList of AbbreviationsAbbreviation Definition2AFC Two-alternative forced choiceBAC Bacterial artificial chromosomeBACHD Transgenic HD mouse modelC57BL/6 Inbred strain of laboratory mouseCAG Cytosine-adenine-guanine repeatCAG140 Knock-in HD mouse modelCCD Charge-coupled device sensorCFF Critical flicker fusion frequencyCMOS Complementary metal oxide semiconductor image sensorCNN Convolutional neural networkCR Conditioned responseCS Conditioned stimulusD1R Dopamine D1 receptorDINO A new self supervised vision transformer model by Facebook AIFVB/N Inbred strain of laboratory mouseGABA Gamma-amino-butyric acidGCaMP6s slow genetically encoded calcium indicatorGCaMP7f and GCaMP8f Fast genetically encoded calcium indicatorGPe Globus pallidus externalGPi Globus pallidus internalGPIO General purpose input/outputGWAS Genome wide associationHD Huntington’s diseaseHdhQ111 Knock-in HD mouse modelHTT Huntingtin geneI2C Inter-integrated circuit, a communication protocolID Inner diameterIPSC Induced pluripotent stem cellIR InfraredJND Just noticeable differenceL2/3 Cortical layer 2/3M1 Primary motor cortex (also known as Mop)M2 Secondary motor cortex (also known as Mos)mHTT Mutatnt huntingtin proteinMop Primary motor cortex (also known as M1)Mos Secondary motor cortex (also known as M2)ixList of AbbreviationsMPR 121 12-channel capacitive touch sensor breakout boardMRI Magnetic resonance imagingMSH3 MutS Homolog 3, a DNA mismatch repair proteinMSN Medium spiny neuron (also known as SPN)NMDA receptor N-methyl-D-aspartate, a glutamate receptorOD Outer diameterPCA Principle component analysisPCR Polymerase chain reactionPM Posterio-medial association areapolyQ PolyglutamineR6/1 Transgenic HD mouse modelR6/2 Transgenic HD mouse modelRCaMP Red genetically encoded calcium indicatorRD RetrosplenialRL Rostrolateral association cortexRPi Raspberry PiRpi-2AFC-rig Raspberry controlled two forced choice task rigS1 Primary somatosensory cortexSCL Serial clock lineSDA Serial data lineSN Substantia nigraSPN Spiny projection neuron (also known as MSN)TSNE t-distributed Stochastic Neighbor EmbeddingTWAS TranscriptomeUR Unconditioned responseUS Unconditioned stimulusUV UltravioletV1 Primary visual cortex (also known as VISp)ViT Vision transformer modelVSDI Voltage sensitive dyeWT WildtypeYAC Yeast artificial chromosomeYAC128 Transgenic HD mouse modelzQ175 Knock-in HD mouse model∆F/F Change in fluorescence over the baseline fluorescenceØ DiameterxAcknowledgementsMy time in the Raymond lab has been an unforgettable journey and I am very grateful forthe support I received along the way. Huge thanks to Dr. Lynn Raymond for her thoughtfulmentorship, endless knowledge, and genuine care — her insights kept me inspired. Thisproject would not have happened without Daniel Ramandi, who taught me everythingfrom rig building to coding, always with patience and plenty of laughs. Dr. Marja Sepersbrought infectious excitement and deep wisdom to every experiment, while Dr. JamesMackay’s unique ideas and kind nature always made discussions more interesting. LilyZhang’s hard work on surgeries, mouse colonies, and genotyping kept everything runningsmoothly. Lastly, thanks to Judy Cheng — her friendship, encouragement, and countlesschats made every day in the lab brighter.Thank you to my Supervisory All-Star Committee members, Dr. Kurt Haas and Dr.Stan Floresco, for their expertise and encouragement. Their knowledge and passion forscience have shown me what dedication and love for discovery can accomplish.Thank you to my first supervisor, Dr. Alan Fine, for introducing me to the world ofbrains and neurons. Those first fluorescent neurons he showed me sparked my fascinationwith neuroscience — a curiosity I have been chasing ever since.Shout out to my mice — for their fascinating, if occasionally frustrating, behaviours andbrain activity. Thank you for your tiny, yet large, contributions to science (and for bitingme only once).To my closest friends: Matt, Cole, Ryler, Alexis, Hannah, Liam, Mikey and Dardan.Your friendship has been my perfect side quest. To Ian for being my rock, to Stephen,Corbin, Helen, Jeremy and Regan, for always being up for an adventure to balance thethesis pandemonium.Endless gratitude to Lhyanne for being the perfect mix of chaotic energy and steadfastsupport. Your joy has been infectious, your care unwavering — I’m so lucky to have youby my side. Couldn’t imagine this journey without you.Lastly, I want to thank my parents for their endless support during both rainy and sunnydays, and for sparking my first love of science. My mom, Kanayo Trappenberg, with herboundless kindness and unwavering encouragement through every hyperfixation - from frogsto neuroscience - has always been my number one fan. My dad, Dr. Thomas Trappenberg,the genetic source of my goofiness and bad puns, has been an inspiration. His knowledge,work ethic, and sharp insights are something I can only hope to live up to - plus, no onebanters with me quite as well. Finally, to my sister, Nami, my built-in, lifelong best friend,thank you for always being by my side, no matter what.xiDedicationTo Ojii-chan, for all his biological fascinations and lasting inspiration.xiiChapter 1Introduction1.1 Huntington’s Disease (HD)Huntington’s disease is an inherited progressive neurodegenerative disorder caused by anexpanded cytosine-adenine-guanine (CAG) repeat in exon-1 of the huntingtin (HTT) gene,located on chromosome 4 (Sepers et al., 2024; The Huntington’s Disease CollaborativeResearch Group, 1993). This genetic mutation leads to the production of mutant hunt-ingtin (mHTT), a protein with an abnormal expansion of a polyglutamine (polyQ) tractnear its N-terminus. While the precise role of normal huntingtin protein remains unclear,its mutated and toxic form has been implicated in cellular dysfunctions such as impairedsynaptic signaling, disrupted intracellular transport, defective DNA repair mechanisms, andincreased susceptibility to neuronal death (Jurcau, 2022). Studies using mouse models andcells expressing HTT with expanded polyQ demonstrate disease pathology. Clinically, HDis best known for its progressive nature, marked by a triad of motor, cognitive, and psy-chiatric symptoms (Sepers et al., 2024). Among the most recognizable features are motordysfunctions, including involuntary dance-like movements known as chorea, and difficultywith fine motor coordination (Panzera et al., 2011). As there is currently no cure andminimal pathology-altering treatment for the terminal disease, it is vital to investigate theunderlying mechanistic cause of the disease.1.1.1 Somatic expansionHealthy individuals typically have less than 27 CAG repeats. Repeats between 27 and 35are considered an intermediate range and can sometimes lead to HD, depending on environ-mental and modulating genetic factors, and they are associated with germline instability11.1. Huntington’s Disease (HD)(Bates et al., 2015; Bunner & Rebec, 2016; Rubinsztein et al., 1996; Wexler et al., 2004).Expansions greater than 39 repeats result in full HD penetrance (within a typical lifespan),while repeat expansions greater than 50 can elicit juvenile-onset, although this thresholdcan be as high as 60, subject to modulating factors discussed below (Ashizawa et al., 1994;Ross et al., 2014; Telenius et al., 1994). The inverse relationship between CAG repeatlength and age of onset was the first indication of the importance of repeat length (Seperset al., 2022; The Huntington’s Disease Collaborative Research Group, 1993). Subsequentfindings revealed region-specific variability in CAG repeat lengths, with notable expansionsoccurring predominantly in basal ganglia structures (Telenius et al., 1994).More recently, the expansion of the CAG repeat in somatic DNA (coined somatic expan-sion) has been a major scientific focus (Monckton, 2021). This process involves the graduallengthening of CAG repeats within individual cells over time, which contributes to bothearlier disease onset and increased severity. The first evidence of somatic expansion in hu-mans was reported by Swami et al. (2009), who used PCR analysis to compare cortical DNAfrom HD individuals with early and late onset of motor symptoms. Further investigationconducted by Moss et al. (2017) in a genome-wide association study (GWAS) linked MSH3,a gene that encodes MSH3 DNA mismatch repair protein, with HD progression. This repairprotein has been implicated as a protein involved in the lengthening of CAG·CTG repeats.Additional evidence was demonstrated by Flower et al. (2019) with a transcriptome-wideassociation study (TWAS), linking increased MSH3 expression with disease progression.This offers an alternative therapeutic approach: rather than preventing the occurrence ofCAG repeat expansion greater than 30 repeats, the focus shifts to mitigating the toxicityand delaying the onset and severity of symptoms caused by somatic expansion over time.This is accomplished by targeting DNA mismatch repair proteins, whose altered expressionhas been linked to somatic expansion. However, recent attempts to modulate the func-tion of MSH3 have proven challenging. One study knocked down MSH3 protein by 50%,and noticed a reduction in somatic expansion; however, it did not affect the progression ofHD (Aldous et al., 2024). Another study using HD IPSC-derived neurons suggested that,21.1. Huntington’s Disease (HD)since the MSH3 knockdown effect is not absolute, residual MSH3 activity may continue topromote repeat expansions (Bunting et al., 2022). As such, alongside MSH3, PMS1 wasidentified as another target for intervention (McLean et al., 2024). PMS1 inactivation hasrecently shown promise when targeted with splice modulators, and has been shown to sig-nificantly reduce somatic expansion (McLean et al., 2024). In spite of the recent success inreducing somatic expansion and targeting of enzymes that modulate CAG repeats, theseare novel theories that require further study. This requirement is highlighted by the clinicalhalting of Ph2b VIBRANT-HD trial of branaplam, a drug involved in the reduction of so-matic expansion via modulating the HTT gene itself, citing safety concerns and off-targetsplice modulation (McLean et al., 2024; Novartis, 2022).1.1.2 Neuroanatomy of Huntington’s diseaseAs a neurodegenerative disease, HD leads to many neuroanatomical changes. The regionparticularly affected is the basal ganglia, a subcortical structure consisting of the globus pal-lidus, subthalamic nucleus, substantia nigra, nucleus accumbens, and striatum (consisting ofthe caudate and putamen nuclei). Many of the motor symptoms arise from the disruption tothe balance of the direct and indirect pathways of the basal ganglia. In healthy brains, thesetwo pathways work together to control motion by initiating desired movements and inhibit-ing unwanted ones. The direct pathway promotes movement, starting with glutamatergic(excitatory) motor cortex signals transmitted to the striatum’s gamma-amino-butyric acid(GABAergic) medium-sized spiny projection neurons (SPNs, also known as MSNs) thatexpress D1 (dopamine) receptors (D1R). These excited striatal D1R-MSNs subsequentlyinhibit the internal segment of the globus pallidus (GPi) and substantia nigra (SN). This,in turn, releases the inhibitory control (“disinhibition”) on the thalamus, resulting in exci-tatory signals being transmitted back to the motor cortex (Sepers et al., 2024; Young et al.,2024). The positive feedback loop further promotes motor cortex signals the basal ganglia,leading to sustained movement. In contrast, the indirect pathway inhibits motor activity(Young et al., 2024). The loop begins at the motor cortex, sending excitatory signals to31.1. Huntington’s Disease (HD)striatal MSNs expressing D2 dopamine receptors (D2-MSN). These neurons send inhibitoryGABAergic connections to the external segment of the globus pallidus (GPe). This inhibitsthe subthalamic nucleus which, in turn, excites the GPi and SN, contrary to the directpathway. Hence, this leads to an inhibited thalamus, further inhibiting the motor cortex,resulting in a stop to the feedback loop and suppressing movement.The disruption of the direct and indirect pathways in Huntington’s disease can be tracedto early neuroanatomical changes. One of the earliest and most pronounced neuroanatomi-cal changes in HD occurs in the caudate nucleus, a critical component of the striatum withinthe basal ganglia. Serial magnetic resonance imaging (MRI) scans of the caudate nucleus inindividuals with presymptomatic Huntington’s disease (HD) were compared to those withsymptomatic HD. The results revealed a significant reduction in caudate nucleus volumeas the disease advanced, with noticeable atrophy evident in the presymptomatic stages(Aylward et al., 2000). As HD advances, the degeneration extends to other basal gangliastructures, including the globus pallidus, subthalamic nucleus, and thalamus (Singh-Bainset al., 2016; Waldvogel et al., 2015).The progressive loss of GABA-ergic MSNs has been implicated as a primary driver ofneuroanatomical degeneration in HD. This loss contributes to the atrophy of key subcorticalstructures, including the putamen and caudate nucleus which project to basal ganglia path-ways, thereby causing major disruptions in direct and indirect pathways (Douaud et al.,2009; Sepers et al., 2024).Although the reason MSNs are particularly vulnerable to the mutant protein remainsunclear, evidence suggests that cell dysfunction, once attributed to expanded polyQ HTTexpression in MSNs, is not the only contributing factor (Ehrlich, 2012). Furthermore,evidence points to the need for cell-cell interactions, particularly with the cortical projectionneurons, for the malfunction of MSNs (Ehrlich, 2012; Gu et al., 2005, 2007; Ilieva et al.,2009).The MSN dysfunction and neuroanatomical changes highlights the importance and un-derstudied nature of cortical involvement in HD. Neuroimaging studies have demonstrated41.1. Huntington’s Disease (HD)significant cortical thinning and ventricular enlargement, both of which are hallmarks ofdisease progression (Burgold et al., 2019; Rosas et al., 2005; Ross & Tabrizi, 2011; Tabriziet al., 2009). The cortical changes, particularly in the frontal and parietal lobes, contributeto the cognitive and psychiatric symptoms seen in later stages, whereas changes to themotor cortex and cingulate cortex lead to some of the motor manifestations (E. H. Kimet al., 2014; Thu et al., 2010). The widespread neuronal degeneration, even outside thebasal ganglia, is accompanied by changes in white matter pathways. Of particular note arethe cortico-striatal and thalamocortical circuits, which are essential for coordinating motorand executive functions. Disruptions in these pathways contribute to the progressive mo-tor deficits and cognitive impairments observed in HD, as altered connectivity between thecortex, striatum, and thalamus leads to dysregulated movement control, impaired decision-making, and executive dysfunction (Blumenstock & Dudanova, 2020).1.1.3 Sensorimotor deficits in patientsThe usual progression of the disease affects both motor and cognitive domains. Motordeficits are among the most evident symptoms, beginning with subtle motor coordinationabnormalities, such as balance difficulties or fine motor tasks. These issues progressivelyworsen, manifesting as chorea with dystonia, ataxia, bradykinesia, and many other debil-itating symptoms, severely limiting patients’ ability to perform daily activities. Hence, aclinical diagnosis is primarily based on these motor symptoms.Alongside these motor challenges, most patients experience equally debilitating cognitivedeficits. These often begin with subtle impairments in executive functions, from problemswith memory, and the ability to acquire new skills, to more severe problems such as demen-tia (Peavy et al., 2010). Patients often score poorly on the Trail Making Test (O’Rourkeet al., 2011), which evaluates processing speed and cognitive flexibility; the Wisconsin CardSorting Test(Paulsen et al., 1995), which measures executive function and problem-solvingskills; and the Stroop Color-Word Test (Beglinger et al., 2005; Montoya et al., 2006), whichassesses selective attention and inhibition. Additionally, cognitive speed is compromised,51.1. Huntington’s Disease (HD)as the dysregulated neural circuits force patients to rely on more deliberate, conscious pro-cessing for tasks that were once automatic, thereby reducing overall processing efficiency(Paulsen, 2011). As a result, HD patients perform significantly worse on standard speededtapping tests compared to control subjects (Paulsen et al., 2008; Paulsen et al., 2006). How-ever, many of the cognitive manifestations of HD often precede notable motor symptoms,and thereby, also precede the clinical diagnosis (Paulsen, 2011; Peavy et al., 2010).Some evidence suggests that these cognitive difficulties may stem from impaired sensoryfeedback and its integration into motor and cognitive planning. A growing body of researchhas shown major sensory deficits in patients with HD, including auditory, tactile, andvisuospatial perception (Hayward et al., 1985; Labuschagne et al., 2016; Profant et al.,2017).These sensory processing deficits in HD may hinder the accurate perception and inter-pretation of external and internal signals, which are essential for adaptive learning and skillacquisition (Boecker et al., 1999; Smith et al., 2000). For instance, tasks requiring fine ad-justments based on tactile, proprioceptive, or visual feedback become increasingly difficult,limiting the patient’s ability to modify motor outputs and optimize performance (Purcellet al., 2019). These deficits may also contribute to broader cognitive impairments, as thebrain’s ability to use sensory information for learning and decision-making is compromised(Zhang et al., 2021). In essence, the inability to effectively process and integrate sensorysignals not only exacerbates motor dysfunction but may also underpin a component of thelearning and cognitive problems observed in HD (Purcell et al., 2019; Smith et al., 2000).This impaired feedback loop creates a significant barrier to skill acquisition and adaptivebehaviour, compounding the challenges patients face as the disease progresses (Smith etal., 2000). Understanding the role of sensory feedback in learning and cognitive functionhighlights a critical area for study.61.1. Huntington’s Disease (HD)1.1.4 Sensorimotor integration in miceSensorimotor integration refers to the process by which sensory inputs are translated intomotor outputs as a mouse interacts with its environment. Due to the ease of recordingneuronal activity and genetic tractability, the mouse cortex has been extensively studied inthis field (Jucker, 2010; Leinweber et al., 2017).The key brain regions involved in sensorimotor integration include the primary so-matosensory cortex (S1), primary visual cortex (V1), and primary and secondary motorcortices (M1 and M2) (Leinweber et al., 2017; Petrof et al., 2015). These areas workin concert to process sensory feedback and generate precise motor outputs. Cortical cir-cuits in mice, particularly the interconnections between S1, V1 and M1, have been exten-sively mapped using techniques such as two-photon calcium imaging (Vickers & McCormick,2024), optogenetics (W. Yang et al., 2018), and electrophysiology (Guo et al., 2020; Petrofet al., 2015).Previous research in the field has identified modalities for the integration of S1 with M1(Ayaz et al., 2019; Kinnischtzke et al., 2016; Mao et al., 2011). One study from the Petersonlab (Ferezou et al., 2007; Kyriakatos et al., 2017) utilized voltage-sensitive dye imaging toinvestigate the spatiotemporal dynamics of cortical activity in awake, behaving mice. Theyfound that sensory-evoked activity in S1 rapidly propagates to M1, suggesting a direct andfast communication pathway between these regions during sensorimotor processing.1.1.5 Mouse models of HDAnimal models are essential tools for studying biological phenomena, and mice are par-ticularly valuable due to their genetic tractability. This is especially true for HD research,following the discovery of the HTT mutation as the cause of the disease. This breakthroughenabled the development of mouse models to study HD pathology in vivo. Currently, thereare three main types of HD mouse models: N-terminal transgenic models, full-length mHTTtransgenic models, and knock-in models.N-terminal transgenic models were the first type of model developed with the R6 strain71.1. Huntington’s Disease (HD)which expresses exon 1 of the human HTT gene with expanded CAG repeats (Mangiarini etal., 1996). R6/1 mice have 116 repeats, while R6/2 mice have 144+, often increasing due tosomatic instability (Pouladi et al., 2013). Both models exhibit HD-like pathology, includingsynaptic changes, striatal degeneration, and widespread protein aggregates (Andre´ et al.,2010). Behaviourally, they show motor coordination difficulty, abnormalities in gait, andexhibit learning and discrimination impairments (Cayzac et al., 2011; Lione et al., 1999;Pallier et al., 2009). R6/2 mice progress more rapidly than the following models, with overtdeficits by 4–6 weeks and death by 13–15 weeks, resembling juvenile-onset HD (Pouladiet al., 2013).The second class of HD models are the transgenic models that incorporate the full-lengthhuman transgene (including all exons, introns and regulatory elements). These models arecreated using either yeast artificial chromosome (YAC) technology or bacterial artificialchromosome technology (BAC) (Pouladi et al., 2013). This allows for the expression offull-length mHTT, which includes all functional domains of the protein. Two commonlyused models in this category are YAC128 and BACHD. These models show limited somaticinstability and present as more slowly progressive compared to the R6 models. Thesemodels also exhibit motor and learning deficits, alongside striatal neuron loss. However,the transgenic models often lead to overexpression of mHTT which may not mimic thenatural disease progression in humans (Pouladi et al., 2010). Additionally, YAC128 modelsare known to go blind after 6 months, as they are on the FVB/N background strain insteadof C57BL/6, making sensory learning tasks difficult to test (Errijgers et al., 2007; Lab,2024; J. Yang et al., 2015).Knock-in models are among the most common and genetically accurate models of Hunt-ington’s disease (HD). These models are a knock-in of human HTT exon 1 with a CAGexpansion into the mouse locus (examples of this model type include the zQ175, CAG140,and HdhQ111 mice) (Pouladi et al., 2013). Knock-in models typically exhibit slower dis-ease progression and are considered the closest to the human HD genotype. They arethe most accurate because they have normal levels of total HTT, as heterozygotes possess81.2. Learningone wild-type and one mutant allele, mirroring the genetic makeup of humans with HD.They show deficits in motor coordination, learning, and behaviour but do not exhibit visualimpairments due to their background strain (C57BL/6) (Menalled et al., 2012). Somaticexpansion occurs in these models at later ages, contributing to their genotypic similarityto humans, unlike transgenic models that exhibit greater CAG repeat stability (Menalledet al., 2012; Pouladi et al., 2013). Although degeneration is observed in striatal and, to alesser extent, cortical neurons at advanced stages, much of the brain remains intact (Kayeet al., 2021). Despite this, knock-in mice display significant behavioural deficits, makingthem ideal for studying the early pathology and underlying circuit mechanisms driving HD(Kaye et al., 2021; Wheeler et al., 2007).1.2 LearningMice have been invaluable tools for investigating the neural mechanisms underlying learningand memory. Many behavioural paradigms have been developed and extensively used to as-certain different aspects of learning and memory. These tests range from freely moving tasksfor spatial memory such as a morris water maze, to tasks that assess associative learningand decision-making, such as go/no-go discrimination or operant conditioning paradigms.These tests allow researchers to probe various types of memory, including spatial, proce-dural, and associative memory (Bjerre & Palmer, 2020; Kohler et al., 2022; Sharma etal., 2010). Freely moving tasks like the Morris water maze evaluate a mouse’s ability tonavigate and remember spatial locations, providing insights into hippocampal-dependentlearning (Vorhees & Williams, 2006). In contrast, tasks such as go/no-go discriminationfocus on sensory processing and decision-making, highlighting the integration of corticalcircuits (Chen et al., 2013; Sachidhanandam et al., 2013).91.2. Learning1.2.1 Sensory learning and associative learningA classic example of learning in animals is Pavlovian or classical conditioning. In this formof learning, a sensory stimulus (referred to as the conditioned stimulus or CS) is repeat-edly presented alongside a reward/punishment (i.e. sucrose, or electric shock), known asan unconditioned stimulus or US. If presented alone, the US normally elicits a particularresponse (known as the unconditioned response or UR, i.e. salivation or startle reaction).Thereby, through repeated pairings, the CS alone becomes sufficient to elicit the UR, and ishenceforth known as a conditioned response (CR). This process demonstrates how associa-tions are formed between previously neutral stimuli and natural responses (Domjan, 2005;Lovibond & Westbrook, 2024).Operant learning, also known as operant conditioning, is a form of associative learningwhich involves the modification of behaviour based on the consequence of actions taken. Inthis paradigm, a positive reinforcement (reward) is used to encourage a particular repeatedaction. The action of learning is the gradual association between the action and reward(Follman et al., 2023; Staddon & Cerutti, 2003).Although some tasks use the pairing of sensory stimuli as a cue to perform a behaviouralaction, this hybrid learning is still often considered operant conditioning. This is becausethe critical distinction lies in the fact that the mice must actively choose and perform aspecific motor response (such as licking a spout, lever pull, button press, etc.) based on thesensory cue to obtain a reward. The reward is contingent on their behaviour, which is thehallmark of operant conditioning (Staddon & Cerutti, 2003; Timberlake, 1994).1.2.2 Discrimination learningDiscrimination learning is a form of associative operant learning in which animals dis-tinguish between two or more sensory stimuli (e.g., visual, auditory, or tactile cues) andassociate them with specific tasks or outcomes. Various discrimination paradigms exist,each employing distinct behavioural measures to evaluate learning success (Goltstein et al.,2018; Helmchen et al., 2018; J. Kim et al., 2020; King et al., 2015; Nomura et al., 2019). A101.3. Mesoscale imaging and cortical dynamicsfundamental goal of these tasks is to determine the smallest detectable difference betweenstimuli that an animal can reliably perceive—a threshold commonly referred to as the just-noticeable difference (JND). This threshold provides valuable insights into the sensitivity ofsensory processing and the mechanisms underlying perceptual learning (Maor et al., 2020).In mice, discrimination tasks such as two-alternative forced-choice (2AFC) paradigmsare commonly used to measure the JND, allowing researchers to determine perceptual limits(Long et al., 2015; Mayrhofer et al., 2013). One study specifically examined visual discrimi-nation capabilities, focusing on visual contrast sensitivity, which represents the threshold fordetecting changes between visible and invisible stimuli (Nomura et al., 2019). A key aspectof contrast sensitivity is its temporal component, known as temporal contrast resolution orcritical flicker-fusion frequency (CFF)—the frequency at which a flickering light becomesindistinguishable from a steady beam. This threshold defines the point at which two stimulino longer appear different. Using a 2AFC task, the study established that mice perceiveflickering light as continuous at a threshold of 14 Hz, marking their temporal flicker-fusionlimit (Nomura et al., 2019).1.3 Mesoscale imaging and cortical dynamicsMesoscale imaging techniques have significantly advanced our understanding of corticaldynamics by enabling the visualization of large-scale neuronal activity across the cortexduring various behaviours. These methods, such as voltage-sensitive dye (VSDI) imagingand wide-field calcium imaging, offer the ability to capture intricate spatiotemporal patternsof neuronal activation that underlie sensorimotor integration and cognitive functions acrossthe whole brain.In one such mesoscale study (Ferezou et al., 2007), researchers injected VSDI into theneocortex of mice and delivered brief whisker deflections. They observed that sensory re-sponses originated in the primary somatosensory barrel cortex before propagating to thewhisker motor cortex, demonstrating the widespread and rapid nature of sensorimotor in-111.3. Mesoscale imaging and cortical dynamicstegration. Their findings emphasize the large-scale activation of cortical networks involvedin processing sensory inputs and coordinating motor responses.Building upon this, a study by Marmor et al. (2023) employed wide-field calcium imagingto monitor cortex-wide activity as mice learned to use their whiskers to discriminate betweendifferent textures. The researchers discovered that trial history information was represented(decoded via a binary classifier) in the barrel cortex during stimulus presentation and thatthis encoding emerged simultaneously with learning. Additionally, trial history informationwas detected in the rostrolateral association cortex prior to stimulus presentation, suggestinga top-down influence on sensory processing areas. These results identify regional specificencoding of information and significance of propagating activity to sensory association areas.Additionally, they highlight the dynamic nature of cortical networks in integrating pastexperiences with current sensory inputs to facilitate learning.Further insights into cortical network dynamics have been gained through studies onneurological disorders and with techniques such as in vivo 2-photon calcium imaging. Onearticle by Donzis et al. (2020) assessed cortical neuronal ensemble activity in behaving miceand found that Huntington’s disease (HD) mutations disrupt normal cortical ensemble activ-ity. Notably, the correlation coefficients between pairs of neurons were generally decreasedin HD mouse models. These findings indicate that HD mutations can differentially affectcortical network dynamics based on disease progression and behavioural context.Finally, in the study ”Spatiotemporal refinement of signal flow through association cor-tex during learning”, Gilad and Helmchen (2020) employed wide-field calcium imagingto investigate how cortical activity patterns evolve as mice learn a whisker-based texturediscrimination task. Their findings revealed a two-phase refinement process in the spa-tiotemporal dynamics of cortical activity. During the early stages of learning, there waswidespread cortical activation in response to sensory stimuli. As learning progressed, thisactivity became more localized and temporally precise, particularly within the rostrolateralassociation cortex (RL). Additionally the posterio-medial (PM) and retrosplenial-dorsal(RD association areas showed a distinct suppression in activity between naive and expert121.4. Research context and objectivesmice. This may indicate the brain’s ability to optimize neural pathways for efficient process-ing through experience-dependent plasticity, leading to more focused and efficient corticalactivation patterns as tasks become well-learned. Therefore, if these circuits are disruptedin the case of HD, the refinement may not occur to the same degree.These studies highlight the dynamic nature of cortical activity and the integration ofsensory and motor information, underscoring the importance of mesoscale imaging in pro-viding a more comprehensive view of cortical processing. They also emphasize the criticalrole of association areas and the cortico-cortical pathways. Given that these pathways arereadily activated by sensory stimuli, they serve as valuable targets for investigating cir-cuit dysfunction in neurodegenerative disorders such as HD, and offer insights into howsensory-driven cortical responses are altered in disease states.1.4 Research context and objectives1.4.1 Sensory spreadPrevious work investigated how cortical sensory processing is altered in HD mouse models,particularly through mesoscale imaging and electrophysiology (Sepers et al., 2022; Wanget al., 2024). In a study by Sepers et al. (2022) it was revealed that HD mice—specificallyYAC128 and zQ175 models—exhibit an exaggerated cortical response to sensory stimuli,with a greater spread and longer duration of sensory-evoked cortical activity compared toWT mice. Using VSDI imaging, genetically encoded calcium indicator (GCaMP6s) imagingand local field potential (LFP) recordings, the study found that limb stimulation leads towider, more prolonged cortical activation, suggesting hyperexcitability in sensory processingcircuits. Electrophysiology in acute brain slice confirmed an elevated Excitatory - Inhibitoryratio in L2/3 sensory cortical pyramidal neurons for zQ175 compared to WT mice (Wang etal., 2024). This prolonged cortical activation in response to sensory stimuli was also observedduring auditory and visual stimulation. This phenomenon, termed ”sensory spread”, refersto the initial activation of the primary sensory cortex, followed by the propagation of this131.4. Research context and objectivesFigure 1.1: The basis of this project is the sensory spread phenotype from unpublished workin awake mice expressing GCaMP6s by Dr. James Mackay. (A) Increase of cortical spatialcortical activation from primary visual cortex to unassociated brain regions, showing thisphenotype occurs in in HD mice and is more prominent at older ages. (B) The temporalcomponent of spread showing the time course of the activity between WT and HD mice inyoung mice compared to (C) old mice, which shows a sustained response in HD mice, inolder ages.signal to association areas and seemingly unrelated cortical regions (Figure 1.1, adaptedfrom unpublished work by Dr. James Mackay). Sensory spread has been observed in otherstudies (B. A. Johnson & Frostig, 2016; Petersen et al., 2003).1.4.2 RationaleThe findings of Sepers et al. (2022) provide a crucial neurophysiological foundation forthe current study, which seeks to explore the behavioural relevance of this sensory spread,particularly in awake, behaving animals. While Sepers et al. (2022) focused on lightlyanesthetized mice to precisely measure sensory-driven cortical activation as a measure forHD circuit dysregulation, further evidence for sensory spread in HD mice comes from un-published data by Dr. James MacKay, who conducted experiments on 6- to 9-month-oldawake zQ175 and WT mice expressing GCaMP6s under the Thy1 promoter. His findingsmirrored those of Sepers et al. (2022), showing similar sensory spread patterns (Figure 1.1),141.4. Research context and objectiveswith cortical responses habituating more in WT mice than in HD mice over the courseof a trial (Figure 1.1). This suggests that sensory processing networks—particularly thoseinvolved in visual stimuli—may be disrupted in HD. Previous behavioural studies have alsoreported sesnory learning deficits in HD mice, as demonstrated in go/no-go discriminationtasks (Mazarakis et al., 2005; Oakeshott et al., 2013).Research questionThis project builds on previous work by examining how exaggerated sensory responsesin HD mice translate into perception and behaviour. Specifically, it aims to determinewhether there is a behavioural and cortical readout for a sensory cue-based learning taskin both HD and WT mice. We hypothesize that sensory spread has a behaviouralconsequence, such that the lack of precise cortical activation in HD mice mayinterfere with the accurate encoding of learned information, contributing todeficits in sensory discrimination and learning.Aims of this studyTo determine the behavioural significance of sensory spread, my first aim was to build arig and design a behavioural paradigm to assess the behavioural readout of sensory spreadwith simultaneous cortical imaging.Building on this, my second aim was to use the two-alternative forced-choice (2AFC)discrimination task from Aim 1 to analyze mesoscale cortical circuits and activity in rela-tion to behaviour, specifically how the visual stimulus (single or double flash of light) isprocessed and translated into the reward-based motor (licking for water reward) response.Furthermore, HD mice compared to WT mice were investigated for cortical processing dif-ferences.By addressing these questions, this study aimed to bridge the gap between corticalsensory processing and behavioural performance, offering insights into how sensory spreadimpacts task engagement, decision-making and discrimination learning in HD. As a future151.4. Research context and objectivesaim/goal, this rig and design can test the temporal flicker fusion point of discriminationbetween genotypes as a behavioural consequence for sensory spread.16Chapter 2Methods2.1 Animal modelzQ175 knock-in mice on a C57BL/6 background expressing GCaMP6s under a Thy1 pro-moter (Thy1-GCaMP6s zQ175dn) were used for this study. The Thy1 promoter allowedfor excitatory neuronal populations to be targeted with the fluorescence indicator. TheQ175 is a knock-in of approximately 160-190 CAG repeats. The transgenic thy1-GCaMP6szQ175dn mouse lines were bred in house with only GCaMP6s positive mice expressing ei-ther Q175 positive (HD) or Q175 negative (WT) used from this colony. Animal tissue wascollected through ear clipping at weaning. DNA extraction and polymerase chain reaction(PCR) analysis were used to determine the genotype of each mouse. Mice were group-housed from weaning to experimental endpoint. The mice were housed in Animal CareSystems OptiMICE cages with wood fiber bedding (NEPCO Beta Chip), a paper hut, andnesting material from birth until 7 days after cranial window surgery. Mice had free accessto food and water and were kept under a 12 hour light/dark cycle (lights on at 07:00, offat 19:00). 7 days after cranial window surgery the mice were transferred into a 12 hourreverse light cycle room (dark/light cycle) with lights on at 19:00 and off at 07:00. Thecages were switched to conventional “shoe-box” cages, also with wood fiber bedding, theiroriginal paper hut and nesting materials. Mice were held in the reverse light cycle room for10 days before they were handled and habituated to the experimental rig. All experimentswere carried out in accordance with the Canadian Council for Animal Care guidelines andapproved by the University of British Columbia animal care committee (protocols A23-0083and A21-0276).172.2. Cranial window surgery2.2 Cranial window surgeryIntact-skull cranial window surgery was conducted on the experimental mice. To do this,mice were placed under anesthesia, then an incision over the cortex was made and theskin retracted. A 1 cm x 1 cm glass coverslip was applied using Metabond clear dentalcement (Parkell; Product: C&B Metabond). Following this, a custom titanium headbarwas cemented on. The animal’s temperature was kept at 37°C via a feedback-controlledheating pad (WPI) for the duration of the surgery. Refer to the appendix (Mouse cranialwindow surgery SOP) for full surgical protocols.2.3 Behavioural paradigmMice were behaviourally tested in a custom built rig using parts from ThorlabsTM, AdafruitTM,LuxeonTM, as well as custom parts and 3D printed parts. These were assembled and changedas the behavioural paradigm was refined for our experimental needs. The extent of the be-havioural paradigm iterations and rig configurations will be explored in further detail inChapter 3. However, each rig fundamentally enabled mice to perform the same behaviouraltask and was built on the same base model.Headfixed mice were water restricted, in accordance with animal care protocols, tomotivate them to perform the given task. Up to 1ml of water was presented as a reward tothe mice. In the event mice did not perform well enough in the task to receive the full 1mlof water, the remaining water was provided at the end of the day, at least 2 hours after thetask was last performed.2.3.1 Mouse habituation7 days post-surgery, mice were transferred to a reverse light cycle room, so experimentstook place during their wake cycle. The mice were given 10 days of habituation in thisroom. Then the mice were handled for 5 days, to habituate to human contact. Followingthis they were habituated to the experimental rig for 5 days, starting with freely exploring182.3. Behavioural paradigmFigure 2.1: First iteration of the behavioural task stages to shape the behaviour of mice toconduct a visual discrimination task. This discrimination task will later be coined stage 3,due to changes outlined in Chapter 3. Mice progress from stage 1 up to stage 3. The firstiteration does not include stage 2the rig on day one. Each subsequent day the mice were head restricted in increasing 5minute incriments up to 20 minutes of head restriction. After every handling session withthe mice, they were given 5g of peanut butter. After 5 days of habituation, the mice werewater restricted to 1 ml a day for 5 day intervals and provided ad libitum water for theweekend. All experiments were carried out in accordance with the Canadian Council forAnimal Care guidelines and approved by the University of British Columbia animal carecommittee.2.3.2 Behavioural stagesAfter the habituation stages, mice underwent two stages (later three) of learning (Figure2.1). Their performance passing preset thresholds determined their progression from onestage to another.Behavioural stage 1In the first stage, mice were head-restricted and presented with a start cue; 4.2 seconds afterthis, they were presented with two spouts (left and right). If the mice licked either spout,the opposing spout would retract from the mouse, and would then receive water for the192.3. Behavioural paradigmremaining 2 seconds. 8 seconds later, the paradigm repeated for a total of 200 trials. Micewould move onto the next stage by successfully licking 75% of the trials for 2 consecutivedays.Behavioural stage 2The second stage of learning began to include an associative component, in which a greenLED light (single flash or double flash) was paired with the presentation of the waterspouts, as follows: 3 seconds after the start cue, the light flash(es) was presented, and 1.2seconds following the light, one of the two spouts were presented. Which spout (left orright) was presented was determined by the corresponding single flash for the left spout, ortwo flashes separated by 0.5 up to 1 second (double flash) paired with the right spout. Sincethere was only one spout presented, if the mouse licked the spout they would be rewarded.Hence this stage did not include any decision making between which spout to lick for water,and instead was intended to reinforce the pairing between the single flash with left spoutversus double flash with right spout. The water was given to the mouse for the remaining2 seconds that the spout was presented. This condition also involved 200 repeated trials.The mice would move onto the following stage if they hit 75% successful licks on both leftand right spout directions for 2 consecutive days.Behavioural stage 3: discrimination taskThe third stage is the discrimination task (Figure 2.2). Like in stage 2 of the task, micewere presented with one of two visual stimuli: a single or double flash of green light. Eachstimulus is paired with a specific spout direction, where a single flash is associated with theleft spout and a double flash with the right spout. The difference is that mice were nowrequired to distinguish between the two stimuli, as they were presented with both spouts(stage 2 only presents the “correct” spout alongside its stimulus pairing).Similar to each other stage, 3 seconds after activating the cameras the trial beginswith a start cue to alert the mice, followed 1.2s later by the presentation of one of the202.4. Rig partsFigure 2.2: First iteration of the discrimination task (now called stage 3) with a 0.02mssingle flash, and two 0.01ms single flash separated by a 0.5s inter-flash-interval.visual stimuli. After a 1.2s delay, both spouts are extended, and the mice must select thespout corresponding to the presented stimulus to receive a water reward. Only the correctresponse—licking the spout associated with the given stimulus—yeilded a water rewardfor the remaining 2 seconds, reinforcing the association. Incorrect responses result in theremoval of the spouts and no reward. After a 6 second intertrial period, this trial wouldrepeat up to 199 more times.2.4 Rig partsThe Raspberry Pi (RPi)-controlled Two-Alternative Forced Choice Task Rig was customdesigned and built for this project. The following parts were used in the development andassembly of the rig. A Raspberry Pi 4 model B Quad Core 64 bit with 4GB ram (upgradedlater to 8GB ram) ran the experiment using a custom python script. The Raspberry Pi(RPi) operated using a SanDisk Ultra 128GB microSD card to store both the data and theRPi operating system (OS). Notably, several other SanDisk microSD cards were tested butencountered technical issues, such as difficulties running the OS or reliably storing data. Thechosen microSD card was the only one that consistently performed without any problemsthroughout the experiments. Additionally, a transparent plastic tube was acquired to holdthe mice with dimensions 31mm inner diameter (ID), 35mm outer diameter (OD).212.4. Rig parts2.4.1 Custom parts: 3D prints, drivers, and headbarsCustom designed parts were printed on an Ender 3, 3D printer. This includes: the baseholding the tube (which in turn holds the mouse’s body in place), a infrared camera holder,servo motor holders, a mouse paw rest (with an insert for the vibrational buzzer), and abreadboard platform.12V dual power LED drivers were custom built by EnixLABs (Figure A.1). Theywere used to power the green LED lights for the stimulus, the infrared light, and the blueexcitation light for cortical recordings.Two variations of headbars were used. The first were laser cut by the UBC physicslaboratory out of stainless steel. These were the first version of headbars used, and alsohad custom laser cut headbar clamps that connected to ThorlabsTM posts. The secondversion of headbars were custom ordered from PCBWay and cut out of titanium. Thesealso included a custom headbar clamp cut out of stainless steel. This second iteration ofheadbars was designed in CAD by Dr. Federico Bolan˜os from Dr. Tim Murphy’s Lab atUBC, incorporating specific input tailored to the requirements of these experiments.2.4.2 Raspberry Pi components, optics and lightsAll Raspberry Pi parts were acquired from AdafruitTM. The Raspberry Pi was connectedto a Full Sized Premium Breadboard (830 Tie Points) via a GPIO Ribbon Cable and Cob-bler+ Breakout Cable. The breadboard contained a Standalone 5-Pad Capacitive TouchSensor Breakout and 8 Channel Darlington Driver Tranistor (Solenoid/Unipolar Stepper).One of the breadboard sides included a Breadboard-friendly 2.1mm DC barrel jack con-nected to a 12V DC 1000mA (1A) regulated switching power adapter, as well as a standardInfrared (IR) Raspberry Pi Camera Module 2 with an RPi 25mm, 5 megapixel telephotolens and 850nm LUXEONTM IR LED (on a Saber Z1, 10mm Square Base with 1050 mW@ 1000mA). The rig connected to two AdafruitTM Micro servo (TowerPro SG92R) viageneral purpose input/output (GPIO) pins. The vibration start cue was generated by anAdafruitTM Vibrating Mini Motor Disc attached to a GPIO pin.222.4. Rig partsThe light for visual stimulation was an AdafruitTM Diffused Green 5mm LED hot gluedto a tube with black tape to direct the light towards the desired direction. The corticalexcitation light was a Blue (470nm) LUXEON Rebel LED (mounted on a 20mm StarCoolBase with 70 lm @ 700mA). These were attached to custom drilled 1 inch diameter (Ø)optical posts with theropaste and encapsulated by a ThorlabsTM C-Mount Extension Tubewith two convex lenses, and a ThorlabsTM Adapter (Ø3 mm LLG to SM1) which connectsto a ThorlabsTM Liquid Light Guide (Ø3 mm Core, 340 - 800 nm).Raspberry pi components after upgrades in chapter 3.The breadboard for the final version included 4 STEMMA non-latching mini relays (de-faulting to an open or closed state until actively switched, allowing 5V RPi control signalsto toggle the 12V power requirement as needed by specific rig parts), each with 2mm 3-Pinmale header cables which connected to the GPIO outputs. Two of the relays were wired totwo large push-pull solenoids. The other two relays were wired to a water-valve solenoid,which was fed via a 1mm tube attached to a water bottle with a hole for the water tube.An MPR 121, 12-key Capacitive Touch Sensor Breakout was connected to SDA and SCLpins on the RPi.The improved lights for visual stimulation were two Green (530nm), LUXEONTM RebelColor LEDs (on a SABER2 Star Base with 102 lm @ 350mA), each attached by the samemeans as the excitation light (custom drilled Ø1” optical posts with theropaste and encap-sulated by a ThorlabsTM C-Mount Extension Tube with two convex lenses, ThorlabsTM Ø3mm Adapter (LLG to SM1) to connect to a ThorlabsTM Liquid Light Guide (Ø3 mm Core,340 - 800 nm).The tone was generated by an AdafruitTM STEMMA Piezo Driver Amp at 10 KHzfrequency connected to a GPIO pin on the RPi breadboard.232.5. Imaging setupFigure 2.3: Version 1.0 of the Raspberry Pi controlled rig. (A) Diagram of components.(B) This configurement included a pipe to direct the light into the right eye, as well as servomotors.2.4.3 Thorlabs components and rig assemblyRefer to Figure 2.3 for an image of the rig assembly. Most parts were attached to therig via Thorlabs components, this included Ø1/2” Optical post in varying lengths fromL = 1” to L = 6” (SS, 8-32 Setscrew, 1/4”-20 Tap). These posts were screwed into thebaseplate or held in place with a Ø1/2” Pedestal Post Holder (Spring-Loaded Hex-LockingThumbscrew, L=1.19”) and a Clamping Fork (1.75” Counterbored Slot, Universal). Themotors and lights were attached at 90 degree angles with a Right-Angle Clamp (for Ø1/2”Posts, 3/16” Hex) and Right-Angle Post Clamp (for Ø1/2” Posts, 1/4”-20 Stud and 3/16”Hex). Additionally the headbars were custom designed to screw into the Ø1/2” Opticalpost.2.5 Imaging setupA Monochromatic Pantera CCD Camera (Dalsa) with green emission filter (525/36nm) andfront-to-front Nikon lenses (f = 1.4, 35mm; f = 2) was used to acquire cortical calcium data.242.6. Custom scripts and pipeline2.6 Custom scripts and pipelineEach experiment (stages 1, 2 and 3) was run with a custom python script. Refer to Github(https://github.com/ktrap06/RPI rig) for further details on the code. These scripts gener-ated the behavioural dataset of success rates as a txt file. The txt files were imported intoa custom python script for further analysis and plotting. The datasets generated in thisproject are videos of calcium dynamics in layer 2/3 of the cortex (stored as tiff stacks in ap-proximately 70 000 depth x 128 height x 128 width). A python pipeline was coded to processthe cortical data (refer to open source Github link - github.com/ktrap06/corticalanalysis).2.6.1 Preprocessing stepThe preprocessing step begins with removing any dark frames from the beginning andend of the experiment, followed by the identification of the green visual stimuli used toelicit cortical network activity. Since this light creates an artifact in the cortical signal,it is removed in order to calculate a change in fluorescence over the baseline fluorescence(∆F/F). The removed frames are then interpolated using the signal before and after theartifact. Then the ∆F/F is calculated to normalize the signal to a 10 second moving averagethereby filtering out any hemodynamic or broader movement artifacts. The tiff stack is thenspatially smoothed with a 3 pixel gaussian blur, and temporally smoothed with a chebyshevtype 1 temporal filtering (high and low bandpass filter). Each session generates three outputdatasets. The first is a primary visual cortex region-of-interest signal. This is defined bythe highest peak within the most caudal 40 x 40 pixels of the window, within 5 frames ofthe visual stimulus artifact. A 10 x 10 pixel ROI is established and the mean signal for thisarea across all single and double trials is extracted as a functionally defined V1. The seconddataset is the whole brain mean of single and double trials. Finally the last set is eachindividual trial in a single dataset. This is the set which will be taken into the heatmapsand brain panel scripts for further analysis.252.6. Custom scripts and pipeline2.6.2 Multi-day heatmaps and brain panelsEach session was imported into a master script and subdivided based on HD or WT, singleor double flash, successful lick or no-lick, and by day. All data was z scored to normalizebetween mice. A mask was applied to each brain panel using regions from the standardisedAllen Brain Atlas. Further details are provided in Chapter 4 (Figure 4.4).26Chapter 3Technical Development andBehavioural TestingThis chapter discusses each iteration of the custom behavioural testing rig. After each ver-sion of the rig was built the behavioural paradigm was completed on the rig and assessedfor its functionality. Often there were additional iterations and changes on the behaviouralparadigm as well. The fundamental design of the behavioural testing and rig design re-mained the same, with many additions and subtractions as new data was collected. Thechapter includes Version 1.0 to Version 4.0 (final version) and cohort 1, cohort 2, cohort3.1, cohort 3.2, and cohort 4 of mice.3.1 Version 1.0 RPi-controlled Two-Alternative ForcedChoice Task Rig (RPI-2AFC-Rig v1)The first version (1.0) of the 2AFC task rig (Figure 3.1) began with a custom 3D printedstage to hold a transparent tube (31mm ID, 35mm OD). The tube holder was screwedinto a baseplate next to a Ø1/2” Pedestal Post Holder attached to the baseplate with a1.75” Clamping Fork from ThorlabsTM. A Ø1/2” Optical post (L=1”) was inserted intothe pedestal post holder with a custom-designed stainless steel head bar clamp (developedin house, and laser cut at the UBC Physics laboratories). Two additional Ø1/2” Opticalposts (L=3”) were attached in front of the tube opening, with a ThorlabsTM Right-Angleclamp holding a 4” optical post. At the end of this post a custom 3D printed motor holderwas attached, with its respective AdafruitTM 360° Micro servo. These servo motors held273.1. Version 1.0 RPi-controlled Two-Alternative Forced Choice Task Rig (RPI-2AFC-Rig v1)Figure 3.1: Version 1.0 of the rig and building progression. (A) It began with the firstheadfixing template, (B) which was replicated, and optical posts were added to hold RPicomponents. (D) The circuit wirings for the breadboard (shown clearly in Figure 3.2). Thisversion uses a regular “cobber plus” rather than the “t-cobbler” shown in Figure 3.2. (E)Completed version 1.0 with light tube (C) and servo motors.a needle with a small tube connected through a solenoid open/close switch to a bottle ofwater. A capacitive sensor (Figure 3.2) was soldered onto the needle to detect any touchesto the spout when mice licked for water. A diffused green LED (565nm wavelength, 2.2-2.5V at 20mA current, 40 mcd brightness) was connected to a black out tube directing thelight toward the right eye of the mouse (when headclamped into the rig). All componentswere controlled and connected to a Raspberry Pi (RPi) 4 Model B Quad Core 64-bit (4GBRAM).While all components of the rig ran on the standard 5V power supply from the RPi,the solenoid valves controlling the water were separated on a 12V system. This requireda transistor to convert RPi controls into the required voltage output (a relevant detail forlater iterations). Figure 3.2 outlines the wiring in this version 1.0 of the rig.283.1. Version 1.0 RPi-controlled Two-Alternative Forced Choice Task Rig (RPI-2AFC-Rig v1)Figure 3.2: Version 1.0 circuit diagram of the RPi breadboard. The circuit diagram showsa T cobbler for ease of viewing. The blue LED is the cortical excitation light, green LED isthe light stimulus. The rig has two solenoid valves (top right), and two servo motors wiredto a touch sensor (top left).293.1. Version 1.0 RPi-controlled Two-Alternative Forced Choice Task Rig (RPI-2AFC-Rig v1)Figure 3.3: Version 1.0 behavioural output from Cohort 1 (n=4, 2 female, 2 male withno sex differences) performing in version one of the RPI controlled rig. This experimentdemonstrated the need for mice to be handled and exposed to the rig with gradual head-fixing.3.1.1 Behavioural testing of RPI-2AFC-Rig v1 (mouse cohort 1)Mice underwent a stage 1 paradigm (Figure 3.3). 2 mice were female, the other 2 micewere male for this set of experiments. During this stage they began to lick the spouts forwater, however, the original design was for the opposing spout to move out of the way oncea particular direction was decided. This would prevent the mouse from being able to lickboth spouts and force a decision between the two spouts. Unfortunately, the speed andpower of the servo motors were insufficient to move the spout quickly enough before themouse licked both spouts. As a result, adjustments to the experimental rig were necessary,as outlined in the following section.Furthermore, as shown in Figure 3.3, mice required 12 days in Stage 1 before theybegan licking the spouts for water, seemingly not recognizing that the water was dispensedthrough the spouts, which were controlled by capacitive sensors, until several days hadpassed. This delay prompted further adjustments to the behavioural design to expedite the303.2. Version 2.0: Motor adjustments (RPI-2AFC-Rig v2)Figure 3.4: Behavioural output from Cohort 2 (n=3, all male) performing in version 2.0of the RPI controlled rig. (A) Diagram of spouts and right-side visual stimuli. (B) Newsolenoid motors, highlighted in yellow are notably larger than the previous servo motors.Additionally, there is a light guide, highlighted in red. (C) Rapidly learned to lick thespouts in stage 1, likely due to the two week-long handling and headfixing exposure whichhabituated the mice to the rig for this cohort. (D) Extreme bias against licking the rightspout in the discrimination task (later called stage 3).learning process and help the mice identify the water source more efficiently.3.2 Version 2.0: Motor adjustments (RPI-2AFC-Rig v2)Version 2.0 of the experimental rig made adjustments to the power by replacing the servomotors with a 12V solenoid-based push-pull motor (Figure 3.4B), using an electromagnet(requiring 250mA to charge up). Due to the increased size of the motors, additional holdingequipment was incorporated to provide adequate support. This included 3 new optical posts(L=3, L=4, and L=5), each equipped with a right-angle clamp giving the motors the abilityto adjust their distance to the mouth of the mouse. This allowed for the spouts to be closerto the mouse at the beginning of the experiments, to teach them water comes out of thespouts. Then the spouts could be moved further away to promote deliberate licking forlater experimental trials in assessing learning. The new motor was powered on the same12V control as the solenoids that controlled the water valve. Additionally, a behaviouralinfrared camera v2 from AdafruitTM was added to collect video footage during the task.In doing so the RPi was upgraded to an 8GB RAM to avoid any memory issues. The newchanges are shown in Figure 3.4A and 3.4B.313.3. Version 3.0: Major overhaul (RPI-2AFC-Rig v3)3.2.1 Behavioural testing of RPI-2AFC-Rig v2 (mouse cohort 2)Additionally, due to the length of time the mice took to begin licking the spouts in version1.0 of the behavioural experiments, a week of handling was incorporated, followed by a weekof exposure to the rig, and another week of headfixing in 5-minute increasing incrementspre-stage 1. This resulted in all 3 male mice successfully surpassing the stage 1, 75% lickthreshold for two consecutive days within 5 days (Figure 3.4C). The mice were then movedonto the discrimination paradigm (now called stage 3); during this stage (as outlined inChapter 2: Methods), mice are presented with both spouts and must make a left or rightlick choice based on a single or double flash of light. In this stage mice exhibited an extremeside bias (Figure 3.4D). All the mice reached a 100% success rate in the left spout directionbut a 0% success rate in the right spout direction. This meant that the mice were onlylicking left, leading to skewed results. Based on these results it was hypothesized that sincethe light stimuli were being presented from the right direction, it was making that sideaversive. Additionally, since there was a tube directing the light to the mouse’s right eye,it was partially obstructing the right-side view of the spout compared to the left spout.3.3 Version 3.0: Major overhaul (RPI-2AFC-Rig v3)Many changes were made to the light system, breadboard, capacitive sensor, and be-havioural design. Additionally, many custom code changes were made in this version (notshown, but appear in the GitHub documentation). Version 3.0 of the rig is shown in Figure3.5A and 3.5B.3.3.1 New lightThe lack of mice making the association between the light and spout direction meant manychanges were made to the rig. This began with the addition of new LuxeonTM 530nm (102lm at 350 mA LED lights). This addressed the worry that the standard RPi LED lightswere not salient enough to cause a pupil dilation response from the mice. These new lights323.3. Version 3.0: Major overhaul (RPI-2AFC-Rig v3)Figure 3.5: Version 3.0 of the rig. (A) Diagram of the components (excluding the circuitboard wiring shown in Figure 3.6). Two light stims on both eyes, with two solenoid motors,each with their respective water spouts and solenoid valves. Complete with a headfixing barand cameras. (B) Representative image of version 3.0 with the breadboard that containsnew non-latching relays and a new capacitive sensor. (C) Updated behavioural shapingparadigm, this includes the addition of an intermediate “stage 2” and addition of stimuluslights into both eyes.333.3. Version 3.0: Major overhaul (RPI-2AFC-Rig v3)were substantially brighter than RPi LEDS and required a custom driver on a 12V controlbut allowed for finetuned brightness scalability. The additional brightness of the light alsoallowed for the removal of the tube that guided the light towards the eye, thereby leavingthe eyes unobstructed, which I postulated could mitigate rig-based side bias. With the aidof ThorlabsTM liquid light guide, and concave lenses for light focusing, the light could befocused onto the mouse’s eye from a distance. The final change to the light was the additionof light stimuli presented from both directions, this was also to prevent any side bias dueto the direction of the stimuli.3.3.2 RPI breadboard changesOne significant challenge introduced with the new motors in the RPI-2AFC-Rig v2 (ver-sion 2.0) and the 12V control system was the occurrence of technical malfunctions duringsome trials. Specifically, the spouts occasionally misfired, extending during periods whenthey were intended to remain retracted. Additionally, the spouts exhibited erratic, rapidtwitching movements, intermittently moving in and out. These unexpected glitches not onlydisrupted the trials but also startled the mice, potentially impacting their behaviour withinthe rig. After numerous unsuccessful attempts to resolve the issue, it was observed thatthe 5-pin capacitive sensor consistently remained active during these technical malfunctions.This observation suggested that the transistor, responsible for converting the 3V GPIO RPisignals to the 12V motors, was inadvertently allowing unintended electrical signals to passthrough. Therefore, the transistors were replaced by Adafruit™ STEMMA non-latchingmini mechanical relays (Figure 3.6), which allow the 12V to be entirely separate from the3V control by using a low-voltage signal to toggle a high-voltage circuit, ensuring completeelectrical isolation between the two systems. The solenoids controlling the water valve werealso switched to these non-latching relays.The second improvement to the system was the replacement of the stand-alone 5-padcapacitive touch sensors with the MPR121 capacitive touch sensor (Figure 3.6). Unlikestand-alone sensors, which have basic functionality, the MPR121 has more advanced capa-343.3. Version 3.0: Major overhaul (RPI-2AFC-Rig v3)Figure 3.6: Circuit diagram of version 3.0 of the rig. The solenoid valve (top right), solenoidmotors and wired touch sensor (top left) are controlled via non-latching relays. This versionincludes a, infrared LED for the behavioural camera, blue LED for cortical excitation andtwo green LEDs for light stimulation on both eyes.353.3. Version 3.0: Major overhaul (RPI-2AFC-Rig v3)bilities, such as customizable touch and release thresholds. This allowed for more precisedetection of spout touches, minimizing false activations and improving overall reliability.This is due to the I2C communication protocols the MPR121 uses, which reduces the num-ber of GPIO pins required, thereby allowing for the sensitivity control that the stand-alonesensors could not provide.3.3.3 Behavioural testing of RPI-2AFC-Rig v3: New stage, extendedstimulus, and sidebias correction (mouse cohort 3.1 and cohort3.2)As the previous set of experiments in version 2.0 suggested that the mice had not successfullyformed the intended associations, changes to the behavioural series of tests were made.Particularly, to reinforce the association between a single flash and a left lick, and a doubleflash with a right lick, an additional intermediate stage, coined stage 2 (Figure 3.5C), wasintroduced between stage 1 (habituation, where mice learn to obtain water from the spouts)and stage 3 (decision-making based on stimuli). This second stage removed the decision-making component and presented the stimuli together with the correct spout direction,reinforcing the desired associations. Mice were required to lick 75% of the trials correctlyon left and right spouts for 2 consecutive days. This additional stage also had the intentionof preventing a case where the mice only licks one side, and encouraged licks on both spouts.Mouse cohort 3.1: New stage and rig version 3.0The revised rig and behavioural design were tested on two cohorts of mice (named cohort3.1 and cohort 3.2, respectively and all males). Cohort 3.1 aimed to determine whether asystematic side bias had been eliminated (Figure 3.7). This appeared to be successful, asall mice exhibited a balanced degree of left and right licking during stage 2 (Figure 3.7B).This behaviour carried over into stage 3 (Figure 3.7C), where mice displayed significantday-to-day variability, showing a strong preference for a particular side on any given day.However, the preferred side varied randomly between mice and across days, suggesting that363.3. Version 3.0: Major overhaul (RPI-2AFC-Rig v3)Figure 3.7: Behavioural output of Cohort 3.1 (n=3, all male) in version 3.0 of the rig.(A) Stage 1 all mice progressed in a week to stage 2. (B) All mice exhibited a balanceddegree of left and right licking during stage 2, surpassing the threshold in on average, aweek. (C) Stage 3. Although mice showed significant day-to-day variability, showing astrong preference for a particular side on a given day, the side preference varied betweenmice and across days. As such the average across showed a stable lick rate of around50%. This displayed the removal of any rig-based sidebias (D) Representative individualmouse showing the variable sidebias per day, and each mouse does not systematically havea sidebias across days.no systematic side bias was introduced. Despite this improvement, the mice showed limitedsuccess in forming the intended associations.Mouse cohort 3.2: Sidebias correction and extended stimulus lengthAs a result, the experiments were paused, and a new cohort (cohort 3.2) was tested using amodified light stimulus duration while maintaining version 3.0 of the rig. This is because the20ms single flash and two 10ms double flash (with 0.5s interval) was suggested to be too shortfor the mice to register it as relevant information (S. Floresco, personal communication,October 11, 2023; Martin et al., 2021). The second consequence of a short flash duration isthat brain imaging did not show much activity in the primary visual cortex (this is exploredin further detail in Chapter 4). As such, the duration of the flash was extended to two 30-msflashes and one 60-ms flash.The second modification introduced was a side bias correction for stage 3, aimed ataddressing the issue that mice could achieve a 50% reward rate by consistently licking inonly one direction, which may have provided enough water to satiate them. The correctionmechanism was implemented based on the method described by Goldstein et al. (2018).This approach used a random number generator that produced values between -1 and 1.373.3. Version 3.0: Major overhaul (RPI-2AFC-Rig v3)Figure 3.8: Cohort 3.2 (n=3, all male) (the second cohort on version 3.0 of the RPIrig), since cohort 3.1 established systematic side bias was removed, the behavioural outputfocused on genotypic groups. (A) Stage 1, HD mice (n = 2) required longer on averageto learn to lick the spouts compared to WT (n = 2). (B) Stage 2, both groups rapidlyprogressed through this stage. (C) Stage 3, extended for 20 days without much improvementin their discrimination capabilities for either HD or WT condition. In this condition thereis a sidebias correction, to encourage licking the opposing spout. This was so mice were notoverly trained in one particular spout directionA threshold value, denoted as ”tb”, was dynamically adjusted based on the outcomes ofthe last 10 trials. Initially set to 0, the threshold determined which spout was presentedin the subsequent trial. If the random number exceeded the threshold, a single flash waspresented (left spout lick was rewarded); if the random number was below the threshold, adouble flash was presented (right spout lick was rewarded). For example, if a mouse showeda strong preference for licking the left spout, the threshold increased (e.g., from 0 to 0.2),making it more likely for random numbers to fall below the threshold and, consequently,for the right spout to be presented. The goal for this correction was to encourage mice toovercome trial-based side bias.The mice in this set of experiments were all male (n=4) (Figure 3.8). Two mice suc-cessfully moved to stage 2 as early as day 3 (Figure 3.8A), while two other mice took 10days. Once in stage 2 all mice passed the threshold within a week without difficulty (Figure3.8B). Issues emerged during stage 3, as the mice showed no improvement in their abilityto choose between the spouts based on the stimulus, even after 20 days. During stage 3(Figure 3.8C) of these experiments, the mice maintained a success rate of approximately30%. This low performance was attributed to the side bias correction, which introduced an383.4. Version 4.0: Tone start cue, and new headbars/cranial windows (RPI-2AFC-Rig v4)unequal distribution of stimuli. Instead of an even 50% left and 50% right stimulus presen-tation, the correction mechanism adjusted the distribution based on the mice’s behaviour.For instance, if a mouse predominantly licked the left spout, more right-spout stimuli werepresented. This led to frequent errors, lowering the overall success rate as the mice en-countered more right-spout trials. Additionally, as the experiments progressed, the micefailed to focus on the visual stimuli and instead adopted a strategy of alternating lickingbehaviour. They would persistently lick the left spout until only right-spout stimuli werepresented, at which point they would switch to licking exclusively in that direction. Thisbehaviour further hindered their ability to learn the intended associations.3.4 Version 4.0: Tone start cue, and new headbars/cranialwindows (RPI-2AFC-Rig v4)Most of the adjustments in this version 4.0 of the rig focused on the behavioural taskdesign (Figure 3.9A). However, notable physical modifications (Figure 3.9B) were made tothe rig as well. Specifically, the vibrational start cue was replaced with a tonal start cue.This change was implemented because it could not be guaranteed that the mice’s pawswere consistently resting on the vibrational pad when the cue was triggered. Additionally,research indicated that Q175 mice retained their hearing at the ages being tested, makingthe tonal cue a more reliable option (Heffner & Heffner, 2007; K. R. Johnson et al., 1997;Park et al., 2013). The second major adjustment was a new headbar and as a consequence,a new headclamp (Figure 3.9C, Figure 3.9D). These new headbars enhanced the qualityof cortical imaging and enabled comprehensive video coverage of the primary visual cortexand barrel fields (somatosensory cortex). They also improved cross-mouse comparisons, aseach headbar could be surgically attached with greater consistency due to its specific design,which fit onto the skull in a precise orientation. In contrast, the previous headbars relied onan arbitrary 1 cm x 1 cm cranial window opening (Figure 3.9E, Figure 3.9F) and a bar thatwas manually positioned based on the back of the skull. The new design encompassed the393.4. Version 4.0: Tone start cue, and new headbars/cranial windows (RPI-2AFC-Rig v4)Figure 3.9: The final iteration. (A) Behavioural paradigm on (B) version 4.0 of the rig,with a (D) new cranial window cortical image and by consequence a (C) new headclamp.Compared to the (F) old cranial window cortical image and (E) old headclamp.entire cortical area and eliminated the need for the subjective placement of the headbars,as their positioning was always relative to the cranial window opening. The final benefitwas that there was no tilt to the cranial window and each cortical video could be analyzedwith greater accuracy.3.4.1 Behavioural testing of RPI-2AFC-Rig v4 (mouse cohort 4):Paradigm changes and stimulus adjustments.For this set of experiments, the stage 1 threshold for progression to stage 2 was lowered to50%. This change was based on the hypothesis that cohort 3.2 (previous set of experiments)was overtraining in stage 1, which led the mice to focus solely on licking the spouts toreceive rewards without paying attention to any stimuli (Figure 3.8A). As a result, themice formed a strong association between the sound of the spouts moving in and lickingwhichever spout appeared or was felt first. When transitioning to stage 2 (Figure 3.8B),this behaviour persisted, with the mice ignoring the stimulus and simply licking the first403.4. Version 4.0: Tone start cue, and new headbars/cranial windows (RPI-2AFC-Rig v4)spout they encountered. Since this form of error could not be adequately assessed, themice advanced through stage 2 with ease but entered stage 3 without learning the intendedassociation. Consequently, this led to numerous failed decisions in stage 3 (Figure 3.8C),despite the extended time spent on the task. For this same reason, a nogo component wasadded to stage 2. Mice were now presented with no stimulus, but one of the two spoutswould move in, if they licked the spout it would retract and no water was presented, withthe addition of a 2-second timeout. This was to ensure the mice only licked when the lightindicated which spout was being presented. The timeout component was also added tostage 3 for any trials that were unsuccessful.Another addition was an increase of the flash duration to a single 120-ms flash and two60-ms flashes. This was because upon assessing the cortical response, only the 60ms singleflash (in cohort 3.2, in version 3.0 of the rig), elicited a robust sustained excitation forapproximately 1s (more on this in Chapter 4). This was thereby the basis for doubling theduration of the flash, as well as increasing the inter-flash duration to 1s between the doubleflash.Behavioural results for mouse cohort 4 in RPI-2AFC-Rig v4In stage 1 (Figure 3.10C), both HD and WT groups exhibited steady improvements insuccess rates, with HD mice requiring more days to learn to lick, compared to WT mice.All mice in this set of experiments were female, and they showed a longer duration oftraining to meet criteria than the male mice. Notably, the male mice required an averageof 5.5 days to surpass the stage 1 threshold, whereas all five female mice needed more thana week (averaging 11 days) to consistently lick the spouts.In spite of successfully licking the spouts, in stage 2 (Figure 3.10D), neither group metthe criteria to progress to stage 3, as success rates for ”success lick” and ”success nogo”failed to exceed 75% for two consecutive days. Both the HD and WT groups exceededthe 75% threshold for successful licks; however, the nogo success rates on those days werenotably low. This suggests that the mice were licking on every trial without paying attention413.5. Concluding insights: Final refinements and optimizations of the RPI-2AFC-RigFigure 3.10: Lick rates for Cohort 4 (all female) on (A) Diagram of version 4.0 of the rig.(B) image of version 4.0 of the rig labeled with all components (C) Mice progressed throughstage 1 after a notable length of time, indicating the extended period of time required totrain female mice to lick spouts. (D) Once in stage 2, mice were not able to meet thethreshold for both left/right stim and nogo condition to move into stage 3.to the stimulus. These results highlight the difficulty in suppressing the incorrect lickingresponses, a behaviour likely overtrained during stage 1.3.5 Concluding insights: Final refinements andoptimizations of the RPI-2AFC-RigThe iterative development of the RPI-2AFC-Rig highlights the challenges and opportunitiesin designing behavioural testing paradigms that are both technically robust and biologi-cally meaningful. Each version of the rig addressed specific limitations identified throughbehavioural testing, ranging from mechanical shortcomings, such as insufficient servo motorpower and side bias-inducing light configurations, to design flaws in behavioural paradigmsthat impeded learning. Key improvements, including the transition to solenoid-based mo-tors, enhanced lighting systems, capacitive sensor upgrades, and redesigned headbars, signif-icantly refined both the precision of the rig and the quality of the data collected. Behaviouraladjustments, such as pre-training handling, the introduction of intermediate training stages,423.5. Concluding insights: Final refinements and optimizations of the RPI-2AFC-Rigand side-bias correction mechanisms, were also necessary for the experiments. While themice did not reliably associate visual stimuli with specific licking responses, they were suc-cessfully trained to lick consistently, demonstrating progress in shaping their behaviour. Afinal proposed refinement involves adding 10-20 stage 3 trials at the end of stage 2 as ameasure to determine readiness for stage 3. This adjustment would provide a clear cri-terion for progression while simultaneously exposing the mice to elements of the Stage 3paradigm, further preparing them for the next phase of training. The ultimate goal wasto gradually reduce the interflash duration of the double flash stimulus and determine thepoint at which discrimination ability differed between genotypes. However, due to timeconstraints, we were unable to reach a stage where this could be tested.43Chapter 4Mesoscale Calcium Dynamics inCortical Layer 2/3 DuringBehaviourEach cohort’s brain activity was imaged throughout the study; however, this chapter willprimarily focus on Cohort 4, as it represents the culmination of iterative refinements made inboth the experimental rig and behavioural task. The development process involved multiplemodifications across previous cohorts, ultimately leading to Version 4 of the behaviouralrig, which provided the most consistent, high-quality, and reliable data. The improvementsin both hardware and task design significantly enhanced the precision of data collection,reducing variability and increasing reproducibility.While the primary focus will be on Cohort 4, a brief discussion of Cohort 3.2 will alsobe included to highlight key observations from an earlier iteration of the paradigm. Theinsights gained from Cohort 3.2 played a crucial role in refining experimental conditions,addressing previous limitations, and informing adjustments that were implemented in thefinal cohort.444.1. Primary visual cortex activation requires visual stimuli of duration greater than 30-ms (cohort 3.2)4.1 Primary visual cortex activation requires visual stimuliof duration greater than 30-ms (cohort 3.2)In Cohort 3.2, the flash stimulus parameters consisted of a 60-ms single flash and a 30-msdouble flash (two flashes separated by 0.5 seconds). However, analysis of cortical activityrevealed a stark difference in the neural response between these two conditions. The 30-ms double flash condition (Figure 4.1) produced minimal activation in the primary visualcortex (V1) across the entire 17-day experimental period in both Huntington’s disease (HD)and wild-type (WT) mice. This lack of a strong response suggested that the shorter flashduration might not be sufficiently salient to drive robust neural activity in V1.In contrast, the single 60-ms flash condition elicited a much stronger and more consistentresponse in the primary visual cortex. On most experimental days, this condition produceda clear peak in cortical activity immediately following stimulus presentation, indicating thatthe visual system was effectively detecting and processing the longer flash duration. Thedisparity between the two conditions suggested that stimulus duration played a critical rolein evoking a measurable neural response in V1.To quantitatively assess differences in peak cortical responses between flash conditions,the peak activity for WT mice during the single-flash condition was extracted across alldays, and the average response was calculated within 1.2 seconds following stimulus onset.This was then compared to the peak activity of the double-flash condition (Figure 4.2).An independent t-test statistical analysis, with a bonferroni multiple comparisons correc-tion, revealed a significant difference between the two conditions in WT mice (p ≺ 0.0001),confirming that the single-flash stimulus generated a stronger cortical response. The sameanalysis was conducted for HD mice, where the difference between the single and double-flash conditions was also statistically significant (p ≺ 0.0001). These findings suggest thatthe reduced response to the double-flash condition was consistent across genotypes.Given these findings—and as discussed in Chapter 3—the flash duration was doubledfor Cohort 4 to enhance stimulus salience and ensure a more reliable cortical response. The454.1. Primary visual cortex activation requires visual stimuli of duration greater than 30-ms (cohort 3.2)Figure 4.1: Cohort 3.2 heatmaps of functionally defined V1 activity averaged by day andacross mice. The green line at time 0 represents the flash onset. In the double flashcondition, the second flash occurs 0.5 seconds later. Spouts move in 1.2 seconds after thefinal flash.464.2. Cortical activity differences between successful lick and no-lick trialsFigure 4.2: Peak Z-score value in V1 within 1.2 seconds after flash onset. A significantdifference (P ≺ 0.0001) was observed in both wild-type and HD mice, with the shorter30-ms double-flash condition eliciting a weaker cortical response compared to the longer60-ms single-flash condition.updated parameters for Cohort 4 included a 120-ms single flash and a 60-ms double flash,which were expected to generate stronger engagement of the visual system and facilitateimproved discrimination between stimulus conditions. These adjustments were made tooptimize the experimental design and ensure that both HD and WT mice could reliablyprocess and respond to the presented visual stimuli.4.2 Cortical activity differences between successful lick andno-lick trialsCohort 4 underwent Stage 2, in which a single spout was presented along with its corre-sponding stimulus – etiher a 120 ms single flash of light paired with the left spout or two60ms flashes paired with the right spout – for a water reward. The double flash conditionis included in the appendix (Figure B.1), but the primary focus of this analysis is on thesingle-flash condition.Each trial was categorized based on whether the mice successfully licked the spout.Layer 2/3 cortical calcium responses were then averaged across all trials within each dailysession for individual mice, followed by averaging across genotypes (WT and HD). To assess474.2. Cortical activity differences between successful lick and no-lick trialsFigure 4.3: Single 120ms flash stimuli, paired with left spout presentation. Cortical windowactivity was average across trial within a session and across genotypes. The top panel foreach condition is an average of the first three session days coined “early days”. The bottompanel is an average of the last three session days, named “last days”. No-lick conditionwere trials where the mice did not lick the spout, whereas successful lick conditions werethe mice licked. Time zero is the light presentation (first black line), the second black linerepresents the spout presentation.changes over time, trials from the first three days were grouped as ”early days,” while thelast three days were averaged to represent ”late days.”4.2.1 Successful lick trials elicit a large motor cortex response comparedto no-lick trialsWhen plotting the time course of cortical activity from stimulus onset to the movementof the spouts, both lick and no-lick trials exhibited a visual cortex response. As expected,successful lick trials (Figure 4.3) evoked a significantly larger motor cortex response followingspout movement, likely due to licking activity. This response extended into the barrel fieldsand other associated sensory regions.This pattern was consistently observed across both WT and HD mice, as well as underboth single and double flash conditions (Figure B.1). To further investigate this effect,the cortical window was segmented into distinct brain regions based on the Allen Brain484.2. Cortical activity differences between successful lick and no-lick trialsFigure 4.4: Allen Brain Atlas regions of a mouse brainAtlas (Figure 4.4), allowing for a more detailed analysis of regional activity (Figure 4.5).Following spout movement, a pronounced increase in cortical activity was evident in motorregions, also illustrated in Figure 4.3. This effect is further quantified in Figure 4.6, whereactivity traces reveal a sharp spike in motor cortical regions during successful lick trials,whereas no-lick trials exhibit a muted response.4.2.2 Increased cortical activity preceding spout movement in no-lickversus successful lick conditionBy observing the cortical activity in the different conditions, an interesting distinctionemerged between successful and no-lick trials—no-lick trials exhibited heightened corticalactivity before the visual stimulus onset at -0.5s (Figure 4.3). This pattern was also presentin the double flash condition (Figure B.1 ), where cortical activity was already elevated attime 0s and continued increasing at 1s. This suggests that when mice were less attentiveto the visual stimulus, their cortical activity was more distributed across unrelated brainregions, potentially leading to failures in recognizing and responding to the spout reward.The heatmaps further illustrate this phenomenon—successful lick trials showed notablyreduced baseline activity before flash onset, whereas no-lick conditions exhibited heightenedactivity across all brain regions. To investigate this in more detail, Figure 4.7 depicts the95th percentile peak activity for each brain region within 1.2 seconds of visual stimulus494.2. Cortical activity differences between successful lick and no-lick trialsFigure 4.5: Heatmaps of cortical activity segmented by Allen Brain Atlas regions. 120msSingle flash condition, with left spout presentation. Panels are all in the right hemisphere(see appendix for left hemisphere and double flash condition (Figure B.2, Figure B.3, andFigure B.3).504.2. Cortical activity differences between successful lick and no-lick trialsFigure 4.6: Brain region activity traces across different conditions. The first vertical line(red) is the flash onset, the first black line is the spout presentation, the second black lineis the spout removal.514.3. Genotypic differences between WT and HD miceFigure 4.7: Successful lick vs no-lick condition peak cortical activity (95% highestz-score values, excluding artifact outliers) within 1.2 seconds of light stimulus onset acrossdifferent brain regions. These representative plots are shown as examples, as all conditionsexhibit a similar pattern of activation (see Appendix for full dataset, Figure B.5 and B.6).As expected, the visual cortex and associated regions show the highest peak activity, whilemotor and select sensory areas demonstrate minimal activation.onset. While both WT and HD mice showed strong activation in the visual cortex andassociated regions regardless of trial outcome, the key difference emerged in non-visualregions. In successful lick trials, motor and sensory areas displayed activity slightly abovebaseline (∆F/F z-score≻ 0), whereas in no-lick trials, these regions were notably suppressed,with z-scores falling below 0. This suggests that successful trials involve more preciseencoding of visual stimuli, potentially enabling a coordinated motor response, whereas failedtrials may reflect disengagement of non-visual cortical areas.4.3 Genotypic differences between WT and HD miceAlthough there are no stark differences between genotypes when comparing successful lickand no-lick trials, certain patterns consistently emerged throughout the cortical data anal-524.3. Genotypic differences between WT and HD miceysis. However, due to the limited sample size, definitive conclusions should be drawn withcaution.4.3.1 Visual responses in HD have a higher peak compared to WTFollowing the visual stimulus, the visual cortex and its associated regions are activated,as expected, to process the sensory input. This response is consistently observed acrossboth the 120 ms single-flash and the two 60 ms double-flash conditions, as well as in bothsuccessful and no-lick trials. Notably, while visual cortex activation is evident in both WTand HD mice, HD mice exhibit an amplified response compared to their WT counterparts.This heightened response is apparent across multiple forms of analysis. In the heatmaps(Figure 4.5), HD mice display a more pronounced visual activation compared to WT. Simi-larly, the traces in Figure 4.6 reveal that both HD no-lick and successful lick trials exhibit asharp and rapid peak immediately following the visual stimulus, whereas WT responses arecomparatively attenuated, with a lower peak amplitude. This pattern is further reinforcedby peak analysis in Figure 4.8, where the highest recorded peak activity in HD mice isconsistently greater than that of WT mice. The convergence of these findings across differ-ent visualization methods suggests that HD mice exhibit exaggerated cortical responses tovisual stimuli.4.3.2 Sustained motor cortex activity following spout presentation inHD mice compared to gradual decrease in WTWhen examining motor cortex activity in secondary motor (Mos or M1) and primary motorcortex (Mop or M1) (Figure 4.6), WT mice exhibit a higher peak response following spoutpresentation compared to HDmice. After reaching this peak, WT activity gradually declinesin 2.8 seconds toward baseline in a time-dependent manner. In contrast, HD mice show aninitial drop from their peak response, but instead of continuing to decline steadily, theiractivity remains elevated for a prolonged 3.5 second period before eventually tapering off.This sustained activation is not limited to the motor cortex but is also observed in other534.4. Exploratory insights into cortical dynamics in WT and HD miceFigure 4.8: WT vs HD Peak cortical activity (95% highest z-score values, excludingartifact outliers) within 1.2 seconds of light stimulus onset across different brain regions.cortical regions, where HD mice maintain a heightened level of activity until the spoutsmove out.4.4 Exploratory insights into cortical dynamics in WT andHD miceThis study provides an exploratory analysis of cortical activity in WT and HD mice during acue-based discrimination task, leveraging simultaneous mesoscale imaging and behaviouralreadouts. HD mice exhibited heightened visual cortex responses compared to WT, suggest-ing differences in sensory processing that may reflect hyperexcitability or altered sensoryintegration.This study provides an exploratory analysis of cortical activity in WT and HD miceduring a cue-based discrimination task, leveraging simultaneous mesoscale imaging andbehavioural readouts. HD mice exhibited heightened visual cortex responses compared544.4. Exploratory insights into cortical dynamics in WT and HD miceto WT, suggesting differences in sensory processing that may reflect hyperexcitability oraltered sensory integration.These findings are observational and should be interpreted with caution due to the lowsample size. However, they offer a first step toward understanding how cortical circuits inHD differ from WT during active behaviour. Future studies with larger cohorts using thisrefined behavioural paradigm are necessary to confirm these patterns and further dissectthe role of cortical dynamics in sensory-guided discrimination task.55Chapter 5Discussion5.1 Results discussionThis study aimed to investigate cortical activity in Huntington’s disease (HD) and wild-type (WT) mice during a sensory-guided behavioural task using mesoscale calcium imag-ing. Through iterative refinements of both the behavioural paradigm and experimental rig(Chapter 3), we developed a robust system for assessing cortical dynamics during behaviour.Our results provide exploratory insights into genotypic differences in sensory processing andmotor activity, as well as differences in cortical engagement between successful and unsuc-cessful trials.5.1.1 Technical refinements and their impact on data qualityA major focus of this study was the iterative development of a behavioural rig capable ofboth precisely delivering sensory stimuli and monitoring behavioural responses (Chapter 3).Early iterations of the rig revealed several challenges, including side bias (mice favoring onespout), insufficient association formation between stimulus and response, and task distrac-tions such as abrupt solenoid-driven movements. The refinements implemented in Version4.0—including a tonal start cue, extended light stimulus duration, and modifications tothe headbar for improved imaging—addressed these issues, allowing for more reliable be-havioural performance and cleaner neural data acquisition (Figure 3.9). However, despitethese improvements, mice in Cohort 4 still did not reach Stage 3, indicating that furthermodifications are needed to optimize learning efficiency. This highlights the continued needfor task refinement in order to reliably assess higher-level cognitive and sensory processing565.1. Results discussiondifferences in HD and WT mice.5.1.2 Sensory processing and visual cortex activationOne of the findings was the increased visual cortex response in HD mice compared toWT mice. This was evident across multiple forms of analysis, including heatmaps (Figure4.5), cortical activity traces (Figure 4.6), and peak response comparisons (Figure 4.8). Theamplified visual response in cortex of HD mice may reflect disruptions in sensory processing,potentially linked to sensory spread, a phenomenon previously observed in HD models(Sepers et al., 2022). Sensory spread describes longer duration and excessive propagationof sensory-evoked cortical activity beyond primary sensory areas, which could contribute toabnormal stimulus perception and difficulty in forming precise sensory-motor associations.Interestingly, despite the stronger visual response in HD mice, their ability to use thisinformation to guide behaviour did not differ significantly from WT mice. Both groupsfailed to advance to Stage 3 of the behavioural paradigm, suggesting that factors beyondsensory encoding—such as decision-making, learning reinforcement, or motivation—mayalso contribute to performance outcomes.In expert mice, refined cortical activity—specifically, a reduction in non-visual regionactivation—was shown to be a stronger predictor of learned behaviour than the absoluteincrease in visual cortical signals (Gilad & Helmchen, 2020). Similarly, in my data, successfullick trials were characterized by increased activation in visual areas , but what distinguishedthem was the reduced activity in motor and sensory regions with the z-score of activityof successful lick conditions persistently was below 0, compared to above 0 for no-lickmice (Figure 4.7). This suggests that effective learning may involve not just strengtheningrelevant signals but also suppressing competing activity, allowing for more precise visualprocessing. With a sufficiently sensitive decoder, these pre-spout presentation differencescould potentially predict trial outcomes, making this an interesting analysis to explore infuture work (Cai et al., 2022).575.1. Results discussion5.1.3 Motor cortex activity and task engagementA second key finding was that successful lick trials elicited a significantly larger motorcortex response compared to no-lick trials (Figure 4.3). This was expected, but furthervalidates that the collected data indeed reflects cortical activity, as successful trials involveactive licking behaviour, which engages motor and sensory circuits. Notably, this responseextended into the barrel fields, suggesting that somatosensory feedback from the tongueand whiskers may play a role in guiding successful responses.In this, a genotypic difference emerged in the temporal dynamics of motor cortex activ-ity. While WT mice exhibited a sharp peak in motor cortex activity followed by a gradualdecline, HD mice showed a more prolonged, sustained response after an initial peak (Figure4.6). This pattern was consistent across multiple brain regions (Figure 4.8), suggesting awidespread disruption in motor circuit regulation. A failure to return to baseline could indi-cate impaired inhibition or excessive recurrent activity, both of which have been implicatedin HD-related motor dysfunction (Donzis et al., 2020). The lower motor peak in HD micecompared to WT may further reflect reduced motor drive or inefficiencies in generatingrapid, precise motor outputs.5.1.4 Pre-stimulus activity and attentional engagementAn unexpected finding was the increased cortical activity before stimulus onset in no-licktrials, particularly at -0.5s (Figure 4.3). This pattern was also observed in the double-flashcondition (Figure B.1), where cortical activity was already elevated at stimulus onset andincreased further at 1s. This suggests that mice that failed to respond correctly were alreadyin a different neural state before the trial began, potentially indicating disengagement,inattention, or spontaneous movement-related activity (Marbach & Zador, 2017).585.2. Addressing the research question5.2 Addressing the research questionThis study aimed to explore the behavioural relevance of exaggerated sensory responsesin Huntington’s disease (HD) mice using a custom-built behavioural paradigm paired withmesoscale cortical imaging. Through multiple iterations of task design and rig development,HD and WT mice were trained in a cue-based discrimination task to assess their abilityto associate visual stimuli with a learned motor response. While refinements successfullyoptimized task delivery, ensuring precise sensory cue presentation, reward delivery, andsimultaneous cortical imaging, mice did not consistently form the intended stimulus-actionassociations between single and double flash stimuli and left/right spout selection. Thismade it difficult to directly link sensory spread to behavioural outcomes. However, corticalimaging revealed heightened visual responses in HD mice, suggesting differences in sensoryprocessing that may contribute to learning deficits. Moreover, mice reliably learned tolick for rewards and likely formed an association between the visual stimulus and spoutmovement. These findings establish a foundation for future studies to further investigatehow sensory processing alterations influence task performance in HD.5.3 LimitationsGiven the exploratory nature of much of the collected data, certain limitations should beacknowledged and addressed.5.3.1 Sex differences and small sample sizeOne notable limitation of this study was the accessibility of the mice sex. Based on wheneach rig was ready for experiments, there were different distributions of the sex of mice. Assuch, cohort 1 contained 2 males and 2 females, cohort 2 contained 3 males, cohort 3.1 con-tained 3 males, cohort 3.2 contained 4 males and cohort 4 used 5 female mice. Unpublishedresearch by Dr. James Mackay reveals sex differences in the sensory spread phenomenon,with females exhibiting a milder phenotype compared to males. This limitation renders595.3. Limitationsdirect comparisons between male and female performance challenging, as the uneven distri-bution of sexes across cohorts may confound the interpretation of sex-related differences insensory spread and behavioural outcomes.Another limitation of this dataset is the small sample size within each cohort. Whilea total of 19 mice were used, the iterative changes made to the rig design required eachcohort to be analyzed separately, preventing data from being combined. This fragmentationresulted in particularly small sample sizes when comparing between genotypes, ultimatelyreducing the statistical power needed to draw definitive conclusions. However, despite theselimitations, the findings provide valuable insights that can inform future studies and guidefurther investigation into this project.5.3.2 Cortical imaging limitationsIn terms of cortical imaging, we used a Thy1-transgenic GCaMP6s sensor, which has rela-tively slow rise and fall times (Barnett et al., 2017), reducing the resolution of fast fluctu-ations in neural activity and potentially obscuring transient or rapid population dynamics.Moreover, while temporal smoothing helped improve signal-to-noise ratio, it augmented theproblem with detecting fast signals. Similarly, we applied spatial smoothing to improvesignal-to-noise ratio but that can blur fine-scale cortical organization, making it difficult toresolve smaller functional domains or localized activity patterns. As a result, some of themore intricate neural dynamics, such as microcircuit interactions or localized oscillatoryactivity, may have been overlooked.Addressing these limitations would require mesoscale2-photon imaging, which provides higher spatial and temporal resolution, allowing for moreprecise measurement of individual neural responses.Furthermore, calcium imaging serves as a proxy for neural activity, capturing large-scale population dynamics but lacking the resolution to discern neuronal-level encoding ona millisecond time scale. Future experiments that include electrophysiological recordingwould help with this issueAnother major limitation lies in the imaging hardware itself. The CCD camera used in605.3. Limitationsthis study is 15 years old and comes with inherent constraints. Advances in CMOS cameratechnology have since surpassed most CCD systems, offering improved resolution, fasteracquisition speeds, and better compatibility with next-generation calcium indicators suchas GCaMP7f or GCaMP8f. Upgrading to a modern CMOS camera would likely enhanceimaging quality, reduce motion artifacts, and better capture rapid neural dynamics, whichare otherwise dampened by both the sensor’s kinetics and the post-processing smoothingtechniques5.3.3 Stimulus artifactWhile the artifact generated by the visual stimulus in the cortical image allowed for precisealignment of cortical data with trials, it was interpolated out during signal processing. Thismeant that the 2–4 frames containing the artifact were removed and averaged over. Giventhe inherent delay in calcium signaling, these removed frames likely did not capture theactual neural response to the stimulus. Nonetheless, this remains a notable limitation.One potential method to overcome this issue was explored during the project but wasultimately not implemented due to hardware constraints. The approach involved usingultraviolet (UV) light at 360 nm as the visual stimulus instead of the 530 nm green light,which falls within the emission range of GCaMP6s. Since mice lack blue cones but possessa green cone peaking at 508 nm and a UV cone peaking at 360 nm (Peirson et al., 2018),UV light could theoretically serve as a viable alternative without interfering with calciumimaging. However, the major challenge was that most UV LEDs tested—sourced fromLuxeon™ and other manufacturers—lacked the necessary brightness for mice to detect themeffectively.Despite these challenges, future studies could explore the use of UV light as a visualstimulus to preserve all imaging frames, eliminating the need for interpolation. This ap-proach may also enhance the capture of activity patterns that would otherwise be lost dueto frame removal615.4. Ultimate goal and rig changes5.4 Ultimate goal and rig changesThere are numerous directions for future work, particularly as this project involved bothrig development and behavioural task design. The refinements made here can serve as afoundation for optimizing experimental design in upcoming studies, enabling more preciseinvestigations into cortical dynamics and behaviour.5.4.1 Ultimate goal for the projectThe ultimate goal of this project was to advance both HD and WT mice to Stage 3, wherethey would consistently discriminate between a single and double flash of light to make thecorrect decision. During training, the time interval between the two flashes in the doubleflash condition was set to 1 second, with the plan to gradually shorten this interval in thefinal stage. This would allow us to determine the threshold at which mice could no longerreliably distinguish between single and double flashes, leading to an error rate approachingchance.The underlying hypothesis was that, due to the temporal aspect of sensory spread ob-served in previous experiments (Sepers et al., 2022), HD mice would perceive the two flashesas a single event at longer intervals than WT mice. This would provide a behavioural read-out of the sensory spread phenotype, linking it directly to sensory processing performance.However, due to the extensive iterative modifications of the rig required to establish arobust behavioural paradigm, as well as time constraints, we were unable to reach this finalstage within the scope of this study. Nevertheless, this remains a key experiment plannedfor the near future, as it holds significant potential for linking cortical sensory dynamics tobehaviour in HD mice.5.4.2 Future rig changesWith the recent release of the Raspberry Pi 5, the rig can now support dual-camera record-ings, allowing for a more comprehensive capture of behavioural data. The improved proces-625.5. Partially complete future projectssor and increased RAM enable simultaneous execution of multiple tasks while maintaininga higher frame rate, ensuring that rapid licking behaviours are accurately recorded withoutdropped frames.Another planned refinement involves modifying the spout movement mechanism. Whilethe current solenoid-based push-pull motors effectively prevent mice from licking bothspouts—forcing a deliberate choice—they may also introduce unintended aversive effects.The rapid spout movement could make it more difficult for mice to associate the light stim-ulus with the reward, potentially slowing learning. Additionally, despite efforts to dampennoise using rubber components, the solenoids generate an audible sound that may act asan unintended sensory cue, potentially confounding the behavioural data.To address these issues, alternative solenoids with finer movement control can be ex-plored. By maintaining fast retraction speeds to enforce decision-making but allowing fora slower and smoother extension, the setup can minimize the potential stress or distrac-tion caused by abrupt spout movements while preserving the integrity of the task. Theserefinements will enhance both data quality and task efficiency, ultimately improving thereliability of behavioural readouts.5.5 Partially complete future projectsWithin this project, several exploratory analyses were attempted but were not includedin the final dataset. However, given more time, these approaches remain viable for futurestudies and could provide valuable insights if further developed.5.5.1 Machine learning to deconvolute mesoscale brain activity patternsOne potential avenue for future exploration is a machine learning-based project utilizingattentional vision transformer models to decode cortical activity. This was originally a col-laborative effort but faced challenges due to limitations in the earlier cohort dataset. Specif-ically, the dataset was affected by rig-based constraints, including unclear visual stimulus635.5. Partially complete future projectsartifacts that could not be effectively interpolated, as well as the use of a short-durationlight stimulus that failed to elicit a measurable response in the visual cortex. These issueshave since been addressed in the most recent dataset (Cohort 4), making it feasible to revisitthis approach.In this project, models such as convolutional neural networks (CNNs) with 2D and 3Dconvolution, as well as vision transformer (ViT) models, were used to identify time pointswithin a trial where cortical activity contained meaningful information. Additionally, theDINO transformer model was employed to analyze which brain regions were most attendedto by the predictive model. Unlike typical vision transformer applications, where the specificregions and pixels contributing to predictions are often incidental, in the context of corticalactivity, these regions are highly relevant. If the model can successfully predict stimulustype, trial outcome (success vs. failure), or whether a mouse has learned the stimulus-rewardassociation, then identifying the brain regions and time points driving these predictions canprovide valuable insights into underlying cortical processing patterns that may otherwisego unnoticed.5.5.2 Two-photon visual cortex retinotopic mappingMy project utilized spatial segmentation of brain regions based on the Allen Brain MouseAtlas, using bregma as a reference point. However, functional mapping methods provide amore precise way to define brain regions. One such approach involves generating a retino-topic map of visual signals in the mouse visual cortex. This is typically achieved by pre-senting a drifting bar stimulus with a checkerboard pattern that alternates between blackand white as it moves across the mouse’s visual field. The bars travel in the four cardinaldirections, sweeping ten times per direction at a frequency of 0.1 Hz (Allen Institute forBrain Science, 2019; Zhuang et al., 2017). By recording cortical activity during this stimuluspresentation—either with a two-photon microscope through a large cranial window or us-ing mesoscale imaging through an intact-skull window—the corresponding neural responsescan be aligned with the stimulus properties. This method enables the functional identifi-645.5. Partially complete future projectscation of cortical regions responsible for processing different aspects of visual information(Allen Institute for Brain Science, 2019; Zhuang et al., 2017). In Cohort 4, three out offive mice underwent this experiment, allowing for a more refined mapping of visual corticalresponses. However, the analysis was incomplete and was therefore not included in the finaldataset. Nonetheless, this approach remains a viable avenue for future exploration andcould enhance data interpretation by providing a more functionally accurate representationof cortical activity.5.5.3 Motion energy and cortical signal comparisonOne hypothesis was that no-lick mice were not paying attention, potentially explainingtheir lack of licking behaviour. This inattentiveness may have been due to engagement inother activities, resulting in a heightened degree of motion. Mouse movement was measuredusing the behavioural camera, quantified as pixel change (Figure 5.1). This motion data wascompared to cortical signals recorded during no-lick and successful lick conditions, offering adirect correlation between mouse activity and cortical activity as an alternative explanationfor cortical signals. Representative analysis from a single WT mouse on day 12 showed noexcessive movement following stimulus presentation, suggesting that cortical signals werelikely internally generated as part of sensory stimulus processing. However, due to timeconstraints and issues with the behavioural camera — which caused many session videosto be cut in half — it was not possible to perform this analysis across the entire dataset oraverage across genotypes due to insufficient data points. While the analysis did not yieldconclusive evidence, it highlights the potential influence of mouse movement on corticalsignals. Despite its limitations, this analysis is highly feasible and could generate valuableinsights with a more complete dataset.5.5.4 Spatiotemporal analysisServeral attempts were made for different forms of temporal and spatial cross correlationdynamics. Some methods include a simple cross-correational analysis with pearson’s cor-655.5. Partially complete future projectsFigure 5.1: Behavioural camera analysis showing ROI-based movement quantification, usedto assess pre-stimulus and motor activity. Representative dataset fromWT mouse on day 12(B) Motion traces across all successful lick trials in the session, measured as pixel change (C)Motion traces across all no-lick trials in the session. (D top) Average motion of successfultraces (in B), shown in blue compared to average cortical activity in red (D bottom) Averagemotion of no-lick traces in blue compared to cortical activity in red.665.6. Future projectsrelation coefficient, to more complex methods such as TSNE or small world analysis (Songet al., 2021) (Song et al., 2021). Often, current analysis techniques focus on two or three ofthe many variables of a network, such as the activity level, the frequency of activity, influ-ences of neighbouring cells, spatial activation and temporal activation (Vanni et al., 2017;Wright et al., 2017; Xiao et al., 2021). One method is a comuptational statistical approachto decoding the complex spatiotemporal dynamics. To analyze how the spatiotemporal cor-tical patterns of activity change over time as a mouse learns, a method from Krzys´ko et al.(2023) “novel Spatio-temporal principal component analysis based on Geary’s contiguityratio” could be employed. Their method expands on PCA since it explicitly includes a timeand place of observation domain, on top of decomposing the variability of features in eachpixel. The addition of neighbouring region influence is of particular note since brain regionsin proximity are often highly impactful on each other.5.6 Future projectsOne upcoming experiment is the use of whisker stimulation as the sensory cue. This methodis functionally better for the mice as they are far more somatosensory animals, than visual(Rodgers et al., 2021). Additionally, mice also have very precisely tuned barrel fields whichmap each whisker making it easier to measure functional cortical activity (Helmchen et al.,2018).One hypothesis for the observed reduction in cortical activity as mice become moreproficient in a task is that these processes are gradually transferred to more brain regionsthat deal with habitual functions (Hwang et al., 2019; Salkoff et al., 2020). This shiftcan be studied using simultaneous two-photon imaging of cortical layer 5 and layer 2/3,allowing for a direct comparison of activity changes across cortical layers during learning(Masamizu et al., 2014). Another approach involves layer-specific targeting using geneticpromoters, such as Rbp4-2A-dcre-camk2a-tTa-TITL-GCamp6f for cortical layer 5 or Rasgrf-2A-dcre-camk2a-tTa-TITL-GCamp6f for layer 2/3, enabling more precise investigation of675.7. Concluding remarks and implicationshow learning reorganizes cortical processing, especially in HD mice (Pollak, 2024).One theory suggests that HD circuits exhibit an imbalance between inhibitory and exci-tatory activity (Chambon et al., 2023; Rangel-Barajas & Rebec, 2016). A potential futuredirection to investigate this phenomenon is dual inhibitory/excitatory recordings using twogenetically encoded calcium indicators, such as GCaMP for excitatory neurons and RCaMPfor inhibitory activity (Najafi et al., 2020). This approach could help disentangle how HDalters the coordination between excitation and inhibition across cortical circuits, providinginsights into network dysfunction and its behavioural consequences. Additionally, pharma-cological interventions, such as low-dose memantine—an NMDA receptor antagonist—couldbe explored for its potential to mitigate sensory spread and test if this improves discrimina-tion performance in the cue-based task (Ogilvie & Schultz, 2023) (unpublished data fromDr. James Mackay). If successful, this would provide further evidence that hyperexcitabil-ity in HD disrupts sensory integration and decision-making, and that targeted interventionscould restore functional processing.5.7 Concluding remarks and implicationsThis study provides an exploratory investigation into cortical dynamics in HD and WT miceduring a sensory-guided behavioural task, revealing differences in sensory processing andmotor engagement. While the findings suggest heightened visual responses and prolongedmotor activity in the cortex of HD mice, the small sample size and task limitations preventdefinitive conclusions. The iterative improvements in the rig and behavioural paradigmestablish a foundation for future studies, which can refine task design, increase samplesizes, and incorporate additional techniques such as dual inhibitory/excitatory imaging.Ultimately, this work serves as a step toward understanding how sensory spread impactsbehaviour in HD and informs future research on early circuit dysfunction in neurodegener-ative disease.68BibliographyAldous, S. G., Smith, E. J., Landles, C., Osborne, G. F., Can˜ibano-Pico, M., Nita, I. M.,Phillips, J., Zhang, Y., Jin, B., Hirst, M. B., Benn, C. L., Bond, B. C., Edelmann, W.,Greene, J. R., & Bates, G. P. (2024). A CAG repeat threshold for therapeutics targetingsomatic instability in Huntington’s disease. Brain: A Journal of Neurology, 147 (5), 1784–1798. https://doi.org/10.1093/brain/awae063Allen Institute for Brain Science. (2019). Allen Brain Observatory: Visual Behavior 2PTechnical Whitepaper. 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Cut a coverslip into a 1 cm x 1 cm square (or to fit the size of the headbar window)using a diamond cutter.2. Autoclave headbar and coverslipMouse Preparation3. Measure the weight of the mouse.4. Induce anesthesia using an isoflurane induction chamber (5 on the anesthesia machinescale for induction, then maintain at 2).5. Insert a temperature sensor into the rectum to maintain the feedback-controlled heat-ing pad (WPI) at 37°C.6. Apply Refresh™ Lacri-Lube ophthalmic ointment to the eyes for hydration.7. Shave the top of the head, extending down to the neck.8. Place the mouse under the microscope and maintain anesthesia.Surgical Procedure9. Clean the shaved area by applying iodine followed by 70% acetone.87Appendix A. Methods: Mouse cranial window surgery SOP and driver schematic10. Administer the following subcutaneous (SQ) injections: - 0.3 ml of 0.9% saline toprevent dehydration during surgery. - 5 mg/kg meloxicam. - 7 mg/kg bupivacaine atsurgery local area11. Carefully remove all skin from the shaved area (from the top of the head to the neck).12. Apply 3M Vetbond tissue adhesive along the edges of the cut area to prevent tissuefluid exudation.13. Use Krazy Glue to attach the edge of the headbar in place. Hold it for 5 minutes toallow the glue to settle.Dental Cement and Coverslip Placement14. In a tray, prepare the dental cement as follows: - Mix Parkell, C&B Metabond QuickBase (solvent) with Parkell, C&B Metabond Clear Powder (powder). - Add Parkell,C&B Amalgambond Universal Catalyst to the mixture to form the cement.15. Immediately apply the dental cement to the cutout area and place the prepared glasscoverslip on top. Press gently to eliminate air bubbles under the coverslip.16. Wait 30 minutes for the dental cement to set.17. Add additional dental cement around the headbar and the base of the skull to securethe structure and to ensure no any tissue exposure in the air. Allow 30 minutes todry.Post-Surgical Care18. Place the mouse in a recovery cage with a heating pad set to 37°C. Once the mouseis fully awake and moving, return it to the home cage.19. Administer a second SQ injection of 5 mg/kg meloxicam 10-12hr after surgery.20. Monitor the mouse’s weight for 3 days or until weight stabilizes.88Appendix A. Methods: Mouse cranial window surgery SOP and driver schematicFigure A.1: Custom dual power LED driver schematic from the EnixLAB89Appendix BSupplementary Analysis FiguresAdditional analysis conducted on the project included the double flash condition on bothWT and HD mice.90Appendix B. Supplementary Analysis FiguresFigure B.1: Double flash condition, showing similar visual activity post stimulus and motorcortex activation on successful lick conditions compared to no-lick conditions. First flash oflight occurs at 0 seconds, followed by the second flash shortly after 1 second. The spoutspresentation are a little past 2 seconds.Figure B.2: Heatmaps of cortical activity segmented by Allen Brain Atlas regions. Singleflash condition, with left spout presentation. Heatmaps are all in the left hemisphere91Appendix B. Supplementary Analysis FiguresFigure B.3: Left hemisphere, double flash heatmaps of brain region activity92Appendix B. Supplementary Analysis FiguresFigure B.4: Right hemisphere, double flash heatmaps of brain region activity93Appendix B. Supplementary Analysis FiguresFigure B.5: Single flash peak analysis for all conditions, see Figure 20 for details94Appendix B. Supplementary Analysis FiguresFigure B.6: Double flash peak analysis for all conditions, see Figure 20 for details95
Thesis/Dissertation
eng
Neuroscience
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Raymond, Lynn A.
Investigating the neural basis of sensory learning in a mouse model of Huntington's disease : insights from mesoscale microscopy
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