Medicine, Faculty of
Anesthesiology, Pharmacology and Therapeutics, Department of
DSpace
UBCV
Choles, Cassandra
Master of Science - MSc
University of British Columbia
Laser evoked potentials (LEPs) provide an avenue to discern individual differences in nociception, to further understand pain perception. We aimed to investigate resting neurobiological differences that may contribute to the small and large responses to noxious laser stimulation in 50 participants (26 females). To do this, participants underwent a two-part protocol. First, LEPs and verbal pain ratings were collected at four different laser intensities, and the resultant signal amplitudes (N1, N2, and P2) were clustered, forming two respective groups for each LEP outcome of small vs. large signal amplitudes, as well as high vs. low pain ratings. Next, participants underwent a magnetic resonance spectroscopy (MRS) scan to measure excitatory glutamatergic activity within the cingulate cortex at rest in four regions: the anterior (ACC), anterior-mid (aMCC), posterior-mid (pMCC), and posterior (PCC) cingulate cortices. Glutamatergic activity (glutamate, glutamine, and combined (Glx)) was compared between each respective cluster across the four cingulate regions, with sex included as a covariate for glutamine specifically. We found three key findings: 1) Resting glutamatergic activity was significantly higher in anterior vs. posterior regions. 2) Participants with higher verbal pain ratings had significantly higher glutamate concentrations in the ACC. 3) Participants with larger P2 amplitudes had significantly higher glutamate concentrations in the PCC. These findings contribute to our knowledge and understanding of glutamatergic activity in the cingulate cortex at rest. Specifically, that pain ratings and P2 amplitudes (reflecting attention) are not equivocal, although they are both influenced by differences in regional cingulate activity, suggesting the involvement of the ‘hubs’ of the salience and default mode networks through the ACC and PCC, respectively. Further research expanding on our sample size, brain regions, metabolites investigated, and LEPs conditions will assist in further understanding the intrinsic neurobiological differences to provide further insight into different responses to nociception and pain.
GLUTAMATE AND LASER EVOKED POTENTIALS: THE ROLE OF THE CINGULATE CORTEX by Cassandra Choles B.Sc., Lauren�an University, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Pharmacology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2023 © Cassandra Choles, 2023 ii The following individuals cer�fy that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis en�tled: Glutamate and laser evoked poten�als: The role of the cingulate cortex submited by Cassandra Choles in par�al fulfilment of the requirements for the degree of Master of Science in Pharmacology Examining Commitee: Dr. John L. K. Kramer, Associate Professor, Anesthesiology, Pharmacology and Therapeu�cs, UBC Supervisor Dr. Alasdair M. Barr, Associate Professor, Anesthesiology, Pharmacology and Therapeu�cs, UBC Supervisory Commitee Member Dr. Brian E. Cairns, Professor, Pharmaceu�cal Sciences, UBC Supervisory Commitee Member iii ABSTRACT Laser evoked poten�als (LEPs) provide an avenue to discern individual differences in nocicep�on, to further understand pain percep�on. We aimed to inves�gate res�ng neurobiological differences that may contribute to the small and large responses to noxious laser s�mula�on in 50 par�cipants (26 females). To do this, par�cipants underwent a two-part protocol. First, LEPs and verbal pain ra�ngs were collected at four different laser intensi�es, and the resultant signal amplitudes (N1, N2, and P2) were clustered, forming two respec�ve groups for each LEP outcome of small vs. large signal amplitudes, as well as high vs. low pain ra�ngs. Next, par�cipants underwent a magne�c resonance spectroscopy (MRS) scan to measure excitatory glutamatergic ac�vity within the cingulate cortex at rest in four regions: the anterior (ACC), anterior-mid (aMCC), posterior-mid (pMCC), and posterior (PCC) cingulate cor�ces. Glutamatergic ac�vity (glutamate, glutamine, and combined (Glx)) was compared between each respec�ve cluster across the four cingulate regions, with sex included as a covariate for glutamine specifically. We found three key findings: 1) Res�ng glutamatergic ac�vity was significantly higher in anterior vs. posterior regions. 2) Par�cipants with higher verbal pain ra�ngs had significantly higher glutamate concentra�ons in the ACC. 3) Par�cipants with larger P2 amplitudes had significantly higher glutamate concentra�ons in the PCC. These findings contribute to our knowledge and understanding of glutamatergic ac�vity in the cingulate cortex at rest. Specifically, that pain ra�ngs and P2 amplitudes (reflec�ng aten�on) are not equivocal, although they are both influenced by differences in regional cingulate ac�vity, sugges�ng the involvement of the ‘hubs’ of the salience and default mode networks through the ACC and PCC, respec�vely. Further research expanding on our sample size, brain regions, metabolites inves�gated, and iv LEPs condi�ons will assist in further understanding the intrinsic neurobiological differences to provide further insight into different responses to nocicep�on and pain. v LAY SUMMARY The experience of pain can be influenced by a mul�tude of factors including an�cipa�on, novelty, and stress. Measuring electroencephalogram (EEG) ac�vity while exposing par�cipants to a painful laser s�mulus provides insight into peoples’ differen�al responses to pain. We inves�gated these EEG outcomes and verbal pain ra�ngs, in rela�on to the res�ng excitatory levels of a brain region involved in pain percep�on called the cingulate cortex. We found the res�ng excitatory levels of the cingulate were higher in par�cipants with higher pain ra�ngs, as well as different excitatory levels in the posterior region of the cingulate in par�cipants that appeared to be paying less aten�on to the incoming laser s�muli (ie. daydreaming). These findings demonstrate pain ra�ngs and EEG outcomes measure different aspects of pain, and yet are both influenced by the res�ng ac�vity of the cingulate, which assists in providing further understanding into differen�al responses in pain percep�on. vi PREFACE Research and data collec�on was conducted at the Blusson Spinal Cord Centre, under Dr. John Kramer, as well as the UBC MRI Research Centre. The concep�on, original ethics submission (UBC CREB #H20-0393) and approval, and analysis pipeline for MRS and EEG was created by Dr. Jessica Archibald, who collected data for the first 23 subjects. Specifically, Jess adjusted the MATLAB scripts, originally created by Dr. Erin MacMillan, for the MRS analysis and created the basis set of metabolites. The MATLAB scripts were adjusted for this project by Oscar Or�z. I was responsible for the recruitment and collec�on of the EEG and MRS data collected on the remaining 27 par�cipants. I analyzed the EEG and MRS data for the full dataset (n=50), and conducted the sta�s�cal analyses, and created the figures and tables using Rstudio and Biorender. An undergraduate student, Nazanin Sadoughian, assisted me with the EEG set up and recording pain ra�ngs for a subset of par�cipants (n=10). The preliminary findings of this thesis were presented at the Canadian Pain Society (CPS) Annual Scien�fic Mee�ng 2023 as a poster presenta�on this past May 2023.vii TABLE OF CONTENTS ABSTRACT ..................................................................................................................................................... iii LAY SUMMARY .............................................................................................................................................. v PREFACE ....................................................................................................................................................... vi TABLE OF CONTENTS .................................................................................................................................... vii LIST OF TABLES ..............................................................................................................................................ix LIST OF FIGURES ............................................................................................................................................ x LIST OF ABBREVIATIONS ................................................................................................................................xi ACKNOWLEDGEMENTS ................................................................................................................................ xii Chapter 1: INTRODUCTION ................................................................................................................. 1 1.1 Pain and Nociception .......................................................................................................................... 1 1.1.1 Dynamic networks ....................................................................................................................... 2 1.1.1.1 The cingulate cortex............................................................................................................. 3 1.1.2 Laser evoked potentials (LEPs) .................................................................................................... 5 1.1.2.1 Glutamatergic activity ......................................................................................................... 7 1.1.2.2 Proton Magnetic resonance spectroscopy (MRS ................................................................ 9 1.2 Experimental pain models and neurobiological measures .............................................................. 11 Chapter 2: RESEARCH AIMS .............................................................................................................. 13 2.1 Summary ........................................................................................................................................... 13 2.2 Research aims ................................................................................................................................... 13 Chapter 3: METHODS ....................................................................................................................... 15 3.1 Experimental protocol ...................................................................................................................... 15 3.1.1 Demographics and ques�onnaires ....................................................................................... 15 3.2 LEPs protocol .................................................................................................................................... 16 3.2.1 EEG set-up ............................................................................................................................ 16 3.2.2 Laser s�mula�on ................................................................................................................... 16 3.2.3 Analysis of EEG data .............................................................................................................. 17 3.3 MRI and MRS protocols .................................................................................................................... 18 3.1.1 Analysis of MRI and 1H-MRS data .......................................................................................... 19 3.4 Statistical Analysis ............................................................................................................................ 21 viii Chapter 4: RESULTS .......................................................................................................................... 23 4.1 Demographics .................................................................................................................................. 23 4.2 Regional differences in excitatory metabolite concentration in the cingulate cortex .................... 23 4.3 The relationship between excitatory metabolite concentration and LEP outcomes ...................... 28 4.4 Regional variations in excitatory metabolites and responses to laser stimulation ......................... 32 Chapter 5: DISCUSSION .................................................................................................................... 41 5.1 Summary of findings ........................................................................................................................ 41 5.1.1 Regional differences .................................................................................................................. 41 5.1.2 Sex differences ........................................................................................................................... 42 5.1.3 Pain and excitatory activity ........................................................................................................ 43 5.2 Dynamic networks ............................................................................................................................ 45 5.2.1 The PCC ...................................................................................................................................... 45 5.2.2 The ACC ...................................................................................................................................... 46 5.3 Strengths, limitations, and future directions ................................................................................... 48 5.3.1 Strengths and limitations of 1H-MRS ........................................................................................ 48 5.3.2 Strengths and limitations of statistical analysis ........................................................................ 49 5.3.3 Future direc�ons ........................................................................................................................ 50 Chapter 6: DISCUSSION .................................................................................................................... 52 REFERENCES ................................................................................................................................................ 53 APPENDIX A: SUPPLEMENTARY FIGURES AND TABLES ............................................................................... 62 APPENDIX B: QUESTIONNAIRES .................................................................................................................. 64 ix LIST OF TABLES Table 1. Quality metrics of 1H-MRS analyses ............................................................................................. 23 Table 2. Results by metabolite and region .................................................................................................. 24 Table 3. Results for metabolites by region and sex for n= 50 par�cipants (26 females) ........................... 25 Table 4. Par�cipants clustered by LEP and PR outcomes (total = 50) ........................................................ 29 Table 5. Results by cluster and region ........................................................................................................ 38 Table 6. Post hoc results from significant interac�ons of P2 and P2 clustering ......................................... 40 Supplementary Table 1. List of R packages and cita�ons ........................................................................... 63 x LIST OF FIGURES Figure 1. The spinothalamic tract and projec�ons ....................................................................................... 2 Figure 2. The dynamic networks of the salience and default mode network ............................................. .5 Figure 3. Individual spectra produced by glutamine, glutamate, and GABA .............................................. 11 Figure 4. Experimental paradigm ................................................................................................................ 16 Figure 5. Example LEP example trace ........................................................................................................ 18 Figure 6. 3DT1 MRI with MRS voxel placement .......................................................................................... 20 Figure 7. Preprocessing of 1H-MRS data..................................................................................................... 20 Figure 8. Regional cingulate metabolite concentra�ons ............................................................................ 26 Figure 9. Regional cingulate differences by sex .......................................................................................... 27 Figure 10. Average EEG traces for the four laser intensi�es ....................................................................... 28 Figure 11. Clustering by LEP (N1, N2, and P2) outcomes ............................................................................ 29 Figure 12. Clustering by N1 outcomes ........................................................................................................ 30 Figure 13. Clustering by N2 outcomes ........................................................................................................ 30 Figure 14. Clustering by P2 outcomes ........................................................................................................ 31 Figure 15. Clustering by PR outcomes ........................................................................................................ 31 Figure 16. Regional concentra�ons across the cingulate cortex by LEP clustering .................................... 33 Figure 17. Regional concentra�ons across the cingulate cortex by N1 clustering ...................................... 34 Figure 18. Regional concentra�ons across the cingulate cortex by N2 clustering ...................................... 35 Figure 19. Regional concentra�ons across the cingulate cortex by P2 clustering ...................................... 36 Figure 20. Regional concentra�ons across the cingulate cortex by PR clustering ...................................... 37 Supplementary Figure 1. Example of a wssplot for clustering of LEPs ....................................................... 62 xi LIST OF ABBREVIATIONS ACC = anterior cingulate cortex AI = anterior insula aMCC = anterior mid-cingulate cortex CEN = central execu�ve network CNS = central nervous system DMN = default mode network EEG = electroencephalogram fMRI-BOLD = func�onal magne�c resonance imaging blood oxygenated level dependent Gln = glutamine Glu = glutamate Glx = glutamate + glutamine LEPs = laser evoked poten�als MRI = magne�c resonance imaging PAG = periaqueductal grey PI = posterior insula PCC = posterior cingulate cortex PFC = prefrontal cortex pMCC = posterior mid-cingulate cortex SN = salience network VPL = ventral posterolateral (nucleus of the thalamus) 1H-MRS = proton magne�c resonance spectroscopy xii ACKNOWLEDGEMENTS I would like to acknowledge everyone who has provided support and guidance throughout my masters, including my commitee, Dr. Alasdair Barr and Dr. Brian Cairns, the Kramer Lab, Dr. Erin MacMillan and the MRI technicians, my friends and family, and most of all my supervisor, Dr. John (Kip) Kramer. Thank you to my commitee, Dr. Cairns and Dr. Barr, for providing valuable feedback and perspec�ves on my methodology, results, and future considera�ons. I am also incredibly apprecia�ve of Dr. Barr for providing and encouraging collabora�ve opportuni�es on mul�ple systema�c reviews. Thank you, Kip, for encouraging me to build upon this project. Learning about MRS and neuroimaging has opened up a world of research that I love and am excited to con�nue learning about. Thank you for your con�nuous encouragement and support, always being willing to chat and answer my ques�ons, and enthusiasm for this project and our findings. Thank you to the Kramer lab for all of your con�nued support, guidance, and encouragement throughout my masters, research, and my thesis. In par�cular, I am incredibly apprecia�ve of Dr. Jessica Archibald, Dr. Lukas Linde, and Oscar Or�z, who each had an integral role in the success of this project. Jess - for your invaluable guidance, insight, and exper�se in teaching me the complex process of analyzing and comprehending MRS research, and the concep�on of this project which resulted in our large sample size. Lukas and Oscar – for your guidance and support in collec�ng and analyzing EEG and MRS data, and always being willing to answer ques�ons on coding and research. Nicole Bailey, Himali Bergeron-Vitez, Jessie McDougall, and Dr. Paulina Scheuren for your con�nued support, guidance, insight, and encouragement of grad school, research, and life. Bobo Tong – thank you for your con�nued support, guidance and pa�ence with me with grad school, research, and in par�cular, learning R. xiii I would also like to thank Dr. Erin MacMillan for her valuable insight, guidance, and support in rela�on to acquiring and analyzing the MRS por�on of my thesis, and all of the MRI technicians at UBC for collec�ng our MRI and MRS data as well as always being willing to answer any and all ques�ons. I am very thankful and apprecia�ve of receiving two ICORD travel awards, and the Dr. K. Leighton Pharmacology and Therapeu�cs Graduate Award, which allowed me to present my preliminary thesis findings at the Canadian Pain Society (CPS) Annual Scien�fic Mee�ng 2023. I am also incredibly apprecia�ve to have received the VCHRI Rising Star award this past spring and I am excited for this opportunity to present my full project at a conference this coming year. Finally, I would like to express deep gra�tude to all of the par�cipants for taking the �me to par�cipate in our study – we would have no results without you!1 Chapter 1: INTRODUCTION 1.1 Pain and nocicep�on Pain is a unique and mul�faceted experience integral to survival. An�cipa�on, stress, distrac�on, and novelty represent some of the numerous factors that contribute to our interpreta�on of pain. As defined by the Interna�onal Associa�on for the Study of Pain (IASP), pain is “an unpleasant sensory or emo�onal experience associated with, or resembling that associated with, actual or poten�al �ssue damage” (Raja et al., 2020). The sensa�on of pain is relayed to our central nervous system (CNS) based on the ac�va�on of peripheral receptors (nociceptors), conveyed by A-delta and C-fibres (Armstrong & Herr, 2019). Excitatory signals arising from the ac�vated nociceptors first synapse in the dorsal horn of the spinal cord, then decussate across the anterior white commissure to ascend in the spinothalamic tracts (Al-Chalabi et al., 2022). The anterior por�on of the spinothalamic tract relays noxious sensory informa�on in rela�on to crude touch and pressure to the ventral posterolateral (VPL) and inferior (VPI) nuclei, which then terminate in the somatosensory cor�ces (Al-Chalabi et al., 2022; De Ridder et al., 2022; Ianne� et al., 2008). In contrast, the lateral por�on of the spinothalamic tract relays pain and temperature informa�on to the mediodorsal and ventromedial nuclei, which then ascend to the dorsal anterior cingulate and anterior insular regions (De Ridder et al., 2022). Other spinothalamic projec�ons synapse within the brainstem, including the parabrachial nucleus, re�cular forma�on, periaqueductal grey (PAG), and the nucleus accumbens (Sengul & Watson, 2015) (Figure 1). The parabrachial nucleus is involved in the sensory relay of interocep�ve and exterocep�ve inputs, with projec�ons to the amygdala, then insular and cingulate cor�ces, contribu�ng to the affec�ve experience of pain (Basbaum et al., 2009). Ac�va�on of the spinothalamic tract and its efferent connec�ons encode for impending or actual �ssue damage (Mischkowski et al., 2018). Following this, pain becomes the experience and interpreta�on of the ac�vated 2 nocicep�ve pathways (Mischkowski et al., 2018). The experience of pain results in the widespread ac�va�on of a variety of cor�cal and subcor�cal regions beyond specifically iden�fied regions involved with somatosensory or nocicep�ve processing. Figure 1. The spinothalamic tract and projec�ons Periaqueductal grey = PAG, anterior cingulate = ACC 1.1.1 Dynamic networks More advanced neuroimaging techniques have demonstrated that the thalamus, anterior cingulate (ACC), posterior cingulate (PCC), insula, amygdala, primary and secondary somatosensory cor�ces, and the PAG are associated with increased ac�vity during the sensa�on of pain in both human and animal studies (Reddan & Wager, 2018). However, this widespread ac�va�on does not necessarily appear to be specific to pain, as exposure to non-noxious s�muli, or noxious s�muli applied to individuals that do not perceive pain, also ac�vate a similar network of regions (Legrain et al., 2011; Salomons et al., 2016). Nonetheless, these regions do lend insight into the complex nature of interpre�ng the percep�on of noxious s�mulus, and in doing so, highlight key regions within the intrinsic connec�vity networks of the 3 default mode network (DMN) and the salience network (SN) which may provide further insight into differences in pain percep�on. The DMN, SN, and central execu�ve network (CEN) play an integral role in higher order cor�cal processing, including the detec�on, interpreta�on, and response to incoming s�muli (ie. noxious input). The SN is considered to work as a “switch” between the DMN which has higher ac�va�on at rest, and the CEN, which is involved in aten�onal control (Schimmelpfennig et al., 2023). The DMN is most ac�ve when the brain is “res�ng” (e.g. daydreaming) and not engaged in a specific task or aten�ve to specific s�muli, and is associated with increased ac�vity in the PCC, ventromedial prefrontal cor�ces (vmPFC), and precuneus (Schimmelpfennig et al., 2023). Whereas SN ac�va�on occurs when there is an incoming salient s�mulus (e.g., a noxious s�mulus), and involves the integra�on of the insula (known for its involvement in integra�ng external s�muli and execu�ve control) with the ACC and amygdala (Schimmelpfennig et al., 2023). Through suppression of the DMN, the SN allows for the amplifica�on of salient signals to be relayed to the cortex to allow for the hierarchical processing of the detec�on and interpreta�on of noxious s�mulus (Uddin, 2017). This SN ac�va�on in turn, allows for the CEN to gain aten�onal control of the cogni�ve task at hand (e.g., provide a pain ra�ng or to remove the body site from noxious s�mulus exposure) through the involvement of the dorsolateral PFC and posterior parietal cor�ces (Schimmelpfennig et al., 2023). These networks, and their delicate balance of ac�va�on and suppression, are con�nually changing to interpret and interact with both internal and external environmental factors, including highly salient noxious s�muli. 1.1.1.1 The cingulate cortex The cingulate cortex is involved in integra�ng emo�ons, sensa�on, and ac�on, and therefore provides an ideal region to capture further understanding of the DMN and SN ac�va�on (Vogt, 2005). It can be subdivided into four func�onally dis�nct areas: the ACC, midcingulate (MCC), PCC and retrosplenial 4 cingulate cortex (Jumah & Dossani, 2020a). The ACC is highly involved with the affec�ve components of pain, with unpleasant s�muli ac�va�ng the pregenual ACC specifically, as well as the anterior MCC (aMCC) to a lesser degree during nocicep�on as an aversive s�mulus (Rolls, 2019; Vogt, 2005). Strong connec�ons to the orbitofrontal regions and amygdala, including the pregenual ACC (pgACC), contribute to its role in the processing of reward related value (Rolls, 2019). Whereas the PCC is well known for its involvement in visuospa�al processes, with connec�ons to the parahippocampal gyrus, precuneus, and lateral parietal areas (Rolls, 2019). Indirect PCC ac�va�on during nocicep�on has also been reported, interpreted as an aspect of somatosensory ac�va�on (Vogt, 2005). Due to its posi�oning in the cingulate and strong connec�ons to the primary and secondary motor regions, the MCC is thought to be involved in combining ACC and PCC informa�on to produce ac�on or outcomes (Jumah & Dossani, 2020). In contrast, the retrosplenial cingulate is involved in the imagina�on, forma�on, and consolida�on of episodic memory (Jumah & Dossani, 2020). Mul�ple regions of the cingulate are ac�vated directly by noxious input, including the caudal ACC/aMCC, as well as indirectly (e.g., caudal MCC & PCC through somatosensory pathways) (Vogt, 2005). The cingulate cortex’s extensive neural connec�ons to other brain regions, and the well-demonstrated ACC ac�va�on in response to nocicep�on, highlight it as a key region to explore DMN and SN ac�va�on in rela�on to further understanding pain (Jumah & Dossani, 2020; Vogt, 2005). 5 Figure 2. The dynamic networks of the salience and default mode network Posterior cingulate = PCC, ventromedial prefrontal cor�ces = vmPFC, anterior cingulate = ACC, anterior insula = AI, posterior insula = PI 1.1.2 Laser evoked poten�als (LEPs) Laser evoked poten�als (LEPs) provide an ideal electrophysiological measure of nocicep�on. In prac�ce, LEPs involve the exposure to a painful laser s�mulus while measuring electroencephalogram (EEG) ac�vity from scalp electrodes. Increases in neural ac�vity (defined as peaks) occur shortly a�er s�mula�on and are labelled as N1, N2, and P2. The N1 peak is a large nega�ve deflec�on that occurs around 160 ms a�er s�mula�on, best characterized on contralateral electrodes to the s�mulated body site (Ianne� et al., 2008). On the basis of its topography, N1 is considered to reflect the “first order processing” of increased neural ac�vity of the bilateral operculoinsular, anterior cingulate, and contralateral primary somatosensory cor�ces (Garcia-Larrea et al., 2003; Ianne� et al., 2008). The N2 and P2 peaks are defined as a nega�ve-posi�ve biphasic complex that occurs around ~200-350 ms a�er laser 6 hand s�mula�on (Mouraux & Ianne�, 2009). It is best extracted from the central (Cz) electrode ac�vity, which is then referenced to the electrodes placed bilaterally over the mastoids (Mouraux & Ianne�, 2009). The anterior regions of the cingulate are consistently reported to be a major generator of N2P2 responses, in par�cular the caudal ACC genera�ng the P2 response concomitantly with opercular and insular regions (Garcia-Larrea et al., 2003). Combined func�onal magne�c resonance imaging blood oxygenated level dependent (fMRI-BOLD) ac�vity with EEG, addi�onally demonstrates that variable N2P2 responses to a painful s�mulus (contact heat) were correlated significantly with the precuneus and contralateral precentral gyrus ac�vity (Mayhew et al., 2013). Overall, N1 and N2 peaks are considered to reflect “top down” detec�on mechanisms to the incoming s�muli, while P2 peaks reflect “botom-up” mechanisms involved in arousal or aten�onal orienta�on a�er detec�on of the incoming s�muli (Ianne� et al., 2008). Inves�ga�ng LEP outcomes (N1, N2, and P2) demonstrates an avenue to quan�fy local neural ac�vity, in par�cular the cingulate neural ac�vity, in rela�on to the experience of elicited pain. The latency and amplitude of N1, N2, and P2 peaks can be influenced by a variety of factors including novelty (Lorenz & Garcia-Larrea, 2003). Nonetheless, the LEP latencies and amplitudes within an individual are usually rela�vely consistent, providing an avenue to quan�fy individual experiences of nocicep�on. Although LEPs are elicited by nocicep�on, Iane� and colleagues demonstrated similar N1, N2, and P2 responses occur during the delivery of equally salient, non-noxious s�mulus, as well as auditory and visual s�muli (Mouraux & Ianne�, 2009). These authors went a step further to inves�gate s�mulus saliency of LEPs responses through delivering trains of three laser pulses in quick succession, in a randomized order of four different laser intensi�es (Ianne� et al., 2008). Iane� et al (2008) found that the amplitude of the N1, N2, and P2 responses decreased with repe��on (while pain ra�ngs did not change), providing evidence that LEPs outcomes are reflec�ve of the saliency of the s�muli and non-selec�ve for nocicep�on. However, another study demonstrated that analgesia reduced the amplitude of LEPs (N1, N2, and P2 peaks) a�er the administra�on of tramadol, in contrast to the placebo group (Truini 7 et al., 2010). In further support of analgesia reducing LEPs amplitudes, a rebound in amplitudes was reported within this same study a�er the administra�on of the opioid antagonist, naloxone (Truini et al., 2010). These studies demonstrate although LEPs are not solely reflec�ve of nocicep�on, they do provide valuable insight into nocicep�ve processing, as pain is a highly salient s�mulus. 1.1.2.1 Glutamatergic ac�vity As an excitatory neurotransmiter, increased concentra�ons of glutamate are associated with the heightened experience of pain peripherally and in the dorsal horn of the spinal cord in preclinical studies (Cervero, 2009; Miller et al., 2011; Zhou & Danbolt, 2014). Similarly, increased concentra�ons of glutamatergic ac�vity in regions associated with pain percep�on including the thalamus and the dorsolateral pFC are associated with increased pain sensi�vity (Zunhammer et al., 2016). Insight into the res�ng glutamatergic ac�vity within the cingulate cortex, as a known brain region involved in pain percep�on, may assist in explaining differen�al excitatory responses in cor�cal ac�vity, reflected in LEP outcomes (ie. different amplitudes). Therefore, measuring glutamate, which is synthesized from alpha-ketoglutarate in the tricyclic acid cycle, and its precursor, glutamine, provides valuable insight into the local neural ac�vity of brain regions including the cingulate (Rothman et al., 2011). Glutamine, an amino acid, is hydrolyzed by the enzyme glutaminase, resul�ng in the produc�on of glutamate (Bak et al., 2006). In turn, the enzyme glutamate decarboxylase (GAD) removes a carboxy group from glutamate, producing the inhibitory neurotransmiter, gamma aminobutyric acid (GABA) (Petroff, 2002). GAD is considered a rate limi�ng enzyme, as it also carboxylates GABA back into glutamate, which can then be converted back into glutamine via glutamine synthetase (Bak et al., 2006; Petroff, 2002). This conversion and turnover of glutamine and glutamate mainly occurs via astrocytes, which are responsible for the majority of glutamate reuptake circula�ng in the synap�c cle�, as well as the conversion and transporta�on of glutamine back into the presynap�c 8 glutamatergic neuron (Schousboe et al., 2014). The reuptake of glutamate, and concentra�on of molecules circula�ng in the synap�c cle� depends on the amount of glutamate released from the pre-synap�c neuron, which can provide insight into how “ac�ve” a neuron is (O’Donovan et al., 2017). Increased glutamate in the synap�c cle� results in an increased number of molecules binding to receptors on the post-synap�c neuron, which results in increased neural ac�vity (O’Donovan et al., 2017). Receptor ac�va�on of the ionotropic receptors, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) or kainate receptors results in fast synap�c responses, whereas ac�va�on of N-methyl-D-aspartate (NMDA) receptors results in slow synap�c responses (Ingram et al., 2018; Purves et al., 2001). These synap�c responses, called excitatory post synap�c poten�als (EPSPs) propagate down the axon, resul�ng in increased neural ac�vity (Huetner, 2003). These EPSPs are triggered by kainate receptors alone, or in conjunc�on with AMPA and NMDA receptors (Huetner, 2003). These ionotropic glutamatergic receptors (NMDA, AMPA, and kainate) are expressed throughout nocicep�ve and somatosensory neural pathways, including within the dorsal horn of the spinal cord and supraspinal regions including the thalamus, sensory cor�ces, amygdala, and ACC (Bhangoo & Swanson, 2013; Bleakman et al., 2006). Kainate and AMPA administra�on has previously shown to increase the dorsal horn response to pain, whereas receptor antagonists produce analgesic effects (Wozniak et al., 2012). Similarly, increased expression of NMDA receptors is reported in neuropathic and inflammatory pain condi�ons, although NMDA receptor antagonists have had limited success in analgesic treatments or induce substan�al side effects due to their widespread occurrence in the nervous system (e.g., ketamine) (Mion & Villevieille, 2013). Through G-protein signalling cascades, metabotropic glutamatergic receptors (mGluRs) also play an important role in indirect regula�on of excitatory ac�vity and are ac�vated by two molecules of glutamate (or two divalent ions) binding to the receptor (Niswender & Conn, 2010). Specifically, Group I mGluRs increase excitatory ac�vity, while Group II and III mGluRs reduce excitatory ac�vity (Maksymetz et al., 2017; Niswender & Conn, 2010). With the excep�on of mGluR6, all mGluR subtypes are expressed 9 throughout the peripheral and central nervous system, and contribute to the transmission and modula�on of incoming noxious signals, including the development of peripheral and central sensi�za�on (Chiechio, 2016). Preclinical studies have inves�gated and reported some intriguing analgesic effects of mGluR ligands; however similar to NMDA antagonism, the widespread nature of these receptors results in the produc�on of numerous side effects (Pereira & Goudet, 2019). Interes�ngly, in addi�on to their ionotropic proper�es producing excitatory ac�vity, kainate receptors have the ability to modulate glutamatergic ac�vity via G-protein signalling, providing further insight into the complexi�es of glutamatergic ac�vity via receptor ac�va�on. Nonetheless, these studies demonstrate that increased glutamatergic ac�vity is associated with increased nocicep�on, through increased sensa�on or modula�on of the noxious signal. Using the non-invasive neurophysiological technique, magne�c resonance spectroscopy (MRS), we can measure the combined presence of glutamate, and glutamine molecules in the presynap�c neuron (to be released), circula�ng in the synap�c cle�, and reuptake into the astrocytes. This provides valuable insight into the level of neural ac�vity within a specific brain region (ie. the cingulate cortex). 1.1.2.2 Proton magne�c resonance spectroscopy (1H-MRS) Proton MRS (1H-MRS) is a non-invasive, neurophysiological technique that is similar to MRI in that it first applies a strong sta�c magne�c field to align the hydrogen protons (de Graaf, 2019). A second radiofrequency pulse is then absorbed by these protons, resul�ng in knocking them off their (newly) aligned axis (de Graaf, 2019). The protons then release this excess energy based on their surrounding chemical bonds, which is detected by the receiver coils and processed by a computer to reconstruct the unique chemical spectra produced for each metabolite, including glutamate, glutamine, and GABA (Figure 3) (de Graaf, 2019; Gruber et al., 2018). Due to the similar molecular structure of glutamine and glutamate, at lower magne�c field strengths (ie. under 7 Tesla (T)), resolu�on of glutamine is considered unreliable, 10 and therefore o�en reported in combina�on with glutamate for increased accuracy, as Glx (de Graaf, 2019). At rest, increased glutamate concentra�ons in anterior cingulate regions in comparison to posterior regions have been reported at 7T (Dou et al., 2013; Oeltzschner et al., 2019). Dou et al (2013) reported in a small sample size of ‘healthy’ males, there were significantly higher glutamate and GABA concentra�ons in the anterior regions (pregenual ACC), in comparison to more posterior regions, which reflects the distribu�on of the GABAergic receptors (subtype B) (Dou et al., 2013). As glutamate is a precursor of GABA, this suggests increased neural ac�vity occurs within the anterior cingulate regions (O’Donovan et al., 2017; Stamford, 2002). Dou et al (2013) also reported a gradual decline of glutamine concentra�ons in the rostrocaudal direc�on, moving from anterior regions to more posterior regions (Dou et al., 2013). These findings suggest AMPA receptor distribu�on may contribute to glutamine cycling (ie. the conversion to glutamate) is reflected in these res�ng regional cingulate concentra�ons (Dou et al., 2013). These findings are intriguing, as lower field strength (3T) studies inves�ga�ng glutamate concentra�ons did not report any differences in the ACC and PCC (Kim et al., 2018; Sailasuta et al., 2008). Dou et al (2013) demonstrated intriguing res�ng cingulate differences in glutamatergic ac�vity are detectable at 7T in males; however, current 3T studies do not appear to capture these regional differences. Detec�on and understanding of these res�ng excitatory differences in neural ac�vity is therefore the first step to further decipher the role of the cingulate in pain percep�on and varying LEPs responses. 11 Figure 3. Individual MRS spectra produced by glutamine, glutamate, and GABA 1.2 Experimental pain models and neurobiological measures Differences in glutamate concentra�ons have also been reported using 1H-MRS during exposure to pain, however these findings vary. A recent systema�c review reported that acute pain exposure increased glutamatergic ac�vity, in comparison to baseline, was reported in only five of 10 included studies (Archibald, MacMillan, Enzler, et al., 2020). Within these 10 studies, five measured glutamatergic ac�vity in the ACC specifically, which included significant increases in two of these studies (glutamate and Glx (Mullins et al., 2005), or Glx alone (Cleve et al., 2015)), and three studies repor�ng no change (in glutamine (Mullins et al., 2005), glutamate (Chiappelli et al., 2018; T. M. Hansen et al., 2014), or Glx (Kupers et al., 2009)). Another experimental pain paradigm demonstrated increased glutamate concentra�ons in the ACC occurred at the onset of pain but were not correlated with the provided verbal pain reports (Archibald, MacMillan, Graf, et al., 2020). These 1H-MRS studies suggest some evidence in support of 12 glutamate concentra�ons in the ACC reflec�ng the an�cipated increase in neural ac�vity elicited by noxious s�mulus. This increase in ACC neural ac�vity has been demonstrated in neuroimaging studies, which reported that increased fMRI ac�vity in the ACC was associated with involvement of cogni�ve tasks during pain (Emmert et al., 2014; Silvestrini et al., 2020), and cogni�ve tasks in comparison to pain (Davis et al., 1997). These studies demonstrate both experiencing pain and cogni�ve tasks, which require SN ac�va�on, are reflected through ACC ac�vity. In further support, increased SN ac�vity has also been correlated with verbal pain ra�ngs of par�cipants exposed to acutely painful s�muli, through increased BOLD ac�va�on of the ACC (in addi�on to the superior frontal gyrus) (Mayhew et al., 2013). Addi�onally, reduced SN ac�vity was associated with increased DMN ac�vity, through repor�ng lower glutamate concentra�ons in the dorsal ACC predicted increased BOLD ac�vity in brain regions associated with the DMN including the retrosplenial cortex, orbitofrontal regions, and the inferior parietal lobe (Falkenberg et al., 2012). Of par�cular relevance to this thesis, a study using fMRI-BOLD with 1H-MRS reported that the level of DMN ac�vity was nega�vely correlated with GABA concentra�ons (higher GABA resulted in less DMN ac�vity), and posi�vely correlated with glutamate concentra�ons (higher glutamate resulted in increased DMN ac�vity) in the PCC/precuneus, in comparison to the control group (Hu et al., 2013). In contrast, another study by Cos�gan et al (2019), reported that neither Glx or GABA concentra�ons in the precuneus/PCC were correlated with fMRI BOLD ac�vity within these regions during a cogni�ve task (Cos�gan et al., 2019). These studies demonstrate measuring glutamatergic ac�vity within the ACC and PCC may provide insight into the rela�ve ac�va�on of the SN, and DMN, respec�vely. However, further research is required to support these current findings, and first understand these dynamic networks at rest, in order to decipher the resultant differences in neural ac�vity that occur in the cingulate during exposure to noxious s�mulus (ie. laser). 13 Chapter 2: RESEARCH AIMS 2.1 Summary Previous research has demonstrated that varied responses in LEP outcomes are reflected mainly from anterior cingulate neural ac�vity and influenced by a mul�tude of factors including saliency and aten�on to the incoming s�muli. We aimed to inves�gate res�ng differences in the neurobiological ac�vity of the cingulate, measured through glutamate, glutamine, and Glx, and this influence on LEP outcomes. We addi�onally inves�gated pain ra�ngs to gain insight into par�cipants’ percep�on of pain. Based on previous research demonstra�ng the involvement of the DMN and SN in pain percep�on, we focused our inves�ga�on of glutamatergic ac�vity on the ACC and PCC, as the respec�ve ‘hubs’ of these networks to further understand their contribu�on to LEP outcomes and pain ra�ngs. 2.2 Research aims: Building on previous studies, the aims and hypotheses (H) of my thesis were to: 1) Examine differences in concentra�on of excitatory metabolites across the cingulate cortex. H: Similar to that reported at 7T, the acquisition of 1H-MRS at 3T will reveal decreasing concentrations of glutamate, glutamine, and Glx in a rostral to caudal manner. 2) Characterize the rela�onship between res�ng concentra�ons of excitatory metabolites in the cingulate cortex and individual responses to noxious s�mula�on. H: Concentrations of glutamate, glutamine, and Glx in the cingulate cortex will be greater in high compared to low laser stimulation responders. 14 3) Determine the extent that regional differences in res�ng concentra�ons of excitatory metabolites in the cingulate cortex contribute to individual responses to noxious s�mula�on. H: Concentrations of glutamate, glutamine, and Glx will be greater in high compared to low laser stimulation responders in regions associated with the SN and DMN. 15 Chapter 3: METHODS 3.1 Experimental protocol Par�cipants were recruited that self-reported no neurological disease or chronic pain, whom were eligible for MRI, and were between 18 - 60 years of age. Par�cipants reported to the laboratory at the Blusson Spinal Cord Centre on the Vancouver General Hospital Campus between 8 and 11 AM a�er a 12 hour fast to complete ques�onnaires and undergo acquisi�on of LEPs (Figure 4A). To acquire MRS, par�cipants then travelled with a research assistant to the MRI Research Centre on the UBC campus by way of UBC shutle services (Figure 4B). During this travel period, and approximately 1 hour before MRS procedures, par�cipants were fed a muffin. The study was designed according to the Declara�on of Helsinki and approval by the Clinical Research Ethics Board at the UBC, with recruitment ongoing. 3.1.1 Demographics and ques�onnaires Demographic informa�on including sex, age, and any health concerns/medica�ons recently ingested was collected a�er comple�on of the consent form and before acquisi�on of LEPs. Addi�onally, par�cipants were asked to not consume alcohol 24 hours prior to tes�ng. A�er an amendment of the ethics approval, a subset of par�cipants (n = 18) filled out two addi�onal ques�onnaires (Appendix A): the Pain Sensi�vity Ques�onnaire and the Depression Anxiety Stress Scale (DASS). 16 Figure 4. Experimental paradigm Par�cipants underwent a two-part protocol. A) Four different laser intensi�es were applied to the forearm in blocks of 20, while recording electroencephalogram (EEG) ac�vity. B) 1H-MRS was completed in four voxels: the anterior (ACC), anterior mid (aMCC), posterior mid (pMCC), and posterior (PCC) cingulate cor�ces, in addi�on to a T1-weighted MRI scan to collect glutamate (Glu), glutamine (Gln) and combined (Glx) concentra�ons. 1H- MRS = proton magne�c resonance spectroscopy, MRI = magne�c resonance imaging 3.2 LEPs protocol 3.2.1 EEG set-up Par�cipants were fited with an EEG cap with 32 ac�ve electrodes using the interna�onal 10-20 posi�oning (Brain Vision Recorder, Vers. 1.24.0001, Brain Products GmbH, Gilching, Germany). Each electrode was prepared with electrode gel to reduce impedances to under 10kΩ. The raw EEG data was sampled at 1000Hz, with a notch filter at 60Hz. 3.2.2 Laser s�mula�on Laser s�mula�on was applied to the right inner forearm. To familiarize par�cipants to s�mula�on (thereby reducing saliency), 8-10 laser s�muli were first applied to the forearm (no EEG acquired). To avoid 17 blink ar�facts, par�cipants were instructed to blink 2 to 3 seconds a�er s�mula�on and to remain with eyes open and s�ll un�l the next s�mulus. LEPs were acquired in response to blocks of 20 s�muli delivered in randomized order at 2.75, 3.0, 3.25, and 3.5J, with 8-12 second interpulse intervals. The selec�on of intensi�es was based on previous experience in the lab (De Schoenmacker et al., 2022). A�er each laser s�muli, par�cipants were asked to provide a verbal pain ra�ng two or three seconds a�er percep�on using the 11-Numeric Ra�ng Scale (0 – no pain, 10 – most pain possible). Each block of s�mula�on was separated by five minutes. In total, all par�cipants were exposed to 80 s�mula�ons. Laser s�mula�on intensity was reduced by 0.25 or 0.5J if a par�cipant had very high pain ra�ngs during the familiariza�on period or was visibly uncomfortable. Par�cipants were blinded to the intensity of s�mula�on during data collec�on (and were informed immediately a�erwards), and frequent checks were conducted with the par�cipant to ensure they were comfortable before moving to the next block of s�mula�on. 3.2.3 Analysis of EEG data Brain Vision Analyzer Version 2.2 was used to process and analyze EEG data. First, EEG data was filtered from 1-30Hz. Next, ocular correc�on was performed to remove blink ar�facts. Noisy trials and channels were also removed, DC trend and baseline correc�on was applied. Trials were segmented between 500 ms pre to 1000 ms post s�mulus and averaged across 20 s�mula�ons at each intensity. N1 waveform amplitudes were extracted from the C3 electrode (referenced to Fz) between 0-200ms. N2P2 amplitudes were extracted from the Cz electrode (referenced to linked M1 and M2 electrodes) between 100-300ms and 200-500ms, respec�vely. An example of a raw N1 and N2P2 trace, with the respec�ve topography and electrode placement is visualized in Figure 5. These N1, N2, and P2 peak amplitudes, latencies, and raw traces were extracted from each par�cipant for sta�s�cal analysis and visualiza�on of figures. 18 Figure 5. LEP example trace An example of a raw LEP traces 500 ms prior to laser delivery (at 0 ms) to 1000 ms post s�mulus. N1 (pink) with the topography of generalized ac�vity at the N1 peak, and the N2P2 trace (dark blue), with respec�ve topography at the N2 and P2 peaks (A). Red on the topography represents increased ac�vity and blue represents decreased ac�vity. N1 was extracted from the C3 electrode and referenced to Fz (in pink), and Cz was extracted from the Cz electrode and referenced to M1 and M2 (in blue) from the standard 10-20 interna�onal system placement for 32 electrodes (B). 3.3 MRI and MRS protocols 1H-MRS scans were performed using a Phillips Eli�on 3.0 Tesla MRI at the UBC MRI Research Centre. A scanning session included a T1 weighted MRI scan (MPRAGE, TE/TR, = 4.33/9.296, FOV(ap/rl/�) =256/256/180 mm3) and four single voxel MRS scans (sLASER, TE/TR = 32/5000, 64 averages, �me = 8 min 5s, voxel size = 24 x 22 x 15 mm3= 7.9 mL) within the cingulate cortex: the anterior cingulate (ACC), anterior mid (aMCC), posterior mid (pMCC), and posterior cingulate (PCC) cor�ces (Figure 6). Voxels were posi�oned by trained MRI technicians. 19 3.3.1 Analysis of MRI and 1H-MRS data Preprocessing of individual transients (frequency alignment, eddy current correc�on, and quality assurance) was completed using an in-house MATLAB script (MATLAB 2022b). The preprocessed metabolite data was then fited using linear combina�on modelling (LCM, version 6.3-0L), with a basis set that was created in-house and simulated using FID-A (James Near, 2020). This basis set included the spectral fit for glutamate (Glu), Glx (glutamate + glutamine), glutamine (Gln), in addi�on to other metabolites including phosphocrea�ne, N-acetyl aspartate, myo-inositol, glycine, and lactate. Separately, the 3DT1 anatomical MRI scan was processed in FMRIB’s so�ware library (FSL V6.0.5.2), using BET and FAST tools of FSL with the default values for brain extrac�on, and segmenta�on of cerebrospinal fluid (CSF), grey and white mater, respec�vely. A second in-house MATLAB script quan�fied the final metabolite levels in millimolar (mM)) within each of the four cingulate voxels, respec�vely, and adjusted for CSF, grey and white mater through combining the LCM outputs were combined with the 3DT1 segmenta�on from FSL (Figure 7). Sta�s�cal analysis focused on glutamate and Glx concentra�ons primarily, with glutamine inves�gated to provide further support of the glutamatergic findings. MRS is an incredibly sensi�ve technique, with movement of the par�cipant, blood, cerebrospinal fluid (CSF), and air flow (ie. voxels placed near the cor�cal surface), contribu�ng to a lower signal to noise ra�o (SNR), and a reduced quality of data overall (de Graaf, 2019; Kreis, 2004). The concern for movement was mi�gated by examining quick T1 weighted anatomical images between each MRS acquisi�on. If a par�cipant moved substan�ally, MRS was reacquired in a given voxel. Quality metrics, including signal to noise ra�o (SNR), full width at half maximum (FWHM) and Cramer Rao lower bounds (CRLBs), were further examined (Kreis, 2004). On the basis of quality metrics, exclusion of a voxel from a par�cipant was done through consensus within study team members. Addi�onally, our quan�fica�on of glutamine was in an exploratory fashion, with cau�on taken when interpre�ng findings due to the spectral overlap with glutamate under 7T (de Graaf, 2019). 20 Figure 6. 3DT1 MRI with MRS voxel placement The approximate placement of the four MRS voxels, from le� to right: the anterior, anterior mid-, posterior-mid, and posterior cingulate cor�ces on the 3DT1 MRI scan. MRI = magne�c resonance imaging, MRS = magne�c resonance spectroscopy Figure 7. Preprocessing of 1H-MRS data Individual proton magne�c resonance spectroscopy (1H-MRS) transients were processed in-house using MATLAB to ensure quality assurance, eddy current correc�on (ECC), and frequency alignment (FA) (A). The 21 MRS data was then fited in LC model (B). The FSL outputs from the raw MRI T1 weighed image, undergo brain extrac�on (C), and segmenta�on of grey (GM), and white mater (WM), and cerebrospinal fluid (CSF) (D), and are combined with the LCM outputs in a second in-house script to quan�fy glutamate (Glu), glutamine (Gln), and the combined Glu + Gln (Glx) within each voxel of interest, adjusted for the CSF, grey and white mater. MRI = magne�c resonance imaging 3.4 Sta�s�cal analysis Normality of the data was examined with visual inspec�on of histograms and Q-Q plots. To address Aim 1, excitatory metabolite concentra�ons were separately compared across the four cingulate regions (ACC, aMCC, pMCC, and PCC) using a one-way repeated measures analysis of variance (ANOVA). In the presence of a significant main effect, differences between regions were further examined by pair-wise student’s independent t-tests, with significant set at p<0.05. The interac�on of sex and region was then examined using a two-way repeated measures ANOVA for each excitatory metabolite. In case of a significant interac�on effect, differences between men and women in excitatory metabolite concentra�on was examined separately in each region (p<0.05). In order to reduce variability in responses to laser s�mula�on to discrete categories (e.g., high, medium, low responders), LEP responses from the four different laser intensi�es were first clustered on the basis of LEP response outcomes. An op�mal number of clusters was first determined by interpre�ng a Within Sum of Squares (WSS) plot (Maria P. Frushicheva, 2016). In brief, the op�mal number of clusters depends on the “elbow” in the plot (Syakur et al., 2018), which was then confirmed using a silhouete plot. K-means clustering was then applied to determine clusters for each LEP outcome. To confirm the accuracy of the clustering approach, LEP outcomes were then ploted and visually inspected. This clustering approach was also applied to the pain ra�ngs from the four laser intensi�es, with confirma�on of accuracy conducted through visually plo�ng the pain ra�ng outcomes. To address Aims 2 and 3, the main effect of LEP cluster and interac�on between each LEP cluster and region were respec�vely examined for each excitatory metabolite concentra�ons using a two-way 22 repeated measures ANOVA. Significant interac�on effects were post-hoc examined with independent student’s t-tests to determine regional differences in excitatory metabolite concentra�ons (p<0.05). All sta�s�cal analysis was performed in R version 4.2.2 (see Supplementary Table 1). 23 Chapter 4: RESULTS 4.1 Demographics LEPs and MRS were acquired from 50 par�cipants (26 females), with the mean age of 27.6 +/- 6.4 years old. The majority (16/26) of female par�cipants were scanned during the follicular phase of their menstrual cycle, with the remaining four scanned during their luteal phase and two during ovula�on. For four females it was unclear when their last period was (ie. due to medica�on/IUD or had missing data). 4.2 Regional differences in excitatory metabolite concentra�ons in the cingulate cortex To address Aim 1, we first examined concentra�ons of excitatory metabolites across the cingulate cortex. Due to a scanner error, MRS data from two par�cipants was unavailable. MRS quality metrics from the remaining subjects (n=48) are shown in Table 1. On the basis of visual inspec�on and SNR, no voxels from any par�cipant needed to be excluded. Table 1. Quality metrics of 1H-MRS analyses Voxel loca�on SNR (+/- SD) LW (ppm (SD)) WM (% of 100) GM (% of 100) CSF (% of 100) Glu CRLB (mM (SD)) Glx CRLB (mM (SD)) Gln CRLB (mM (SD)) ACC 23 (4.5) 2.86e-2 (6.66e-3) 21.6 57.9 20.6 0.43 (0.09) 0.56 (0.30) 0.39 (0.09) aMCC 26 (4.4) 2.63e-2 (6.17e-3) 29.6 53.7 16.7 0.37 (0.09) 0.46 (0.11) 0.32 (0.10) pMCC 28 (3.8) 2.73e-3 (4.99e-3) 33.3 52.8 15.3 0.35 (0.05) 0.42 (0.06) 0.29 (0.07) PCC 29 (4.8) 2.75e-2 (4.60e-3) 30.5 57.7 11.8 0.34 (0.08) 0.43 (0.23) 0.31 (0.07) SNR = signal to noise ra�o, FWHM = full width at half maximum, Glu = glutamate, Glx = glutamate + glutamine, Gln = glutamine, CRLB = Cramer Rao lower bounds, WM = white mater, GM = grey mater, CSF = cerebrospinal fluid, SD = standard devia�on, mM = millimolar, LW = line width, ppm = parts per million *Note: SNR and LW are reported for each voxel from the LCM outputs and provide insight into the ability to discern spectral features. The WM, GM, and CSF are extracted from the FSL segmentation and contribute to the final metabolite concentrations calculated. CRLBs are reported with the final metabolite concentrations, as a measure of variability. 24 Cingulate region had a significant main effect on all excitatory metabolite concentra�ons (Figure 8). Post hoc analysis indicated that concentra�ons were significantly higher in the ACC and aMCC in comparison to the more posterior regions (Table 2). This confirms our hypothesis that excitatory metabolite concentra�on followed a rostral to caudal, high to low gradient. There was no main effect of sex on glutamate or Glx, nor did sex interact with region in rela�on to glutamate, Glx, or glutamine (Figure 9). The absence of a main and interac�on effects suggests that overall concentra�ons and a rostral-caudal excitatory metabolite concentra�on gradient is similar in both men and women. There was, however, a trend (p=0.05) for a main effect of sex on glutamine, with men tending to have higher overall concentra�ons of glutamine (Table 3). On the basis of this main effect, subsequent analyses of glutamine (Aims 2 and 3) included sex as a covariate. Table 2: Results by metabolite and region Metabolite Concentra�on Results Post hocs Figure (mM +/- SD) Comparisons p <0.05 Glu ACC: aMCC: 11.4 +/- 1.13 10.5 +/- 0.95 F(3,141) = 29.2, p= 9.78e-15, ACC:aMCC ACC:pMCC 2.95e-8 2.04e-10 8A pMCC: 9.99 +/- 0.69 Ƞ2G = 0.2 ACC:PCC 7.02e-8 PCC: 10.2 +/- 0.93 aMCC:pMCC 2.00e-3 aMCC:PCC NS pMCC:PCC NS Glx ACC: aMCC: 14.8 +/- 1.88 13.7 +/- 1.51 F(3,141) = 24.2, p= 1.09e-12, ACC:aMCC ACC:pMCC 5.47e-5 5.50e-8 8B pMCC: 12.9 +/- 0.92 Ƞ2G = 0.23 ACC:PCC 9.90e-8 PCC: 13.0 +/- 1.24 aMCC:pMCC 5.01e-4 aMCC:PCC NS pMCC:PCC NS Gln ACC: aMCC: 3.52 +/- 0.81 3.19 +/- 0.85 F(3,141) = 15.1, p= 1.38e-08, ACC:aMCC ACC:pMCC 0.047 2.66e-6 8C pMCC: 2.86 +/- 0.55 Ƞ2G =0.15 ACC:PCC 1.87e-6 PCC: 2.76 +/- 0.58 aMCC:pMCC 1.00e-3 aMCC:PCC 1.00e-3 pMCC:PCC NS ACC = anterior cingulate cortex, aMCC = anterior mid-cingulate cortex, pMCC = posterior mid-cingulate, PCC = posterior cingulate cortex, Glu = glutamate, Glsx = glutamate + glutamine, Gln = glutamine, Ƞ2G = generalized eta squared (effect size), NS = not significant, SD = standard devia�on, mM = millimolar, p<0.05 25 Table 3: Results for metabolites by region and sex for n= 48 par�cipants (25 females) ACC = anterior cingulate cortex, aMCC = anterior mid-cingulate cortex, pMCC = posterior mid-cingulate, PCC = posterior cingulate cortex, Glu = glutamate, Glx = glutamate + glutamine, Gln = glutamine, Ƞ2G = generalized eta squared (effect size), SD = standard devia�on, F = female, M = male, mM = millimolar, p<0.05 Metabolite Concentra�on (mM (SD)) F M Main effect (sex) Main effect (region) Interac�on (sex x region) Figure Glu ACC aMCC pMCC PCC 11.4 (1.18) 10.5 (1.10) 9.94 (0.72) 10.4 (0.85) 11.5 (1.09) 10.5 (0.77) 10.0 (0.68) 10.0 (0.99) F(1,46) = 0.021, p = 0.88 F(3,138) = 29.2, p = 1.13e-14, Ƞ2G = 0.26 F(3,138) = 0.806, p = 0.49 9A Glx ACC aMCC pMCC PCC 14.9 (1.73) 13.5 (1.64) 12.6 (0.99) 13.1 (1.31) 14.7 (2.07) 13.9 (1.36) 13.2 (0.73) 12.9 (1.18) F(1,46) = 0.48, p = 0.49 F(3,138) = 24.06, p = 1.38e-12, Ƞ2G = 0.23 F(3,138) = 1.26, p = 0.29 9B Gln ACC aMCC pMCC PCC 3.54 (0.86) 2.99 (0.77) 2.63 (0.51) 2.66 (0.62) 3.50 (0.77) 3.41 (0.91) 3.10 (0.50) 2.88 (0.53) F(1,46) = 4.02, p = 0.05, Ƞ2G = 0.04 F(3,138) = 15.1, p = 1.5e-8, Ƞ2G = 0.16 F(3,138) = 1.67, p = 0.18 9C 26 Figure 8. Regional cingulate metabolite concentra�ons The median, upper and lower quar�le ranges for the respec�ve concentra�ons of glutamate (A), Glx (glutamate and glutamine combined) (B), and glutamine (C), across the anterior (ACC), anterior mid (aMCC), posterior mid (pMCC), and posterior (PCC) cingulate cortex for n=48 par�cipants. mM= millimolar, *denotes p<0.05, **denotes p<0.001 27 Figure 9. Regional cingulate differences by sex The median, upper and lower quar�le ranges for the respec�ve concentra�ons of glutamate (A) Glx (glutamate and glutamine combined) (B), and glutamine (C), across the anterior (ACC), anterior mid (aMCC), posterior mid (pMCC), and posterior (PCC) cingulate cortex. Concentra�ons are grouped by sex (25 female and 23 male). mM= millimolar 28 4.3 The rela�onship between excitatory metabolite concentra�on and LEP outcomes To address Aim 2, we first determined the op�mal number of clusters needed to characterize LEP outcomes. N1, N2, and P2 waveforms were detected in all subjects, with four subjects missing a single laser trial (four missing trials of intensity 4 – one trial missing due to technological difficul�es and three due to par�cipants having had enough of the laser, in which we ceased LEPs data collec�on a�er 3 trials). Ra�ngs were not available for one par�cipant due to a recording error. Visual inspec�on of N1 revealed contralateral topography, while N2 and P2 were maximal at the Cz electrode. Amplitudes and numeric pain ra�ngs increased in response to increasing s�mula�on intensi�es, with the highest intensity generally elici�ng the largest amplitude responses and highest ra�ng (Figure 10). Figure 10. Average EEG traces for the four laser intensi�es The average EEG traces from n = 50 par�cipants for the N1 outcomes (A) and N2P2 outcomes (B) at the four laser intensi�es. For each of our laser outcomes (i.e., N1, N2, P2 and pain ra�ngs (PR)), par�cipants were clustered into high and low responders to s�mula�on. We confirmed dis�nct differences in large and small amplitude responders for LEPs (N1, N2, and P2 combined) (Figure 11), N1 alone (Figure 12), N2 alone 29 (Figure 13), P2 alone (Figure 14), and high and low pain ra�ngs (Figure 15). A descrip�on of each of the clusters, including the number of par�cipants in each, is outlined in Table 4. Table 4. Par�cipants clustered by LEP and PR outcomes (total = 50) Clustering (for Intensity 1, 2, 3,4 combined) Cluster 1 (n) Cluster 2 (n) LEPs (N1, N2, P2 combined) Smaller amplitudes (39) Larger amplitudes (11) N1 Smaller amplitudes (27) Larger amplitudes (23) N2 Smaller amplitudes (37) Larger amplitudes (13) P2 Smaller amplitudes (35) Larger amplitudes (15) PR Higher pain ra�ngs (18) Lower pain ra�ngs (31) LEPs = laser evoked poten�als, PR = pain ra�ngs, n = number of par�cipants Figure 11. Clustering by LEP (N1, N2, and P2) outcomes Clustering of the four intensi�es of the combined LEP (N1, N2, and P2) outcomes determined there were two op�mal clusters (A). Based on the LEP amplitudes grouped by cluster 1 and 2 (B), the N1, N2, and P2 outcomes were visually compared between clusters by amplitude (C) and N1 and N2P2 traces averaged across the four laser intensi�es by cluster (D). 30 Figure 12. Clustering by N1 outcomes Clustering of the four intensi�es of N1 outcomes determined there were two op�mal clusters (A) based on N1 outcomes from n=50 par�cipants (B). N1 amplitudes were visually compared between cluster 1 and 2 by amplitude (C), and N1 traces averaged across the 4 laser intensi�es by cluster (D). Figure 13. Clustering by N2 outcomes Clustering of the four intensi�es of N2 outcomes determined there were two op�mal clusters (A) based on N2 outcomes from n=50 par�cipants (B). N2 amplitudes were visually compared between cluster 1 and 2 (C), and N2P2 traces averaged across the 4 laser intensi�es by cluster (D). 31 Figure 14. Clustering by P2 outcomes Clustering of the four intensi�es of P2 outcomes determined there were two op�mal clusters (A) based on P2 outcomes from n=50 par�cipants (B). P2 amplitudes were visually compared between cluster 1 and 2 (C), and N2P2 traces averaged across the 4 laser intensi�es by cluster (D). Figure 15. Clustering by PR outcomes Clustering of the four laser intensi�es determined there were two op�mal clusters (A) based on pain ra�ng (PR) outcomes from n=50 par�cipants (B). Pain ra�ngs across the four intensi�es were compared between Cluster 1 and 2 (C). 32 There was no significant main effect of LEPs (Figure 16), N1 (Figure 17), N2 (Figure 18), or P2 clustering (Figure 19) on any metabolite concentra�on (Table 5). This observa�on indicates no overall rela�onship between amplitude of responses to laser s�mula�on and overall cingulate cortex excitatory metabolite concentra�on. There was, however, a significant main effect of pain ra�ng cluster for glutamate (but not Glx or glutamine) (Table 5). Par�ally confirming our Aim 2 hypothesis, this significant main effect indicates that par�cipants with higher glutamate concentra�ons in the cingulate cortex are associated with greater sensi�vity to laser s�mula�on (Figure 20). 4.4 Regional varia�ons in excitatory metabolites and responses to laser s�mula�on Next, we examined interac�on effects between region and cluster. This was done to assess the associa�on between regional excitatory metabolite concentra�on and responses to laser s�mula�on (Aim 3). While there were no significant interac�on effects between LEPs (Figure 16), N1 (Figure 17), or N2 (Figure 18) clusters and region for any excitatory metabolite, there was for P2 amplitude in rela�on to glutamate. This interac�on was characterized by low and high concentra�ons of glutamate in the ACC and PCC, respec�vely, in par�cipants with larger P2 amplitudes (Figure 19A). However, there were no significant interac�on effects for P2 cluster and region for Glx or glutamine (Figures 19B, C). There was also a significant region and pain ra�ngs cluster interac�on effect in rela�on to Glx, and a trend found for glutamate, however not for glutamine (Table 5). Dis�nct from P2 amplitude, these interac�ons were characterized by higher glutamate and Glx concentra�ons in the rostral regions of the cingulate (i.e., ACC and aMCC) compared to pMCC and PCC in par�cipants with high pain ra�ngs (Figures 20A, B). Collec�vely, these interac�on effects confirm our Aim 3 hypothesis that excitatory metabolites concentra�ons in the hubs of the DMN (PCC, P2 amplitude) and SN (ACC, pain ra�ngs) are associated with responses to laser s�mula�on. 33 Further examina�on of the significant interac�ons for glutamate by region and P2 clustering demonstrated significant concentra�on differences in the PCC, whereas significant interac�ons for Glx by region and PR clustering demonstrated significant concentra�on differences in the ACC (Table 6). Figure 16. Regional concentra�ons across the cingulate cortex between LEP clusters The median, upper and lower quar�le ranges for the respec�ve concentra�ons of glutamate (A), Glx (glutamate and glutamine combined) (B), and glutamine (C), across the anterior (ACC), anterior mid (aMCC), posterior mid (pMCC), and posterior (PCC) cingulate cortex. Concentra�ons are grouped by small 34 (Cluster 1) and large (Cluster 2) evoked poten�al (EP) responses based on the combined N1, N2, and P2 outcomes at the four different laser intensi�es for n=48. mM= millimolar Figure 17. Regional concentra�ons across the cingulate cortex between N1 clusters The median, upper and lower quar�le ranges for the respec�ve concentra�ons of glutamate (A) Glx (glutamate and glutamine combined) (B), and glutamine (C), across the anterior (ACC), anterior mid (aMCC), posterior mid (pMCC), and posterior (PCC) cingulate cortex. Concentra�ons are grouped by small (Cluster 1) and large (Cluster 2) evoked poten�al (EP) responses based on N1 outcomes at the four different intensi�es for n=48. mM= millimolar 35 Figure 18. Regional concentra�ons across the cingulate cortex between N2 clusters The median, upper and lower quar�le ranges for the respec�ve concentra�ons of glutamate (A), Glx (glutamate and glutamine combined) (B), and glutamine (C), across the anterior (ACC), anterior mid (aMCC), posterior mid (pMCC), and posterior (PCC) cingulate cortex. Concentra�ons are grouped by small (Cluster 1) and large (Cluster 2) evoked poten�al (EP) responses based on N2 outcomes at the four intensi�es of n=48. mM= millimolar 36 Figure 19. Regional concentra�ons across the cingulate cortex between P2 clusters The median, upper and lower quar�le ranges for the respec�ve concentra�ons of glutamate (A), Glx (glutamate and glutamine combined) (B), and glutamine (C), across the anterior (ACC), anterior mid (aMCC), posterior mid (pMCC), and posterior mid (PCC) cingulate cortex. Concentra�ons are grouped by small (Cluster 1) and large (Cluster 2) evoked poten�al (EP) responses based on P2 outcomes for n=48. mM= millimolar, *denotes p<0.05 37 Figure 20. Regional concentra�ons across the cingulate cortex between PR clusters The median, upper and lower quar�le ranges for the respec�ve concentra�ons of glutamate (A) Glx (glutamate and glutamine combined) (B), and glutamine (C), across the anterior (ACC), anterior mid (aMCC), posterior mid (pMCC), and posterior (PCC) cingulate cortex. Concentra�ons are grouped by high (Cluster 1) and low (Cluster 2) pain ra�ngs (PR) averaged across the four different laser intensi�es of n=48. mM= millimolar, *denotes p<0.05 38 Tables 5: Results by cluster and region Laser outcome Metabolite Concentra�on (mM (SD)) Cluster 1 Cluster 2 Main effect (cluster) Main effect (region) Interac�on (cluster x region) Sex as a covariate Figure LEPs Glu ACC aMCC pMCC PCC 11.5 (1.12) 10.6 (0.95) 10.0 (0.67) 10.1 (0.91) 11.0 (10.7) 10.2 (0.96) 9.96 (0.79) 10.5 (0.97) F(1,46) = 0.37, p = 0.55 F(3,138) = 16.2, p = 4.65e-09, Ƞ2G = 0.16 F(1,138) = 2.19, p=0.09 NA 16A Glx ACC aMCC pMCC PCC 14.9 (1.97) 13.8 (1.34) 13.0 (0.82) 12.8 (1.10) 14.5 (1.57) 13.2 (1.99) 12.7 (1.25) 13.4 (1.40) F(1,46) = 0.30, p = 0.59 F(3,138) = 14.9, p = 1.75e-8, Ƞ2G = 0.16 F(3, 138) = 1.54, p=0.21 NA 16B Gln ACC 3.52 (0.80) 3.54 (0.88) F(1,45) = F(3,135) = F(3,135) = Sex: 16C aMCC pMCC PCC 3.27 (0.76) 2.89 (0.48) 2.72 (0.57) 2.95 (1.12) 2.73 (0.75) 2.92 (0.61) 2-e3, p = 0.97 p = 1.58e-5, Ƞ2 G = 0.10 0.91, p = 0.44 F(3,135) = 0.39, p = 0.25 Sex x region: F(3,135) = 0.39, p=0.25 N1 Glu ACC aMCC pMCC PCC 11.5 (1.07) 10.4 (0.77) 10.1 (0.51) 10.1 (1.06) 11.3 (1.20) 10.6 (1.12) 9.91 (0.85) 10.4 (0.74) F(1,46) = 0.06, p = 0.81 F(3,138) = 28.9, p = 1.51e-12, Ƞ2G = 0.26 F(3,138) = 1.01, p = 0.39 NA 17A Glx ACC aMCC pMCC PCC 14.6 (1.95) 13.6 (1.31) 12.9 (0.81) 12.7 (1.30) 15.1 (1.82) 13.8 (1.72) 12.9 (1.05) 12.3 (1.15) F(1,46) = 0.41, p = 0.29 F(3,138) = 23.9, p = 1.51e-12, Ƞ2G = 0.23 F(3, 138) = 0.41, p = 0.75 NA 17B Gln ACC 3.39 (0.76) 3.67 (0.86) F(1,44) = F(3, 135) = F(3,135) = Sex: 17C aMCC pMCC PCC 3.17 (0.78) 2.85 (0.49) 2.6 (0.49) 3.22 (0.94) 2.87 (0.63) 2.86 (0.66) 1.96, p = 0.17 14.8, p = 2.08e-08, Ƞ2G = 0.16 0.21, p = 0.88 F(1,45) = 4.99, p = 0.03, Ƞ2G = 0.04 Sex x region: F(3, 135) = 1.41, p = 0.88 N2 Glu ACC aMCC pMCC PCC 11.5 (1.13) 10.5 (0.79) 10.0 (0.66) 10.2 (1.03) 11.3 (1.15) 10.5 (1.33) 9.89 (0.80) 10.3 (0.59) F(1,46) = 0.06, p = 0.81 F(3,138) = 21.5, p = 1.67e-11, Ƞ2G = 0.21 F(3,138) = 0.31, p=0.12 NA 18A Glx ACC aMCC pMCC PCC 14.8 (1.97) 13.8 (1.32) 13.0 (0.83) 13.0 (1.34) 14.9 (1.68) 13.3 (1.93) 12.6 (1.12) 12.9 (0.95) F(1,46) = 0.73, p = 0.39 F(3,138) = 20.4, p = 5.41e-11, Ƞ2G = 0.21 F(3,138) = 0.52, p =0.67 NA 18B Gln ACC 3.51 (0.83) 3.57 (0.81) F(1, 45) = F(3,135) = F(3,135) = Sex: aMCC pMCC PCC 3.35 (0.80) 2.92 (0.56) 2.82 (0.61) 2.78 (0.89) 2.68 (0.51) 2.62 (0.48) 1.08, p = 0.30 12.4, p = 3.15e-6, Ƞ2G = 0.14 1.16, p = 0.33 F(1,45) = 2.55, p = 0.12 Sex x region: F(3,135) = 1.10, p = 0.35 18C 39 ACC = anterior cingulate cortex, aMCC = anterior mid-cingulate cortex, pMCC = posterior mid-cingulate, PCC = posterior cingulate cortex, Glu = glutamate, Glx = glutamate + glutamine, Gln = glutamine, LEPs = laser evoked poten�als (combined N1, N2, and P2 outcomes), PR = pain ra�ngs, NA = not applicable, Ƞ2G = generalized eta squared, SD = standard devia�on, p<0.05 Laser outcomes Metabolite Concentra�on (mM (SD)) Cluster 1 Cluster 2 Main effect (cluster) Main effect (region) Interac�on (cluster x region) Sex as a covariate Figure P2 Glu ACC aMCC pMCC PCC 11.6 (1.10) 10.5 (1.02) 9.97 (0.69) 10.0 (0.88) 11.1 (1.13) 10.4 (0.80) 10.0 (0.72) 10.6 (0.93) F(1,46) = 3e-3, p = 0.96 F(3,138) = 21.5, p = 2.09e-11, Ƞ2G = 0.21 F(3,138) = 3.46, P = 1.80e-02, Ƞ2G = 0.04 NA 19A Glx ACC aMCC pMCC PCC 14.9 (2.01) 13.8 (1.46) 12.9 (0.82) 12.7 (1.03) 14.6 (1.59) 13.4 (1.65) 12.8 (1.15) 13.5 (1.50) F(1,46) = 2e-3, p = 0.97 F(3,138) = 18.8, p = 2.76e-10, Ƞ2G = 0.19 F(3,138) = 1.98, p = 0.12 NA 19B Gln ACC 3.55 (0.81) 3.46 (0.85) F(1,45) = F(3,135) = F(3, 135) = Sex: 19C aMCC pMCC PCC 3.28 (0.80) 2.90 (0.47) 2.69 (0.50) 3.01 (0.96) 2.77 (0.71) 2.93 (0.71) 1.80e-2, p = 0.89 10.5, p = 2.98e-6, Ƞ2G = 0.12 1.22, p = 0.31 F(1,45) = 3.79, p = 5.90e-2, Ƞ2G = 0.04 Sex x region: F(3, 135) = 1.62, p = 0.19 PR Glu ACC aMCC pMCC PCC 12.0 (1.03) 10.8 (1.22) 9.98 (0.64) 10.3 (0.54) 11.1 (1.08) 10.3 (0.72) 9.97 (0.74) 10.1 (1.05) F(1,45) = 4.35, p = 0.04, Ƞ2 G = 0.04 F(3,135) = 34.3, p = 1.54e-16, Ƞ2G = 0.30 F(3,135) = 2.38, p = 7.20e-2, Ƞ2G = 0.03 NA 20A Glx ACC aMCC pMCC PCC 15.7 (1.37) 14.0 (1.88) 12.7 (1.09) 13.0 (0.99) 14.4 (2.00) 13.5 (1.30) 13.0 (0.82) 12.9 (1.28) F(1,45) = 2.06, p = 0.16 F(3,135) = 30.6, p = 3.80 e-15, Ƞ2G = 0.28 F(3,135) = 3.39, p = 0.02, Ƞ2G = 0.04 NA 20B Gln ACC 3.72 (0.69) 3.42 (0.88) F(1,44) = F(3,132)= F(3,132) = Sex: 20C aMCC pMCC PCC 3.15 (1.01) 2.73 (0.54) 2.67 (0.70) 3.22 (0.78) 2.91 (0.55) 2.78 (0.48) 1.0e-3, p = 0.97 11.5, p = 9.34e-7, Ƞ2G = 0.13 0.84, p = 0.48 F(1,44) = 4.00, p=0.05, Ƞ2G = 0.04 Sex x region: F(3,132) = 1.77, p = 0.16 40 Table 6. Post hoc results from significant interac�ons of P2 and P2 clustering Metabolite Region t-test results Effect size (cohen’s d) Figure Glu (by P2) ACC t(46) = 1.50, p=0.14 0.47 19A aMCC t(46) = 0.24, p=0.81 0.08 pMCC t(46) = -0.21, p=0.84 0.06 PCC t(46) = -2.11, p=0.04 0.65 Glx (by PR) ACC t(45) = 2.41, p=0.02 0.77 20B aMCC t(45) = 1.03, p= 0.31 0.30 pMCC t(45) = -0.90, p=0.37 0.26 PCC t(45) = 0.15, p=0.87 0.05 Glu = glutamate, Glx – glutamate and glutamine, ACC = anterior cingulate cortex, aMCC = anterior midcingulate cortex, pMCC = posterior midcingulate cortex, PCC = posterior cingulate cortex, p<0.05 41 Chapter 5: DISCUSSION 5.1 Summary of findings 5.1.1 Regional differences Consistent with previous research at the higher magne�c field strength (Dou et al., 2013) and confirming our preliminary analysis, anterior regions of the cingulate cortex (ACC and aMCC) had significantly higher excitatory metabolite concentra�ons (glutamate, glutamine, Glx) in comparison to more posterior regions (pMCC and PCC). Sex was associated with excitatory metabolite concentra�ons, specifically that men exhibited overall higher concentra�ons of glutamine across the cingulate. Also confirming previous studies (Cervero, 2009; Spisak et al., 2020), greater sensi�vity to noxious laser s�mula�on was generally associated with overall higher excitatory metabolite concentra�ons in the cingulate cortex. This rela�onship was largely atributable to higher concentra�ons of excitatory metabolites in the ACC. Conversely, objec�ve readouts of sensa�on to laser s�mula�on, in par�cular P2 amplitudes, were associated with higher excitatory metabolite concentra�ons in the PCC. These results provide important insights into res�ng excitatory metabolite physiology in the cingulate cortex and a role in sensi�vity to pain. Higher concentra�ons of glutamate in the anterior compared to the posterior cingulate have been reported in mul�ple MRS studies using 7T (Dou et al., 2013; Oeltzschner et al., 2019). These observa�ons appear to converge with histological, post-mortem evidence of a rostral-caudal gradient in glutamate receptor density (Palomero-Gallagher et al., 2009). In line with these findings, in vivo positron emission tomography studies report the highest densi�es of excitatory glutamatergic receptor mGluR5 in anterior regions of the cingulate (Hansen et al., 2022; Kaulen et al., 2022). Hansen et al (2022) addi�onally reported high mGluR and NMDA densi�es in the posterior cingulate. At lower magne�c field strengths, however, detec�on of regional differences via MRS have yielded mixed outcomes (Kim et al., 2018; Sailasuta et al., 2008; Zhang & Shen, 2015). In our preliminary review of excitatory 42 metabolite concentra�ons in 30 par�cipants (unpublished), we first detected robust differences across the cingulate, which we confirmed here (n=48). To our knowledge, this is the first �me that differences in metabolite concentra�ons across the cingulate in a large and balanced sex cohort, observing a significant rostral-caudal gradient, have been reported at 3T. A number of technical factors may have contributed to finding an effect where others have not, including differences in voxel placement, hardware and acquisi�on parameters (e.g., sLASER), the absolute quan�fica�on of excitatory metabolite concentra�ons, and so�ware used for segmenta�on of �ssue volumes. Addi�onally, the �ming of MRS acquisi�on and food intake was accounted for in our study, and par�cipants were rela�vely young (<45 years, with the average age of 27 years old). The later is notable insofar as other studies conducted at 3T included much older subjects (Sailasuta et al., 2008), with age-related decreases in excitatory metabolite concentra�ons have been previously reported, as well as an increased in glutamine in rela�on to age in the ACC (Hädel et al., 2013; Zahr et al., 2013). However, Hadel et al (2013) reported their glutamine findings did not survive Bonferroni correc�ons. 5.1.2 Sex differences Sex differences in excitatory metabolite concentra�ons have been variably reported over the last two decades, in par�cular the concern of the contribu�on of the phases of females’ menstrual cycles, which have been reported in specific pa�ent popula�ons (e.g., women experiencing dysmenorrhea) (Ghuman et al., 2022; Liu et al., 2015; Yap et al., 2021). To circumvent this concern, some studies exclude females completely (Dou et al., 2013), which creates concerns for biased results when applied to the general popula�on. A 2009 study reported no significant effect of sex on concentra�ons of glutamate in the basal ganglia, parietal gray mater, and frontal gray/white mater (Chang et al., 2009). It should also be noted despite the large sample size of 62 par�cipants in this study by Chang et al (2009), they included nearly double the number of males compared to females (39 males and 23 females). We 43 recruited a balanced number of males and females to ensure equal representa�on of sex, in order to further inves�gate these poten�al differences. Our observa�ons largely suggest no difference in excitatory metabolites glutamate and Glx concentra�ons across the cingulate. The excep�on was glutamine, which was higher in men, par�cularly in the mid-cingulate regions. Previous studies have reported higher levels of glutamate in the hippocampus (Hädel et al., 2013), as well as in the striatum, cerebellum, and pons (Zahr et al., 2013) in women. Addi�onally, Hadel et al (2013) measured glutamine (at 3T) and did not report sta�s�cal differences between sex. Conversely, glutamate and Glx concentra�ons were reported to be higher in men in the dorsolateral prefrontal cortex, and equal concentra�ons reported in the ACC (O’Gorman et al., 2011). This corresponds with observa�ons in vitro electrophysiological recordings of rodent PFC slices, which found that males have higher res�ng and spontaneous glutamate release (Pena-Bravo et al., 2019). These variable outcomes in repor�ng sex differences in excitatory metabolite concentra�ons could be atributable to a variety of factors, including those that explain regional varia�ons across the cingulate (see above). Challenges related to the reliable acquisi�on of glutamine at 3T should, however, be considered, in rela�on to our glutamine findings. At present, there are no MRS studies conducted at 7T that have examined sex differences in excitatory metabolite concentra�ons. This represents a major gap in knowledge, in that 7T could provide a clearer picture of the “ground truth” as the higher field strength improves the quality of data and is considered more sensi�ve to detect smaller physiological changes (Ladd et al., 2018). 5.1.3 Pain and excitatory ac�vity The rela�onship between res�ng concentra�ons of excitatory metabolite concentra�ons and pain has been examined previously (Archibald, MacMillan, Enzler, et al., 2020; Peek et al., 2020). In general, studies demonstrate a posi�ve rela�onship, such that higher levels of glutamate, wherever the 44 voxel is posi�oned in the brain, is associated with increased sensi�vity to pain. Our observa�on, namely the significant main effect of our pain ra�ng clusters on glutamate concentra�ons, corresponds with these studies. Elevated concentra�ons of excitatory metabolite concentra�ons of Glx, as well as GABA in the brain have also been reported in individuals who experience migraines (Peek et al., 2020), which is a disease characterized by heightened sensi�vity to a variety of afferent s�muli. Similarly, increased glutamate in the posterior insula, a region with strong connec�ons to the cingulate, was associated with increased sensa�on of pain (Martucci & MacKey, 2018). Based on our evalua�on of interac�on effects (region by pain ra�ng cluster), Glx concentra�ons were par�cularly elevated in the ACC among individuals highly sensi�ve to pain. The ACC is a key region of the SN and integrally involved in the percep�on of noxious and innocuous afferent s�muli (Schimmelpfennig et al., 2023). High glutamate concentra�ons in the ACC at rest are associated with decreased connec�vity in the saliency network (McCutcheon et al., 2021). Aberrant saliency network connec�vity is a hallmark of a variety of diseases (Manoliu et al., 2014; Schimmelpfennig et al., 2023), including among individuals with chronic pain (Baliki et al., 2014; Van E�nger-Veenstra et al., 2019). In ‘healthy’ individuals, connec�vity in the saliency network is associated with trait anxiety, with higher levels giving rise to less connec�vity (Geng et al., 2016). This is relevant to our study insofar as anxiety has a long- and well-established history in modula�ng the experience of pain, in the direc�on of increasing sensi�vity (Edwards et al., 2000; Jones & Zachariae, 2002; Von Graffenried et al., 1977). Although within our current sample size we did not have enough par�cipant scores on anxiety (or stress or depression) to explore this considera�on further, inves�ga�ng anxiety in rela�on to res�ng excitatory ac�vity in the ACC may provide further insight on the SN’s involvement in LEP and PR outcomes. 45 5.2 Dynamic networks 5.2.1 The PCC We demonstrated par�cipants with large P2 amplitudes had significantly higher glutamate concentra�ons in the PCC, which is associated with increased DMN ac�vity (ie. daydreaming) at rest, in comparison to par�cipants with smaller P2 amplitudes. Posi�ve correla�ons with higher glutamate concentra�ons in the PCC and increased DMN ac�vity (Hu et al., 2013), as well as higher DMN ac�vity and higher daydreaming frequency (Kucyi & Davis, 2014), suggests that the increased glutamate concentra�ons in the PCC we found are associated with increased DMN ac�vity within par�cipants with reduced awareness/concern of their surroundings at rest. Exposure to a noxious s�mulus then produces an involuntary shi� in aten�on to orient the par�cipant to the incoming s�mulus, through the ac�va�on of the dorsolateral prefrontal, orbitofrontal and ACC regions, sugges�ng ac�va�on of the SN (Legrain et al., 2003). As the ACC is considered a major generator of the detected neural ac�vity of LEP outcomes, including P2 amplitudes (Garcia-Larrea et al., 2003), we therefore suggest that P2 amplitudes reflect this involuntary shi� in aten�on to an incoming noxious s�mulus, through increased ACC ac�vity, elici�ng the ‘switch’ from DMN (daydreaming/reduced aten�on) into SN ac�va�on. Previous research has demonstrated that reduced DMN ac�vity occurs a�er noxious s�mulus exposure through fMRI (Mayhew et al., 2013), which is also reflected through increased concentra�ons of GABA in the PCC (and precuneus) occurring during a cogni�ve task (Hu et al., 2013). Mul�ple studies provide evidence to support this concept, including higher concentra�ons of glutamate in the ACC that were nega�vely correlated with reduced fMRI BOLD ac�vity in the PCC during a cogni�ve task (Overbeek et al., 2019) and viewing of salient pictures (von Düring et al., 2019). In line with research demonstra�ng increased ACC ac�vity is associated with reduced DMN ac�vity, we suggest our par�cipants with higher res�ng glutamate concentra�ons in the PCC, require increased ACC ac�vity to ac�vate the SN, which is reflected in the larger P2 amplitudes. In contrast, par�cipants with less DMN ac�vity and lower 46 glutamate concentra�ons in the PCC, produce smaller P2 amplitudes reflec�ng the smaller increase in ACC ac�vity that is required to ac�vate their SN, a�er exposure to noxious s�mulus. 5.2.2 The ACC Increased neural ac�vity in the ACC a�er exposure to noxious s�mulus has been demonstrated through increased local fMRI BOLD ac�vity with higher pain ra�ngs (Mayhew et al., 2013), and increased glutamate concentra�ons at the onset of exposure to a noxious s�mulus (Archibald, MacMillan, Graf, et al., 2020). However, in the later, no rela�onship between glutamate concentra�ons and pain ra�ngs was found (Archibald, MacMillan, Graf, et al., 2020). These findings, combined with another study repor�ng ACC ac�va�on is influenced by the percep�on of unpleasantness of the applied noxious s�mulus (Rainville et al., 1997), suggest increased glutamate occurs with the detection of noxious (salient) s�mulus. However, these concentra�ons may not be reflected in the con�nual ac�va�on of the ACC reported in neuroimaging studies. We found par�cipants with higher pain sensi�vity (via higher pain ra�ngs) had higher glutamate concentra�ons in the ACC, providing evidence that res�ng glutamatergic ACC ac�vity may contribute to the how painful par�cipants perceive a noxious s�mulus to be. Further evidence of the rela�onship between glutamatergic ac�vity of the ACC and the ac�va�on of other SN regions at rest, however, is limited and varies. One study reported a nega�ve correla�on with glutamate in the ACC and salience network fMRI ac�va�on (described as the ACC and insula) (McCutcheon et al., 2021), and in contrast, another study reported a posi�ve correla�on with Glx and fMRI ac�vity in the insular and the putamen regions (Maximo et al., 2021). Addi�onally, a recent review reported no significant rela�onship between ACC glutamate and local (ACC) fMRI ac�vity in a meta-analysis of six studies during an�cipated ac�va�on of the SN via cogni�ve tasks (Kiemes et al., 2021). Preclinical rodent studies demonstrate the direct, yet precise, involvement of glutamatergic ac�vity in the ACC influencing pain percep�on through AMPA/NMDA antagonism reducing pain 47 detec�on in viscerally hypersensi�ve rats (Yan et al., 2012). Furthermore, NMDA injec�ons into the rostral and caudal ACC, respec�vely decreased and increased rats’ emo�onal responses (measured via vocaliza�ons) that followed exposure to a noxious s�mulus (Mussio et al., 2020). Our findings, combined with previous research, highlight that although increased ACC neural ac�vity has direct influences on pain percep�on, and indirect influences on the DMN suppression that occurs a�er exposure to salient s�muli, the neuropharmacological underpinnings producing these effects require further research, both within the ACC locally as well as other SN regions (e.g., the insular cor�ces). Interes�ngly, within a study with no changes in glutamate or Glx concentra�ons in the ACC a�er noxious s�mulus exposure, significant increases in GABA concentra�ons were reported (Kupers et al., 2009). These GABA findings, combined with previous research demonstra�ng the higher density of GABAergic receptors and GABA concentra�ons at rest in the ACC (Dou et al., 2013; Palomero-Gallagher et al., 2009), may lend insight into the inhibitory ac�vity of the ACC playing an important role, in addi�on to glutamatergic ac�vity in the SN connec�vity during exposure to noxious s�mulus. Although not discussed in their ar�cle, the supplementary informa�on from Zunhammer et al (2016), reported a mild correla�on with Glx and GABA concentra�ons in the ACC, while the comparison of Glx and GABA concentra�ons in the insula was not significant, lending insight into the relevant involvement of both glutamatergic and GABAergic ac�vity in the cingulate. Addi�onally, in chronic pain studies, GABA concentra�ons in the ACC were nega�vely correlated with pain ra�ngs in pa�ents with osteoarthri�s (Reckziegel et al., 2016), as well as a preclinical chronic pain rodent model which demonstrated deficits in both excitatory and inhibitory neurons within the ACC (Blom et al., 2014). These studies demonstrate GABAergic ac�vity may provide further insight into understanding the dynamic balance between the SN and DMN in rela�on to the experience of both acute and chronic pain. Con�nual incoming noxious signals (ie. in pa�ents who experience chronic pain) produces dysregula�on of these dynamic networks, through increased func�onal connec�vity in the SN (ie. anterior insula and superior temporal gyrus) and 48 reduced func�onal connec�vity in DMN regions (ie. PCC, mPFC) (Baliki et al., 2014; Van E�nger-Veenstra et al., 2019). Considering the effect of chronic pain on the SN and DMN provides further evidence of this integral rela�onship, which we must first understand in acute experimental pain paradigms. Our findings provide a basis for, and highlight the importance of, considering res�ng glutamatergic ac�vity in the cingulate cortex, in rela�on to the experience of pain and noxious s�muli. Combined with previous research, we suggest the inves�ga�on of GABAergic ac�vity in combina�on with glutamatergic ac�vity in the ACC at rest may provide further insight into the excitatory and inhibitory involvement of the ACC, and its resultant influence on both dynamic networks. 5.3 Strengths, limita�ons, and future direc�ons 5.3.1 Strengths and limita�ons of 1H-MRS There are a number of limita�ons to consider. We used to a field strength of 3T to quan�fy glutamatergic ac�vity which is considered reliable for glutamate and Glx quan�fica�on; however, glutamine is substan�ally less reliable due to its overlapping spectra with other macromolecules (de Graaf, 2019). Our 3T glutamine findings do reflect previous glutamine findings at 7T, with repor�ng higher glutamine concentra�ons within the rostral ACC, with it slight decrease moving in the caudal direc�on (Dou et al., 2013). Nonetheless, despite our sex differences in glutamine being intriguing, 1H-MRS at 7T is required to confirm these findings, in par�cular as Dou et al (2013) only recruited male par�cipants for their study. In general, the quan�fica�on of glutamatergic ac�vity via 1H-MRS has the inherent limita�on of lacking the ability to discern intracellular vs extracellular glutamatergic ac�vity (de Graaf, 2019). Insight into the quan�fica�on of both intracellular and extracellular glutamine and glutamate would provide further understanding of glutamatergic turnover and local excitability within the cingulate. Although our focus on glutamatergic ac�vity was in rela�on to neurotransmission, glutamate metabolism also 49 contributes to the biosynthesis of numerous nucleic acids and proteins (Yelamanchi et al., 2016). Therefore, it is important to keep in mind the detected glutamate concentra�ons may be also involved in these metabolic processes, and not neurotransmission specifically. Addi�onally, the sta�c nature of our 1H-MRS measurements is also a factor to consider, as each measurement of glutamate, Glx, and glutamine is quan�fied over a ~8.5 minute �mepoint to ensure a high enough signal to noise ra�o (SNR). Within our current 1H-MRS analysis, our data has reasonable SNR (>20) and FWHM (<0.07ppm) values. Glutamate and glutamine CRLBs were reasonable (<50), whereas Glx CRLBs were around ~50, which suggests less reliability of the Glx findings. The overall spectral quality was lowest in the anterior cingulate regions, which is likely in part due to �ssue ar�facts, including the branches of the anterior cerebral artery that may run through the placement of our ACC voxel (Jumah & Dossani, 2020a; Matos Casano & Ciofoaia, 2018). This is somewhat expected, as previous research has demonstrated frontal regions tend to have reduced spectral resolu�on due to the increased suscep�bility of movement (of the par�cipant, blood, or cerebrospinal flow etc.), resul�ng in lower spectral resolu�on (de Graaf, 2019). Despite these considera�ons, our glutamate data is of reasonable overall quality, with Glx and glutamine findings providing addi�onal support to our glutamate results despite the reduced reliability of Glx (due to ~ 50 CRLBs) and glutamine (with the 3T scanner). Addi�onally, we calculated the metabolite concentra�ons based on the grey mater, white mater, and cerebrospinal fluid percentages within each of the cingulate voxels for further accuracy (Kreis, 2004; Wilson et al., 2019). Our findings are in line with previous research at 7T repor�ng a rostrocaudal gradient of glutamatergic ac�vity which decreases when moving caudally, with the largest (significant) differences in glutamate concentra�ons within the anterior cingulate regions (Dou et al., 2013). 5.3.2 Strengths and limita�ons of sta�s�cal analysis Our clustering approach had substan�al advantages including allowing for a reduc�onist approach to our LEP outcomes and reducing poten�al bias through using the K-means clustering 50 algorithm (Li & Wu, 2012) to determine a) if there are mul�ple clusters, and b) which par�cipants fall into the clusters produced. This resulted in some clustered groups having a smaller sample size (<30 par�cipants) in comparison to the other cluster within the same outcome. However, our large sample size of n=50 increased the chances of “catching” large and small responders of laser responses (LEP amplitudes and PR), which smaller sample sizes may not be able to discern. Perhaps most importantly, it demonstrated LEP responders can be categorized into subgroups based on their amplitude size and pain ra�ngs. Our study provides a valuable founda�on to build upon by con�nuing to increase our sample size to reveal further evidence and sta�s�cal power to compare differences in laser responders. In par�cular, our findings demonstrate the importance of further inves�ga�on of the underlying biological differences that may drive res�ng glutamatergic differences within the ACC and PCC, and their contribu�on to P2 and PR outcomes. 5.3.3 Future direc�ons Future direc�ons of this project include increasing our sample size to determine whether our findings remain sta�s�cally significant with increased sample sizes of individual LEP and PR clusters, as well as if the trend of sex differences in glutamine becomes more apparent or not. This increased sample size will also allow for the inves�ga�on of addi�onal outcomes that may contribute to differen�al metabolite concentra�ons that we were unable to include within our current sta�s�cal comparisons due to limited sample size within subgroups (e.g., menstrual cycle phase - the majority of female par�cipants were scanned during their luteal phase, therefore we do not have enough females scanned during their follicular phase to compare). This included mental health state (via the DASS ques�onnaire), perceived pain sensi�vity (via the Pain Sensi�vity Ques�onnaire), menstrual cycle phase, and specific �me of scan. Addi�onally, expanding on our LEPs protocol (to include a condi�on with an�cipated laser pain and one without), and our MRS protocol to addi�onally quan�fy GABA in the cingulate (and brain regions involved in the DMN and SN, ie. the insula) would provide further insight into res�ng glutamatergic 51 ac�vity and individual differences in LEP responses. Using func�onal 1H-MRS to detect smaller �mepoint changes of glutamatergic/GABAergic ac�vity, a stronger magne�c field, and dynamic neuroimaging such as fMRI or arterial spin labeling would be beneficial to discern res�ng neurobiological differences between individuals and these contribu�ons to LEP and PR outcomes. 52 Chapter 6: CONCLUSION We demonstrated within our n=48 par�cipants that there are dis�nct small and large LEP responders, for N1, N2, P2 separately, and combined outcomes, in addi�on to high and low pain raters. We found large P2 responders had significantly higher glutamate concentra�ons in the PCC in comparison to small P2 responders. Combined with previous research demonstra�ng P2 amplitudes are influenced by arousal and aten�onal factors, we suggest the large P2 amplitudes are reflected within par�cipants with increased DMN ac�va�on at rest, resul�ng in increased ACC ac�vity required to suppress the DMN and shi� into the SN a�er noxious s�mulus exposure. Addi�onally, par�cipants with higher pain ra�ngs associated with higher glutamate concentra�ons across the cingulate cortex, driven by the ACC, may provide further insight into differences in pain percep�on to laser s�muli. As each cluster (ie. small vs. large responses) did not necessarily contain the same par�cipants, this highlights the inter-person variability and dynamic nature of pain percep�on and LEP outcomes. Our findings highlight that LEPs and pain percep�on are not synonymous, although res�ng ACC and PCC ac�vity (measured via glutamatergic ac�vity) both play an important role in the individual differences of both these outcomes, and may provide further insight into the contribu�on of nocicep�on within the experience of pain, through exploring the dynamic rela�onship between the SN and DMN. 53 REFERENCES Al-Chalabi, M., Reddy, V., & Gupta, S. (2022). Neuroanatomy, Spinothalamic Tract. StatPearls. Archibald, J., MacMillan, E. L., Enzler, A., Jutzeler, C. 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Pain, 157(10). htps://doi.org/10.1097/j.pain.0000000000000634 62 APPENDIX A: SUPPLEMENTARY FIGURES AND TABLES Supplementary Figure 1: Example of a wssplot for clustering of LEPs The combined LEP outcomes (N1, N2, and P2 amplitudes) at the four different laser intensi�es were included for N= 50 par�cipants (A) and were input into the WSS func�on, producing the “elbow” at Number of Clusters = 2 (B). The silhouete plot then confirmed 2 clusters was op�mal (C), and determined which par�cipants were categorized as cluster 1 or 2 (D). 63 Supplementary Table 1. List of R packages and cita�on R packages used Reference ggplot2 H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016 dplyr Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. R package version 1.1.0, https://CRAN.R-project.org/package=dplyr tidyr Wickham H, Vaughan D, Girlich M (2023). _tidyr: Tidy Messy Data_. R package version 1.3.0, https://CRAN.R-project.org/package=tidyr rstatix Kassambara A (2023). _rstatix: Pipe-Friendly Framework for Basic Statistical Tests_. R package version 0.7.2, https://CRAN.R-project.org/package=rstatix factoextra Kassambara A, Mundt F (2020). _factoextra: Extract and Visualize the Results of Multivariate Data Analyses_. R package version 1.0.7, https://CRAN.R-project.org/package=factoextra readxl Wickham H, Bryan J (2023). _readxl: Read Excel Files_. R package version 1.4.2, https://CRAN.R-project.org/package=readxl writexl Ooms J (2023). _writexl: Export Data Frames to Excel 'xlsx' Format_. R package version 1.4.2, https://CRAN.R-project.org/package=writexl stats R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ ggpubr Kassambara A (2023). _ggpubr: 'ggplot2' Based Publication Ready Plots_. R package version 0.6.0, https://CRAN.R-project.org/package=ggpubr 64 APPENDIX B: QUESTIONNAIRES Pain Sensi�vity Ques�onnaire 65 66 Depression Anxiety Stress Scale 67
Thesis/Dissertation
eng
Pharmacology and Therapeutics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Kramer, John L. K.
Glutamate and laser evoked potentials : the role of the cingulate cortex
Text