UBC Faculty Research and Publications

Effects of computerized cognitive training on neuroimaging outcomes in older adults: a systematic review ten Brinke, Lisanne F; Davis, Jennifer C; Barha, Cindy K; Liu-Ambrose, Teresa Jul 10, 2017

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
52383-12877_2017_Article_529.pdf [ 1.42MB ]
Metadata
JSON: 52383-1.0362039.json
JSON-LD: 52383-1.0362039-ld.json
RDF/XML (Pretty): 52383-1.0362039-rdf.xml
RDF/JSON: 52383-1.0362039-rdf.json
Turtle: 52383-1.0362039-turtle.txt
N-Triples: 52383-1.0362039-rdf-ntriples.txt
Original Record: 52383-1.0362039-source.json
Full Text
52383-1.0362039-fulltext.txt
Citation
52383-1.0362039.ris

Full Text

RESEARCH ARTICLE Open AccessEffects of computerized cognitive trainingon neuroimaging outcomes in older adults:a systematic reviewLisanne F. ten Brinke, Jennifer C. Davis, Cindy K. Barha and Teresa Liu-Ambrose*AbstractBackground: Worldwide, the population is aging and the number of individuals diagnosed with dementia is risingrapidly. Currently, there are no effective pharmaceutical cures. Hence, identifying lifestyle approaches that may prevent,delay, or treat cognitive impairment and dementia in older adults is becoming increasingly important. ComputerizedCognitive Training (CCT) is a promising strategy to combat cognitive decline. Yet, the underlying mechanisms of the effectof CCT on cognition remain poorly understood. Hence, the primary objective of this systematic review was to examinepeer-reviewed literature ascertaining the effect of CCT on both structural and functional neuroimaging measures amongolder adults to gain insight into the underlying mechanisms by which CCT may benefit cognitive function.Methods: In accordance with PRISMA guidelines, we used the following databases: MEDLINE, EMBASE, and CINAHL. Twoindependent reviewers abstracted data using pre-defined terms. These included: main study characteristics such as thetype of training (i.e., single- versus multi-domain), participant demographics (age ≥ 50 years; no psychiatric conditions),and the inclusion of neuroimaging outcomes. The Physiotherapy Evidence Database (PEDro) scale was used to assessquality of all studies included in this systematic review.Results: Nine studies were included in this systematic review, with four studies including multiple MRI sequences. Resultsof this systematic review are mixed: CCT was found to increase and decrease both brain structure and function in olderadults. In addition, depending on region of interest, both increases and decreases in structure and function were associatedwith behavioural performance.Conclusions: Of all studies included in this systematic review, results from the highest quality studies, which were tworandomized controlled trials, demonstrated that multi-domain CCT could lead to increases in hippocampal functionalconnectivity. Further high quality studies that include an active control, a sample size calculation, and an appropriatetraining dosage, are needed to confirm these findings and their relation to cognition.Keywords: Computerized cognitive training, Neuroimaging, Brain structure, Brain function, Older adultsBackgroundWith our ageing population, the incidence of dementiais rising rapidly. Currently, over 47 million people world-wide are diagnosed with dementia and this number isexpected to triple by 2050 [1]. In 2010 it was estimatedthat the worldwide cost of dementia was 604 billion USdollars [1]. Thus it is imperative to find strategies thatpromote cognitive healthy aging to minimize the projectedsocietal, health, and economic burden by reducing ordelaying the potential progression to mild cognitive impair-ment or dementia.Currently, there is no pharmaceutical cure for dementia.As such, identifying lifestyle approaches that may prevent,delay, or even treat cognitive impairment and dementia inolder adults is becoming increasingly important [2]. Evenwhen an effective pharmacological therapy is available,lifestyle approaches (i.e., exercise, nutrition, and cognitivetraining) can be used in conjunction as lifestyle interven-tions result in multidimensional benefits [3]. In recentyears, there is growing interest in complex mental activity* Correspondence: teresa.ambrose@ubc.caAging, Mobility, and Cognitive Neuroscience Laboratory, Department ofPhysical Therapy, Djavad Mowafaghian Centre for Brain Health, University ofBritish Columbia, 2215 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.ten Brinke et al. BMC Geriatrics  (2017) 17:139 DOI 10.1186/s12877-017-0529-xas a strategy to promote healthy cognitive aging. Complexmental activity comprises all activities that are cognitivelychallenging for an individual [4], such as memory andexecutive functioning training, or dance. A meta-analysisof human cohort studies provides robust evidence thatcomplex patterns of mental activity in early, mid-life, andlate-life stages is associated with a significant reduction indementia incidence [5]. Furthermore, they found an asso-ciation between increased levels of complex mental activ-ity in late life and lower dementia rates, independent ofother predictors. Finally, it showed a dose-response rela-tionship between the amount of complex mental activitiesin late life and dementia risk [5].Computerized cognitive training (CCT) is one exampleof complex mental activity that could be used to promotehealthy cognitive aging. CCT is defined as cognitive train-ing on an individual electronic device (e.g., computer,laptop, tablet/iPad) that requires a physical response suchas a button press, and excludes training that primarily re-quires an individual to perform two tasks simultaneously,in order to compare performance with single-task con-ditions (i.e., dual-task training). Notably, CCT is an ap-proach that could be used by those who are limited intheir ability to physically participate in other strategies,such as exercise. A meta-analyses shows that CCTimproved overall cognitive performance in older adults[6]. Specifically it showed improvements in verbal andnon-verbal memory, working memory, processing speed,and visuospatial skills [6]. Recent randomized controlledtrials (RCT’s) of CCT in older adults showed that bothtwo and three months of training resulted in improvedglobal cognition compared with an active control group[7, 8]. Additionally, an RCT showed that CCT resultedin improvements in memory and processing speedwhich were still visible twelve months post-training[7], and shows that CCT is able to maintain its bene-fits. Playing a real-time strategy video game for 23.5 himproved performance in executive functions, indicat-ing transfer of training after participating in complexmental activities [9]. Thus, current evidence suggeststhat CCT is a promising strategy for promoting healthycognitive aging.Cognitive training is based on the notion that the brain,even with age, can change for the better, if given the ap-propriate environmental stimuli, thoughts, and emotions[10]. This capacity of the brain is called “neuroplasticity”.In the same way that physical training improves physicalabilities, cognitive training (or brain training) may induceneuroplastic changes in the brain, resulting in improvedcognitive abilities. One of the fundamental principles ofneuroplasticity is the concept of synaptic plasticity – thenotion that individual connections within the brain areconstantly being removed or recreated, largely dependentupon how they are used [11]. Cognitive training aims toharness this principle of neuroplasticity by using guidedpractice on a set of tasks related to memory, attention, orother cognitive processes.To gain more insight in what potential neuroplasticchanges CCT may induce; incorporating different neuro-imaging techniques in studies could be a good approachto help demonstrate these changes in the brain. Forexample, synaptic plasticity as a result of stimulationby CCT could potentially be captured by functionalconnectivity, measured with resting-state functionalmagnetic resonance imaging (rsfMRI), by strengthen-ing connections within and between networks [12]. Todate, it is not well established how CCT impacts re-gional brain volume, functional activity, and functionalor structural connectivity in older adults. Althoughwork has been done among younger adults illustratingchanges in functional activity in the middle frontalgyrus and superior and inferior parietal cortices afterworking memory training [13], these findings don’tnecessarily translate to an older adult population.Therefore, gaps remain in understanding the under-lying mechanisms of training-induced neuroplasticityin older adults. Addressing this knowledge void, thissystematic review aims to ascertain the mechanisms bywhich CCT exerts an impact on brain structure andfunction by using different neuroimaging techniquessuch as volumetric magnetic resonance imaging (MRI),task-based functional MRI (fMRI), rsfMRI, and diffu-sion tensor imaging (DTI). Through understanding theunderlying neural mechanisms of CCT, our goal is toprovide knowledge on how to design improved and tar-geted interventions that help combat or prevent cogni-tive decline throughout life.MethodsSearch strategyIn accordance with the Preferred Reporting Items forSystematic reviews and Meta-Analyses (PRISMA) state-ment [14], we conducted a comprehensive search ofMEDLINE, EMBASE, and CINAHL databases to identifyall the studies that investigated neuroimaging outcomesresulting from CCT interventions. We limited oursearch to adults aged 55 years and older with and with-out cognitive impairment, who have not been diagnosedwith dementia. We did not limit the search based onpublication date, as CCT is a relative novel researchtopic. The final search (see Fig. 1a for search strategy)was done on July 7 (2016) and included a check forrecent publications in PubMed.Study selectionWe selected studies that had a CCT intervention withneuroimaging outcomes (e.g. volumetric structural MRI,functional MRI, DTI) in an older adult population (ageten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 2 of 2055 years and older). Study designs included in this sys-tematic review were RCT’s and quasi-experimentalstudies. Studies that used samples of younger and olderadults but reported group results separately were in-cluded in this systematic review. We included studiesthat focused on both single- and multi-domain CCTprograms. We considered single-domain CCT trainingas training that targeted a specific cognitive ability, suchas working memory. In contrast, multi-domain CCT wasconsidered training that consisted of a series of tasksA BFig. 1 (a) Search Strategy retrieved from Ovid; (b) Exclusion pathway for study selectionten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 3 of 20that targeted multiple cognitive abilities (e.g., executivefunctions and memory). We excluded studies that didnot focus on CCT or studies that used CCT in combin-ation with other types of intervention (e.g., non-CCT,exercise), reviews and short reports. A full list of exclu-sion criteria and the exclusion pathway is displayed inFig. 1b. Critical review of titles and abstracts resulted in26 articles for full-text review.Data extraction and quality assessmentWe developed a list of data extraction items. This list in-cluded reference, study sample, study design, MRI magnet,neuroimaging outcomes, cognitive function measured,training program/task, cognitive domain trained, descrip-tion of training, training frequency and duration, totalhours of training, supervised/home-based training, andcontrol group. Two authors [LTB and CKB] independentlyextracted the data from the included studies. Discrepancieswere discussed and solved by two authors [JCD and TLA].The Physiotherapy Evidence Database (PEDro) scale[15] was used to assess the quality of the included stud-ies. We [LTB and TLA] added three additional items tothe PEDro scale to ensure a proper assessment of inter-vention studies using neuroimaging outcomes. Thesethree items included were: 1) cognition measured to as-sist the interpretation of neuroimaging results; 2) samplesize calculation; and 3) compliance reported (yes/no). Toanswer the items in the quality assessment, we used a ‘+’for items that were present and a ‘-‘ for items that wereabsent. The quality assessment was performed independ-ently by two authors [LTB and CKB]. Discrepancies werediscussed and reviewed by two authors [JCD and TLA].Consensus between two authors [LTB and CKB] wasachieved after discussion (K=0.98). Because item one ofthe PEDro scale is related to external validity, it is notincluded in the overall PEDro score. Therefore, the max-imum quality assessment score calculated by the PEDrowas 10 points (each ‘+’ indicates one point), and will bereported in the results. Studies with a PEDro score of 6/10 or higher were considered studies of moderate tohigh quality. The additional item list had a maximumscore of three points and trends from this list will bedescriptively discussed in the results.ResultsOverview of studies includedOf the 684 articles identified, nine were included in thissystematic review (Table 1). These nine papers includedfour RCT’s [16–19] and five quasi-experimental studies[20–24]; all nine studies had a different study duration.Details of the interventions included are provided inTable 2. The results are categorized into four categories:1) Volumetric structural imaging (n = 4) [16, 19, 20, 22];2) Task-based fMRI (n = 3) [18, 21, 22]; 3) Connectivity(n = 7) [16, 17, 19, 20, 22–24]; and 4) Correlationbetween imaging outcomes and cognitive function out-comes (n = 8) [16–22, 24], (Table 3). Results are re-ported in order of study quality, starting with the highestquality.Structural imaging (n = 4)Four studies [16, 19, 20, 22] reported volumetric and cor-tical thickness outcomes (Table 3). A randomized con-trolled study (full factorial design) multi-domain cognitivetraining study using Cogpack [19], older adults with mildcognitive impairment (MCI) trained for a total of 78 hover a period of 6 months under supervision. Combinedcognitive training with resistance training resulted in in-creased cortical thickness in the posterior cingulate cortex.However, in the same study they found that cognitivetraining alone led to a decrease in the posterior cingulatecortex thickness. However, there was no difference in de-crease in thickness compared with the control group.In addition, a twelve-week supervised multi-domainCCT study [16] using the same program (CogPack)showed that 36 h of training resulted in an increase ingrey matter density in the right post-central gyrus com-pared with a decrease in the active control group. Add-itionally, the training resulted in a difference in rate ofthickness change over time in both the left fusiformgyrus and the supramarginal and post-central gyri.In contrast, in an object-location learning paradigmstudy [20] participants performed training on threeconsecutive days where they had to learn the correctspatial location of buildings on a street map. On eachtraining day, the training was followed by a cued recalland recognition task. Hippocampal volumes was mea-sured pre- and post-training. The authors found thatthe object-location learning paradigm did not lead tochanges in hippocampal volume.In another quasi-experimental study [22], participantsperformed an adaptive working memory training (n-Back) for twelve 45-min sessions over 4 weeks. Difficultylevel of the training was based on individual perform-ance and increases over time. Results showed that thetraining did not result in changes in grey matter volumein the working memory network.In summary, one RCT [19] found cortical thinning asa result of cognitive training alone. In contrast, anotherRCT [16] found an increase in grey matter density fol-lowing training. Finally, one study [22] found that cogni-tive training did not result in changes in grey matter,and one study [20] found that cognitive training did notlead to changes in hippocampal volume.Task-based fMRI (n = 3)Three [18, 21, 22] of the eight included studies examinedthe effect of a CCT intervention on brain function asten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 4 of 20Table1CharacteristicsofstudiesincludedReferenceStudySampleaStudyDesignLengthoffollow-upMRIMagnetNeuroimagingOutcomeMeasuresCognitionmeasured(testname)Suoetal.[19]2016OlderadultswithMCIN=10070.1±6.7yearsCompletedMRI:N=79RCTAssessmentsatbaselineand6months3T•VolumetricStructuralMRI•Resting-statefMRIGlobalCognition(ADAS-Cog)[41]○MemoryDomain○ExecutiveFunction○Attention-SpeedRosenetal.[18]2011OlderadultswithMCIN=1274.34±9.25yearsTrainingN=670.67±10.58yearsControlN=678±7.92yearsRCTAssessmentsconductedonaverage72±26daysapart3T•Task-basedfMRI•IncidentalAuditory-VerbalRepetitionparadigmMemory(RepeatableBatteryfortheAssessmentofNeuropsychologicalStatus:RBANS)[42]Lampitetal.[16]2015Healthyolderadults:SubsamplefromTimecourseTrialN=1271.43±7.48yearsTrainingN=772.3±8yearsControlN=570.2±6.7yearsRCTAssessmentsatbaseline,3weeks:FollowUp1(FU1),3months:FollowUp2(FU2)Secondaryanalysis3T•VolumetricStructuralMRI•Resting-statefMRI•ProtonMagneticResonanceSpectroscopy•DTIGlobalCognition:Compositeofmemoryandinformationprocessingspeed(Mindstreamsbattery)[43]aswellasexecutivefunction(AverageMindstreamsStroopInterferencetestforInhibition[43]andCambridgeNeuropsychologicalTestAutomatedBattery(CANTAB)StockingsofCambridgeproblemssolving)[44,45]Bellevilleetal.[21]2014Healthycommunity-dwellingolderadultsN=4069±6.27yearsTraininggroup1N=1268.58±8.16yearsTraininggroup2N=1469.57±5.81yearsTraininggroup3N=1468.79±5.13yearsQuasi-experimentalPre-postAssessments1weekbeforeand1weekaftertraining3T•Task-basedfMRI•Alphanumericequationtask•VisualdetectiontaskTasksperformedassingle-taskanddual-taskReactionTime(Alphanumericequationtaskandvisualdetectiontask)Accuracy(Alphanumericequationtaskandvisualdetectiontask)Linetal.[17]2014OlderadultswithahistoryofastrokeN=3469.21±4.93yearsTrainingN=1662.4±6.0yearsControlN=1863.2±5.7yearsRCTAssessmentsatbaselineand10weeks3T•RestingstatefMRIMemory(WechslerMemoryScale)[46]ExecutiveFunction(TrailMakingTest)[47]ten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 5 of 20Table1Characteristicsofstudiesincluded(Continued)Strenzioketal.[24]2014HealthyolderadultsN=4269.21±4.93yearsTraininggroup1N=1469.70±6.9yearsTraininggroup2N=1468.52±5.6yearsTraininggroup3N=1469.41±2.3yearsQuasi-experimentalPre-postLengthoffollowup:NotstatedNotstated•Resting-statefMRI•DTIReasoning/ProblemSolving(WAISIIIMatrixReasoningsubtest,[48]EverydayproblemsTest,[49]WordSeriesandLetterSeriesTests)[50]EpisodicMemory(WechslerMemoryScaleLogicalMemorySubtest)[51,52]SpatialWorkingMemory(Information-processingVisuo-SpatialDelayedMatch-to-SampleTest)[53,54]AuditoryWorkingMemory(LetterNumberSequencingsubtestofWAISIII)[48]Lövdenetal.[23]2010Healthyolderadultsb:SubsampleCOGITOstudyN=2569.32±3.12yearsTrainingN=1268.9±2.7yearsControlN=1369.7±3.5yearsQuasi-experimentalPre-postTraining:Pre-postMRIonaverage179±25.2daysapartControl:Pre-postMRIonaverage184±15.0daysapart1.5T•DTISpatialWorkingMemory(3-Back)cNumericalWorkingMemory(MemoryUpdating)cFigural-SpatialEpisodicMemory(Object-PositionMemory)cNumericalEpisodicMemory(Number-nounpairs)cVerbalEpisodicMemory(Wordlist)cPerceptualSpeed(ChoiceReactionTask,Comparisontasks)cAntonenkoetal.[20]2016HealthyolderadultsN=2569±6yearsQuasi-experimentalPre-postAssessments1daybefore(pre),1dayafter(post)and1monthafter(follow-up)trainingd3T•VolumetricStructuralMRI•DTICuedrecall(3-alternative-forced-choicerecalltask(AFC);mainoutcome)[55]andrecognitionEpisodicMemorycontroltask(GermanReyAuditoryVerbalLearningTest)[56]Heinzeletal.[22]2014HealthyolderadultscN=1965.95±3.73yearsQuasi-experimentalPre-postSubsetof15olderindividualsperformedpre-postMRILengthoffollowup:Notstated3T•VolumetricStructuralMRI•Task-basedfMRI•N-back[57]:tworuns(16blocks/run)with4workingmemoryloads(0,1,2,3)•FunctionalConnectivity(PPI)RelativeWorkingMemoryTraininggain(n-Back)[57]Short-termmemory(DigitspanFwdandBwdWAISIII)[51]ProcessingSpeed(DigitSymbolWAISIII,[51]D2Test[58])ExecutiveFunctions:VerbalFluency(ControlledOralWordAssociationTest)[59]Inhibition(Stroop)[60]AbstractReasoning(Raven’sSPM[61],FiguralRelationssubtest[62])MRIMagneticResonanceImaging,DTIDiffusionTensorImaging,fMRIfunctionalMagneticResonanceImaging,RCTRandomizedControlledTriala Meanage±standarddeviationbAsampleofyoungadultswasincludedinthestudyaswellc BehaviouraloutcomesonlymeasuredforinterventiongroupsdOnlycognitiveassessmentsatonemonthfollow-up(noMRI)ten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 6 of 20Table2DetailsofthecomputerizedcognitivetraininginterventionforthestudiesincludedReferenceTrainingprogram/taskCognitiveDomainTrainedDescriptionofTrainingTrainingFrequencyTrainingDurationTotalhoursoftrainingSupervised/Home-basedControlGroupSuoetal.[19]2016COGPACKMultidomain:memory,attention,responsespeed,executivefunctions,languageCOGPACK:Exercisesfocusedonmemory,attention,responsespeed,executivefunctions,andlanguage26weeks52sessions90min/session78 SupervisedActive:watchedvideosoncomputer,followedbyquestionsRosenetal.[18]2011PositScienceMultidomain:processingspeed,accuracyinauditoryprocessingAuditoryverbalrepetitionparadigm:7exercisesaimedatimprovingprocessingspeedandaccuracyinauditoryprocessing5weeks24sessions100min/session36 HomeActive:3differentcomputer-basedactivities(listeningtoaudiobooks,readingonlinenews,playingvisuospatialcomputergame)Lampitetal.[16]2015COGPACKMultidomain:memory,attention,responsespeed,executivefunctions,languageExercisesfocusedonmemory,attention,responsespeed,executivefunctions,andlanguage12weeks3×/week60min/session36 SupervisedActive:viewed7NationalGeographicvideospersessiononacomputerwithmultiplechoicequestionsBellevilleetal.[21]2014CustomizedprogramExecutiveFunction:AttentionAlphanumericequationtask:judgeaccuracyofvisuallypresentedletterandnumberequations.Visualdetectiontask:detecttheredrectangles(pressabutton)inaseriesofwhiteandredrectanglesGroups:1.Singlerepeated:Completebothtasksindividually(focusedattention)2.Dividedfixed:Complete2taskssimultaneouslywithdividedattention(50%)3.Dividedvariable:Completetwotaskssimultaneouslywithdifferentattentionallocationlevels(80%,50%,20%)2weeks3×/week1h/session6 SupervisedNoControlLinetal.[17]2014RehaComExecutiveFunctionandmemoryComputer-assistedexercisefocusedonmemoryandexecutivefunction10weeks6×/week60min/session60 SupervisedPassiveStrenzioketal.[24]2014Multidomain:BrainFitness(BF):auditoryperception;SpaceFortress(SF):visuomotorandworkingmemoryRiseofNation(RoN):attention,motorprocessing,workingmemory,reasoning,visuospatialshort-termmemory,task-switching1.BrainFitness(BF):Adaptiveauditoryperceptioncomputergame2.SpaceFortress(SF):Complexskillacquisitioncomputergame3.RiseofNations(RoN):Off-theshelfreal-timestrategycomputergame6weeks36sessions60min/session36 Supervised+Home(50–50%)NoControlLövdenetal.[23]2010CustomizedprogramMultidomain:workingmemory,episodicmemory,perceptualspeedWorkingMemory(3-Back,Memoryupdating,Alphaspan)Episodicmemory(Object-positionmemory,Number-nounpairs,Wordlists)>4monthsAverageof100±3.7sessions60min/sessionAverageof100SupervisedPassive:Pre-postMRIonlyten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 7 of 20Table2Detailsofthecomputerizedcognitivetraininginterventionforthestudiesincluded(Continued)Perceptualspeed(Choicereactiontasks,ComparisonTasks)Antonenkoetal.[20]2016Object-locationLearningParadigmMemoryObject-locationLearningParadigm:Learnthecorrectspatiallocationsofbuildingsonastreetmap.Fiveblocksof120stimulus-locationpairingwitharesponseintervalof3s.Eachblockwasfollowedbyacuedrecallandarecognitiontask3consecutivedays5learningblocks/dayUnknownUnspecifiedNoControlHeinzeletal.[22]2014n-BacktrainingExecutiveFunction:WorkingMemoryAdaptiven-backtraining,3runs(12blocks/run)eachsession.Difficultylevelincreasedaccordingtoindividualperformance(higherworkingmemoryload,shortenedinterstimulusinterval(ISI).ISIrangedfrom1500to500msinstepsof500ms.4weeks3×/week45min/session9 SupervisedNocontrolten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 8 of 20Table3ResultsforImagingOutcomemeasuresReferenceStructuralchangesFunctionalchangesChangesinconnectivityCognitionOutcomeCognitionrelatedtoimagingoutcomeSuoetal.[19]2016Combinedcognitivetrainingandprogressiveresistancetrainingledtoincreasedcorticalthicknessinposteriorcingulatecortex.Cognitivetrainingaloneledtoatrophy.-CognitivetraininggroupsshowedGroupXTimeinteractionindicatingdecreasedconnectivitybetweentheposteriorcingulateandsuperiorfrontallobe(F(67)=31.7,p<0.001)andbetweentheposteriorcingulateandtheanteriorcingulatecortex(F(67)=13.9,p<0.001)a .Cognitivetraininggroup(aloneorcombinedwithexercise)showedaGroupXTimeinteractionindicatingincreasedconnectivitybetweenhippocampusandtheleftsuperiorfrontallobecomparedwithnon-computerizedcognitivetraining(p=0.012)aComputerizedcognitivetraining(aloneandwithresistancetraining):Memorydomain:GroupXTimeinteraction(F(90)=5.7,p<0.02)showingnodeclineincognitivetraininggroupcomparedtonon-cognitivetraininggroupsaADAS-Cog:NoeffectofcognitivetrainingChangeinposteriorcingulategreymattercorrelatedwithimprovementintheADAS-Cog(r=0.25,p=0.030)a .Increasedconnectivitybetweenhippocampusandsuperiorfrontallobewascorrelatedwithimprovedmemorydomainperformance(r=0.33,p=0.005)aRosenetal.[18]2011-Significantincreaseofactivationinleftanteriorhippocampusinexperimentalgroupcomparedwithcontrols.-Anon-significantbutgreatergaininmemoryperformanceinexperimentalgroupcomparedwithcontrolgroup(F(1,10)=4.76,p=0.054).Changescoresshowedimprovedmemoryperformanceininterventiongroupcomparedwithdecreaseinperformanceinthecontrolgroup(t(10)=2.61,p<.0027,Cohen’sd=1.38)Non-significanttrendshowingchangesinhippocampalactivationcorrelatedpositivelywithchangesinmemoryscoreonRBANSinallparticipants(r=0.49,p=0.10,Cohen’sd=1.14)Lampitetal.[16]2015Significantincreaseingreymatterdensityinrightpost-centralgyrusintraininggroupcomparedwithadecreaseincontrol.Vertex-basedanalysisshowedsignificantdifferenceinrateofthicknesschangeovertimebetweentrainingandcontrolinboththeleftfusiformgyrus(T>3.39)andthesupramarginalandpost-centralgyri(T>2.24).-GroupxTimeinteractionshowedfunctionalconnectivitydecreasebetweenposteriorcingulateandrightsuperiorfrontalgyrusintraininggroupwhilefunctionalconnectivityincreasedinthecontrolgroup(p=.006)atFU1.GroupxTimeinteractionshowedfunctionalconnectivityincreasebetweenrighthippocampusandleftsuperiortemporalgyrusinCCT,whiledecreasedincontrolatfirstFU1(p=.029).NosignificantGroupxTimeinteractionsfoundforMagneticResonanceSpectroscopy(MRS)andwholebrainDiffusionTensorImaging(DTI)Repeated-measuredANOVAshowedimprovedglobalcognitionintraininggroupcomparedtocontrol(GroupXTime,F=7.833,p=0.003).EffectsizeonGlobalCognition(d=0.94baselineversusFU1andd=2.18baselineversusFU2)Significantpositivecorrelationbetweenchangeingreymatterdensityinrightpost-centralgyrusatFU2andchangeinglobalcognitionatFU1(r=0.647,p=.023)andFU2(r=0.584,p=0.046)inbothtrainingandcontrol.InversecorrelationbetweenfunctionalconnectivitybetweenposteriorcingulateandrightsuperiorfrontalgyrusatFU1andchangeinglobalcognitionatFU2(r=−.771,p=.003).SignificantpositivecorrelationinfunctionalconnectivitybetweentherighthippocampusandleftsuperiortemporalgyrusatFU1andchangeinglobalcognitionatFU2(r=0.591,p=.043).Bellevilleetal.[21]2014-SingleRepeated:Alphanumericsingletask:Decreasedpost--Alphanumericsingletask:Allgroupsshowedimprovedreactiontime(RT;F(1,34)=9.75,p<.001,η2=.22)andaccuracy(AC;F(1,34)=14.8,p=.001,η2=.30)SingleRepeated:Alphanumericsingletask:Significantpositivecorrelationbetweenrightinferiorandmiddlefrontalgyrusten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 9 of 20Table3ResultsforImagingOutcomemeasures(Continued)trainingactivationininferiorandrightmiddlefrontalgyrus(t=5.91),leftmiddlefrontalgyrus(t=4.57)andleftthalamus(t=5.37).Visualdetectionsingletask:nochangeDualtask:nochangeDividedFixedAlphanumericsingletask:nochangeVisualdetectionsingletask:Decreasedpost-trainingactivationinrightcerebellum(t=4.73)andrightmiddleoccipitalgyrus(t=4.68)whenperformingthevisualdetectiontask.Dualtask(50/50):Smallincreaseinpost-trainingactivationinrightandleftmiddlefrontalgyrus(areas11,47;t=4.41andt=4.52respectively).DividedVariableAlphanumericsingletask:nochangeVisualdetectionsingletask:nochangeDualtask:Significantincreasedactivationinrightmiddlefrontalgyrus(area10;for20%attentionallocationt=5.35and50%attentionallocationt=4.78).Noreducedpost-trainingactivationin80%attentionallocation.Visualdetectionsingletask:NochangeDualtask(costscore)b:Singlerepeated:NoimprovementsindualtaskingDividedFixed:Reduceddual-taskcost(F(1,34)=6.97,p<.001,η2=.45)DividedVariable:Reduceddual-taskcostandwereabletomodifyattentionalpriority(F(2,33)=5.17,p<.001,η2=.34)activationandreactiontime(r=.56,p<.05).DividedVariable:Significantnegativecorrelation(posttraining)betweenactivationofrightsuperiorandmiddlefrontalgyrus(Brodmannarea10)andattentionalcost(r=−.55,p<.05)ten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 10 of 20Table3ResultsforImagingOutcomemeasures(Continued)Linetal.[17]2014--Traininggroup:Significantincreasedfunctionalconnectivityin(allp’s<0.005):1.Lefthippocampus-rightinferiorfrontalgyrus2.Lefthippocampus-rightmiddlefrontalgyrus3.Righthippocampus-leftmiddlefrontalgyrus4.Righthippocampus-leftinferiorfrontalgyrus5.Righthippocampus-leftsuperiorfrontalgyrus6.Righthippocampus-leftparietallobeControlgroup:Significantlydecreasedfunctionalconnectivity(allp’s<0.005):1.Lefthippocampus-rightmiddleoccipitalgyrus2.Righthippocampus-rightposteriorlobeorcerebellum3.Righthippocampus-leftsuperiortemporalgyrusTraininggroup:1.Significantimprovedscoreson5/7subtestsfromWechslerMemoryScale,namely:Mentalcontrol(p=0.003),Logicalmemory(p<0.001),Digitsforwardandbackward(p=0.014),Visualreproduction(p=0.008),andAssociatedlearning(p<0.001).2.ImprovedMemoryquotient(p=0.005)3.ImprovedperformanceonTrailMakingTest-A(p<0.001)Controlgroup:nosignificantchangesbetweenbaselineand10-weekscoresTraininggroup:significantpositivecorrelationsbetween(allp’s<0.001):1.Memoryquotientandfunctionalconnectivityoflefthippocampus-rightfrontallobe(r=0.64)2.Memoryquotientandfunctionalconnectivityofrighthippocampus-leftfrontallobe(r=0.85)3.Memoryquotientandfunctionalconnectivityofrighthippocampus-leftparietallobe(r=0.79)4.TrailMakingTest-Ascoreandfunctionalconnectivityoflefthippocampus-rightfrontallobe(r=0.94)5.TrailMakingTest-Aandfunctionalconnectivityofrighthippocampus-leftfrontallobe(r=0.68)Controlgroup:nosignificantcorrelationsbetweencognitionandfunctionalconnectivitycStrenzioketal.[24]2014--VentralNetwork:Axialdiffusivity(AD)intherightoccipito-temporalwhitemattersignificantlyincreasedafterBFcomparedwithadecreaseafterSFandRON(p<0.05)DorsalNetwork:Functionalconnectivitybetweenrightsuperiorparietalcortex(SPC)andleftposteriorinferiortemporallobe(ITL)decreasedinSFandincreasedinRON(p=0.02).FunctionalconnectivitybetweenrightSPCandleftanteriorITLdecreasedinBFandshowedanincreaseinRON(p=0.03)UnivariateANOVAshowedmaineffectsoftraininggroup:ReasoningonEverydayProblemsTest:Maineffectoftraininggroup(F(2,39)=5.34,p<0.01,partialη2=0.215).BFandSFshowedimprovedperformanceaftertrainingandRONshowednoeffect.SpatialWorkingMemory:Maineffectoftraininggroup(F(2,39)=5.03,p<0.001,partialη2=0.205).SFimprovedperformanceaftertraining,RONdecreasedperformance,andBFshowednoeffect.MatrixReasoning:Maineffectoftraininggroup(F(2,39)=3.40,p<0.044,partialη2=0.148).LargestgainsseeninBFandasmallergaininRON.TheSFgroupshowedadecreaseinreasoningaftertrainingCognitionandWhiteMatterIntegrityPositivecorrelationbetweenchangeinthalamicADandchangeinworkingmemoryperformanceinallparticipants(r=0.44,p<0.005).Negativecorrelationbetweenchangesinoccipito-temporalADandeverydayproblemsolving(r=−0.32,p<.05)andspatialworkingmemoryaccuracy(r=−0.35,p<.05).Negativecorrelationbetweenchangesinoccipito-temporal-parietalADandspatialworkingmemoryaccuracy(r=−0.40,p<0.05).Cognition&FunctionalConnectivityPositivecorrelationbetweenchangesinSPC-posteriorITLconnectivityandchangesineverydayproblemsolvingtime(r=−0.57,p<.001).Lövdenetal.[23]2010--MeanDiffusivity(MD)GroupXTimeinteractionfoundforsegment1(genu)ofcorpuscallosum,showingadecreaseinMDUnknown:analysiscombinedyoungerandoldersubsetsUnknown:analysiscombinedyoungerandoldersubsetsten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 11 of 20Table3ResultsforImagingOutcomemeasures(Continued)(t(11)=2.39,p=.036).NochangesincontrolgroupFractionalAnisotropy(FA)GroupXTimeinteractionfoundforsegment1ofcorpuscallosum,showinganincreaseinFA(t(11)=3.12,p=.010)Antonenkoetal.[20]2016Hippocampalvolume:nodifferencepretoposttraining(p=0.505)MeanDiffusivity(MD):AsignificantdecreaseinfornixMDwasfoundatpost-trainingcomparedwithpre-training(p=0.036).NodifferenceinhippocampalMDfrompre-topost-training(p=0.669).FractionalAnisotropy(FA):Anon-significantincreaseinfornixFAwasfoundbetweenpre-andpost-training(p=0.114)%Correctduringtraining:Taskperformancesignificantlyimprovedinacurvilinearconvexmanneroverthe3trainingdayslearning-HigherincreaseinfornixFAfrompretopostassessmentwassignificantlyrelatedtobetteraveragerecallperformanceontheobject-locationtaskduringtraining,at1-daypostandfollow-up(r=0.431,p=0.031)-ChangeinfornixFAdidnotcorrelatewithepisodicmemoryperformanceonthecontroltask(ReyAuditoryVerbalLearningTest;p=0.214)-ChangeinfornixMDdidnotcorrelatewithrecallperformancep=0.728-ChangeinhippocampalMDorvolumedidnotcorrelatewithrecallperformance(p=0.688andp=0.758,respectively)Heinzeletal.[22]2014Nosignificantchangeingreymattervolumeofworkingmemorynetworkposttraining(t(14)=0.83,p=.421)Nosignificant2(time)×3(workingmemoryload)interaction(F=.24,p=.714,partialη2=.024).Significantmaineffectoftime(F=12.68,p=.003,partialη2=.475)drivenbyBOLDdecreasein1-back(t=.99,p=.029).A2(time)×3(load)repeatedmeasuresANOVAshowednochangesinconnectivityinworkingmemorynetwork(F(2,28)=1.08,p=.355,partialη2=.071)n-Back:pairedt-testsshowedimprovedperformanceon1-Back(t(18)=3.37,p=.003),2-ack(t(18)=7.47,p<.001),and3-Back(t(18)=4.86,p<.001)d.Repeated-measuresMANOVA(factortime)showedimprovementsinneuropsychologicalmeasuresaftertraining.Posthocpairedt-testsshowedimprovementsinDigitSpanFwd(t(18)=2.97,p=0.008),D2test(t(18)=6.48,p<0.001),DigitSymbol(t(18)=2.76,p=0.013),StroopInterference(t(18)=3.28,p=0.004),andFiguralRelations(t(18)=4.73,p<0.001).NoimprovementsaftertrainingwerefoundinDigitSpanBwd,VerbalFluency,andRaven’sSPM.dNon-significanttrendbetweenBOLDactivationatbaselineandrelativeimprovementinDigitSpanFwd(r=.43,p=.067)a ThisstudywasafullfactorialdesignbThisdual-taskcostrepresentstheproportionallossofperformanceinthedual-taskconditionasafunctionofperformanceinthesingle-taskcondition.Alargerscorerepresentsalargerdual-taskcostc Notspecifiedwhethercorrelationswerebasedonchangescoresorscoresatweek10dResultsreportedforallolderparticipants(N=19)ten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 12 of 20measured via task-based fMRI (Table 3). An RCT [18]showed that 2200 min of cognitive training over a periodof 5 weeks resulted in a significant increase in leftanterior hippocampus activity compared with an activecontrol group. The cognitive training consisted of sevengames aimed to improve auditory processing speed andaccuracy. Task difficulty was adjusted throughout thetraining based on individual performance. The activecontrol group performed computer-based activities suchas reading online newspapers and playing computergames targeting visuospatial abilities.A two-week quasi-experimental study looked at fo-cused and divided (fixed and variable) attention training[21]. In the focused attention training, two tasks (i.e.,alphanumeric task and a visual detection task) were per-formed back to back but separate so participants focusedon one task at a time. In the divided attention training,participants performed two tasks at the same time withan equal amount of attention (fixed) or under differentattention allocations (variable). Results showed thattraining a single alphanumeric task for 6 h over twoweeks decreased activation in the inferior and right mid-dle frontal gyrus, in the left middle frontal gyrus and inthe left thalamus. No differences in functional brain acti-vation were found after performing the single visualdetection task or the in the dual task condition. Partici-pants who were assigned to training where they per-formed both the alphanumeric task and the visualdetection task at the same time (i.e., dual task) did notshow differences in performance during the alpha-numeric task in the scanner. However, participantsshowed decreased functional brain activation at post-training compared with pre-training in the cerebellumand right middle occipital gyrus during the single visualdetection task. Additionally, participants showed a slightincrease in activation in both the right and left middlefrontal gyrus. Finally, participants who were assigned tothe training group where they had to perform dual tasksunder different attention allocation levels (i.e., 80%, 50%,or 20%), showed increased activation in the right middlefrontal gyrus (area 10) for 20% and 50% attention alloca-tion when performing the dual task. No significantchanges in functional brain activation were found duringthe 80% attention allocation task, neither during thealphanumeric single task, nor during the visual detectionsingle task performance.In an adaptive n-back training program [22], partici-pants performed 12 sessions of approximately 45 mineach over 4 weeks. The difficulty level of the trainingwas based on individual performance and was increasedacross training sessions by increasing working memoryload and decreasing the interstimulus interval. Results ofthis study showed a non-significant time (2) by workingmemory load (3) interaction, with a significant maineffect of time. This main effect of time demonstrates areduction in working memory network functional brainactivity measured by the Blood Oxygen Level Dependent(BOLD) signal after 12 training sessions. Only decreasesin the 1-back (and not 2-back or 3-back) condition weresignificant, which indicates this main effect of time isdriven by the BOLD signal during the 1-back condition.In summary, an RCT [18] showed that 2200 min ofCCT resulted in increased in left anterior hippocampusactivity compared with an active control group. Onequasi-experimental study [21] showed that depending onthe task and region of interest, all training conditions re-sulted in both increased and decreased activity. Finally, asecond quasi-experimental study [22] found that 12 ses-sions of n-back training resulted in a significant decreasein working memory activity; however decrease in activitywas driven by performance on the 1-back condition.ConnectivityResting-state fMRI (n = 5)Five studies [16, 17, 19, 22, 24] looked at changes in func-tional connectivity after CCT (Table 3). An RCT [19] ex-amined the effect of progressive resistance training (PRT),computerized multi-domain cognitive training (CCT), ora combined intervention on brain structure and functionin older adults with mild cognitive impairment (MCI).The study duration was 26 weeks, with a total of 78 h oftraining. In the cognitive training groups (i.e., PRT + CCT,and CCT + Sham), the posterior cingulate cortex showedsignificant decreases in resting-state functional connectiv-ity with both the superior frontal lobe and the anteriorcingulate cortex. In addition, increases in resting-statefunctional connectivity between the hippocampus and theleft superior frontal lobe were found compared withgroups without CCT.A second RCT of 12 weeks of multimodal CCT [16]showed that 36 h of cognitive training resulted in de-creases in resting-state functional connectivity betweenthe posterior cingulate and the right superior frontalgyrus, while the control group showed significant in-creases in resting-state functional connectivity. In con-trast, CCT resulted in increased resting-state functionalconnectivity between the right hippocampus and the leftsuperior temporal gyrus compared with a decrease inconnectivity in the control group.Another RCT [17] looked at the effects of a 10-weekcomputer assisted training focused on executive func-tioning and memory in older adults with a history ofstroke. The authors found that training, compared witha passive control group, significantly increased resting-state functional connectivity in multiple areas. The lefthippocampus showed significantly increased connectivitywith the right inferior frontal gyrus and the right middlefrontal gyrus. Additionally, the right hippocampus showedten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 13 of 20increased resting-state functional connectivity with the leftmiddle frontal gyrus, the left inferior frontal gyrus, the leftsuperior frontal gyrus and the left parietal lobe. In con-trast, the control group showed significant decreases inresting-state functional connectivity over the 10 weeks(see Table 3 for connectivity decreases).A quasi experiment investigating the effect of threedifferent computer programs [24] found an increasedresting-state functional connectivity in the dorsal net-work between the right superior parietal cortex (SPC)and left posterior inferior temporal lobe (ITL) in Rise OfNation (RON) compared with a decrease in Space Fort-ress (SF). Finally, Brain Fitness (BF) resulted in signifi-cantly decreased resting-state functional connectivitybetween the right SPC and the left anterior ITL com-pared with an increase in RON.Finally, a quasi-experimental study [22] looking at theeffects of an adaptive n-back training program in olderadults found that the 5-week training did not result inchanges in task-based functional connectivity in theworking memory network.Structural connectivity (n = 4)Four studies [16, 20, 23, 24] examined changes in struc-tural connectivity, using DTI, after CCT (Table 3).Whole brain diffusion tensor imaging (DTI) of an RCTof 12 weeks of multimodal CCT [16] showed that 36 hof cognitive training did not result in changes in struc-tural connectivity after training.A quasi-experiment in healthy older adults looked atthe effect of three different training protocols on brainstructure [24]. The participants trained for 36 h over aperiod of 6 weeks; half of the training was supervised,and the other half was performed at their own homes.One training group performed BF, an auditory percep-tion game; the second training group performed SF, acomplex skill acquisition game focused on visuomotorand working memory skills; and the third group per-formed RON, an off-the shelf real-time strategy gamefocused on for example attention, motor processing,working memory and reasoning. The authors foundchanges in the ventral and dorsal network. Axial diffu-sivity (AD) was increased in the right occipito-temporalwhite matter in the BF group, compared with a decreasein SF and RON.Another quasi-experimental study [23] of approxi-mately 100 h of multi-domain cognitive training in bothyoung and healthy older adults performed DiffusionTensor Imaging (DTI) to look at the effects of trainingon structural connectivity in the brain. Result showed asignificant decrease in MD in the genu of the corpuscallosum compared with a passive control group whoshowed no changes in MD. They also found a significantincrease of fractional anisotropy (FA) in the genu of thecorpus callosum compared with the control group.Diffusion Tensor Imaging results from a third quasi-experimental study [20] that involved 3 consecutive daysof training an object-location learning paradigm, showedthat the 3-day training resulted in a significant decreasein mean diffusivity (MD) in the fornix at post-trainingcompared with pre-training. No changes in MD werefound in the hippocampus as a result of the training. Inaddition, the results showed an increase in FA in the for-nix, however this increase was not significant.In summary, the seven [16, 17, 19, 20, 22–24] abovementioned rsfMRI and DTI studies showed both in-creases and decreases in functional and structural con-nectivity after CCT. The variety in study protocol (i.e.,training type, duration) and the regions of interestchosen for neuroimaging analysis makes the comparisonbetween studies difficult.Correlation between imaging outcomes and cognitivefunction outcomes (n = 8)Eight studies [16–22, 24] assessed the association betweencognitive performance and neuroimaging findings (Table 3).An RCT in older adults with a history of stroke [19]found that increases in posterior cingulate grey matterwere associated with improvements in global cognition.Additionally, a cognitive training by time interactionshowed that the increased connectivity between thehippocampus and the left superior frontal lobe was relatedto increased memory domain performance. However, thisinteraction takes into account all training groups thathad a cognitive training component (i.e., also cognitivetraining combined with resistance training). The inclu-sion of the combination group might have influencedthis interaction.In contrast, an RCT looking at CCT in olderadults with MCI [18] found no significant correla-tions between neuroimaging and cognitive results.However, the authors found a non-significant trendsuggesting that, in both groups, increases in hippocampalactivity might be related to improved memory scores onthe RBANS.An RCT of multimodal CCT [16] found that increasedgrey matter density in the right posterior central gyrus wasassociated with improved global cognition at 3 weeks and3 months. This association was found in both the trainingand control group. In addition, it was found that a decreasein resting-state functional connectivity between the poster-ior cingulate and the superior frontal gyrus after 3 weeksof training was related to an increased change in globalcognition after 3 months of training. Increased resting-state functional connectivity between the right hippo-campus and the left superior temporal gyrus measuresten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 14 of 20after three weeks of training was associated with in-creases in global cognition after 3 months of training.A quasi-experimental study [21] found that in partici-pants performing the alphanumeric task in the single taskcondition (i.e., focus on one task at the time), there was asignificant positive correlation between both the right in-ferior and the middle frontal gyrus activation and reactiontime. Thus shorter reaction time (i.e., better performance)was associated with a decrease in brain activation. In thedivided variable condition (i.e., dual task with different at-tention allocation levels), there was a negative correlationbetween activation of the right superior and middle frontalgyrus and attentional cost post training. This correlationindicates that a better training performance (i.e., lowerattentional cost during dual task performance) was associ-ated with higher levels of brain activation.An RCT in older adults with a history of stroke [17]revealed that in the multimodal cognitive training group,resting-state functional connectivity between the lefthippocampus and both the right frontal lobe and rightfrontal lobe, was associated with improved performancein memory executive function respectively. Additionally,increases in resting-state functional connectivity betweenthe right hippocampus and the left frontal lobe and theleft parietal lobe were associated with increases of memoryand executive functioning. No significant associationsbetween functional connectivity and behavioural perform-ance were found in the control group.A quasi-experimental study looking at the effect of threedifferent types of cognitive training on brain structure andfunction [24] found that in the BF training group an in-crease in thalamic AD was associated with an increase inworking memory performance. By comparing BF and SF,the authors found that an increase in occipito-temporalAD was associated with a decrease in everyday problemsolving time. Additionally, they found an associationbetween the increase in both the occipito-temporal ADand occipito-temporal-parietal AD and accuracy of spatialworking memory tasks, indicating that a greater AD wasassociated with a smaller increase in accuracy on thememory task. Finally, looking at the contrast between SFand RON, functional connectivity decreases between thesuperior parietal cortex (SPC) and the posterior inferiortemporal lobe (ITL) were related to better performanceon every day problem solving tasks (i.e., decrease in timefor task completion).In another quasi-experimental study [20], participantstraining for 3 consecutive days on an object-locationlearning paradigm. The authors found that the previousmentioned increase in fornix FA on the post-test com-pared with pre-test was significantly associated with betterrecall performance. Thus, a higher increase in fornix FAover the course of the training resulted in a better recallperformance on the object-location learning paradigmtask. Changes in fornix MD, hippocampal MD, and hippo-campal volume were not associated with recall perform-ance. Performance on the episodic memory control taskwas not associated with changes in fornix FA.The last quasi-experimental study [22] looked atchanges in short term memory (digit span) and found anon-significant trend between task-based functional acti-vation at baseline and improvement in digit span, whichindicates that an increased activation might lead to in-creased short term memory performance.In summary, eight [16–22, 24] of the nine studies[16–24] included demonstrated an association betweenchanges in neuroimaging measures (volumetric orconnectivity) and changes in behavioural outcomes. De-pending on the region of interest (i.e., both volumetricand connectivity), both increases and decreases in ac-tivity resulted in improved cognitive performance. Onestudy [18] found no significant association between neu-roimaging and behavioural measures. One study [23] didnot report the association between neuroimaging and cog-nition in older adults specifically.Quality assessment of the included studiesThe quality of studies included in this systematic reviewvaried substantially (Table 4). On average, the nine in-cluded studies met 7 of the 11 PEDro criteria. Two stud-ies of the highest quality [18, 19] meeting 9 of the 10PEDro criteria; however, five [17, 20, 22–24] studiesfailed to meet five or more study quality criteria. Item 11(i.e., included point measures and variability measures)was met for all nine studies. Item 8 (key outcome mea-sured for 85% of subjects) and nine (outcome data ana-lyzed by intention to treat) were met by seven of thenine studies [18–24]. Item 6, (i.e., blinding of all who ad-ministered the training) commonly received a negativeresponse (i.e., one of the studies [19] blinded trainingadministers). Frequent issues were failure to meet or re-port: 1) allocation concealment (n = 4) [20, 22–24]; 2)blinding of all subjects (n = 6) [17, 20–24]; 3) blinding ofall who administered the training (n = 8) [16–18, 20–24]; 4)blinding of assessors who measured at least one keyoutcome (n = 5) [20–24]; and 5) between-group statis-tical comparisons for at least one key outcome (n = 4)[17, 20–22]. Item 9 (participants with available outcomemeasures received the treatment or control conditionallocated) received 78% overall rater agreement betweenthe authors [LTB and CKB], where the remaining ques-tions received a 100% overall rater agreement between theauthors [LTB and CKB].Of the three additional items, selected by the authors[LTB and TLA], item 12 (inclusion of cognitive out-comes to assist neuroimaging interpretation) was ad-dressed by all nine studies [16–24]. Items 13 (sampleten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 15 of 20size calculation) and 14 (reported compliance) were notaddressed by eight studies [16–18, 20–24].DiscussionFindings from two high-quality studies examining the ef-fect of CCT on volumetric changes, suggest that multi-domain CCT programs with a duration ranging from 12to 26 weeks could result in an increase in grey matterdensity [16], but in contrast could also result in a decreasein cortical thickness in the posterior cingulate [19]. Thisindicates that in a relatively short time span, multi-domainCCT might be able to alter brain structure. However, theoverall heterogeneity of the findings between studies (i.e.,potential functional improvements versus declines), whichcould be in part due to the differences in region of interest,makes it difficult to draw definitive conclusions regardingthe effect of CCT on brain structure.Task-based functional brain activity decreased aftertraining of a single task [21]; however, an increase intask-based brain activation was found in a more com-plex dual-task training [21] and a multi-domain CCTprogram [18]. This highlights that the CCT method (i.e.,multi-domain versus single domain CCT) may play acritical role in task-based functional brain activity.Conversely, multi-domain CCT did not result in changesin structural connectivity [16], where an auditoryperception-training program resulted in increased AD[24]. Resting-state functional connectivity was found toincrease [16, 19] or decrease [16, 19, 24] depending ontraining type (e.g., single- versus multi-domain) and re-gion of interest. Below, we will discuss as to why weTable 4 Quality Assessment of Included Studies (N = 9)Quality item Suo et al.[19] 2016Rosen et al.[18] 2011Lampit et al.[16] 2015Belleville et al.[21] 2014Lin et al.[17] 2014Strenziok et al.[24] 2014Lövden et al.[23] 2010Antonenko et al.[20] 2016Heinzel et al.[22] 2014PEDro Scale Items1 + + + + + − + + −2 + + + + + + − − −3 + + + + + − − − −4 − + + + + + + − −5 + + + − − − − − −6 + − − − − − − − −7 + + + − + − − − −8 + + − + − + + + +9 + + − + − + + + +10 + + + − − + + − −11 + + + + + + + + +Additional Items12 + + + + + + + + +13 + − − − − − − − −14 + − − − − − − − −PEDro scoring system: receive a point (+) for each item that is met. When criteria were not met (−), no points were givenThe maximum number of points is 10, which means excellent quality based on PEDro’s quality assessmentAdditional Quality Assessment Items: Maximum score of 3PEDro Scale1. Eligibility criteria were specified (this item is not used to calculate the PEDro score)2. Subjects were randomly allocated to groups3. Allocation was concealed4. The groups were similar at baseline regarding the most important prognostic indicators5. There was blinding of all subjects6. There was blinding of all therapists who administered the therapy7. There was blinding of all assessors who measured at least one key outcome8. Measures of at least one key outcome were obtained from more than 85% of the subjects initially allocated to groups9. All subjects for whom outcome measures were available received the treatment or control condition as allocated or, where this was not the case, data for atleast one key outcome was analyzed by “intention to treat”10. The results of between-group statistical comparisons are reported for at least one key outcome11. The study provides both point measures and measures of variability for at least one key outcomeAdditional Items12. Was cognition measured to assist the interpretation of neuroimaging results?13. Was there a sample size calculation?14. Was the compliance reported?ten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 16 of 20might see a discrepancy between single- and multi-domain CCT effects, and why this discrepancy mightaffect both structural and resting-state functional con-nectivity differently.Task-based functional activityFunctional activation patterns in the brain change withaging as a result of neurophysiological changes. Comparedwith younger adults, functional activation patterns be-come less coordinated and localized in older adults, whichresult in loss of cognitive performance [25]. In the currentreview, three studies looked at functional activity in thebrain while performing a task in the scanner. Activitylevels in the brain while performing a task were both in-creased and decreased, depending on the type of trainingand region of interest. All three studies focused on differ-ent brain regions, which makes comparison difficult.However, results suggest that engaging in a more diverseor complex training (e.g., multi-domain CCT or dual-tasktraining) might lead to an increased functional activation[18, 21] compared with training of a single task [21, 22].In contrast, a short report focusing on transfer of trainingshowed results that five weeks of training (i.e., letter mem-ory and updating tasks) resulted in increases in task-related functional activity in the striatum compared with apassive control group [26]. Though, besides the focus ondifferent brain regions, the vast differences in study de-sign, such as the training duration, the presence or ab-sence of a control group, and the small number of studiesask for prudence for making assumptions.Structural connectivity and type of trainingDTI is an imaging technique used to determine thewhite matter microstructure of the brain by looking athow water molecules diffuse within the brain (i.e., thedirection and amount of diffusion) [27, 28]. DTI is oftenquantified by measures such as FA and MD; which pro-vide information about the direction of diffusivity andmolecular diffusion rate, respectively. Decreases in FAand increases in MD might indicate lower levels of mye-lin or the presence of axonal injury, as water moleculesare able to diffuse more freely (i.e., isotropic) [29, 30].However, rather than looking at one specific DTI scalar(e.g., FA, MD), scalars need to be combined with otherneuroimaging measures (e.g., T2, PD, FLAIR) to give amore detailed and accurate picture of for example whitematter abnormalities that might occur within the brain[30]. Studies have linked loss of white matter integrity,as measured with DTI, to be associated with age-relatedcognitive decline in otherwise healthy older adults [31].In addition, a meta-analysis focusing on DTI in MCIand Alzheimer’s Disease found increased MD in bothMCI and Alzheimer’s Disease, as well as decreased FA inAlzheimers’ Disease compared with controls. More se-vere levels of Alzheimer’s Disease (i.e., lower scores onthe Mini-Mental State Examination) were associatedwith reductions in FA [32].Few studies looked at the effect of CCT on structuralconnectivity using DTI. One study of moderate-to-highquality (PEDro score of 7/10) found no changes in struc-tural connectivity after 12 weeks of multi-domain CCT,which could be due to the small sample size [16]. Thesefindings are in contrast with a quasi-experimental study[22] that found that an average of 100 h of training overfour months resulted in decreased MD and increased FAin the genu of the corpus callosum. These findings sug-gest that multi-domain CCT is able to alter white mattermicrostructure in the brain in older adults. This findingcould be promising as disruptions in white matterorganization are often paired with cognitive decline [33].However, a limitation of this quasi-experimental study isthe lack of an active control group. Thus, we need morehigh quality studies to replicate these findings and toexamine how multi-domain CCT might be able to alterwhite matter microstructure.Increases in AD in the right occipito-temporal whitematter were found in a study examining the effect of anadaptive auditory perception computer game (i.e., single-domain). This increased AD was correlated with a lowerscore in everyday problem solving and spatial workingmemory accuracy [24]. However, due to the absence ofan included control group, this study used contrastsbetween the three training groups to look at improve-ments between groups. Therefore, results will morelikely provide information about the effect of the train-ing groups in relation to each other (i.e., which interven-tion shows the best results), than give informationwhether the intervention actually works.Functional connectivity and type of trainingResting-state fMRI is used to map networks in the brain,such as the well-established Default Mode Network(DMN) and the Central Executive Network (CEN). Thesenetworks are activated in both the presence [34] or the ab-sence of a (cognitive) task [35, 36]. In patients with MCIor Alzheimer’s Disease, these functional networks in thebrain are found to be disrupted [37, 38]. In addition, wecan measure functional networks in the brain while per-forming a task with task-based fMRI.Two studies [16, 19] showed that a multi-domain CCTintervention resulted in increased resting-state functionalconnectivity of the hippocampus. One high quality study(i.e., PEDro score of 9/10) found that a 26-week multi-domain CCT program alone (versus combination of CCTwith resistance training) resulted in increased resting-statefunctional connectivity between the hippocampus and theten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 17 of 20left superior frontal lobe [19]. Additionally, a study withthe same CCT program (i.e., COGPACK) found thatmulti-domain CCT resulted in increased resting-statefunctional connectivity between the right hippocampusand the left superior temporal gyrus after only three weeksof training [16]. These improvements in resting-statefunctional connectivity were significantly correlated withimproved memory performance [19] and changes in glo-bal cognition at follow-up [16], respectively.In accordance, an RCT of multi-domain CCT in olderadults with a history of a stroke [17] found that CCT in-creased resting-state functional connectivity between thehippocampus and both the inferior frontal gyrus and themiddle frontal gyrus. These increases in resting-sate func-tional connectivity were associated with significant positivechanges in memory quotient and processing speed (TrailMaking Test-A). Literature shows that resting-state func-tional connectivity between the hippocampus and the su-perior frontal lobe is reduced in MCI [37, 38]. Therefore,the current findings might indicate that multi-domain CCTcould lead to improved cognitive performance throughstrengthening hippocampal functional networks and pre-venting memory loss that might be manifested by loss inhippocampal functional connectivity. However, the bio-logical underpinnings of this change in connectivity are stillunclear. Current histological findings suggest training in-duced neuroplasticity could be a result of dendritic branch-ing, synaptogenesis or other factors such as angiogenesis[39]. Besides more human studies, we need to combineknowledge acquired from both human and animal (histo-logical analyses), to help understand how multi-domainCCTcould result in these functional changes in the brain.Immediate comparison between the results of a single-versus multi-domain program can be made within onequasi-experimental study [24]. Participants in this studywere randomly assigned to one of three included cognitivetraining programs. Participants who were randomized inBrain Fitness, a training program considered more single-domain in nature, showed decreased resting-state func-tional connectivity between the superior parietal cortex andthe inferior temporal lobe. In contrast, participants whowere assigned to Rise of Nation, a more multi-domaintraining, showed increased resting-state functional connect-ivity between the superior parietal cortex and the inferiortemporal lobe. This contrast could be due to the nature ofthe training (i.e., single-domain versus multi-domain), asanother quasi-experimental study [22] of single-domainCCT showed no changes in task-based functional connect-ivity following training.A recent study [40] comparing non-computerizedsingle-domain and multi-domain training found thatmulti-domain cognitive training mainly resulted in in-creased memory proficiency, while single-domain train-ing primarily – but not only - enhances visuospatialand attentional benefits. Results of the current system-atic review are in accordance with these findings, as themulti-domain CCT shows improvements in resting-state functional connectivity of hippocampus-frontallobe and hippocampus-temporal lobe, which was asso-ciated with improvements in memory. Single-domainCCT did not result in similar findings. Gains in cogni-tion resulting from multi-domain were more prone tosustain compared to gains acquired in single-domaincognitive training. Thus, multi-domain cognitive train-ing might result in more widespread gains in cognitivefunctions, which maintain visible over a longer periodof time compared to single-domain cognitive training.Quality assessmentThe quality of studies was heterogeneous. Commonlymissed criteria, were those that focused on blinding of par-ticipants, blinding of individuals who delivered the CCT,and blinding of the assessors. These issues could result intobias (i.e., either positively or negatively) during training andfollow-up measurements due to expectations of both studyexaminers (treatment delivery or assessors) and partici-pants. However, five [20–24] of the nine included studieswere quasi-experimental and therefore the key characteris-tic of the more superior RCT, randomization into either anexperimental or a control group, was lacking in thesestudies. The absence of a proper control group in thesefive quasi-experimental designs affects the interpret-ation of the results of the study; instead of whether atreatment works, quasi-experimental studies provideinformation on whether an intervention is more effect-ive than a standard or alternative treatment.Finally, of the three additionally included quality assess-ment criteria (i.e., item 12–14) two criteria (i.e., sample sizecalculation, compliance reported) were only met by onestudy [19]. The absence of sample size calculations and re-ported compliance in the remaining studies [16–18, 20–24],could result in a lack of power, which increases the chancesof false negatives (i.e., type-II errors). This could mean thatpotential effects of CCT on neuroimaging parameterssimply could not be detected due to a small samplesize, and not because they were not present.LimitationsThe studies included in this systematic review variedvastly in study design and CCT delivery, which resultedin a great deal of heterogeneity mainly in outcomes offunctional and structural connectivity. Only four of thenine included studies were RCT’s [16–19]. However, thetype of control group used varied; some studies includedactive controls, whether other control groups were of apassive nature (i.e., usual care). The inclusion of a con-trol group, with a preference for the so-called activecontrol groups, is recommendable in future studies. Inten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 18 of 20addition, the heterogeneity of the findings in thissystematic review might also be due to the large vari-ability in type of training (single- versus multi-domain)and the dosage and duration of training (i.e., daysversus months). Thus, the heterogeneous nature of thestudy designs in this review makes it difficult to drawconclusions. To better understand the relevant mecha-nisms of CCT, neuroimaging outcomes need to be accom-panied with behavioural data. Furthermore, there arelimited investigations regarding the transfer effects ofCCT and the pattern of neuroplasticity associated withtransfer. A high-quality study design, which includesfor example an active control group, a literature-basedtraining duration and dosage, and a sample size calcula-tion, would help increase the consistency and compar-ability of findings, which in turn would help increasethe ability to draw appropriate conclusions.ConclusionsThis systematic review is an essential first step towardsunderstanding the complex volumetric and functionalchanges, as well as changes in structural and functionalconnectivity that underlie CCT in older adults. However,the highly heterogeneous nature of the results in thissystematic review, potentially due to the large variabilityin study design, indicates that more high-quality studiesare needed to confirm and expand upon these findings.In addition, these studies do not provide informationregarding the physiological and cellular mechanisms caus-ing these structural changes. More histological studies areneeded to gain insight whether these CCT induced changesmight be a result of for example neurogenesis or synapticplasticity. Future studies should focus on multi-domainCCT, since this type of training has the potential to inducemore widespread and long-lasting effects on cognition.AbbreviationsAD: Axial diffusivity; BF: Brain fitness; BOLD: Blood oxygen level dependent;CCT: Computerized cognitive training; CEN: Central executive network;DMN: Default mode network; DTI: Diffusion tensor imaging; FA: Fractionalanisotropy; fMRI: functional magnetic resonance imaging; ITL: Inferior temporallobe; MCI: Mild cognitive impairment; MD: Mean diffusivity; MRI: Magneticresonance imaging; PEDro: Physiotherapy evidence database; PRT: Progressiveresistance training; RCT: Randomized controlled trial; RON: Rise of Nation;rsfMRI: resting-state functional magnetic resonance imaging; SF: Spacefortress; SPL: Superior parietal cortexFundingThis work was supported by funding from the Jack Brown and FamilyAlzheimer Research Foundation Society.Availability of data and materialsAll data supporting the conclusions of this article are included within the article.Authors’ contributionsLTB wrote the first draft of the manuscript. JCD and CKB provided helpedwith data extraction and drafting of tables. TLA and JCD conceived the studyconcept and design. TLA, JCD, and CKB wrote portions of the manuscriptand critically reviewed the manuscript. All authors (TLA, JCD, CKB and LTB)have read and approved the manuscript.Authors’ informationLTB is a Mitacs Accelerate Doctoral Trainee. CKB is a Michael Smith Foundationfor Health Research Postdoctoral Fellow. TLA is a Canada Research Chair in PhysicalActivity, Mobility and Cognitive Neuroscience.Ethics approval and consent to participateNot applicable.Consent for publicationNot applicable.Competing interestsThe authors declare that they have no competing interests.Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in publishedmaps and institutional affiliations.Received: 11 October 2016 Accepted: 30 June 2017References1. World Health Organization: Governments commit to advancements indementia research and care. http://www.who.int/mediacentre/news/releases/2015/action-on-dementia/en/. Accessed 1 Aug 2016.2. Polidori MC, Nelles G, Pientka L. Prevention of dementia: focus on lifestyle.Int J Alzheimers Dis. 2010;2010:1–9.3. Ngandu T, Lehtisalo J, Solomon A, Levalahti E, Ahtiluoto S, Antikainen R,Backman L, Hanninen T, Jula A, Laatikainen T, et al. A 2 year multidomainintervention of diet, exercise, cognitive training, and vascular risk monitoringversus control to prevent cognitive decline in at-risk elderly people (FINGER): arandomised controlled trial. Lancet. 2015;385(9984):2255–63.4. Valenzuela M, Sachdev PS. Harnessing brain and cognitive reserve for theprevention of dementia. Indian J Psychiatry. 2009;51(Suppl 1):S16–21.5. Valenzuela MJ, Sachdev P. Brain reserve and dementia: a systematic review.Psychol Med. 2006;36(4):441–54.6. Lampit A, Hallock H, Valenzuela M. Computerized cognitive training incognitively healthy older adults: a systematic review and meta-analysis ofeffect modifiers. PLoS Med. 2014;11(11):e1001756.7. Lampit A, Hallock H, Moss R, Kwok S, Rosser M, Lukjanenko M, et al. Thetimecourse of global cognitive gains from supervised computer-assistedcognitive training: a randomised, active-controlled trial in elderly withmultiple dementia risk factors. J Prev Alzheimers Dis. 2014;1(1):33–9.8. Smith GE, Housen P, Yaffe K, Ruff R, Kennison RF, Mahncke HW, Zelinski EM.A cognitive training program based on principles of brain plasticity: resultsfrom the Improvement in Memory with Plasticity-based Adaptive CognitiveTraining (IMPACT) study. J Am Geriatr Soc. 2009;57(4):594–603.9. Basak C, Boot WR, Voss MW, Kramer AF. Can training in a real-time strategyvideo game attenuate cognitive decline in older adults? Psychol Aging.2008;23(4):765–77.10. Bruel-Jungerman E, Davis S, Laroche S. Brain plasticity mechanisms andmemory: a party of four. Neuroscientist. 2007;13(5):492–505.11. Citri A, Malenka RC. Synaptic plasticity: multiple forms, functions, andmechanisms. Neuropsychopharmacology. 2008;33(1):18–41.12. Guerra-Carrillo B, Mackey AP, Bunge SA. Resting-state fMRI: a window intohuman brain plasticity. Neuroscientist. 2014;20(5):522–33.13. Olesen PJ, Westerberg H, Klingberg T. Increased prefrontal and parietalactivity after training of working memory. Nat Neurosci. 2004;7(1):75–9.14. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reportingitems for systematic reviews and meta-analyses: the PRISMA statement. AnnIntern Med. 2009;151(4):264–9. W26415. Maher CG, Sherrington C, Herbert RD, Moseley AM, Elkins M. Reliability ofthe PEDro scale for rating quality of randomized controlled trials. Phys Ther.2003;83(8):713–21.16. Lampit A, Hallock H, Suo C, Naismith SL, Valenzuela M. Cognitive training-induced short-term functional and long-term structural plastic change isrelated to gains in global cognition in healthy older adults: a pilot study.Front Aging Neurosci. 2015;7:14.17. Lin ZC, Tao J, Gao YL, Yin DZ, Chen AZ, Chen LD. Analysis of centralmechanism of cognitive training on cognitive impairment after stroke:ten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 19 of 20Resting-state functional magnetic resonance imaging study. J Int Med Res.2014;42(3):659–68.18. Rosen AC, Sugiura L, Kramer JH, Whitfield-Gabrieli S, Gabrieli JD. Cognitivetraining changes hippocampal function in mild cognitive impairment: apilot study. J Alzheimers Dis. 2011;26(Suppl 3):349–57.19. Suo C, Singh MF, Gates N, Wen W, Sachdev P, Brodaty H, Saigal N, Wilson GC,Meiklejohn J, Singh N, et al. Therapeutically relevant structural and functionalmechanisms triggered by physical and cognitive exercise. Mol Psychiatry. 2016;20. Antonenko D, Kulzow N, Cesarz ME, Schindler K, Grittner U, Floel A.Hippocampal pathway plasticity is associated with the ability to form novelmemories in older adults. Front Aging Neurosci. 2016;8:61.21. Belleville S, Mellah S, de Boysson C, Demonet JF, Bier B. The pattern and lociof training-induced brain changes in healthy older adults are predicted bythe nature of the intervention. PLoS One. 2014;9(8):e102710.22. Heinzel S, Lorenz RC, Brockhaus WR, Wustenberg T, Kathmann N, Heinz A,Rapp MA. Working memory load-dependent brain response predictsbehavioral training gains in older adults. J Neurosci. 2014;34(4):1224–33.23. Lovden M, Bodammer NC, Kuhn S, Kaufmann J, Schutze H, Tempelmann C,Heinze HJ, Duzel E, Schmiedek F, Lindenberger U. Experience-dependentplasticity of white-matter microstructure extends into old age.Neuropsychologia. 2010;48(13):3878–83.24. Strenziok M, Parasuraman R, Clarke E, Cisler DS, Thompson JC, GreenwoodPM. Neurocognitive enhancement in older adults: comparison of threecognitive training tasks to test a hypothesis of training transfer in brainconnectivity. NeuroImage. 2014;85(Pt 3):1027–39.25. Bishop NA, Lu T, Yankner BA. Neural mechanisms of ageing and cognitivedecline. Nature. 2010;464(7288):529–35.26. Dahlin E, Neely AS, Larsson A, Backman L, Nyberg L. Transfer of learning afterupdating training mediated by the striatum. Science. 2008;320(5882):1510–2.27. Mori S. Introduction to diffusion tensor imaging. 1st ed. Amsterdam: ElsevierScience; 2007.28. Mori S, Zhang J. Principles of diffusion tensor imaging and its applicationsto basic neuroscience research. Neuron. 2006;51(5):527–39.29. Soares JM, Marques P, Alves V, Sousa N. A hitchhiker’s guide to diffusiontensor imaging. Front Neurosci. 2013;7:31.30. Alexander AL, Lee JE, Lazar M, Field AS. Diffusion tensor imaging of thebrain. Neurotherapeutics. 2007;4(3):316–29.31. Kerchner GA, Racine CA, Hale S, Wilheim R, Laluz V, Miller BL, Kramer JH.Cognitive processing speed in older adults: relationship with white matterintegrity. PLoS One. 2012;7(11):e50425.32. Sexton CE, Kalu UG, Filippini N, Mackay CE, Ebmeier KP. A meta-analysis ofdiffusion tensor imaging in mild cognitive impairment and Alzheimer’sdisease. Neurobiol Aging. 2011;32(12):2322 e2325–18.33. Bartzokis G. Alzheimer's disease as homeostatic responses to age-relatedmyelin breakdown. Neurobiol Aging. 2011;32(8):1341–71.34. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, Reiss AL,Greicius MD. Dissociable intrinsic connectivity networks for salienceprocessing and executive control. J Neurosci. 2007;27(9):2349–56.35. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, ShulmanGL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98(2):676–82.36. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in theresting brain: a network analysis of the default mode hypothesis. Proc NatlAcad Sci U S A. 2003;100(1):253–8.37. Bai F, Watson DR, Yu H, Shi Y, Yuan Y, Zhang Z. Abnormal resting-statefunctional connectivity of posterior cingulate cortex in amnestic type mildcognitive impairment. Brain Res. 2009;1302:167–74.38. Wang Z, Liang P, Jia X, Qi Z, Yu L, Yang Y, Zhou W, Lu J, Li K.Baseline and longitudinal patterns of hippocampal connectivity in mildcognitive impairment: evidence from resting state fMRI. J Neurol Sci.2011;309(1–2):79–85.39. Zatorre RJ, Fields RD, Johansen-Berg H. Plasticity in gray and white:neuroimaging changes in brain structure during learning. Nat Neurosci.2012;15(4):528–36.40. Cheng Y, Wu W, Feng W, Wang J, Chen Y, Shen Y, Li Q, Zhang X, Li C.The effects of multi-domain versus single-domain cognitive training innon-demented older people: a randomized controlled trial. BMC Med.2012;10:30.41. Graham DP, Cully JA, Snow AL, Massman P, Doody R. The Alzheimer’sDisease Assessment Scale-Cognitive subscale: normative data for older adultcontrols. Alzheimer Dis Assoc Disord. 2004;18(4):236–40.42. Randolph C, Tierney MC, Mohr E, Chase TN. The repeatable battery for theassessment of neuropsychological status (RBANS): preliminary clinicalvalidity. J Clin Exp Neuropsychol. 1998;20(3):310–9.43. Dwolatzky T, Whitehead V, Doniger GM, Simon ES, Schweiger A, Jaffe D,Chertkow H. Validity of a novel computerized cognitive battery for mildcognitive impairment. BMC Geriatr. 2003;3:4.44. Sahakian BJ, Owen AM. Computerized assessment in neuropsychiatry usingCANTAB: discussion paper. J R Soc Med. 1992;85(7):399–402.45. Robbins TW, James M, Owen AM, Sahakian BJ, McInnes L, Rabbitt P.Cambridge neuropsychological test automated battery (CANTAB): a factoranalytic study of a large sample of normal elderly volunteers. Dementia.1994;5(5):266–81.46. Wechsler D. A standardized memory scale for clinical use. Aust J Psychol.1945;19:87–95.47. Hachinski V, Iadecola C, Petersen RC, Breteler MM, Nyenhuis DL, Black SE,Powers WJ, DeCarli C, Merino JG, Kalaria RN, et al. National Institute ofNeurological Disorders and Stroke-Canadian Stroke Network vascularcognitive impairment harmonization standards. Stroke. 2006;37(9):2220–41.48. Wechsler D. Wechsler Adult Intelligence Scale. 3rd ed. San Antonio:Psychological Corporation; 1997.49. Willis SLM, M. Manual for the Everyday Problems Test. University Park:Pennsylvania State University; 1993.50. Schaie KW. Manual for the Schaie-Thurstone adult mental abilities test(STAMAT). Palo Alto: Consulting Psychological Press; 1985.51. Wechsler D. Wechsler memory scale–revised: manual. San Antonio:Psychology Corporation; 1987.52. Wechsler D. Wechsler memory scale—revised. San Antonio: PsychologicalCorporation; 2009.53. Greenwood PM, Lambert C, Sunderland T, Parasuraman R. Effects ofapolipoprotein E genotype on spatial attention, working memory, and theirinteraction in healthy, middle-aged adults: results From the National Instituteof Mental Health’s BIOCARD study. Neuropsychology. 2005;19(2):199–211.54. Parasuraman R, Greenwood PM, Kumar R, Fossella J. Beyond heritability:neurotransmitter genes differentially modulate visuospatial attention andworking memory. Psychol Sci. 2005;16(3):200–7.55. Floel A, Suttorp W, Kohl O, Kurten J, Lohmann H, Breitenstein C, Knecht S.Non-invasive brain stimulation improves object-location learning in theelderly. Neurobiol Aging. 2012;33(8):1682–9.56. Helmstadter CL, M. & Lux S. Verbaler Lern- und Merkfähigkeitstest (VLMT),Manual. Göttingen: Belz-Test; 2001.57. Cohen JD, Perlstein WM, Braver TS, Nystrom LE, Noll DC, Jonides J, Smith EE.Temporal dynamics of brain activation during a working memory task.Nature. 1997;386(6625):604–8.58. Brickenkamp R. Test d2. The d2 test of attention. 9th ed. Goettingen:Hogrefe; 2002.59. Benton AH, K. Multilingual aphasia examination. Iowa City: AJA Associates; 1989.60. Stroop JR. Studies of Interference in serial verbal reactions. J Exp Psychol.1935;18:643–62.61. Raven J, Summer B, Birchfield M, Brosier G, Burciaga L, Bykrit B. Manual forraven’s progressive matrices and vocabulary scales. Research supplementno. 3: a compendium of North American normative and validity studies.Oxford: Oxford Psychologists Press; 1990.62. Horn W: Leistungspruefsystem LPS, vol. Hogrefe, 2 edn. Goettingen:Germany; 1983.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:ten Brinke et al. BMC Geriatrics  (2017) 17:139 Page 20 of 20

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.52383.1-0362039/manifest

Comment

Related Items