Identification of Two Distinct Working Memory-Related Brain Networks in Healthy Young Adults

Abstract Working memory (WM) is an important cognitive domain for everyday life functioning and is often disturbed in neuropsychiatric disorders. Functional magnetic resonance imaging (fMRI) studies in humans show that distributed brain areas typically described as fronto-parietal regions are implicated in WM tasks. Based on data from a large sample of healthy young adults (N = 1369), we applied independent component analysis (ICA) to the WM-fMRI signal and identified two distinct networks that were relevant for differences in individual WM task performance. A parietally-centered network was particularly relevant for individual differences in task measures related to WM performance (“WM dependent”) and a frontally-centered network was relevant for differences in attention-dependent task performance. Importantly, frontal areas that are typically considered as key regions for WM were either involved in both WM-dependent and attention-dependent performance, or in attention-dependent performance only. The networks identified here are provided as publicly available datasets. These networks can be applied in future studies to derive a low-dimensional representation of the overall WM brain activation.

[1]  Volker Schmid,et al.  Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R , 2011 .

[2]  D. Bowers,et al.  Is the n-back task a valid neuropsychological measure for assessing working memory? , 2009, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[3]  Torkel Klingberg,et al.  Structural Maturation and Brain Activity Predict Future Working Memory Capacity during Childhood Development , 2014, The Journal of Neuroscience.

[4]  J. Sweeney,et al.  White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. , 2007, Brain : a journal of neurology.

[5]  Russ B. Altman,et al.  Independent component analysis: Mining microarray data for fundamental human gene expression modules , 2010, J. Biomed. Informatics.

[6]  Kathryn M. McMillan,et al.  N‐back working memory paradigm: A meta‐analysis of normative functional neuroimaging studies , 2005, Human brain mapping.

[7]  S. Faraone,et al.  Neurocognition in first-episode schizophrenia: a meta-analytic review. , 2009, Neuropsychology.

[8]  H. Gunshin,et al.  A review of independent component analysis application to microarray gene expression data. , 2008, BioTechniques.

[9]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[10]  Edward E. Smith,et al.  Mechanisms of Working Memory Impairment in Schizophrenia , 2016, Biological Psychiatry.

[11]  Torkel Klingberg,et al.  The role of fronto-parietal and fronto-striatal networks in the development of working memory: a longitudinal study. , 2015, Cerebral cortex.

[12]  N. Bargalló,et al.  White matter integrity related to functional working memory networks in traumatic brain injury , 2012, Neurology.

[13]  A M McIntosh,et al.  Working memory in schizophrenia: a meta-analysis , 2008, Psychological Medicine.

[14]  Sébastien Ourselin,et al.  Fast free-form deformation using graphics processing units , 2010, Comput. Methods Programs Biomed..

[15]  C. Constantinidis,et al.  The neuroscience of working memory capacity and training , 2016, Nature Reviews Neuroscience.

[16]  P. Lang.  International affective picture system (IAPS) : affective ratings of pictures and instruction manual , 2005 .

[17]  Volker Schmid,et al.  Working with the DICOM and NIfTI Data Standards in R , 2011 .

[18]  J. Jay Todd,et al.  Capacity limit of visual short-term memory in human posterior parietal cortex , 2004, Nature.

[19]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[20]  Susan F Tapert,et al.  Neuropsychological Predictors of BOLD Response During a Spatial Working Memory Task in Adolescents: What Can Performance Tell Us About fMRI Response Patterns? , 2005, Journal of clinical and experimental neuropsychology.

[21]  Anna S. Huang,et al.  Load-related brain activation predicts spatial working memory performance in youth aged 9–12 and is associated with executive function at earlier ages , 2015, Developmental Cognitive Neuroscience.

[22]  J. Ioannidis,et al.  Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature , 2017, PLoS biology.

[23]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[24]  A. Lansner,et al.  Neurocognitive Architecture of Working Memory , 2015, Neuron.

[25]  Daniel S. Margulies,et al.  NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain , 2016, NeuroImage.

[26]  Thomas R. Barrick,et al.  White matter pathways associated with working memory in normal aging , 2010, Cortex.

[27]  Angela R. Laird,et al.  Modelling neural correlates of working memory: A coordinate-based meta-analysis , 2012, NeuroImage.

[28]  Bruno Torrésani,et al.  Blind Source Separation and the Analysis of Microarray Data , 2004, J. Comput. Biol..

[29]  R. Marois,et al.  Posterior parietal cortex activity predicts individual differences in visual short-term memory capacity , 2005, Cognitive, affective & behavioral neuroscience.

[30]  K. Uğurbil,et al.  fMRI: From Nuclear Spins to Brain Functions , 2015, Biological Magnetic Resonance.

[31]  Tyrone D. Cannon,et al.  Memory systems in schizophrenia: Modularity is preserved but deficits are generalized , 2015, Schizophrenia Research.

[32]  Olaf B. Paulson,et al.  White Matter Microstructure in Superior Longitudinal Fasciculus Associated with Spatial Working Memory Performance in Children , 2011, Journal of Cognitive Neuroscience.

[33]  A. Baddeley.  Working memory: theories, models, and controversies. , 2012, Annual review of psychology.

[34]  Irene E. Nagel,et al.  Microstructure of frontoparietal connections predicts cortical responsivity and working memory performance. , 2011, Cerebral cortex.

[35]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[36]  J. M. Moran,et al.  Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses , 2016, Neuroscience & Biobehavioral Reviews.

[37]  N. Macmillan,et al.  Response bias : characteristics of detection theory, threshold theory, and nonparametric indexes , 1990 .

[38]  Hans Forssberg,et al.  Increased Brain Activity in Frontal and Parietal Cortex Underlies the Development of Visuospatial Working Memory Capacity during Childhood , 2002, Journal of Cognitive Neuroscience.

[39]  P. Basser.  Inferring microstructural features and the physiological state of tissues from diffusion‐weighted images , 1995, NMR in biomedicine.

[40]  Michael W. Cole,et al.  Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence , 2012, The Journal of Neuroscience.

[41]  W. Niessen,et al.  White matter lesions relate to tract-specific reductions in functional connectivity , 2017, Neurobiology of Aging.

[42]  Justin L. Vincent,et al.  Disruption of Large-Scale Brain Systems in Advanced Aging , 2007, Neuron.

[43]  L. Marstaller,et al.  Aging and large-scale functional networks: White matter integrity, gray matter volume, and functional connectivity in the resting state , 2015, Neuroscience.

[44]  Daniel R. Weinberger,et al.  Imaging genetics—days of future past , 2010, NeuroImage.

[45]  Jonathan D. Power,et al.  Intrinsic and Task-Evoked Network Architectures of the Human Brain , 2014, Neuron.

[46]  Neda Jahanshad,et al.  Whole-genome analyses of whole-brain data: working within an expanded search space , 2014, Nature Neuroscience.

[47]  Deanna M. Barch,et al.  Cognition in schizophrenia: core psychological and neural mechanisms , 2012, Trends in Cognitive Sciences.

[48]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[49]  Andreas Kleinschmidt,et al.  Cingulo-opercular network activity maintains alertness , 2016, NeuroImage.

[50]  Y. Zang,et al.  Intrinsic resting‐state activity predicts working memory brain activation and behavioral performance , 2013, Human brain mapping.

[51]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.

[52]  M. Posner,et al.  The attention system of the human brain: 20 years after. , 2012, Annual review of neuroscience.

[53]  M. Minzenberg,et al.  Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. , 2009, Archives of general psychiatry.

[54]  John Muschelli,et al.  fslr: Connecting the FSL Software with R , 2015, R J..

[55]  Thomas E. Nichols,et al.  Scanning the horizon: towards transparent and reproducible neuroimaging research , 2016, Nature Reviews Neuroscience.

[56]  Junghee Lee,et al.  Working memory impairments in schizophrenia: a meta-analysis. , 2005, Journal of abnormal psychology.

[57]  P. Sullivan,et al.  Converging Genetic and Functional Brain Imaging Evidence Links Neuronal Excitability to Working Memory, Psychiatric Disease, and Brain Activity , 2014, Neuron.

[58]  Nadim Joni Shah,et al.  The relationship between BOLD fMRI response and the underlying white matter as measured by fractional anisotropy (FA): A systematic review , 2017, NeuroImage.

[59]  L. Kasper,et al.  Attention-deficit/hyperactivity disorder (ADHD) and working memory in adults: a meta-analytic review. , 2013, Neuropsychology.

[60]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[61]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[62]  D. Murdoch,et al.  P-Values are Random Variables , 2008 .

[63]  Michael W. Cole,et al.  Activity flow over resting-state networks shapes cognitive task activations , 2016, Nature Neuroscience.

[64]  Christos Davatzikos,et al.  Functional Maturation of the Executive System during Adolescence , 2013, The Journal of Neuroscience.

[65]  Jin Fan,et al.  Human attentional networks. , 2004, Psychiatrische Praxis.

[66]  H Takao,et al.  Effects of Age and Gender on White Matter Integrity , 2011, American Journal of Neuroradiology.

[67]  Nicolas Y. Masse,et al.  Task-specific versus generalized mnemonic representations in parietal and prefrontal cortices , 2015, Nature Neuroscience.

[68]  Jing Liu,et al.  Dissociated roles of the parietal and frontal cortices in the scope and control of attention during visual working memory , 2017, NeuroImage.

[69]  C. Beaulieu,et al.  The basis of anisotropic water diffusion in the nervous system – a technical review , 2002, NMR in biomedicine.

[70]  Olaf Sporns,et al.  Network attributes for segregation and integration in the human brain , 2013, Current Opinion in Neurobiology.

[71]  Donatella Marazziti,et al.  Cognitive impairment in major depression. , 2010, European journal of pharmacology.

[72]  Barry D. Van Veen,et al.  Context-specific differences in fronto-parieto-occipital effective connectivity during short-term memory maintenance , 2015, NeuroImage.

[73]  B. Miller,et al.  Neurodegenerative Diseases Target Large-Scale Human Brain Networks , 2009, Neuron.

[74]  Efstathios D. Gennatas,et al.  Predicting Regional Neurodegeneration from the Healthy Brain Functional Connectome , 2012, Neuron.

[75]  B. T. Thomas Yeo,et al.  Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex , 2014, NeuroImage.