Distributed patterns of occipito-parietal functional connectivity predict the precision of visual working memory

Abstract Limitations in visual working memory (WM) quality (i.e., WM precision) may depend on perceptual and attentional limitations during stimulus encoding, thereby affecting WM capacity. WM encoding relies on the interaction between sensory processing systems and fronto‐parietal ‘control’ regions, and differences in the quality of this interaction are a plausible source of individual differences in WM capacity. Accordingly, we hypothesized that the coupling between perceptual and attentional systems affects the quality of WM encoding. We combined fMRI connectivity analysis with behavioral modeling by fitting a variable precision and fixed capacity model to the performance data obtained while participants performed a visual delayed continuous response WM task. We quantified functional connectivity during WM encoding between occipital and parietal brain regions activated during both perception and WM encoding, as determined using a conjunction of two independent experiments. The multivariate pattern of voxel‐wise inter‐areal functional connectivity significantly predicted WM performance, most specifically the mean of WM precision but not the individual number of items that could be stored in memory. In particular, higher occipito‐parietal connectivity was associated with higher behavioral mean precision. These results are consistent with a network perspective of WM capacity, suggesting that the efficiency of information flow between perceptual and attentional neural systems is a critical determinant of limitations in WM quality. HighlightsWorking memory (WM) limitations depend on individual differences in WM precision.We combined fMRI with behavioral modeling to explore underlying neural mechanisms.Behavioral performance was modeled with a variable precision + fixed capacity model.Voxel‐wise occipito‐parietal functional coupling was examined during WM encoding.These connectivity patterns were predictive of WM precision.

[1]  Rainer Goebel,et al.  Cortical capacity constraints for visual working memory: dissociation of fMRI load effects in a fronto-parietal network , 2003, NeuroImage.

[2]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[3]  Stephen M. Smith,et al.  Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data , 2010, Front. Syst. Neurosci..

[4]  S. Luck,et al.  Discrete fixed-resolution representations in visual working memory , 2008, Nature.

[5]  H Pashler,et al.  Familiarity and visual change detection , 1988, Perception & psychophysics.

[6]  J. Palmer.  Attentional limits on the perception and memory of visual information. , 1990, Journal of experimental psychology. Human perception and performance.

[7]  Daniel J. Mitchell,et al.  Flexible, capacity-limited activity of posterior parietal cortex in perceptual as well as visual short-term memory tasks. , 2008, Cerebral cortex.

[8]  M. Goldberg,et al.  Attention, intention, and priority in the parietal lobe. , 2010, Annual review of neuroscience.

[9]  Adam Gazzaley,et al.  Measuring functional connectivity during distinct stages of a cognitive task , 2004, NeuroImage.

[10]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[11]  A. Treisman,et al.  Attentional demands predict short-term memory load response in posterior parietal cortex , 2009, Neuropsychologia.

[12]  Sabine Windmann,et al.  How to trust a perfect stranger: predicting initial trust behavior from resting-state brain-electrical connectivity. , 2015, Social cognitive and affective neuroscience.

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

[14]  M. Chun,et al.  Dissociable neural mechanisms supporting visual short-term memory for objects , 2006, Nature.

[15]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[16]  Mark D'Esposito,et al.  Top-down flow of visual spatial attention signals from parietal to occipital cortex. , 2009, Journal of vision.

[17]  A. Gazzaley.  Influence of early attentional modulation on working memory , 2011, Neuropsychologia.

[18]  Edward Awh,et al.  A Neural Measure of Precision in Visual Working Memory , 2013, Journal of Cognitive Neuroscience.

[19]  Markus Siegel,et al.  Neural substrates of cognitive capacity limitations , 2011, Proceedings of the National Academy of Sciences.

[20]  A. Wagner,et al.  Sources of Method Bias in Social Science Research and Recommendations on How to Control It , 2015 .

[21]  B. Postle,et al.  The cognitive neuroscience of working memory. , 2007, Annual review of psychology.

[22]  E. Vogel,et al.  PSYCHOLOGICAL SCIENCE Research Article Visual Working Memory Represents a Fixed Number of Items Regardless of Complexity , 2022 .

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

[24]  Paul J. Laurienti,et al.  Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data , 2010, NeuroImage.

[25]  Yasuyuki Taki,et al.  Training of Working Memory Impacts Structural Connectivity , 2010, The Journal of Neuroscience.

[26]  Theodore P. Zanto,et al.  Causal role of the prefrontal cortex in top-down modulation of visual processing and working memory , 2011, Nature Neuroscience.

[27]  Adam Gazzaley,et al.  Early Top–Down Control of Visual Processing Predicts Working Memory Performance , 2010, Journal of Cognitive Neuroscience.

[28]  R. Sekuler,et al.  Recognizing spatial patterns: a noisy exemplar approach , 2002, Vision Research.

[29]  D G Pelli,et al.  The VideoToolbox software for visual psychophysics: transforming numbers into movies. , 1997, Spatial vision.

[30]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[31]  W. Ma,et al.  Factorial comparison of working memory models. , 2014, Psychological review.

[32]  W. Ma,et al.  A detection theory account of change detection. , 2004, Journal of vision.

[33]  Karl J. Friston,et al.  Multisubject fMRI Studies and Conjunction Analyses , 1999, NeuroImage.

[34]  Erin D. Bigler,et al.  Quantitative temporal lobe differences: Autism distinguished from controls using classification and regression tree analysis , 2007, Brain and Development.

[35]  H Sompolinsky,et al.  Simple models for reading neuronal population codes. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[36]  Kartik K. Sreenivasan,et al.  Revisiting the role of persistent neural activity during working memory , 2014, Trends in Cognitive Sciences.

[37]  Cornelis J. Stam,et al.  Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain , 2008, NeuroImage.

[38]  Jon Driver,et al.  Human Visual Short-Term Memory Precision Can Be Varied at Will When the Number of Retained Items Is Low , 2012, Psychological science.

[39]  A. Nobre,et al.  Oscillatory Brain State Predicts Variability in Working Memory , 2014, The Journal of Neuroscience.

[40]  J. Palva,et al.  Neuronal synchrony reveals working memory networks and predicts individual memory capacity , 2010, Proceedings of the National Academy of Sciences.

[41]  R. Kahn,et al.  Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.

[42]  P. Cavanagh,et al.  The Capacity of Visual Short-Term Memory is Set Both by Visual Information Load and by Number of Objects , 2004, Psychological science.

[43]  K. Oberauer,et al.  Effects and mechanisms of working memory training: a review , 2013, Psychological Research.

[44]  J. Jay Todd,et al.  Posterior parietal cortex activity predicts individual differences in visual short-term memory capacity , 2010 .

[45]  V. Menon.  Large-scale brain networks and psychopathology: a unifying triple network model , 2011, Trends in Cognitive Sciences.

[46]  Gregor M. Hörzer,et al.  Theta coupling between V4 and prefrontal cortex predicts visual short-term memory performance , 2012, Nature Neuroscience.

[47]  Dejan Gjorgjevikj,et al.  A Multi-class SVM Classifier Utilizing Binary Decision Tree , 2009, Informatica.

[48]  Maro G. Machizawa,et al.  Electrophysiological Measures of Maintaining Representations in Visual Working Memory , 2007, Cortex.

[49]  Julia P. Owen,et al.  Autism and Sensory Processing Disorders: Shared White Matter Disruption in Sensory Pathways but Divergent Connectivity in Social-Emotional Pathways , 2014, PloS one.

[50]  Leslie G. Ungerleider,et al.  A neural system for human visual working memory. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[51]  J. Kaiser,et al.  Activity in Human Visual and Parietal Cortex Reveals Object-Based Attention in Working Memory , 2015, The Journal of Neuroscience.

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

[53]  Paul M Bays,et al.  Dynamic Shifts of Limited Working Memory Resources in Human Vision , 2008, Science.

[54]  M. Paradiso,et al.  A theory for the use of visual orientation information which exploits the columnar structure of striate cortex , 2004, Biological Cybernetics.

[55]  A. Nobre,et al.  Top-down modulation: bridging selective attention and working memory , 2012, Trends in Cognitive Sciences.

[56]  N. Cowan.  The magical number 4 in short-term memory: A reconsideration of mental storage capacity , 2001, Behavioral and Brain Sciences.

[57]  Thomas E. Nichols.  Multiple testing corrections, nonparametric methods, and random field theory , 2012, NeuroImage.

[58]  Karl J. Friston,et al.  Conjunction revisited , 2005, NeuroImage.

[59]  W. Ma,et al.  Changing concepts of working memory , 2014, Nature Neuroscience.

[60]  Adam C. Riggall,et al.  Distributed Patterns of Activity in Sensory Cortex Reflect the Precision of Multiple Items Maintained in Visual Short-Term Memory , 2013, The Journal of Neuroscience.

[61]  George A. Alvarez,et al.  Variability in the quality of visual working memory , 2012, Nature Communications.

[62]  A. Stevens,et al.  Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity , 2012, PloS one.

[63]  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.

[64]  E. Vogel,et al.  Capacity limit of visual short-term memory in human posterior parietal cortex , 2004 .

[65]  C. Fiebach,et al.  Modulation of Inferotemporal Cortex Activation during Verbal Working Memory Maintenance , 2006, Neuron.

[66]  Rainer Goebel,et al.  Common neural substrates for visual working memory and attention , 2007, NeuroImage.

[67]  Christian C Ruff,et al.  Sensory processing: who's in (top‐down) control? , 2013, Annals of the New York Academy of Sciences.

[68]  T. Klingberg,et al.  Prefrontal cortex and basal ganglia control access to working memory , 2008, Nature Neuroscience.

[69]  W. Singer,et al.  Neural Anatomy of Primary Visual Cortex Limits Visual Working Memory. , 2016, Cerebral cortex.

[70]  J. Serences,et al.  Neural system for controlling the contents of object working memory in humans. , 2006, Cerebral cortex.

[71]  Stephen M. Emrich,et al.  Strengthened Effective Connectivity Underlies Transfer of Working Memory Training to Tests of Short-Term Memory and Attention , 2013, The Journal of Neuroscience.

[72]  J L Lancaster,et al.  Automated Talairach Atlas labels for functional brain mapping , 2000, Human brain mapping.

[73]  Paul J. Laurienti,et al.  An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets , 2003, NeuroImage.

[74]  Wei Ji Ma,et al.  Does precision decrease with set size? , 2012, Journal of vision.

[75]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .

[76]  X Leclerc,et al.  Brain-behaviour relationships. Some models and related statistical procedures for the study of brain-damaged patients. , 1998, Brain : a journal of neurology.

[77]  M. D’Esposito,et al.  Functional connectivity during working memory maintenance , 2004, Cognitive, affective & behavioral neuroscience.

[78]  Timothy F. Brady,et al.  Modeling visual working memory with the MemToolbox. , 2013, Journal of vision.

[79]  Janaina Mourão Miranda,et al.  Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data , 2005, NeuroImage.

[80]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[81]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[82]  J. Jonides,et al.  Overlapping mechanisms of attention and spatial working memory , 2001, Trends in Cognitive Sciences.

[83]  D. Heeger,et al.  Decoding and Reconstructing Color from Responses in Human Visual Cortex , 2009, The Journal of Neuroscience.

[84]  Edward K. Vogel,et al.  The capacity of visual working memory for features and conjunctions , 1997, Nature.

[85]  T. Klingberg.  Training and plasticity of working memory , 2010, Trends in Cognitive Sciences.

[86]  Jesper Andersson,et al.  Valid conjunction inference with the minimum statistic , 2005, NeuroImage.

[87]  Brian A Wandell,et al.  Corrigendum: Visual field maps and stimulus selectivity in human ventral occipital cortex , 2005, Nature Neuroscience.

[88]  D. Mantini,et al.  Modulation of alpha power at encoding and retrieval tracks the precision of visual short-term memory , 2014, Journal of neurophysiology.

[89]  Russell A. Poldrack,et al.  Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses , 2012, NeuroImage.

[90]  Julie D. Golomb,et al.  A taxonomy of external and internal attention. , 2011, Annual review of psychology.

[91]  B. Postle,et al.  Superior Parietal Cortex Is Critical for the Manipulation of Information in Working Memory , 2009, The Journal of Neuroscience.

[92]  Maro G. Machizawa,et al.  Neural measures reveal individual differences in controlling access to working memory , 2005, Nature.

[93]  Paul J. Laurienti,et al.  Modularity maps reveal community structure in the resting human brain , 2009 .

[94]  Evgueni A. Haroutunian,et al.  Information Theory and Statistics , 2011, International Encyclopedia of Statistical Science.

[95]  R. Engle,et al.  The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective , 2002, Psychonomic bulletin & review.

[96]  Hauke R. Heekeren,et al.  Normative shifts of cortical mechanisms of encoding contribute to adult age differences in visual–spatial working memory , 2013, NeuroImage.

[97]  R. Engle.  Working Memory Capacity as Executive Attention , 2002 .

[98]  Yaoda Xu,et al.  Decoding the content of visual short-term memory under distraction in occipital and parietal areas , 2015, Nature Neuroscience.

[99]  R. Deichmann,et al.  Distinct causal influences of parietal versus frontal areas on human visual cortex: evidence from concurrent TMS-fMRI. , 2008, Cerebral cortex.

[100]  Alex R. Wade,et al.  Visual field maps and stimulus selectivity in human ventral occipital cortex , 2005, Nature Neuroscience.

[101]  S. Luck,et al.  Sudden Death and Gradual Decay in Visual Working Memory , 2009, Psychological science.

[102]  C. Fiebach,et al.  Superior Intraparietal Sulcus Controls the Variability of Visual Working Memory Precision , 2016, The Journal of Neuroscience.

[103]  C. Fiebach,et al.  Trait anxiety and the neural efficiency of manipulation in working memory , 2012, Cognitive, affective & behavioral neuroscience.

[104]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[105]  W. K. Simmons,et al.  Circular analysis in systems neuroscience: the dangers of double dipping , 2009, Nature Neuroscience.

[106]  J Martinerie,et al.  Complex modular structure of large-scale brain networks. , 2009, Chaos.

[107]  Jeffrey N Rouder,et al.  An assessment of fixed-capacity models of visual working memory , 2008, Proceedings of the National Academy of Sciences.

[108]  J. Wallis,et al.  Capacity and precision in an animal model of visual short-term memory. , 2012, Journal of vision.

[109]  Maro G. Machizawa,et al.  Neural activity predicts individual differences in visual working memory capacity , 2004, Nature.

[110]  Jan Derrfuss,et al.  Different Roles of Direct and Indirect Frontoparietal Pathways for Individual Working Memory Capacity , 2016, The Journal of Neuroscience.

[111]  P. Skudlarski,et al.  Brain Connectivity Related to Working Memory Performance , 2006, The Journal of Neuroscience.

[112]  Wei Ji Ma,et al.  Variability in encoding precision accounts for visual short-term memory limitations , 2012, Proceedings of the National Academy of Sciences.

[113]  M. Chun.  Visual working memory as visual attention sustained internally over time , 2011, Neuropsychologia.

[114]  Arnaud Cachia,et al.  Feature selection and classification of imbalanced datasets Application to PET images of children with autistic spectrum disorders , 2011, NeuroImage.

[115]  K. Zilles,et al.  Differentiated parietal connectivity of frontal regions for “what” and “where” memory , 2012, Brain Structure and Function.