Complementary topology of maintenance and manipulation brain networks in working memory

Working memory (WM) is assumed to consist of a process that sustains memory representations in an active state (maintenance) and a process that operates on these activated representations (manipulation). We examined evidence for two distinct, concurrent cognitive functions supporting maintenance and manipulation abilities by testing brain activity as participants performed a WM alphabetization task. Maintenance was investigated by varying the number of letters held in WM and manipulation by varying the number of moves required to sort the list alphabetically. We found that both maintenance and manipulation demand had significant effects on behavior that were associated with different cortical regions: maintenance was associated with bilateral prefrontal and left parietal cortex, and manipulation with right parietal activity, a link that is consistent with the role of parietal cortex in symbolic computations. Both structural and functional architecture of these systems suggested that these cognitive functions are supported by two dissociable brain networks. Critically, maintenance and manipulation functional networks became increasingly segregated with increasing demand, an effect that was positively associated with individual WM ability. These results provide evidence that network segregation may act as a protective mechanism to enable successful performance under increasing WM demand.

[1]  Raymond J Dolan,et al.  Maintenance versus manipulation in verbal working memory revisited: an fMRI study , 2003, NeuroImage.

[2]  Felix Blankenburg,et al.  Maintenance and manipulation of somatosensory information in ventrolateral prefrontal cortex , 2014, Human brain mapping.

[3]  Erik A. Wing,et al.  Excitatory TMS modulates memory representations , 2018, Cognitive neuroscience.

[4]  R. Marois,et al.  "What" and "where" in the intraparietal sulcus: an FMRI study of object identity and location in visual short-term memory. , 2010, Cerebral cortex.

[5]  R. Cabeza,et al.  Frequency-specific neuromodulation of local and distant connectivity in aging & episodic memory function , 2016, bioRxiv.

[6]  Giulio Tononi,et al.  Repetitive Transcranial Magnetic Stimulation Dissociates Working Memory Manipulation from Retention Functions in the Prefrontal, but not Posterior Parietal, Cortex , 2006, Journal of Cognitive Neuroscience.

[7]  Jonathan D. Power,et al.  Multi-task connectivity reveals flexible hubs for adaptive task control , 2013, Nature Neuroscience.

[8]  Andrew Zalesky,et al.  Reconfiguration of Brain Network Architectures between Resting-State and Complexity-Dependent Cognitive Reasoning , 2017, The Journal of Neuroscience.

[9]  S. Dehaene,et al.  THREE PARIETAL CIRCUITS FOR NUMBER PROCESSING , 2003, Cognitive neuropsychology.

[10]  M. D’Esposito,et al.  Category-specific modulation of inferior temporal activity during working memory encoding and maintenance. , 2004, Brain research. Cognitive brain research.

[11]  Jessica R. Cohen,et al.  The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition , 2016, The Journal of Neuroscience.

[12]  Edward E. Smith,et al.  A Parametric Study of Prefrontal Cortex Involvement in Human Working Memory , 1996, NeuroImage.

[13]  Janice Chen,et al.  Dynamic reconfiguration of the default mode network during narrative comprehension , 2016, Nature Communications.

[14]  E. J. Carter,et al.  Functional Imaging of Numerical Processing in Adults and 4-y-Old Children , 2006, PLoS biology.

[15]  Edward T. Bullmore,et al.  Network Scaling Effects in Graph Analytic Studies of Human Resting-State fMRI Data , 2010, Front. Syst. Neurosci..

[16]  Alan Connelly,et al.  SIFT: Spherical-deconvolution informed filtering of tractograms , 2013, NeuroImage.

[17]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[18]  Michael X. Cohen,et al.  Inferior Temporal, Prefrontal, and Hippocampal Contributions to Visual Working Memory Maintenance and Associative Memory Retrieval , 2004, The Journal of Neuroscience.

[19]  Jessica R. Cohen,et al.  Quantifying the Reconfiguration of Intrinsic Networks during Working Memory , 2014, PloS one.

[20]  Alan Connelly,et al.  Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution , 2007, NeuroImage.

[21]  B. Biswal,et al.  Evidence for multiple manipulation processes in prefrontal cortex , 2006, Brain Research.

[22]  Andrew S. Bock,et al.  Predicting future learning from baseline network architecture , 2016, NeuroImage.

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

[24]  J D Gabrieli,et al.  A resource model of the neural basis of executive working memory. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Todd S Braver,et al.  Preparation for integration: the role of anterior prefrontal cortex in working memory , 2008, Neuroreport.

[26]  J. Desmond,et al.  Load-Dependent Roles of Frontal Brain Regions in the Maintenance of Working Memory , 1999, NeuroImage.

[27]  Andreas Heinz,et al.  Test–retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures , 2012, NeuroImage.

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

[29]  Denise C. Park,et al.  Decreased segregation of brain systems across the healthy adult lifespan , 2014, Proceedings of the National Academy of Sciences.

[30]  B M Bly,et al.  Functional magnetic resonance imaging of working memory impairment after traumatic brain injury , 2001, Journal of neurology, neurosurgery, and psychiatry.

[31]  A. Baddeley The episodic buffer: a new component of working memory? , 2000, Trends in Cognitive Sciences.

[32]  D. Bassett,et al.  Dynamic reconfiguration of frontal brain networks during executive cognition in humans , 2015, Proceedings of the National Academy of Sciences.

[33]  B. Postle,et al.  Maintenance versus Manipulation of Information Held in Working Memory: An Event-Related fMRI Study , 1999, Brain and Cognition.

[34]  Zhentao Zuo,et al.  Effects of number magnitude and notation at 7T: separating the neural response to small and large, symbolic and nonsymbolic number. , 2014, Cerebral cortex.

[35]  Chun-Hung Yeh,et al.  Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics , 2016, NeuroImage.

[36]  B. Rypma,et al.  Factors controlling neural activity during delayed-response task performance: Testing a memory organization hypothesis of prefrontal function , 2006, Neuroscience.

[37]  Degang Zhang,et al.  Complex span tasks and hippocampal recruitment during working memory , 2011, NeuroImage.

[38]  Denise C Park,et al.  Parietal functional connectivity in numerical cognition. , 2013, Cerebral cortex.

[39]  David Thomas,et al.  The Art in Computer Programming , 2001 .

[40]  S. Dehaene,et al.  A Magnitude Code Common to Numerosities and Number Symbols in Human Intraparietal Cortex , 2007, Neuron.

[41]  Kim F. Nimon,et al.  Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity , 2012, Front. Psychology.

[42]  Elizabeth M Brannon,et al.  Neural connectivity patterns underlying symbolic number processing indicate mathematical achievement in children. , 2014, Developmental science.

[43]  T. Griffiths,et al.  A Brain System for Auditory Working Memory , 2016, The Journal of Neuroscience.

[44]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.