Working Memory

Working memory, the ability to transiently keep, process, and use information as part of ongoing mental processes is an essential feature of cognitive functioning. The largest number of items that people can hold in their working memory, referred to as the capacity of working memory, is limited and varies substantially among individuals. Uncovering the biological factors that underlie these two defining properties of working memory capacity remains a key undertaking of modern cognitive neuroscience since capacity strongly predicts how well we reason, learn, and even do math. In this work we review data that highlights the role white matter, which provides the wiring of the extensive neural networks that activate during working memory tasks, may play in interindividual variations in capacity. We also describe advanced diffusion imaging methods, which may be uniquely suited in capturing those white matter features that are most relevant to capacity. Finally, we discuss several possible mechanisms through which white matter may both contribute to and limit working memory.

[1]  Nikolaus Weiskopf,et al.  Microstructural parameter estimation in vivo using diffusion MRI and structured prior information , 2015, Magnetic resonance in medicine.

[2]  B. Postle Neural Bases of the Short-term Retention of Visual Information , 2016 .

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

[4]  Julien Cohen-Adad,et al.  In vivo histology of the myelin g-ratio with magnetic resonance imaging , 2015, NeuroImage.

[5]  Anders Petersen,et al.  Structural Variability within Frontoparietal Networks and Individual Differences in Attentional Functions: An Approach Using the Theory of Visual Attention , 2015, The Journal of Neuroscience.

[6]  S. Nagarajan,et al.  White Matter Changes of Neurite Density and Fiber Orientation Dispersion during Human Brain Maturation , 2015, PloS one.

[7]  Jeremy F. P. Ullmann,et al.  Brain tissue compartment density estimated using diffusion‐weighted MRI yields tissue parameters consistent with histology , 2015, Human brain mapping.

[8]  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.

[9]  K. Trinkaus,et al.  Differentiation and quantification of inflammation, demyelination and axon injury or loss in multiple sclerosis. , 2015, Brain : a journal of neurology.

[10]  R. Engle,et al.  Working memory capacity and the scope and control of attention , 2015, Attention, Perception, & Psychophysics.

[11]  Peter J. Basser,et al.  Glial Regulation of the Neuronal Connectome through Local and Long-Distant Communication , 2015, Neuron.

[12]  Jelle Veraart,et al.  One diffusion acquisition and different white matter models: How does microstructure change in human early development based on WMTI and NODDI? , 2015, NeuroImage.

[13]  Julien Cohen-Adad,et al.  The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter , 2015, NeuroImage.

[14]  Joseph A. Helpern,et al.  Kurtosis analysis of neural diffusion organization , 2015, NeuroImage.

[15]  Earl K. Miller,et al.  Working Memory Capacity: Limits on the Bandwidth of Cognition , 2015, Daedalus.

[16]  R. Marois The Brain Mechanisms of Working Memory: An Evolving Story , 2015 .

[17]  F. Castellanos,et al.  Constrained by Our Connections: White Matter's Key Role in Interindividual Variability in Visual Working Memory Capacity , 2014, The Journal of Neuroscience.

[18]  Pascale Tremblay,et al.  The Language Connectome , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[19]  P. J. Basser,et al.  Role of myelin plasticity in oscillations and synchrony of neuronal activity , 2014, Neuroscience.

[20]  Lars T. Westlye,et al.  TVA–based assessment of attentional capacities–associations with age and indices of brain white matter microstructure , 2014, Front. Psychol..

[21]  P. DeRosse,et al.  Age-Related Differences in White Matter Tract Microstructure Are Associated with Cognitive Performance from Childhood to Adulthood , 2014, Biological Psychiatry.

[22]  M. Catani,et al.  Can spherical deconvolution provide more information than fiber orientations? Hindrance modulated orientational anisotropy, a true‐tract specific index to characterize white matter diffusion , 2013, Human brain mapping.

[23]  E. Vogel,et al.  Visual working memory capacity: from psychophysics and neurobiology to individual differences , 2013, Trends in Cognitive Sciences.

[24]  Carly J. Leonard,et al.  The relationship between working memory capacity and broad measures of cognitive ability in healthy adults and people with schizophrenia. , 2013, Neuropsychology.

[25]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[26]  P. Sterling,et al.  Why Do Axons Differ in Caliber? , 2012, The Journal of Neuroscience.

[27]  Roberto Cabeza,et al.  The architecture of cross-hemispheric communication in the aging brain: linking behavior to functional and structural connectivity. , 2012, Cerebral cortex.

[28]  Derek K. Jones,et al.  Using the biophysical CHARMED model to elucidate the underpinnings of contrast in diffusional kurtosis analysis of diffusion-weighted MRI , 2011, Magnetic Resonance Materials in Physics, Biology and Medicine.

[29]  K. Trinkaus,et al.  Quantification of increased cellularity during inflammatory demyelination. , 2011, Brain : a journal of neurology.

[30]  L. Westlye,et al.  The brain dynamics of intellectual development: Waxing and waning white and gray matter , 2011, Neuropsychologia.

[31]  Yasuyuki Taki,et al.  Verbal working memory performance correlates with regional white matter structures in the frontoparietal regions , 2011, Neuropsychologia.

[32]  Joseph A. Helpern,et al.  White matter characterization with diffusional kurtosis imaging , 2011, NeuroImage.

[33]  T. Salthouse Neuroanatomical substrates of age-related cognitive decline. , 2011, Psychological bulletin.

[34]  A. Alexander,et al.  Diffusion tensor imaging of the brain , 2007, Neurotherapeutics.

[35]  Tim B. Dyrby,et al.  Orientationally invariant indices of axon diameter and density from diffusion MRI , 2010, NeuroImage.

[36]  Mariana Lazar,et al.  Mapping brain anatomical connectivity using white matter tractography , 2010, NMR in biomedicine.

[37]  N. Cowan,et al.  The Magical Mystery Four , 2010, Current directions in psychological science.

[38]  Sheng-Kwei Song,et al.  Axial Diffusivity Is the Primary Correlate of Axonal Injury in the Experimental Autoimmune Encephalomyelitis Spinal Cord: A Quantitative Pixelwise Analysis , 2009, The Journal of Neuroscience.

[39]  C. Nicholson,et al.  Diffusion in brain extracellular space. , 2008, Physiological reviews.

[40]  P. Basser,et al.  Axcaliber: A method for measuring axon diameter distribution from diffusion MRI , 2008, Magnetic resonance in medicine.

[41]  Joel Myerson,et al.  Variation in Working Memory across the Life Span , 2008 .

[42]  T. Klingberg Development of a superior frontal–intraparietal network for visuo-spatial working memory , 2006, Neuropsychologia.

[43]  P. Hagmann,et al.  Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[44]  Yaniv Assaf,et al.  Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain , 2005, NeuroImage.

[45]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[46]  R. Engle,et al.  Individual differences in working memory capacity and learning: Evidence from the serial reaction time task , 2005, Memory & cognition.

[47]  Alan Connelly,et al.  Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution , 2004, NeuroImage.

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

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

[50]  Andrew R. A. Conway,et al.  Working memory capacity and its relation to general intelligence , 2003, Trends in Cognitive Sciences.

[51]  Jerry M Crutchfield,et al.  Individual differences in working memory capacity predict visual attention allocation , 2003, Psychonomic bulletin & review.

[52]  R. Engle,et al.  Working-memory capacity and the control of attention: the contributions of goal neglect, response competition, and task set to Stroop interference. , 2003, Journal of experimental psychology. General.

[53]  Derek K. Jones,et al.  Diffusion‐tensor MRI: theory, experimental design and data analysis – a technical review , 2002 .

[54]  John Russell,et al.  Dysmyelination Revealed through MRI as Increased Radial (but Unchanged Axial) Diffusion of Water , 2002, NeuroImage.

[55]  G. Baylis,et al.  Individual differences in working memory capacity and enumeration , 2001, Memory & cognition.

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

[57]  J E Lisman,et al.  Storage of 7 +/- 2 short-term memories in oscillatory subcycles , 1995, Science.

[58]  R. Engle,et al.  Individual differences in working memory and comprehension: a test of four hypotheses. , 1992, Journal of experimental psychology. Learning, memory, and cognition.