How recent learning shapes the brain: Memory-dependent functional reconfiguration of brain circuits

The process of storing recently encoded episodic mnestic traces so that they are available for subsequent retrieval is accompanied by specific brain functional connectivity (FC) changes. In this fMRI study, we examined the early processing of memories in twenty-eight healthy participants performing an episodic memory task interposed between two resting state sessions. Memory performance was assessed through a forced-choice recognition test after the scanning sessions. We investigated resting state system configuration changes via Independent Component Analysis by cross-modeling baseline resting state spatial maps onto the post-encoding resting state, and post-encoding resting state spatial maps onto baseline. We identified both persistent and plastic components of the overall brain functional configuration between baseline and post-encoding. While FC patterns within executive, default mode, and cerebellar circuits persisted from baseline to post-encoding, FC within the visual circuit changed. A significant session × performance interaction characterized medial temporal lobe and prefrontal cortex FC with the visual circuit, as well as thalamic FC within the executive control system. Findings reveal early-stage FC changes at the system-level subsequent to a learning experience and associated with inter-individual variation in memory performance.

[1]  Adrian Preda,et al.  The spatial chronnectome reveals a dynamic interplay between functional segregation and integration , 2019, Human brain mapping.

[2]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[3]  F. Sambataro,et al.  Thalamic connectivity measured with fMRI is associated with a polygenic index predicting thalamo-prefrontal gene co-expression , 2019, Brain Structure and Function.

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

[5]  Boris Suchan,et al.  The Regulatory Role of the Human Mediodorsal Thalamus , 2018, Trends in Cognitive Sciences.

[6]  Michael W. Deem,et al.  Brain Modularity Mediates the Relation between Task Complexity and Performance , 2017, bioRxiv.

[7]  M. Raichle The brain's default mode network. , 2015, Annual review of neuroscience.

[8]  M. Erb,et al.  Fast track to the neocortex: A memory engram in the posterior parietal cortex , 2018, Science.

[9]  L. Davachi,et al.  Awake Reactivation of Prior Experiences Consolidates Memories and Biases Cognition , 2019, Trends in Cognitive Sciences.

[10]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[11]  P. Frankland,et al.  The organization of recent and remote memories , 2005, Nature Reviews Neuroscience.

[12]  Michael M. Halassa,et al.  Thalamocortical Circuit Motifs: A General Framework , 2019, Neuron.

[13]  Yuhui Du,et al.  Group information guided ICA for fMRI data analysis , 2013, NeuroImage.

[14]  G. Pergola,et al.  Flexible and specific contributions of thalamic subdivisions to human cognition , 2021, Neuroscience & Biobehavioral Reviews.

[15]  Yadin Dudai,et al.  The Consolidation and Transformation of Memory , 2015, Neuron.

[16]  Aapo Hyvärinen,et al.  Validating the independent components of neuroimaging time series via clustering and visualization , 2004, NeuroImage.

[17]  Fabio Sambataro,et al.  Treatment with Olanzapine is Associated with Modulation of the Default Mode Network in Patients with Schizophrenia , 2010, Neuropsychopharmacology.

[18]  Y. Dudai The neurobiology of consolidations, or, how stable is the engram? , 2004, Annual review of psychology.

[19]  Scott T. Grafton,et al.  Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.

[20]  J. Pekar,et al.  A method for making group inferences from functional MRI data using independent component analysis , 2001, Human brain mapping.

[21]  Caitlin R. Bowman,et al.  The Neural Basis of Recollection Rejection: Increases in Hippocampal–Prefrontal Connectivity in the Absence of a Shared Recall-to-Reject and Target Recollection Network , 2016, Journal of Cognitive Neuroscience.

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

[23]  R. Olsen,et al.  Zooming in and zooming out: the importance of precise anatomical characterization and broader network understanding of MRI data in human memory experiments , 2020, Current Opinion in Behavioral Sciences.

[24]  Y. Kareev,et al.  Correct acceptance weighs more than correct rejection: a decision bias induced by question framing , 2011, Psychonomic bulletin & review.

[25]  Guillén Fernández,et al.  Thalamo-cortical coupling during encoding and consolidation is linked to durable memory formation , 2019, NeuroImage.

[26]  J. Callicott,et al.  Normal aging modulates prefrontoparietal networks underlying multiple memory processes , 2012, The European journal of neuroscience.

[27]  Rex E. Jung,et al.  A Baseline for the Multivariate Comparison of Resting-State Networks , 2011, Front. Syst. Neurosci..

[28]  P Alvarez,et al.  Memory consolidation and the medial temporal lobe: a simple network model. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[29]  J. Kim,et al.  Episodic memory in aspects of large-scale brain networks , 2015, Front. Hum. Neurosci..

[30]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[31]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[32]  L. Nadel,et al.  Memory consolidation, retrograde amnesia and the hippocampal complex , 1997, Current Opinion in Neurobiology.

[33]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[34]  G. Winocur,et al.  Episodic Memory and Beyond: The Hippocampus and Neocortex in Transformation. , 2016, Annual review of psychology.

[35]  M. Parazzini,et al.  Modulating Human Procedural Learning by Cerebellar Transcranial Direct Current Stimulation , 2013, Cerebellum.

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

[37]  Anna S. Mitchell,et al.  The mediodorsal thalamus as a higher order thalamic relay nucleus important for learning and decision-making , 2015, Neuroscience & Biobehavioral Reviews.

[38]  Onur Güntürkün,et al.  Recall deficits in stroke patients with thalamic lesions covary with damage to the parvocellular mediodorsal nucleus of the thalamus , 2012, Neuropsychologia.

[39]  Boris Suchan,et al.  The role of the thalamic nuclei in recognition memory accompanied by recall during encoding and retrieval: An fMRI study , 2013, NeuroImage.

[40]  Karl J. Friston Learning and inference in the brain , 2003, Neural Networks.

[41]  Karen R. Brandt,et al.  Measuring the speed of the conscious components of recognition memory: Remembering is faster than knowing , 2006, Consciousness and Cognition.

[42]  G. Pergola,et al.  Association of familial risk for schizophrenia with thalamic and medial prefrontal functional connectivity during attentional control , 2016, Schizophrenia Research.

[43]  Nicholas A. Ketz,et al.  Enhanced Brain Correlations during Rest Are Related to Memory for Recent Experiences , 2010, Neuron.

[44]  J. Callicott,et al.  Age-related alterations in default mode network: Impact on working memory performance , 2010, Neurobiology of Aging.

[45]  Craig E. L. Stark,et al.  When zero is not zero: The problem of ambiguous baseline conditions in fMRI , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[46]  Greg G. Brown,et al.  Dysregulation of working memory and default‐mode networks in schizophrenia using independent component analysis, an fBIRN and MCIC study , 2009, Human brain mapping.

[47]  Hans J. Johnson,et al.  Advanced Normalization Tools (ANTs) , 2020 .

[48]  Vince D. Calhoun,et al.  Automatic Identification of Functional Clusters in fMRI Data Using Spatial Dependence , 2011, IEEE Transactions on Biomedical Engineering.

[49]  G. Pergola,et al.  Associative Learning Beyond the Medial Temporal Lobe: Many Actors on the Memory Stage , 2013, Front. Behav. Neurosci..