Neural representations of task context and temporal order during action sequence execution

Since routine action sequences can share a great deal of similarity in terms of their stimulus response mappings, their correct execution relies crucially on the ability to preserve contextual and temporal information (Lashley, 1951). However, there are few empirical studies on the neural mechanism and the brain areas maintaining such information. To address this gap in the literature, we recently recorded the blood-oxygen level dependent (BOLD) response in a newly developed coffee-tea making task (Holroyd et al., 2018). The task involves the execution of 4 action sequences that each feature 6 decision states. Here we report a reanalysis of this dataset using a data-driven approach, namely multivariate pattern analysis (MVPA), that examines context-dependent neural activity across several predefined regions of interest. Results highlight involvement of the inferior-temporal gyrus and lateral prefrontal cortex in maintaining temporal and contextual information for the execution of hierarchically-organized action sequences. Furthermore, temporal information seems to be more strongly encoded in areas over the left hemisphere.

[1]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[2]  Clay B. Holroyd,et al.  Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach , 2017, Psychonomic Bulletin & Review.

[3]  David Badre,et al.  Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes , 2008, Trends in Cognitive Sciences.

[4]  Matthijs A. A. van der Meer,et al.  Hippocampal Replay Is Not a Simple Function of Experience , 2010, Neuron.

[5]  Theresa M. Desrochers,et al.  What is a Sequence? The Neural Mechanisms of Perceptual, Motor, and Task Sequences Across Species and Their Interaction with Addiction , 2019, Oxford Research Encyclopedia of Neuroscience.

[6]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[7]  A. Redish,et al.  Representational changes of latent strategies in rat medial prefrontal cortex precede changes in behaviour , 2016, Nature Communications.

[8]  D. Hassabis,et al.  Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network , 2016, Neuron.

[9]  R. Poldrack Region of interest analysis for fMRI. , 2007, Social cognitive and affective neuroscience.

[10]  Stefano Fusi,et al.  The geometry of abstraction in hippocampus and pre-frontal cortex , 2018, bioRxiv.

[11]  J. S. Guntupalli,et al.  Decoding neural representational spaces using multivariate pattern analysis. , 2014, Annual review of neuroscience.

[12]  Theresa M. Desrochers,et al.  Sequential Control Underlies Robust Ramping Dynamics in the Rostrolateral Prefrontal Cortex , 2018, The Journal of Neuroscience.

[13]  Clay B. Holroyd,et al.  Motivation of extended behaviors by anterior cingulate cortex , 2012, Trends in Cognitive Sciences.

[14]  J. Shaoul Human Error , 1973, Nature.

[15]  Kristjan Kalm,et al.  Reading positional codes with fMRI: Problems and solutions , 2016, bioRxiv.

[16]  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.

[17]  Kristjan Kalm,et al.  A shared representation of order between encoding and recognition in visual short-term memory , 2016, NeuroImage.

[18]  K. Lashley The problem of serial order in behavior , 1951 .

[19]  D. Plaut,et al.  Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action. , 2004, Psychological review.

[20]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Johan D. Carlin,et al.  Choosing the Rules: Distinct and Overlapping Frontoparietal Representations of Task Rules for Perceptual Decisions , 2013, The Journal of Neuroscience.

[22]  Rainer Goebel,et al.  Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns , 2008, NeuroImage.

[23]  A. Georgopoulos,et al.  Neural activity in prefrontal cortex during copying geometrical shapes , 2003, Experimental brain research.

[24]  C. Felser,et al.  Negative magnetoresistance without well-defined chirality in the Weyl semimetal TaP , 2015, Nature Communications.

[25]  Massimo Silvetti,et al.  Human midcingulate cortex encodes distributed representations of task progress , 2018, Proceedings of the National Academy of Sciences.

[26]  Nancy Kanwisher,et al.  Divide and conquer: A defense of functional localizers , 2006, NeuroImage.

[27]  Matthias J. Gruber,et al.  Hippocampal Activity Patterns Carry Information about Objects in Temporal Context , 2014, Neuron.

[28]  C. Ranganath,et al.  Two cortical systems for memory-guided behaviour , 2012, Nature Reviews Neuroscience.

[29]  Julia Uddén,et al.  A rostro-caudal gradient of structured sequence processing in the left inferior frontal gyrus , 2012, Philosophical Transactions of the Royal Society B: Biological Sciences.

[30]  Gabriel Baud-Bovy,et al.  Encoding of Serial Order in Working Memory: Neuronal Activity in Motor, Premotor, and Prefrontal Cortex during a Memory Scanning Task , 2018, The Journal of Neuroscience.

[31]  Y. Niv Learning task-state representations , 2019, Nature Neuroscience.

[32]  William D. Marslen-Wilson,et al.  Conserved Sequence Processing in Primate Frontal Cortex , 2017, Trends in Neurosciences.

[33]  J. Tanji,et al.  Integration of temporal order and object information in the monkey lateral prefrontal cortex. , 2004, Journal of neurophysiology.

[34]  Theresa M. Desrochers,et al.  The Necessity of Rostrolateral Prefrontal Cortex for Higher-Level Sequential Behavior , 2015, Neuron.

[35]  Karim Jerbi,et al.  Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy , 2015, Journal of Neuroscience Methods.

[36]  Cathy J. Price,et al.  A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading , 2012, NeuroImage.

[37]  Daeyeol Lee,et al.  Prefrontal Neural Correlates of Memory for Sequences , 2007, The Journal of Neuroscience.

[38]  Lila Davachi,et al.  How the hippocampus preserves order: the role of prediction and context , 2015, Trends in Cognitive Sciences.

[39]  E. Procyk,et al.  Anterior cingulate activity during routine and non-routine sequential behaviors in macaques , 2000, Nature Neuroscience.

[40]  Jeremy K. Seamans,et al.  Tracking Progress toward a Goal in Corticostriatal Ensembles , 2014, The Journal of Neuroscience.

[41]  Marisa Carrasco,et al.  Specific Visual Subregions of TPJ Mediate Reorienting of Spatial Attention , 2018, Cerebral cortex.

[42]  E. Koechlin,et al.  Managing competing goals — a key role for the frontopolar cortex , 2017, Nature Reviews Neuroscience.

[43]  D. Durstewitz,et al.  Contextual encoding by ensembles of medial prefrontal cortex neurons , 2012, Proceedings of the National Academy of Sciences.

[44]  Leila Reddy,et al.  Multivoxel Object Representations in Adult Human Visual Cortex Are Flexible: An Associative Learning Study , 2016, Journal of Cognitive Neuroscience.

[45]  Jörn Diedrichsen,et al.  Reliability of dissimilarity measures for multi-voxel pattern analysis , 2016, NeuroImage.

[46]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[47]  E. Miller,et al.  Task-Dependent Changes in Short-Term Memory in the Prefrontal Cortex , 2010, The Journal of Neuroscience.

[48]  D. Norman Categorization of action slips. , 1981 .

[49]  Apostolos P. Georgopoulos,et al.  Neural activity in prefrontal cortex during copying geometrical shapes , 2003, Experimental Brain Research.

[50]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..