Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network

Motor imagery (MI)-based brain-computer interfaces (BCIs) have been widely used for rehabilitation of motor abilities and prosthesis control for patients with motor impairments. However, MI-BCI performance exhibits a wide variability across subjects, and the underlying neural mechanism remains unclear. Several studies have demonstrated that both the fronto-parietal attention network (FPAN) and MI are involved in high-level cognitive processes that are crucial for the control of BCIs. Therefore, we hypothesized that the FPAN may play an important role in MI-BCI performance. In our study, we recorded multi-modal datasets consisting of MI electroencephalography (EEG) signals, T1-weighted structural and resting-state functional MRI data for each subject. MI-BCI performance was evaluated using the common spatial pattern to extract the MI features from EEG signals. One cortical structural feature (cortical thickness (CT)) and two measurements (degree centrality (DC) and eigenvector centrality (EC)) of node centrality were derived from the structural and functional MRI data, respectively. Based on the information extracted from the EEG and MRI, a correlation analysis was used to elucidate the relationships between the FPAN and MI-BCI performance. Our results show that the DC of the right ventral intraparietal sulcus, the EC and CT of the left inferior parietal lobe, and the CT of the right dorsolateral prefrontal cortex were significantly associated with MI-BCI performance. Moreover, the receiver operating characteristic analysis and machine learning classification revealed that the EC and CT of the left IPL could effectively predict the low-aptitude BCI users from the high-aptitude BCI users with 83.3% accuracy. Those findings consistently reveal that the individuals who have efficient FPAN would perform better on MI-BCI. Our findings may deepen the understanding of individual variability in MI-BCI performance, and also may provide a new biomarker to predict individual MI-BCI performance.

[1]  Frederik Barkhof,et al.  Brain network alterations in Alzheimer's disease measured by eigenvector centrality in fMRI are related to cognition and CSF biomarkers , 2013, Alzheimer's & Dementia.

[2]  Qing Gao,et al.  Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality , 2011, NeuroImage.

[3]  G. Mangun,et al.  The neural mechanisms of top-down attentional control , 2000, Nature Neuroscience.

[4]  D. Yao,et al.  An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface , 2011, PloS one.

[5]  Bernhard Schölkopf,et al.  A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs) , 2013 .

[6]  J. Duncan,et al.  Lateral Prefrontal Cortex Subregions Make Dissociable Contributions during Fluid Reasoning , 2010, Cerebral cortex.

[7]  Rui Zhang,et al.  Predicting Inter-session Performance of SMR-Based Brain–Computer Interface Using the Spectral Entropy of Resting-State EEG , 2015, Brain Topography.

[8]  Anina N. Rich,et al.  Multimodal functional imaging of motor imagery using a novel paradigm , 2013, NeuroImage.

[9]  Patrizia Baraldi,et al.  Human parietofrontal networks related to action observation detected at rest. , 2013, Cerebral cortex.

[10]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[11]  B. Varkuti,et al.  Prediction of brain-computer interface aptitude from individual brain structure , 2013, Front. Hum. Neurosci..

[12]  O. Sporns.  Structure and function of complex brain networks , 2013, Dialogues in clinical neuroscience.

[13]  Peng Xu,et al.  Efficient resting-state EEG network facilitates motor imagery performance , 2015, Journal of neural engineering.

[14]  Frederik Barkhof,et al.  Brain network alterations in Alzheimer's disease measured by Eigenvector centrality in fMRI are related to cognition and CSF biomarkers , 2014, Human brain mapping.

[15]  Theodore P. Zanto,et al.  Fronto-parietal network: flexible hub of cognitive control , 2013, Trends in Cognitive Sciences.

[16]  David G. Norris,et al.  Relationship Between White Matter Hyperintensities, Cortical Thickness, and Cognition , 2015, Stroke.

[17]  Robert J. Zatorre,et al.  Early visual deprivation changes cortical anatomical covariance in dorsal-stream structures , 2015, NeuroImage.

[18]  Rui Zhang,et al.  Local Temporal Correlation Common Spatial Patterns for Single Trial EEG Classification during Motor Imagery , 2013, Comput. Math. Methods Medicine.

[19]  JapkowiczNathalie,et al.  The class imbalance problem: A systematic study , 2002 .

[20]  M. Desmurget,et al.  An ‘automatic pilot’ for the hand in human posterior parietal cortex: toward reinterpreting optic ataxia , 2000, Nature Neuroscience.

[21]  Michael Breakspear,et al.  Graph analysis of the human connectome: Promise, progress, and pitfalls , 2013, NeuroImage.

[22]  Maarten De Vos,et al.  Real-time EEG feedback during simultaneous EEG–fMRI identifies the cortical signature of motor imagery , 2015, NeuroImage.

[23]  T. Paus,et al.  Functional coactivation map of the human brain. , 2008, Cerebral cortex.

[24]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[25]  Sophie K Scott,et al.  Monitoring and the controlled processing of meaning: distinct prefrontal systems. , 2004, Cerebral cortex.

[26]  Silvia A. Bunge,et al.  Evolutionary and Developmental Changes in the Lateral Frontoparietal Network: A Little Goes a Long Way for Higher-Level Cognition , 2014, Neuron.

[27]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[28]  Dezhong Yao,et al.  L1 Norm based common spatial patterns decomposition for scalp EEG BCI , 2013, BioMedical Engineering OnLine.

[29]  Wolfgang Rosenstiel,et al.  Neural mechanisms of brain–computer interface control , 2011, NeuroImage.

[30]  Eric W Sellers,et al.  Manipulating attention via mindfulness induction improves P300-based brain–computer interface performance , 2011, Journal of neural engineering.

[31]  G. Pfurtscheller,et al.  How many people are able to operate an EEG-based brain-computer interface (BCI)? , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  Suliann Ben Hamed,et al.  Multimodal Convergence within the Intraparietal Sulcus of the Macaque Monkey , 2013, The Journal of Neuroscience.

[33]  Heinz Holling,et al.  Is functional integration of resting state brain networks an unspecific biomarker for working memory performance? , 2015, NeuroImage.

[34]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[35]  Klaus-Robert Müller,et al.  Playing Pinball with non-invasive BCI , 2008, NIPS.

[36]  Dinggang Shen,et al.  Inter-modality Relationship Constrained Multi-Task Feature Selection for AD/MCI Classification , 2013, MICCAI.

[37]  Marianna D. Eddy,et al.  Regionally localized thinning of the cerebral cortex in schizophrenia , 2003, Schizophrenia Research.

[38]  P. Jackson,et al.  The neural network of motor imagery: An ALE meta-analysis , 2013, Neuroscience & Biobehavioral Reviews.

[39]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[40]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[41]  Arthur W Toga,et al.  Relationships between IQ and regional cortical gray matter thickness in healthy adults. , 2007, Cerebral cortex.

[42]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[43]  Mikhail A. Lebedev,et al.  Brain-Machine Interfaces: From Macro- to Microcircuits , 2015 .

[44]  Suzanne E. Welcome,et al.  Longitudinal Mapping of Cortical Thickness and Brain Growth in Normal Children , 2022 .

[45]  R. Ptak.  The Frontoparietal Attention Network of the Human Brain , 2012, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

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

[47]  A. Sirigu,et al.  The Mental Representation of Hand Movements After Parietal Cortex Damage , 1996, Science.

[48]  Alan C. Evans,et al.  Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.

[49]  Kirstie J. Whitaker,et al.  Increased Functional Selectivity over Development in Rostrolateral Prefrontal Cortex , 2011, The Journal of Neuroscience.

[50]  R. Goebel,et al.  Tracking the Mind's Image in the Brain I Time-Resolved fMRI during Visuospatial Mental Imagery , 2002, Neuron.

[51]  L. Jäncke,et al.  The effects of working memory training on functional brain network efficiency , 2013, Cortex.

[52]  Olaf Sporns,et al.  Making sense of brain network data , 2013, Nature Methods.

[53]  G. Pfurtscheller,et al.  Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. , 2005, Brain research. Cognitive brain research.

[54]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

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

[56]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[57]  Guglielmo Foffani,et al.  Brain-Machine Interfaces beyond Neuroprosthetics , 2015, Neuron.

[58]  Anders M. Dale,et al.  Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.

[59]  A. Sack,et al.  The cross-functional role of frontoparietal regions in cognition: internal attention as the overarching mechanism , 2014, Progress in Neurobiology.

[60]  V. Calhoun,et al.  The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery , 2014, Neuron.

[61]  Nicholas Lange,et al.  Longitudinal changes in cortical thickness in autism and typical development. , 2014, Brain : a journal of neurology.

[62]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[63]  R. Andersen,et al.  Decoding motor imagery from the posterior parietal cortex of a tetraplegic human , 2015, Science.

[64]  T. Benzinger,et al.  Weighted brain networks in disease: centrality and entropy in human immunodeficiency virus and aging , 2015, Neurobiology of Aging.

[65]  Benjamin Blankertz,et al.  Towards a Cure for BCI Illiteracy , 2009, Brain Topography.

[66]  Christopher R. Madan,et al.  Motor imagery and higher-level cognition: four hurdles before research can sprint forward , 2012, Cognitive Processing.

[67]  Katharina N. Seidl-Rathkopf,et al.  Functions of the human frontoparietal attention network: Evidence from neuroimaging , 2015, Current Opinion in Behavioral Sciences.

[68]  Cameron S. Carter,et al.  Maintaining structured information: An investigation into functions of parietal and lateral prefrontal cortices , 2008, Neuropsychologia.

[69]  May D. Wang,et al.  Histological image classification using biologically interpretable shape-based features , 2013, BMC Medical Imaging.

[70]  Michael A Ferguson,et al.  Topographic maps of multisensory attention , 2010, Proceedings of the National Academy of Sciences.

[71]  Chunshui Yu,et al.  Increased cortical thickness and altered functional connectivity of the right superior temporal gyrus in left-handers , 2015, Neuropsychologia.

[72]  Carl Gabbard,et al.  Using Motor Imagery Therapy to Improve Movement Efficiency and Reduce Fall Injury Risk , 2013 .

[73]  A. Sack.  Parietal cortex and spatial cognition , 2009, Behavioural Brain Research.

[74]  C. Neuper,et al.  Long-term evaluation of a 4-class imagery-based brain–computer interface , 2013, Clinical Neurophysiology.

[75]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[76]  Andrés Marino Álvarez-Meza,et al.  Feature relevance analysis supporting automatic motor imagery discrimination in EEG based BCI systems , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[77]  Takashi Hanakawa,et al.  Neuroanatomical correlates of brain–computer interface performance , 2015, NeuroImage.

[78]  M. Corbetta,et al.  Neural Systems for Visual Orienting and Their Relationships to Spatial Working Memory , 2002, Journal of Cognitive Neuroscience.

[79]  B. Luna,et al.  What has fMRI told us about the Development of Cognitive Control through Adolescence? , 2010, Brain and Cognition.

[80]  Rajesh P. N. Rao,et al.  Cortical activity during motor execution, motor imagery, and imagery-based online feedback , 2010, Proceedings of the National Academy of Sciences.

[81]  Klaus-Robert Müller,et al.  Neurophysiological predictor of SMR-based BCI performance , 2010, NeuroImage.

[82]  Teresa Schuhmann,et al.  Hemispheric Differences within the Fronto-Parietal Network Dynamics Underlying Spatial Imagery , 2012, Front. Psychology.

[83]  C. Montag,et al.  Assessing the function of the fronto‐parietal attention network: Insights from resting‐state fMRI and the attentional network test , 2014, Human brain mapping.

[84]  Koji Jimura,et al.  Dynamically Allocated Hub in Task-Evoked Network Predicts the Vulnerable Prefrontal Locus for Contextual Memory Retrieval in Macaques , 2015, PLoS biology.

[85]  G. DeAngelis,et al.  Representation of Vestibular and Visual Cues to Self-Motion in Ventral Intraparietal Cortex , 2011, The Journal of Neuroscience.

[86]  Alan C. Evans,et al.  Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI , 2006, NeuroImage.

[87]  M. Rietschel,et al.  Positive Association of Video Game Playing with Left Frontal Cortical Thickness in Adolescents , 2014, PloS one.

[88]  Jonathan D. Power,et al.  Evidence for Hubs in Human Functional Brain Networks , 2013, Neuron.

[89]  D Le Bihan,et al.  The Dorsolateral Prefrontal Cortex (dlpfc) Plays a Key Role in Working Memory (wm). yet Its Precise Contribution , 2022 .

[90]  Alan C. Evans,et al.  Accelerated longitudinal cortical thinning in adolescence , 2015, NeuroImage.

[91]  Yangsong Zhang,et al.  Z-Score Linear Discriminant Analysis for EEG Based Brain-Computer Interfaces , 2013, PloS one.

[92]  Geoffrey Bird,et al.  Selective disruption of sociocognitive structural brain networks in autism and alexithymia. , 2014, Cerebral cortex.

[93]  O. Sporns,et al.  Network centrality in the human functional connectome. , 2012, Cerebral cortex.

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

[95]  Deanna Greenstein,et al.  Trajectories of Cerebral Cortical Development in Childhood and Adolescence and Adult Attention-Deficit/Hyperactivity Disorder , 2013, Biological Psychiatry.

[96]  João Ricardo Sato,et al.  Decreased centrality of subcortical regions during the transition to adolescence: A functional connectivity study , 2015, NeuroImage.

[97]  Jeffrey M. Zacks,et al.  Neuroimaging Studies of Mental Rotation: A Meta-analysis and Review , 2008, Journal of Cognitive Neuroscience.

[98]  Huafu Chen,et al.  Multivariate classification of social anxiety disorder using whole brain functional connectivity , 2013, Brain Structure and Function.

[99]  R. Turner,et al.  Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain , 2010, PloS one.

[100]  L. Nyberg,et al.  Common fronto-parietal activity in attention, memory, and consciousness: Shared demands on integration? , 2005, Consciousness and Cognition.

[101]  M. Hallett,et al.  Functional properties of brain areas associated with motor execution and imagery. , 2003, Journal of neurophysiology.

[102]  Dinggang Shen,et al.  Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's Disease and mild cognitive impairment identification , 2014, NeuroImage.

[103]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.