Unilateral, 3D arm movement kinematics are encoded in ipsilateral human cortex

There is increasing evidence that the hemisphere ipsilateral to a moving limb plays a role in planning and executing movements. However, the exact relationship between cortical activity and ipsilateral limb movements is uncertain. We sought to determine whether 3D arm movement kinematics (speed, velocity, and position) could be decoded from cortical signals recorded from the hemisphere ipsilateral to the moving limb. By having invasively monitored patients perform unilateral reaches with each arm, we also compared the encoding of contralateral and ipsilateral limb kinematics from a single cortical hemisphere. In four motor-intact human patients (three male, one female) implanted with electrocorticography electrodes for localization of their epileptic foci, we decoded 3D movement kinematics of both arms with accuracies above chance. Surprisingly, the spatial and spectral encoding of contralateral and ipsilateral limb kinematics was similar, enabling cross-prediction of kinematics between arms. These results clarify our understanding that the ipsilateral hemisphere robustly contributes to motor execution and supports that the information of complex movements is more bihemispherically represented in humans than has been previously understood

Electrocorticographic Representations of Semantic Information in the Human Cortex

ABSTRACT OF THE DISSERTATION Electrocorticographic Representations of Semantic Information in the Human Cortex by Nicholas Paul Szrama Doctor of Philosophy in Biomedical Engineering Washington University in St. Louis, 2016 Professor Eric C. Leuthardt, Chair Semantics is a broad field of research that examines the literal meanings behind words, objects, concepts, and actions. Semantic memory is a critical component of our daily interactions with the world and it describes the cumulative knowledge that we acquire throughout our lifetime. Despite its importance, there is an incomplete understanding of how semantic information is organized, accessed, and retrieved in the brain. In this body of work, electrocorticographic (ECoG) potentials are recorded from five subjects while they perform a variant of the Deese, Roediger, and McDermott false memory experiment. By design, the auditory word stimuli used in this experiment were chosen to have strong semantic relationships with multiple non-presented semantic concepts. This design allowed the neural correlates to semantic information to be examined without an excessive repetition of identical word stimuli. In addition to these predefined semantic concepts, higher order semantic relationships across all sets of words were also studied using word2vec word embeddings. The DRM task was found to elicit strong high gamma ECoG power increases along the superior temporal gyrus during the auditory presentation of the stimuli. However, ECoG spectral power estimates did not distinguish individual semantic concepts or low-level semantic categories. Weak but significant correlations with semantic word embeddings particularly in the delta band were observed both in the within-electrode univariate ECoG spectral power patterns and the cross-electrode spatial ECoG power patterns. Additionally, repetition suppression patterns in ECoG power were unable to significantly discriminate different semantic concepts. Event-related potentials were also unable to distinguish individual semantic categories. In contrast to semantics, ECoG power at multiple frequencies were shown to reliably track the auditory envelope with a high correlation, statistically distinguish multiple vowels and consonants, and significantly (negatively) correlated with estimates of word imageability and word frequency. Taken together, these findings illustrate the degree of language-based information available within macroscale electrocorticographic recordings

Resting state network estimation in individual subjects

Resting state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive functions. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative. (C) 2013 Elsevier Inc. All rights reserved