Eigenvector centrality reveals the time course of task-specific electrode connectivity in human ECoG

Newman, Geoffrey I.; Fifer, Matthew S.; Benz, Heather L.; Crone, Nathan E.; Thakor, Nitish V.

doi:10.1109/ner.2015.7146628

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several connectivity measures have been applied to the analysis of ECoG. Bayesian networks (TV-DBN) and eigenvector centrality analysis have been used to identify brain regions relevant to motor tasks (Newman et al, 2015). Benz et al (2012) used TV-DBN to quantify task-related changes in connectivity and to decode hand kinematics.…”

Section: Decoding Algorithmsmentioning

confidence: 99%

Decoding Movement From Electrocorticographic Activity: A Review

Volkova

Lebedev

Kaplan

et al. 2019

Front. Neuroinform.

View full text Add to dashboard Cite

Electrocorticography (ECoG) holds promise to provide efficient neuroprosthetic solutions for people suffering from neurological disabilities. This recording technique combines adequate temporal and spatial resolution with the lower risks of medical complications compared to the other invasive methods. ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients. During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of different complexity. Several research groups have advanced toward the development of assistive devices driven by brain-computer interfaces (BCIs) that decode motor commands from multichannel ECoG recordings. Here we review the evolution of this field and its recent tendencies, and discuss the potential areas for future development.

show abstract

Section: Decoding Algorithmsmentioning

confidence: 99%

Decoding Movement From Electrocorticographic Activity: A Review

Volkova

Lebedev

Kaplan

et al. 2019

Front. Neuroinform.

View full text Add to dashboard Cite

show abstract

“…Second, the location of a site within the FNC architecture may be indicative of that site’s importance in task processing relative to other activated sites. For instance, nodes with high centrality in an FNC may be seen as local “hubs” for a particular processing stage (Bullmore and Sporns, 2009; Newman et al, 2015); hence, resecting such central sites may be more likely to cause cognitive damage specific to that FNC’s processing role, while resecting sites that are more peripheral in the FNC could potentially carry a reduced risk of deficits. Similarly, sites that participate in multiple FNCs may facilitate communication between subnetworks, serving as the “bridge” or “connector” between computation stages; such sites, when resected, might also lead to a specific pattern of impairment.…”

Section: Discussionmentioning

confidence: 99%

Cortical subnetwork dynamics during human language tasks

et al. 2016

View full text Add to dashboard Cite

Language tasks require the coordinated activation of multiple subnetworks—groups of related cortical interactions involved in specific components of task processing. Although electrocorticography (ECoG) has sufficient temporal and spatial resolution to capture the dynamics of event-related interactions between cortical sites, it is difficult to decompose these complex spatiotemporal patterns into functionally discrete subnetworks without explicit knowledge of each subnetwork’s timing. We hypothesized that subnetworks corresponding to distinct components of task-related processing could be identified as groups of interactions with co-varying strengths. In this study, five subjects implanted with ECoG grids over language areas performed word repetition and picture naming. We estimated the interaction strength between each pair of electrodes during each task using a time-varying dynamic Bayesian network (tvDBN) model constructed from the power of high gamma (70–110 Hz) activity, a surrogate for population firing rates. We then reduced the dimensionality of this model using principal component analysis (PCA) to identify groups of interactions with co-varying strengths, which we term functional network components (FNCs). This data-driven technique estimates both the weight of each interaction’s contribution to a particular subnetwork, and the temporal profile of each subnetwork’s activation during the task. We found FNCs with temporal and anatomical features consistent with articulatory preparation in both tasks, and with auditory and visual processing in the word repetition and picture naming tasks, respectively. These FNCs were highly consistent between subjects with similar electrode placement, and were robust enough to be characterized in single trials. Furthermore, the interaction patterns uncovered by FNC analysis correlated well with recent literature suggesting important functional-anatomical distinctions between processing external and self-produced speech. Our results demonstrate that subnetwork decomposition of event-related cortical interactions is a powerful paradigm for interpreting the rich dynamics of large-scale, distributed cortical networks during human cognitive tasks.

show abstract

Eigenvector centrality reveals the time course of task-specific electrode connectivity in human ECoG

Cited by 2 publications

References 11 publications

Decoding Movement From Electrocorticographic Activity: A Review

Decoding Movement From Electrocorticographic Activity: A Review

Cortical subnetwork dynamics during human language tasks

Contact Info

Product

Resources

About