Griffin Milsap

Presurgical language localization with visual naming associated ECoG high- gamma modulation in pediatric drug-resistant epilepsy

This prospective study compared presurgical language localization with visual naming–associated high‐γ modulation (HGM) and conventional electrical cortical stimulation (ECS) in children with intracranial electrodes.

Cortical subnetwork dynamics during human language tasks

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 subnetworks 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 (70-110Hz) 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 interactions contribution to a particular subnetwork, and the temporal profile of each subnetworks 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.

Semantic attributes are encoded in human electrocorticographic signals during visual object recognition.

Abstract Non‐invasive neuroimaging studies have shown that semantic category and attribute information are encoded in neural population activity. Electrocorticography (ECoG) offers several advantages over non‐invasive approaches, but the degree to which semantic attribute information is encoded in ECoG responses is not known. We recorded ECoG while patients named objects from 12 semantic categories and then trained high‐dimensional encoding models to map semantic attributes to spectral‐temporal features of the task‐related neural responses. Using these semantic attribute encoding models, untrained objects were decoded with accuracies comparable to whole‐brain functional Magnetic Resonance Imaging (fMRI), and we observed that high‐gamma activity (70–110 Hz) at basal occipitotemporal electrodes was associated with specific semantic dimensions (manmade‐animate, canonically large‐small, and places‐tools). Individual patient results were in close agreement with reports from other imaging modalities on the time course and functional organization of semantic processing along the ventral visual pathway during object recognition. The semantic attribute encoding model approach is critical for decoding objects absent from a training set, as well as for studying complex semantic encodings without artificially restricting stimuli to a small number of semantic categories. HighlightsRecognized objects can be accurately decoded from intracranial electrode responses.Semantic attribute encoding models generalize to new objects.Timing of semantic decoding is consistent with prior electrophysiological studies.Animacy and canonical size are represented in ventral temporal ECoG signals.

Cortical Responses to Input From Distant Areas are Modulated by Local Spontaneous Alpha/Beta Oscillations

Any given area in human cortex may receive input from multiple, functionally heterogeneous areas, potentially representing different processing threads. Alpha (8-13 Hz) and beta oscillations (13-20 Hz) have been hypothesized by other investigators to gate local cortical processing, but their influence on cortical responses to input from other cortical areas is unknown. To study this, we measured the effect of local oscillatory power and phase on cortical responses elicited by single-pulse electrical stimulation (SPES) at distant cortical sites, in awake human subjects implanted with intracranial electrodes for epilepsy surgery. In 4 out of 5 subjects, the amplitudes of corticocortical evoked potentials (CCEPs) elicited by distant SPES were reproducibly modulated by the power, but not the phase, of local oscillations in alpha and beta frequencies. Specifically, CCEP amplitudes were higher when average oscillatory power just before distant SPES (-110 to -10 ms) was high. This effect was observed in only a subset (0-33%) of sites with CCEPs and, like the CCEPs themselves, varied with stimulation at different distant sites. Our results suggest that although alpha and beta oscillations may gate local processing, they may also enhance the responsiveness of cortex to input from distant cortical sites.

Neuroprosthetic limb control with electrocorticography: approaches and challenges.

Advanced upper limb prosthetics, such as the Johns Hopkins Applied Physics Lab Modular Prosthetic Limb (MPL), are now available for research and preliminary clinical applications. Research attention has shifted to developing means of controlling these prostheses. Penetrating microelectrode arrays are often used in animal and human models to decode action potentials for cortical control. These arrays may suffer signal loss over the long-term and therefore should not be the only implant type investigated for chronic BMI use. Electrocorticographic (ECoG) signals from electrodes on the cortical surface may provide more stable long-term recordings. Several studies have demonstrated ECoGs potential for decoding cortical activity. As a result, clinical studies are investigating ECoG encoding of limb movement, as well as its use for interfacing with and controlling advanced prosthetic arms. This overview presents the technical state of the art in the use of ECoG in controlling prostheses. Technical limitations of the current approach and future directions are also presented.

Feature Selection Methods for Zero-Shot Learning of Neural Activity

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

Time-Varying Parametric Modeling of ECoG for Syllable Decoding

As a step toward developing neuroprostheses, the purpose of this study is to explore syllable decoding in a subject with implanted electrocorticographic (ECoG) recordings. For this study, we use ECoG signals recorded while a subject volunteered to perform a task in which the patient has been visually cued to speak isolated consonant-vowel syllables varying in their articulatory features. We propose a recursive estimation method to calculate the parametric model coefficients in each time instant and band power features from individual ECoG sites are extracted to decode the articulated syllables. Our findings may contribute to the development of brain machine interface (BMI) systems for syllable-level speech rehabilitation in handicapped individuals.

Design and implementation of a human ECoG simulator for testing brain-machine interfaces

This paper presents the design and implementation of a signal simulator that emulates event-related human electrocorticographic (ECoG) signals. This real-time simulator renders a representative model of human ECoG encompassing prominent physiological modulation in the time domain (e.g., event-related potentials, or ERPs) and the frequency domain (e.g., alpha/mu, beta, and high gamma band). The simulated signals were generated in a MATLAB SIMULINK framework and output through a National Instruments PCI card for recording by a standard research-grade ECoG amplifier system. Trial-averaged event-related spectrograms computed offline from simulated signals exhibit characteristics similar to those of experimental human ECoG recordings. The presented simulator can serve as a useful tool for testing real-time brain-machine interface (BMI) applications. It can also serve as a potential framework for future implementation of neuronal models for generation of extracellular field potentials.

Listening to the music of the brain: Live analysis of ECoG recordings using digital audio workstation software

A process is presented for analyzing electrocorticographic (ECoG) recordings and prototyping brain computer interfaces in which complex signal processing chains are able to be rapidly developed and iterated in digital audio workstation (DAW) software. DAW software includes many built-in “drag and drop” blocks that perform common, low-level signal processing algorithms such as filtering and envelope extraction. In addition to being optimized for real-time performance, DAW software also produces audio output, allowing for listening to raw and processed signals. Hearing these sonifications can impart new insights that may not be apparent in purely visual representations. A simple functional mapping analysis is performed in a DAW called Pure Data and compared to the results from a more traditional spatiotemporal analysis in MATLAB. Channels exhibiting qualitative activation in the resulting functional maps were further analyzed in another DAW called Renoise, wherein several high frequency (i.e., >400 Hz) features were observed. This study demonstrates an example use of DAW software, which we suggest is an easy-to-use and intuitive environment for real-time exploratory analyses and sophisticated sonification of ECoG recordings.