Statistical Properties of a Modified Welch Method That Uses Sample Percentiles

Schwock, Felix; Abadi, Shima

doi:10.1109/icassp39728.2021.9415074

Cited by 3 publications

(2 citation statements)

References 10 publications

(12 reference statements)

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“…Felix Schwock: a novel graph diffusion framework for estimating neural communication towards personalized neurorehabilitation Most neurological diseases are associated with altered patterns of neural communication; however, their specific changes due to disease or subsequent rehabilitative treatments could be better understood. As a step towards that, we propose a new computational framework for estimating dynamic network level neural communication by modeling the evolution of neural activity as a parameterized graph diffusion process [137]. To demonstrate the utility of our framework for neurorehabilitative applications, we have applied it to electrocorticography recordings from the sensorimotor cortex of a macaque monkey that underwent focal ischemic lesioning and acute electrical stimulation in the ipsilesional hemisphere [138].…”

Section: Modeling Adaptation and Plasticitymentioning

confidence: 99%

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

Valero-Cuevas,

Finley,

Orsborn

et al. 2024

J NeuroEngineering Rehabil

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We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation’s Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling ‘In-the-Wild’. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

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Section: Modeling Adaptation and Plasticitymentioning

confidence: 99%

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

Valero-Cuevas,

Finley,

Orsborn

et al. 2024

J NeuroEngineering Rehabil

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“…To address these shortcoming, we propose an alternative framework for estimating network level neural communication dynamics from LFP recordings, by combining a biologically plausible network diffusion process 35,36 with the autoregressive framework 37 . The resulting graph diffusion autoregressive (GDAR) model naturally gives rise to a communication signal with millisecond temporal resolution between nodes of a predefined graph, therefore incorporating the spatial information of the recording array and describing highly transient communication events.…”

Section: Introductionmentioning

confidence: 99%

Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression

Schwock,

Bloch,

Khateeb

et al. 2024

Preprint

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Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not model neural interactions in a biologically plausible way, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce a graph diffusion autoregressive model. Designed for distributed field potential recordings, our model combines vector autoregression with a network communication process to produce a high-resolution communication signal. We successfully validated the model on simulated neural activity and recordings from subdural and intracortical micro-electrode arrays placed in macaque sensorimotor cortex demonstrating its ability to describe rapid communication dynamics induced by optogenetic stimulation, changes in resting state communication, and the trial-by-trial variability during a reach task.

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