A guide towards optimal detection of transient oscillatory bursts with unknown parameters

Journal of Neural Engineering, Journal Year: 2023, Volume and Issue: 20(4), P. 046007 - 046007

Published: June 20, 2023

Abstract Objectives . Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a signature that bridges dynamic states to cognition and behaviors. Following this insight, our study aimed (1) compare efficacy common burst detection algorithms under varying signal-to-noise ratios event durations using synthetic signals (2) establish strategic guideline for selecting optimal algorithm real datasets with undefined properties. Approach. We tested robustness simulation dataset comprising multiple frequencies. To systematically assess their performance, we used metric called ‘detection confidence’, quantifying classification accuracy temporal precision in balanced manner. Given properties empirical data are often unknown advance, then proposed selection rule identify an given validated its application on local field potentials basolateral amygdala recorded from male mice (n=8) exposed natural threat. Main Results. Our simulation-based evaluation demonstrated is contingent upon duration, whereas accurately pinpointing onsets more susceptible noise level. For data, chosen based exhibited superior accuracy, although statistical significance differed across frequency bands. Notably, by human visual screening one recommended rule, implying potential misalignment between priors mathematical assumptions algorithms. Significance. Therefore, findings underscore precise fundamentally influenced algorithm. The algorithm-selection suggests potentially viable solution, while also emphasizing inherent limitations originating algorithmic design volatile performances datasets. Consequently, cautions against relying solely heuristic-based approaches, advocating careful studies.

Language: Английский

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Post-stroke upper limb recovery is correlated with dynamic resting-state network connectivity

Brain Communications, Journal Year: 2023, Volume and Issue: 6(1)

Published: Dec. 28, 2023

Motor recovery is still limited for people with stroke especially those greater functional impairments. In order to improve outcome, we need understand more about the mechanisms underpinning recovery. Task-unbiased, blood flow-independent post-stroke neural activity can be acquired from resting brain electrophysiological recordings and offers substantial promise investigate physiological mechanisms, but behaviourally relevant features of resting-state sensorimotor network dynamics have not yet been identified. Thirty-seven subcortical ischaemic unilateral hand paresis any degree were longitudinally evaluated at 3 weeks (early subacute) 12 (late after stroke. Resting-state magnetoencephalography clinical scores motor function recorded compared matched controls. Magnetoencephalography data decomposed using a data-driven hidden Markov model into 10 time-varying networks. People showed statistically significantly improved Action Research Arm Test Fugl-Meyer upper extremity between (both

Language: Английский

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osl-dynamics, a toolbox for modeling fast dynamic brain activity

eLife, Journal Year: 2024, Volume and Issue: 12

Published: Jan. 29, 2024

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic span across networks of brain regions, all which can occur on timescales tens milliseconds. While these processes be accessed through recordings imaging, modeling them presents methodological challenges due their fast transient nature. Furthermore, the exact timing duration interesting cognitive events are often a priori unknown. Here, we present OHBA Software Library Dynamics Toolbox (osl-dynamics), Python-based package identify describe recurrent dynamics in functional neuroimaging data as At its core machine learning generative models able adapt learn timing, well spatial spectral characteristics, with few assumptions. osl-dynamics incorporates state-of-the-art approaches be, have been, used elucidate wide range types, including magneto/electroencephalography, magnetic resonance invasive local field potential recordings, electrocorticography. It also provides novel summary measures inform our understanding cognition, behavior, disease. We hope will further function, ability enhance processes.

Language: Английский

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Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel

eLife, Journal Year: 2024, Volume and Issue: 13

Published: March 7, 2024

Predicting an individual's cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This commonly done either structural aspects, such as connectivity cortical thickness, aggregated measures of activity that average over time. But these approaches are missing aspect function: the unique ways which unfolds One reason why dynamic patterns not usually considered they have to be described by complex, high-dimensional models; and it unclear how best use models for prediction. We here propose approach describes functional amplitude Hidden Markov model (HMM) combines with Fisher kernel, can used predict individual traits. The kernel constructed from HMM mathematically principled manner, thereby preserving structure underlying model. show here, fMRI data, HMM-Fisher accurate reliable. compare other prediction methods, both time-varying time-averaged connectivity-based models. Our leverages information about has broad applications neuroscience personalised medicine.

Language: Английский

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Transformer aided Dynamic Causal Model for Scalable Estimation of Effective Connectivity

Imaging Neuroscience, Journal Year: 2024, Volume and Issue: 2, P. 1 - 22

Published: Jan. 1, 2024

Abstract Dynamic Causal Models (DCMs) in functional Magnetic Resonance Imaging (fMRI) decipher causal interactions, known as Effective Connectivity, among neuronal populations. However, their utility is often constrained by computational limitations, restricting analysis to a small subset of interacting brain areas, typically fewer than 10, thus lacking scalability. While the regression DCM (rDCM) has emerged faster alternative traditional DCMs, it not without its including linearization terms, reliance on fixed Hemodynamic Response Function (HRF), and an inability accommodate modulatory influences. In response these challenges, we propose novel hybrid approach named Transformer encoder decoder (TREND), which combines with state-of-the-art physiological (P-DCM) decoder. This innovative method addresses scalability issue while preserving nonlinearities inherent equations. Through extensive simulations, validate TREND’s efficacy demonstrating ability accurately predict effective connectivity values dramatically reduced time relative original P-DCM even networks comprising up to, for instance, 100 regions. Furthermore, showcase TREND empirical fMRI dataset superior accuracy and/or speed compared other variants. summary, amalgamating Transformer, introduce pioneering determining regions, extending applicability seamlessly large-scale networks.

Language: Английский

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