Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics DOI Creative Commons
Hohyun Cho, Markus Adamek, Jon T. Willie

et al.

eLife, Journal Year: 2023, Volume and Issue: 12

Published: Nov. 17, 2023

Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence frequency are determined by identifying peaks over 1/f noise within power spectrum. However, this approach solely operates domain thus cannot adequately distinguish between fundamental a non-sinusoidal oscillation its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing false-positive detection rate — confounding factor in analysis oscillations. To overcome these limitations, we define criteria that characterize introduce Cyclic Homogeneous Oscillation (CHO) method implements based on an auto-correlation determines oscillation's periodicity frequency. We evaluated CHO verifying performance simulated sinusoidal oscillatory bursts convolved with noise. Our results demonstrate outperforms conventional techniques accurately detecting Specifically, sensitivity specificity as function signal-to-noise ratio (SNR). further assessed testing it electrocorticographic (ECoG, 8 subjects) electroencephalographic (EEG, 7 recorded during pre-stimulus period auditory reaction time task (6 SEEG subjects 6 ECoG collected resting state. In task, detected alpha pre-motor beta occipital EEG signals. Moreover, hippocampal human hippocampus state subjects). summary, demonstrates high precision domains. The method's enables detailed study characteristics oscillations, such degree asymmetry waveform oscillation. Furthermore, can be applied identify how govern interactions throughout determine biomarkers index abnormal

Language: Английский

Electroencephalogram Correlates of Infant Spinal Anesthesia DOI
Chang Amber Liu,

Johanna M. Lee,

Ashlee E. Holman

et al.

Anesthesia & Analgesia, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Liu, Chang Amber MD, MSc, FAAP; Lee, Johanna M. MD; Holman, Ashlee Heydinger, Grant Whitaker, Emmett E. Chao, Jerry Y. MSc Author Information

Language: Английский

Citations

0
Multivariate linear time-series modeling and prediction of cerebral physiologic signals: review of statistical models and implications for human signal analytics DOI Creative Commons
Nuray Vakitbilir, Amanjyot Singh Sainbhi,

Abrar Islam

et al.

Frontiers in Network Physiology, Journal Year: 2025, Volume and Issue: 5

Published: April 16, 2025

Cerebral physiological signals embody complex neural, vascular, and metabolic processes that provide valuable insight into the brain's dynamic nature. Profound comprehension analysis of these are essential for unraveling cerebral intricacies, enabling precise identification patterns anomalies. Therefore, advancement computational models in physiology is pivotal exploring links between measurable underlying states. This review provides a detailed explanation models, including their mathematical formulations, discusses relevance to dynamics. It emphasizes importance linear multivariate statistical particularly autoregressive (AR) Kalman filter, time series modeling prediction processes. The focuses on operational principles such as AR filter. These examined ability capture intricate relationships among parameters, offering holistic representation brain function. use enables capturing signals. insights nature by representing highlights clinical implications using understand physiology, while also acknowledging inherent limitations, need stationary data, challenges with high dimensionality, complexity, limited forecasting horizons.

Language: Английский

Citations

0
Neuro-GSTH: A Geometric Scattering and Persistent Homology Framework for Uncovering Spatiotemporal Signatures in Neural Activity DOI Creative Commons
Dhananjay Bhaskar, Yanlei Zhang, Jessica L. Moore

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: March 24, 2023

Abstract Neural signals are high-dimensional, noisy, and dynamic, making it challenging to extract interpretable features linked behavior or disease. We introduce Neurospectrum , a framework that encodes neural activity as latent trajectories shaped by spatial temporal structure. At each timepoint, represented on graph capturing relationships, with learnable attention mechanism highlighting important regions. These embedded using wavelets passed through manifold-regularized autoencoder preserves geometry. The resulting trajectory is summarized principled set of descriptors - including curvature, path signatures, persistent homology, recurrent networks -that capture multiscale geometric, topological, dynamical features. drive downstream prediction in modular, interpretable, end-to-end trainable framework. evaluate simulated experimental datasets. It tracks phase synchronization Kuramoto simulations, reconstructs visual stimuli from calcium imaging, identifies biomarkers obsessive-compulsive disorder fMRI. Across tasks, uncovers meaningful dynamics outperforms traditional analysis methods.

Language: Английский

Citations

3
Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics DOI Creative Commons
Hohyun Cho, Markus Adamek, Jon T. Willie

et al.

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

Published: Sept. 6, 2024

Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/ f noise within power spectrum fail distinguish between fundamental harmonics often highly non-sinusoidal oscillations. To overcome this limitation, we define criteria characterize introduce cyclic homogeneous oscillation (CHO) detection method. We implemented these based on an autocorrelation approach determine oscillation’s frequency. evaluated CHO by verifying its performance simulated oscillatory bursts validated ability in electrocorticographic (ECoG), electroencephalographic (EEG), stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate outperforms conventional techniques accurately detecting In summary, demonstrates high precision specificity time domains. The method’s enables detailed study characteristics oscillations, such as degree asymmetry waveform oscillation. Furthermore, can be applied identify how govern interactions throughout biomarkers index abnormal

Language: Английский

Citations

0
Detection of High-Frequency Oscillations from Intracranial EEG Data with Switching State Space Model DOI Creative Commons

Zeyu Gu,

Shihao Yang, Zhongyuan Yu

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 4, 2024

Abstract High Frequency Oscillations (HFOs) is an important biomarker that can potentially pinpoint the epileptogenic zones (EZs). However, duration of HFO short with around 4 cycles, which might be hard to recognize when embedded within signals lower frequency oscillatory background. In addition, annotating HFOs manually time-consuming given long-time recordings and up hundreds intracranial electrodes. We propose leverage a Switching State Space Model (SSSM) identify events automatically instantaneously without relying on extracting features from sliding windows. The effectiveness SSSM for detection fully validated in EEG recording human subjects undergoing presurgical evaluations showed improved accuracy capturing occurrence their duration.

Language: Английский

Citations

0
An unbiased method to partition diverse neuronal responses into functional ensembles reveals interpretable population dynamics during innate social behavior DOI Creative Commons
Alexander J. Lin, Cyril Akafia, Olga Dal Monte

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 9, 2024

In neuroscience, understanding how single-neuron firing contributes to distributed neural ensembles is crucial. Traditional methods of analysis have been limited descriptions whole population activity, or, when analyzing individual neurons, criteria for response categorization varied significantly across experiments. Current lack scalability large datasets, fail capture temporal changes and rely on parametric assumptions. There's a need robust, scalable, non-parametric functional clustering approach interpretable dynamics. To address this challenge, we developed model-based, statistical framework unsupervised multiple time series datasets that exhibit nonlinear dynamics into an

Language: Английский

Citations

0
eLife assessment: Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics DOI Open Access
Björn Herrmann

Published: June 7, 2024

Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence frequency are determined by identifying peaks over 1/f noise within power spectrum. However, this approach solely operates domain thus cannot adequately distinguish between fundamental a non-sinusoidal oscillation its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing false-positive detection rate — confounding factor in analysis oscillations. To overcome these limitations, we define criteria that characterize introduce Cyclic Homogeneous Oscillation (CHO) method implements based on an auto-correlation determines oscillation's periodicity frequency. We evaluated CHO verifying performance simulated sinusoidal oscillatory bursts convolved with noise. Our results demonstrate outperforms conventional techniques accurately detecting Specifically, sensitivity specificity as function signal-to-noise ratio (SNR). further assessed testing it electrocorticographic (ECoG, 8 subjects) electroencephalographic (EEG, 7 recorded during pre-stimulus period auditory reaction time task (6 SEEG subjects 6 ECoG collected resting state. In task, detected alpha pre-motor beta occipital EEG signals. Moreover, hippocampal human hippocampus state subjects). summary, demonstrates high precision domains. The method's enables detailed study characteristics oscillations, such degree asymmetry waveform oscillation. Furthermore, can be applied identify how govern interactions throughout determine biomarkers index abnormal

Language: Английский

Citations

0
Reviewer #2 (Public Review): Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics DOI Open Access
Hohyun Cho, Markus Adamek, Jon T. Willie

et al.

Published: June 7, 2024

Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence frequency are determined by identifying peaks over 1/f noise within power spectrum. However, this approach solely operates domain thus cannot adequately distinguish between fundamental a non-sinusoidal oscillation its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing false-positive detection rate — confounding factor in analysis oscillations. To overcome these limitations, we define criteria that characterize introduce Cyclic Homogeneous Oscillation (CHO) method implements based on an auto-correlation determines oscillation's periodicity frequency. We evaluated CHO verifying performance simulated sinusoidal oscillatory bursts convolved with noise. Our results demonstrate outperforms conventional techniques accurately detecting Specifically, sensitivity specificity as function signal-to-noise ratio (SNR). further assessed testing it electrocorticographic (ECoG, 8 subjects) electroencephalographic (EEG, 7 recorded during pre-stimulus period auditory reaction time task (6 SEEG subjects 6 ECoG collected resting state. In task, detected alpha pre-motor beta occipital EEG signals. Moreover, hippocampal human hippocampus state subjects). summary, demonstrates high precision domains. The method's enables detailed study characteristics oscillations, such degree asymmetry waveform oscillation. Furthermore, can be applied identify how govern interactions throughout determine biomarkers index abnormal

Language: Английский

Citations

0
Reviewer #1 (Public Review): Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics DOI Open Access
Hohyun Cho, Markus Adamek, Jon T. Willie

et al.

Published: June 7, 2024

Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence frequency are determined by identifying peaks over 1/f noise within power spectrum. However, this approach solely operates domain thus cannot adequately distinguish between fundamental a non-sinusoidal oscillation its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing false-positive detection rate — confounding factor in analysis oscillations. To overcome these limitations, we define criteria that characterize introduce Cyclic Homogeneous Oscillation (CHO) method implements based on an auto-correlation determines oscillation's periodicity frequency. We evaluated CHO verifying performance simulated sinusoidal oscillatory bursts convolved with noise. Our results demonstrate outperforms conventional techniques accurately detecting Specifically, sensitivity specificity as function signal-to-noise ratio (SNR). further assessed testing it electrocorticographic (ECoG, 8 subjects) electroencephalographic (EEG, 7 recorded during pre-stimulus period auditory reaction time task (6 SEEG subjects 6 ECoG collected resting state. In task, detected alpha pre-motor beta occipital EEG signals. Moreover, hippocampal human hippocampus state subjects). summary, demonstrates high precision domains. The method's enables detailed study characteristics oscillations, such degree asymmetry waveform oscillation. Furthermore, can be applied identify how govern interactions throughout determine biomarkers index abnormal

Language: Английский

Citations

0
Author response: Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics DOI Open Access
Hohyun Cho, Markus Adamek, Jon T. Willie

et al.

Published: June 7, 2024

Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence frequency are determined by identifying peaks over 1/f noise within power spectrum. However, this approach solely operates domain thus cannot adequately distinguish between fundamental a non-sinusoidal oscillation its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing false-positive detection rate — confounding factor in analysis oscillations. To overcome these limitations, we define criteria that characterize introduce Cyclic Homogeneous Oscillation (CHO) method implements based on an auto-correlation determines oscillation's periodicity frequency. We evaluated CHO verifying performance simulated sinusoidal oscillatory bursts convolved with noise. Our results demonstrate outperforms conventional techniques accurately detecting Specifically, sensitivity specificity as function signal-to-noise ratio (SNR). further assessed testing it electrocorticographic (ECoG, 8 subjects) electroencephalographic (EEG, 7 recorded during pre-stimulus period auditory reaction time task (6 SEEG subjects 6 ECoG collected resting state. In task, detected alpha pre-motor beta occipital EEG signals. Moreover, hippocampal human hippocampus state subjects). summary, demonstrates high precision domains. The method's enables detailed study characteristics oscillations, such degree asymmetry waveform oscillation. Furthermore, can be applied identify how govern interactions throughout determine biomarkers index abnormal

Language: Английский

Citations

0