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

и другие.

eLife, Год журнала: 2023, Номер 12

Опубликована: Ноя. 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

Язык: Английский

Electroencephalogram Correlates of Infant Spinal Anesthesia DOI
Chang Amber Liu,

Johanna M. Lee,

Ashlee E. Holman

и другие.

Anesthesia & Analgesia, Год журнала: 2025, Номер unknown

Опубликована: Янв. 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

Язык: Английский

Процитировано

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

и другие.

Frontiers in Network Physiology, Год журнала: 2025, Номер 5

Опубликована: Апрель 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.

Язык: Английский

Процитировано

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

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Март 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.

Язык: Английский

Процитировано

3
Detection of High-Frequency Oscillations from Intracranial EEG Data with Switching State Space Model DOI Creative Commons

Zeyu Gu,

Shihao Yang, Zhongyuan Yu

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Май 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.

Язык: Английский

Процитировано

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

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Май 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

Язык: Английский

Процитировано

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

Опубликована: Июнь 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

Язык: Английский

Процитировано

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

и другие.

Опубликована: Июнь 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

Язык: Английский

Процитировано

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

и другие.

Опубликована: Июнь 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

Язык: Английский

Процитировано

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

и другие.

Опубликована: Июнь 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

Язык: Английский

Процитировано

0
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

и другие.

Опубликована: Июнь 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

Язык: Английский

Процитировано

0