Biology subjects

Shriki, O.

Publications and source records attributed to Shriki, O..

12 recordsLinked to original sources

bioRxiv2026

A cortical gradient of distance to criticality governs large-scale resting-state fMRI dynamics

The cortex generates a remarkable diversity of regional dynamics despite relying on broadly shared principles of recurrent organization. Here, we identify distance to criticality (DTC) as a unifying axis along which these dynamics are spatially organized. Analyzing resting-state fMRI BOLD signals and leveraging simple network models of randomly connected recurrent units, we show that DTC robustly explains key dynamical features, in particular, local power spectra and functional connectivity, across the full set of 360 cortical areas. Our analysis shows that a rank-order distribution of DTC values is highly conserved across subjects. Moreover, the empirical analysis of cortical slow dynamics and its fitted network simulations demonstrate similar power-laws across hierarchies of the cortical sheet. These results suggest that recurrent neuronal networks, operating close to criticality, can generate a remarkably rich dynamical repertoire which fit the entire range of experimentally observed cortical slow-timescale dynamics. Our findings underscore the importance of DTC as a powerful, fundamental generator underlying the spectrum of diverse cortical dynamics. HighlightsO_LISpontaneous (resting-state) activity in the human cortex is shown to be organized along a conserved spatial gradient of distance from criticality (DTC), with regions exhibiting a stable cross-individual rank order along this axis. C_LIO_LIMulti-subject fMRI data of regional power spectra and functional connectivity can be fitted with a single parameter simulation model based on DTC. C_LIO_LIQuantitative estimation of the DTC across cortical regions can be achieved using a simple sparse recurrent neural network model. C_LIO_LIThe model fits the power spectra of low frequency fluctuations and the distribution of functional connectivity. C_LIO_LIShape collapse analysis of the power spectrum demonstrates a universal profile across the resting cortex depending only on the DTC. C_LI

neuroscience↗

bioRxiv2025

The Hypno-PC: Uncovering Sleep Dynamics through Principal Component Analysis and Hidden Markov Modeling of Electrophysiological Signals

The conventional approach to sleep analysis relies on pre-defined, visually scored stages derived from electrophysiological signals. This manual method demands substantial effort and is influenced by subjective assessments, implicitly assuming that these categories accurately reflect underlying biological processes. Recent advancements indicate that low-dimensional representations of complex brain activity can provide objective means of identifying brain states. These approaches can potentially uncover inherent patterns within sleep, offering valuable insights into its organization. In this study, we applied Principal Component Analysis (PCA) to spectral features extracted from high-density EEG, EOG, EMG, and ECG recorded overnight at both 30- and 4-second resolutions. Notably, the first principal component--the "Hypno-PC"--strongly aligns with the hypnogram at both time scales. Subsequently, we employed a Gaussian Hidden Markov Model (GHMM) to delineate discrete states in the PCA-transformed data and to quantify their temporal dynamics. Using minimal supervision (less than 0.5% of the data labeled) and a cross-subject approach, the model achieved alignment with standard sleep labels comparable to the typical inter-rater agreement. Finally, independent component analysis (ICA) was applied to the PCA space, decomposing it into an independent set of components that potentially represent distinct physiological processes. The integrated use of PCA, GHMM, and ICA provides a reproducible and scalable methodology that aligns with traditional sleep staging, while offering a more flexible and comprehensive perspective on sleep states. Our findings indicate that these data-driven, unsupervised methods effectively uncover the intrinsic dynamics of sleep, advancing automated sleep analysis and enhancing our understanding of sleep organization. Statement of SignificanceThis study introduces a data-driven framework for sleep analysis designed to objectively identify brain states using low-dimensional representations of electrophysiological signals during sleep. By integrating a suite of unsupervised learning techniques, our methodology offers an alternative to subjective manual scoring, potentially enhancing both efficiency and reproducibility. In addition to aligning with traditional sleep staging, this approach uncovers subtle sleep dynamics across multiple time scales, enabling the discovery of patterns that conventional methods might overlook. These advancements hold promise for automated sleep monitoring and the study of sleep disorders, potentially improving diagnostic accuracy and facilitating large-scale sleep research. By addressing current limitations in sleep analysis techniques, this framework lays the groundwork for more elaborate and scalable assessments.

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bioRxiv2024

Neural field theory as a framework for modeling and understanding consciousness states in the brain

Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we explore the potential of neural field theory (NFT) as a computational framework for representing consciousness states. While prior research has validated NFTs capacity to differentiate between normal and pathological states of consciousness, the relationship of its parameters to the representation of consciousness levels remains unclear. Here, we fitted a corticothalamic NFT model to EEG data collected from healthy individuals and patients with disorders of consciousness. We then comprehensively explored the correlations between the fitted NFT parameters and features extracted from both experimental and simulated EEG data, across various states of consciousness. The identified correlations not only highlight the models ability to differentiate between states of consciousness, but also shed light on the physiological bases of these states, pinpointing potential biomarkers. Our results provide valuable insights into how consciousness levels are represented within the NFT framework and into the dynamics of brain activity across various consciousness states. This underscores the potential of NFT as a useful tool for consciousness research, facilitating in-silico experimentation.

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bioRxiv2024

Personalized stimulation therapies for disorders of consciousness: A computational approach to inducing healthy-like brain activity based on neural field theory

Disorders of consciousness (DoC) pose significant challenges in neurology. Conventional neuromodulation therapies for DoC have exhibited limited success, with varying effectiveness among patients. In this study, we introduce a computational approach for constructing personalized stimulus signals capable of inducing healthy-like neural activity patterns in DoC patients. Leveraging a simplified brain model based on neural field theory, we fit this model to the power spectrum of a patient with DoC and derive a personalized stimulus time series to induce a healthy-like power spectrum. By applying this stimulus to brain regions typically targeted by stimulation therapies such as deep brain stimulation and repetitive transcranial magnetic stimulation, we demonstrate in silico the ability of our method to elicit EEG power spectra resembling those of healthy individuals. We speculate that in the course of a long-term treatment, when the brain produces healthy-like activity, it may trigger intrinsic plasticity mechanisms, potentially leading to sustained improvements in the patients condition. While further clinical adjustments and validation are needed, this novel approach offers promise in tailoring brain stimulation therapies for DoC patients. Moreover, it presents potential extensions to other conditions that could also benefit from brain stimulation therapies. Author summaryIn this research, we tackled a challenging issue in the field of neurology - disorders of consciousness (DoC). These conditions, which include states like coma and the minimally consciousness state, have been difficult to treat effectively. While traditional brain stimulation treatments have been tried, they do not always work well, and their effects can vary greatly from one person to another. Thus, we set out to find a new way to help these patients. Our approach involves using computer models of the brain to design personalized treatments. We designed a method to generate stimulation signals tailored to individual DoC patients. These signals aim to trigger healthier brain activity patterns, similar to those found in individuals without consciousness disorders. What is exciting is that if the brain begins to produce these healthy-like patterns, it could potentially lead to lasting improvements in the patients consciousness level. We show that this approach works in different simulations, and can be applied in multiple neuromodulation techniques like deep brain stimulation and transcranial magnetic stimulation. This research opens up new possibilities for more effective treatments for people dealing with these challenging disorders. Plus, it might work for other conditions treated with similar brain stimulation therapies.

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bioRxiv2023

Seizure dynamotype classification using non-invasive recordings

ObjectiveRecently, a seizure classification approach derived from complex systems and nonlinear dynamics has been suggested, termed the "taxonomy of seizure dynamotypes." This framework is based on modeling the dynamical process of the transition in and out of a seizure. It has been examined in computational and animal models in-vitro and recently in human intracranial data. However, its applicability and value in surface EEG remain unclear. This study examined the applicability of dynamotype classification to seizure information extracted from surface EEG and tested how it relates to clinical factors. MethodsSurface EEG recordings from 1,215 seizures were analyzed. We used an automated pre-processing pipeline, resulting in independent components (ICs) for each seizure. Subsequently, we visually identified ICs with clear seizure information and classified them based on the suggested taxonomy. To examine the possibility of automatic classification, we applied a random forest classifier combined with EEG features and evaluated its performance in identifying seizure-related ICs and classifying dynamical types. Lastly, we used a Bayesian estimator to examine the likelihood of the different dynamical types under various clinical conditions. ResultsWe found an apparent onset and offset bifurcation in 49.5% and 40.3% of seizures, respectively. Bifurcation prevalence aligns with that previously reported using intracranial data and computational modeling. The automated classifiers, evaluated with a leave-one-patient-out paradigm, provided good performance. In addition, bifurcation prevalence differed between vigilance states and seizure classes. SignificanceWe demonstrated a method to extract seizure information and classify dynamotypes in non-invasive recordings with a visual as well as an automated framework. Extending this classification to a larger scale and a broader population may provide further insights into seizure dynamics. Key Points BoxO_LIWe characterized the dynamical types of transitions at seizure onset and offset based on seizure information extracted from surface EEG. C_LIO_LIWe classified the dynamical types (dynamotypes) in 49.5% and 40.3% of seizure onsets and offsets, respectively. C_LIO_LIThe dynamotype distribution in surface EEG data aligns with previous findings from intracranial EEG and theoretical expectations. C_LIO_LIThe likelihood of the dynamical type of a seizure exhibits differences across clinical seizure classes and vigilance states. C_LIO_LIAutomated detection and classification of seizure bifurcations are possible using relevant features and pre-existing tools. C_LI

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bioRxiv2023

Modulation of proximity to criticality enhances slow activity fluctuations during free recall

Ultra-slow fluctuations are a hallmark of spontaneous cortical activity. We examine the hypothesis that these unique dynamics arise from recurrent neuronal networks operating near a phase transition, a state characterized by critical slowing down. A further prediction of such dynamics is that a small modulation towards the critical transition should lead to specific amplification of slow fluctuations. Here, we relate this phenomenon to experimental findings using a simulation of a simple random recurrent network. Importantly, the model aligns with direct intracranial electroencephalography recordings from human visual cortex during both rest and visual free-recall, specifically replicating the observed enhancement of slow fluctuations during free recall. These simulations illuminate a simple and powerful mechanism underlying slow spontaneous fluctuations, while enabling the rapid transition between different spontaneous states. They propose that modulation towards criticality might be a universal strategy employed by cortical networks to engage in a spontaneous generative mode.

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bioRxiv2022

Alpha rhythm induces attenuation-amplification dynamics in neural activity cascades

The alpha rhythm is a distinctive feature of the awake resting-state of the human brain. Recent evidence suggests that alpha plays an active role in information processing, modulating behavioral and cognitive performance. However, the functional role of alpha oscillations in the resting-state neuronal dynamics remains poorly understood. To address this question, we investigate collective neural activity during resting wake and NREM sleep, a physiologic state with marginal presence of alpha rhythm. We show that, during resting wake, alpha oscillations drive an alternation of attenuation and amplification bouts in neural activity. Our analysis indicates that inhibition is activated in pulses that last a single alpha cycle and gradually suppress neural activity, while excitation is successively enhanced over timescales of a few alpha cycles to amplify neural activity. Furthermore, we show that long-term, intermittent fluctuations in alpha amplitude--known as the "waxing and waning" phenomenon--are associated with an attenuation-amplification mechanism acting over the timescales of several seconds and described by a power law decay of the activity rate in the "waning" phase. Importantly, we do not observe such dynamics during NREM sleep. The results suggest that the alpha rhythm acts as a "pacemaker" for the alternation of inhibition and excitation bouts across multiple timescales, the "waxing and waning" being a long-term control mechanism of cortical excitability. The amplification regime observed beyond the timescales of the individual alpha cycle suggests in turn that alpha oscillations might modulate the intensity of neural activity not only through pulses of inhibition, as proposed in the pulsed inhibition hypothesis, but also by timely enhancing excitation (or dis-inhibition).

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bioRxiv2021

Tinnitus-like "hallucinations" elicited by sensory deprivation in an entropy maximization recurrent neural network

Sensory deprivation has long been known to cause hallucinations or "phantom" sensations, the most common of which is tinnitus induced by hearing loss, affecting 10-20% of the population. An observable hearing loss, causing auditory sensory deprivation over a band of frequencies, is present in over 90% of people with tinnitus. Existing plasticity-based computational models for tinnitus are usually driven by homeostasis mechanisms, modeled to fit phenomenological findings. Here, we use an objective-driven learning algorithm to model an early auditory processing neuronal network, e.g., in the dorsal cochlear nucleus. The learning algorithm maximizes the networks output entropy by learning the feed-forward and recurrent interactions in the model. We show that the connectivity patterns and responses learned by the model display several hallmarks of early auditory neuronal networks. We further demonstrate that attenuation of peripheral inputs drives the recurrent network towards its critical point and transition into a tinnitus-like state. In this state, the network activity resembles responses to genuine inputs even in the absence of external stimulation, namely, it "hallucinates" auditory responses. These findings demonstrate how objective-driven plasticity mechanisms that normally act to optimize the networks input representation can also elicit pathologies such as tinnitus as a result of sensory deprivation. Author summaryTinnitus or "ringing in the ears" is a common pathology. It may result from mechanical damage in the inner ear, as well as from certain drugs such as salicylate (aspirin). A common approach toward a computational model for tinnitus is to use a neural network model with inherent plasticity applied to early auditory processing, where the input layer models the auditory nerve and the output layer models a nucleus in the brain stem. However, most of the existing computational models are phenomenological in nature, driven by a homeostatic principle. Here, we use an objective-driven learning algorithm based on information theory to learn the feed-forward interactions between the layers, as well as the recurrent interactions within the output layer. Through numerical simulations of the learning process, we show that attenuation of peripheral inputs drives the network into a tinnitus-like state, where the network activity resembles responses to genuine inputs even in the absence of external stimulation; namely, it "hallucinates" auditory responses. These findings demonstrate how plasticity mechanisms that normally act to optimize network performance can also lead to undesired outcomes, such as tinnitus, as a result of reduced peripheral hearing.

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bioRxiv2020

Long-range Temporal Correlations in the Broadband Resting state Activity of the Human Brain revealed by Neuronal Avalanches

Resting-state brain activity is characterized by the presence of neuronal avalanches showing absence of characteristic size. Such evidence has been interpreted in the context of criticality and associated with the normal functioning of the brain. At criticality, a crucial role is played by long-range power-law correlations. Thus, to verify the hypothesis that the brain operates close to a critical point and consequently assess deviations from criticality for diagnostic purposes, it is of primary importance to robustly and reliably characterize correlations in resting-state brain activity. Recent works focused on the analysis of narrow band electroencephalography (EEG) and magnetoencephalography (MEG) signal amplitude envelope, showing evidence of long-range temporal correlations (LRTC) in neural oscillations. However, this approach is not suitable for assessing long-range correlations in broadband resting-state cortical signals. To overcome such limitation, here we propose to characterize the correlations in the broadband brain activity through the lens of neuronal avalanches. To this end, we consider resting-state EEG and long-term MEG recordings, extract the corresponding neuronal avalanche sequences, and study their temporal correlations. We demonstrate that the broadband resting-state brain activity consistently exhibits long-range power-law correlations in both EEG and MEG recordings, with similar values of the scaling exponents. Importantly, although we observe that avalanche size distribution depends on scale parameters, scaling exponents characterizing long-range correlations are quite robust. In particular, they are independent of the temporal binning (scale of analysis), indicating that our analysis captures intrinsic characteristics of the underlying dynamics. Because neuronal avalanches constitute a fundamental feature of neural systems with universal characteristics, the proposed approach may serve as a general, systems- and experiment-independent procedure to infer the existence of underlying long-range correlations in extended neural systems, and identify pathological behaviors in the complex spatio-temporal interplay of cortical rhythms.

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bioRxiv2019

Multiscale criticality measures as general-purpose gauges of proper brain function

The brain is universally regarded as a system for processing information. If so, any behavioral or cognitive dysfunction should lend itself to depiction in terms of information processing deficiencies. Information is characterized by recursive, hierarchical complexity. The brain accommodates this complexity by a hierarchy of large/slow and small/fast spatiotemporal loops of activity. Thus, successful information processing hinges upon tightly regulating the spatiotemporal makeup of activity, to optimally match the underlying multiscale delay structure of such hierarchical networks. Reduced capacity for information processing will then be expressed as deviance from this requisite multiscale character of spatiotemporal activity. This deviance is captured by a general family of multiscale criticality measures (MsCr). We applied MsCr to MEG and EEG data in four telling degraded information processing scenarios: disorders of consciousness, mild cognitive impairment, schizophrenia and preictal activity. Consistently with our previous modeling work, MsCr measures systematically varied with information processing capacity. MsCr measures might thus be able to serve as general gauges of information processing capacity and, therefore, as normative measures of brain health.

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bioRxiv2019

Surprise response as a probe for compressed memory states

The limited capacity of recent memory inevitably leads to partial memory of past stimuli. There is also evidence that behavioral and neural responses to novel or rare stimuli are dependent on ones memory of past stimuli. Thus, these responses may serve as a probe of different individuals remembering and forgetting characteristics. Here, we utilize two lossy compression models of stimulus sequences that inherently involve forgetting, which in addition to being a necessity under many conditions, also has theoretical and behavioral advantages. One model is based on a simple stimulus counter and the other on the Information Bottleneck (IB) framework. These models are applied to analyze a novelty-detection event-related potential commonly known as the P300. The trial-by-trial variations of the P300 response, recorded in an auditory oddball paradigm, were subjected to each model to extract two stimulus-compression parameters for each subject: memory length and representation accuracy. These parameters were then utilized to estimate the subjects recent memory capacity limit under the task conditions. The results, along with recently published findings on single neurons on the IB model, underscore how a lossy compression framework can be utilized to account for trial-by-trial variability of neural responses at different spatial scales and in different individuals, while at the same time providing estimates of individual memory characteristics at different levels of representation using a theoretically-based parsimonious model.\n\nAuthor summarySurprise responses reflect expectations based on preceding stimuli representations, and hence can be used to infer the characteristics of memory utilized for a task. We suggest a quantitative method for extracting an individual estimate of effective memory capacity dedicated for a task based on the correspondence between a theoretical surprise model and electrophysiological single-trial surprise responses. We demonstrate this method on EEG responses recorded while participants were performing a simple auditory task; we show the correspondence between the theoretical and physiological surprise, and calculate an estimate of the utilized memory. The generality of this framework allows it to be applied to different EEG features that reflect different modes and levels of the processing hierarchy, as well as other physiological measures of surprise responses. Future studies may use this framework to construct a handy diagnostic tool for a quantitative, individualized characterization of memory-related disorders.

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bioRxiv2019

EEG-based Prediction of Cognitive Load in Intelligence Tests

Measuring and assessing the cognitive load associated with different tasks is crucial for many applications, from the design of instructional materials to monitoring the mental wellbeing of aircraft pilots. The goal of this paper is to utilize EEG to infer the cognitive workload of subjects during intelligence tests. We chose the well established advanced progressive matrices test, an ideal work-frame because it presents problems at increasing levels of difficulty, and has been rigorously validated in past experiments. We train classic machine learning models using basic EEG measures as well as measures of network connectivity and signal complexity. Our findings demonstrate that cognitive load can be well predicted using these features, even for a low number of channels. We show that by creating an individually tuned neural network for each subject, we can improve prediction compared to a general model and that such models are robust to decreasing the number of available channels as well.

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