Biology subjects

Ezzyat, Y.

Publications and source records attributed to Ezzyat, Y..

9 recordsLinked to original sources

bioRxiv2026

Structural and Functional Connectivity Predict the Effects of Direct Brain Stimulation on Memory

Intracranial stimulation can enhance episodic memory in humans; however, the behavioral effects vary substantially across individuals and stimulation sites. Here, we investigated whether the network embedding of a stimulation target, defined by MRI-based normative structural and functional connectivity, accounts for variability in stimulation-linked memory enhancement. We analyzed data from 50 adults with medically refractory epilepsy who underwent intracranial EEG monitoring and completed a verbal delayed free-recall task during stimulation of left temporal cortex sites across 61 sessions (39 closed-loop; 22 random). On average, closed-loop stimulation delivered during classifier-detected low-encoding states increased recall rates, whereas random stimulation produced no reliable benefit. Diffusion tractography from a normative database showed that sites yielding greater memory enhancement were characterized by stronger structural coupling to a distributed fronto-temporo-parietal network. Greater structure-function congruence with a normative verbal-encoding activation network predicted larger closed-loop memory benefit (Spearman {rho} = 0.58, P < 0.0001). Functional connectivity exhibited overlapping trends but did not yield robust regional associations after permutation correction. Multivariate Partial Least Squares Structural Equation Modeling further identified stimulation mode, baseline memory, and a structural profile factor as independent predictors of memory enhancement, with no independent contribution of functional connectivity. These findings indicate that reliable stimulation-driven memory improvement depends not only on the timing of stimulation, but also on whether the stimulated target is structurally embedded within an encoding-relevant network scaffold. Significance statementMemory enhancement through direct brain stimulation holds substantial clinical promise, yet inconsistent outcomes have limited its therapeutic translation. This study shows that the effectiveness of closed-loop brain stimulation for memory improvement is determined by the structural network architecture of the stimulation target. Sites more deeply embedded within white-matter pathways connecting a distributed verbal encoding network yield the greatest mnemonic benefits when stimulation is delivered adaptively during poor encoding states. These findings establish a principled, network-based rationale for precision-guided neuromodulation: optimizing both the targets structural embedding and the timing of stimulation delivery are necessary and complementary conditions for reliable, individualized memory enhancement.

neuroscience↗

bioRxiv2025

Neural components underlying successful free recall are specific to episodic memory

AbstractEpisodic memory depends upon activity distributed across the brain. However, the activity underlying memory has largely been examined within single tasks in isolation. Thus it is unclear to what extent prior findings reflect task-general rather than memory-specific cognitive processes. Here we address this question using data from 371 patients recorded intracranially who performed a free recall task with encoding and retrieval phases alongside an arithmetic distractor phase. We ask whether neural decoders fit to predict behavior from one phase transfer to the others. Encoding-retrieval transfer exceeds both arithmetic-encoding and arithmetic-retrieval transfer and therefore cannot be explained solely by processes supporting arithmetic. We further detect transfer between arithmetic and retrieval but not between arithmetic and encoding. The brain-behavioral relations observed in these tasks thus do not merely reflect a single task-general factor of activity. We propose cross-task decoding as a method for identifying the neural factor structure underlying distinct cognitive processes.

neuroscience↗

bioRxiv2023

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulations behavioral and physiological effects depend on the stimulation targets anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brains memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulations behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

neuroscience↗

bioRxiv2020

Distinct cortical systems reinstate the content and context of episodic memories

Episodic recall depends upon the reinstatement of cortical activity present during the formation of a memory. We identified dissociable cortical networks via functional connectivity that uniquely reinstated semantic content and temporal context of previously studied stimuli during free recall. Network-specific reinstatement predicted the temporal and semantic organization of recall sequences, demonstrating how specialized cortical systems enable the human brain to target specific memories.

neuroscience↗

bioRxiv2019

Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

High-frequency gamma activity of verbal-memory encoding using invasive-electroencephalogram coupled has laid the foundation for numerous studies testing the integrity of memory in diseased populations. Yet, the functional connectivity characteristics of networks subserving these HFA-memory linkages remains uncertain. By integrating this electrophysiological biomarker of memory encoding from IEEG with resting-state BOLD fluctuations, we estimated the segregation and hubness of HFA-memory regions in drug-resistant epilepsy patients and matched healthy controls. HFA-memory regions express distinctly different hubness compared to neighboring regions in health and in epilepsy, and this hubness was more relevant than segregation in predicting verbal memory encoding. The HFA-memory network comprised regions from both the cognitive control and primary processing networks, validating that effective verbal-memory encoding requires multiple functions, and is not dominated by a central cognitive core. Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding.\n\nHighlightsO_LIHigh frequency memory activity in IEEG corresponds to specific BOLD changes in resting-state data.\nC_LIO_LIHFA-memory regions had lower hubness relative to control brain nodes in both epilepsy patients and healthy controls.\nC_LIO_LIHFA-memory network displayed hubness and participation (interaction) values distinct from other cognitive networks.\nC_LIO_LIHFA-memory network shared regional membership and interacted with other cognitive networks for successful memory encoding.\nC_LIO_LIHFA-memory network hubness predicted both concurrent task (phasic) and baseline (tonic) verbal-memory encoding success.\nC_LI

neuroscience↗

bioRxiv2019

Spectral tilt underlies mathematical problem solving

Neural activity associated with successful cognition appears as a tilt in the power spectrum of the local field potential, wherein increases in high-frequency power accompany decreases in low frequency power. Whereas this pattern has been shown in a wide range of memory tasks, it is unknown whether this increased spectral tilt reflects underlying memory-specific processes or rather a domain-general index of task engagement. To address the question of whether increased spectral tilt reflects increased attention to a cognitive task, we collected intracranial recordings from three hundred thirty neurosurgical patients as they performed a mathematical problem solving task. We used a mathematical problem solving task, because it allowed us to decouple task-specific processes with domain-general attention in a novel way. Using a statistical model to control for inherent problem complexity, we classified individual math problems based on whether a subject performed faster than predicted (high-attention or fast) or slower than predicted (low-attention, or slow) based on residual response times. In contrast to the domain-general attentional account, problems that took longer than predicted produced stronger evidence for the spectral tilt: widespread increases in high frequency (31-180 Hz) power and decreases in low frequency (3-17 Hz) power across frontal, temporal, and parietal cortices. The pattern emerged early within each trial and was sustained throughout the response period but was not observed in the medial temporal lobe. The data show that engaging in mathematical problem solving leads to a distributed spectral tilt pattern, even when accounting for variability in performance driven by the arithmetic demands of the problems themselves, and suggest that broadband changes in the power spectrum reflect an index of information processing in the brain beyond simple attention to the cognitive task.

neuroscience↗

bioRxiv2019

Does Data Cleaning Improve Brain State Classification?

BackgroundNeuroscientists routinely seek to identify and remove noisy or artifactual observations from their data. They do so guided by the belief that including such data would reduce power to detect effects of interest. Whereas standard methods can exclude certain well defined noise sources (e.g., line noise), most forms of noise do not easily separate from signals of interest. Here we ask how well methods routinely used to "clean" human electrophysiological recordings actually boost power to detect brain-behavior correlations. New MethodThis, to the authors knowledge, is the first large-scale study of the impact of intracranial EEG preprocessing on brain state classification. ResultsWe find that several commonly used data cleaning methods (automated methods based on statistical properties of the signal and manual methods based on expert review) reduce statistical power for both univariate and multivariate classification of successful memory encoding, a behavioral state with very well-characterized electrophysiological biomarkers. Comparison with Existing MethodsBy reallocating resources towards collecting more within-patient data instead of attempting to "clean" data, neuroscientists may see increases in the statistical power to detect physiological phenomena. ConclusionsThese findings highlight the challenge of partitioning signal and noise in the analysis of brain-behavior relations. They also prescribe increases in sample size and numbers of observations, rather than data cleaning, as the best approach to improving statistical power.

neuroscience↗

bioRxiv2018

Predictive control of electrophysiological network architecture using direct, single-node neurostimulation in humans

Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multi-modal intracranial recordings and diffusion tensor imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition.

neuroscience↗

bioRxiv2017

The medial temporal lobe organizes memory across time and space: Causal evidence from brain stimulation

The medial temporal lobe (MTL) is widely implicated in supporting episodic memory and navigation, but its precise functional role in organizing memory across time and space remains elusive. Here we examine the specific cognitive processes implemented by MTL structures (hippocampus and entorhinal cortex) to organize memory by using electrical brain stimulation, leveraging its ability to establish causal links between brain regions and features of behavior. We studied neurosurgical patients who performed spatial-navigation and verbal-episodic memory tasks while brain stimulation was applied in various regions during learning. During the verbal memory task, stimulation in the MTL disrupted the temporal organization of encoded memories such that items learned with stimulation tended to be recalled in a more randomized order. During the spatial task, MTL stimulation impaired subjects abilities to remember items using view-independent (allocentric) navigation strategies. These stimulation effects were specific to the MTL. Our findings thus provide the first causal demonstration in humans of the specific memory processes that are performed by the MTL to encode when and where events occurred.\n\nSignificance StatementNumerous studies have implicated the medial temporal lobe (MTL) in encoding spatial and temporal memories, but they have not been able to causally demonstrate the nature of the cognitive processes by which this occurs in real-time. Electrical brain stimulation is able to demonstrate causal links between a brain region and a given function with high temporal precision. By examining behavior in a memory task as subjects received MTL stimulation, we provide the first causal evidence demonstrating the role of the MTL in organizing the spatial and temporal aspects of episodic memory.

neuroscience↗