Biology subjects

Zaghloul, K. A.

Publications and source records attributed to Zaghloul, K. A..

17 recordsLinked to original sources

bioRxiv2026

Inflammatory signaling differentially changes chromatin accessibility and gene expression of the PD- associated kinase LRRK2 between human and mice

The genomic locus that encodes the Leucine-rich repeat kinase 2 (LRRK2) gene is highly pleiotropic, being associated with both Parkinsons disease (PD) and Crohns disease (CD). Coding variants associated with risk of PD and CD act as gain of function kinase mutations increasing phosphorylation of RAB substrates, and non-coding variants in the promoter region of LRRK2 increase expression of the gene, notably in immune cells. If regulation of LRRK2 expression is a causal contributor to age-related diseases, it is important to understand the mechanism(s) by which LRRK2 is regulated, particularly in the context of inflammation. Here, we show that interferon-{gamma} exposure induces robust LRRK2 activation in human iPSC-derived microglia through signaling of the Janus-activated Kinase complex to phosphorylate STAT1, which then binds to the LRRK2 promoter and is associated with remodeling of chromatin structure in this genomic locus. Additional regulatory mechanisms include the stress-induced transcription factor and long non-coding RNA encoded at the same locus, resulting in increased LRRK2 mRNA levels. We also show evidence of the same effect in acutely cultured human brain slices. While we were unable to demonstrate any induction of Lrrk2 mRNA in the mouse brain, the introduction of a human bacterial artificial chromosome transgene into the mouse genome recapitulated sensitivity to interferon-{gamma} in microglia. A comparative genomic analysis across mammals suggests that these species differences are driven by regulatory regions upstream of LRRK2 that are specific to anthropoid primates. These results demonstrate that there are differences between species in how genes associated with human diseases are regulated and provide important information that should be incorporated in disease modeling.

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↗

bioRxiv2025

A novel, evolutionarily conserved inhibitory circuit selectively regulates dentate gyrus mossy cell function.

The mammalian dentate gyrus contributes to mnemonic function by parsing similar events and places. The disparate activity patterns of mossy cells and granule cells is believed to enable this function yet the mechanisms that drive this circuit dynamic remain elusive. We identified a novel inhibitory interneuron subtype, characterized by VGluT3 expression, with overwhelming target selectivity for mossy cells while also revealing that CCK, PV, SOM and VIP interneurons preferentially innervate granule cells. Leveraging pharmacology and novel enhancer viruses, we find that this target-specific inhibitory innervation pattern is evolutionarily conserved in non-human primates and humans. In addition, in vivo chemogenetic manipulation of VGluT3+ interneurons selectively alters the activity and functional properties of mossy cells. These findings establish that mossy cells and granule cells have unique, evolutionarily conserved inhibitory innervation patterns and suggest selective inhibitory circuits may be necessary to maintain DG circuit dynamics and enable pattern separation across species.

neuroscience↗

bioRxiv2024

Reactivation of reward representations associated with the reinforcement of behavior

Reward-related representations are found distributed throughout many human subcortical and neocortical regions that support different neural processes. These representations get reinstated for different but related tasks. There is rising evidence that representations reactivate during learning to reinforce prior rewarding choices. However, there remains a critical lack of understanding for whether and how reactivations in humans can facilitate behavioral reinforcement. To investigate this, we recorded from the temporal lobe and prefrontal cortex with intracranial electrocorticography while human subjects learnt two-choice decisions across two different scenes. Crucially, subjects were able to straightforwardly reactivate knowledge gained in a prior scene to make decisions in the current scene, but only when the reward contingencies stayed the same between the two scenes. Using a Bayesian learner, we inferred reward expectation from choice behavior, and measured representations of these reward expectations in electrocorticography data. We found that the representations of reward expectation in the medial temporal lobe and orbitofrontal cortex were reactivated, only when the subjects could straightforwardly transfer knowledge between the two scenes. In the anterior temporal lobe, the reactivation of reward representations increased as learning increased, suggesting an area of plasticity during learning. This work presents a novel framework for measuring the reinforcement, or reactivation, of representations from human electrophysiology during the reinforcement of behavior. Significance statementThis study provides a framework for how the brain processes and reinstates reward-related information during the reinforcement of behavior. Using intracranial electrocorticography, the researchers found that reward information is encoded in distant brain regions, the medial temporal lobe and orbitofrontal cortex. These representations are dynamically reinstated across scenes when reward contingencies align and when knowledge can be transferred. Additionally, reward-related representations in the anterior temporal lobe become increasingly reinforced or reinstated as learning progresses, revealing an area of plasticity during learning. These findings offer new insights into the neural mechanisms underlying decision-making and highlight potential avenues for addressing conditions like addiction or depression, where reward processing and decision-making are disrupted.

neuroscience↗

bioRxiv2021

Source-sink connectivity: a novel resting-state EEG marker of the epileptogenic zone

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy (DRE). Successful surgery is a standard of care treatment for DRE but can only be achieved through complete resection or disconnection of the epileptogenic zone (EZ), the brain region(s) where seizures originate. Surgical success rates vary between 20-80% because no clinically validated biological markers of the EZ exist. Localizing the EZ is a costly and time-consuming process beginning with non-invasive neuroimaging and often followed by days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity (e.g., low-voltage high frequency activity) on individual channels occurring immediately before seizures or spikes that occur on interictal iEEG (i.e., between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in EZ localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. Intracranial EEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aim to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the EZ. We hypothesize that when a patient is not clinically seizing, it is because the EZ is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, patient-specific dynamical network models (DNMs) were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics (SSMs). We validated the SSMs in a retrospective analysis of 65 patients by using the SSMs of the annotated EZ to predict surgical outcomes. The SSMs predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians predictions (surgical success rate of this dataset). In failed outcomes, we identified regions of the brain with high SSMs that were untreated. When compared to high frequency oscillations, the most commonly proposed interictal iEEG feature for EZ localization, SSMs outperformed in predictive power (by a factor of 1.2) suggesting SSMs may be an interictal iEEG fingerprint of the EZ.

bioengineering↗

bioRxiv2021

Discrete ripples reflect a spectrum of synchronous spiking activity in human association cortex

Direct brain recordings have provided important insights into how persistent oscillatory activity support human memory retrieval, but the extent to which transient fluctuations in intracranial EEG (iEEG) captures the dynamic coordination of underlying neurons involved in memory processing remains unclear. Here, we simultaneously record iEEG, local field potential (LFP), and single unit activity in the human temporal cortex. We demonstrate that cortical ripples contribute to broadband high frequency activity and exhibit a spectrum of amplitudes and durations related to the amount of underlying neuronal spiking. Ripples in the macro-scale iEEG are related to the number and synchrony of ripples in the micro-scale LFP, which in turn are related to the synchrony of neuronal spiking. Our data suggest that neural activity in the human cortex is organized into dynamic, discrete packets of information.

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↗

bioRxiv2020

Low frequency traveling waves in the human cortex coordinate neural activity across spatial scales

Traveling waves of oscillatory activity are thought to influence neuronal spiking, providing a spatiotemporal frame-work for neural communication. However, no direct link has been established between traveling waves and neuronal spiking in humans. We examined traveling waves in the human lateral temporal lobe by using recordings from intracranial electrodes implanted in twenty participants for seizure monitoring as they performed a paired-associates verbal memory task. We observed ubiquitous low frequency traveling waves across the temporal lobe. While wave occurrence in a broad low frequency range did not differ between successful and unsuccessful memory conditions, in a subset of participants with microelectrode recordings, we found that macro-scale waves co-occurred with micro-scale waves, which in turn were temporally locked to single unit spiking. This temporal coordination between traveling waves at different spatial scales and between waves and neuronal spiking in the human brain suggests a role for traveling waves in neural communication.

neuroscience↗

bioRxiv2019

Viral manipulation of functionally distinct neurons from mice to humans

Recent success in identifying gene regulatory elements in the context of recombinant adeno-associated virus vectors have enabled cell type-restricted gene expression. However, within the cerebral cortex these tools are presently limited to broad classes of neurons. To overcome this limitation, we developed a strategy that led to the identification of multiple novel enhancers to target functionally distinct neuronal subtypes. By investigating the regulatory landscape of the disease gene Scn1a, we identified enhancers that target the breadth of its expression, including two that are selective for parvalbumin and vasoactive intestinal peptide cortical interneurons. Demonstrating the functional utility of these elements, we found that the PV-specific enhancer allowed for the selective targeting and manipulation of these neurons across species, from mice to humans. Finally, we demonstrate that our selection method is generalizable to other genes and characterize four additional PV-specific enhancers with exquisite specificity for distinct regions of the brain. Altogether, these viral tools can be used for cell-type specific circuit manipulation and hold considerable promise for use in therapeutic interventions.

neuroscience↗

bioRxiv2019

The effects of direct brain stimulation in humans depend on frequency, amplitude, and white-matter proximity.

Researchers have used direct electrical brain stimulation to treat a range of neurological and psychiatric disorders. However, for brain stimulation to be maximally effective, clinicians and researchers should optimize stimulation parameters according to desired outcomes. To examine how different kinds of stimulation affect human brain activity, we compared the changes in neuronal activity that resulted from stimulation at a range of frequencies, amplitudes, and locations with direct human brain recordings. We recorded human brain activity directly with electrodes that were implanted in widespread regions across 106 neurosurgical epilepsy patients while systematically stimulating across a range of parameters and locations. Overall, stimulation most often had an inhibitory effect on neuronal activity, consistent with earlier work. When stimulation excited neuronal activity, it most often occurred from high-frequency stimulation. These effects were modulated by the location of the stimulating electrode, with stimulation sites near white matter more likely to cause excitation and sites near gray matter more likely to inhibit neuronal activity. By characterizing how different stimulation parameters produced specific neuronal activity patterns on a large scale, our results help guide clinicians and researchers when designing stimulation protocols to cause precisely targeted changes in human brain activity.

neuroscience↗

bioRxiv2019

Risk of Seizures Induced by Intracranial Research Stimulation: Analysis of 770 Stimulation Sessions

BackgroundPatients with medically refractory epilepsy often undergo intracranial electroencephalography (iEEG) monitoring to identify a seizure focus and determine their candidacy for surgical intervention. This clinically necessary monitoring period provides an increasingly utilized research opportunity to study human neurophysiology, however, ethical concerns demand a thorough appreciation of the associated risks.\n\nObjectiveWe measured the incidence of research stimulation-associated seizures in a large multi-institutional study on brain stimulations effect on memory in order to determine if brain stimulation was statistically associated with seizure incidence, and identify potential risk factors for stimulation-associated seizures.\n\nMethods188 subjects undergoing iEEG monitoring across 10 institutions participated in 770 research stimulation sessions over 3.5 years. Seizures within 30 minutes of a stimulation session were included in our retrospective analysis. We analyzed stimulation parameters, seizure incidence, and typical seizure patterns, to assess the likelihood that recorded seizures were stimulation-induced, rather than events that occurred by chance in epilepsy patients prone to seizing.\n\nResultsIn total, 14 seizures were included in our analysis. All events were single seizures, and no adverse events occurred. The mean amplitude of seizure-associated stimulation did not differ significantly from the mean amplitude delivered in sessions without seizures.\n\nIn order to determine the likelihood that seizures were stimulation induced, we used three sets of analyses: Visual iEEG analysis, statistical frequency, and power analyses. We determined that three of the 14 seizures were likely stimulation-induced, five were possibly stimulation-induced, and six were unlikely stimulation-induced. Overall, we estimate a rate of stimulation-induced seizures between 0.39% and 1.82% of sessions.\n\nConclusionsThe rarity of stimulation-associated seizures, and that none added morbidity or affected the clinical course of any patient, are important findings for understanding the feasibility and safety of intracranial stimulation for research purposes.

neuroscience↗

bioRxiv2019

Identifying site- and stimulation-specific TMS-evoked EEG potentials using a quantitative cosine similarity metric

The ability to interpret transcanial magnetic stimulation (TMS) evoked EEG potentials (TEPs) is limited by artifacts, such as auditory evoked responses produced by the TMS coil. TEPs generated from direct cortical stimulation should vary in spatial distribution with stimulation site and from responses to sham stimulation. Responses that do not show these effects are likely to be artifactual. In 20 healthy volunteers, we delivered active and sham TMS to right prefrontal, left primary motor, and left posterior parietal cortex and compared the waveform similarity of TEPs between stimulation sites and active and sham TMS with a cosine similarity-based analysis method. We looked for epochs after the stimulus when the spatial pattern of TMS-evoked activation had greater than random similarity between stimulation sites and sham vs. active TMS, indicating presence of a dominant artifact, such as the auditory brain response. We calculated and binarized the derivatives of the TEPs recorded from 30 EEG channels and calculated cosine similarity between conditions at each time point with millisecond resolution. Only TEP components occurring before approximately 80 ms differed across stimulation sites and between active and sham, indicating site and condition-specific responses. TEP components before about 80 ms can be safely interpreted as stimulation location-specific responses to TMS, but components beyond this latency should be interpreted with caution due to high similarity in their spatial distribution.

neuroscience↗

bioRxiv2018

MTL functional connectivity predicts stimulation-induced theta power

Abstract/SummaryFocal electrical stimulation of the brain incites a cascade of neural activity that propagates from the stimulated region to both nearby and remote areas, offering the potential to control the activity of brain networks. Understanding how exogenous electrical signals perturb such networks in humans is key to its clinical translation. To investigate this, we applied electrical stimulation to subregions of the medial temporal lobe in 26 neurosurgical patients fitted with indwelling electrodes. Networks of low-frequency (5-13 Hz) spectral coherence predicted stimulation-evoked changes in theta (5-8 Hz) power, but only when stimulation was applied in or adjacent to white matter. Furthermore, these power changes aligned with control-theoretic predictions of how exogenous stimulation flows through complex networks, such as a dispersal of induced activity when functional hubs are targeted. Our results demonstrate that functional connectivity is predictive of causal changes in the brain, but that access to structural connections is necessary to observe such effects.

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↗

bioRxiv2018

Using Network Analysis to Localize the Epileptogenic Zone from Invasive EEG Recordings in Intractable Focal Epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60-65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.\n\nAUTHOR SUMMARYEpilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often requires observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patients brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

neuroscience↗

bioRxiv2017

Signal complexity of human intracranial EEG tracks successful associative memory formation across individuals

Memory performance is highly variable between individuals. Most studies examining human memory, however, have largely focused on the neural correlates of successful memory formation within individuals, rather than the differences between them. As such, what gives rise to this variability is poorly understood. Here, we examined intracranial EEG (iEEG) recordings captured from 43 participants (23 male) implanted with subdural electrodes for seizure monitoring as they performed a paired-associates verbal memory task. We identified three separate but related signatures of neural activity that tracked differences in successful memory formation across individuals. High performing individuals consistently exhibited less broadband power, flatter power spectral density (PSD) slopes, and greater complexity in their iEEG signals. Furthermore, within individuals across three separate time scales ranging from seconds to days, successful recall was positively associated with these same metrics. Our data therefore suggest that memory ability across individuals can be indexed by increased neural signal complexity.\n\nSignificance StatementWe show that participants whose intracranial EEG exhibits less low frequency power, flatter power spectrums, and greater sample entropy overall are better able to memorize associations, and that the same metrics track fluctuations in memory performance across time within individuals. These metrics together signify greater neural signal complexity which may index the brains ability to flexibly engage with information and generate separable memory representations. Critically, the current set of results provide a unique window into the neural markers of individual differences in memory performance which have hitherto been underexplored.

neuroscience↗

bioRxiv2016

Dual origins of measured phase-amplitude coupling reveal distinct neural mechanisms underlying episodic memory in the human cortex

Phase-amplitude coupling (PAC) is hypothesized to coordinate neural activity, but its role in successful memory formation in the human cortex is unknown. Measures of PAC are difficult to interpret, however. Both increases and decreases in PAC have been linked to memory encoding, and PAC may arise due to different neural mechanisms. Here, we use a waveform analysis to examine PAC in the human cortex as participants with intracranial electrodes performed a paired associates memory task. We found that successful memory formation exhibited significant decreases in left temporal lobe and prefrontal cortical PAC, and these two regions exhibited changes in PAC within different frequency bands. Two underlying neural mechanisms, nested oscillations and sharp waveforms, were responsible for the changes in these regions. Our data therefore suggest that decreases in measured cortical PAC during episodic memory reflect two distinct underlying mechanisms that are anatomically segregated in the human brain.

neuroscience↗