Biology subjects

Friedman, D.

Publications and source records attributed to Friedman, D..

45 records · Page 3Linked to original sources

bioRxiv2020

Spatiotemporal dynamics between interictal epileptiform discharges and ripples during associative memory processing

We describe the spatiotemporal course of cortical high-gamma activity (HGA), hippocampal ripple activity and interictal epileptiform discharges (IEDs) during an associative memory task in 15 epilepsy patients undergoing invasive electroencephalography. Successful encoding trials manifested significantly greater HGA in hippocampus and frontal regions. Successful cued recall trials manifested sustained HGA in hippocampus compared to failed responses. Hippocampal ripple rates were greater during successful encoding and retrieval trials. IEDs during encoding were associated with 15% decreased odds of remembering in hippocampus (95% CI 6-23%). Hippocampal IEDs during retrieval predicted 25% decreased odds of remembering (15-33%). Odds of remembering were reduced by 25-52% if IEDs occurred during the 500-2000 ms window of encoding or by 41% during retrieval. During encoding and retrieval, hippocampal IEDs were followed by a transient decrease in ripple rate. We hypothesize that IEDs impair associative memory in a regionally and temporally specific manner by decreasing physiologic hippocampal ripples necessary for effective encoding and recall. Because dynamic memory impairment arises from pathological IED events competing with physiological ripples, IEDs represent a promising therapeutic target for memory remediation in patients with epilepsy. SummaryHippocampal interictal epileptiform discharges in hippocampus acutely impair declarative memory, potentially by hijacking physiological processes essential for encoding and recall.

neuroscience↗

bioRxiv2020

Proteomic Differences in the Hippocampus and Cortex of Epilepsy Brain Tissue

Epilepsy is a common neurological disorder affecting over 70 million people worldwide, with a high rate of pharmaco-resistance, diverse comorbidities including progressive cognitive and behavioral disorders, and increased mortality from direct (e.g., Sudden Unexpected Death in Epilepsy [SUDEP], accidents, drowning) or indirect effects of seizures and therapies. Extensive research with animal models and human studies provides limited insights into the mechanisms underlying seizures and epileptogenesis, and these have not translated into significant reductions in pharmaco-resistance, morbidities or mortality. To help define changes in molecular signaling networks associated with epilepsy, we examined the proteome of brain samples from epilepsy and control cases. Label-free quantitative mass spectrometry (MS) was performed on the hippocampal CA1-3 region, frontal cortex, and dentate gyrus microdissected from epilepsy and control cases (n=14/group). Epilepsy cases had significant differences in the expression of 777 proteins in the hippocampal CA1-3 region, 296 proteins in the frontal cortex, and 49 proteins in the dentate gyrus in comparison to control cases. Network analysis showed that proteins involved in protein synthesis, mitochondrial function, G-protein signaling, and synaptic plasticity were particularly altered in epilepsy. While protein differences were most pronounced in the hippocampus, similar changes were observed in other brain regions indicating broad proteomic abnormalities in epilepsy. Among the most significantly altered proteins, G-protein Subunit Beta 1 (GNB1) was one of the most significantly decreased proteins in epilepsy in all regions studied, highlighting the importance of G-protein subunit signaling and G-protein-coupled receptors (GPCRs) in epilepsy. Our results provide insights into the molecular mechanisms underlying epilepsy, which may allow for novel targeted therapeutic strategies.

neuroscience↗

bioRxiv2020

Dual mechanisms of ictal high frequency oscillations in human rhythmic onset seizures

High frequency oscillations (HFOs) recorded from intracranial electrodes during epileptiform discharges are a proposed biomarker of epileptic brain tissue and may also be useful for seizure forecasting, with mixed results. Despite such potential for HFOs, there is limited investigation into the spatial context of HFOs and their relationship to simultaneously recorded neuronal activity. We sought to further understand the biophysical underpinnings of ictal HFOs using unit recordings in the human neocortex and mesial temporal lobe during rhythmic onset seizures. We compare features of ictal discharges in both the seizure core and penumbra (spatial seizure domains defined by multiunit activity patterns). We report differences in spectral features, unit-local field potential coupling, and information theoretic characteristics of HFOs before and after local seizure invasion. Furthermore, we tie these timing-related differences to spatial domains of seizures, showing that penumbral discharges are widely distributed and less useful for seizure localization.

physiology↗

bioRxiv2019

Shared neural codes for eye-gaze and valence

The eye-gaze of others is a prominent social cue in primates and crucial for communication1-7, and atypical processing occurs in several conditions as autism-spectrum-disorder (ASD)1,9-14. The neural mechanisms that underlie eye-gaze remain vague, and it is still debated if these computations developed in dedicated neural circuits or shared with non-social elements. In many species, eye-gaze signals a threat and elicits anxiety, yet can also serve as a predictor for the outcome of the encounter: negative or positive2,4,8. Here, we hypothesized and find that neural codes overlap between eye-gaze and valence. Monkeys participated in a modified version of the human-intruder-test8,15 that includes direct and averted eye-gaze and interleaved with blocks of aversive and appetitive conditioning16,17. We find that single-neurons in the amygdala encode gaze18, whereas neurons in the anterior-cingulate-cortex encode the social context19,20 but not gaze. We identify a shared amygdala circuitry where neural responses to averted and direct gaze parallel the responses to appetitive and aversive value, correspondingly. Importantly, we distinguish two shared coding mechanisms: a shared-intensity scheme that is used for gaze and the unconditioned-stimulus, and a shared-activity scheme that is used for gaze and the conditioned-stimulus. The shared-intensity points to overlap in circuitry, whereas the shared-activity requires also correlated activity. Our results demonstrate that eye-gaze is coded as a signal of valence, yet also as the expected value of the interaction. The findings may suggest new insights into the mechanisms that underlie the malfunction of eye-gaze in ASD and the comorbidity with impaired social skills and anxiety.

neuroscience↗

bioRxiv2019

Statistical learning shapes neural sequence representations

Sensory input arrives in continuous sequences that humans experience as units, e.g., words and events. The brains ability to discover extrinsic regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words); while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits.

neuroscience↗

bioRxiv2018

More is Better: Using Machine Learning Techniques and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

A basic aim of marketing research is to predict consumers preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects individual future preferences over the products and the commercials population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials effectiveness using a standard questionnaire commonly used in marketing research. We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects preferences and marketing campaigns success.

neuroscience↗

bioRxiv2018

Hippocampal signature of associative memory measured by chronic ambulatory intracranial EEG

Some patients with medically refractory focal epilepsy are chronically implanted with a brain-responsive neurostimulation device (the RNS(R) System), permitting neurophysiological measurements at millisecond resolution. This clinical device can be adapted to measure hippocampal dynamics time-locked to cognitive tasks. We illustrate the technique with a proof of concept in three patients previously implanted with the RNS System as they engage in an associative memory task, measured months apart. Hippocampal activity measured in successful encoding in RNS System patients mirrors that in surgical patients during intracranial electroencephalography (iEEG), suggesting that chronic iEEG allows sensitive measurements of hippocampal physiology over prolonged timescales.

neuroscience↗

bioRxiv2017

Replay of large-scale spatio-temporal patterns from waking during subsequent slow-wave sleep in human cortex

Animal studies support the hypothesis that in slow-wave sleep, replay of waking neocortical activity under hippocampal guidance leads to memory consolidation. However, no intracranial electrophysiological evidence for replay exists in humans. We identified consistent sequences of population firing peaks across widespread cortical regions during complete waking periods. The occurrence of these Motifs were compared between sleeps preceding the waking period (Sleep-Pre) when the Motifs were identified, and those following (Sleep-Post). In all subjects, the majority of waking Motifs (most of which were novel) had more matches in Sleep-Post than in Sleep-Pre. In rodents, hippocampal replay occurs during local sharp-wave ripples, and the associated neocortical replay tends to occur during local sleep spindles and down-to-up transitions. These waves may facilitate consolidation by sequencing cell-firing and encouraging plasticity. Similarly, we found that Motifs were coupled to neocortical spindles, down-to-up transitions, theta bursts, and hippocampal sharp-wave ripples. While Motifs occurring during cognitive task performance were more likely to have more matches in subsequent sleep, our studies provide no direct demonstration that the replay of Motifs contributes to consolidation. Nonetheless, these results confirm a core prediction of the dominant neurobiological theory of human memory consolidation.

neuroscience↗

bioRxiv2017

Mean-Field Equations For Neuronal Networks With Arbitrary Degree Distributions

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections which are made randomly and independently with a fixed probability (Erdos-Renyi network) (ER), or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of pre-synaptic and post-synaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

neuroscience↗