Biology subjects

Rey, H. G.

Publications and source records attributed to Rey, H. G..

6 recordsLinked to original sources

bioRxiv2026

Electrophysiological dissociation of human posterior cingulate cortex contributions to value- and memory-based decision-making

The human posterior cingulate cortex (PCC) is routinely implicated in cognition and disease, yet its specific functional contributions remain unclear. Historically, human neuroimaging has linked the region to episodic memory and the default mode network, while a distinct non-human primate electrophysiology literature has focused on economic decision-making. Integrating anatomical evidence with these literatures, it has recently been proposed that this divergence reflects subregional organization, with dorsal PCC as a potential convergence site for value-based and memory-based decisions. Here, we recorded local field potentials (LFPs) and single units from human PCC while the same participants performed matched value- and memory-based decision tasks. LFPs in dorsal but not ventral PCC showed sustained engagement across both tasks, with risk sensitivity emerging only after the decision. In contrast, single-unit activity was more temporally circumscribed and could be grouped into response profiles active before or after the decision. Dorsal but not ventral PCC engagement further extended to memory encoding, recognition, and confidence judgments. Together, these findings reveal a consistent functional dissociation, identifying dorsal PCC as a domain-general interface between evaluative and mnemonic systems. In doing so, they align human and non-human primate accounts of PCC function and help orient future targeted studies of its role in cognition and disease.

neuroscience↗

bioRxiv2025

MCWs (MiCroWire sorter): A new framework for automated and reliable spike sorting in human intracerebral recordings.

Efficient and accurate spike sorting is critical for isolating single neurons from extracellular recordings to distinguish neural activity of interest. However, while the electrodes and acquisition systems for non-human electrophysiology have been enhanced over the past decades to enable higher-yield single-neuron detections, those advances have not been translated into human electrophysiology. Single-wire electrodes are still ubiquitously used, and although acquisition systems have augmented their signal-to-noise ratio over the last 15 years, we are still limited by their low electrode count. Moreover, unlike non-human recordings, human recordings often take place in hospitals where different noise sources and subject breaks can compromise the recording quality during experimental sessions. To bridge this gap, this work presents an automatic, open-source spike sorting pipeline that leverages contemporary computational capabilities and is tailored to single-neuron recordings from humans acquired via microwires. The pipeline is implemented in both MATLAB and Python, ensuring accessibility and compatibility across computational environments. Its modular and comprehensive structure supports customization and even opportunities for new developments as per the requirements of the user and the application. One feature is a data-driven automatic module to remove narrow-band interference, besides electrical line noise, which can be an essential tool while recording in clinical settings, particularly for online processing implementations. Following spike detection, the pipeline implements an artifact rejection module that separates waveforms that are unlikely to be associated with actual spikes. Additionally, we introduce a configurable feature-extraction, clustering, and benchmarking framework that not only allows flexibility in employing user-defined or conventional algorithms, such as wavelet transform with superparamagnetic clustering, but can also evaluate multi-method agreement among the different sorters. The pipeline also utilizes established and novel quality metrics to support semiautomatic curation of isolated clusters. Furthermore, we can integrate the customized pipeline with experimental tasks by removing task-unrelated waveforms (e.g., during a break in a task) and prevent over-clustering with the aid of metrics for comparing response profiles. Thus, the presented pipeline addresses the three-pronged objectives of algorithm-adaptability, rigorous validation, and human single-neuron recording optimization to support clinical and cognitive neuroscience applications.

neuroscience↗

bioRxiv2025

An online dynamic experimental framework to study single neuron responses in humans

Single-neuron activity can be recorded from patients being evaluated for neurosurgical treatment of drug-resistant epilepsy who volunteer to have microwires implanted for single cell research that aims to study various aspects of the human brain. These recordings provide an unprecedented window into the human brain to observe how information is encoded and retrieved at the cellular level. Current single cell research paradigms require laborious offline analysis to identify stimuli eliciting neuronal responses and introduce new relevant stimuli in a follow up task. Any delays due to patient availability can compromise the follow up task, risking loss of experimental progress as recording electrodes may move away from previously recorded neurons. This paper introduces a novel experimental framework for online single cell data analysis and dynamic sampling of the stimulus space. It allows for real-time data acquisition and analysis to identify response-eliciting stimuli, introduce new relevant stimuli, and discard those that fail to elicit responses.

neuroscience↗

bioRxiv2025

Parietal cortex is recruited by frontal and cingulate areas to support action monitoring and updating during stopping

Recent evidence indicates that the intraparietal sulcus (IPS) may play a causal role in action stopping, potentially representing a novel neuromodulation target for inhibitory control dysfunctions. Here, we leverage intracranial recordings in human subjects to establish the timing and directionality of information flow between IPS and prefrontal and cingulate regions during action stopping. Prior to successful inhibition, information flows primarily from the inferior frontal gyrus (IFG), a critical inhibitory control node, to IPS. In contrast, during stopping errors the communication between IPS and IFG is lacking, and IPS is engaged by posterior cingulate cortex, an area outside of the classical inhibition network and typically associated with default mode. Anterior cingulate and orbitofrontal cortex also display performance-dependent connectivity with IPS. Our functional connectivity results provide direct electrophysiological evidence that IPS is recruited by frontal and anterior cingulate areas to support action plan monitoring/updating, and by posterior cingulate during control failures. In briefFunctional connectivity between the intraparietal sulcus (IPS) and a set of frontal and cingulate regions indicates that IPS is recruited to aid inhibitory control. Control failures are associated with increased communication with posterior cingulate. IPS could be a novel and tractable neuromodulation target for control-related neuropsychiatric disorders. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/640787v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@e04699org.highwire.dtl.DTLVardef@3b1ea6org.highwire.dtl.DTLVardef@15961f4org.highwire.dtl.DTLVardef@12e144f_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIParietal cortex displays performance-dependent activity in action stopping C_LIO_LIFunctional connectivity between IPS and IFG underlies successful stopping C_LIO_LIEarly communication from ACC and OFC to IPS is also specific to successful stopping C_LIO_LICommunication from PCC to IPS is higher during lapses in control C_LI

neuroscience↗

bioRxiv2022

Distinct population and single-neuron selectivity for executive and episodic processing in human dorsal posterior cingulate

Posterior cingulate cortex (PCC) is an enigmatic region implicated in psychiatric and neurological disease, yet its role in cognition remains unclear. Human studies link PCC to episodic memory and default mode network (DMN), while findings from the non-human primate emphasize executive processes more associated with the cognitive control network (CCN) in humans. We hypothesized this difference reflects an important functional division between dorsal (executive) and ventral (episodic) PCC. To test this, we utilized human intracranial recordings of population and single unit activity targeting dorsal PCC during an alternated executive/episodic processing task. Dorsal PCC population responses were significantly enhanced for executive, compared to episodic, task conditions, consistent with the CCN. Single unit recordings, however, revealed four distinct functional types with unique executive (CCN) or episodic (DMN) response profiles. Our findings provide critical electrophysiological data from human PCC, bridging incongruent views within and across species, furthering our understanding of PCC function.

neuroscience↗

bioRxiv2020

General Framework for Tracking Neural Activity Over Long-Term Extracellular Recordings

The recent advances in the chronic implantation of electrodes have allowed the collection of extracellular activity from neurons over long periods of time. To fully take advantage of these recordings, it is necessary to track single neurons continuously, particularly when their associated waveform changes over time. Multiple spike sorting algorithms can track drifting neurons but they do not perform well in conditions like a temporary increase in the noise level and changes in the number of detectable neurons. In this work, we present Tracking_Graph, a general framework to track neurons under these conditions. Tracking_Graph can be implemented with different spike sorting algorithms, allowing the experimenter to use the algorithm best fitted for their setup. The main idea behind Tracking_Graph is the blockwise analysis of the recording and application of a classification algorithm to match spikes to templates in different segments, leading to a directional metric that can be used to link clusters across blocks. Moreover, the algorithm can detect and fix sorting errors (splits and merges) in isolated blocks. We compared an implementation of Tracking_Graph with other algorithms using long-term simulations and obtained superior performance in all the metrics.

neuroscience↗