Biology subjects

Roychowdhury, V.

Publications and source records attributed to Roychowdhury, V..

5 recordsLinked to original sources

bioRxiv2026

Self-Exciting Population Event Models Reveal Abnormal Temporal Amplification in MAPT-Mutant Human Brain Assembloids

Human brain assembloids provide a tractable platform for studying mutation-specific network dysfunction, but most studies do not distinguish whether abnormal activity arises from increased spontaneous initiation or stronger history-dependent amplification. We introduce a low-dimensional discrete-time self-exciting population model for calcium-imaging event sequences that separates baseline initiation (%[mu]), integrated history-dependent gain (%[eta]), and memory decay (%[tau]). Across 175 recordings from 13 MAPT p.R406W or CRISPR-corrected isogenic assembloids, biological-unit inference identified a 4.31-fold increase in %[eta] in mutants (95% CI 3.20- 5.84; exact p=7.8x10-4), without increased baseline activity. Parametric predictive checks exposed residual Poisson overdispersion; fixed-decay and beta-binomial sensitivity analyses nevertheless preserved the mutant history effect. Median %[eta] separated all 13 held-out assembloids, while a multifeature recording-level RBF-SVM achieved AUC 0.816 under nested whole-assembloid holdout. These results identify robust history-dependent temporal amplification as a functional MAPT-mutant network phenotype.

neuroscience↗

bioRxiv2026

Time-Resolved Neuronal Network Dynamics Distinguish Pathological States in Organoid Models

Human brain assembloids offer a powerful platform for modeling neurological diseases, yet comprehensive methods for analyzing their complex network dynamics are lacking. Here, we developed a time-resolved network analysis pipeline that extracts quantitative biomarkers from two-photon calcium imaging, enabling the detection of subtle differences between disease and control models. We applied this pipeline to assembloids containing a pathogenic MAPT p.R406W variant--clinically associated with an Alzheimers disease-like phenotype--and their isogenic controls. Our analysis revealed that mutant networks exhibit significantly increased degree variance and clustering. This indicates a "hub-like", interconnected topology prone to hypersynchrony, a finding that parallels the network hyperexcitability and seizure-like features observed in in-vivo models of Alzheimers disease. Furthermore, a Random Forest classifier trained on these dynamic network features distinguished between diseased and control states with high accuracy (F1 score = 0.90). These results establish that dynamic network properties can serve as potent biomarkers for identifying pathological states in assembloid models, providing a quantitative framework to investigate disease mechanisms and potential therapeutic interventions.

neuroscience↗

bioRxiv2023

PyHFO: Lightweight Deep Learning-poweredEnd-to-End High-Frequency Oscillations AnalysisApplication

In the context of epilepsy studies, intracranially-recorded interictal high-frequency oscillations (HFOs) in EEG signals are emerging as promising spatial neurophysiological biomarkers for epileptogenic zones. While significant efforts have been made in identifying and understanding these biomarkers, deep learning is carving novel avenues for biomarker detection and analysis. Yet, transitioning such methodologies to clinical environments is difficult due to the rigorous computational needs of processing EEG data via deep learning. This paper presents our development of an advanced end to end software platform, PyHFO, aimed at bridging this gap. PyHFO provides an integrated and user-friendly platform that includes time-efficient HFO detection algorithms such as short-term energy (STE) and Montreal Neurological Institute and Hospital (MNI) detectors and deep learning models for artifact and HFO with spike classification. This application functions seamlessly on conventional computer hardware. Our platform has been validated to adeptly handle datasets from 10-minute EEG recordings captured via grid/strip electrodes in 19 patients. Through implementation optimization, PyHFO achieves speeds up to 50 times faster than the standard HFO detection method. Users can either employ our pre-trained deep learning model for their analyses or use their EEG data to train their model. As such, PyHFO holds great promise for facilitating the use of advanced EEG data analysis tools in clinical practice and large-scale research collaborations.

neuroscience↗

bioRxiv2021

Decoding of human identity by computer vision and neuronal vision

Extracting meaning from a dynamic and variable flow of incoming information is a major goal of both natural and artificial intelligence. Computer vision (CV) guided by deep learning (DL) has made significant strides in recognizing a specific identity despite highly variable attributes1,2. This is the same challenge faced by the nervous system and partially addressed by the concept cells--neurons exhibiting selective firing in response to specific persons/places, described in the human medial temporal lobe (MTL)3-6. Yet, access to neurons representing a particular concept is limited due to these neurons sparse coding. It is conceivable, however, that the information required for such decoding is present in relatively small neuronal populations. To evaluate how well neuronal populations encode identity information in natural settings, we recorded neuronal activity from multiple brain regions of nine neurosurgical epilepsy patients implanted with depth electrodes, while the subjects watched an episode of the TV series "24". We implemented DL models that used the time-varying population neural data as inputs and decoded the visual presence of the main characters in each frame. Before training and testing the DL models, we devised a minimally supervised CV algorithm (with comparable performance against manually-labelled data7) to detect and label all the important characters in each frame. This methodology allowed us to compare "computer vision" with "neuronal vision"--footprints associated with each character present in the activity of a subset of neurons--and identify the brain regions that contributed to this decoding process. We then tested the DL models during a recognition memory task following movie viewing where subjects were asked to recognize clip segments from the presented episode. DL model activations were not only modulated by the presence of the corresponding characters but also by participants subjective memory of whether they had seen the clip segment, and by the associative strengths of the characters in the narrative plot. The described approach can offer novel ways to probe the representation of concepts in time-evolving dynamic behavioral tasks. Further, the results suggest that the information required to robustly decode concepts is present in the population activity of only tens of neurons even in brain regions beyond MTL.

neuroscience↗

bioRxiv2021

Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach

Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, visual verification of HFOs is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish HFOs generated from the epileptogenic zone (epileptogenic HFOs: eHFOs) from those generated from other areas (non-epileptogenic HFOs: non-eHFOs). To address these issues, we constructed a deep learning (DL)-based algorithm using HFO events from chronic intracranial electroencephalogram (iEEG) data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: 1) replicate human expert annotation of artifacts and HFOs with or without spikes, and 2) discover eHFOs by designing a novel weakly supervised model (HFOs from the resected brain regions are initially labeled as eHFOs, and those from the preserved brain regions as non-eHFOs). The "purification power" of DL is then used to automatically relabel the HFOs to distill eHFOs. Using 12,958 annotated HFO events from 19 patients, the model achieved 96.3% accuracy on artifact detection (F1 score = 96.8%) and 86.5% accuracy on classifying HFOs with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the DL-based algorithm trained from 84,602 HFO events from nine patients who achieved seizure-freedom after resection, the majority of such DL-discovered eHFOs were found to be HFOs with spikes (78.6%, p < 0.001). While the resection ratio of detected HFOs (number of resected HFOs/number of detected HFOs) did not correlate significantly with post-operative seizure freedom (the area under the curve [AUC]=0.76, p=0.06), the resection ratio of eHFOs positively correlated with post-operative seizure freedom (AUC=0.87, p=0.01). We discovered that the eHFOs had a higher signal intensity associated with ripple (80-250 Hz) and fast ripple (250-500 Hz) bands at the HFO onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-eHFOs. We then designed perturbations on the input of the trained model for non-eHFOs to determine the models decision-making logic. The model probability significantly increased towards eHFOs by the artificial introduction of signals in the inverted T-shaped frequency bands (mean probability increase: 0.285, p < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, p < 0.001). With this DL-based framework, we reliably replicated HFO classification tasks by human experts. Using a reverse engineering technique, we distinguished eHFOs from others and identified salient features of eHFOs that aligned with current knowledge. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/458385v1_ufig1.gif" ALT="Figure 1"> View larger version (81K): org.highwire.dtl.DTLVardef@39977dorg.highwire.dtl.DTLVardef@108b182org.highwire.dtl.DTLVardef@1bdf864org.highwire.dtl.DTLVardef@fe9e57_HPS_FORMAT_FIGEXP M_FIG C_FIG

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