Biology subjects

Granger, R.

Publications and source records attributed to Granger, R..

4 recordsLinked to original sources

bioRxiv2026

Mechanistic simulation identifies predictive dose-dependent biomarkers of propofol anesthesia

Understanding how receptor-level pharmacological modulation reorganizes large-scale brain circuits remains a central challenge in neuropharmacology. We introduce a multiscale mechanistic model with explicit core-matrix thalamocortical architecture, driven solely by GABA-A modulation without parameter fitting to any anesthesia data, to examine how propofol reorganizes brainwide activity from individual receptors to systems-level circuits. The model exhibits anesthetic effects spanning individual synaptic conductances to widespread changes in spiking, field potentials, and coherence. Without training on any task-specific data, our simulation of sensory processing in a standard auditory oddball paradigm matches independent macaque datasets. The same simulation, unmodified, also reproduces changes to functional connectivity in anesthetized humans, exhibiting selective attenuation of matrix thalamocortical loops relative to core loops. Most importantly, the simulation identified a dose-dependent biomarker of propofol concentration -- elevated residual inter-stimulus cortical activity -- that was subsequently confirmed in empirical macaque data where it had previously gone unnoticed. This simulation-first discovery, arising from mechanistic circuit dynamics rather than statistical comparison of clinical populations, illustrates a generative framework for translating receptor-level modulation into circuit-scale biomarkers with potential applications across predictive neuropharmacology.

neuroscience↗

bioRxiv2025

A formal relation between two disparate mathematical algorithms is ascertained from biological circuit analyses

We simulate and formally analyze the emergent operations from the specific anatomical layout and physiological activation patterns of a particular local excitatory-inhibitory circuit architecture that occurs throughout superficial layers of cortex. The circuit carries out two effective procedures on its inputs, depending on the strength of its local feedback inhibitory cells. Both procedures can be formally characterized in terms of well-studied statistical operations: clustering, and component analyses, under high-feedback-inhibition and low-feedback-inhibition conditions, respectively. The detailed nature of these clustering and component procedures is studied in the context of extensive related literatures in statistics, machine learning, and computational neuroscience. The two operations (clustering and component analysis) have not previously been shown to contain deep connections, let alone to each be derivable from a single overarching algorithmic precursor. The identification of this deep formal mathematical connection, which arose from analysis of a detailed biological circuit, represents a rare instance of novel mathematical relations arising from biological analyses. 1

neuroscience↗

bioRxiv2023

New learning principles emerge from biomimetic computational primitives

Although computational models have deepened our understanding of neuroscience, it is still highly challenging to link actual low-level physiological activity (spiking, field potentials) and biochemistry (transmitters and receptors) with high-level cognitive abilities (decision-making, working memory) nor with corresponding disorders. We introduce an anatomically-organized multi-scale model directly generating simulated physiology from which extended neural and cognitive phenomena emerge. The model produces spiking, fields, phase synchronies, and synaptic change, directly generating working memory, decisions, and categorization, all of which were then validated on extensive experimental macaque data from which the model received zero prior training of any kind. Moreover, the simulation uncovered a previously unknown neural code specifically predicting upcoming erroneous ("incongruous") behaviors, also subsequently confirmed in empirical data. The biomimetic model thus directly and predictively links novel decision and reinforcement signals, of computational interest, with novel spiking and field codes, of potential behavioral and clinical relevance.

neuroscience↗

bioRxiv2017

DLPFC Transcriptome Defines Two Molecular Subtypes of Schizophrenia

The Clinical Brain Disorders Branch of the Intramural Research Program at the National Institutes of Health assembled a large collection of frozen post-mortem human brains from individuals diagnosed with schizophrenia and other psychiatric diagnoses, as well as matched control individuals. Illumina HumanHT-12 v4 expression array data was collected from dorsolateral prefrontal cortex (DLPFC) and the data deposited at dbGaP (Study ID: phs000979). We report an analysis of the data from the 189 adult schizophrenics and 206 adult controls in that cohort.\n\nTranscripts from 633 genes are differentially expressed in the DLPFC of schizophrenics as compared to the controls at levels of statistical significance which survive Bonferroni correction. Seventeen of those genes are differentially expressed at a level of statistical significance less than 10-8 after Bonferroni correction.\n\nWeighted Gene Co-expression Network Analysis (WGCNA) of the schizophrenic subjects using differentially expressed gene module eigengenes divides them into two groups, \"type 1\" and \"type 2.\" There are 2,635 genes differentially expressed in the DLPFC of type 2 schizophrenics at a level of statistical significance which survives Bonferroni correction. Of them 221 have P-values less than 10-15 after Bonferroni correction. In the type 1 schizophrenics however, only 6 genes are differentially expressed in the DLPFC at a level of statistical significance which survives Bonferroni correction. This striking difference in their DLPFC transcriptomes emphasizes the fundamental biologic difference between these two groups of patients.

neuroscience↗