Biology subjects

Robinson, P. A.

Publications and source records attributed to Robinson, P. A..

17 recordsLinked to original sources

bioRxiv2026

Adversity severity shapes caregiving predictors and neural correlates of infant social behavior

Early-life adversity is associated with highly variable developmental outcomes, but identifying sources of this heterogeneity is difficult because variation in outcome is typically accompanied by variation in exposure. Animal models provide experimental leverage by standardizing features of the early environment while allowing variation in developmental experience and outcome to remain observable. Using a graded limited bedding and nesting model spanning normative rearing, moderate restriction, and severe scarcity, we asked what distinguishes social outcomes among animals exposed to the same resource restriction. Severe scarcity increased adverse maternal handling while leaving organized nurturing behaviors intact. Following the P8-P12 rearing manipulation, pups were tested outside the home cage in a P13 social approach assay. Severe scarcity reduced the proportion of pups that approached the dam, but outcomes varied substantially among scarcity litters. Greater typical maternal pup transport predicted greater approach, whereas a greater proportion of rough transport predicted poorer approach. Transport composition did not differ across rearing conditions, but its relationship with social behavior did. Amygdala intercalated-cell recruitment also covaried with social approach under severe scarcity despite limited condition-level differences in regional c-Fos. Plasma corticosterone differed across rearing conditions but did not covary detectably with the principal caregiving, behavioral, or neural measures. Maternal transport and intercalated-cell recruitment were not robustly associated, identifying separable experiential and neural correlates of behavioral variation. These findings show that standardizing an environmental constraint does not produce uniform developmental experience or outcome, and that caregiving experiences preserved across rearing conditions can distinguish social outcomes under severe scarcity.

neuroscience↗

bioRxiv2026

Maternal defense against intruders changes her subsequent maternal behavior and neural circuitry

Although female rats are typically less aggressive than male rats, lactating females will vigorously defend their nests and pups against an intruder. Much attention has been directed at the consequences of this aggression on the intruder and less on the consequences for the mother and her subsequent interactions with her pups. Here, we exposed resident Sprague-Dawley dams to the resident-intruder paradigm twice daily for five consecutive days, beginning when the dams (RES) pups were 7 days old, to assess social stress effects on maternal behavior and neurobiology. Controls were dams that had time-matched (TMC) separation from their pups but were not exposed to intruders, and naive moms which were never separated nor exposed to an intruder (CTL). We assessed the dams subsequent behavior and interactions with her pups on Day 1 and Day 5, and Fos expression after Day 5 in select regions of the prefrontal cortex, amygdala, hypothalamus and periaqueductal gray of the midbrain. In separate cohorts, after pups were weaned, the dams underwent restraint stress and plasma corticosterone assayed. PCA analysis of the dams behaviors identified three components: normal self-focused behaviors; nurturing behaviors and rough non-nurturing behaviors. Relative to CTL, RES dams exhibited more disrupted behaviors towards their pups, including, rough transport, stepping on pups, and flinging/tossing pups around the cage. In contrast, TMC Dams showed some, but fewer changes relative to CTL, suggesting that separation from pups alone does not account for all disrupted behavior in RES dams. The bulk of these behavioral effects occurred in the first 5-10 min after reunion with the pups and were seen on both the first and fifth day of testing. Of the brain regions examined, the prefrontal cortex was activated by both the defeat/intruder stress (RES) and separation stress (TMC), whereas the dorsal PAG was activated specifically by the defeat/intruder stress. The medial and basolateral amygdala exhibited differential neuronal activity between the RES defeat/intruder-exposed dams and the other two groups. The RES moms exhibited an insufficient adrenocortical response to acute restraint stress. The results suggest that amygdala-dPAG activity is important for dissociating disrupted maternal care in RES (due to defense of the nest against an intruder) from simple pup separation, both of which activate the mPFC. The experience of repeatedly defending the nest may induce subsequent disruptions in HPA responses. The amygdala-dPAG pathway may regulate aspects of stress and emotional regulation exhibited by mothers who defend their offspring against intruders.

animal behavior and cognition↗

bioRxiv2026

Cortical geometry constrains the unimodal anchors of sensory integration

The human cerebral cortex is organized along a unimodal-to-transmodal hierarchy, which provides a putative substrate for the integration of sensory signals from primary cortical fields with information from other modalities. Diverse structural and functional properties of the cortex, including myelination, gene expression, neurodevelopmental timing, and inter-regional functional connectivity are patterned along this hierarchy. One exception to this hierarchy are the dominant eigenmodes of cortical geometry, which are instead patterned along rostrocaudal, mediolateral, and dorsoventral axes, each anchored by a primary sensory area at one extreme. Recent work has reconstructed the unimodal-to-transmodal hierarchy by integrating seed-based functional connectivity from three primary sensory areas, suggesting hierarchical organization may be driven by converging sensory input. Although geometric eigenmodes do not directly express the unimodal-to-transmodal hierarchy, they may encode modality-specific sensory organization originating from primary areas. Using MRI data from the Human Connectome Project, we tested whether geometric eigenmodes encode sensory function by modelling multisensory integration directly from cortical geometry. Specifically, we substituted functional connectivity maps from each primary sensory area with the rostrocaudal, mediolateral, and dorsoventral geometric eigenmodes, following a previously validated sensory integration mapping framework. Each geometric eigenmode corresponded to a distinct sensory domain (rostrocaudal-visual: |r| = 0.516; mediolateral-somatosensory: |r| = 0.551; dorsoventral-auditory: |r| = 0.342). Together, the three geometric eigenmodes created a mapping space that differentiated between unimodal brain regions with similar accuracy as fMRI-based models ({delta} = 64.74{degrees}; p < .001); however, differences between geometric and functional maps were largest within the transmodal association cortex. Reproducing the full unimodal-to-transmodal hierarchy required additional higher-frequency geometric eigenmodes (15 eigenmodes: r2 = 0.64). These findings suggest that the unimodal anchors of sensory integration are shaped by cortical geometry, with low-frequency geometric eigenmodes providing a structural basis for sensory organization, while the transmodal apex requires additional structural information to emerge.

neuroscience↗

bioRxiv2025

Geometric constraints on the architecture of mammalian cortical connectomes

The intricate network of axonal fibres that forms the mammalian cortical connectome has a complex topology, being organized in a way that is neither completely regular nor random, as well as a characteristic topography, in which specific anatomical locations are imbued with distinctive connectivity profiles. The mechanisms that give rise to such properties remain a mystery. Here, we formulate a simple analytic model derived from neural field theory that prioritizes physical constraints on connectome architecture by assuming that connectivity is preferentially concentrated between pairs of cortical locations that facilitate the excitation of resonant geometric modes of the cortex. We show that the model outperforms existing approaches in reproducing multiple topological and topographical properties of cortical connectomes mapped at spatial scales spanning orders of magnitude in humans, chimpanzees, macaques, marmosets, and mice, as mapped with either non-invasive diffusion magnetic resonance imaging or invasive viral tract-tracing. Our findings thus point to a fundamental role of geometry in shaping the multiscale architecture of cortical connectomes that has been conserved across 90 million years of evolution.

neuroscience↗

bioRxiv2025

A unified model of cortico-hippocampal interactions through neural field theory

Numerous physical systems evolve on curved manifolds whose geometry constrains their dynamics. The human brain provides a canonical example, with large-scale neural activity evolving on distinct anatomical surfaces such as the cortex and hippocampus. Within each structure, intrinsic feedback loops and manifold geometry shape characteristic neural rhythms. However, core cognitive functions of the brain arise from reciprocal interactions between these spatially distinct neural structures. Here, we introduce a general framework for geometry-constrained coupling between spatially extended dynamical systems evolving on separate manifolds. Using quasi-conformal mapping, we construct neighbourhood-preserving interactions that enable continuous neural activity on distinct geometries to interact while preserving local neighbourhood structure. Applying this framework to neural activity evolving on cortical and hippocampal surfaces, we show that increasing inter-manifold coupling reorganises system dynamics, producing frequency shifts, mode interactions, and coupling-driven instabilities consistent with critical transitions. These effects reproduce key features of large-scale brain activity, including topographically organised synchronisation between cortex and hippocampus during healthy cognition and critical transitions to seizure-like spectral dynamics. These results identify inter-manifold coupling as a core influence on dynamics in the human brain and highlight the broader role of geometry-constrained coupling on emergent dynamics in complex spatially extended systems.

neuroscience↗

bioRxiv2025

Geometric influences on the regional organization of the mammalian brain

The mammalian brain comprises anatomically and functionally distinct regions, yet the principles governing their organization across scales remain unclear. Efforts to map regional boundaries use expert judgement or data-driven clustering of functional, connectional, and/or architectonic properties, but these approaches are often descriptive, have limited generalizability, and do not elucidate generative mechanisms. Here, we develop a geometrically constrained framework for the hierarchical, multiscale regional organization of neocortical and non-neocortical structures. The framework yields parcels at any resolution with high internal homogeneity across hundreds of anatomical, functional, cellular, and molecular properties in humans, macaques, marmosets, and mice, and it generalizes to understudied mammalian species. Finally, we demonstrate that the framework captures the essence of a reaction-diffusion mechanism in which brain geometry shapes the spatial expression of putative patterning molecules that establish a blueprint of regional identity during development. Our findings point to a conserved, universal influence of geometry on mammalian brain organization.

neuroscience↗

bioRxiv2024

Neural field theory as a framework for modeling and understanding consciousness states in the brain

Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we explore the potential of neural field theory (NFT) as a computational framework for representing consciousness states. While prior research has validated NFTs capacity to differentiate between normal and pathological states of consciousness, the relationship of its parameters to the representation of consciousness levels remains unclear. Here, we fitted a corticothalamic NFT model to EEG data collected from healthy individuals and patients with disorders of consciousness. We then comprehensively explored the correlations between the fitted NFT parameters and features extracted from both experimental and simulated EEG data, across various states of consciousness. The identified correlations not only highlight the models ability to differentiate between states of consciousness, but also shed light on the physiological bases of these states, pinpointing potential biomarkers. Our results provide valuable insights into how consciousness levels are represented within the NFT framework and into the dynamics of brain activity across various consciousness states. This underscores the potential of NFT as a useful tool for consciousness research, facilitating in-silico experimentation.

neuroscience↗

bioRxiv2024

Reply to: Eigenmodes of the brain: revisiting connectomics and geometry

In Pang, Aquino et al. (2023)1, we presented multiple lines of evidence to indicate that brain geometry plays a previously under-appreciated role in shaping dynamics. Mansour et al. raise concerns about one specific analysis, in which we showed that eigenmodes derived from the geometry of the human cortex can reconstruct diverse activity maps generated with functional magnetic resonance imaging (fMRI) better than eigenmodes derived from connectomes estimated with diffusion MRI (dMRI). Here, we address their concerns and show how our findings and conclusions remain valid.

neuroscience↗

bioRxiv2024

Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes

The brain expresses activity in complex spatiotemporal patterns, reflected in the influence of spatially distributed cytoarchitectural, biochemical, and genetic properties. The correspondence between these multimodal "brain maps" may reflect underlying causal pathways and is hence a topic of substantial interest. However, these maps possess intrinsic smoothness (spatial autocorrelation, SA) which can inflate spurious cross-correlations, leading to false positive associations. Identifying true associations requires knowledge about the distribution of correlations that arise by chance in the presence of SA. This null distribution can be generated from an ensemble of surrogate brain maps that preserve the intrinsic SA but break the correlations between maps. The present work introduces the "eigenstrapping" method, which uses a spectral decomposition of cortical and subcortical surfaces in terms of geometric eigenmodes, and then randomly rotating these modes to produce SA-preserving surrogate brain maps. It is shown that these surrogates appropriately represent the null distribution of chance pairwise correlations, with similar or superior false positive control to current state-of-the-art procedures. Eigenstrapping is fast, eschews the need for parametric assumptions about the nature of a maps SA, and works with maps defined on smooth surfaces with or without a boundary. Moreover, it generalizes to broader classes of null models than existing techniques, offering a unified approach for inference on cortical and subcortical maps, spatiotemporal processes, and complex patterns possessing higher-order correlations.

neuroscience↗

bioRxiv2023

Reply to: Commentary on Pang et al. (2023) Nature

In Pang et al. (2023)1, we identified a close link between the geometry and function of the human brain by showing that: (1) eigenmodes derived from cortical geometry parsimoniously reconstruct activity patterns recorded with functional magnetic resonance imaging (fMRI); (2) task-evoked cortical activity results from excitations of brain-wide modes with long wavelengths; (3) wave dynamics, constrained by geometry and distance-dependent connectivity, can account for diverse aspects of spontaneous and evoked brain activity; and (4) geometry and function are strongly coupled in the subcortex. Faskowitz et al. (2023)2 raise concerns about the framing of our paper and the specificity of the eigenmode reconstructions in result (1). Here, we address these concerns and show how specificity is established by using appropriate benchmarks.

neuroscience↗

bioRxiv2022

Geometric constraints on human brain function

The brains anatomy constrains its function, but precisely how remains unclear. Here, we show that human cortical and subcortical activity, measured with magnetic resonance imaging under spontaneous and diverse task-evoked conditions, can be parsimoniously understood as resulting from excitations of fundamental, resonant modes of the brains geometry (i.e., its shape) rather than modes from complex inter-regional connectivity, as classically assumed. We then use these modes to show that task-evoked activations across >10,000 brain maps are not confined to focal areas, as widely believed, but instead excite brain-wide modes with wavelengths spanning >60 mm. Finally, we confirm theoretical predictions that the close link between geometry and function is explained by a dominant role for wave-like dynamics, showing that such dynamics can reproduce numerous canonical spatiotemporal properties of spontaneous and evoked recordings. Our findings challenge prevailing views of brain function and identify a previously under-appreciated role of brain geometry that is predicted by a unifying and physically principled approach.

neuroscience↗

bioRxiv2020

Cortical Depth-Dependent Modeling of Visual Hemodynamic Responses

A physiologically based three-dimensional (3D) hemodynamic model is used to predict the experimentally observed blood oxygen level dependent (BOLD) responses versus the cortical depth induced by visual stimuli. Prior 2D approximations are relaxed in order to analyze 3D blood flow dynamics as a function of cortical depth. Comparison of the predictions with experimental data for typical stimuli demonstrates that the full 3D model matches at least as well as previous approaches while requiring significantly fewer assumptions and model parameters (e.g., there is no more need to define depth-specific parameter values for spatial spreading, peak amplitude, and hemodynamic velocity).

neuroscience↗

bioRxiv2019

Effects of Parcellation and Threshold on Brain Connectivity Measures

It is shown that the statistical properties of connections between regions of the brain and their dependence on coarse-graining and thresholding in published data can be reproduced by a simple distance-based physical connectivity model. This allows studies with differing parcellation and thresholding to be interrelated objectively, and for the results of future studies on more finely grained or differently thresholded networks to be predicted. The dependence of network measures on thresholding and parcellation implies that chosen brain regions can appear to form a small world network in many studies, even though the network of individual neurons may not be a small world network itself.

neuroscience↗

bioRxiv2018

Gamma-Band Correlations in Primary Visual Cortex

Neural field theory is used to quantitatively analyze the two-dimensional spatiotemporal correlation properties of gamma-band (30 - 70 Hz) oscillations evoked by stimuli arriving at the primary visual cortex (V1), and modulated by patchy connectivities that depend on orientation preference (OP). Correlation functions are derived analytically under different stimulus and measurement conditions. The predictions reproduce a range of published experimental results, including the existence of two-point oscillatory temporal cross-correlations with zero time-lag between neurons with similar OP, the influence of spatial separation of neurons on the strength of the correlations, and the effects of differing stimulus orientations.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC=\"FIGDIR/small/339184_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (44K):\norg.highwire.dtl.DTLVardef@12be853org.highwire.dtl.DTLVardef@1a1b228org.highwire.dtl.DTLVardef@b785dcorg.highwire.dtl.DTLVardef@b53a2a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIncorporate orientation preference map into patchy connectivities of neurons in V1.\nC_LIO_LIGeneralize spatiotemporal correlation function of neural activity to 2D spatially.\nC_LIO_LIReproduce experimental results: synchronization of neural activities in gamma band.\nC_LIO_LITemporal correlation between 2 measurements decreases with increasing separation.\nC_LIO_LIPredict 2D spatial map of temporal correlation strength between 2 measurement sites.\nC_LI

neuroscience↗

bioRxiv2018

Pathological Pattern Formation in Absence Epilepsy: A Theoretical Framework to Unify Global and Focal Aspects

A physiology-based corticothalamic model is investigated with focal spatial heterogeneity, to unify global and focal aspects of absence epilepsy. Numerical and analytical calculations are employed to investigate the emergent spatiotemporal dynamics induced by focal activity as well as their underlying dynamical mechanisms. The spatiotemporal dynamics can be categorized into three scenarios: suppression, localization, and generalization of the focal activity, as summarized from a phase diagram vs. focal width and characteristic axon range. The corresponding temporal frequencies and spatial extents of wave activity during seizure generalization and localization agree well with experimental observations of global and focal aspects of absence epilepsy, respectively. The emergent seizure localization provide a biophysical explanation of the temporally higher frequency but spatially more localized cortical waves observed in genetic rat models that display characteristics of human absence epilepsy. Predictions are also presented for further experimental test.\n\nAuthor SummaryAbsence epilepsy is characterized by a sudden paroxysmal loss of consciousness accompanied by oscillatory activity propagating over many brain areas. Although primary generalized absence seizures are supported by the global corticothalamic system, converging experimental evidence supports a focal theory of absence epilepsy. Here we propose a dynamical mechanism to unify the global and focal aspects of absence epilepsy, with focal absence seizures associated with seizure localization, and the global ones associated with seizure generalization. Our corticothalamic model is used to investigate how seizure rhythms and spatial extents are related in these two different aspects of absence epilepsy. The results account for the difference of the experimentally observed seizure rhythms and spatial extents between humans and genetic rat models, which has previously been used to argue against the validity of such rats as animal models of absence epilepsy in humans.

neuroscience↗

bioRxiv2017

NeuroField: Theory and Simulation of Multiscale Neural Field Dynamics

A user ready, portable, documented software package, NFTsim, is presented to facilitate numerical simulations of a wide range of brain systems using continuum neural field modeling. NFTsim enables users to simulate key aspects of brain activity at multiple scales. At the microscopic scale, it incorporates characteristics of local interactions between cells, neurotransmitter effects, synaptodendritic delays and feedbacks. At the mesoscopic scale, it incorporates information about medium to large scale axonal ranges of fibers, which are essential to model dissipative wave transmission and to produce synchronous oscillations and associated cross-correlation patterns as observed in local field potential recordings of active tissue. At the scale of the whole brain, NFTsim allows for the inclusion of long range pathways, such as thalamocortical projections, when generating macroscopic activity fields. The multiscale nature of the neural activity produced by NFTsim has the potential to enable the modeling of resulting quantities measurable via various neuroimaging techniques. In this work, we give a comprehensive description of the design and implementation of the software. Due to its modularity and flexibility, NFTsim enables the systematic study of an unlimited number of neural systems with multiple neural populations under a unified framework and allows for direct comparison with analytic and experimental predictions. The code is written in C++ and bundled with Matlab routines for a rapid quantitative analysis and visualization of the outputs. The output of NFTsim is stored in plain text file enabling users to select from a broad range of tools for offline analysis. This software enables a wide and convenient use of powerful physiologically-based neural field approaches to brain modeling. NFTsim is distributed under the Apache 2.0 license.

neuroscience↗

bioRxiv2017

Biophysical modeling of neural plasticity induced by transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a widely used noninvasive brain stimulation method capable of inducing plastic reorganisation of cortical circuits in humans. Changes in neural activity following TMS are often attributed to synaptic plasticity (e.g long-term potentiation and depression; LTP/LTD). However, the precise way in which synaptic processes such as LTP/LTD modulate the activity of large populations of neurons, as stimulated en masse by TMS, are unclear. The recent development of biophysically-informed models, which capture the physiological properties of TMS-induced plasticity using mathematics, provide an excellent framework for reconciling synaptic and macroscopic plasticity. In this article, we overview the TMS paradigms used to induce plasticity, and their limitations. We then describe the development of biophysically-based numerical models of the mechanisms underlying LTP/LTD on population-level neuronal activity, and the application of these models to TMS plasticity paradigms, including theta burst and paired associative stimulation. Finally, we outline how modeling can complement experiment to improve mechanistic understandings and optimize outcomes of TMS-induced plasticity.\n\nAbbreviations

neuroscience↗