Biology subjects

Shinn, M.

Publications and source records attributed to Shinn, M..

17 recordsLinked to original sources

bioRxiv2026

Autoantibodies from recovered Guillain-Barre syndrome patients exhibit altered effector functions that prevent subsequent neuron degeneration

Guillain-Barre syndrome (GBS) is an autoimmune polyneuropathy that is the leading cause of nonpoliovirus-associated acute flaccid paralysis worldwide. In most cases, GBS occurs following an infection, most commonly Campylobacter jejuni, through the induction of antibodies recognizing bacterial ganglioside-mimicking lipooligosaccharides that cross-react with human neuronal gangliosides. This post-infectious autoimmune cascade causes neuropathy, from which patients can recover as their anti-ganglioside antibody titers diminish and immunostimulation decreases. In this study, we find 10% of clinically confirmed GBS patients maintain high titers of circulating anti-ganglioside antibodies more than one decade after recovery. These antibodies no longer cause neuropathy compared to acute sera from the same patients using a human pluripotent stem cell-derived sensory neuron model with human complement. We found both IgG subclass and glycoform differences between paired acute and recovered GBS patient sera, including anti-inflammatory modifications on isolated anti-GM1 ganglioside antibodies. Together, these data suggest that patients with GBS select for non-pathogenic variants of autoantibodies that are no longer capable of damaging their neurons, but may still protect against C. jejuni infection.

immunology↗

bioRxiv2026

The mouse superior colliculus promotes competing actions independently of sensory inputs

In many circumstances the brain makes decisions according to logistic classification: summing weighted factors in favor of each option and choosing an option according to a softmax function. Here we find that the superior colliculus (SC) contributes additively to this computation, promoting competing options independently of stimuli. We trained mice to locate an audiovisual stimulus on their left or right, and found that SC encodes the auditory, visual, and action signals in largely separate neurons in different layers. Unilateral SC inactivation did not affect sensory sensitivity but reduced contralateral actions, mostly in favor of inaction. The effects of bilateral inactivation were additive, with balanced inactivations restoring left-right balance. A simple 3-option logistic classification model captured the results, with sensory and prefrontal cortex processing sensory signals and each side of SC additively promoting the contralateral action while slightly opposing the ipsilateral action. The SC thus simultaneously promotes all competing eligible actions.

neuroscience↗

bioRxiv2026

A low-variance subspace underlies individual differences in resting state fMRI

Non-invasive whole-brain recordings of human brain activity, such as those from resting state fMRI (rs-fMRI), contain reliable individual differences across thousands of features, but it remains unknown whether this reliability can be isolated in a small number of dimensions. Here, we directly optimize test-retest reliability across many of these features and identify a low-dimensional linear subspace with very high reliability. These dimensions form personal fingerprints, allowing participants to be identified with high accuracy despite fingerprints explaining only a fraction of the total variance. Several dimensions are strongly associated with a single anatomical, demographic, or behavioral variable, and most dimensions can be predicted from the anatomical layout of cortical regions. Together, our findings suggest that stable individual signatures can be isolated from rsfMRI. These signatures reflect persistent anatomical and physiological differences, and provide a principled low-dimensional basis for biomarker discovery.

neuroscience↗

bioRxiv2025

Mapping the visual cortex with Zebra noise and wavelets

Studies of the early visual system often require characterizing the visual preferences of large populations of neurons. This task typically requires multiple stimuli such as sparse noise and drifting gratings, each of which probe only a limited set of visual features. Here we introduce a new dynamic stimulus with sharp-edged stripes called Zebra noise and a new analysis model based on wavelets, and show that in combination they are highly efficient for mapping multiple aspects of the visual preferences of thousands of neurons. We used two-photon calcium imaging to record the activity of neurons in the mouse visual cortex. Zebra noise elicited strong responses that were more repeatable than those evoked by traditional stimuli. The wavelet-based model captured the repeatable aspects of the resulting responses, providing measures of neuronal tuning for multiple stimulus features: position, orientation, size, spatial frequency, drift rate, and direction. The method proved efficient, requiring only 5 minutes of stimulus (repeated 3 times) to characterize the tuning of thousands of neurons across visual areas. In combination, the Zebra noise stimulus and the wavelet-based model provide a broadly applicable toolkit for the rapid characterization of visual representations, promising to accelerate future studies of visual function.

neuroscience↗

bioRxiv2025

Temporal and Spatial Scales of Resting-state Human Cortical Activity Throughout Lifespan

Sensorimotor and cognitive abilities undergo substantial changes throughout the human lifespan, but the corresponding changes in the functional properties of cortical networks remain poorly understood. This can be studied using temporal and spatial scales of functional magnetic resonance imaging (fMRI) signals, which provide a robust description of the topological structure and temporal dynamics of neural activity. For example, timescales of resting-state fMRI signals parsimoniously predict a significant amount of the individual variability in functional connectivity networks identified in adult human brains. In the present study, we quantified and compared temporal and spatial scales in resting-state fMRI data collected from 2,352 subjects between the ages of 5 and 100 in Developmental, Young Adult, and Aging datasets from the Human Connectome Project. For most cortical regions, we found that both temporal and spatial scales decreased with age throughout the lifespan, with the visual cortex and the limbic network consistently showing the largest and smallest scales, respectively. For some prefrontal regions, however, these two scales displayed non-monotonic trajectories and peaked around the same time during adolescence and decreased throughout the rest of the lifespan. We also found that cortical myelination increased monotonically throughout the lifespan, and its rate of change was significantly correlated with the changes in both temporal and spatial scales across different cortical regions in adulthood. These findings suggest that temporal and spatial scales in fMRI signals, as well as cortical myelination, are closely coordinated during both development and aging. Significance StatementTemporal and spatial scales of resting-state cortical activity in humans measured by fMRI largely decreased throughout the lifespan, except that for some regions in the prefrontal cortex they peaked similarly during adolescence. In addition, whereas cortical myelination consistently increased throughout the lifespan, its variation across different cortical networks and the rate of age-related changes were correlated with the dynamics of temporal and spatial scales of rs-fMRI activity, suggesting that the spatio-temporal scales of cortical activity and cortical myelination might be co-regulated during development and aging.

neuroscience↗

bioRxiv2025

Learning decouples accuracy and reaction time for rapid decisions in a transitive inference task

Transitive inference (TI) is a cognitive process in which decisions are guided by internal representations of abstract relationships. While the mechanisms underlying transitive learning have been well studied, the dynamics of the decision-making process during learning and inference remain less clearly understood. In this study, we investigated whether a modeling framework traditionally applied to perceptual decision-making--the drift diffusion model (DDM)--can account for performance in a TI transfer task involving rapid decisions that deviate from standard accuracy and response time (RT) patterns. We trained six macaque monkeys on a TI transfer task, in which they learned the implied order of a novel list of seven images in each behavioral session, indicating their decisions with saccadic eye movements or reaching movements. Consistent learning of the list structure was achieved within 200-300 trials per session. Behavioral performance exhibited a symbolic distance effect, with accuracy increasing as the ordinal distance between items grew. Notably, RTs remained relatively stable across learning, despite improvements in accuracy. We applied a generalized DDM implementation (PyDDM; Shinn et al., 2020) to jointly fit accuracy and RT data. Model fits were achieved by incorporating both an increasing evidence accumulation rate and a collapsing decision bound, successfully capturing the RT distribution shapes observed during learning. Learning and transfer were fit by varying drift rate with little change in other parameters. Eye and reaching movements showed similar dynamics, with the difference in RT accounted for mainly by non-decision time. Our results highlight a distinct dynamical regime of the DDM framework, extending its applicability to cognitive domains involving symbolic reasoning and serial relational learning.

animal behavior and cognition↗

bioRxiv2023

Phantom oscillations in principal component analysis

Principal component analysis (PCA) is a dimensionality reduction technique that is known for being simple and easy to interpret. Principal components are often interpreted as low-dimensional patterns in high-dimensional data. However, this simple interpretation of PCA relies on several unstated assumptions that are difficult to satisfy. When these assumptions are violated, non-oscillatory data may have oscillatory principal components. Here, we show that two common properties of data violate these assumptions and cause oscillatory principal components: smooth-ness, and shifts in time or space. These two properties implicate almost all neuroscience data. We show how the oscillations that they produce, which we call "phantom oscillations", impact data analysis. We also show that traditional cross-validation does not detect phantom oscillations, so we suggest procedures that do. Our findings are supported by a collection of mathematical proofs. Collectively, our work demonstrates that patterns which emerge from high-dimensional data analysis may not faithfully represent the underlying data.

neuroscience↗

bioRxiv2021

A transcriptomic axis predicts state modulation of cortical interneurons

Transcriptomics has revealed the exquisite diversity of cortical inhibitory neurons1-7, but it is not known whether these fine molecular subtypes have correspondingly diverse activity patterns in the living brain. Here, we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, but that this diversity is organized by a single factor: position along their main axis of transcriptomic variation. We combined in vivo 2-photon calcium imaging of mouse V1 with a novel transcriptomic method to identify mRNAs for 72 selected genes in ex vivo slices. We used transcriptomic clusters (t-types)4 to classify inhibitory neurons imaged in layers 1-3 using a three-level hierarchy of 5 Families, 11 Classes, and 35 t-types. Visual responses differed significantly only across Families, but modulation by brain state differed at all three hierarchical levels. Nevertheless, this diversity could be predicted from the first transcriptomic principal component, which predicted a cell types brain state modulation and correlations with simultaneously recorded cells. Inhibitory t-types with narrower spikes, lower input resistance, weaker adaptation, and less axon in layer 1 as determined in vitro8 fired more in resting, oscillatory brain states. Transcriptomic types with the opposite properties fired more during arousal. The former cells had more inhibitory cholinergic receptors, and the latter more excitatory receptors. Thus, despite the diversity of V1 inhibitory neurons, a simple principle determines how their joint activity shapes state-dependent cortical processing.

neuroscience↗

bioRxiv2021

Spatial and temporal autocorrelation weave human brain networks

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler low-dimensional statistics is largely unknown. To explore this question, we examine resting state fMRI (rs-fMRI) data using complex topology measures from network neuroscience. We show that spatial and temporal autocorrelation are reliable statistics which explain numerous measures of network topology. Surrogate timeseries with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely-used complexity measures may help link them to neurobiology.

neuroscience↗

bioRxiv2020

Transient neuronal suppression for exploitation of new sensory evidence

In noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals' behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is an important component of dynamic perceptual decision making.

neuroscience↗

bioRxiv2020

A flexible framework for simulating and fitting generalized drift-diffusion models

The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building, simulating, and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are simulated numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We show that a GDDM fit with our framework explains a classic open dataset with better accuracy and fewer parameters than several DDMs implemented using the latest methodology. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

neuroscience↗

bioRxiv2020

Generative modeling of brain maps with spatial autocorrelation

Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene ontology enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization.

neuroscience↗

bioRxiv2019

Confluence of timing and reward biases in perceptual decision-making dynamics

Although the decisions of our daily lives often occur in the context of temporal and reward structures, the impact of such regularities on decision-making strategy is poorly understood. Here, to explore how temporal and reward context modulate strategy, we trained rhesus monkeys to perform a novel perceptual decision-making task with asymmetric rewards and time-varying evidence reliability. To model the choice and response time patterns, we developed a computational framework for fitting generalized drift-diffusion models (GDDMs) which flexibly accommodates diverse evidence accumulation strategies. We found that a dynamic urgency signal and leaky integration, in combination with two independent forms of reward biases, best capture behavior. We also tested how temporal structure influences urgency by systematically manipulating the temporal structure of sensory evidence, and found that the time course of urgency was affected by temporal context. Overall, our approach identified key components of cognitive mechanisms for incorporating temporal and reward structure into decisions.

neuroscience↗

bioRxiv2017

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network (SCN) from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we use this to define, transcriptomic brain networks (TBN) by estimating gene co-expression between pairs of cortical regions. Finally, we explore the hypothesis that TBN and the SCN are coupled.\n\nTBN and SCN were correlated across connection weights and showed qualitatively similar complex topological properties. There were differences between networks in degree and distance distributions. However, cortical areas connected to each other within modules of the SCN network had significantly higher levels of whole genome co-expression than expected by chance.\n\nNodes connected in the SCN had significantly higher levels of expression and co-expression of a Human Supragranular Enriched (HSE) gene set that are known to be important for large-scale cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not completely related to the common constraint of physical distance on both networks.

neuroscience↗

bioRxiv2017

Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation

Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping, based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organisation comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs to tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.

neuroscience↗

bioRxiv2017

Adolescent Tuning Of Association Cortex In Human Structural Brain Networks

Motivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organisation of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N=297 healthy participants, aged 14-24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.

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↗