Biology subjects

Fulcher, B. D.

Publications and source records attributed to Fulcher, B. D..

7 recordsLinked to original sources

bioRxiv2018

Multimodal gradients across mouse cortex

The primate cerebral cortex displays a hierarchical organization that extends from primary sensorimotor to association areas, supporting increasingly integrated function that is underpinned by a gradient of heterogeneity in the brain s microcircuits. The extent to which these properties of brain organization are unique to primate or may be conserved across mammals remains unknown. Here we report the topographic similarity of large-scale gradients in cytoarchitecture, brain-related gene expression, interneuron cell densities, and long-range axonal connectivity, which vary from primary sensory through to prefrontal areas of mouse cortex, highlighting an underappreciated spatial dimension of mouse cortical specialization. Using the T1w:T2w magnetic resonance imaging (MRI) map as a common spatial reference for comparison across species, we report interspecies agreement in a range of cortical gradients, including a significant correspondence between gene transcriptional maps in mouse cortex with their human orthologs in human cortex. The interspecies correspondence of large-scale cortical gradients suggests that a conserved anatomical organization may underlie hierarchical specialization in mammalian brains.

neuroscience↗

bioRxiv2018

A practical guide to linking brain-wide gene expression and neuroimaging data

The recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on the molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.

neuroscience↗

bioRxiv2017

Hub connectivity, neuronal diversity, and gene expression in the C. elegans connectome

Studies of nervous system connectivity, in a wide variety of species and at different scales of resolution, have identified several highly conserved motifs of network organization. One such motif is a heterogeneous distribution of connectivity across neural elements, such that some elements act as highly connected and functionally important network hubs. These brain network hubs are also densely interconnected, forming a so-called rich-club. Recent work in mouse has identified a distinctive transcriptional signature of neural hubs, characterized by tightly coupled expression of oxidative metabolism genes, with similar genes characterizing macroscale inter-modular hub regions of the human cortex. Here, we sought to determine whether hubs of the neuronal C. elegans connectome also show tightly coupled gene expression. Using open data on the chemical and electrical connectivity of 279 C. elegans neurons, and binary gene expression data for each neuron across 948 genes, we computed a correlated gene expression score for each pair of neurons, providing a measure of their gene expression similarity. We demonstrate that connections between hub neurons are the most similar in their gene expression while connections between nonhubs are the least similar. Genes with the greatest contribution to this effect are involved in glutamatergic and cholinergic signalling, and other communication processes. We further show that coupled expression between hub neurons cannot be explained by their neuronal subtype (i.e., sensory, motor, or interneuron), separation distance, chemically secreted neurotransmitter, birth time, pairwise lineage distance, or their topological module affiliation. Instead, this coupling is intrinsically linked to the identity of most hubs as command interneurons, a specific class of interneurons that regulates locomotion. Our results suggest that neural hubs may possess a distinctive transcriptional signature, preserved across scales and species, that is related to the involvement of hubs in regulating the higher-order behaviors of a given organism.\n\nAuthor summarySome elements of neural systems possess many more connections than others, marking them as network hubs. These hubs are often densely interconnected with each other, forming a so-called rich-club that is thought to support integrated function. Recent work in the mouse suggests that connected pairs of hubs show higher levels of transcriptional coupling than other pairs of brain regions. Here, we show that hub neurons of the nematode C. elegans also show tightly coupled gene expression and that this effect cannot be explained by the spatial proximity or anatomical location of hub neurons, their chemical composition, birth time, neuronal lineage or topological module affiliation. Instead, we find that elevated coexpression is driven by the identity of most hubs of the C. elegans connectome as command interneurons, a specific functional class of neurons that regulate locomotion. These findings suggest that coupled gene expression is a highly conserved genomic signature of neural hubs that may be related to the specific functional role that hubs play in broader network function.

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↗

bioRxiv2017

An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI

Estimates of functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) are sensitive to artefacts caused by in-scanner head motion. This susceptibility has motivated the development of numerous denoising methods designed to mitigate motion-related artefacts. Here, we compare popular retrospective rs-fMRI denoising methods, such as regression of head motion parameters and mean white matter (WM) and cerebrospinal fluid (CSF) (with and without expansion terms), aCompCor, volume censoring (e.g., scrubbing and spike regression), global signal regression and ICA-AROMA, combined into 19 different pipelines. These pipelines were evaluated across five different quality control benchmarks in four independent datasets associated with varying levels of motion. Pipelines were benchmarked by examining the residual relationship between in-scanner movement and functional connectivity after denoising; the effect of distance on this residual relationship; whole-brain differences in functional connectivity between high- and low-motion healthy controls (HC); the temporal degrees of freedom lost during denoising; and the test-retest reliability of functional connectivity estimates. We also compared the sensitivity of each pipeline to clinical differences in functional connectivity in independent samples of schizophrenia and obsessive-compulsive disorder. Our results indicate that (1) simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals (with or without expansion terms) is not sufficient to remove head motion artefacts; (2) aCompCor pipelines may only be viable in low-motion data; (3) volume censoring performs well at minimising motion-related artefact but a major benefit of this approach derives from the exclusion of high-motion individuals; (4) while not as effective as volume censoring, ICA-AROMA performed well across our benchmarks for relatively low cost in terms of data loss; and (5) group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy. We offer some recommendations for best practice and outline some simple analyses to facilitate transparent reporting of the degree to which a given set of findings may be affected by motion-related artefact.

neuroscience↗

bioRxiv2016

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal populations; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations in brain dynamics (also called functional connectivity), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics at each brain region by computing 6 930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag{tau} = 34s (partial Spearman correlation{rho} = 0.58), as well as a range of related measures such as relative high frequency power (f > 0.4 Hz:{rho} = -0.43). Our results indicate that the topology of inter-regional axonal connections of the mouse brain is closely related to intrinsic, spontaneous dynamics such that regions with a greater aggregate strength of incoming projections display longer timescales of activity fluctuations.

neuroscience↗

bioRxiv2016

Automatic time-series phenotyping using massive feature extraction

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of a model organism to their genotype, or measurements of brain dynamics of a patient to their disease diagnosis. Here we report a new tool, hctsa, that automatically selects interpretable and useful properties of time series by comparing over 7 700 time-series features drawn from diverse scientific literatures. Using exemplar applications to high throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to understand and quantify informative structure in time-series data.

bioinformatics↗