Biology subjects

Fornito, A.

Publications and source records attributed to Fornito, A..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

On the intersection between data quality and dynamical modelling of large-scale fMRI signals

Large-scale dynamics of the brain are routinely modelled using systems of nonlinear dynamical equations that describe the evolution of population-level activity, with distinct neural populations often coupled according to an empirically measured structural connection matrix. This modelling approach has been used to generate insights into the neural underpinnings of spontaneous brain dynamics, as recorded with techniques such as resting state functional MRI (fMRI). In fMRI, researchers have many degrees of freedom in the way that they can process the data and recent evidence indicates that the choice of pre-processing steps can have a major effect on empirical estimates of functional connectivity. However, the potential influence of such variations on modelling results are seldom considered. Here we show, using three popular whole-brain dynamical models, that different choices during fMRI preprocessing can dramatically affect model fits and interpretations of findings. Critically, we show that the ability of these models to accurately capture patterns in fMRI dynamics is mostly driven by the degree to which they fit global signals rather than interesting sources of coordinated neural dynamics. We show that widespread deflections can arise from simple global synchronisation. We introduce a simple two-parameter model that captures these fluctuations and which performs just as well as more complex, multi-parameter biophysical models. From our combined analyses of data and simulations, we describe benchmarks to evaluate model fit and validity. Although most models are not resilient to denoising, we show that relaxing the approximation of homogeneous neural populations by more explicitly modelling inter-regional effective connectivity can improve model accuracy at the expense of increased model complexity. Our results suggest that many complex biophysical models may be fitting relatively trivial properties of the data, and underscore a need for tighter integration between data quality assurance and model development.

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bioRxiv2020

Dynamical consequences of regional heterogeneity in the brains transcriptional landscape

Brain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles and MRI-derived estimates of myeloarchitecture. We further show that regional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional activity timescales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptional data to constrain models of large-scale brain function.

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bioRxiv2020

Genetic influences on hub connectivity of the human connectome

Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics. The mechanisms driving this organization are poorly understood. Using diffusion-weighted imaging in twins, we identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome. Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity. Finally, comparing over thirteen generative models of network growth, we show that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints. Our findings indicate that genes play a strong and preferential role in shaping the functionally valuable, metabolically costly connections between connectome hubs.

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bioRxiv2020

Scaling of gene transcriptional gradients with brain size across mouse development

The structure of the adult brain is the result of complex physical mechanisms acting through development. These physical processes, acting in threedimensional space, mean that the brains spatial embedding plays a key role in its organization, including the gradient-like patterning of gene expression that encodes the molecular underpinning of functional specialization. However, we do not yet understand how the dramatic changes in brain shape and size that occur in early development influence the brains transcriptional architecture. Here we investigate the spatial embedding of transcriptional patterns of over 1800 genes across seven time points through mousebrain development using data from the Allen Developing Mouse Brain Atlas. We find that transcriptional similarity decreases exponentially with separation distance across all developmental time points, with a correlation length scale that follows a powerlaw scaling relationship with a linear dimension of brain size. This scaling suggests that the mouse brain achieves a characteristic balance between local molecular similarity (homogeneous gene expression within a specialized brain area) and longer-range diversity (between functionally specialized brain areas) throughout its development. Extrapolating this mouse developmental scaling relationship to the human cortex yields a prediction consistent with the value measured from microarray data. We introduce a simple model of brain growth as spatially autocorrelated gene-expression gradients that expand through development, which captures key features of the mouse developmental data. Complementing the well-known exponential distance rule for structural connectivity, our findings characterize an analogous exponential distance rule for transcriptional gradients that scales across mouse brain development, providing new understanding of spatial constraints on the brains molecular patterning.

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bioRxiv2020

A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders

Numerous studies have investigated gray matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.

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bioRxiv2020

Multi-day rTMS exerts site-specific effects on functional connectivity but does not influence associative memory performance

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique with the capacity to modulate brain network connectivity and cognitive function. Recent studies have demonstrated long-lasting improvements in associative memory and resting-state connectivity following multi-day repetitive TMS (rTMS) to individualised parietal-hippocampal networks. We aimed to assess the reproducibility and network- and cognitive-specificity of these effects following multi-day rTMS. Participants received four days of 20 Hz rTMS to a subject-specific region of left lateral parietal cortex exhibiting peak functional connectivity to the left hippocampus. In a separate week, the same stimulation protocol was applied to a subject-specific region of pre-supplementary motor area (pre-SMA) exhibiting peak functional connectivity to the left putamen. We assessed changes to associative memory before and after each week of stimulation (N = 39), and changes to resting-state functional connectivity before and after stimulation in week one (N = 36). We found no evidence of long-lasting enhancement of associative memory or increased parieto-hippocampal connectivity following multi-day rTMS to the parietal cortex, nor increased pre-SMA-putamen connectivity following multi-day rTMS to pre-SMA. Instead, we observed some evidence of site-specific modulations of functional connectivity lasting [~]24 hours, with reduced connectivity within targeted networks and increased connectivity across distinct non-targeted networks. Our findings suggest a complex interplay between multi-day rTMS and network connectivity. Further work is required to develop reliable rTMS paradigms for driving changes in functional connectivity between cortical and subcortical regions.

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bioRxiv2020

Overcoming bias in gene-set enrichment analyses of brain-wide transcriptomic data

The recent availability of whole-brain atlases of gene expression, which quantify the transcriptional activity of thousands of genes across many different brain regions, has opened new opportunities to understand how gene-expression patterns relate to spatially varying properties of brain structure and function. To aid interpretation of a given neural phenotype, gene-set enrichment analysis (GSEA) has become a standard statistical methodology to identify functionally related groups of genes, annotated using systems such as the Gene Ontology (GO), that are associated with a given phenotype. While GSEA has identified groups of genes related to diverse aspects of brain structure and function in mouse and human, here we show that these results are affected by substantial statistical biases. Quantifying the falsepositive rates of individual GO categories across an ensemble of random phenotypic maps, we found an average 875-fold inflation of significant findings relative to expectation in mouse, and a 582-fold inflation in human, with some categories being judged as significant for over 20% of random phenotypes. Concerningly, the probability of a GO category being reported as significant in the extant literature increases with its estimated false-positive rate, suggesting that published reports are strongly affected by the reporting of false-positive bias. We show that the bias is primarily driven by within-category gene-gene coexpression and spatial autocorrelation, which are not accounted for in conventional GSEA nulls, and we introduce flexible ensemble-based null models that can account for these effects. Testing a range of structural connectivity and cell density phenotypes in mouse and human, we demonstrate that many GO categories that would conventionally be judged as highly significant are in fact consistent with ensembles of random phenotypes. Our results highlight major pitfalls with applying standard GSEA to brain-wide transcriptomic data and outline solutions to this pervasive problem, which is made available as an open toolbox.

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bioRxiv2020

The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

Head motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 252), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction. HighlightsO_LIWe assess how motion affects structural connectivity in 240 preprocessing pipelines C_LIO_LIMotion contamination of structural connectivity depends on preprocessing choices C_LIO_LIAdvanced motion correction tools reduce motion confounds C_LIO_LIFA edge weighting is more susceptible to motion effects than streamline count C_LI

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bioRxiv2019

Individual differences in haemoglobin concentration influence BOLD fMRI functional connectivity and its correlation with cognition

Resting-state connectivity measures the temporal coherence of the spontaneous neural activity of spatially distinct regions, and is commonly measured using BOLD-fMRI. The BOLD response follows neuronal activity, when changes in the relative concentration of oxygenated and deoxygenated haemoglobin cause fluctuations in the MRI T2* signal. Since the BOLD signal detects changes in relative concentrations of oxy/deoxy-haemoglobin, individual differences in haemoglobin levels may influence the BOLD signal-to-noise ratio in a manner independent of the degree of neural activity. In this study, we examined whether group differences in haemoglobin may confound measures of functional connectivity. We investigated whether relationships between measures of functional connectivity and cognitive performance could be influenced by individual variability in haemoglobin. Finally, we mapped the neuroanatomical distribution of the influence of haemoglobin on functional connectivity to determine where group differences in functional connectivity are manifest. In a cohort of 518 healthy elderly subjects (259 men) each sex group was median split into two groups with high and low haemoglobin concentration. Significant differences were obtained in functional connectivity between the high and low haemoglobin groups for both men and women (Cohens d 0.17 and 0.03 for men and women respectively). The haemoglobin connectome in males showed a widespread systematic increase in functional connectivity correlational scores, whilst the female connectome showed predominantly parietal and subcortical increases and temporo-parietal decreases. Despite the haemoglobin groups having no differences in cognitive measures, significant differences in the linear relationships between cognitive performance and functional connectivity were obtained for all 5 cognitive tests in males, and 4 out of 5 tests in females. Our findings confirm that individual variability in haemoglobin levels that give rise to group differences are an important confounding variable in BOLD-fMRI-based studies of functional connectivity. Controlling for haemoglobin variability as a potentially confounding variable is crucial to ensure the reproducibility of human brain connectome studies, especially in studies that compare groups of individuals, compare sexes, or examine connectivity-cognition relationships. HighlightsO_LIIndividual differences in haemoglobin significantly impact measures of functional connectivity in the elderly. C_LIO_LISignificant differences in connectivity-cognition relationships are shown between groups separated by haemoglobin values without accompanying cognitive differences. C_LIO_LIThe influence of haemoglobin on functional connectivity differs between men and women. C_LI

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bioRxiv2019

Do TMS evoked responses measured from scalp and hand represent the same cortical mechanisms?

Transcranial magnetic stimulation (TMS) is a powerful tool to investigate cortical circuits. Changes in cortical excitability following TMS are typically assessed by measuring changes in either conditioned motor-evoked potentials (MEPs) following paired-pulse TMS over motor cortex or evoked potentials measured with electroencephalography following single-pulse TMS (TEPs). However, it is unclear whether these two measures of cortical excitability index the same cortical response. Twenty-four healthy participants received local and interhemispheric paired-pulse TMS over motor cortex with eight inter-pulse intervals, suband suprathreshold conditioning intensities, and two different pulse waveforms, while MEPs were recorded from a hand muscle. TEPs were also recorded in response to single-pulse TMS using the conditioning pulse alone. The relationships between TEPs and conditioned-MEPs were evaluated using metrics sensitive to both their magnitude at each timepoint and their overall shape across time. The impacts of undesired sensory potentials resulting from TMS pulse and muscle contractions were also assessed on both measures. Both conditioned-MEPs and TEPs were sensitive to re-afferent somatosensory activity following motor-evoked responses, but over different post-stimulus timepoints. Moreover, the amplitude of low-frequency oscillations in TEPs was strongly correlated with the sensory potentials, whereas early and local high-frequency responses showed minimal relationships. Accordingly, conditioned-MEPs did not correlate with TEPs in the time domain but showed high shape similarity with the amplitude of high-frequency oscillations in TEPs. Therefore, despite the effects of sensory confounds, the TEP and MEP measures share a response component, suggesting that they index a similar cortical response and perhaps the same neuronal populations.

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bioRxiv2019

Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem.

One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire brain, from voxel-wise or regional time series. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts. Its removal via GSR improves various quality control metrics, enhances the anatomical specificity of functional connectivity patterns, and can increase the behavioural variance explained by such patterns. On the other hand, GSR alters the distribution of regional signal correlations in the brain, can induce artifactual anticorrelations, may remove real neural signal, and can distort case-control comparisons of functional-connectivity measures. Global signal fluctuations can be identified by visualizing a matrix of colour-coded signal intensities, called a carpet plot, in which rows represent voxels and columns represent time. Prior to GSR, large, periodic bands of coherent signal changes that affect most of the brain are often apparent; after GSR, these apparent global changes are greatly diminished. Here, using three independent datasets, we show that reordering carpet plots to emphasize cluster structure in the data reveals a greater diversity of spatially widespread signal deflections (WSDs) than previously thought. Their precise form varies across time and participants and GSR is only effective in removing specific kinds of WSDs. We present an alternative, iterative correction method called Diffuse Cluster Estimation and Regression (DiCER), that identifies representative signals associated with large clusters of coherent voxels. DiCER is more effective than GSR at removing diverse WSDs as visualized in carpet plots, reduces correlations between functional connectivity and head-motion estimates, reduces inter-individual variability in global correlation structure, and results in comparable or improved identification of canonical functional-connectivity networks. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER.

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bioRxiv2019

Timescales of spontaneous activity fluctuations relate to structural connectivity in the brain

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brains underlying structural-connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural-connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale-related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6000 time-series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain regions spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

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bioRxiv2018

Characterizing and minimizing the contribution of sensory inputs to TMS-evoked potentials

BackgroundTranscranial magnetic stimulation (TMS) evokes voltage deflections in electroencephalographic (EEG) recordings, known as TMS-evoked potentials (TEPs), which are increasingly used to study brain dynamics. However, the extent to which TEPs reflect activity directly evoked by magnetic rather than sensory stimulation is unclear.\n\nObjectiveTo characterize and minimize the contribution of sensory inputs to TEPs.\n\nMethodsTwenty-four healthy participants received TMS over the motor cortex using two different intensities (below and above cortical motor threshold) and waveforms (monophasic, biphasic). TMS was also applied over the shoulder as a multisensory control condition. Common sensory attenuation measures, including coil padding and noise masking, were adopted. We examined spatiotemporal relationships between the EEG responses to the scalp and shoulder stimulations at sensor and source levels. Furthermore, we compared three different filters (independent component analysis, signal-space projection with source informed reconstruction (SSP-SIR) and linear regression) designed to attenuate the impact of sensory inputs on TEPs.\n\nResultsThe responses to the scalp and shoulder stimulations were correlated in both temporal and spatial domains, especially after [~]60 ms, regardless of the intensity and stimuli waveform. Among the three filters, SSP-SIR showed the best trade-off between removing sensory-related signals while preserving data not related to the control condition.\n\nConclusionsThe findings demonstrate that TEPs elicited by motor cortex TMS reflect a combination of transcranially and peripherally evoked brain responses despite adopting sensory attenuation methods during experiments, thereby highlighting the importance of adopting sensory control conditions in TMS-EEG studies. Offline filters may help to isolate the transcranial component of the TEP from its peripheral component, but only if these components express different spatiotemporal patterns. More realistic control conditions may help to improve the characterization and attenuation of sensory inputs to TEPs, especially in early responses.

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bioRxiv2018

Functional Connectivity of Corticostriatal Circuitry and Psychosis-Like Experiences in the General Community

BackgroundPsychotic symptoms are proposed lie on a continuum, ranging from isolated psychosis-like experiences (PLEs) in non-clinical populations to frank disorder. Here, we investigate neurobiological correlates of this symptomatologic continuum by examining whether functional connectivity of dorsal corticostriatal circuitry, which is disrupted in patients and high-risk individuals, is associated with the severity of subclinical PLEs.\n\nMethodsA community sample of 672 adults with no history of psychiatric or neurological illnesses completed a battery of seven questionnaires spanning various PLE domains. Principal component analysis (PCA) estimated major dimensions of PLEs from the questionnaires. PCA dimension scores were then correlated with whole-brain voxelwise functional connectivity (FC) maps of the striatum in a subset of 353 participants who completed a resting-state neuroimaging protocol.\n\nResultsPCA identified two dimensions of PLEs accounting for 62.57% of variance in the measures, corresponding to positive and negative PLEs. Reduced FC between the dorsal striatum and prefrontal cortex correlated with higher positive PLEs. Negative PLEs correlated with increased FC between the dorsal striatum and visual and sensorimotor areas. In the ventral corticostriatal system, positive and negative PLEs were both associated with FC between the ventro-rostral putamen and sensorimotor cortices.\n\nConclusionsConsistent with past findings in patients and high-risk individuals, subthreshold positive symptomatology is associated with reduced FC of the dorsal circuit. These findings suggest that the connectivity of this circuit tracks the expression of psychotic phenomena across a broad spectrum of severity, extending from the subclinical domain to clinical diagnosis.

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bioRxiv2018

Transdiagnostic variations in impulsivity and compulsivity in obsessive-compulsive disorder and gambling disorder correlate with effective connectivity in cortical-striatal-thalamic-cortical circuits.

BackgroundIndividual differences in impulsivity and compulsivity is thought to underlie vulnerability to a broad range of disorders and are closely tied to cortical-striatal-thalamic-cortical (CSTC) function. However, whether impulsivity and compulsivity in clinical disorders is continuous with the healthy population and explains CSTC dysfunction across different disorders remains unclear.\n\nMethodsWe characterized the relationship between CSTC effective connectivity, estimated using dynamic causal modelling of functional magnetic resonance imaging data, and dimensional phenotypes of impulsivity and compulsivity in two symptomatically distinct but phenotypically related disorders, obsessive-compulsive disorder (OCD) and gambling disorder (GD). 487 online participants provided data for modelling of dimensional phenotypes. These data were combined with 34 OCD patients, 22 GD patients, and 39 healthy controls, who underwent functional magnetic resonance imaging.\n\nResultsThree core dimensions were identified: disinhibition, impulsivity, and compulsivity. Patients scores on these dimensions were continuously distributed with the healthy participants, supporting a continuum model of psychopathology. Across all participants, higher disinhibition correlated with lower bottom-up connectivity in the dorsal circuit and increased bottom-up connectivity in the ventral circuit, and higher compulsivity correlated with reduced bottom-up connectivity in the dorsal circuit. Similar changes in effective connectivity were observed with increasing clinical severity that were not accounted for by phenotypic variation, demonstrating convergence towards behaviourally and clinically relevant changes in brain dynamics. Effective connectivity did not differ as a function of traditional diagnostic labels.\n\nConclusionsCSTC dysfunction across OCD and GD is better characterized by dimensional phenotypes than diagnostic comparisons, supporting investigation of quantitative liability phenotypes.

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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.

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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.

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bioRxiv2017

The effect of stimulation interval on plasticity following repeated blocks of intermittent theta burst stimulation

IntroductionTheta burst stimulation (TBS) is a non-invasive brain stimulation paradigm capable of influencing cortical circuits in humans by inducing neural plasticity. Applying spaced blocks of TBS can affect both the direction and magnitude of plasticity, but the impact of interval duration on these interactions following intermittent TBS (iTBS) is unclear.\n\nObjectivesTo assess the effect of interval duration on plasticity magnitude/direction following spaced iTBS.\n\nMethods15 healthy participants received three different iTBS conditions on separate days: single iTBS; spaced iTBS with a 5 minute interval (iTBS-5); and spaced iTBS with a 15 minute interval (iTBS-15). Changes in cortical excitability and short-interval cortical inhibition (SICI) resulting from iTBS were assessed via motor-evoked potentials (MEPs) measured from the first dorsal interosseus muscle before and up to 60 mins following stimulation.\n\nResultsiTBS-15 increased MEP amplitude up to 60 mins post stimulation, whereas iTBS-5 decreased MEP amplitude. In contrast, MEP amplitude was not altered by single iTBS. Despite the significant effect of iTBS-15 on MEP amplitude at the group level, there was still considerable inter-individual variability, with only 53% of individuals meeting response criteria. Modulation of SICI did not differ between conditions.\n\nConclusionsThe interval duration between spaced iTBS plays an important role in determining the direction of plasticity on excitatory, but not inhibitory circuits in human motor cortex. While iTBS-15 can increase the magnitude of facilitation in some individuals compared to single iTBS, this approach still suffers from high inter-individual variability.

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