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Noninvasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function in vivo, and it has been used extensively to map the neural changes associated with various brain disorders. However, most neuroimaging techniques offer only indirect measures of underlying pathological mechanisms. The recent development of anatomically comprehensive gene expression atlases has opened new opportunities for studying the transcriptional correlates of noninvasively measured neural phenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms. Here, we provide an overview of some fundamental methods in imaging transcriptomics and outline their application to understanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidence indicates that spatial variations in gene expression are linked to normative changes in brain structure during age-related maturation and neurodegeneration that are in part associated with cell-specific gene expression markers of gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked to transcriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstrate that spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spread of disease across the brain. This growing body of work supports the utility of transcriptional atlases in testing hypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimaging phenotypes.

Human brain networks undergo pronounced changes during development. The emergence of highly connected hub regions that can support integrated brain function is central to this maturational process, with these areas undergoing a particularly protracted period of development that extends into adulthood. The location of cortical network hubs emerges early but connections to and from hubs continue to strengthen throughout childhood and adolescence. Patterns of functional coupling in cortical association hubs are immature and incomplete at birth, but gradually strengthen during development. Early establishment of hub connectivity may provide a stable substrate that is refined by changes in tissue organization and microstructure, resulting in the emergence of complex functional dynamics by adulthood.

Numerous neuroimaging techniques have been used to identify biomarkers of disease progression in Huntington's disease (HD). To date, the earliest and most sensitive of these is caudate volume; however, it is becoming increasingly evident that numerous changes to cortical structures, and their interconnected networks, occur throughout the course of the disease. The mechanisms by which atrophy spreads from the caudate to these cortical regions remains unknown. In this review, the neuroimaging literature specific to T1-weighted and diffusion-weighted magnetic resonance imaging is summarized and new strategies for the investigation of cortical morphometry and the network spread of degeneration in HD are proposed. This new avenue of research may enable further characterization of disease pathology and could add to a suite of biomarker/s of disease progression for patient stratification that will help guide future clinical trials.

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.

Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain – cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18–20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures., Measurement(s) glucose import • haemodynamic response • functional brain measurement Technology Type(s) FDG-Positron Emission Tomography • Blood Oxygen Level-Dependent Functional MRI Factor Type(s) resting human brain Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12994508

Animal neurophysiological studies have identified neural signals within dorsal frontoparietal areas that trace a perceptual decision by accumulating sensory evidence over time and trigger action upon reaching a threshold. Although analogous accumulation-to-bound signals are identifiable on extracranial human electroencephalography, their cortical origins remain unknown. Here neural metrics of human evidence accumulation, predictive of the speed of perceptual reports, were isolated using electroencephalography and related to dorsal frontoparietal network (dFPN) connectivity using diffusion and resting-state functional magnetic resonance imaging. The build-up rate of evidence accumulation mediated the relationship between the white matter macrostructure of dFPN pathways and the efficiency of perceptual reports. This association between steeper build-up rates of evidence accumulation and the dFPN was recapitulated in the resting-state networks. Stronger connectivity between dFPN regions is thus associated with faster evidence accumulation and speeded perceptual decisions. Our findings identify an integrated network for perceptual decisions that may be targeted for neurorehabilitation in cognitive disorders. How efficiently humans make perceptual decisions varies between people. Based on EEG and structural and functional MRI data, Brosnan et al. suggest a role for dorsal frontoparietal network connectivity in the speed of perceptual decisions.

Objectives: We aimed to investigate whether severity of cannabis dependence is associated with the neuroanatomy of key brain regions of the stress and reward brain circuits. Methods: To examine dependence-specific regional brain alterations, we compared the volumes of regions relevant to reward and stress, between high-dependence cannabis users (CD+, n = 25), low-dependence cannabis users (CD−, n = 20) and controls ( n = 37). Results: Compared to CD− and/or controls, the CD+ group had lower cerebellar white matter and hippocampal volumes, and deflation of the right hippocampus head and tail. Conclusion: These findings provide initial support for neuroadaptations involving stress and reward circuits that are specific to high-dependence cannabis users.

Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as ρ ≥ 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.

Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12–68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r = 0.067, pFDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r = 0.285, pspin = 0.024), but not BD (r = 0.166, pspin = 0.205) or MDD (r = −0.274, pspin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = −0.690, pspin = 0.006), BD (rho = −0.672, pspin = 0.009), and MDD (rho = −0.692, pspin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.

Understanding how the living human brain functions requires sophisticated in vivo neuroimaging technologies to characterise the complexity of neuroanatomy, neural function, and brain metabolism. Fluorodeoxyglucose positron emission tomography (FDG-PET) studies of human brain function have historically been limited in their capacity to measure dynamic neural activity. Simultaneous [18 F]-FDG-PET and functional magnetic resonance imaging (fMRI) with FDG infusion protocols enable examination of dynamic changes in cerebral glucose metabolism simultaneously with dynamic changes in blood oxygenation. The Monash vis-fPET-fMRI dataset is a simultaneously acquired FDG-fPET/BOLD-fMRI dataset acquired from n = 10 healthy adults (18–49 yrs) whilst they viewed a flickering checkerboard task. The dataset contains both raw (unprocessed) images and source data organized according to the BIDS specification. The source data includes PET listmode, normalization, sinogram and physiology data. Here, the technical feasibility of using opensource frameworks to reconstruct the PET listmode data is demonstrated. The dataset has significant re-use value for the development of new processing pipelines, signal optimisation methods, and to formulate new hypotheses concerning the relationship between neuronal glucose uptake and cerebral haemodynamics., Measurement(s)brain activityTechnology Type(s)functional magnetic resonance imaging • FDG-Positron Emission TomographySample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14977851

White matter abnormalities associated with schizophrenia have been widely reported, although the consistency of findings across studies is moderate. In this study, neuroimaging was used to investigate white matter pathology and its impact on whole-brain white matter connectivity in one of the largest samples of patients with schizophrenia. Fractional anisotropy (FA) and mean diffusivity (MD) were compared between patients with schizophrenia or schizoaffective disorder (n = 326) and age-matched healthy controls (n = 197). Between-group differences in FA and MD were assessed using voxel-based analysis and permutation testing. Automated whole-brain white matter fiber tracking and the network-based statistic were used to characterize the impact of white matter pathology on the connectome and its rich club. Significant reductions in FA associated with schizophrenia were widespread, encompassing more than 40% (234ml) of cerebral white matter by volume and involving all cerebral lobes. Significant increases in MD were also widespread and distributed similarly. The corpus callosum, cingulum, and thalamic radiations exhibited the most extensive pathology according to effect size. More than 50% of cortico-cortical and cortico-subcortical white matter fiber bundles comprising the connectome were disrupted in schizophrenia. Connections between hub regions comprising the rich club were disproportionately affected. Pathology did not differ between patients with schizophrenia and schizoaffective disorder and was not mediated by medication. In conclusion, although connectivity between cerebral hubs is most extensively disturbed in schizophrenia, white matter pathology is widespread, affecting all cerebral lobes and the cerebellum, leading to disruptions in the majority of the brain's fiber bundles.

Asymmetries of the cerebral cortex are found across diverse phyla and occur at multiple spatial scales, from the global morphology of the two hemispheres to fine-grained sulcal and gyral anatomy. Cortical asymmetries are particularly pronounced in humans and have important implications for brain function and disease. However, most studies conflate asymmetries in regional size and shape and only consider effects at fairly fine-grained resolutions, such as image voxels or a priori regions-of-interest. As such, whether asymmetries are preferentially expressed at specific spatial resolutions over others remains unclear. Here, we apply a spectral shape analysis to magnetic resonance imaging (MRI) data from three independent samples to derive a multiscale description of asymmetries in cortical shape that is distinct from regional size-related descriptors such as volume or thickness. We show that our proposed shape asymmetry signature (SAS) is a highly individualized feature, akin to a cortical fingerprint, that more accurately identifies individuals than size-based descriptors or measures of inter-regional functional coupling of brain activity. Notably, individual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries at specific scales show differences between males and females but are unrelated to the handedness. We further demonstrate that while unihemispheric shape descriptors show significant heritability at coarse scales, the SAS are determined primarily by unique environmental effects across all scales considered. Our findings thus identify coarse-scale scale asymmetries of cortical shape as being highly unique to individuals, sexually dimorphic, and largely driven by stochastic environmental influences.

Regional heterogeneity in the brain’s transcriptional landscape supports complex neuronal dynamics., 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 or MRI-derived estimates of myeloarchitecture. We further show that regional transcriptional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional-activity time scales, 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 transcriptomic data to constrain models of large-scale brain function.

Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.

Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3,004 unmedicated healthy individuals (12-68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r=.07, pFDR=.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r=.33, pspin=.01), but not BD (r=.19, pspin=.16) or MDD (r=-.22, pspin=.10). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho=-.65, pspin=.01), BD (rho=-.63, pspin=.01), and MDD (rho=-.69, pspin=.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.

Dysfunction of fronto-striato-thalamic (FST) circuits is thought to contribute to dopaminergic dysfunction and symptom onset in psychosis, but it remains unclear whether this dysfunction is driven by aberrant bottom-up subcortical signaling or impaired top-down cortical regulation. Here, we used spectral dynamic causal modelling (DCM) of resting-state functional magnetic resonance imaging (fMRI) to characterize the effective connectivity of dorsal and ventral FST circuits in a sample of 46 antipsychotic-naïve first-episode psychosis (FEP) patients and 23 controls and an independent sample of 36 patients with established schizophrenia (SCZ) patients and 100 controls. We found that midbrain and thalamic connectivity were implicated across both patient groups. Dysconnectivity in FEP patients was mainly restricted to the subcortex, with positive symptom severity being associated with midbrain connectivity. Dysconnectivity between the cortex and subcortical systems was only apparent in SCZ patients. In another independent sample of 33 healthy individuals who underwent concurrent fMRI and [18F]DOPA positron emission tomography, we found that striatal dopamine synthesis capacity was associated with the effective connectivity of nigrostriatal and striatothalamic pathways, implicating similar circuits as those associated with psychotic symptom severity in patients. Our findings thus indicate that subcortical dysconnectivity is salient in the early stages of psychosis, that cortical dysfunction may emerge later in the illness, and that nigrostriatal and striatothalamic signaling are closely related to striatal dopamine synthesis capacity, which is a robust risk marker for psychosis.

The structure of the adult brain is the result of complex physical mechanisms acting through development. These physical processes, acting in three-dimensional space, mean that the brain’s 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 brain’s transcriptional architecture. Here we investigate the spatial embedding of transcriptional patterns of over 1800 genes across seven time points through mouse-brain 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 power-law 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 brain’s molecular patterning.

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.

Key points TMS is commonly used to study excitatory/inhibitory neurotransmission in cortical circuits. Changes in cortical excitability following TMS are typically measured from hand (using EMG; limited to motor cortex) or scalp (using EEG); however, it is unclear whether these two measures represent the same activity when assessing motor cortex. We found that TMS-EMG and TMS-EEG measures of motor cortex excitability are differentially affected by sensory confounds at different time points, masking any actual relationship between them in the time domain. In the frequency domain, local high-frequency oscillations in EEG recordings were minimally confounded by sensory artefacts and demonstrated strong correlations with EMG measures of cortical excitability across time, regardless of TMS intensity or waveform. Therefore, despite the effects of sensory artefacts, the two measures of motor cortex excitability share a response component, suggesting that they index a similar cortical activity and perhaps the same neuronal population. Abstract Transcranial magnetic stimulation (TMS) is a powerful tool for investigating 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, sub- and 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 time point 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 time points. 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.

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 brain’s 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 brain’s 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 brain’s molecular patterning.

Continuous theta burst stimulation (cTBS) is thought to reduce cortical excitability and modulate functional connectivity, possibly by altering cortical inhibition at the site of stimulation. However, most evidence comes from the motor cortex and it remains unclear whether similar effects occur following stimulation over other brain regions. We assessed whether cTBS over left dorsolateral prefrontal cortex altered gamma aminobutyric acid (GABA) concentration, functional connectivity and brain dynamics at rest, and brain activation and memory performance during a working memory task. Seventeen healthy individuals participated in a randomised, sham-controlled, cross-over experiment. Before and after either real or sham cTBS, magnetic resonance spectroscopy was obtained at rest to measure GABA concentrations. Functional magnetic resonance imaging (fMRI) was also recorded at rest and during an n-back working memory task to measure functional connectivity, regional brain activity (low-frequency fluctuations), and task-related patterns of brain activity. We could not find evidence for changes in GABA concentration (P=0.66, Bayes factor [BF10]=0.07), resting-state functional connectivity (P(FWE)>0.05), resting-state low-frequency fluctuations (P=0.88, BF10=0.04), blood-oxygen level dependent activity during the n-back task (P(FWE) >0.05), or working memory performance (P=0.13, BF10=0.05) following real or sham cTBS. Our findings add to a growing body of literature suggesting the effects of cTBS are highly variable between individuals and question the notion that cTBS is a universal ‘inhibitory’ paradigm.

A series of parallel, integrated circuits link distinct regions of prefrontal cortex with specific nuclei of the striatum and thalamus. Dysfunction of these fronto-striato-thalamic systems is thought to play a major role in the pathogenesis of psychosis. In this review, we examine evidence from human and animal investigations that dysfunction of a specific dorsal fronto-striato-thalamic circuit, linking the dorsolateral prefrontal cortex, dorsal (associative) striatum, and mediodorsal nucleus of the thalamus, is apparent across different stages of psychosis, including prior to the onset of a first episode, suggesting that it represents a candidate risk biomarker. We consider how abnormalities at distinct points in the circuit may give rise to the pattern of findings seen in patient populations, and how these changes relate to disruptions in dopamine, glutamate and GABA signaling.

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.

Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different species and scales ranging from C. elegans to mouse, rat, cat, macaque, and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.

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 brain’s 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 6,000 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 region’s spontaneous activity fluctuations are closely related to their surrounding structural scaffold., Author Summary Reflecting structural and functional differences across brain regions, the spontaneous dynamics of neural activity vary correspondingly. Dynamical timescales are thought to be organized hierarchically, with slower timescales in integrative association areas, consistent with longer durations of information processing. In the mouse brain, this variation in BOLD dynamical properties follows the variation in structural connectivity strength, with more strongly connected regions exhibiting slower dynamics. Here we show a consistent variation in human cortex that holds at the level of individuals, and characterize a range of BOLD properties that vary strongly with structural connectivity strength. Our results indicate a conserved property of mouse and human brain organization in which a brain area’s spontaneous activity fluctuations are closely related to its structural connectivity strength.

Individual 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 function. However, whether impulsivity and compulsivity in clinical disorders is continuous with the healthy population and explains cortical-striatal-thalamic-cortical dysfunction across different disorders remains unclear. Here, we characterized the relationship between cortical-striatal-thalamic-cortical effective connectivity, estimated using dynamic causal modelling of resting-state functional magnetic resonance imaging data, and dimensional phenotypes of impulsivity and compulsivity in two symptomatically distinct but phenotypically related disorders, obsessive-compulsive disorder and gambling disorder. 487 online participants provided data for modelling of dimensional phenotypes. These data were combined with 34 obsessive-compulsive disorder patients, 22 gambling disorder patients, and 39 healthy controls, who underwent functional magnetic resonance imaging. Three 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 greater bottom-up connectivity in the ventral circuit, and higher compulsivity correlated with lower bottom-up connectivity in the dorsal circuit. In patients, higher clinical severity was also linked to lower bottom-up connectivity in the dorsal circuit, but these findings were independent of 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 and only weak associations were observed for functional connectivity measures. Together, our results demonstrate that cortical-striatal-thalamic-cortical dysfunction across obsessive-compulsive disorder and gambling disorder may be better characterized by dimensional phenotypes than diagnostic comparisons, supporting investigation of quantitative liability phenotypes.

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.ObjectiveTo characterize and minimize the contribution of sensory inputs to TEPs.MethodsTwenty-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.ResultsThe 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.ConclusionsThe 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.

Dysfunction of dorsal corticostriatal (CST) circuitry is thought to play an important role in psychosis. Here, we use multivariate analysis to characterize covariance between CST functional connectivity and psychosis-like experiences (PLEs) in non-clinical individuals. In 353 healthy adults (155 males), we use partial least squares (PLS) to identify latent variables (LV) describing covariance between seven PLE questionnaire measures and functional connectivity estimated between each of six striatal seed regions and the rest of the brain using multiband resting-state fMRI. Hypothesis-driven PLS of the dorsal caudate (DC) seed identified one significant LV, accounting for 23.88% of covariance, with loadings from nearly all PLE subscales. Cortical regions implicated in this LV comprise anterior cingulate and left dorsolateral prefrontal cortex. Lower connectivity between these cortical areas and the DC seed was associated with more severe PLEs. Using multivariate modeling, we identified an association between dorsal CST connectivity and PLEs in the general community that implicates similar brain regions to those identified in patient groups. Our results highlight that the severity of both positive/negative symptom-like PLEs is related with functional coupling between the DC and dorsolateral PFC, suggesting this neural circuit may play a role in mediating risk for general psychosis-related psychopathology.

BackgroundSalience network (SN) dysconnectivity has been hypothesized to contribute to schizophrenia. Nevertheless, little is known about the functional and structural dysconnectivity of SN in subjects at risk for psychosis. We hypothesized that SN functional and structural connectivity would be disrupted in subjects with At-Risk Mental State (ARMS) and would be associated with symptom severity and disease progression.MethodWe examined 87 ARMS and 37 healthy participants using both resting-state functional magnetic resonance imaging and diffusion tensor imaging. Group differences in SN functional and structural connectivity were examined using a seed-based approach and tract-based spatial statistics. Subject-level functional connectivity measures and diffusion indices of disrupted regions were correlated with CAARMS scores and compared between ARMS with and without transition to psychosis.ResultsARMS subjects exhibited reduced functional connectivity between the left ventral anterior insula and other SN regions. Reduced fractional anisotropy (FA) and axial diffusivity were also found along white-matter tracts in close proximity to regions of disrupted functional connectivity, including frontal-striatal-thalamic circuits and the cingulum. FA measures extracted from these disrupted white-matter regions correlated with individual symptom severity in the ARMS group. Furthermore, functional connectivity between the bilateral insula and FA at the forceps minor were further reduced in subjects who transitioned to psychosis after 2 years.ConclusionsOur findings support the insular dysconnectivity of the proximal SN hypothesis in the early stages of psychosis. Further developed, the combined structural and functional SN assays may inform the prognosis of persons at-risk for psychosis.

Background Cortical neuroanatomical abnormalities have been reported along a continuum between individuals with chronic schizophrenia, first-episode psychosis, clinical high risk for psychosis, and healthy individuals self-reporting subclinical psychotic-like experiences (or schizotypy). Recently, the Schizophrenia Working Group within the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium provided meta-analytic evidence for robust cortical thickness abnormalities in schizophrenia, while also indicating that these abnormalities are influenced by illness severity and treatment with antipsychotic medications. In this context, schizotypy research allows the investigation of cortical neuroanatomy associated with the expression of subclinical psychotic-like symptoms without the potential influence of a psychotic illness, its severity, or the use of antipsychotics. This study presents the first large-scale imaging meta-analysis of cortical thickness in schizotypy using standardized methods from 23 datasets worldwide. Methods Cortical thickness and surface area were assessed in MRI scans of 2,695 healthy individuals (mean [range] age of 29.1 [17–55.8], 46.3% male) who had also completed validated self-report schizotypy questionnaires. Each site processed their local T1-weighted MRI scans using FreeSurfer and, following the protocol outlined in the ENIGMA Schizophrenia Working Group study, extracted cortical thickness for 70 Desikan-Killiany (DK) atlas regions (34 regions per hemisphere + left and right hemisphere mean thickness). At each site, partial correlation analyses were performed between regional cortical thickness by ROI and total schizotypy scores in R, predicting the left, right and mean cortical thickness, adjusting for sex, age and site. Random-effects meta-analyses of partial correlation effect sizes for each of the DK atlas regions were performed using R’s metafor package. False discovery rate (pFDR < .05) was used to control for multiple comparisons. Results We found significant positive associations between subclinical psychotic-like experiences and mean cortical thickness of the medial orbitofrontal cortex (r = .077; pFDR = .006) and the frontal pole (r = .073; pFDR = .006). When assessed separately by hemisphere, meta-analysis revealed a significant positive association between subclinical psychotic-like experiences and cortical thickness of the left medial orbitofrontal cortex (r = .066; pFDR = .044), and at trend-level with the right medial orbitofrontal cortex (r = .062; pFDR = .053) and the left frontal pole (r = .062; pFDR = .053). No significant associations were observed for surface area. Discussion Worldwide cooperative analyses of large-scale brain imaging data support a profile of cortical thickness abnormalities involving prefrontal cortical regions positively related to schizotypy in healthy individuals. These findings are not secondary to potential influences of disease chronicity or antipsychotic medication on the neuroanatomical correlates of psychotic-like experiences. The directionality of the observed meta-analytical effects in schizotypy is opposite to those previously reported in patients with schizophrenia (i.e., thinner cortex). The present findings of increased thickness may indicate early microstructural deficits (e.g. in myelination) that contribute to vulnerability for psychosis. Alternatively, these may reflect mechanisms of resilience associated with the expression of subclinical manifestations of psychotic symptoms in otherwise healthy individuals.

© 2019 Elsevier Inc. Neuroethics is central to the Australian Brain Initiative's aim to sustain a thriving and responsible neurotechnology industry. Diverse and inclusive community and stakeholder engagement and a trans-disciplinary approach to neuroethics will be key to the success of the Australian Brain Initiative.

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.MethodsA 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.ResultsPCA 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.ConclusionsConsistent 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.

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.ObjectivesTo assess the effect of interval duration on plasticity magnitude/direction following spaced iTBS.Methods15 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.ResultsiTBS-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.ConclusionsThe 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.

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 signaling, 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., Author summary Some 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.

Previous research has demonstrated that individuals with schizophrenia who are homozygous at the c.267C>A single nucleotide polymorphism (rs2067477) within the cholinergic muscarinic M1 receptor (CHRM1) perform less well on the Wisconsin Card Sorting Test (WCST) than those who are heterozygous. This study sought to determine whether variation in the rs2067477 genotype was associated with differential changes in brain structure. Data from 227 patients with established schizophrenia or schizoaffective disorder were obtained from the Australian Schizophrenia Research Bank. Whole-brain voxel-based morphometry was performed to compare regional grey matter volume (GMV) between the 267C/C (N=191) and 267C/A (N=36) groups. Secondary analyses tested for an effect of genotype on cognition (the WCST was not available). Individuals who were homozygous (267C/C) demonstrated significantly reduced GMV in the right precentral gyrus compared to those who were heterozygous (267C/A). These preliminary results suggest that the rs2067477 genotype is associated with brain structure in the right precentral gyrus in individuals with schizophrenia/schizoaffective disorder. Future studies are required to replicate these results and directly link the volumetric reductions with specific cognitive processes.

In recent years, pathophysiological models of brain disorders have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disturbances of interconnected neural systems. This shift has paralleled rapid advances in connectomics, a field concerned with comprehensively mapping the neural elements and inter-connections that constitute the brain. Magnetic resonance imaging (MRI) has played a central role in these efforts, as it allows relatively cost-effective in vivo assessment of the macro-scale architecture of brain network connectivity. In this paper, we provide a brief introduction to some of the basic concepts in the field and review how recent developments in imaging connectomics are yielding new insights into brain disease, with a particular focus on Alzheimer's disease and schizophrenia. Specifically, we consider how research into circuit-level, connectome-wide and topological changes is stimulating the development of new aetiopathological theories and biomarkers with potential for clinical translation. The findings highlight the advantage of conceptualizing brain disease as a result of disturbances in an interconnected complex system, rather than discrete pathology in isolated sub-sets of brain regions.

Pathological perturbations of the brain are rarely confined to a single locus; instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease.

Background Brain abnormalities in fronto-temporal structures have been implicated in major depressive disorder (MDD). This study aims to identify their anatomical distribution and their relation to the time course of the disease. Methods A whole-brain voxel based morphometry analysis was conducted to assess gray and white matter alterations in 56 participants with a lifetime history of MDD, including currently depressed (cMDD) and remitted patients (rMDD), and 33 matched healthy controls (HC). Results Compared to HC, MDD participants showed increased white matter volume (WMV) in the uncinate fasciculus (UF) and decreased gray matter density (GMD) on the ventromedial prefrontal cortex (vmPFC). The increased WMV in UF was driven by both cMDD and rMDD groups and positively correlated with depression scores. The GMD decrease in the vmPFC resulted mainly from abnormalities in rMDD and was not correlated with depression scores. Finally, temporal UF and vmPFC white matter showed strong structural covariance suggesting functional interactions between these two brain regions. Limitations The retrospective and cross-sectional design of the study limits the generalizability of the results. Information concerning ongoing treatment did not allow the exploration of interactions between medication and observed abnormalities. The duration of the remission period could have influenced abnormalities in the subgroup of remitted patients. Conclusions Fronto-temporal alterations in MDD consist of alterations in a cortico-limbic network involving the ventromedial prefrontal cortex and temporal white matter tracts. State-like abnormalities in the UF survive remission and persist as trait-like abnormalities together with alteration in the vmPFC.

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.AbbreviationsTMStranscranial magnetic stimulationLTPlong-term potentiationLTDlong-term depressionrTMSrepetitive TMSTBStheta burst stimulationPASpaired-associative stimulationMEPmotor-evoked potentialcTBScontinuous TBSiTBSintermittent TBSNMDAn-methyl-d-aspartateCaDPcalcium dependent plasticityEEGelectroencephalographyMRImagnetic resonance imagingSTDPspike timing dependent plasticityBCMBienenstock-Cooper-MunroGABAgamma-aminobutyric-acidISIinter-stimulus interval

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 τ = 34s (partial Spearman correlation ρ = 0.58), as well as a range of related measures such as relative high frequency power (f > 0.4 Hz: ρ= −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.

Few studies have sought to identify, in a regionally unbiased way, the precise cortical and subcortical regions that are affected by white matter abnormalities in attention-deficit/hyperactivity disorder (ADHD). This study aimed to derive a comprehensive, whole-brain characterization of connectomic disturbances in ADHD.Using diffusion tensor imaging, whole-brain tractography, and an imaging connectomics approach, we characterized altered white matter connectivity in 71 children and adolescents with ADHD compared with 26 healthy control subjects. White matter differences were further delineated between patients with (n = 40) and without (n = 26) the predominantly hyperactive/impulsive subtype of ADHD.A significant network comprising 25 distinct fiber bundles linking 23 different brain regions spanning frontal, striatal, and cerebellar brain regions showed altered white matter structure in ADHD patients (p.05, family-wise error-corrected). Moreover, fractional anisotropy in some of these fiber bundles correlated with attentional disturbances. Attention-deficit/hyperactivity disorder subtypes were differentiated by a right-lateralized network (p.05, family-wise error-corrected) predominantly linking frontal, cingulate, and supplementary motor areas. Fractional anisotropy in this network was also correlated with continuous performance test scores.Using an unbiased, whole-brain, data-driven approach, we demonstrated abnormal white matter connectivity in ADHD. The correlations observed with measures of attentional performance underscore the functional importance of these connectomic disturbances for the clinical phenotype of ADHD. A distributed pattern of white matter microstructural integrity separately involving frontal, striatal, and cerebellar brain regions, rather than direct frontostriatal connectivity, appears to be disrupted in children and adolescents with ADHD.

Schizophrenia - a severe psychiatric condition characterized by hallucinations, delusions, loss of initiative and cognitive function - is hypothesized to result from abnormal anatomical neural connectivity and a consequent decoupling of the brain's integrative thought processes. The rise of in vivo neuroimaging techniques has refueled the formulation of dysconnectivity hypotheses, linking schizophrenia to abnormal structural and functional connectivity in the brain at both microscopic and macroscopic levels. Over the past few years, advances in high-field structural and functional neuroimaging techniques have made it increasingly feasible to reconstruct comprehensive maps of the macroscopic neural wiring system of the human brain, know as the connectome. In parallel, advances in network science and graph theory have improved our ability to study the spatial and topological organizational layout of such neural connectivity maps in detail. Combined, the field of neural connectomics has created a novel platform that provides a deeper understanding of the overall organization of brain wiring, its relation to healthy brain function and human cognition, and conversely, how brain disorders such as schizophrenia arise from abnormal brain network wiring and dynamics. In this review we discuss recent findings of connectomic studies in schizophrenia that examine how the disorder relates to disruptions of brain connectivity.

Functionally distinct regions of the brain are thought to possess a characteristic connectional fingerprint - a profile of incoming and outgoing connections that defines the function of that area. This observation has motivated efforts to subdivide brain areas using their connectivity patterns. However, it remains unclear whether these connectomically-defined subregions can be distinguished at the molecular level. Here, we combine high-resolution diffusion-weighted magnetic resonance imaging with transcriptomic data to show that connectomically-defined subregions of the striatum carry distinct transcriptional signatures. Using data-driven clustering of diffusion tractography, seeded from the striatum in 100 healthy individuals, we identify a tripartite organization of the caudate and putamen that comprises ventral, dorsal and caudal subregions. We then use microarray data of gene expression levels in 19 343 genes, taken from 98 tissue samples distributed throughout the striatum, to accurately discriminate the three connectomically-defined subregions with 80-90% classification accuracy using linear support vector machines. This classification accuracy was robust at the group and individual level and was superior for our parcellation of the striatum when compared with parcellations based on anatomical boundaries or other criteria. Genes contributing strongly to classification were enriched for gene ontology categories including dopamine signaling, glutamate secretion, response to amphetamine and metabolic pathways, and were implicated in risk for disorders such as schizophrenia, autism and Parkinson's disease. Our findings highlight a close link between regional variations in transcriptional activity and inter-regional connectivity in the brain, and suggest that there may be a strong genomic signature of connectomically-defined subregions of the brain.

Background The frontostriatal circuits linking different parts of the frontal cortex to subregions of the striatum are proposed to regulate different aspects of cognition, executive function, affect and reward processing. Dysregulation of these brain circuits is also known to be important in the etiology of psychotic disorders, with the magnitude of dysfunction correlating with the severity of positive symptoms. These observations suggest that the integrity of brain circuits connected to the striatum is important for antipsychotic treatment response as well as specific cognitive processes. However, not all individuals with schizophrenia benefit from antipsychotic treatment, with up to 20% of individuals considered to be treatment-resistant. These individuals also show pervasive impairments in cognition, including cognitive flexibility. Nevertheless, few studies have examined striatal connectivity in treatment-resistant schizophrenia (TRS), particularly in relation to positive symptomatology and specific cognitive deficits subserved by the striatal circuits. This study therefore aimed to (i) assess for disruptions in frontostriatal connectivity in a sample of TRS and (ii) assess the relationship between the frontostriatal circuits with positive symptoms and attentional set-shifting (cognitive flexibility) given recent associations with the dorsal striatal circuit. Methods Resting-state functional magnetic resonance imaging was used to investigate functional connectivity (FC) in 42 TRS participants prescribed clozapine (30 males, mean age=41.3(10)), and 42 healthy controls (24 males, mean age=38.4(10)). The whole striatum (caudate, putamen and nucleus accumbens) and the left and right dorsal striatum were separately seeded as regions of interest, and Pearson’s correlations between the seeds and all other voxels comprising cortical and subcortical gray matter were investigated. For brain regions that showed significant group differences in FC with the striatal seeds, Pearson’s correlations explored the relationship between the strength of connectivity with positive symptoms and attentional set-shifting (extradimensional shift errors) as measured with the CANTAB intra-/extradimensional set shift task. Results In comparison with healthy controls, TRS patients displayed significantly reduced FC between the whole striatum and the bilateral anterior cingulate, cerebellum, precuneus, right and left frontal pole and left insular/temporal pole, and reduced FC of the left and right dorsal striatum with cerebellum, and between the right dorsal striatum and bilateral cingulate and right frontal pole. Reduced FC between the whole striatum and precuneus and insular/temporal pole was associated with greater delusions of jealousy (p

The self-concept – the set of beliefs that a person has about themselves – shows significant development from adolescence to early adulthood, in parallel with brain development over the same period. We sought to investigate how age-related changes in self-appraisal processes corresponded with brain network segregation and integration in healthy adolescents and young adults. We scanned 88 participants (46 female), aged from 15 to 25 years, as they performed a self-appraisal task. We first examined their patterns of activation to self-appraisal, and replicated prior reports of reduced dorsomedial prefrontal cortex activation with older age, with similar reductions in precuneus, right anterior insula/operculum, and a region extending from thalamus to striatum. We used independent component analysis to identify distinct anterior and posterior components of the default mode network (DMN), which were associated with the self-appraisal and rest-fixation parts of the task, respectively. Increasing age was associated with reduced functional connectivity between the two components. Finally, analyses of task-evoked interactions between pairs of nodes within the DMN identified a subnetwork that demonstrated reduced connectivity with increasing age. Decreased network integration within the DMN appears to be an important higher-order maturational process supporting the emerging adult self. Keywords: Adolescent development, Connectivity, Default mode network, Functional MRI, Self

Highlights • Some brain regions are highly connected, acting as network hubs. • Structural hubs are present in an adult spatial configuration from birth. • Functional hubs emerge in primary areas and shift to association areas during childhood. • Hub connectivity, especially of frontal areas, strengthens from birth to adulthood. • Methodological differences across studies can lead to inconsistent findings., Some brain regions have a central role in supporting integrated brain function, marking them as network hubs. Given the functional importance of hubs, it is natural to ask how they emerge during development and to consider how they shape the function of the maturing brain. Here, we review evidence examining how brain network hubs, both in structural and functional connectivity networks, develop over the prenatal, neonate, childhood, and adolescent periods. The available evidence suggests that structural hubs of the brain arise in the prenatal period and show a consistent spatial topography through development, but undergo a protracted period of consolidation that extends into late adolescence. In contrast, the hubs of brain functional networks show a more variable topography, being predominantly located in primary cortical areas in early development, before moving to association areas by late childhood. These findings suggest that while the basic anatomical infrastructure of hubs may be established early, the functional viability and integrative capacity of these areas undergoes extensive postnatal maturation. Not all findings are consistent with this view however. We consider methodological factors that might drive these inconsistencies, and which should be addressed to promote a more rigorous investigation of brain network development.