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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.

The clinical heterogeneity of autism spectrum disorders majorly challenges their genetic study. Autism spectrum disorders symptoms occur in milder forms in the general population, as autistic-like traits, and share genetic factors with autism spectrum disorders. Here, we investigate the genetics of individual autistic-like traits to improve our understanding of autism spectrum disorders. We meta-analysed four population-based genome-wide association studies investigating four autistic-like traits – ‘attention-to-detail’, ‘imagination’, ‘rigidity’ and ‘social-skills’ ( n = 4600). Using autism spectrum disorder summary statistics from the Psychiatric Genomic Consortium ( N = 46,350), we applied polygenic risk score analyses to understand the genetic relationship between autism spectrum disorders and autistic-like traits. Using MAGMA, we performed gene-based and gene co-expression network analyses to delineate involved genes and pathways. We identified two novel genome-wide significant loci – rs6125844 and rs3731197 – associated with ‘attention-to-detail’. We demonstrated shared genetic aetiology between autism spectrum disorders and ‘rigidity’. Analysing top variants and genes, we demonstrated a role of the immune-related genes RNF114, CDKN2A, KAZN, SPATA2 and ZNF816A in autistic-like traits. Brain-based genetic expression analyses further linked autistic-like traits to genes involved in immune functioning, and neuronal and synaptic signalling. Overall, our findings highlight the potential of the autistic-like trait–based approach to address the challenges of genetic research in autism spectrum disorders. We provide novel insights showing a potential role of the immune system in specific autism spectrum disorder dimensions. Lay abstract Autism spectrum disorders are complex, with a strong genetic basis. Genetic research in autism spectrum disorders is limited by the fact that these disorders are largely heterogeneous so that patients are variable in their clinical presentations. To address this limitation, we investigated the genetics of individual dimensions of the autism spectrum disorder phenotypes, or autistic-like traits. These autistic-like traits are continuous variations in autistic behaviours that occur in the general population. Therefore, we meta-analysed data from four different population cohorts in which autistic-like traits were measured. We performed a set of genetic analyses to identify common variants for autistic-like traits, understand how these variants related to autism spectrum disorders, and how they contribute to neurobiological processes. Our results showed genetic associations with specific autistic-like traits and a link to the immune system. We offer an example of the potential to use a dimensional approach when dealing with heterogeneous, complex disorder like autism spectrum disorder. Decomposing the complex autism spectrum disorder phenotype in its core features can inform on the specific biology of these features which is likely to account to clinical variability in patients.

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.

Cognitive impairments are a core feature of first-episode psychosis (FEP), arising before illness onset and antipsychotic exposure. Individuals with chronic psychosis experience poorer physical health while taking antipsychotic medication, but health disparities may be evident at FEP onset, prior to antipsychotic exposure. Given the links between cognition and physical health in healthy populations, the aim was to explore whether cognition and physical health are associated in FEP, which could inform early physical health interventions for cognition in FEP. Participants were aged 15 to 25 and included 86 individuals experiencing FEP with limited antipsychotic exposure and duration of untreated psychosis of ≤six months, and 43 age- and sex-matched controls. Individuals with FEP performed significantly poorer than controls in most cognitive domains (Cohen's d = 0.38 to 1.59). Groups were similar in metabolic health measures, excluding a significantly faster heart rate in FEP (d = 0.68). Through hierarchical regression analyses, we found that in the overall sample, BMI was negatively related to current IQ after controlling for education and group (FEP/control). Relationships between BMI and cognition were consistent across the FEP and healthy control groups. In FEP, current IQ and working memory were negatively correlated with lipid profiles. Findings suggest that in FEP, impaired cognition is exhibited earlier than physical health problems, and that compared to controls, similar relationships with cognition are demonstrated. Causal pathways and trajectories of relationships between health and cognition in FEP require investigation, especially as antipsychotic medications are introduced. The findings have implications for cognitive and health interventions.

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.

BackgroundAltered functional connectivity (FC) is a common finding in resting-state functional Magnetic Resonance Imaging (rs-fMRI) studies of people with psychosis, yet how FC disturbances evolve in the early stages of illness, and how antipsychotics may influence the temporal evolution of these disturbances, remains unclear. Here, we scanned first episode psychosis (FEP) patients who were and were not exposed to antipsychotic medication during the first six months of illness at baseline, three months, and 12 months, to characterize how FC changes over time and in relation to medication use.MethodsSixty-two antipsychotic-naïve patients with FEP received either an atypical antipsychotic or a placebo pill over a treatment period of 6 months. Both FEP groups received intensive psychosocial therapy. A healthy control group (n=27) was also recruited. A total of 202 rs-fMRI scans were obtained across three timepoints: baseline, 3-months and 12-months. Our primary aim was to differentiate patterns of FC in antipsychotic-treated and antipsychotic-naive patients within the first 3 months of treatment, and to examine associations with clinical and functional outcomes. A secondary aim was to investigate long-term effects at the 12-month timepoint.ResultsAt baseline, FEP patients showed widespread functional dysconnectivity in comparison to controls, with reductions predominantly affecting interactions between the default mode network (DMN), limbic systems, and the rest of the brain. From baseline to 3 months, patients receiving placebo showed increased FC principally within the same systems, and some of these changes correlated with improved clinical outcomes. Antipsychotic exposure was associated with increased FC primarily between the thalamus and the rest of the brain. At the 12-month follow-up, antipsychotic treatment was associated with a prolonged increase of FC primarily in the DMN and limbic systems.Conclusions and RelevanceAntipsychotic-naïve FEP patients show widespread functional dysconnectivity at baseline, followed by an early normalization of DMN and paralimbic dysfunction in patients receiving a psychosocial intervention only. Antipsychotic exposure is associated with distinct FC changes, principally concentrated on thalamo-cortical and limbic networks.

The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

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.

Objective Synthetic cannabinoids (SCs) have become increasingly popular in recent years, especially among adolescents. The first aim of the current study was to examine resting-state functional connectivity (rsFC) in SC users compared to controls. Our second aim was to examine the influence of comorbid attention-deficit/hyperactivity disorder (ADHD) symptomatology on rsFC changes in SC users compared to controls. Methods Resting-state functional magnetic resonance imaging (fMRI) analysis included 25 SC users (14 without ADHD and 11 with ADHD combined type) and 12 control subjects. Results We found (i) higher rsFC between the default mode network (DMN) and salience network, dorsal attention network and cingulo-opercular network, and (ii) lower rsFC within the DMN and between the DMN and visual network in SC users compared to controls. There were no significant differences between SC users with ADHD and controls, nor were there any significant differences between SC users with and without ADHD. Conclusions We found the first evidence of abnormalities within and between resting state networks in adolescent SC users without ADHD. in contrast, SC users with ADHD showed no differences compared to controls. These results suggest that comorbidity of ADHD and substance dependence may show different rsFC alterations than substance use alone., Ege University Science and Technology Application and Research CenterEge University [2015 EGEBAM 001]; TUBITAK-BIDEB 2219-International Postdoctoral Research Fellowship Programme [53325897-115.02-39691], This research was funded by Ege University Science and Technology Application and Research Center (grant number 2015 EGEBAM 001) which had no role in the design of the study, collection and analysis of data and decision to publish. N. Zorlu received financial support from TUBITAK-BIDEB 2219-International Postdoctoral Research Fellowship Programme (Grant no. 53325897-115.02-39691) for this study.

BackgroundASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project is one of the first large-scale, family-based studies to take a truly transdiagnostic approach to ASD and ADHD. Using a comprehensive phenotyping protocol capturing dimensional traits central to ASD and ADHD, the MAGNET project aims to identify data-driven clusters across ADHD-ASD spectra using deep phenotyping of symptoms and behaviours; investigate the degree of familiality for different dimensional ASD-ADHD phenotypes and clusters; and map the neurocognitive, brain imaging, and genetic correlates of these data-driven symptom-based clusters.MethodsThe MAGNET project will recruit 1,200 families with children who are either typically developing, or who display elevated ASD, ADHD, or ASD-ADHD traits, in addition to affected and unaffected biological siblings of probands, and parents. All children will be comprehensively phenotyped for behavioural symptoms, comorbidities, neurocognitive and neuroimaging traits and genetics.ConclusionThe MAGNET project will be the first large-scale family study to take a transdiagnostic approach to ASD-ADHD, utilising deep phenotyping across behavioural, neurocognitive, brain imaging and genetic measures.

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.

Imaging studies of young people with a family history of alcohol use disorder (AUD) have found structural and/or functional differences within and between anatomically distributed and functionally specialised systems throughout the brain. Differences in brain connectivity among adolescents with a family history of AUD may account for the increased risk of later alcohol use problems; however, to date, no prospective studies have directly examined this hypothesis across the entire connectome in a regionally unbiased way. Our analysis included 52 adolescents (Mage = 16.5 years ± 0.6 SD) assessed with diffusion-weighted magnetic resonance imaging, of whom 20 had a family history of AUD and 32 did not. All participants were followed-up 2.3 years later and completed a questionnaire measuring past year alcohol use and alcohol-related harms. Subject-specific connectomic maps of structural connectivity were constructed using two parcellation schemes (82-node anatomical and 530-node random) and five measures of connectivity weight (streamline count, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity), and a connectome-wide network-based statistic analysis was used to compare group differences at each and every connection between adolescents with and without a family history of AUD. Baseline connectivity measures did not differentiate these groups, and we did not find an association between baseline connectivity measures and alcohol outcomes at follow-up. These findings suggest that atypical inter-regional structural connectivity may not contribute to the risk of developing alcohol use problems in this particular age group, or during this particular period of development.

Alcohol consumption is common in adolescence, a time when the human brain undergoes substantial development, raising concerns about the neurodevelopmental impact of drinking alcohol, especially at high levels. Risky drinking may adversely affect the developing white matter, comprised of axonal fibre pathways that integrate anatomically distributed, and functionally specialised, neural systems. We used diffusion-weighted magnetic resonance imaging (MRI) to perform the first prospective, comprehensive and regionally unbiased connectome-wide analysis of longitudinal changes in inter-regional structural connectivity between 16.5 and 18.8 years of age, comparing adolescents who reported frequent risky drinking (n = 20) with those who reported limited risky drinking (n = 19) across this developmental period. We found no difference in the development of structural connectivity between these groups, regardless of whether inter-regional connections were quantified using streamline count, fractional anisotropy, mean diffusivity, axial diffusivity, or radial diffusivity. These findings suggest that risky drinking may have limited adverse effects on the development of inter-regional structural connectivity during mid to late adolescence.

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.

Changes in brain volume are a common finding in Magnetic Resonance Imaging (MRI) studies of people with psychosis and numerous longitudinal studies suggest that volume deficits progress with illness duration. However, a major unresolved question concerns whether these changes are driven by the underlying illness or represent iatrogenic effects of antipsychotic medication. In this study, 62 antipsychotic-naïve patients with first-episode psychosis (FEP) received either a second-generation antipsychotic (risperidone or paliperidone) or a placebo pill over a treatment period of 6 months. Both FEP groups received intensive psychosocial therapy. A healthy control group (n = 27) was also recruited. Structural MRI scans were obtained at baseline, 3 months and 12 months. Our primary aim was to differentiate illness-related brain volume changes from medication-related changes within the first 3 months of treatment. We secondarily investigated long-term effects at the 12-month timepoint. From baseline to 3 months, we observed a significant group x time interaction in the pallidum (p

Objectives Age and female sex are risk factors for dementia, and menopause is associated with cognitive dysfunction. Previous work largely considered the effects of sex and menopause as being independent of age. We studied whether age interacts with sex or menopause in explaining imaging biomarkers of dementia during midlife. Methods In this cross-sectional study of UK Biobank participants with brain magnetic resonance imaging (MRI), we explored the interaction of age with sex or menopausal status in explaining total brain volume (TBV), gray matter volume (GMV), white matter volume (WMV), white matter hyperintensity volume (WMHV), regional cortical volume , and subcortical volume. Results Data were available for 1827 postmenopausal women, 230 pre/perimenopausal women and 2165 men (median age 63.3 years). There was a significant interaction between age and sex (P = .024) for TBV, where the inverse association age with TBV was steeper in women (β = –5.35 mL/year) than in men (β = –4.77 mL/year). Similar age–sex interactions were also observed for GMV and WMV. In women, there was a significant interaction between age and menopausal status (P = .007) where the inverse association of age with TBV was steeper in postmenopausal (β = –5.89 mL/year) than in pre/perimenopausal women (β = –1.61 mL/year). Similar age–menopause interactions were found in predicting lower GMV and higher WMHV. Differences in the direction of these age–sex and age–menopause interactions were found for regional cortical and subcortical brain volumes. Conclusion Sex and menopause both interact with age during midlife in explaining MRI biomarkers of dementia. Further work is required to understand the mechanisms driving these interactions to develop strategies for delaying dementia.

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.

Changes in brain volume are a common finding in Magnetic Resonance Imaging (MRI) studies of people with psychosis and numerous longitudinal studies suggest that volume deficits progress with illness duration. However, a major unresolved question concerns whether these changes are driven by the underlying illness or represent iatrogenic effects of antipsychotic medication. Here, we report MRI findings from a triple-blind randomised placebo-controlled study where 62 antipsychotic-naïve patients with first episode psychosis (FEP) received either an atypical antipsychotic or a placebo pill over a treatment period of 6 months. Both FEP groups received intensive psychosocial therapy. A healthy control group (n=27) was also recruited. Structural MRI scans were obtained at baseline, 3-months and 12- months. Our primary aim was to differentiate illness-related brain volume changes from medication-related changes within the first 3 months of treatment. We secondarily investigated long-term effects at the 12-month timepoint. From baseline to 3 months, we observed a significant group x time interaction in the pallidum (p < 0.05 FWE-corrected), such that patients receiving antipsychotic medication showed increased volume, patients on placebo showed decreased volume, and healthy controls showed no change. In patients, a greater increase in pallidal grey matter volume over 3 months was associated with a greater reduction in symptom severity. We additionally found preliminary evidence for illness- related volume reductions in prefrontal cortices at 12 months and medication-related volume reductions in cerebellum at both 3-months and 12-months. Our findings indicate that psychotic illness and antipsychotic exposure exert distinct and spatially distributed effects on brain volume. Our results align with prior work in suggesting that the therapeutic efficacy of antipsychotic medications may be primarily mediated through their effects on the basal ganglia.

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.

This triple-blind (participants, clinicians, and researchers) randomized controlled noninferiority trial examined whether intensive psychosocial intervention (cognitive-behavioral case management, CBCM) for first-episode psychosis (FEP) in 15–25 year-olds managed in a specialized early intervention for psychosis service was noninferior to usual treatment of antipsychotic medication plus CBCM delivered during the first 6 months of treatment. To maximize safety, participants were required to have low levels of suicidality and aggression, a duration of untreated psychosis (DUP) of less than 6 months, and be living in stable accommodation with social support. The primary outcome was level of functioning as assessed by the Social and Occupational Functioning Scale (SOFAS) at 6 months. Ninety young people were randomized by computer, 46 to placebo, and 44 antipsychotic medication and 33% of those who commenced trial medication completed the entire 6-month trial period. On the SOFAS, both groups improved, and group differences were small and clinically trivial, indicating that treatment with placebo medication was no less effective than conventional antipsychotic treatment (mean difference = −0.2, 2-sided 95% confidence interval = −7.5 to 7.0, t = 0.060, P = .95). Within the context of a specialized early intervention service, and with a short DUP, the immediate introduction of antipsychotic medication may not be required for all cases of FEP in order to see functional improvement. However, this finding can only be generalized to a very small proportion of FEP cases at this stage, and a larger trial is required to clarify whether antipsychotic-free treatment can be recommended for specific subgroups of those with FEP. Trial Registration: ACTRN12607000608460 (www.anzctr.org.au).

Estimates of functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) are highly sensitive to artefacts caused by in-scanner head motion. This susceptibility has motivated the development of numerous denoising methods designed to mitigate motion-related artefacts. Here, we compare 8 popular retrospective rs-fMRI denoising methods, including methods such as regression of head motion parameters (with or without expansion terms), aCompCor, volume censoring (e.g., scrubbing and spike regression), global signal regression and ICA-AROMA, combined into 16 different pipelines. These pipelines were evaluated across five different quality control benchmarks in three independent datasets that were characterized by both high and low levels of motion. Pipelines were benchmarked by examining the residual relationship between in-scanner movement and functional connectivity after denoising; the effect of distance on this residual relationship; whole-brain differences in functional connectivity between high- and low-motion healthy controls (HC); the temporal degrees of freedom lost during denoising; and the test-retest reliability of functional connectivity estimates. We also compared the sensitivity of each pipeline to clinical differences in functional connectivity in comparisons between people with schizophrenia (SCZ; n = 50) and HCs (n = 121) and people with obsessive-compulsive disorder (OCD; n = 34) and HCs (n = 39). Our results indicate that (1) simple linear regression of regional fMRI time series against head motion parameters (with or without expansion terms) is not sufficient to remove head motion artefacts; (2) aCompCor pipelines can exacerbate motion artefacts in low-motion data; (3) the primary benefit of volume censoring comes from the exclusion of high-motion individuals rather than censoring of data in remaining participants; and (4) that ICA-AROMA consistently performed well across all benchmarks and datasets, particularly when applied after the exclusion of high-motion individuals. ICA-AROMA was also the most sensitive to clinical differences in case-control analyses, suggesting that its denoising efficacy is associated with enhanced power for detecting pathophysiological effects. Crucially, the comparison between HC and SCZ revealed that the specific choice of noise correction pipeline had a major effect on the findings, affecting both the location and direction of group differences. Putative increases in functional connectivity in patients only emerged in pipelines incorporating either global signal regression or aCompCor. Thus, group comparisons in functional connectivity are highly dependent on preprocessing strategy. We offer some recommendations for best practice and outline some simple analyses to facilitate transparent reporting of the degree to which a given set of findings may be affected by motion-related artefact.

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.

Key Points Question Are there different patterns of brain connectivity in antipsychotic-treated and antipsychotic-naive patients with psychosis, and how do these patterns evolve over time? Findings In this secondary analysis of a triple-blind, longitudinal, placebo-controlled, randomized clinical trial in antipsychotic-naive patients, patients showed widespread brain dysconnectivity at baseline relative to healthy controls. Patients treated with therapy and placebo or with therapy and antipsychotics showed evidence of circuit-specific and treatment-specific normalization of connectivity over time. Meaning In this study, some prominent baseline connectivity differences in first-episode psychosis normalized with both psychosocial therapy and placebo early in the illness course, with antipsychotics exerting circuit-specific effects., This secondary analysis of a randomized clinical trial investigates differential patterns of brain connectivity in antipsychotic-treated and antipsychotic-naive patients with psychosis and how these patterns change over time., Importance Altered functional connectivity (FC) is a common finding in resting-state functional magnetic resonance imaging (rs-fMRI) studies of people with psychosis, yet how FC disturbances evolve in the early stages of illness, and how antipsychotic treatment influences these disturbances, remains unknown. Objective To investigate longitudinal FC changes in antipsychotic-naive and antipsychotic-treated patients with first-episode psychosis (FEP). Design, Setting, and Participants This secondary analysis of a triple-blind, randomized clinical trial was conducted over a 5-year recruitment period between April 2008 and December 2016 with 59 antipsychotic-naive patients with FEP receiving either a second-generation antipsychotic or a placebo pill over a treatment period of 6 months. Participants were required to have low suicidality and aggression, to have a duration of untreated psychosis of less than 6 months, and to be living in stable accommodations with social support. Both FEP groups received intensive psychosocial therapy. A healthy control group was also recruited. Participants completed rs-fMRI scans at baseline, 3 months, and 12 months. Data were analyzed from May 2019 to August 2020. Interventions Resting-state functional MRI was used to probe brain FC. Patients received either a second-generation antipsychotic or a matched placebo tablet. Both patient groups received a manualized psychosocial intervention. Main Outcomes and Measures The primary outcomes of this analysis were to investigate (1) FC differences between patients and controls at baseline; (2) FC changes in medicated and unmedicated patients between baseline and 3 months; and (3) associations between longitudinal FC changes and clinical outcomes. An additional aim was to investigate long-term FC changes at 12 months after baseline. These outcomes were not preregistered. Results Data were analyzed for 59 patients (antipsychotic medication plus psychosocial treatment: 28 [47.5%]; mean [SD] age, 19.5 [3.0] years; 15 men [53.6%]; placebo plus psychosocial treatment: 31 [52.5%]; mean [SD] age, 18.8 [2.7]; 16 men [51.6%]) and 27 control individuals (mean [SD] age, 21.9 [1.9] years). At baseline, patients showed widespread functional dysconnectivity compared with controls, with reductions predominantly affecting interactions between the default mode network, limbic systems, and the rest of the brain. From baseline to 3 months, patients receiving placebo showed increased FC principally within the same systems; some of these changes correlated with improved clinical outcomes (canonical correlation analysis R = 0.901; familywise error–corrected P = .005). Antipsychotic exposure was associated with increased FC primarily between the thalamus and the rest of the brain. Conclusions and Relevance In this secondary analysis of a clinical trial, antipsychotic-naive patients with FEP showed widespread functional dysconnectivity at baseline, followed by an early normalization of default mode network and cortical limbic dysfunction in patients receiving placebo and psychosocial intervention. Antipsychotic exposure was associated with FC changes concentrated on thalamocortical networks. Trial Registration ACTRN12607000608460

Overvaluation of palatable food is a primary driver of obesity, and is associated with brain regions of the reward system. However, it remains unclear if this network is specialized in food reward, or generally involved in reward processing. We used functional magnetic resonance imaging (fMRI) to characterize functional connectivity during processing of food and monetary rewards. Thirty-nine adults with excess weight and 37 adults with normal weight performed the Willingness to Pay for Food task and the Monetary Incentive Delay task in the fMRI scanner. A data-driven graph approach was applied to compare whole-brain, task-related functional connectivity between groups. Excess weight was associated with decreased functional connectivity during the processing of food rewards in a network involving primarily frontal and striatal areas, and increased functional connectivity during the processing of monetary rewards in a network involving principally frontal and parietal areas. These two networks were topologically and anatomically distinct, and were independently associated with BMI. The processing of food and monetary rewards involve segregated neural networks, and both are altered in individuals with excess weight.

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.

Background Negative symptoms can be seen to represent a continuum from subclinical manifestations in the general population to severe symptoms in schizophrenia. Neuroanatomical studies show evidence of fronto-striatal structural abnormalities linked to negative symptoms in patients with schizophrenia (Walton et al. 2018). However, it remains an open question whether these structural associations are also observed in ostensibly healthy individuals reporting subclinical negative symptoms. The present study used structural T1-weighted brain imaging data from the ENIGMA Schizotypy Working Group to investigate the relationship between subclinical negative symptoms and fronto-striatal structural measures. Methods We included 2,235 healthy unmedicated individuals with varying levels of schizotypy from 17 centers around the world. The complete sample had a weighted mean (range) age of 29.2 (15.9–39.6) and 59.4% (51–100) were male. Subclinical negative symptoms were assessed at each site separately using factor scores from self-report schizotypy questionnaires (i.e., the Community Assessment of Psychic Experiences, the Oxford-Liverpool Inventory of Feelings and Experiences, or the Schizotypal Personality Questionnaire). Based on prior studies in schizophrenia, we obtained cortical thickness from 22 frontal regions-of-interest (ROIs) and subcortical volumes from 6 striatal ROIs using FreeSurfer. We performed meta-analyses of effect sizes (standardized regression coefficients) from a model predicting mean cortical thickness by subclinical negative symptom scores, adjusting for age, sex, and site. The same analysis was repeated for subcortical volumes including intracranial volume as additional covariate. Results Meta-analyses revealed significant positive associations between subclinical negative symptoms and cortical thickness of the left frontal pole (βstd=0.091; pFDR=0.009), right medial orbitofrontal cortex (βstd=0.083; pFDR=0.009) and right anterior cingulate cortex (βstd=0.07; pFDR=0.011). Discussion Using a large sample of healthy unmedicated individuals with varying levels of schizotypal personality traits, this ENIGMA meta-analysis showed that subclinical negative symptoms are associated with thicker prefrontal cortex. The present data are contrary to previous findings in schizophrenia, which demonstrates a relationship between negative symptoms and lower prefrontal cortical thickness (Walton et al. 2018). These divergent neural correlates suggest that thicker cortex could be a potential compensatory mechanism preventing individuals with schizotypy from the clinical manifestation of severe negative symptoms. Alternatively, greater prefrontal cortical thickness could also be associated with pathological processes along the negative symptom continuum prior to clinical manifestation.

Background Neuroimaging studies have found dysconnectivity of frontostriatal circuits across a broad spectrum of psychotic symptoms. However, it is unknown whether dysconnectivity within frontostriatal circuits originates from disrupted bottom-up or top-down control signaling within these systems. Here, we used dynamic causal modelling (DCM) to examine the effective connectivity of frontostriatal systems in first-episode psychosis (FEP). Methods A total of 55 FEP patients (26 males; mean [SD] age = 19.24 [2.89]) and 24 healthy controls (15 males; mean [SD] age = 21.83 [1.93]) underwent a resting-state functional magnetic resonance imaging protocol. Biologically plausible connections between eight left hemisphere regions encompassing the dorsal and ventral frontostriatal systems were modelled using spectral DCM. The regions comprise dorsolateral prefrontal cortex, ventromedial prefrontal cortex, anterior hippocampus, amygdala, dorsal caudate, nucleus accumbens, thalamus, and the midbrain. Effective connectivity between groups were assessed using a parametric Bayesian model. Associations between effective connectivity parameters and positive symptoms, measured by the Brief Psychiatric Rating Scale positive subscale, was assessed in the patient group in a separate Bayesian general linear model. Results DCM shows evidence for differences in effective connectivity between patients and healthy controls, namely in the bottom-down connections distributed in the frontostriatal system encompassing the hippocampus, amygdala, striatum, and midbrain. Compared to healthy controls, patients also demonstrated increased disinhibition of the midbrain. In patients, positive symptoms are associated with increased top-down connections to the midbrain. Outgoing connection from the midbrain to the nucleus accumbens is also increased in association with positive symptoms. Discussion Aberrant top-down connectivity in the frontostriatal system in patients is consistent with top-down dysregulation of dopamine function in FEP, as dopaminergic activity in the midbrain is proposed to be under the control of higher brain areas. In patients, increased self-inhibition of the midbrain, as well as symptom associations in both ingoing and outgoing connections of this region, are congruous with hyperactivity of the midbrain as proposed by the dopamine dysregulation hypothesis. Here, we demonstrate that mathematical models of brain imaging signals can be used to identify the key disruptions driving brain circuit dysfunction, identifying new targets for treatment.

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.

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. On one hand, the global mean signal contains variance associated with respiratory, scanner-, and motion-related artifacts, and its removal via GSR improves various quality-control metrics, enhances the anatomical specificity of functional-connectivity patterns, and can increase the behavioral 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 visually from 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 apparently 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. Using task fMRI data across 47 contrasts from 7 tasks in the Human Connectome Project, we also present evidence that DiCER is more successful than GSR in preserving the spatial structure of expected task-related activation patterns. Our findings indicate that care must be exercised when examining WSDs (and their possible removal) in rsfMRI data, and that DiCER is a viable alternative to GSR for removing anatomically widespread and temporally coherent signals. All code for implementing DiCER and replicating our results is available at https://github.com/BMHLab/DiCER.

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.