Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

INTRODUCTION

The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures.

METHODS

In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025.

FINDINGS

Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2-6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5-5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4-10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32-4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23-11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation.

INTERPRETATION

After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification.

FUNDING

UK Research and Innovation and National Institute for Health Research.

Early adversity changes the economic conditions of mouse structural brain network organization

Early adversity can change educational, cognitive, and mental health outcomes. However, the neural processes through which early adversity exerts these effects remain largely unknown. We used generative network modeling of the mouse connectome to test whether unpredictable postnatal stress shifts the constraints that govern the organization of the structural connectome. A model that trades off the wiring cost of long-distance connections with topological homophily (i.e., links between regions with shared neighbors) generated simulations that successfully replicate the rodent connectome. The imposition of early life adversity shifted the best-performing parameter combinations toward zero, heightening the stochastic nature of the generative process. Put simply, unpredictable postnatal stress changes the economic constraints that reproduce rodent connectome organization, introducing greater randomness into the development of the simulations. While this change may constrain the development of cognitive abilities, it could also reflect an adaptive mechanism that facilitates effective responses to future challenges.

Neuroscience Needs Network Science

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.

Neuroscience needs Network Science

The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions.

Genetic variants associated with psychiatric disorders are enriched at epigenetically active sites in lymphoid cells

Multiple psychiatric disorders have been associated with abnormalities in both the innate and adaptive immune systems. The role of these abnormalities in pathogenesis, and whether they are driven by psychiatric risk variants, remains unclear. We test for enrichment of GWAS variants associated with multiple psychiatric disorders (cross-disorder or trans-diagnostic risk), or 5 specific disorders (cis-diagnostic risk), in regulatory elements in immune cells. We use three independent epigenetic datasets representing multiple organ systems and immune cell subsets. Trans-diagnostic and cis-diagnostic risk variants (for schizophrenia and depression) are enriched at epigenetically active sites in brain tissues and in lymphoid cells, especially stimulated CD4⁺ T cells. There is no evidence for enrichment of either trans-risk or cis-risk variants for schizophrenia or depression in myeloid cells. This suggests a possible model where environmental stimuli activate T cells to unmask the effects of psychiatric risk variants, contributing to the pathogenesis of mental health disorders.

Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study

BACKGROUND

No effective pharmacological or non-pharmacological interventions exist for patients with long COVID. We aimed to describe recovery 1 year after hospital discharge for COVID-19, identify factors associated with patient-perceived recovery, and identify potential therapeutic targets by describing the underlying inflammatory profiles of the previously described recovery clusters at 5 months after hospital discharge.

METHODS

The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a prospective, longitudinal cohort study recruiting adults (aged ≥18 years) discharged from hospital with COVID-19 across the UK. Recovery was assessed using patient-reported outcome measures, physical performance, and organ function at 5 months and 1 year after hospital discharge, and stratified by both patient-perceived recovery and recovery cluster. Hierarchical logistic regression modelling was performed for patient-perceived recovery at 1 year. Cluster analysis was done using the clustering large applications k-medoids approach using clinical outcomes at 5 months. Inflammatory protein profiling was analysed from plasma at the 5-month visit. This study is registered on the ISRCTN Registry, ISRCTN10980107, and recruitment is ongoing.

FINDINGS

2320 participants discharged from hospital between March 7, 2020, and April 18, 2021, were assessed at 5 months after discharge and 807 (32·7%) participants completed both the 5-month and 1-year visits. 279 (35·6%) of these 807 patients were women and 505 (64·4%) were men, with a mean age of 58·7 (SD 12·5) years, and 224 (27·8%) had received invasive mechanical ventilation (WHO class 7-9). The proportion of patients reporting full recovery was unchanged between 5 months (501 [25·5%] of 1965) and 1 year (232 [28·9%] of 804). Factors associated with being less likely to report full recovery at 1 year were female sex (odds ratio 0·68 [95% CI 0·46-0·99]), obesity (0·50 [0·34-0·74]) and invasive mechanical ventilation (0·42 [0·23-0·76]). Cluster analysis (n=1636) corroborated the previously reported four clusters: very severe, severe, moderate with cognitive impairment, and mild, relating to the severity of physical health, mental health, and cognitive impairment at 5 months. We found increased inflammatory mediators of tissue damage and repair in both the very severe and the moderate with cognitive impairment clusters compared with the mild cluster, including IL-6 concentration, which was increased in both comparisons (n=626 participants). We found a substantial deficit in median EQ-5D-5L utility index from before COVID-19 (retrospective assessment; 0·88 [IQR 0·74-1·00]), at 5 months (0·74 [0·64-0·88]) to 1 year (0·75 [0·62-0·88]), with minimal improvements across all outcome measures at 1 year after discharge in the whole cohort and within each of the four clusters.

INTERPRETATION

The sequelae of a hospital admission with COVID-19 were substantial 1 year after discharge across a range of health domains, with the minority in our cohort feeling fully recovered. Patient-perceived health-related quality of life was reduced at 1 year compared with before hospital admission. Systematic inflammation and obesity are potential treatable traits that warrant further investigation in clinical trials.

FUNDING

UK Research and Innovation and National Institute for Health Research.

Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia

BACKGROUND AND HYPOTHESIS

Schizophrenia is increasingly understood as a disorder of brain dysconnectivity. Recently, graph-based approaches such as graph convolutional network (GCN) have been leveraged to explore complex pairwise similarities in imaging features among brain regions, which can reveal abstract and complex relationships within brain networks.

STUDY DESIGN

We used GCN to investigate topological abnormalities of functional brain networks in schizophrenia. Resting-state functional magnetic resonance imaging data were acquired from 505 individuals with schizophrenia and 907 controls across 6 sites. Whole-brain functional connectivity matrix was extracted for each individual. We examined the performance of GCN relative to support vector machine (SVM), extracted the most salient regions contributing to both classification models, investigated the topological profiles of identified salient regions, and explored correlation between nodal topological properties of each salient region and severity of symptom.

STUDY RESULTS

GCN enabled nominally higher classification accuracy (85.8%) compared with SVM (80.9%). Based on the saliency map, the most discriminative brain regions were located in a distributed network including striatal areas (ie, putamen, pallidum, and caudate) and the amygdala. Significant differences in the nodal efficiency of bilateral putamen and pallidum between patients and controls and its correlations with negative symptoms were detected in post hoc analysis.

CONCLUSIONS

The present study demonstrates that GCN allows classification of schizophrenia at the individual level with high accuracy, indicating a promising direction for detection of individual patients with schizophrenia. Functional topological deficits of striatal areas may represent a focal neural deficit of negative symptomatology in schizophrenia.

Prefrontal connectomics: from anatomy to human imaging

The fundamental importance of prefrontal cortical connectivity to information processing and, therefore, disorders of cognition, emotion, and behavior has been recognized for decades. Anatomic tracing studies in animals have formed the basis for delineating the direct monosynaptic connectivity, from cells of origin, through axon trajectories, to synaptic terminals. Advances in neuroimaging combined with network science have taken the lead in developing complex wiring diagrams or connectomes of the human brain. A key question is how well these magnetic resonance imaging (MRI)-derived networks and hubs reflect the anatomic "hard wiring" first proposed to underlie the distribution of information for large-scale network interactions. In this review, we address this challenge by focusing on what is known about monosynaptic prefrontal cortical connections in non-human primates and how this compares to MRI-derived measurements of network organization in humans. First, we outline the anatomic cortical connections and pathways for each prefrontal cortex (PFC) region. We then review the available MRI-based techniques for indirectly measuring structural and functional connectivity, and introduce graph theoretical methods for analysis of hubs, modules, and topologically integrative features of the connectome. Finally, we bring these two approaches together, using specific examples, to demonstrate how monosynaptic connections, demonstrated by tract-tracing studies, can directly inform understanding of the composition of PFC nodes and hubs, and the edges or pathways that connect PFC to cortical and subcortical areas.

Challenges and opportunities for conducting a vaccine trial during the COVID-19 pandemic in the United Kingdom

The COVID-19 pandemic has resulted in unprecedented challenges for healthcare systems worldwide. It has also stimulated research in a wide range of areas including rapid diagnostics, novel therapeutics, use of technology to track patients and vaccine development. Here, we describe our experience of rapidly setting up and delivering a novel COVID-19 vaccine trial, using clinical and research staff and facilities in three National Health Service Trusts in Cambridgeshire, United Kingdom. We encountered and overcame a number of challenges including differences in organisational structures, research facilities available, staff experience and skills, information technology and communications infrastructure, and research training and assessment procedures. We overcame these by setting up a project team that included key members from all three organisations that met at least daily by teleconference. This group together worked to identify the best practices and procedures and to harmonise and cascade these to the wider trial team. This enabled us to set up the trial within 25 days and to recruit and vaccinate the participants within a further 23 days. The lessons learned from our experiences could be used to inform the conduct of clinical trials during a future infectious disease pandemic or public health emergency.

The Human Brain Is Best Described as Being on a Female/Male Continuum: Evidence from a Neuroimaging Connectivity Study

Psychological androgyny has long been associated with greater cognitive flexibility, adaptive behavior, and better mental health, but whether a similar concept can be defined using neural features remains unknown. Using the neuroimaging data from 9620 participants, we found that global functional connectivity was stronger in the male brain before middle age but became weaker after that, when compared with the female brain, after systematic testing of potentially confounding effects. We defined a brain gender continuum by estimating the likelihood of an observed functional connectivity matrix to represent a male brain. We found that participants mapped at the center of this continuum had fewer internalizing symptoms compared with those at the 2 extreme ends. These findings suggest a novel hypothesis proposing that there exists a neuroimaging concept of androgyny using the brain gender continuum, which may be associated with better mental health in a similar way to psychological androgyny.

Invited Review: The spectrum of neuropathology in COVID-19

There is increasing evidence that patients with Coronavirus disease 19 (COVID-19) present with neurological and psychiatric symptoms. Anosmia, hypogeusia, headache, nausea and altered consciousness are commonly described, although there are emerging clinical reports of more serious and specific conditions such as acute cerebrovascular accident, encephalitis and demyelinating disease. Whether these presentations are directly due to viral invasion of the central nervous system (CNS) or caused by indirect mechanisms has yet to be established. Neuropathological examination of brain tissue at autopsy will be essential to establish the neuro-invasive potential of the SARS-CoV-2 virus but, to date, there have been few detailed studies. The pathological changes in the brain probably represent a combination of direct cytopathic effects mediated by SARS-CoV-2 replication or indirect effects due to respiratory failure, injurious cytokine reaction, reduced immune response and cerebrovascular accidents induced by viral infection. Further large-scale molecular and cellular investigations are warranted to clarify the neuropathological correlates of the neurological and psychiatric features seen clinically in COVID-19. In this review, we summarize the current reports of neuropathological examination in COVID-19 patients, in addition to our own experience, and discuss their contribution to the understanding of CNS involvement in this disease.

The scope of mental health research during the COVID-19 pandemic and its aftermath

The effects of the COVID-19 pandemic on population mental health are unknown. We need to understand the scale of any such impact in different sections of the population, who is most affected and how best to mitigate, prevent and treat any excess morbidity. We propose a coordinated and interdisciplinary mental health science response.

A survey of psychosexual arousability in male patients on depot neuroleptic medication

Sexual and endocrine function in a geographically defined population of male patients receiving depot neuroleptic medication were studied (n = 119). It was predicted that psychosexual arousability would be reduced in this patient group, perhaps because of the endocrine effects of medication. Arousability was measured in 63 patients (53% of the population) using a validated questionnaire of sexual function for which normative data were available (Sexuality Experience Scales, SES 2). Blood levels of prolactin, testosterone and gonadotrophins were assayed. Physical sexual dysfunction was common, as was endocrine dysfunction. However, the sample's mean score on the global arousability scale was not significantly different from the normative mean. Arousability was negatively correlated with age and positively correlated with frequency of spontaneous penile erections, but not significantly correlated with endocrine variables or exposure to neuroleptic medication. These results suggest that sexual arousability in response to imagined or audio-visual erotic stimuli is surprisingly unimpaired in medicated male patients suffering from chronic mental illness.

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting‐state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low‐frequency fluctuation, regional homogeneity and two connectome‐wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10‐fold cross‐validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.

Fractionation of impulsive and compulsive trans-diagnostic phenotypes and their longitudinal associations

OBJECTIVE

Young adulthood is a crucial neurodevelopmental period during which impulsive and compulsive problem behaviours commonly emerge. While traditionally considered diametrically opposed, impulsive and compulsive symptoms tend to co-occur. The objectives of this study were as follows: (a) to identify the optimal trans-diagnostic structural framework for measuring impulsive and compulsive problem behaviours, and (b) to use this optimal framework to identify common/distinct antecedents of these latent phenotypes.

METHOD

In total, 654 young adults were recruited as part of the Neuroscience in Psychiatry Network, a population-based cohort in the United Kingdom. The optimal trans-diagnostic structural model capturing 33 types of impulsive and compulsive problem behaviours was identified. Baseline predictors of subsequent impulsive and compulsive trans-diagnostic phenotypes were characterised, along with cross-sectional associations, using partial least squares.

RESULTS

Current problem behaviours were optimally explained by a bi-factor model, which yielded dissociable measures of impulsivity and compulsivity, as well as a general disinhibition factor. Impulsive problem behaviours were significantly explained by prior antisocial and impulsive personality traits, male gender, general distress, perceived dysfunctional parenting and teasing/arguments within friendships. Compulsive problem behaviours were significantly explained by prior compulsive traits and female gender.

CONCLUSION

This study demonstrates that trans-diagnostic phenotypes of 33 impulsive and compulsive problem behaviours are identifiable in young adults, utilising a bi-factor model based on responses to a single questionnaire. Furthermore, these phenotypes have different antecedents. The findings yield a new framework for fractionating impulsivity and compulsivity, and suggest different early intervention targets to avert emergence of problem behaviours. This framework may be useful for future biological and clinical dissection of impulsivity and compulsivity.

Developmental cognitive neuroscience using latent change score models: A tutorial and applications

Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the literature is based largely on cross-sectional comparisons, which reflect a variety of influences and cannot directly represent change. We advocate using latent change score (LCS) models in longitudinal samples as a statistical framework to tease apart the complex processes underlying lifespan development in brain and behaviour using longitudinal data. LCS models provide a flexible framework that naturally accommodates key developmental questions as model parameters and can even be used, with some limitations, in cases with only two measurement occasions. We illustrate the use of LCS models with two empirical examples. In a lifespan cognitive training study (COGITO, N = 204 (N = 32 imaging) on two waves) we observe correlated change in brain and behaviour in the context of a high-intensity training intervention. In an adolescent development cohort (NSPN, N = 176, two waves) we find greater variability in cortical thinning in males than in females. To facilitate the adoption of LCS by the developmental community, we provide analysis code that can be adapted by other researchers and basic primers in two freely available SEM software packages (lavaan and Ωnyx).

Adolescent friendships predict later resilient functioning across psychosocial domains in a healthy community cohort

BACKGROUND

Adolescence is a key time period for the emergence of psychosocial and mental health difficulties. To promote adolescent adaptive ('resilient') psychosocial functioning (PSF), appropriate conceptualisation and quantification of such functioning and its predictors is a crucial first step. Here, we quantify resilient functioning as the degree to which an individual functions better or worse than expected given their self-reported childhood family experiences, and relate this to adolescent family and friendship support.

METHOD

We used Principal Component and regression analyses to investigate the relationship between childhood family experiences and PSF (psychiatric symptomatology, personality traits and mental wellbeing) in healthy adolescents (the Neuroscience in Psychiatry Network; N = 2389; ages 14-24). Residuals from the relation between childhood family experiences and PSF reflect resilient functioning; the degree to which an individual is functioning better, or worse, than expected given their childhood family experiences. Next, we relate family and friendship support with resilient functioning both cross-sectionally and 1 year later.

RESULTS

Friendship and family support were positive predictors of immediate resilient PSF, with friendship support being the strongest predictor. However, whereas friendship support was a significant positive predictor of later resilient functioning, family support had a negative relationship with later resilient PSF.

CONCLUSIONS

We show that friendship support, but not family support, is an important positive predictor of both immediate and later resilient PSF in adolescence and early adulthood. Interventions that promote the skills needed to acquire and sustain adolescent friendships may be crucial in increasing adolescent resilient PSF.

Wavelets and statistical analysis of functional magnetic resonance images of the human brain

Wavelets provide an orthonormal basis for multiresolution analysis and decorrelation or ‘whitening’ of nonstationary time series and spatial processes. Wavelets are particularly well suited to analysis of biological signals and images, such as human brain imaging data, which often have fractal or scale-invariant properties. We briefly define some key properties of the discrete wavelet transform (DWT) and review its applications to statistical analysis of functional magnetic resonance imaging (fMRI) data. We focus on time series resampling by ‘wavestrapping’ of wavelet coefficients, methods for efficient linear model estimation in the wavelet domain, and wavelet-based methods for multiple hypothesis testing, all of which are somewhat simplified by the decorrelating property of the DWT.

Opening bottlenecks on weighted networks by local adaptation to cascade failures

Structure and dynamics of complex systems are often described using weighted networks in which the position, weight and direction of links quantify how activity propagates between system elements, or nodes. Nodes with only few outgoing links of low weight have low out-strength and thus form bottlenecks that hinder propagation. It is currently not well understood how systems can overcome limits imposed by such bottlenecks. Here, we simulate activity cascades on weighted networks and show that, for any cascade length, activity initially propagates towards high out-strength nodes before terminating in low out-strength bottlenecks. Increasing the weights of links that are active early in the cascade further enhances already strong pathways, but worsens the bottlenecks thereby limiting accessibility to other pathways in the network. In contrast, strengthening only links that propagated the activity just prior to cascade termination, i.e. links that point into bottlenecks, eventually removes these bottlenecks and increases the accessibility of all paths on the network. This local adaptation rule simply relies on the relative timing to a global failure signal and allows systems to overcome engrained structure to adapt to new challenges.

Semi-Metric Topology of the Human Connectome: Sensitivity and Specificity to Autism and Major Depressive Disorder

Introduction

The human functional connectome is a graphical representation, consisting of nodes connected by edges, of the inter-relationships of blood oxygenation-level dependent (BOLD) time-series measured by MRI from regions encompassing the cerebral cortices and, often, the cerebellum. Semi-metric analysis of the weighted, undirected connectome distinguishes an edge as either direct (metric), such that there is no alternative path that is accumulatively stronger, or indirect (semi-metric), where one or more alternative paths exist that have greater strength than the direct edge. The sensitivity and specificity of this method of analysis is illustrated by two case-control analyses with independent, matched groups of adolescents with autism spectrum conditions (ASC) and major depressive disorder (MDD).

Results

Significance differences in the global percentage of semi-metric edges was observed in both groups, with increases in ASC and decreases in MDD relative to controls. Furthermore, MDD was associated with regional differences in left frontal and temporal lobes, the right limbic system and cerebellum. In contrast, ASC had a broadly increased percentage of semi-metric edges with a more generalised distribution of effects and some areas of reduction. In summary, MDD was characterised by localised, large reductions in the percentage of semi-metric edges, whilst ASC is characterised by more generalised, subtle increases. These differences were corroborated in greater detail by inspection of the semi-metric backbone for each group; that is, the sub-graph of semi-metric edges present in >90% of participants, and by nodal degree differences in the semi-metric connectome.

Conclusion

These encouraging results, in what we believe is the first application of semi-metric analysis to neuroimaging data, raise confidence in the methodology as potentially capable of detection and characterisation of a range of neurodevelopmental and psychiatric disorders.

Whole-brain functional hypoconnectivity as an endophenotype of autism in adolescents

Endophenotypes are heritable and quantifiable markers that may assist in the identification of the complex genetic underpinnings of psychiatric conditions. Here we examined global hypoconnectivity as an endophenotype of autism spectrum conditions (ASCs). We studied well-matched groups of adolescent males with autism, genetically-related siblings of individuals with autism, and typically-developing control participants. We parcellated the brain into 258 regions and used complex-network analysis to detect a robust hypoconnectivity endophenotype in our participant group. We observed that whole-brain functional connectivity was highest in controls, intermediate in siblings, and lowest in ASC, in task and rest conditions. We identified additional, local endophenotype effects in specific networks including the visual processing and default mode networks. Our analyses are the first to show that whole-brain functional hypoconnectivity is an endophenotype of autism in adolescence, and may thus underlie the heritable similarities seen in adolescents with ASC and their relatives.

Network topology and functional connectivity disturbances precede the onset of Huntington's disease

Cognitive, motor and psychiatric changes in prodromal Huntington's disease have nurtured the emergent need for early interventions. Preventive clinical trials for Huntington's disease, however, are limited by a shortage of suitable measures that could serve as surrogate outcomes. Measures of intrinsic functional connectivity from resting-state functional magnetic resonance imaging are of keen interest. Yet recent studies suggest circumscribed abnormalities in resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease, despite the spectrum of behavioural changes preceding a manifest diagnosis. The present study used two complementary analytical approaches to examine whole-brain resting-state functional magnetic resonance imaging connectivity in prodromal Huntington's disease. Network topology was studied using graph theory and simple functional connectivity amongst brain regions was explored using the network-based statistic. Participants consisted of gene-negative controls (n = 16) and prodromal Huntington's disease individuals (n = 48) with various stages of disease progression to examine the influence of disease burden on intrinsic connectivity. Graph theory analyses showed that global network interconnectivity approximated a random network topology as proximity to diagnosis neared and this was associated with decreased connectivity amongst highly-connected rich-club network hubs, which integrate processing from diverse brain regions. However, functional segregation within the global network (average clustering) was preserved. Functional segregation was also largely maintained at the local level, except for the notable decrease in the diversity of anterior insula intermodular-interconnections (participation coefficient), irrespective of disease burden. In contrast, network-based statistic analyses revealed patterns of weakened frontostriatal connections and strengthened frontal-posterior connections that evolved as disease burden increased. These disturbances were often related to long-range connections involving peripheral nodes and interhemispheric connections. A strong association was found between weaker connectivity and decreased rich-club organization, indicating that whole-brain simple connectivity partially expressed disturbances in the communication of highly-connected hubs. However, network topology and network-based statistic connectivity metrics did not correlate with key markers of executive dysfunction (Stroop Test, Trail Making Test) in prodromal Huntington's disease, which instead were related to whole-brain connectivity disturbances in nodes (right inferior parietal, right thalamus, left anterior cingulate) that exhibited multiple aberrant connections and that mediate executive control. Altogether, our results show for the first time a largely disease burden-dependent functional reorganization of whole-brain networks in prodromal Huntington's disease. Both analytic approaches provided a unique window into brain reorganization that was not related to brain atrophy or motor symptoms. Longitudinal studies currently in progress will chart the course of functional changes to determine the most sensitive markers of disease progression.

Schizophrenia and abnormal brain network hubs

Schizophrenia is a heterogeneous psychiatric disorder of unknown cause or characteristic pathology. Clinical neuroscientists increasingly postulate that schizophrenia is a disorder of brain network organization. In this article we discuss the conceptual framework of this dysconnection hypothesis, describe the predominant methodological paradigm for testing this hypothesis, and review recent evidence for disruption of central/hub brain regions, as a promising example of this hypothesis. We summarize studies of brain hubs in large-scale structural and functional brain networks and find strong evidence for network abnormalities of prefrontal hubs, and moderate evidence for network abnormalities of limbic, temporal, and parietal hubs. Future studies are needed to differentiate network dysfunction from previously observed gray- and white-matter abnormalities of these hubs, and to link endogenous network dysfunction phenotypes with perceptual, behavioral, and cognitive clinical phenotypes of schizophrenia.

Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease

Alzheimer's disease (AD) is increasingly recognized as a disconnection syndrome, which leads to cognitive impairment due to the disruption of functional activity across large networks or systems of interconnected brain regions. We explored abnormal functional magnetic resonance imaging (fMRI) resting-state dynamics, functional connectivity, and weighted functional networks, in a sample of patients with severe AD (N = 18) and age-matched healthy volunteers (N = 21). We found that patients had reduced amplitude and regional homogeneity of low-frequency fMRI oscillations, and reduced the strength of functional connectivity, in several regions previously described as components of the default mode network, for example, medial posterior parietal cortex and dorsal medial prefrontal cortex. In patients with severe AD, functional connectivity was particularly attenuated between regions that were separated by a greater physical distance; and loss of long distance connectivity was associated with less efficient global and nodal network topology. This profile of functional abnormality in severe AD was consistent with the results of a comparable analysis of data on 2 additional groups of patients with mild AD (N = 17) and amnestic mild cognitive impairment (MCI; N = 18). A greater degree of cognitive impairment, measured by the mini-mental state examination across all patient groups, was correlated with greater attenuation of functional connectivity, particularly over long connection distances, for example, between anterior and posterior components of the default mode network, and greater reduction of global and nodal network efficiency. These results indicate that neurodegenerative disruption of fMRI oscillations and connectivity in AD affects long-distance connections to hub nodes, with the consequent loss of network efficiency. This profile was evident also to a lesser degree in the patients with less severe cognitive impairment, indicating that the potential of resting-state fMRI measures as biomarkers or predictors of disease progression in AD.

Powerlaw: a Python package for analysis of heavy-tailed distributions

Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible.

Imaging structural co-variance between human brain regions

Brain structure varies between people in a markedly organized fashion. Communities of brain regions co-vary in their morphological properties. For example, cortical thickness in one region influences the thickness of structurally and functionally connected regions. Such networks of structural co-variance partially recapitulate the functional networks of healthy individuals and the foci of grey matter loss in neurodegenerative disease. This architecture is genetically heritable, is associated with behavioural and cognitive abilities and is changed systematically across the lifespan. The biological meaning of this structural co-variance remains controversial, but it appears to reflect developmental coordination or synchronized maturation between areas of the brain. This Review discusses the state of current research into brain structural co-variance, its underlying mechanisms and its potential value in the understanding of various neurological and psychiatric conditions.

The convergence of maturational change and structural covariance in human cortical networks

Large-scale covariance of cortical thickness or volume in distributed brain regions has been consistently reported by human neuroimaging studies. The mechanism of this population covariance of regional cortical anatomy has been hypothetically related to synchronized maturational changes in anatomically connected neuronal populations. Brain regions that grow together, i.e., increase or decrease in volume at the same rate over the course of years in the same individual, are thus expected to demonstrate strong structural covariance or anatomical connectivity across individuals. To test this prediction, we used a structural MRI dataset on healthy young people (N = 108; aged 9-22 years at enrollment), comprising 3-6 longitudinal scans on each participant over 6-12 years of follow-up. At each of 360 regional nodes, and for each participant, we estimated the following: (1) the cortical thickness in the median scan and (2) the linear rate of change in cortical thickness over years of serial scanning. We constructed structural and maturational association matrices and networks from these measurements. Both structural and maturational networks shared similar global and nodal topological properties, as well as mesoscopic features including a modular community structure, a relatively small number of highly connected hub regions, and a bias toward short distance connections. Using resting-state functional magnetic resonance imaging data on a subset of the sample (N = 32), we also demonstrated that functional connectivity and network organization was somewhat predictable by structural/maturational networks but demonstrated a stronger bias toward short distance connections and greater topological segregation. Brain structural covariance networks are likely to reflect synchronized developmental change in distributed cortical regions.

Structural brain changes in First Episode Schizophrenia compared with Fronto-Temporal Lobar Degeneration: a meta-analysis

BACKGROUND

The authors sought to compare gray matter changes in First Episode Schizophrenia (FES) compared with Fronto-Temporal Lobar Degeneration (FTLD) using meta-analytic methods applied to neuro-imaging studies.

METHODS

A systematic search was conducted for published, structural voxel-based morphometric MRI studies in patients with FES or FTLD. Data were combined using anatomical likelihood estimation (ALE) to determine the extent of gray matter decreases and analysed to ascertain the degree of overlap in the spatial distribution of brain changes in both diseases.

RESULTS

Data were extracted from 18 FES studies (including a total of 555 patients and 621 comparison subjects) and 20 studies of FTLD or related disorders (including a total of 311 patients and 431 comparison subjects). The similarity in spatial overlap of brain changes in the two disorders was significant (p = 0.001). Gray matter deficits common to both disorders included bilateral caudate, left insula and bilateral uncus regions.

CONCLUSIONS

There is a significant overlap in the distribution of structural brain changes in First Episode Schizophrenia and Fronto-Temporal Lobar Degeneration. This may reflect overlapping aetiologies, or a common vulnerability of these regions to the distinct aetio-pathological processes in the two disorders.

The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia

The modular organization of the brain network can vary in two fundamental ways. The amount of inter- versus intra-modular connections between network nodes can be altered, or the community structure itself can be perturbed, in terms of which nodes belong to which modules (or communities). Alterations have previously been reported in modularity, which is a function of the proportion of intra-modular edges over all modules in the network. For example, we have reported that modularity is decreased in functional brain networks in schizophrenia: There are proportionally more inter-modular edges and fewer intra-modular edges. However, despite numerous and increasing studies of brain modular organization, it is not known how to test for differences in the community structure, i.e., the assignment of regional nodes to specific modules. Here, we introduce a method based on the normalized mutual information between pairs of modular networks to show that the community structure of the brain network is significantly altered in schizophrenia, using resting-state fMRI in 19 participants with childhood-onset schizophrenia and 20 healthy participants. We also develop tools to show which specific nodes (or brain regions) have significantly different modular communities between groups, a subset that includes right insular and perisylvian cortical regions. The methods that we propose are broadly applicable to other experimental contexts, both in neuroimaging and other areas of network science.

The future of functional MRI in clinical medicine

In the last 20 years or so, functional MRI has matured very rapidly from being an experimental imaging method in the hands of a few labs to being a very widely available and widely used workhorse of cognitive neuroscience and clinical neuroscience research internationally. FMRI studies have had a considerable impact on our understanding of brain system phenotypes of neurological and psychiatric disorders; and some impact already on development of new therapeutics. However, the direct benefit of fMRI to individual patients with brain disorders has so far been minimal. Here I provide a personal perspective on what has already been achieved, and imagine how the further development of fMRI over the medium term might lead to even greater engagement with clinical medicine.

Failure of adaptive self-organized criticality during epileptic seizure attacks

Critical dynamics are assumed to be an attractive mode for normal brain functioning as information processing and computational capabilities are found to be optimal in the critical state. Recent experimental observations of neuronal activity patterns following power-law distributions, a hallmark of systems at a critical state, have led to the hypothesis that human brain dynamics could be poised at a phase transition between ordered and disordered activity. A so far unresolved question concerns the medical significance of critical brain activity and how it relates to pathological conditions. Using data from invasive electroencephalogram recordings from humans we show that during epileptic seizure attacks neuronal activity patterns deviate from the normally observed power-law distribution characterizing critical dynamics. The comparison of these observations to results from a computational model exhibiting self-organized criticality (SOC) based on adaptive networks allows further insights into the underlying dynamics. Together these results suggest that brain dynamics deviates from criticality during seizures caused by the failure of adaptive SOC.

The discovery of population differences in network community structure: new methods and applications to brain functional networks in schizophrenia

The modular organization of the brain network can vary in two fundamental ways. The amount of inter- versus intra-modular connections between network nodes can be altered, or the community structure itself can be perturbed, in terms of which nodes belong to which modules (or communities). Alterations have previously been reported in modularity, which is a function of the proportion of intra-modular edges over all modules in the network. For example, we have reported that modularity is decreased in functional brain networks in schizophrenia: There are proportionally more inter-modular edges and fewer intra-modular edges. However, despite numerous and increasing studies of brain modular organization, it is not known how to test for differences in the community structure, i.e., the assignment of regional nodes to specific modules. Here, we introduce a method based on the normalized mutual information between pairs of modular networks to show that the community structure of the brain network is significantly altered in schizophrenia, using resting-state fMRI in 19 participants with childhood-onset schizophrenia and 20 healthy participants. We also develop tools to show which specific nodes (or brain regions) have significantly different modular communities between groups, a subset that includes right insular and perisylvian cortical regions. The methods that we propose are broadly applicable to other experimental contexts, both in neuroimaging and other areas of network science.

Are there progressive brain changes in schizophrenia? A meta-analysis of structural magnetic resonance imaging studies

BACKGROUND

It is well established that schizophrenia is associated with structural brain abnormalities, but whether these are static or progress over time remains controversial.

METHODS

A systematic review of longitudinal volumetric studies using region-of-interest structural magnetic resonance imaging in patients with schizophrenia and healthy control subjects. The percentage change in volume between scans for each brain region of interest was obtained, and data were combined using random effects meta-analysis.

RESULTS

Twenty-seven studies were included in the meta-analysis, with 928 patients and 867 control subjects, and 32 different brain regions of interest. Subjects with schizophrenia showed significantly greater decreases over time in whole brain volume, whole brain gray matter, frontal gray and white matter, parietal white matter, and temporal white matter volume, as well as larger increases in lateral ventricular volume, than healthy control subjects. The time between baseline and follow-up magnetic resonance imaging scans ranged from 1 to 10 years. The differences between patients and control subjects in annualized percentage volume change were -.07% for whole brain volume, -.59% for whole brain gray matter, -.32% for frontal white matter, -.32% for parietal white matter, -.39% for temporal white matter, and +.36% for bilateral lateral ventricles.

CONCLUSIONS

These findings suggest that schizophrenia is associated with progressive structural brain abnormalities, affecting both gray and white matter. We found no evidence to suggest progressive medial temporal lobe involvement but did find evidence that this may be partly explained by heterogeneity between studies in patient age and illness duration. The causes and clinical correlates of these progressive brain changes should now be the focus of investigation.

Overall brain connectivity maps show cortico-subcortical abnormalities in schizophrenia

Abnormal interactions between areas of the brain have been pointed as possible causes for schizophrenia. However, the nature of these disturbances and the anatomical location of the regions involved are still unclear. Here, we describe a method to estimate maps of net levels of connectivity in the resting brain, and we apply it to look for differential patterns of connectivity in schizophrenia. This method uses partial coherences as a basic measure of covariability, and it minimises the effect of major physiological noise. When overall (net) connectivity maps of a sample of 40 patients with schizophrenia were compared with the maps from a matched sample of 40 controls, a single area of abnormality was found. It is an area of patient hyper-connectivity and is located frontally, in medial and orbital structures, clearly overlapping the anterior node of the default mode network (DMN). When this area is used as a region of interest in a second-level analysis, it shows functional hyper-connections with several cortical and subcortical structures. Interestingly, the most significant abnormality is found with the caudate, which has a bilateral pattern of abnormality, pointing to a possible DMN-striatum deviant relation in schizophrenia. However, hyper-connectivity observed with other regions (right hippocampus and amygdala, and other cortical structures) suggests a more pervasive alteration of brain connectivity in this disease.

Anatomy of bipolar disorder and schizophrenia: a meta-analysis

BACKGROUND

Recent genetic results have indicated that the two major, classically distinct forms of psychosis - schizophrenia and bipolar disorder - may share causative factors in common. However it is not clear to what extent they may also have similar profiles of brain abnormality. We used meta-analytic techniques to generate and compare maps of brain structural abnormality in the large samples of patients with both disorders that have been studied using magnetic resonance imaging.

METHOD

A systematic search was conducted for voxel-based morphometry studies examining gray matter in patients with schizophrenia or bipolar disorder. The anatomical distribution of the co-ordinates of gray matter differences was meta-analysed using Anatomical Likelihood Estimation.

RESULTS

Forty-two schizophrenia studies including 2058 patients with schizophrenia and 2131 comparison subjects were compared with fourteen bipolar studies including 366 patients with bipolar disorder and 497 comparison subjects. In schizophrenia, there were extensive gray matter deficits in frontal, temporal, cingulate and insular cortex and thalamus, and increased gray matter in the basal ganglia. In bipolar disorder, gray matter reductions were present in the anterior cingulate and bilateral insula. These substantially overlapped with areas of gray matter reduction in schizophrenia, except for a region of anterior cingulate where gray matter reduction was specific to bipolar disorder.

IMPLICATIONS

In bipolar disorder studies there were consistent regional gray matter reductions in paralimbic regions (anterior cingulate and insula) implicated in emotional processing. Gray matter reductions in schizophrenia studies were more extensive and involved limbic and neocortical structures as well as the paralimbic regions affected in bipolar disorder.

Cognitive performance and grey matter density in psychosis: functional relevance of a structural endophenotype

BACKGROUND

Structural brain changes and cognitive impairments have been identified as indicators of genetic risk for schizophrenia. However, the pattern of associations between such structural and functional liability markers has been less well investigated.

METHODS

Magnetic resonance imaging data and cognitive assessments were acquired in 31 patients with psychosis, 32 non-psychotic first-degree relatives and 28 controls. The relationship between cerebral grey matter density and cognitive performance was examined using computational morphometry.

RESULTS

Two out of 6 cognitive tests revealed significant associations with grey matter density in regions of the frontal lobe, basal ganglia, thalamus and cerebellum in patients and relatives. In patients, poorer executive functioning was associated with cerebellar grey matter density deficits. In relatives, poorer executive functioning was associated with increased grey matter density in the cerebellum and frontal lobe. In both patients and relatives, strategic retrieval from semantic memory was positively associated with grey matter density in basal ganglia structures. Some additional negative associations in the patients differentiated this group from relatives.

CONCLUSIONS

The overlap in structure-function relationships in individuals with schizophrenia and those with liability for the disorder may suggest that regional grey matter density alterations functionally alter particular neurocircuits, which could lead to cognitive deficits. The non-overlapping structure-function correlations may reflect disease-related or compensatory mechanisms.

Protecting peer review: correspondence chronology and ethical analysis regarding Logothetis vs. Shmuel and Leopold

Editors of scientific journals are ethically bound to provide a fair and impartial peer-review process and to protect the rights of contributing authors to publish research results. If, however, a dispute arises among investigators regarding data ownership and the right to publish, the ethical responsibilities of journal editors become more complex. The editors of Human Brain Mapping recently had the unusual experience of learning of an ongoing dispute regarding data-access rights pertaining to a manuscript already accepted for publication. Herein the editors describe the nature of the dispute, the steps taken to explore and resolve the conflict, and discuss the ethical principles that govern such circumstances. Drawing on this experience and with the goal of avoiding future controversies, the editors have formulated a Data Rights Policy and a Data Rights Procedure for Human Brain Mapping. Human Brain Mapping adopts this policy effective immediately and respectfully suggests that other journals consider adopting this or similar policies.

Why psychiatry can't afford to be neurophobic

The original vision of psychiatry was as a medicine - or physic - of the mind. If psychiatry aspires to be a progressive modern medicine of the mind, it should be fully engaged with the science of the brain. We summarise and rebut three countervailing or 'neurophobic' propositions and aim to show that not one provides a compelling argument for neurophobia. We suggest that there are several ways in which psychiatry could organise itself professionally to better advance and communicate the theoretical and therapeutic potential of a brain-based medicine of the mind.

Meta-analysis of diffusion tensor imaging studies in schizophrenia

The objective of the study was to identify whether there are consistent regional white matter changes in schizophrenia. A systematic search was conducted for voxel-based diffusion tensor imaging fractional anisotropy studies of patients with schizophrenia (or related disorders) in relation to comparison groups. The authors carried out meta-analysis of the co-ordinates of fractional anisotropy differences. For the meta-analysis they used the Activation Likelihood Estimation (ALE) method hybridized with the rank approach used in Genome Scan Meta-Analysis (GSMA). This system detects three-dimensional conjunctions of co-ordinates from multiple studies and permits the weighting of studies in relation to sample size. Fifteen articles were identified for inclusion in the meta-analysis, including a total of 407 patients with schizophrenia and 383 comparison subjects. The studies reported fractional anisotropy reductions at 112 co-ordinates in schizophrenia and no fractional anisotropy increases. Over all studies, significant reductions were present in two regions: the left frontal deep white matter and the left temporal deep white matter. The first region, in the left frontal lobe, is traversed by white matter tracts interconnecting the frontal lobe, thalamus and cingulate gyrus. The second region, in the temporal lobe, is traversed by white matter tracts interconnecting the frontal lobe, insula, hippocampus-amygdala, temporal and occipital lobe. This suggests that two networks of white matter tracts may be affected in schizophrenia, with the potential for 'disconnection' of the gray matter regions which they link.

The dopamine D2 receptor antagonist sulpiride modulates striatal BOLD signal during the manipulation of information in working memory

RATIONALE

Dopamine (DA) plays an important role in working memory. However, the precise functions supported by different DA receptor subtypes in different neural regions remain unclear.

OBJECTIVE

The present study used pharmacological, event-related fMRI to test the hypothesis that striatal dopamine is important for the manipulation of information in working memory.

METHODS

Twenty healthy human subjects were scanned twice, once after placebo and once after sulpiride 400 mg, a selective DA D2 receptor antagonist, while performing a verbal working memory task requiring different levels of manipulation.

RESULTS

Whilst there was no overall effect of sulpiride on task-dependent activation, individual variation in sulpiride plasma levels predicted the effect of working memory manipulation on activation in the putamen, suggesting a dose-dependent effect of DA antagonism on a striatally based manipulation process. These effects occurred in the context of a drug-induced improvement in performance on trials requiring the manipulation of information in working memory but not on simple retrieval trials. No significant drug effects were observed in the prefrontal cortex.

CONCLUSIONS

These results support models of dopamine function that posit a 'gating' function for dopamine D2 receptors in the striatum, which enables the flexible updating and manipulation of information in working memory.

Age-related changes in modular organization of human brain functional networks

Graph theory allows us to quantify any complex system, e.g., in social sciences, biology or technology, that can be abstractly described as a set of nodes and links. Here we derived human brain functional networks from fMRI measurements of endogenous, low frequency, correlated oscillations in 90 cortical and subcortical regions for two groups of healthy (young and older) participants. We investigated the modular structure of these networks and tested the hypothesis that normal brain aging might be associated with changes in modularity of sparse networks. Newman's modularity metric was maximised and topological roles were assigned to brain regions depending on their specific contributions to intra- and inter-modular connectivity. Both young and older brain networks demonstrated significantly non-random modularity. The young brain network was decomposed into 3 major modules: central and posterior modules, which comprised mainly nodes with few inter-modular connections, and a dorsal fronto-cingulo-parietal module, which comprised mainly nodes with extensive inter-modular connections. The mean network in the older group also included posterior, superior central and dorsal fronto-striato-thalamic modules but the number of intermodular connections to frontal modular regions was significantly reduced, whereas the number of connector nodes in posterior and central modules was increased.

Components of variance in a multicentre functional MRI study and implications for calculation of statistical power

This article firstly presents a theoretical analysis of the statistical power of a parallel-group, repeated-measures (two-session) and two-centre design suitable for a placebo-controlled pharmacological MRI study. For arbitrary effect size, power is determined by the pooled between-session error, the pooled measurement error, the ratio of centre measurement errors, the total number of subjects and the proportion of subjects studied at the centre with greatest measurement error. Secondly, an experiment is described to obtain empirical estimates of variance components in task-related and resting state functional magnetic resonance imaging. Twelve healthy volunteers were scanned at two centres during performance of blocked and event-related versions of an affect processing task (each repeated twice per session) and rest. In activated regions, variance components were estimated: between-subject (23% of total), between-centre (2%), between-paradigm (4%), within-session occasion (paradigm repeat; 2%) and residual (measurement) error (69%). The between-centre ratio of measurement errors was 0.8. A similar analysis for the Hurst exponent estimated in resting data showed negligible contributions of between-subject and between-centre variability; measurement error accounted for 99% of total variance. Substituting these estimates in the theoretical expression for power, incorporation of two centres in the design necessitates a modest (10%) increase in the total number of subjects compared with a single-centre study. Furthermore, considerable improvements in power can be attained by repetition of the task within each scanning session. Thus, theoretical models of power and empirical data indicate that between-centre variability can be small enough to encourage multicentre designs without major compensatory increases in sample size.