Nicolas Crossley

The hubs of the human connectome are generally implicated in the anatomy of brain disorders

See Sporns (doi:10.1093/brain/awu148) for a scientific commentary on this article. Brain networks contain a minority of highly connected hub nodes with high topological value and biological cost. Using network analysis of DTI data from healthy volunteers, and meta-analyses of published MRI studies in 26 brain disorders, Crossley et al. show that lesions across disorders tend to be concentrated at hubs.

Cognitive relevance of the community structure of the human brain functional coactivation network

There is growing interest in the complex topology of human brain functional networks, often measured using resting-state functional MRI (fMRI). Here, we used a meta-analysis of the large primary literature that used fMRI or PET to measure task-related activation (>1,600 studies; 1985–2010). We estimated the similarity (Jaccard index) of the activation patterns across experimental tasks between each pair of 638 brain regions. This continuous coactivation matrix was used to build a weighted graph to characterize network topology. The coactivation network was modular, with occipital, central, and default-mode modules predominantly coactivated by specific cognitive domains (perception, action, and emotion, respectively). It also included a rich club of hub nodes, located in parietal and prefrontal cortex and often connected over long distances, which were coactivated by a diverse range of experimental tasks. Investigating the topological role of edges between a deactivated and an activated node, we found that such competitive interactions were most frequent between nodes in different modules or between an activated rich-club node and a deactivated peripheral node. Many aspects of the coactivation network were convergent with a connectivity network derived from resting state fMRI data (n = 27, healthy volunteers); although the connectivity network was more parsimoniously connected and differed in the anatomical locations of some hubs. We conclude that the community structure of human brain networks is relevant to cognitive function. Deactivations may play a role in flexible reconfiguration of the network according to cognitive demand, varying the integration between modules, and between the periphery and a central rich club.

Superior temporal lobe dysfunction and frontotemporal dysconnectivity in subjects at risk of psychosis and in first-episode psychosis.

Background: Superior temporal lobe dysfunction is a robust finding in functional neuroimaging studies of schizophrenia and is thought to be related to a disruption of fronto‐temporal functional connectivity. However, the stage of the disorder at which these functional alterations occur is unclear. We addressed this issue by using functional MRI (fMRI) to study subjects in the prodromal and first episode phases of schizophrenia. Methods: Subjects with an at risk mental state (ARMS) for psychosis, a first psychotic episode (FEP), and controls were studied using fMRI while performing a working memory task. Activation in the superior temporal gyrus (STG) was assessed using statistical parametric mapping, and its relationship to frontal activation was examined using dynamic causal modeling. Results: The STG was differentially engaged across the three groups. There was deactivation of this region during the task in controls, whereas subjects with FEP showed activation and the response in subjects with ARMS was intermediately relative to the two other groups. There were corresponding differences in the effective connectivity between the STG and the middle frontal gyrus across the three groups, with a negative coupling between these areas in controls, a positive coupling in the FEP group, and an intermediate value in the ARMS group. Conclusions: A failure to deactivate the superior temporal lobe during tasks that engage prefrontal cortex is evident at the onset of schizophrenia and may reflect a disruption of fronto‐temporal connectivity. Qualitatively similar alterations are evident in people with prodromal symptoms of the disorder. Hum Brain Mapp, 2009.

Efficacy of atypical v. typical antipsychotics in the treatment of early psychosis: meta-analysis

BACKGROUND There is an ongoing debate about the use of atypical antipsychotics as a first-line treatment for first-episode psychosis. AIMS To examine the evidence base for this recommendation. METHOD Meta-analyses of randomised controlled trials in the early phase of psychosis, looking at long-term discontinuation rates, short-term symptom changes, weight gain and extrapyramidal side-effects. Trials were identified using a combination of electronic (Cochrane Central, EMBASE, MEDLINE and PsycINFO) and manual searches. RESULTS Fifteen randomised controlled trials with a total of 2522 participants were included. No significant differences between atypical and typical drugs were found for discontinuation rates (odds ratio (OR) = 0.7, 95% CI 0.4 to 1.2) or effect on symptoms (standardised mean difference (SMD) = -0.1, 95% CI -0.2 to 0.02). Participants on atypical antipsychotics gained 2.1 kg (95% CI 0.1 to 4.1) more weight than those on typicals, whereas those on typicals experienced more extrapyramidal side-effects (SMD = -0.4, 95% CI -0.5 to -0.2). CONCLUSIONS There was no evidence for differences in efficacy between atypical and typical antipsychotics, but there was a clear difference in the side-effect profile.

Age effects on the default mode and control networks in typically developing children

BACKGROUND The investigation of neurodevelopment during late childhood and pre-adolescence has recently attracted a great deal of interest in the field of neuroimaging. One promising topic in this field is the formation of brain networks in healthy subjects. The integration between neural modules characterizes the ability of the network to process information globally. Although many fMRI-based neurodevelopment studies can be found in the literature, the analyses of very large samples (on the order of hundreds of subjects) that focus on the late childhood/pre-adolescence period and resting state fMRI are scarce, and most studies have focused solely on North American and European populations. AIMS In this study, we present a descriptive investigation of the developmental formation of the Default Mode Network and the Control Network based on a Brazilian, cross-sectional community sample of 447 typically developing subjects aged 7-15 years old. METHODS Resting state fMRI data were acquired using two MRI systems from the same manufacturer using the same acquisition parameters. We estimated the age effects on the strength of the links (between brain regions) and the network features (graph descriptors: degree and eigenvector centrality). RESULTS Our findings showed an increase in the antero-posterior connectivity in both studied networks during brain development. The graph analyses showed an increase in centrality with age for most regions in the Default Mode Network and the dorsal anterior and posterior cingulate, the right anterior insula and the left posterior temporal cortex in the Control Network. CONCLUSION We conclude that the period of 7-15 years of age is crucial for the development of both the Default Mode and Control networks, with integration between the posterior and anterior neuronal modules and an increase in the centrality measures of the hub regions.

Decreased centrality of subcortical regions during the transition to adolescence: A functional connectivity study

Investigations of brain maturation processes are a key step to understand the cognitive and emotional changes of adolescence. Although structural imaging findings have delineated clear brain developmental trajectories for typically developing individuals, less is known about the functional changes of this sensitive development period. Developmental changes, such as abstract thought, complex reasoning, and emotional and inhibitory control, have been associated with more prominent cortical control. The aim of this study is to assess brain networks connectivity changes in a large sample of 7- to 15-year-old subjects, testing the hypothesis that cortical regions will present an increasing relevance in commanding the global network. Functional magnetic resonance imaging (fMRI) data were collected in a sample of 447 typically developing children from a Brazilian community sample who were submitted to a resting state acquisition protocol. The fMRI data were used to build a functional weighted graph from which eigenvector centrality (EVC) was extracted. For each brain region (a node of the graph), the age-dependent effect on EVC was statistically tested and the developmental trajectories were estimated using polynomial functions. Our findings show that angular gyrus become more central during this maturation period, while the caudate; cerebellar tonsils, pyramis, thalamus; fusiform, parahippocampal and inferior semilunar lobe become less central. In conclusion, we report a novel finding of an increasing centrality of the angular gyrus during the transition to adolescence, with a decreasing centrality of many subcortical and cerebellar regions.

Altered Hub Functioning and Compensatory Activations in the Connectome: A Meta-Analysis of Functional Neuroimaging Studies in Schizophrenia

Background: Functional neuroimaging studies of schizophrenia have identified abnormal activations in many brain regions. In an effort to interpret these findings from a network perspective, we carried out a meta-analysis of this literature, mapping anatomical locations of under- and over-activation to the topology of a normative human functional connectome. Methods: We included 314 task-based functional neuroimaging studies including more than 5000 patients with schizophrenia and over 5000 controls. Coordinates of significant under- or over-activations in patients relative to controls were mapped to nodes of a normative connectome defined by a prior meta-analysis of 1641 functional neuroimaging studies of task-related activation in healthy volunteers. Results: Under-activations and over-activations were reported in a wide diversity of brain regions. Both under- and over-activations were significantly more likely to be located in hub nodes that constitute the “rich club” or core of the normative connectome. In a subset of 121 studies that reported both under- and over-activations in the same patients, we found that, in network terms, these abnormalities were located in close topological proximity to each other. Under-activation in a peripheral node was more frequently associated specifically with over-activation of core nodes than with over-activation of another peripheral node. Conclusions: Although schizophrenia is associated with altered brain functional activation in a wide variety of regions, abnormal responses are concentrated in hubs of the normative connectome. Task-specific under-activation in schizophrenia is accompanied by over-activation of topologically central, less functionally specialized network nodes, which may represent a compensatory response.

Quantitative Prediction of Individual Psychopathology in Trauma Survivors Using Resting-State fMRI

Neuroimaging techniques hold the promise that they may one day aid the clinical assessment of individual psychiatric patients. However, the vast majority of studies published so far have been based on average differences between groups. This study employed a multivariate approach to examine the potential of resting-state functional magnetic resonance imaging (MRI) data for making accurate predictions about psychopathology in survivors of the 2008 Sichuan earthquake at an individual level. Resting-state functional MRI data was acquired for 121 survivors of the 2008 Sichuan earthquake each of whom was assessed for symptoms of post-traumatic stress disorder (PTSD) using the 17-item PTSD Checklist (PCL). Using a multivariate analytical method known as relevance vector regression (RVR), we examined the relationship between resting-state functional MRI data and symptom scores. We found that the use of RVR allowed quantitative prediction of clinical scores with statistically significant accuracy (correlation=0.32, P=0.006; mean squared error=176.88, P=0.001). Accurate prediction was based on functional activation in a number of prefrontal, parietal, and occipital regions. This is the first evidence that neuroimaging techniques may inform the clinical assessment of trauma-exposed individuals by providing an accurate and objective quantitative estimation of psychopathology. Furthermore, the significant contribution of parietal and occipital regions to such estimation challenges the traditional view of PTSD as a disorder specific to the fronto-limbic network.

Neuroimaging distinction between neurological and psychiatric disorders

Background It is unclear to what extent the traditional distinction between neurological and psychiatric disorders reflects biological differences. Aims To examine neuroimaging evidence for the distinction between neurological and psychiatric disorders. Method We performed an activation likelihood estimation meta-analysis on voxel-based morphometry studies reporting decreased grey matter in 14 neurological and 10 psychiatric disorders, and compared the regional and network-level alterations for these two classes of disease. In addition, we estimated neuroanatomical heterogeneity within and between the two classes. Results Basal ganglia, insula, sensorimotor and temporal cortex showed greater impairment in neurological disorders; whereas cingulate, medial frontal, superior frontal and occipital cortex showed greater impairment in psychiatric disorders. The two classes of disorders affected distinct functional networks. Similarity within classes was higher than between classes; furthermore, similarity within class was higher for neurological than psychiatric disorders. Conclusions From a neuroimaging perspective, neurological and psychiatric disorders represent two distinct classes of disorders.

Temporal stability of network centrality in control and default mode networks: Specific associations with externalizing psychopathology in children and adolescents

Abnormal connectivity patterns have frequently been reported as involved in pathological mental states. However, most studies focus on “static,” stationary patterns of connectivity, which may miss crucial biological information. Recent methodological advances have allowed the investigation of dynamic functional connectivity patterns that describe non‐stationary properties of brain networks. Here, we introduce a novel graphical measure of dynamic connectivity, called time‐varying eigenvector centrality (tv‐EVC). In a sample 655 children and adolescents (7–15 years old) from the Brazilian “High Risk Cohort Study for Psychiatric Disorders” who were imaged using resting‐state fMRI, we used this measure to investigate age effects in the temporal in control and default‐mode networks (CN/DMN). Using support vector regression, we propose a network maturation index based on the temporal stability of tv‐EVC. Moreover, we investigated whether the network maturation is associated with the overall presence of behavioral and emotional problems with the Child Behavior Checklist. As hypothesized, we found that the tv‐EVC at each node of CN/DMN become more stable with increasing age (P < 0.001 for all nodes). In addition, the maturity index for this particular network is indeed associated with general psychopathology in children assessed by the total score of Child Behavior Checklist (P = 0.027). Moreover, immaturity of the network was mainly correlated with externalizing behavior dimensions. Taken together, these results suggest that changes in functional network dynamics during neurodevelopment may provide unique insights regarding pathophysiology. Hum Brain Mapp 36:4926–4937, 2015.