Abnormal cortical growth in schizophrenia targets normative modules of synchronized development.

BACKGROUND: Schizophrenia is a disorder of brain connectivity and altered neurodevelopmental processes. Cross-sectional case-control studies in different age groups have suggested that deficits in cortical thickness in childhood-onset schizophrenia may normalize over time, suggesting a disorder-related difference in cortical growth trajectories. METHODS: We acquired magnetic resonance imaging scans repeated over several years for each subject, in a sample of 106 patients with childhood-onset schizophrenia and 102 age-matched healthy volunteers. Using semiparametric regression, we modeled the effect of schizophrenia on the growth curve of cortical thickness in ~80,000 locations across the cortex, in the age range 8 to 30 years. In addition, we derived normative developmental modules composed of cortical regions with similar maturational trajectories for cortical thickness in typical brain development. RESULTS: We found abnormal nonlinear growth processes in prefrontal and temporal areas that have previously been implicated in schizophrenia, distinguishing for the first time between cortical areas with age-constant deficits in cortical thickness and areas whose maturational trajectories are altered in schizophrenia. In addition, we showed that when the brain is divided into five normative developmental modules, the areas with abnormal cortical growth overlap significantly only with the cingulo-fronto-temporal module. CONCLUSIONS: These findings suggest that abnormal cortical development in schizophrenia may be modularized or constrained by the normal community structure of developmental modules of the human brain connectome.

Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study.

CONTEXT: There is consensus that autism spectrum disorder (ASD) is accompanied by differences in neuroanatomy. However, the neural substrates of ASD during adulthood, as well as how these relate to behavioral variation, remain poorly understood. OBJECTIVE: To identify brain regions and systems associated with ASD in a large, well-characterized sample of adults. DESIGN: Multicenter case-control design using quantitative magnetic resonance imaging. SETTING: Medical Research Council UK Autism Imaging Multicentre Study (MRC AIMS), with sites comprising the Institute of Psychiatry, Kings College London; the Autism Research Centre, University of Cambridge; and the Autism Research Group, University of Oxford. PARTICIPANTS: Eighty-nine men with ASD and 89 male control participants who did not differ significantly in mean age (26 and 28 years, respectively) and full-scale IQ (110 and 113, respectively). MAIN OUTCOME MEASURES: (1) Between-group differences in regional neuroanatomy assessed by voxel-based morphometry and (2) distributed neural systems maximally correlated with ASD, as identified by partial least-squares analysis. RESULTS: Adults with ASD did not differ significantly from the controls in overall brain volume, confirming the results of smaller studies of individuals in this age group without intellectual disability. However, voxelwise comparison between groups revealed that individuals with ASD had significantly increased gray matter volume in the anterior temporal and dorsolateral prefrontal regions and significant reductions in the occipital and medial parietal regions compared with controls. These regional differences in neuroanatomy were significantly correlated with the severity of specific autistic symptoms. The large-scale neuroanatomic networks maximally correlated with ASD identified by partial least-squares analysis included the regions identified by voxel-based analysis, as well as the cerebellum, basal ganglia, amygdala, inferior parietal lobe, cingulate cortex, and various medial, orbital, and lateral prefrontal regions. We also observed spatially distributed reductions in white matter volume in participants with ASD. CONCLUSIONS: Adults with ASD have distributed differences in brain anatomy and connectivity that are associated with specific autistic features and traits. These results are compatible with the concept of autism as a syndrome characterized by atypical neural "connectivity."

Brain network analysis: separating cost from topology using cost-integration.

A statistically principled way of conducting brain network analysis is still lacking. Comparison of different populations of brain networks is hard because topology is inherently dependent on wiring cost, where cost is defined as the number of edges in an unweighted graph. In this paper, we evaluate the benefits and limitations associated with using cost-integrated topological metrics. Our focus is on comparing populations of weighted undirected graphs that differ in mean association weight, using global efficiency. Our key result shows that integrating over cost is equivalent to controlling for any monotonic transformation of the weight set of a weighted graph. That is, when integrating over cost, we eliminate the differences in topology that may be due to a monotonic transformation of the weight set. Our result holds for any unweighted topological measure, and for any choice of distribution over cost levels. Cost-integration is therefore helpful in disentangling differences in cost from differences in topology. By contrast, we show that the use of the weighted version of a topological metric is generally not a valid approach to this problem. Indeed, we prove that, under weak conditions, the use of the weighted version of global efficiency is equivalent to simply comparing weighted costs. Thus, we recommend the reporting of (i) differences in weighted costs and (ii) differences in cost-integrated topological measures with respect to different distributions over the cost domain. We demonstrate the application of these techniques in a re-analysis of an fMRI working memory task. We also provide a Monte Carlo method for approximating cost-integrated topological measures. Finally, we discuss the limitations of integrating topology over cost, which may pose problems when some weights are zero, when multiplicities exist in the ranks of the weights, and when one expects subtle cost-dependent topological differences, which could be masked by cost-integration.

A quantitative meta-analysis of fMRI studies in bipolar disorder.

OBJECTIVES: Functional magnetic resonance imaging (fMRI) has been widely used to identify state and trait markers of brain abnormalities associated with bipolar disorder (BD). However, the primary literature is composed of small-to-medium-sized studies, using diverse activation paradigms on variously characterized patient groups, which can be difficult to synthesize into a coherent account. This review aimed to synthesize current evidence from fMRI studies in midlife adults with BD and to investigate whether there is support for the theoretical models of the disorder. METHODS: We used voxel-based quantitative meta-analytic methods to combine primary data on anatomical coordinates of activation from 65 fMRI studies comparing normal volunteers (n = 1,074) and patients with BD (n = 1,040). RESULTS: Compared to normal volunteers, patients with BD underactivated the inferior frontal cortex (IFG) and putamen and overactivated limbic areas, including medial temporal structures (parahippocampal gyrus, hippocampus, and amygdala) and basal ganglia. Dividing studies into those using emotional and cognitive paradigms demonstrated that the IFG abnormalities were manifest during both cognitive and emotional processing, while increased limbic activation was mainly related to emotional processing. In further separate comparisons between healthy volunteers and patient subgroups in each clinical state, the IFG was underactive in manic but not in euthymic and depressed states. Limbic structures were not overactive in association with mood states, with the exception of increased amygdala activation in euthymic states when including region-of-interest studies. CONCLUSIONS: In summary, our results showed abnormal frontal-limbic activation in BD. There was attenuated activation of the IFG or ventrolateral prefrontal cortex, which was consistent across emotional and cognitive tasks and particularly related to the state of mania, and enhanced limbic activation, which was elicited by emotional and not cognitive tasks, and not clearly related to mood states.

White matter abnormalities in patients with obsessive-compulsive disorder and their first-degree relatives.

OBJECTIVE: Obsessive-compulsive disorder (OCD) is a common, heritable neuropsychiatric disorder, hypothetically underpinned by dysconnectivity of large-scale brain systems. The extent of white matter abnormalities in OCD is unknown, and the genetic basis of this disorder is poorly understood. The authors used diffusion tensor imaging, a magnetic resonance imaging technique, for examining white matter abnormalities in brain structure through quantification of water diffusion, to confirm whether white matter abnormalities exist in OCD. They also explored whether such abnormalities occur in healthy first-degree relatives of patients, indicating they may be endophenotypes representing increased genetic risk for OCD. METHOD: The authors used diffusion tensor imaging to measure fractional anisotropy of white matter in 30 patients with OCD, 30 unaffected first-degree relatives, and 30 matched healthy comparison subjects. Regions of significantly abnormal fractional anisotropy in patients in relation to healthy comparison subjects were identified by permutation tests. The authors assessed whether these abnormalities were also evident in the first-degree relatives. A secondary region-of-interest analysis was undertaken to assess the extent of replication between our data and previous relevant literature. RESULTS: Patients with OCD demonstrated significantly reduced fractional anisotropy in a large region of right inferior parietal white matter and significantly increased fractional anisotropy in a right medial frontal region. Relatives also exhibited significant abnormalities of fractional anisotropy in these regions. CONCLUSIONS: These findings indicate that OCD is associated with white matter abnormalities in parietal and frontal regions. Similar abnormalities in unaffected first-degree relatives suggest these may be white matter endophenotypes for OCD.

Integrating evidence from neuroimaging and neuropsychological studies of obsessive-compulsive disorder: the orbitofronto-striatal model revisited.

Obsessive-compulsive disorder (OCD) is a common, heritable and disabling neuropsychiatric disorder. Theoretical models suggest that OCD is underpinned by functional and structural abnormalities in orbitofronto-striatal circuits. Evidence from cognitive and neuroimaging studies (functional and structural magnetic resonance imaging (MRI) and positron emission tomography (PET)) have generally been taken to be supportive of these theoretical models; however, results from these studies have not been entirely congruent with each other. With the advent of whole brain-based structural imaging techniques, such as voxel-based morphometry and multivoxel analyses, we consider it timely to assess neuroimaging findings to date, and to examine their compatibility with cognitive studies and orbitofronto-striatal models. As part of this assessment, we performed a quantitative, voxel-level meta-analysis of functional MRI findings, which revealed consistent abnormalities in orbitofronto-striatal and other additional areas in OCD. This review also considers the evidence for involvement of other brain areas outside orbitofronto-striatal regions in OCD, the limitations of current imaging techniques, and how future developments in imaging may aid our understanding of OCD.

Substance use in a population-based clinic sample of people with first-episode psychosis.

BACKGROUND: Substance use is implicated in the cause and course of psychosis. AIMS: To characterise substance and alcohol use in an epidemiologically representative treatment sample of people experiencing a first psychotic episode in south Cambridgeshire. METHOD: Current and lifetime substance use was recorded for 123 consecutive referrals to a specialist early intervention service. Substance use was compared with general population prevalence estimates from the British Crime Survey. RESULTS: Substance use among people with first-episode psychosis was twice that of the general population and was more common in men than women. Cannabis abuse was reported in 51% of patients (n=62) and alcohol abuse in 43% (n=53). More than half (n=68, 55%) had used Class A drugs, and 38% (n=43) reported polysubstance abuse. Age at first use of cannabis, cocaine, ecstasy and amphetamine was significantly associated with age at first psychotic symptom. CONCLUSIONS: Substance misuse is present in the majority of people with first-episode psychosis and has major implications for management. The association between age at first substance use and first psychotic symptoms has public health implications.

The multiscale character of evoked cortical activity.

Both the architecture and the dynamics of the brain have characteristic features at different spatial scales. However, the existence, nature and function of dynamical interdependencies between such scales have not been investigated. We studied the multiscale properties of functional magnetic resonance imaging (fMRI) data acquired while human subjects viewed a visual image. Traditional "region of interest" analysis of this data set revealed evoked activity in primary and extrastriate visual cortex. Wavelet transform in the spatial domain provides a multiscale representation of this evoked brain activity. Studying the correlation structure of this representation revealed strong and novel interdependencies in these data within and between different spatial scales. We found that such correlations are stronger than those evident in the original data and comparable in magnitude to those obtained after Gaussian smoothing. However, analysis of the data in the wavelet domain revealed additional structure such as positive correlations, strong anti-correlations and phase-lagged interdependencies. Statistical significance of these effects was inferred through nonparametric bootstrap techniques. We conclude that the spatial analysis of functional neuroimaging data in the wavelet domain provides novel information which may reflect complex spatiotemporal neuronal activity and information encoding. It also affords a quantitative means of testing hierarchical and multiscale models of cortical activity.

Formal characterization and extension of the linearized diffusion tensor model.

We analyzed the properties of the logarithm of the Rician distribution leading to a full characterization of the probability law of the errors in the linearized diffusion tensor model. An almost complete lack of bias, a simple relation between the variance and the signal-to-noise ratio in the original complex data, and a close approximation to normality facilitated estimation of the tensor components by an iterative weighted least squares algorithm. The theory of the linear model has also been used to derive the distribution of mean diffusivity, to develop an informative statistical test for relative lack of fit of the ellipsoidal (or spherical) model compared to an unrestricted linear model in which no specific shape is assumed for the diffusion process, and to estimate the signal-to-noise ratios in the original data. The false discovery rate (FDR) has been used to control thresholds for statistical significance in the context of multiple comparisons at voxel level. The methods are illustrated by application to three diffusion tensor imaging (DTI) datasets of clinical interest: a healthy volunteer, a patient with acute brain injury, and a patient with hydrocephalus. Interestingly, some salient features, such as a region normally comprising the basal ganglia and internal capsule, and areas of edema in patients with brain injury and hydrocephalus, had patterns of error largely independent from their mean diffusivities. These observations were made in brain regions with sufficiently large signal-to-noise ratios (>2) to justify the assumptions of the log Rician probability model. The combination of diffusivity and its error may provide added value in diagnostic DTI of acute pathologic expansion of the extracellular fluid compartment in brain parenchymal tissue.

Spatiotemporal wavelet resampling for functional neuroimaging data.

The study of dynamic interdependences between brain regions is currently a very active research field. For any connectivity study, it is important to determine whether correlations between two selected brain regions are statistically significant or only chance effects due to non-specific correlations present throughout the data. In this report, we present a wavelet-based non-parametric technique for testing the null hypothesis that the correlations are typical of the data set and not unique to the regions of interest. This is achieved through spatiotemporal resampling of the data in the wavelet domain. Two functional MRI data sets were analysed: (1) Data from 8 healthy human subjects viewing a checkerboard image, and (2) "Null" data obtained from 3 healthy human subjects, resting with eyes closed. It was demonstrated that constrained resampling of the data in the wavelet domain allows construction of bootstrapped data with four essential properties: (1) Spatial and temporal correlations within and between slices are preserved, (2) The irregular geometry of the intracranial images is maintained, (3) There is adequate type I error control, and (4) Expected experiment-induced correlations are identified. The limitations and possible extensions of the proposed technique are discussed.

Differential neural responses to overt and covert presentations of facial expressions of fear and disgust.

There is debate in cognitive neuroscience whether conscious versus unconscious processing represents a categorical or a quantitative distinction. The purpose of the study was to explore this matter using functional magnetic resonance imaging (fMRI). We first established objective thresholds of the critical temporal parameters for overt and covert presentations of fear and disgust. Next we applied these stimulus parameters in an fMRI experiment to determine whether non-consciously perceived (covert) facial expressions of fear and disgust show the same double dissociation (amygdala response to fear, insula to disgust) observed with consciously perceived (overt) stimuli. A backward masking paradigm was used. In the psychophysics experiment, the following parameters were established: 30-ms target duration for the covert condition, and 170-ms target duration for the overt condition. Results of the block-design fMRI study indicated substantial differences underlying the perception of fearful and disgusted facial expressions, with significant effects of both emotion and target duration. Findings for the overt condition (170 ms) confirm previous evidence of amygdala activation to fearful faces, and insula activation to disgusted faces, and a double dissociation between these two emotions. In the covert condition (30 ms), the amygdala was not activated to fear, nor was the insula activated to disgust. Overall, findings demonstrate significant differences between the neural responses to fear and to disgust, and between the covert presentations of these two emotions. These results therefore suggest distinct neural correlates of conscious and unconscious emotion perception.