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The article discusses a research study on the relationship of brain anatomy to the behavior of adults who have autism spectrum disorder (ASD). Assessed in the multicenter case-control design study were the difference in regional neuroanatomy through voxel-based morphometry and neural systems correlated with ASD. Results showed in voxel-based comparison that people with ASD have increased volume of gray matter in the dorsolateral prefrontal and anterior temporal regions.

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. [ABSTRACT FROM AUTHOR]

Chen C-H, Suckling J, Lennox BR, Ooi C, Bullmore ET. A quantitative meta-analysis of fMRI studies in bipolar disorder. Bipolar Disord 2011: 13: 1-15. © 2011 The Authors. Journal compilation © 2011 John Wiley & Sons A/S. 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. 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). 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. 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. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented regarding a longitudinal fMRI study of the manic and euthymic states of bipolar disorder in 2010 issue.

Objective: Obsessive-compulsive disorder (OCO) is a common, heritable neuropsychiatric disorder, hypothetically underpinned by dysconnectivity of largescale 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Hum. Brain Mapping 24:144-155, 2005. © 2004 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

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. Hum. Brain Mapp. 23:1-25, 2004. © 2004 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

The precise localization of executive functions such as response inhibition within the prefrontal cortex (PFC), although theoretically crucial, has proven to be controversial and difficult. Functional neuroimaging has contributed importantly to this debate, but as human cortical lesions are seldom discrete, the literature still lacks definitive neuropsychological evidence that a specific region is necessary for task performance. We overcame this limitation by using a new observer-independent method to relate the degree of damage within a specific prefrontal region to performance on a stop-signal task that is sensitive to the neurodevelopmental aspects of stopping behavior and to attention-deficit/hyperactivity disorder (ADHD) as well as its amelioration by methylphenidate. [ABSTRACT FROM AUTHOR]

Presents a corrected reprint of the article "Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans," by Adam R. Aron, Paul C. Fletcher et al., published in the Number 6, 2003 issue of the "Nature Neuroscience" journal.