Article

Grey matter abnormality in autism spectrum disorder: an activation likelihood estimation meta-analysis study.

CCS fMRI, Koelliker Hospital, Turin, Italy.
Journal of neurology, neurosurgery, and psychiatry (Impact Factor: 4.87). 06/2011; 82(12):1304-13. DOI: 10.1136/jnnp.2010.239111
Source: PubMed

ABSTRACT Autism spectrum disorder (ASD) is defined on a clinical basis by impairments in social interaction, verbal and non-verbal communication, and repetitive or stereotyped behaviours. Voxel based morphometry (VBM), a technique that gives a probabilistic measure of local grey matter (GM) and white matter concentration, has been used to study ASD patients: modifications in GM volume have been found in various brain regions, such as the corpus callosum, brainstem, amygdala, hippocampus and cerebellum. However, the findings are inconsistent with respect to the specific localisation and direction of GM modifications, and no paper has attempted to statistically summarise the results available in the literature.
The present study is a quantitative meta-analysis of the current VBM findings aimed at delineating the cortical regions with consistently increased or reduced GM concentrations. The activation likelihood estimation (ALE) was used, which is a quantitative voxel based meta-analysis method which can be used to estimate consistent activations across different imaging studies. Co-occurrence statistics of a PubMed query were generated, employing 'autism spectrum disorder' as the neuroanatomical lexicon.
Significant ALE values related to GM increases were observed bilaterally in the cerebellum, in the middle temporal gyrus, in the right anterior cingulate cortex, caudate head, insula, fusiform gyrus, precuneus and posterior cingulate cortex, and in the left lingual gyrus. GM decreases were observed bilaterally in the cerebellar tonsil and inferior parietal lobule, in the right amygdala, insula, middle temporal gyrus, caudate tail and precuneus and in the left precentral gyrus.

1 Bookmark
 · 
291 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Psychological models of mental disorders guide research into psychological and environmental factors that elicit and maintain mental disorders as well as interventions to reduce them. This paper addresses four areas. (1) Psychological models of mental disorders have become increasingly transdiagnostic, focusing on core cognitive endophenotypes of psychopathology from an integrative cognitive psychology perspective rather than offering explanations for unitary mental disorders. It is argued that psychological interventions for mental disorders will increasingly target specific cognitive dysfunctions rather than symptom-based mental disorders as a result. (2) Psychotherapy research still lacks a comprehensive conceptual framework that brings together the wide variety of findings, models and perspectives. Analysing the state-of-the-art in psychotherapy treatment research, "component analyses" aiming at an optimal identification of core ingredients and the mechanisms of change is highlighted as the core need towards improved efficacy and effectiveness of psychotherapy, and improved translation to routine care. (3) In order to provide more effective psychological interventions to children and adolescents, there is a need to develop new and/or improved psychotherapeutic interventions on the basis of developmental psychopathology research taking into account knowledge of mediators and moderators. Developmental neuroscience research might be instrumental to uncover associated aberrant brain processes in children and adolescents with mental health problems and to better examine mechanisms of their correction by means of psychotherapy and psychological interventions. (4) Psychotherapy research needs to broaden in terms of adoption of large-scale public health strategies and treatments that can be applied to more patients in a simpler and cost-effective way. Increased research on efficacy and moderators of Internet-based treatments and e-mental health tools (e.g. to support "real time" clinical decision-making to prevent treatment failure or relapse) might be one promising way forward. Copyright © 2013 John Wiley & Sons, Ltd.
    International Journal of Methods in Psychiatric Research 01/2014; 23(1):58-91. · 1.76 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In the clinical area, some symptoms of attention deficit hyperactivity disorder (ADHD) also present in patients with autism spectrum disorders (ASD). Research has shown that there are alterations in brain circuits that have an impact upon specific cognitive and behavioural failures in each of these disorders. Yet, little research has been conducted on the brain correlates underlying both the similarities and the differences in the symptoms. In this review, the structural and functional meta-analytical studies that have been carried out to date on ADHD and ASD have been analysed. On the one hand, there are convergences in the attentional dorsal, executive functions, visual, somatomotor circuits and the default activation circuit. These similarities can account for the comorbid manifestations between the disorders, such as failure in the integration of information, fine motor control and specific attention processes. On the other hand, specifically in ADHD, there is a deficit in the reward circuit and in the attentional ventral, which are systems involved in the measurement of the effects of reinforcement and monitoring of attention. In ASD, the circuits that are most strongly affected are those involved in social cognition and language processes. In conclusion, there are neuronal correlates in both disorders that explain both the convergent and divergent clinical and behavioural manifestations.
    Revista de neurologia 09/2013; 57(s01):S163-S175. · 1.18 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: This article describes a novel approach to identify autism spectrum disorder (ASD) utilizing regional and interregional morphological patterns extracted from structural magnetic resonance images. Two types of features are extracted to characterize the morphological patterns: (1) Regional features, which includes the cortical thickness, volumes of cortical gray matter, and cortical-associated white matter regions, and several subcortical structures extracted from different regions-of-interest (ROIs); (2) Interregional features, which convey the morphological change pattern between pairs of ROIs. We demonstrate that the integration of regional and interregional features via multi-kernel learning technique can significantly improve the classification performance of ASD, compared with using either regional or interregional features alone. Specifically, the proposed framework achieves an accuracy of 96.27% and an area of 0.9952 under the receiver operating characteristic curve, indicating excellent diagnostic power and generalizability. The best performance is achieved when both feature types are weighted approximately equal, indicating complementary between these two feature types. Regions that contributed the most to classification are in line with those reported in the previous studies, particularly the subcortical structures that are highly associated with human emotional modulation and memory formation. The autistic brains demonstrate a significant rightward asymmetry pattern particularly in the auditory language areas. These findings are in agreement with the fact that ASD is a behavioral- and language-related neurodevelopmental disorder. By concurrent consideration of both regional and interregional features, the current work presents an effective means for better characterization of neurobiological underpinnings of ASD that facilitates its identification from typically developing children.
    Human Brain Mapping 01/2013; · 6.88 Impact Factor

Full-text

View
223 Downloads
Available from
May 21, 2014