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Introduction: Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD). Method: We compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age=14-50, IQ=92-132), 16 AS (age=7-58, IQ=93-133) and 28 HC (age=9-39, IQ=95-144). Result: The one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is altered in ASD. Conclusion: This changed structural covariance could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.
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Introduction: It has been shown that intelligence as a general mental ability is related to the structure and function of the brain regions. However, the specificity of these regional dependencies to the intelligence scores in the typical and atypical developed individuals needs to be well understood. In this study, we hypothesized that neural correlates of IQ should not have a fixed pattern rather they must follow a dynamic pattern to compensate for the functional deficits caused by a neurodevelopmental disorder. Therefore, electroencephalography (EEG) correlates of normal IQ in various subtypes of attention deficit hyperactive disorder (ADHD) were compared with a group of healthy controls. Methods: Sixty-three ADHD subjects comprising combined, inattentive, and hyperactive individuals diagnosed by a psychiatrist using structural clinical interview for DSM-V, and 46 healthy controls with similar normal IQ scores were recruited in this study. The subjects' EEG data were then recorded during an eye-closed resting condition. The subjects' intelligence level was measured by Raven's standard progressive matrices. Then, the association between IQ and the power of the EEG signal was computed in the conventional frequency bands. Subsequently, topographical representations of these associations were compared between the groups. Results: Our results demonstrated that the association between IQ score and EEG power is not the same in various ADHD subtypes and healthy controls. Conclusion: This finding suggests a compensatory mechanism in ADHD individuals for changing the regional oscillatory pattern to maintain the IQ within a normal range.
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Introduction: Neuroimaging evidence has shown the relationship of intelligence with several structural brain properties in normal individuals. However, this relationship with attention deficit hyperactivity disorder (ADHD) needs to be investigated. Methods: We estimated grey matter (GM) density of the brain using magnetic resonance imaging (MRI) scan on 56 ADHD individuals, including 30 combined individuals (Mean±SD age: 10.44±2.41, intelligence quotient: [IQ]=112.13±13.15, male, 24 right hands) and 26 inattentive individuals (mean age =11.39±2.1, IQ=107.44±13.98, male, 28 right hands) as well as 30 IQ matched healthy control group (mean age=11.08±2.15, IQ=115±13.56, male, 23 right hands). Results: In this study, two statistical approaches were used. In the first approach, region-based as well as the whole association patterns between full-scale IQ and GM were computed and compared between groups. The second approach was to examine the differential pattern of GM density without considering IQ in three groups. Conclusion: Results showed significant differences between the ADHD group and the control. This finding could indicate that intelligence is not purely based on the density of GM in certain brain regions; it is a dynamic phenomenon and drastically changes neurodevelopmental disorders. Highlights: In ADHDs as compared to healthy controls the relation between GM and IQ was decreased at right hemisphere;In ADHDs as compared to healthy controls the relation between GM and IQ was increased at left hemisphere;Differenceses of the observed relation between control group and IQ matched ADHDs suggest a compensatory mechanism in ADHDs to maintaine an adequate cognitive performance;GM is not the only determiner of intelligence. IQ score may be affected by neural dynamic of the brain; therefore, the structural covariate could be a better alternative for GM density. Plain Language Summary: In this study, we estimated the relation between GM density and IQ score in 2 subtypes of ADHD (combined and inattentive) and IQ matched healthy control group. We compared the association between groups and found that the pattern of association in ADHDs were different from controls. In the other words, the decreased association at right hemisphere, were compensated by increased association at left hemisphere in ADHDs to maintaine adequate performance. We conclude that, the brain structure is not the single determiner of intelligence, rather intelligence may underpine by neural dynamics of the brain. Therefore, the structural covariate may be a better alternative for GM density.
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Asperger syndrome (AS) is a subtype of Autism Spectrum Disorder (ASD) characterized by major problems in social and nonverbal communication, together with limited and repetitive forms of behavior and interests. The linguistic and cognitive development in AS is preserved which help us to differentiate it from other subtypes of ASD. However, significant effects of AS on cognitive abilities and brain functions still need to be researched. Although a clear cut pathology for Asperger has not been identified yet, recent studies have largely focused on brain imaging techniques to investigate AS. In this regard, we carried out a systematic review on behavioral, cognitive, and neural markers (specifically using MRI and fMRI) studies on AS. In this paper, behavior, motor skills and language capabilities of individuals with Asperger are compared to those in healthy controls. In addition, common findings across MRI and fMRI based studies associated with behavior and cognitive disabilities are highlighted.
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Transcription is a tightly regulated process ensuring the proper expression of numerous genes regulating all aspects of cellular behavior. Transcription factors regulate multiple genes including other transcription factors that together control a highly complex gene network. The transcriptional machinery can be "hijacked" by oncogenic transcription factors, thereby leading to malignant cell transformation. Oncogenic transcription factors manipulate a variety of epigenetic control mechanisms to fulfill gene regulatory and cell transforming functions. These factors assemble epigenetic regulators at target gene promoter sequences, thereby disturbing physiological gene expression patterns. Retroviral vector technology and the availability of "healthy" human hematopoietic CD34+ progenitor cells enable the generation of pre-leukemic cell models for the analysis of aberrant human hematopoietic progenitor cell expansion mediated by leukemogenic transcription factors. This review summarizes recent findings regarding the mechanism by which leukemogenic gene products control human hematopoietic CD34+ progenitor cell expansion by disrupting the normal epigenetic program.