Reduced Gyral Window and Corpus Callosum Size in Autism: Possible Macroscopic Correlates of a Minicolumnopathy

Casanova, Manuel F.; El-Baz, Ayman; Mott, Meghan; Mannheim, Glenn B.; Hassan, Hossam S.; Fahmi, Rachid; Giedd, Jay N.; Rumsey, Judith M.; Switala, Andrew E.; Farag, Aly A.

doi:10.1007/s10803-008-0681-4

Cited by 78 publications

(56 citation statements)

References 88 publications

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“…Atypical cortical morphology has previously been reported in ASD (33-36) and may be more "complex" than in controls (37,38). Our finding of reduced MSDs further supports the hypothesis that increased geometric complexity may underpin the development of shorter cortico-cortical fibers at the expense of longer connections (25,39). If so, a relative imbalance of short-to long-range cortical projections most likely also affects the way information is processed in the brain in ASD and may favor what some have suggested as a preference for local over global information (e.g., ref.…”

Section: Discussionsupporting

confidence: 88%

Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder

Ecker

Ronan

Feng

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

101

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Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions that are accompanied by atypical brain connectivity. So far, in vivo evidence for atypical structural brain connectivity in ASD has mainly been based on neuroimaging studies of cortical white matter. However, genetic studies suggest that abnormal connectivity in ASD may also affect neural connections within the cortical gray matter. Such intrinsic gray-matter connections are inherently more difficult to describe in vivo but may be inferred from a variety of surface-based geometric features that can be measured using magnetic resonance imaging. Here, we present a neuroimaging study that examines the intrinsic cortico-cortical connectivity of the brain in ASD using measures of "cortical separation distances" to assess the global and local intrinsic "wiring costs" of the cortex (i.e., estimated length of horizontal connections required to wire the cortex within the cortical sheet). In a sample of 68 adults with ASD and matched controls, we observed significantly reduced intrinsic wiring costs of cortex in ASD, both globally and locally. Differences in global and local wiring cost were predominantly observed in fronto-temporal regions and also significantly predicted the severity of social and repetitive symptoms (respectively). Our study confirms that atypical cortico-cortical "connectivity" in ASD is not restricted to the development of white-matter connections but may also affect the intrinsic gray-matter architecture (and connectivity) within the cortical sheet. Thus, the atypical connectivity of the brain in ASD is complex, affecting both gray and white matter, and forms part of the core neural substrates underlying autistic symptoms.brain structure | gyrification | morphometry A utism spectrum disorders (ASDs) are a group of neurodevelopmental conditions characterized by impaired social communication, social reciprocity, and repetitive/stereotypic behavior (1). There is consensus that ASD is accompanied by developmental differences in brain anatomy and connectivity (2). However, the specific neurobiological substrate of ASD remains elusive.Neuroimaging evidence suggests that ASD is accompanied by anatomical differences in several large-scale neurocognitive networks (3), which typically include the cerebellum (4), the amygdalahippocampal complex (5), fronto-temporal regions (6), and the caudate nuclei (7). These gross-anatomical abnormalities are likely to reflect differences in the cytoarchitectonic make-up of individual brain regions in ASD, as can be observed in postmortem studies. For instance, individuals with ASD may have a reduced size, but increased number, of minicolumns (8), which could underpin differences in cortical surface area (9, 10). Also, individuals with ASD may have an excess number of neurons in some brain regions (11), which in turn may affect measures of cortical thickness (9, 12). Therefore, different morphometric features of the cortical surface provide proxy measures of various aspects of local gray-matter arc...

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Section: Discussionsupporting

confidence: 88%

Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder

Ecker

Ronan

Feng

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

101

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“…• A future work of this dissertation is to investigate the integration of the proposed work with the BioImaging lab work for the detection of other brain disorders such as autism [313,[336][337][338][339][340][341][342][343].…”

Section: B Directions For Future Researchmentioning

confidence: 99%

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Elnakib¹

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DEDICATIONThis dissertation is dedicated to my mother, my father, and my wife for their love, patience, and support during the completion of this endeavor.iii ACKNOWLEDGMENTSIn the Name of Allah, the most beneficent, the most merciful. All deepest thanks are due to Allah, the compassionate for the uncountable gifts given to me.I would like to express my sincere gratitude to Professor El-Baz, my dissertation advisor, for the immeasurable amount of support and guidance he has provided throughout this study. He gave me the opportunity to work in his distinguished research group and provided me with an exciting working environment that facilitate many opportunities to develop new ideas and working on promising applications. Dr. El-Baz is always willing to give, share, and appreciate. I knew that I could always expect full credit for my accomplishments. He always supported me with novel ideas that helped me to publish my works in the top international conference and journal papers. It is worthy to mention that Professor El-Baz is a great advisor as well as a good friend who helps any Ph.D. student who works with him in high energy level. I cannot thank him enough, and shall remain grateful for all he has done. Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality.This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in vi the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure-the largest fiber bundle that connects the two hemispheres in the brain-for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object's visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching...

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“…A future extension of this work would be analyzing the extracted brain and testing these measurements not only to diagnose autism [134][135][136][137][138][139][140][141], but also to characterize physiological processes and other disease entities or to characterize the severity of other diseases such as dyslexia [142,143], brain tumors, strokes, seizure disorders, depression, and Alzheimer's disease [33].…”

Section: B Future Workmentioning

confidence: 99%

Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

Alansary¹

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Reduced Gyral Window and Corpus Callosum Size in Autism: Possible Macroscopic Correlates of a Minicolumnopathy

Cited by 78 publications

References 88 publications

Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder

Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

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