Focal cortical dysplasias in autism spectrum disorders

Casanova, Manuel F.; El-Baz, Ayman; Kamat, Shweta Sunil; Dombroski, Brynn A.; Khalifa, Fahmi; Elnakib, Ahmed; Soliman, Ahmed; Allison-McNutt, Anita; Switala, Andrew E.

doi:10.1186/2051-5960-1-67

Cited by 128 publications

(100 citation statements)

References 59 publications

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“…Further, abnormalities in local circuits could impede the formation of long-distance connectivity between various, functionally specialised parts of the cortex , resulting in significant hypo-or under-connectivity crosscortically (Casanova et al, 2002;Courchesne & Pierce, 2005;Lewis & Elman, 2008;Rubenstein & Merzenich, 2003). The model is now being supported at the cytoarchitectural level (Casanova et al, 2013;Casanova & Trippe, 2009;Stoner et al, 2014) with additional support from in vivo measures of GABA in ASD populations (Gaetz et al, 2014). However, a recent review of neurophysiological and psychophysiological research into alterations in the balance of excitation-inhibition in ASD has concluded that, while the evidence for an imbalance is strong, it may arise not only from excitation being increased relative to inhibition (as above) but also from increased inhibitory processes resulting in imbalance relative to excitatory processes (Dickinson, Jones, & Milne, 2016).…”

Section: Excitation-inhibition In Autismmentioning

confidence: 91%

Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

Kessler

Seymour

Rippon

2016

Neuroscience & Biobehavioral Reviews

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Please cite this article as: Kessler, K., Seymour, R.A., Rippon, G., Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling.Neuroscience and Biobehavioral Reviews http://dx.doi.org/10. 1016/j.neubiorev.2016.10.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Abstract:Although atypical social behaviour remains a key characterisation of ASD, the presence of sensory and perceptual abnormalities has been given a more central role in recent classification changes. An understanding of the origins of such aberrations could thus prove a fruitful focus for ASD research. Early neurocognitive models of ASD suggested that the study of high frequency activity in the brain as a measure of cortical connectivity might provide the key to understanding the neural correlates of sensory and perceptual deviations in ASD. As our review shows, the findings from subsequent research have been inconsistent, with a lack of agreement about the nature of any high frequency disturbances in ASD brains.Based on the application of new techniques using more sophisticated measures of brain synchronisation, direction of information flow, and invoking the coupling between high and low frequency bands, we propose a framework which could reconcile apparently conflicting findings in this area and would be consistent both with emerging neurocognitive models of autism and with the heterogeneity of the condition.

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Section: Excitation-inhibition In Autismmentioning

confidence: 91%

Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

Kessler

Seymour

Rippon

2016

Neuroscience & Biobehavioral Reviews

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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 [191][192][193][194][195][196][197][198], but also to characterize physiological processes and other disease entities or to characterize the severity of other diseases such as dyslexia [199,200], attention deficit disorder (ADD), brain tumors, strokes, seizure disorders, depression, and Alzheimer's disease [201].…”

Section: B Future Workmentioning

confidence: 99%

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

Mostapha¹

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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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Focal cortical dysplasias in autism spectrum disorders

Cited by 128 publications

References 59 publications

Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

A novel diffusion tensor imaging-based computer-aided diagnostic system for early diagnosis of autism.

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

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