Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach

Ecker, Christine; Rocha-Rego, Vanessa; Johnston, Patrick G.; Mourão-Miranda, Janaı́na; Marquand, André F.; Daly, Eileen; Brammer, Michael; Murphy, Clodagh; Murphy, Declan

doi:10.1016/j.neuroimage.2009.08.024

Cited by 374 publications

(334 citation statements)

References 58 publications

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“…Abnormalities in the organization of and communication between cortical neural networks are implied by a range of reports on atypical structural and functional connectivity in ASC (Casanova & Trippe, 2009;Courchesne & Pierce, 2005;Di Martino et al, 2014;Ecker et al, 2010;Geschwind & Levitt, 2007;Moseley, Shtyrov et al, 2015;Moseley, Ypma et al, 2015;Nomi & Uddin, 2015;Vissers, Cohen, & Geurts, 2012;Ypma et al, 2016). 9 These are supplemented by theoretical accounts of ASC in terms of brain-wide dysfunction characterized by 'noisy' or dysfunctional neural communication (Belmonte et al, 2004;Minshew & Goldstein, 1998;Rubenstein & Merzenich, 2003).…”

Section: The Neuroanatomical Correlates Of Movement Impairmentmentioning

confidence: 99%

What can autism teach us about the role of sensorimotor systems in higher cognition? New clues from studies on language, action semantics, and abstract emotional concept processing

Moseley

Pulvermüller

2018

Cortex

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Section: The Neuroanatomical Correlates Of Movement Impairmentmentioning

confidence: 99%

What can autism teach us about the role of sensorimotor systems in higher cognition? New clues from studies on language, action semantics, and abstract emotional concept processing

Moseley

Pulvermüller

2018

Cortex

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“…Support vector machines (SVM) are a class of machine learning algorithms that are well suited for neuroimaging data as they are fast, flexible and can be readily automated. They have been studied extensively in computer science and have been applied to neuroimaging data from a variety of neurological diseases including Alzheimer's disease (Klöppel et al, 2008), autism spectrum disorder (Ecker et al, 2010) and ADHD (Colby et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Machine learning classification of mesial temporal sclerosis in epilepsy patients

Rudie¹,

Colby²,

Salamon

2015

Epilepsy Research

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a b s t r a c tBackground and purpose: Novel approaches applying machine-learning methods to neuroimaging data seek to develop individualized measures that will aid in the diagnosis and treatment of brain-based disorders such as temporal lobe epilepsy (TLE). Using a large cohort of epilepsy patients with and without mesial temporal sclerosis (MTS), we sought to automatically classify MTS using measures of cortical morphology, and to further relate classification probabilities to measures of disease burden. Materials and methods: Our sample consisted of high-resolution T1 structural scans of 169 adults with epilepsy collected across five different 1.5 T and four different 3 T scanners at UCLA. We applied a multiple support vector machine recursive feature elimination algorithm to morphological measures generated from FreeSurfer's automated segmentation and parcellation in order to classify Epilepsy patients with MTS (n = 85) from those without MTS (N = 84). Results: In addition to hippocampal volume, we found that alterations in cortical thickness, surface area, volume and curvature in inferior frontal and anterior and inferior temporal regions contributed to a classification accuracy of up to 81% (p = 1.3 × 10 −17 ) in identifying MTS. We also found that MTS classification probabilities were associated with a longer duration of disease for epilepsy patients both with and without MTS. Conclusions: In addition to implicating extra-hippocampal involvement of MTS, these findings shed further light on the pathogenesis of TLE and may ultimately assist in the development of automated tools that incorporate multiple neuroimaging measures to assist clinicians in detecting more subtle cases of TLE and MTS.

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“…Additionally, the right TPJ has been proposed as related to mentalizing and has been repeatedly implicated in autism [Barnea‐Goraly et al, 2004], including a fMRI study of a subsample of this same AIMS dataset [Lombardo et al, 2011]. The right superior frontal gyrus (region A) is more equivocal, with some studies [Ecker et al, 2010, 2012] reporting abnormalities in this area, while others [Hadjikhani et al, 2006; Segovia et al, 2014] report no significant differences. Our analyses reveal no differences in the insula and amygdala, brain structures frequently linked to autism.…”

Section: Discussionmentioning

confidence: 99%

On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance‐weighted principal component analysis

Martínez-Murcia

Lai

Górriz

et al. 2016

Human Brain Mapping

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Neuroimaging studies have reported structural and physiological differences that could help understand the causes and development of Autism Spectrum Disorder (ASD). Many of them rely on multisite designs, with the recruitment of larger samples increasing statistical power. However, recent large‐scale studies have put some findings into question, considering the results to be strongly dependent on the database used, and demonstrating the substantial heterogeneity within this clinically defined category. One major source of variance may be the acquisition of the data in multiple centres. In this work we analysed the differences found in the multisite, multi‐modal neuroimaging database from the UK Medical Research Council Autism Imaging Multicentre Study (MRC AIMS) in terms of both diagnosis and acquisition sites. Since the dissimilarities between sites were higher than between diagnostic groups, we developed a technique called Significance Weighted Principal Component Analysis (SWPCA) to reduce the undesired intensity variance due to acquisition site and to increase the statistical power in detecting group differences. After eliminating site‐related variance, statistically significant group differences were found, including Broca's area and the temporo‐parietal junction. However, discriminative power was not sufficient to classify diagnostic groups, yielding accuracies results close to random. Our work supports recent claims that ASD is a highly heterogeneous condition that is difficult to globally characterize by neuroimaging, and therefore different (and more homogenous) subgroups should be defined to obtain a deeper understanding of ASD. Hum Brain Mapp 38:1208–1223, 2017. © 2016 Wiley Periodicals, Inc.

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Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach

Cited by 374 publications

References 58 publications

What can autism teach us about the role of sensorimotor systems in higher cognition? New clues from studies on language, action semantics, and abstract emotional concept processing

What can autism teach us about the role of sensorimotor systems in higher cognition? New clues from studies on language, action semantics, and abstract emotional concept processing

Machine learning classification of mesial temporal sclerosis in epilepsy patients

On the brain structure heterogeneity of autism: Parsing out acquisition site effects with significance‐weighted principal component analysis

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