scholarly journals Support Vector Machines, Multidimensional Scaling and Magnetic Resonance Imaging Reveal Structural Brain Abnormalities Associated With the Interaction Between Autism Spectrum Disorder and Sex

2018 ◽  
Vol 12 ◽  
Author(s):  
Andrei Irimia ◽  
Xiaoyu Lei ◽  
Carinna M. Torgerson ◽  
Zachary J. Jacokes ◽  
Sumiko Abe ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Autism Spectrum Disorder ◽  
Support Vector Machines ◽  
Magnetic Resonance ◽  
Multidimensional Scaling ◽  
Autism Spectrum ◽  
Support Vector ◽  
Resonance Imaging ◽  
Vector Machines ◽  
Structural Brain

scholarly journals Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0239615
Author(s):  
Maeri Yamamoto ◽  
Epifanio Bagarinao ◽  
Itaru Kushima ◽  
Tsutomu Takahashi ◽  
Daiki Sasabayashi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Magnetic Resonance ◽  
Support Vector Machine Classifier ◽  
Classification Performance ◽  
Support Vector ◽  
Resonance Imaging ◽  
Vector Machines ◽  
Structural Brain Alterations

Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.

scholarly journals Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network

2021 ◽  
Vol 11 (13) ◽  
pp. 6216
Author(s):  
Aikaterini S. Karampasi ◽  
Antonis D. Savva ◽  
Vasileios Ch. Korfiatis ◽  
Ioannis Kakkos ◽  
George K. Matsopoulos
Keyword(s):  
Magnetic Resonance Imaging ◽  
Autism Spectrum Disorder ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Default Mode Network ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Dynamic Connectivity

Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis, although the scarcity of potent autism-related biomarkers is a bottleneck. More importantly, the variability of the imported attributes among different sites (e.g., acquisition parameters) and different individuals (e.g., demographics, movement, etc.) pose additional challenges, eluding adequate generalization and universal modeling. The present study focuses on a data-driven approach for the identification of efficacious biomarkers for the classification between typically developed (TD) and ASD individuals utilizing functional magnetic resonance imaging (fMRI) data on the default mode network (DMN) and non-physiological parameters. From the fMRI data, static and dynamic connectivity were calculated and fed to a feature selection and classification framework along with the demographic, acquisition and motion information to obtain the most prominent features in regard to autism discrimination. The acquired results provided high classification accuracy of 76.63%, while revealing static and dynamic connectivity as the most prominent indicators. Subsequent analysis illustrated the bilateral parahippocampal gyrus, right precuneus, midline frontal, and paracingulate as the most significant brain regions, in addition to an overall connectivity increment.

scholarly journals High Resolution Magnetic Resonance Imaging for Characterization of the Neuroligin-3 Knock-in Mouse Model Associated with Autism Spectrum Disorder

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e109872 ◽  
Author(s):  
Manoj Kumar ◽  
Jeffery T. Duda ◽  
Wei-Ting Hwang ◽  
Charles Kenworthy ◽  
Ranjit Ittyerah ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Autism Spectrum Disorder ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Mouse Model ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Resonance Imaging

Training the social brain: Clinical and neural effects of an 8-week real-time functional magnetic resonance imaging neurofeedback Phase IIa Clinical Trial in Autism

Autism ◽  
2021 ◽  
pp. 136236132110020
Author(s):  
Bruno Direito ◽  
Susana Mouga ◽  
Alexandre Sayal ◽  
Marco Simões ◽  
Hugo Quental ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Clinical Trial ◽  
Autism Spectrum Disorder ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Social Brain

Autism spectrum disorder is characterized by abnormal function in core social brain regions. Here, we demonstrate the feasibility of real-time functional magnetic resonance imaging volitional neurofeedback. Following up the demonstration of neuromodulation in healthy participants, in this repeated-measure design clinical trial, 15 autism spectrum disorder patients were enrolled in a 5-session training program of real-time functional magnetic resonance imaging neurofeedback targeting facial emotion expressions processing, using the posterior superior temporal sulcus as region-of-interest. Participants were able to modulate brain activity in this region-of-interest, over multiple sessions. Moreover, we identified the relevant clinical and neural effects, as documented by whole-brain neuroimaging results and neuropsychological measures, including emotion recognition of fear, immediately after the intervention and persisting after 6 months. Neuromodulation profiles demonstrated subject-specificity for happy, sad, and neutral facial expressions, an unsurprising variable pattern in autism spectrum disorder. Modulation occurred in negative or positive directions, even for neutral faces, in line with their often-perceived ambiguity in autism spectrum disorder. Striatal regions (associated with success/failure of neuromodulation), saliency (insula/anterior cingulate cortex), and emotional control (medial prefrontal cortex) networks were recruited during neuromodulation. Recruitment of the operant learning network is consistent with participants’ engagement. Compliance, immediate intervention benefits, and their persistence after 6 months pave the way for a future Phase IIb/III, randomized controlled clinical trial, with a larger sample that will allow to conclude on clinical benefits from neurofeedback training in autism spectrum disorder (NCT02440451). Lay abstract Neurofeedback is an emerging therapeutic approach in neuropsychiatric disorders. Its potential application in autism spectrum disorder remains to be tested. Here, we demonstrate the feasibility of real-time functional magnetic resonance imaging volitional neurofeedback in targeting social brain regions in autism spectrum disorder. In this clinical trial, autism spectrum disorder patients were enrolled in a program with five training sessions of neurofeedback. Participants were able to control their own brain activity in this social brain region, with positive clinical and neural effects. Larger, controlled, and blinded clinical studies will be required to confirm the benefits.

scholarly journals Resting State Functional Magnetic Resonance Imaging Elucidates Neurotransmitter Deficiency in Autism Spectrum Disorder

2021 ◽  
Vol 11 (10) ◽  
pp. 969
Author(s):  
Patrick J. McCarty ◽  
Andrew R. Pines ◽  
Bethany L. Sussman ◽  
Sarah N. Wyckoff ◽  
Amanda Jensen ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Autism Spectrum Disorder ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Brain Structures ◽  
Autism Spectrum ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Deep Brain

Resting-state functional magnetic resonance imaging provides dynamic insight into the functional organization of the brains’ intrinsic activity at rest. The emergence of resting-state functional magnetic resonance imaging in both the clinical and research settings may be attributed to recent advancements in statistical techniques, non-invasiveness and enhanced spatiotemporal resolution compared to other neuroimaging modalities, and the capability to identify and characterize deep brain structures and networks. In this report we describe a 16-year-old female patient with autism spectrum disorder who underwent resting-state functional magnetic resonance imaging due to late regression. Imaging revealed deactivated networks in deep brain structures involved in monoamine synthesis. Monoamine neurotransmitter deficits were confirmed by cerebrospinal fluid analysis. This case suggests that resting-state functional magnetic resonance imaging may have clinical utility as a non-invasive biomarker of central nervous system neurochemical alterations by measuring the function of neurotransmitter-driven networks. Use of this technology can accelerate and increase the accuracy of selecting appropriate therapeutic agents for patients with neurological and neurodevelopmental disorders.

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