scholarly journals Increased contrast of the grey-white matter boundary in the motor, visual and auditory areas in Autism Spectrum Disorders

2019 ◽  
Author(s):  
Marion Fouquet ◽  
Nicolas Traut ◽  
Anita Beggiato ◽  
Richard Delorme ◽  
Thomas Bourgeron ◽  
...  
Keyword(s):  
White Matter ◽  
Data Exchange ◽  
Primary Motor Cortex ◽  
Autism Spectrum ◽  
Imaging Data ◽  
Sensory Motor ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data

AbstractThe contrast of the interface between the neocortical grey matter and the white matter is emerging as an important neuroimaging phenotype for several brain disorders. To date, a single in vivo study has analysed the cortical grey-to-white matter percent contrast (GWPC) on Magnetic Resonance Imaging (MRI), and has shown a significant decrease of this contrast in several areas in individuals with Autism Spectrum Disorder (ASD). Our goal was to replicate this study across a larger cohort, using the multicenter data from the Autism Brain Imaging Data Exchange 1 and 2 gathering data from 2,148 subjects. Multiple linear regression was used to study the effect of the diagnosis of ASD on the GWPC. Contrary to the first study, we found a statistically significant increase of GWPC among individuals with ASD in left auditory and bilateral visual sensory areas, as well as in the left primary motor cortex. These results were still statistically significant after inclusion of cortical thickness as covariate. There are numerous reports of sensory-motor atypicalities in patients with ASD, which may be the reason for the differences in GWPC that we observed. Further investigation could help us determine the potential role of a defect or a delay in intra-cortical myelination of sensory-motor regions in ASD. Code: https://github.com/neuroanatomy/GWPC.

scholarly journals An Autism Spectrum Disorder Adaptive Identification Based on the Elimination of Brain Connections: A Proof of Long-Range Underconnectivity

2021 ◽  
Author(s):  
Fatima zahra Benabdallah ◽  
Ahmed Drissi El Maliani ◽  
Dounia Lotfi ◽  
Rachid Jennane ◽  
Mohammed El hassouni
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Long Range ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

scholarly journals Cross-species machine learning improves diagnostic classification of human psychiatric disorders

2019 ◽  
Author(s):  
Yafeng Zhan ◽  
Jianze Wei ◽  
Jian Liang ◽  
Xiu Xu ◽  
Ran He ◽  
...  
Keyword(s):  
Machine Learning ◽  
Psychiatric Disorders ◽  
Brain Imaging ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Diagnostic Classification ◽  
Imaging Data ◽  
Primate Model ◽  
Compulsive Disorder ◽  
Brain Imaging Data

AbstractPsychiatric disorders often exhibit shared (co-morbid) symptoms, raising controversies over accurate diagnosis and the overlap of their neural underpinnings. Because the complexity of data generated by clinical studies poses a formidable challenge, we have pursued a reductionist framework using brain imaging data of a transgenic primate model of autism spectrum disorder (ASD). Here we report an interpretable cross-species machine learning approach which extracts transgene-related core regions in the monkey brain to construct the classifier for diagnostic classification in humans. The cross-species classifier based on core regions, mainly distributed in frontal and temporal cortex, identified from the transgenic primate model, achieved an accuracy of 82.14% in one clinical ASD cohort obtained from Autism Brain Imaging Data Exchange (ABIDE-I), significantly higher than the human-based classifier (61.31%, p < 0.001), which was validated in another independent ASD cohort obtained from ABIDE-II. Such monkey-based classifier generalized to achieve a better classification in obsessive-compulsive disorder (OCD) cohorts, and enabled parsing of differential connections to right ventrolateral prefrontal cortex being attributable to distinct traits in patients with ASD and OCD. These findings underscore the importance of investigating biologically homogeneous samples, particularly in the absence of real-world data adequate for deconstructing heterogeneity inherited in the clinical cohorts.One Sentence SummaryFeatures learned from transgenic monkeys enable improved diagnosis of autism-related disorders and dissection of their underlying circuits.

scholarly journals Machine Learning Framework for Fully Automatic Quality Checking of Rigid and Affine Registrations in Big Data Brain MRI

2020 ◽  
Author(s):  
Sudhakar Tummala ◽  
Niels K. Focke
Keyword(s):  
Machine Learning ◽  
Data Exchange ◽  
Brain Mri ◽  
Structural Mri ◽  
Imaging Data ◽  
Normalized Mutual Information ◽  
Learning Framework ◽  
Fully Automatic ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Imaging Data

ABSTRACTRigid and affine registrations to a common template are the essential steps during pre-processing of brain structural magnetic resonance imaging (MRI) data. Manual quality check (QC) of these registrations is quite tedious if the data contains several thousands of images. Therefore, we propose a machine learning (ML) framework for fully automatic QC of these registrations via local computation of the similarity functions such as normalized cross-correlation, normalized mutual-information, and correlation ratio, and using these as features for training of different ML classifiers. To facilitate supervised learning, misaligned images are generated. A structural MRI dataset consisting of 215 subjects from autism brain imaging data exchange is used for 5-fold cross-validation and testing. Few classifiers such as kNN, AdaBoost, and random forest reached testing F1-scores of 0.98 for QC of both rigid and affine registrations. These tested ML models could be deployed for practical use.

scholarly journals Cerebellar volume in autism: Meta-analysis and analysis of the ABIDE cohort

2017 ◽  
Author(s):  
Nicolas Traut ◽  
Anita Beggiato ◽  
Thomas Bourgeron ◽  
Richard Delorme ◽  
Laure Rondi-Reig ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Effect Size ◽  
Data Exchange ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Imaging Data ◽  
Autism Diagnosis ◽  
Cerebellar Volume ◽  
Cerebellar Anatomy ◽  
Brain Imaging Data

AbstractCerebellar volume abnormalities have been often suggested as a possible endophenotype for autism spectrum disorder (ASD). We aimed at objectifying this possible alteration by performing a systematic meta-analysis of the literature, and an analysis of the Autism Brain Imaging Data Exchange (ABIDE) cohort. Our meta-analysis sought to determine a combined effect size of ASD diagnosis on different measures of the cerebellar anatomy, as well as the effect of possible factors of variability across studies. We then analysed the cerebellar volume of 328 patients and 353 controls from the ABIDE project. The meta-analysis of the literature suggested a weak but significant association between ASD diagnosis and increased cerebellar volume (p=0.049, uncorrected), but the analysis of ABIDE did not show any relationship. The studies in the literature were generally underpowered, however, the number of statistically significant findings was larger than expected. Although we could not provide a conclusive explanation for this excess of significant findings, our analyses would suggest publication bias as a possible reason. Finally, age, sex and IQ were important sources of cerebellar volume variability, however, independent of autism diagnosis.

scholarly journals Behavioural profiling of autism connectivity abnormalities

BJPsych Open ◽  
2020 ◽  
Vol 6 (1) ◽  
Author(s):  
William Snyder ◽  
Vanessa Troiani
Keyword(s):  
Functional Diversity ◽  
Data Exchange ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Brain Disorders ◽  
Imaging Data ◽  
Functional Magnetic Resonance ◽  
Magnetic Resonance Imaging Data ◽  
Brain Imaging Data ◽  
Functionally Diverse

Background Brain regions are functionally diverse, and a given region may engage in a variety of tasks. This functional diversity of brain regions may be one factor that has prevented the finding of consistent biomarkers for brain disorders such as autism spectrum disorder (ASD). Thus, methods to characterise brain regions would help to determine how functional abnormalities contribute to affected behaviours. Aims As the first illustration of the meta-analytic behavioural profiling procedure, we evaluated how the regions with disrupted connectivity in ASD contributed to various behaviours. Method Connectivity abnormalities were determined from a published degree centrality group comparison based on functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange. Using BrainMap's database of task-based neuroimaging studies, behavioural profiles were created for abnormally connected regions by relating these regions to tasks activating them. Results Hyperconnectivity in ASD brains was significantly related to memory, attention, reasoning, social, execution and speech behaviours. Hypoconnectivity was related to vision, execution and speech behaviours. Conclusions The procedure outlines the first clinical neuroimaging application of a behavioural profiling method that estimates the functional diversity of brain regions, allowing for the relation of abnormal functional connectivity to diagnostic criteria. Behavioural profiling and the computational insights it provides can facilitate better understanding of the functional manifestations of various disorders, including ASD.

scholarly journals Atypical development of subcortico-cortical effective connectivity in autism

2021 ◽  
Author(s):  
Luigi Lorenzini ◽  
Guido van Wingen ◽  
Leonardo Cerliani
Keyword(s):  
Cognitive Processing ◽  
Sensory Processing ◽  
Data Exchange ◽  
Effective Connectivity ◽  
Sensory Information ◽  
Autism Spectrum ◽  
Cortical Processing ◽  
Imaging Data ◽  
Brain Imaging Data ◽  
Atypical Development

Hypersensitivity, stereotyped behaviors and attentional problems in autism spectrum disorder (ASD) are compatible with inefficient filtering of undesired or irrelevant sensory information at early stages of neural processing. This could stem from delays in the neurotypical development of the functional segregation between cortical and subcortical brain processes, as suggested by previous findings of overconnectivity between primary sensory regions and deep brain nuclei in ASD. To test this hypothesis, we used dynamic causal modelling to quantify the effect of age on the development of (1) cortical functional segregation from subcortical activity and (2) directional influence of subcortical activity on cortical processing in 166 participants with ASD and 193 typically developing controls (TD) from the Autism Brain Imaging Data Exchange (ABIDE). We found that in TD participants age was significantly associated with increased functional segregation of cortical sensory processing from subcortical activity, paralleled by a decreased influence of subcortical activity on cortical processing. Instead these effects were highly reduced and mostly absent in ASD participants, suggesting a delayed or arrested development of the segregation between subcortical and cortical sensory processing in ASD. This atypical configuration of subcortico-cortical connectivity in ASD can result in an excessive amount of unprocessed sensory information relayed to the cortex, which is likely to impact cognitive functioning in everyday situations where it is beneficial to limit the influence of basic sensory information on cognitive processing, such as activities requiring focused attention or social interactions.

scholarly journals Body mass index, time of day, and genetics affect perivascular spaces in the white matter

2020 ◽  
Author(s):  
Giuseppe Barisano ◽  
Farshid Sepehrband ◽  
Nasim Sheikh-Bahaei ◽  
Meng Law ◽  
Arthur W. Toga
Keyword(s):  
Body Mass Index ◽  
White Matter ◽  
Body Mass ◽  
Structural Changes ◽  
Large Population ◽  
Time Of Day ◽  
Perivascular Spaces ◽  
Healthy Human ◽  
Magnetic Resonance Imaging Mri

AbstractThe analysis of cerebral perivascular spaces (PVS) using magnetic resonance imaging (MRI) allows to explore in vivo their contributions to neurological disorders. To date the normal amount and distribution of PVS in healthy human brains are not known, thus hampering our ability to define with confidence pathogenic alterations. Furthermore, it is unclear which biological factors can influence the presence and size of PVS on MRI. We performed exploratory data analysis of PVS volume and distribution in a large population of healthy individuals (n = 897, age = 28.8 ± 3.7). Here we describe the global and regional amount of PVS in the white matter, which can be used as a reference for clinicians and researchers investigating PVS and may help the interpretation of the structural changes affecting PVS in pathological states. We found a relatively high inter-subject variability in the PVS amount in this population of healthy adults (range: 1.31-14.49 cm3). We then identified body mass index, time of day, and genetics as new elements significantly affecting PVS in vivo under physiological conditions, offering a valuable foundation to future studies aimed at understanding the physiology of perivascular flow.

Dynamics of the Tracheal Airway and Its Influences on Respiratory Airflows: An Exemplar Study

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Bora Sul ◽  
Talissa Altes ◽  
Kai Ruppert ◽  
Kun Qing ◽  
Daniel S. Hariprasad ◽  
...  
Keyword(s):  
Morphological Changes ◽  
Minute Ventilation ◽  
Correlation Coefficients ◽  
Dynamic Imaging ◽  
Imaging Data ◽  
Cross Sectional ◽  
Mild Conditions ◽  
Large Airways ◽  
Magnetic Resonance Imaging Mri

Respiration is a dynamic process accompanied by morphological changes in the airways. Although deformation of large airways is expected to exacerbate pulmonary disease symptoms by obstructing airflow during increased minute ventilation, its quantitative effects on airflow characteristics remain unclear. Here, we used in vivo dynamic imaging and examined the effects of tracheal deformation on airflow characteristics under different conditions based on imaging data from a single healthy volunteer. First, we measured tracheal deformation profiles of a healthy lung using magnetic resonance imaging (MRI) during forced exhalation, which we simulated to characterize the subject-specific airflow patterns. Subsequently, for both inhalation and exhalation, we compared the airflows when the modeled deformation in tracheal cross-sectional area was 0% (rigid), 33% (mild), 50% (moderate), or 75% (severe). We quantified differences in airflow patterns between deformable and rigid airways by computing the correlation coefficients (R) and the root-mean-square of differences (Drms) between their velocity contours. For both inhalation and exhalation, airflow patterns were similar in all branches between the rigid and mild conditions (R > 0.9; Drms < 32%). However, airflow characteristics in the moderate and severe conditions differed markedly from those in the rigid and mild conditions in all lung branches, particularly for inhalation (moderate: R > 0.1, Drms < 76%; severe: R > 0.2, Drms < 96%). Our exemplar study supports the use of a rigid airway assumption to compute flows for mild deformation. For moderate or severe deformation, however, dynamic contraction should be considered, especially during inhalation, to accurately predict airflow and elucidate the underlying pulmonary pathology.

Sign in / Sign up

Export Citation Format

Share Document