scholarly journals Motion and morphometry in clinical populations

2015 ◽  
Author(s):  
Heath R Pardoe ◽  
Rebecca Kucharsky Hiess ◽  
Ruben Kuzniecky
Keyword(s):  
Cortical Thickness ◽  
Resting State ◽  
Motion Artifact ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Hyperactivity Disorder ◽  
Statistical Validity ◽  
Proxy Measure ◽  
And Gender ◽  
Contrast Reduction

Introduction The relationship between participant motion, demographic variables and MRI-derived morphometric estimates was investigated in autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia. Participant motion was estimated using resting state fMRI and used as a proxy measure for motion during T1-weighted MRI acquired in the same session. Analyses were carried out in scans qualitatively assessed as free from motion-related artifact. Methods Whole brain T1-weighted MRI and resting state fMRI acquisitions from the ABIDE, ADHD-200 and COBRE databases were included in our analyses. Motion was estimated using coregistration of sequential resting state volumes. Morphometric estimates were obtained using Freesurfer v5.3. We investigated if motion is related to diagnosis, age and gender, and scanning site. We further determined if there is a relationship between participant motion and cortical thickness, contrast, and volumetric estimates. Results 2131 participants were included in our analyses. Participant motion was higher in all clinical groups compared with healthy controls. Younger (age < 20 years) and older (age > 40 years) people move more than individuals aged 20 – 40 years. Increased motion is associated with reduced average cortical thickness (-0.02 mm thickness per mm motion, p = 4.03×10-5) and cortical contrast (0.95% contrast reduction per mm motion, p = 5.25×10-11) in scans that have been qualitatively assessed as free from motion artifact. Conclusions Participant motion is increased in clinical groups and is systematically associated with morphometric estimates. These findings indicate that accounting for participant motion may be important for improving the statistical validity of morphometric studies.

scholarly journals Motion and morphometry in clinical populations

2015 ◽  
Author(s):  
Heath R Pardoe ◽  
Rebecca Kucharsky Hiess ◽  
Ruben Kuzniecky
Keyword(s):  
Cortical Thickness ◽  
Resting State ◽  
Motion Artifact ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Hyperactivity Disorder ◽  
Statistical Validity ◽  
Proxy Measure ◽  
And Gender ◽  
Contrast Reduction

Introduction The relationship between participant motion, demographic variables and MRI-derived morphometric estimates was investigated in autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia. Participant motion was estimated using resting state fMRI and used as a proxy measure for motion during T1-weighted MRI acquired in the same session. Analyses were carried out in scans qualitatively assessed as free from motion-related artifact. Methods Whole brain T1-weighted MRI and resting state fMRI acquisitions from the ABIDE, ADHD-200 and COBRE databases were included in our analyses. Motion was estimated using coregistration of sequential resting state volumes. Morphometric estimates were obtained using Freesurfer v5.3. We investigated if motion is related to diagnosis, age and gender, and scanning site. We further determined if there is a relationship between participant motion and cortical thickness, contrast, and volumetric estimates. Results 2131 participants were included in our analyses. Participant motion was higher in all clinical groups compared with healthy controls. Younger (age < 20 years) and older (age > 40 years) people move more than individuals aged 20 – 40 years. Increased motion is associated with reduced average cortical thickness (-0.02 mm thickness per mm motion, p = 4.03×10-5) and cortical contrast (0.95% contrast reduction per mm motion, p = 5.25×10-11) in scans that have been qualitatively assessed as free from motion artifact. Conclusions Participant motion is increased in clinical groups and is systematically associated with morphometric estimates. These findings indicate that accounting for participant motion may be important for improving the statistical validity of morphometric studies.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

Functional abnormalities in Broca’s area in adolescents with ADHD: A resting-state fMRI study

2015 ◽  
Vol 51 (1) ◽  
Author(s):  
Michał Pikusa ◽  
Rafał Jończyk
Keyword(s):  
Resting State ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Broca’S Area ◽  
Broca's Area ◽  
Hyperactivity Disorder ◽  
Spontaneous Neural Activity ◽  
Fmri Study ◽  
Brodmann Areas ◽  
Underlying Causes

AbstractThere is evidence that attention-deficit/hyperactivity disorder (ADHD) is associated with linguistic difficulties. However, the pathophysiology underlying these difficulties is yet to be determined. This study investigates functional abnormalities in Broca’s area, which is associated with speech production and processing, in adolescents with ADHD by means of resting-state fMRI. Data for the study was taken from the ADHD-200 project and included 267 ADHD patients (109 with combined inattentive/hyperactive subtype and 158 with inattentive subtype) and 478 typically-developing control (TDC) subjects. An analysis of fractional amplitude of low-frequency fluctuations (fALFF), which reflects spontaneous neural activity, in Broca’s area (Brodmann Areas 44/45) was performed on the data and the results were compared statistically across the participant groups. fALFF was found to be significantly lower in the ADHD inattentive group as compared to TDC in BA 44, and in the ADHD combined group as compared to TDC in BA 45. The results suggest that there are functional abnormalities in Broca’s area with people suffering from ADHD, and that the localization of these abnormalities might be connected to particular language deficits associated with ADHD subtypes, which we discuss in the article. The findings might help explore the underlying causes of specific language difficulties in ADHD.

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

scholarly journals Sex Differences in the Default Mode Network with Regard to Autism Spectrum Traits: A Resting State fMRI Study

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0143126 ◽  
Author(s):  
Minyoung Jung ◽  
Maria Mody ◽  
Daisuke N. Saito ◽  
Akemi Tomoda ◽  
Hidehiko Okazawa ◽  
...  
Keyword(s):  
Sex Differences ◽  
Default Mode Network ◽  
Resting State ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Default Mode ◽  
Fmri Study

Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI

2020 ◽  
pp. 169-186
Author(s):  
S. Vidhusha ◽  
A. Kavitha
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Resting State ◽  
Cognitive Task ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Control Group ◽  
Neural Connectivity ◽  
Activation Patterns ◽  
The Brain

Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.

Obstetric oxytocin exposure and ADHD and ASD among Danish and Finnish children

2020 ◽  
Author(s):  
Lonny Stokholm ◽  
Mette Juhl ◽  
Nicole M Talge ◽  
Mika Gissler ◽  
Carsten Obel ◽  
...  
Keyword(s):  
Population Based ◽  
Autism Spectrum ◽  
Labour Induction ◽  
Prenatally Exposed ◽  
Hyperactivity Disorder ◽  
Diagnostic Codes ◽  
Increased Risk ◽  
Specific Association ◽  
And Gender ◽  
Confounder Adjustment

Abstract Background Some studies have indicated an increased risk of attention deficit hyperactivity disorder (ADHD) and a small, sex-specific association with autism spectrum disorder (ASD) among children prenatally exposed to obstetric oxytocin. Since oxytocin is widely used in the obstetric ward, these potentially deleterious effects are of concern. Thus, we aimed to examine whether obstetric oxytocin treatment for labour induction or augmentation is associated with ADHD and ASD in offspring born in a two-country design based on data from Denmark and Finland. Methods This population-based study used data from national registers in Denmark and Finland. Singletons born in Denmark 2000–10 (n = 577 380) and Finland 1991–2010 (n = 945 543), who survived infancy, were followed until 31 December 2015. ADHD and ASD were defined using diagnostic codes. For ADHD, we also included information on prescribed and redeemed ADHD medication in the definition. Hazards ratios (HRs) with 95% confidence intervals (CI), modelled with age as the underlying time scale, were calculated to estimate the associations. Results Oxytocin was used in 31% and 46% of the included deliveries in Denmark and Finland, respectively. In crude analyses, prenatal oxytocin was associated with an approximately 20% increased risk of ADHD and ASD, but confounder adjustment attenuated the association. The adjusted HR was 1.03, 95% CI 1.01–1.05, for ADHD and 1.05, 95% CI 1.02–1.08, for ASD. The results were similar in across country and gender. Conclusions We found an association between synthetic oxytocin and ADHD or ASD which is unlikely to reflect a causal association and thus should not support the concern of clinical use. Our results help to allay concerns of obstetric use of oxytocin causing ADHD or ASD.

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