scholarly journals Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning

2021 ◽  
Vol 15 ◽  
Author(s):  
Alexandra M. Reardon ◽  
Kaiming Li ◽  
Xiaoping P. Hu
Keyword(s):  
Functional Connectivity ◽  
Effect Size ◽  
Site Effects ◽  
Data Exchange ◽  
Site Effect ◽  
Superior Performance ◽  
Imaging Data ◽  
Control Subjects ◽  
Intermediate Phenotypes ◽  
And Control

Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients.Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls.Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%.Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.

scholarly journals Sex-dependent and sex-independent brain resting-state functional connectivity in children with autism spectrum disorder

2016 ◽  
Author(s):  
Xin Di ◽  
Bharat B Biswal
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Main Effects

Background: Males are more likely to suffer from autism spectrum disorder (ASD) than females. As to whether females with ASD have similar brain alterations remain an open question. The current study aimed to examine sex-dependent as well as sex-independent alterations in resting-state functional connectivity in individuals with ASD compared with typically developing (TD) individuals. Method: Resting-state functional MRI data were acquired from the Autism Brain Imaging Data Exchange (ABIDE). Subjects between 6 to 20 years of age were included for analysis. After matching the intelligence quotient between groups for each dataset, and removing subjects due to excessive head motion, the resulting effective sample contained 28 females with ASD, 49 TD females, 129 males with ASD, and 141 TD males, with a two (diagnosis) by two (sex) design. Functional connectivity among 153 regions of interest (ROIs) comprising the whole brain was computed. Two by two analysis of variance was used to identify connectivity that showed diagnosis by sex interaction or main effects of diagnosis. Results: The main effects of diagnosis were found mainly between visual cortex and other brain regions, indicating sex-independent connectivity alterations. We also observed two connections whose connectivity showed diagnosis by sex interaction between the precuneus and medial cerebellum as well as the precunes and dorsal frontal cortex. While males with ASD showed higher connectivity in these connections compared with TD males, females with ASD had lower connectivity than their counterparts. Conclusions: Both sex-dependent and sex-independent functional connectivity alterations are present in ASD.

scholarly journals Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling

2021 ◽  
Vol 15 ◽  
Author(s):  
Naoki Okamoto ◽  
Hiroyuki Akama
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Cross Validation ◽  
Data Augmentation ◽  
Rapid Change ◽  
Autism Spectrum ◽  
Batch Size ◽  
Imaging Data ◽  
Resting State Functional Connectivity

Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; however, unlike the original IIC, it is characterized by transfer learning with labeled data pairs, but without the need for a data augmentation technique. Each site in LOSO-CV is left out in turn from the remaining sites used for training and receives a value for modeling evaluation. We applied the EIIC to the resting state functional connectivity magnetic resonance imaging dataset of the Autism Brain Imaging Data Exchange. The challenging nature of brain analysis for autism spectrum disorder (ASD) can be attributed to the variability of subjects, particularly the rapid change in the neural system of children as the target ASD age group. However, EIIC demonstrated higher LOSO-CV classification accuracy for the majority of scanning locations than previously used methods. Particularly, with the adjustment of a mini-batch size, EIIC outperformed other classifiers with an accuracy >0.8 for the sites with highest mean age of the subjects. Considering its effectiveness, our proposed method might be promising for harmonization in other domains, owing to its simplicity and intrinsic flexibility.

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 Extended Invariant Information Clustering is Effective for Leave-One- Site-Out Cross-Validation in Resting State Functional Connectivity Modelling

2021 ◽  
Author(s):  
Naoki Okamoto ◽  
Hiroyuki Akama
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Cross Validation ◽  
Data Augmentation ◽  
Rapid Change ◽  
Autism Spectrum ◽  
Batch Size ◽  
Imaging Data ◽  
Resting State Functional Connectivity

Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; however, unlike the original IIC, it is characterized by supervised learning with labeled data pairs, but without the need for a data augmentation technique. Each site in LOSO-CV is left out in turn from the remaining sites used for training and receives a value for modeling evaluation. We applied the EIIC to the resting state functional connectivity magnetic resonance imaging dataset of the Autism Brain Imaging Data Exchange. The challenging nature of brain analysis for autism spectrum disorder (ASD) can be attributed to the variability of subjects, particularly the rapid change in the neural system of children as the target ASD age group. However, EIIC demonstrated higher LOSO-CV classification accuracy for the majority of scanning locations than previously used methods. Particularly, with the adjustment of a mini-batch size, EIIC outperformed other classifiers with an accuracy >0.8 for the sites with highest mean age of the subjects. Considering its effectiveness, our proposed method might be promising for harmonization in other domains, owing to its simplicity and intrinsic flexibility. We are currently submitting this manuscript to Frontiers in Neuroinformatics.

scholarly journals The functional brain organization of an individual predicts measures of social abilities in autism spectrum disorder: Predicting symptoms in autism with brain imaging

2018 ◽  
Author(s):  
Evelyn MR Lake ◽  
Emily S Finn ◽  
Stephanie M Noble ◽  
Tamara Vanderwal ◽  
Xilin Shen ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Brain Imaging ◽  
Data Exchange ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Organization ◽  
Functional Brain ◽  
Functional Brain Organization

ABSTRACTAutism Spectrum Disorder (ASD) is associated with multiple complex abnormalities in functional brain connectivity measured with functional magnetic resonance imaging (fMRI). Despite much research in this area, to date, neuroimaging-based models are not able to characterize individuals with ASD with sufficient sensitivity and specificity; this is likely due to the heterogeneity and complexity of this disorder. Here we apply a data-driven subject-level approach, connectome-based predictive modeling, to resting-state fMRI data from a set of individuals from the Autism Brain Imaging Data Exchange. Using leave-one-subject-out and split-half analyses, we define two functional connectivity networks that predict continuous scores on the Social Responsiveness Scale (SRS) and Autism Diagnostic Observation Schedule (ADOS) and confirm that these networks generalize to novel subjects. Notably, these networks were found to share minimal anatomical overlap. Further, our results generalize to individuals for whom SRS/ADOS scores are unavailable, predicting worse scores for ASD than typically developing individuals. In addition, predicted SRS scores for individuals with attention-deficit/hyperactivity disorder (ADHD) from the ADHD-200 Consortium are linked to ADHD symptoms, supporting the hypothesis that the functional brain organization changes relevant to ASD severity share a component associated with attention. Finally, we explore the membership of predictive connections within conventional (atlas-based) functional networks. In summary, our results suggest that an individual’s functional connectivity profile contains information that supports dimensional, non-binary classification in ASD, aligning with the goals of precision medicine and individual-level diagnosis.

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.

Attention deficit hyperactivity disorder (ADHD): gender- and age-related differences in neurocognition

2008 ◽  
Vol 39 (8) ◽  
pp. 1337-1345 ◽  
Author(s):  
S. Bálint ◽  
P. Czobor ◽  
S. Komlósi ◽  
Á. Mészáros ◽  
V. Simon ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Effect Size ◽  
Adult Adhd ◽  
Healthy Controls ◽  
Age And Gender ◽  
Hyperactivity Disorder ◽  
Control Subjects ◽  
The Difference ◽  
And Gender ◽  
And Control

BackgroundDespite the growing recognition that the clinical symptom characteristics associated with attention deficit hyperactivity disorder (ADHD) persist into adulthood in a high proportion of subjects, little is known about the persistence of neurocognitive deficits in ADHD. The objective was twofold: (1) to conduct a meta-analysis of neuropsychological studies to characterize attentional performance in subjects with adult ADHD by examining differences in ADHD versus normal control subjects; and (2) to investigate whether these differences vary as a function of age and gender.MethodTwenty-five neuropsychological studies comparing subjects with adult ADHD and healthy controls were evaluated. Statistical effect size was determined to characterize the difference between ADHD and control subjects. Meta-regression analysis was applied to investigate whether the difference between ADHD and control subjects varied as a function of age and gender across studies.ResultsTests measuring focused and sustained attention yielded an effect size with medium to large magnitude whereas tests of simple attention resulted in a small to medium effect size in terms of poorer attention functioning of ADHD subjects versus controls. On some of the measures (e.g. Stroop interference), a lower level of attention functioning in the ADHD group versus the controls was associated with male gender.ConclusionsAdult ADHD subjects display significantly poorer functioning versus healthy controls on complex but not on simple tasks of attention, and the degree of impairment varies with gender, with males displaying a higher level of impairment.

scholarly journals Correcting Cardiorespiratory Noise in Resting-state Functional MRI Data Acquired in Critically Ill Patients

2021 ◽  
Author(s):  
Suk-tak Chan ◽  
William R Sanders ◽  
David J Fischer ◽  
John E Kirsch ◽  
Vitaly Napadow ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Resting State ◽  
Brain Injuries ◽  
Control Subjects ◽  
Healthy Control ◽  
Severe Tbi ◽  
And Control

Resting-state functional MRI (rs-fMRI) is being used to develop diagnostic, prognostic, and therapeutic biomarkers for critically ill patients with severe brain injuries. In studies of healthy volunteers and non-critically ill patients, prospective cardiorespiratory data are routinely collected to remove non-neuronal fluctuations in the rs-fMRI signal during analysis. However, the feasibility and utility of collecting cardiorespiratory data in critically ill patients on a clinical MRI scanner are unknown. We concurrently acquired rs-fMRI (TR=1250ms), cardiac and respiratory data in 23 critically ill patients with acute severe traumatic brain injury (TBI), and 12 healthy control subjects. We compared the functional connectivity results after denoising with cardiorespiratory data (i.e., RETROICOR) with the results obtained after standard bandpass filtering. Rs-fMRI data in 7 patients could not be analyzed due to imaging artifacts. In 6 of the remaining 16 patients (37.5%), cardiorespiratory data were either incomplete or corrupted. In both patients and control subjects, the functional connectivity corrected with RETROICOR did not significantly differ from that corrected with bandpass filtering of 0.008-0.125 Hz. Collectively, these findings suggest that there is a limited feasibility and utility to prospectively acquire high-quality cardiorespiratory data during rs-fMRI in critically ill patients with severe TBI for physiological correction.

scholarly journals Altered Functional Connectivity in a Triple-Network Model in Autism With Co-occurring Attention Deficit Hyperactivity Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Kai Wang ◽  
Ke Li ◽  
Xiaoyu Niu
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Functional Connectivity ◽  
Network Model ◽  
Attention Deficit ◽  
Data Exchange ◽  
Network Connectivity ◽  
Autism Spectrum ◽  
Adhd Group ◽  
Imaging Data ◽  
Hyperactivity Disorder

Purpose: This study aimed to explore alterations in functional connectivity (FC) within and between default mode network (DMN), central executive network, and salience network in autism spectrum disorder (ASD) with co-occurring attention deficit hyperactivity disorder (ADHD).Method: A total of 135 individuals' date of the Autism Brain Imaging Data Exchange II was used to compare the ASD+ADHD group with the ASD group in relation to the abnormal within-network and between-network connectivity of the ASD group relative to the TD group; consequently, the correlation analysis between abnormal FC and behavior was performed.Results: The ASD+ADHD group exhibited decreased within-network connectivity in the precuneus of the ventral DMN compared with the ASD group. Among the three groups, the ASD+ADHD group showed lower connectivity, whereas the ASD group had higher connectivity than the TD group, although the effect of the separate post hoc test was not significant. Meanwhile, the ASD+ADHD group showed increased between-network connectivity between the ventral DMN and dorsal DMN and between the ventral DMN and left executive control network, compared with the ASD and TD groups.Conclusion: Dysfunction of DMN in the “triple-network model” is the core evidence for ASD with co-occurring ADHD.

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