The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives

Craddock, R. Cameron; Benhajali, Yassine; Carlton, Chu; Francois, Chouinard; Alan, Evans; András, Jakab; Budhachandra, Khundrakpam; Liu, John; Li, Qingyang; Michael, Milham; Chen, Yan; Pierre, Bellec

doi:10.3389/conf.fninf.2013.09.00041

Cited by 231 publications

(114 citation statements)

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“…This study used a publicly available dataset from the ABIDE Initiative [26]. To ensure that our results were not affected by any custom preprocessing pipeline, we used the preprocessed data provided by ABIDE in the C-PAC [27] pipeline.…”

Section: Data and Preprocessingmentioning

confidence: 99%

The Importance of Anti-Correlations in Graph Theory Based Classification of Autism Spectrum Disorder

Kazeminejad

Sotero

2019

Preprint

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With the release of the multi-site Autism Brain Imaging Data Exchange, many researchers have applied machine learning methods to distinguish between healthy subjects and autistic individuals by using features extracted from resting state functional MRI data. An important part of applying machine learning to this problem is how to extract these features. Specifically, whether to include anti-correlations between brain regions as relevant features and how best to define these features. For the second question, the graph theoretical properties of the brain network may provide a reasonable answer. In this study, we investigated the first issue by comparing three different approaches. These included using the unaltered correlation matrix, the absolute value of the correlation matrix or the inverted correlation matrix (negative of the original) as the starting point for extracting relevant features using graph theory. We then trained a multi-layer perceptron in a leave-one-site out manner in which the data from a single site was left out as testing data and the model was trained on the data from the other sites. Our results show that on average, using graph features extracted from the anticorrelation-based matrix led to the highest accuracy and AUC scores. We also show that adding the PCA transformation of the original correlation matrix to the feature space leads to an increase in accuracy. This suggests that anti-correlations should not simply be discarded as they may include useful information that would aid the classification task.

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Section: Data and Preprocessingmentioning

confidence: 99%

The Importance of Anti-Correlations in Graph Theory Based Classification of Autism Spectrum Disorder

Kazeminejad

Sotero

2019

Preprint

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“…We used single-site data (NYU) from the ABIDE Preprocessed Initiative (Cameron et al, 2013). Institutional review board approval was provided by each data contributor in the ABIDE database.…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder

Harlalka

Raju

Vinod

et al. 2018

Preprint

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Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the age, disease phenotype and their interaction in the whole brain transient connectivity patterns. Contrasting with most extant literature in the present study, we investigated precisely how age and disease phenotypes factors into dynamic changes in functional connectivity of TD and ASD using resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7-11 years) and adolescents (12-17 years), and adults (18+) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures: flexibility, cohesion strength and disjointness were explored for each subject to characterize the differences between ASD and TD.In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hypervariability relates to the symptom severity in ASD. We found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a coreperiphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with the flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing the high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.

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“…One of the possible solutions suggested in many recent studies [10][11][12][13][14][15][16][17] is to investigate potential biomarkers on larger and heterogeneous dataset, as provided by ABIDE (Autism Brain Imaging Data Exchange). It is an open-access repository that aggregates neuroimaging data from 539 autistic individuals and 573 typical controls (TC) across 17 international imaging sites [18]. Based on the rs-fMRI data from ABIDE, previous work on connectivity measures have made some considerable achievements, which resulted in accurate prediction of ASD.…”

Section: Introductionmentioning

confidence: 99%

Attentional Connectivity-based Prediction of Autism Using Heterogeneous rs-fMRI Data from CC200 Atlas

Ya-ya

et al. 2020

Exp Neurobiol

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Autism spectrum disorder (ASD) is a developmental syndrome characterized by obvious drawbacks in sociality and communication. It has crucial significance to exactly discern the individuals with ASD and typical controls (TC). Previous imaging studies on ASD/TC identification have made remarkable progress in the exploration of objective as well as crucial biomarkers associated with ASD. However, glaring deficiency is manifested by the investigation on solely homogeneous and small datasets. Thus, we attempted to unveil some replicable and robust neural patterns of autism using a heterogeneous multi-site brain imaging dataset from ABIDE (Autism Brain Imaging Data Exchange). Experiments were carried out with an attention mechanism based on Extra-Trees algorithm, taking the study object of brain connectivity measured with the resting-state functional magnetic resonance imaging (fMRI) data of CC200 atlas. With cross-validation strategy, our proposed method resulted in a mean classification accuracy of 72.2% (sensitivity=68.6%, specificity=75.4%). It raised the precision of ASD prediction by about 2% and specificity by 3.2% in comparison with the most competitive reported effort. Connectivity analysis on the optimal model highlighted informative regions strongly involved in the social cognition as well as interaction, and manifested lower correlation between the anterior and posterior default mode network (DMN) in autistic individuals than controls. This observation is concordant with previous studies, which enables our proposed method to effectively identify the individuals with risk of ASD.

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The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives

Cited by 231 publications

References 0 publications

The Importance of Anti-Correlations in Graph Theory Based Classification of Autism Spectrum Disorder

The Importance of Anti-Correlations in Graph Theory Based Classification of Autism Spectrum Disorder

Atypical flexibility in dynamic functional connectivity quantifies the severity in autism spectrum disorder

Attentional Connectivity-based Prediction of Autism Using Heterogeneous rs-fMRI Data from CC200 Atlas

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