Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC)

Sikka, Sharad; Brian, Cheung; Ranjit, Khanuja; Satra, Ghosh; Chao-gan, Yan; Li, Qingyang; Vogelstein, Joshua T.; Randal, Burns; Stanley, Colcombe; Craddock, R. Cameron; Maarten, Mennes; Clare, Kelly; Adriana, Dimartino; Francisco, Castellanos; Michael, Milham

doi:10.3389/conf.fninf.2014.08.00117

Cited by 23 publications

(13 citation statements)

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“…We preprocessed the data using a widely adopted pipeline called Configurable Pipeline for the Analysis of Connectomes (C-PAC) (Craddock et al, 2013), which involves skull striping, slice timing correction, realignment to correct for motion, and bandpass filtering (0.01-0.1 Hz). The functional images were smoothed with a 5-mm full width half maximum (FWHM) Gaussian kernel, registered to a standard anatomical space (MNI152) and resampled to 4 mm.…”

Section: Pre-processing Of Functional Mri Datamentioning

confidence: 99%

Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model

Yang

Cao

Chen

et al. 2021

Front. Hum. Neurosci.

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GoalBrain functional networks (BFNs) constructed using resting-state functional magnetic resonance imaging (fMRI) have proven to be an effective way to understand aberrant functional connectivity in autism spectrum disorder (ASD) patients. It is still challenging to utilize these features as potential biomarkers for discrimination of ASD. The purpose of this work is to classify ASD and normal controls (NCs) using BFNs derived from rs-fMRI.MethodsA deep learning framework was proposed that integrated convolutional neural network (CNN) and channel-wise attention mechanism to model both intra- and inter-BFN associations simultaneously for ASD diagnosis. We investigate the effects of each BFN on performance and performed inter-network connectivity analysis between each pair of BFNs. We compared the performance of our CNN model with some state-of-the-art algorithms using functional connectivity features.ResultsWe collected 79 ASD patients and 105 NCs from the ABIDE-I dataset. The mean accuracy of our classification algorithm was 77.74% for classification of ASD versus NCs.ConclusionThe proposed model is able to integrate information from multiple BFNs to improve detection accuracy of ASD.SignificanceThese findings suggest that large-scale BFNs is promising to serve as reliable biomarkers for diagnosis of ASD.

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Section: Pre-processing Of Functional Mri Datamentioning

confidence: 99%

Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model

Yang

Cao

Chen

et al. 2021

Front. Hum. Neurosci.

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“…For the brain activity modality, we derived the following comparison function. For each scan, (i) run Configurable Pipeline for the Analysis of Connectomes pipeline (Craddock et al, 2013) to process the raw brain images yielding a parcellation into 197 regions of interest, (ii) run a spectral analysis on each region and keep the power of band, (iii) bandpass and normalize it to sum to one, (iv) calculate the Kullback-Leibler divergence across regions to obtain a similarity matrix across comparing all regions. Then, use the normalized Hellinger distance to compute distances between each subject.…”

Section: Real Data Processingmentioning

confidence: 99%

Discovering and deciphering relationships across disparate data modalities

Vogelstein

Bridgeford

Wang

et al. 2019

eLife

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Understanding the relationships between different properties of data, such as whether a genome or connectome has information about disease status, is increasingly important. While existing approaches can test whether two properties are related, they may require unfeasibly large sample sizes and often are not interpretable. Our approach, ‘Multiscale Graph Correlation’ (MGC), is a dependence test that juxtaposes disparate data science techniques, including k-nearest neighbors, kernel methods, and multiscale analysis. Other methods may require double or triple the number of samples to achieve the same statistical power as MGC in a benchmark suite including high-dimensional and nonlinear relationships, with dimensionality ranging from 1 to 1000. Moreover, MGC uniquely characterizes the latent geometry underlying the relationship, while maintaining computational efficiency. In real data, including brain imaging and cancer genetics, MGC detects the presence of a dependency and provides guidance for the next experiments to conduct.

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“…FMRIPrep is analysis-agnostic to currently-available analysis choices, as it supports a wide range of higher-level analysis and modeling options. Alternative workflows such as afni_proc.py (AFNI 12 ), feat (FSL 15 ), C-PAC 24 (configurable pipeline for the analysis of connectomes), Human Connectome Project (HCP 25 ) Pipelines 26 , or the Batch Editor of SPM, are not agnostic because they prescribe particular methodologies to analyze the preprocessed data. Important limitations to compatibility with downstream analysis derive from the coordinates space of the outputs and the regular (volume) vs. irregular (surface) sampling of the BOLD signal.…”

Section: Resultsmentioning

confidence: 99%

fMRIPrep: a robust preprocessing pipeline for functional MRI

et al. 2018

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Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep , an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.

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Towards Automated Analysis of Connectomes: The Configurable Pipeline for the Analysis of Connectomes (C-PAC)

Cited by 23 publications

References 0 publications

Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model

Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model

Discovering and deciphering relationships across disparate data modalities

fMRIPrep: a robust preprocessing pipeline for functional MRI

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