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An open science resource for establishing reliability and reproducibility in functional connectomics.

ABSTRACT: Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals' resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

SUBMITTER: Zuo XN

PROVIDER: S-EPMC4421932 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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An open science resource for establishing reliability and reproducibility in functional connectomics.

Zuo Xi-Nian XN Anderson Jeffrey S JS Bellec Pierre P Birn Rasmus M RM Biswal Bharat B BB Blautzik Janusch J Breitner John C S JC Buckner Randy L RL Calhoun Vince D VD Castellanos F Xavier FX Chen Antao A Chen Bing B Chen Jiangtao J Chen Xu X Colcombe Stanley J SJ Courtney William W Craddock R Cameron RC Di Martino Adriana A Dong Hao-Ming HM Fu Xiaolan X Gong Qiyong Q Gorgolewski Krzysztof J KJ Han Ying Y He Ye Y He Yong Y Ho Erica E Holmes Avram A Hou Xiao-Hui XH Huckins Jeremy J Jiang Tianzi T Jiang Yi Y Kelley William W Kelly Clare C King Margaret M LaConte Stephen M SM Lainhart Janet E JE Lei Xu X Li Hui-Jie HJ Li Kaiming K Li Kuncheng K Lin Qixiang Q Liu Dongqiang D Liu Jia J Liu Xun X Liu Yijun Y Lu Guangming G Lu Jie J Luna Beatriz B Luo Jing J Lurie Daniel D Mao Ying Y Margulies Daniel S DS Mayer Andrew R AR Meindl Thomas T Meyerand Mary E ME Nan Weizhi W Nielsen Jared A JA O'Connor David D Paulsen David D Prabhakaran Vivek V Qi Zhigang Z Qiu Jiang J Shao Chunhong C Shehzad Zarrar Z Tang Weijun W Villringer Arno A Wang Huiling H Wang Kai K Wei Dongtao D Wei Gao-Xia GX Weng Xu-Chu XC Wu Xuehai X Xu Ting T Yang Ning N Yang Zhi Z Zang Yu-Feng YF Zhang Lei L Zhang Qinglin Q Zhang Zhe Z Zhang Zhiqiang Z Zhao Ke K Zhen Zonglei Z Zhou Yuan Y Zhu Xing-Ting XT Milham Michael P MP

Scientific data 20141209

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address th ...[more]

PMID: 25977800

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Project description:Understanding the relationship between functional connectivity (FC) of higher-order neurocognitive networks and age-related cognitive decline is a complex and evolving field of research. Decreases in FC have been associated with cognitive decline in persons with Alzheimer's disease and related dementias (ADRD). However, the contributions of FC have been less straightforward in typical cognitive aging. Some investigations suggest relatively robust FC within neurocognitive networks differentiates unusually successful cognitive aging from average aging, while others do not. Methodologic limitations in data processing and varying definitions of 'successful aging' may have contributed to the inconsistent results to date. The current study seeks to address previous limitations by optimized MRI methods to examine FC in the well-established SuperAging phenotype, defined by age and cognitive performance as individuals 80 and older with episodic memory performance equal to or better than 50-to-60-year-olds. Within- and between-network FC of large-scale neurocognitive networks were compared between 24 SuperAgers and 16 cognitively average older-aged control (OACs) with stable cognitive profiles using resting-state functional MRI (rs-fMRI) from a single visit. Group classification was determined based on measures of episodic memory, executive functioning, verbal fluency and picture naming. Inclusion criteria required stable cognitive status across two visits. First, we investigated the FC within and between seven resting-state networks from a common atlas parcellation. A separate index of network segregation was also compared between groups. Second, we investigated the FC between six subcomponents of the default mode network (DMN), the neurocognitive network commonly associated with memory performance and disrupted in persons with ADRD. For each analysis, FCs were compared across groups using two-sample independent t-tests and corrected for multiple comparisons. There were no significant between-group differences in demographic characteristics including age, sex and education. At the group-level, within-network FC, between-network FC, and segregation measurements of seven large-scale networks, including subcomponents of the DMN, were not a primary differentiator between cognitively average aging and SuperAging phenotypes. Thus, FC within or between large-scale networks does not appear to be a primary driver of the exceptional memory performance observed in SuperAgers. These results have relevance for differentiating the role of FC changes associated with cognitive aging from those associated with ADRD.

Project description:Objective.New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC (ML.FC) which efficiently captures linear and nonlinear aspects.Approach.To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity (ML.EC), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality (SP.GC) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms ofreproducibilityand theability to predict individual traitsin order to demonstrate these measures' internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits.Main results.The proposed new FC measure ofML.FCattains high reproducibility (mean intra-subjectR2of 0.44), while the proposed EC measure ofSP.GCattains the highest predictive power (meanR2across prediction tasks of 0.66).Significance.The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.

Dataset Information

An open science resource for establishing reliability and reproducibility in functional connectomics.

Publications

An open science resource for establishing reliability and reproducibility in functional connectomics.

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