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

Zuo, Xi‐Nian; Anderson, Jeffrey S.; Bellec, Pierre; Birn, Rasmus M.; Biswal, Bharat B.; Blautzik, Janusch; Breitner, John C.S.; Buckner, Randy L.; Calhoun, Vince D.; Castellanos, F. Xavier; Chen, Antao; Chen, Bing; Chen, Jiangtao; Chen, Xu; Colcombe, Stanley J.; Courtney, William; Craddock, R. Cameron; Martino, Adriana Di; Dong, Hao-Ming; Fu, Xiaolan; Gong, Qiyong; Gorgolewski, Krzysztof J.; Han, Ying; He, Ye; He, Yong; Ho, Erica J.; Holmes, Avram J.; Hou, Xiaohui; Huckins, Jeremy F.; Jiang, Tianzi; Jiang, Yiguo; Kelley, William M.; Kelly, Clare; King, Margaret D.; LaConte, Stephen M.; Lainhart, Janet E.; Lei, Xu; Li, Huijie; Li, Kaiming; Li, Kuncheng; Lin, Qixiang; Liu, Dongqiang; Liu, Jia; Liu, Xun; Liu, Yijun; Lu, Guangming; Lu, Jie; Luna, Beatríz; Luo, Jing; Lurie, Daniel J; Mao, Ying; Margulies, Daniel S.; Mayer, Andrew R.; Meindl, Thomas; Meyerand, M. Elizabeth; Nan, Weizhi; Nielsen, Jared A.; O’Connor, David; Paulsen, David J.; Prabhakaran, Vivek; Qi, Zhigang; Qiu, Jiang; Shao, Chunhong; Shehzad, Zarrar; Tang, Weijun; Villringer, Arno; Wang, Huiling; Wang, Kai; Wei, Dongtao; Wei, Gao-Xia; Weng, Xuchu; Wu, Xuehai; Xu, Ting; Yang, Ning; Yang, Zhi; Zang, Yu‐Feng; Zhang, Lei; Zhang, Qinglin; Zhang, Zhe; Zhang, Zhiqiang; Zhao, Ke; Zhen, Zonglei; Zhou, Yuan; Zhu, Xing-Ting; Milham, Michael P.

doi:10.1038/sdata.2014.49

Cited by 372 publications

(303 citation statements)

References 111 publications

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“…Cohorts included: the first and second phases of the Alzheimer's Disease Neuroimaging Initiative (ADNI_1, ADNI_2) [15], the Queensland Twin Imaging Study (QTIM), Brain Genomics Superstruct Project (BrainGSP) [16], and multiple datasets from the Consortium for Reliability and Reproducibility (CoRR) [17] including These cohorts cover a range of ages (18-30 years, to late adulthood, 60-85) and were collected on scanners around the world. These datasets provide us with a range of scan qualities (information in Table 1); image voxels range in size (0.9x0.9x0.9-1.25x1.25x2.0 mm) as do magnetic field strengths of the scanners used (1.5-4T).…”

Section: Cohorts and Image Acquisitionsmentioning

confidence: 99%

Comparison of template registration methods for multi-site meta-analysis of brain morphometry

et al. 2016

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Neuroimaging consortia such as ENIGMA can significantly improve power to discover factors that affect the human brain by pooling statistical inferences across cohorts to draw generalized conclusions from populations around the world. Voxelwise analyses such as tensor-based morphometry also allow an unbiased search for effects throughout the brain. Even so, such consortium-based analyses are limited by a lack of high-powered methods to harmonize voxelwise information across study populations and scanners. While the simplest approach may be to map all images to a single standard space, the benefits of cohort-specific templates have long been established. Here we studied methods to pool voxel-wise data across sites using templates customized for each cohort but providing a meaningful common space across all studies for voxelwise comparisons. As non-linear 3D MRI registrations represent mappings between images at millimeter resolution, we need to consider the reliability of these mappings. To evaluate these mappings, we calculated test-retest statistics on the volumetric maps of expansion and contraction. Further, we created study-specific brain templates for ten T1-weighted MRI datasets, and a common space from four study-specific templates. We evaluated the efficacy of using a two-step registration framework versus a single standard space. We found that the two-step framework more reliably mapped subjects to a common space.

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Section: Cohorts and Image Acquisitionsmentioning

confidence: 99%

Comparison of template registration methods for multi-site meta-analysis of brain morphometry

et al. 2016

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“…A prominent approach for mapping this architecture is to study interregional correlations in the fMRI signal fluctuations, which, even during rest, appear to be indicative of networks supporting specific functions. However, despite the promise and rapidly increasing application of this technique in the endeavor of brain connectomics (2)(3)(4), its sensitivity and specificity are compromised by unexplained variability arising from multiple neural and nonneural sources (e.g., refs. 5-11).…”

mentioning

confidence: 99%

Tracking brain arousal fluctuations with fMRI

Chang

Leopold

Schölvinck

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

282

273

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Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the timevarying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.resting-state fMRI | spontaneous fluctuations | arousal | electrophysiology D uring both active task engagement and rest, the human brain exhibits fluctuations in neural activity that can be readily measured using functional MRI (fMRI). In recent years, examining the spatiotemporal organization of these fluctuations has generated novel insight into the functional architecture of the human brain and its changes with development and disease (1). A prominent approach for mapping this architecture is to study interregional correlations in the fMRI signal fluctuations, which, even during rest, appear to be indicative of networks supporting specific functions. However, despite the promise and rapidly increasing application of this technique in the endeavor of brain connectomics (2-4), its sensitivity and specificity are compromised by unexplained variability arising from multiple neural and nonneural sources (e.g., refs. 5-11). As a result, the interpretation of resting-state fMRI data and the efficacy of these data as a biomarker rely critically on understanding and accounting for such sources of variability (11-13).Changes in arousal, mediated by an interaction between the ascending arousal system and the neocortex, may strongly modulate neuronal activity in much of the brain (14-18). Indeed, changes in vigilance and arousal (hereafter described jointly as "arousal"), which can be especially prevalent during the passive and uncontrolled resting state, result in fMRI signal variability that may confound the extraction of functional networks (5, 19). For example, the amplitude and extent of correlations in restingstate fMRI data vary with EEG-and behaviorally defined indicators of drowsiness and light sleep (20)(21)(22)(23)(24)(25) and are altered by sleep deprivation (26-28) and caffeine-induced changes in arousal state (29). Distinct patterns of functional connectivity across multiple networks have been associated with distinct EEG-defined sleep stages (30, 31) with sufficient reliabilit...

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“…The first is a test-retest reliability analysis using a subset of the Consortium for Reliability and Reproducibility (CoRR) dataset [15]; we compare the reliability of our method versus the standard counting method. We also compare three parcellations using the criteria defined in Equations 4, 5, and 6.…”

Section: Preprocessing and Proceduresmentioning

confidence: 99%

“…Our first dataset is comprised of 29 subjects from the Institute of Psychology, Chinese Academy of Sciences (IPCAS) sub-dataset of the larger Consortium for Reliability and Reproducibility (CoRR) dataset [15]. T1-weighted (T1w) and diffusion weighted (DWI) images were obtained on 3T Siemens TrioTim using an 8-channel head coil and 60 directions.…”

Section: Preprocessing and Proceduresmentioning

confidence: 99%

Continuous representations of brain connectivity using spatial point processes

Moyer

Gutman

Faskowitz

et al. 2017

Medical Image Analysis

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We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each streamline curve in a tractography as an observed event in connectome space, here the product space of the gray matter/white matter interfaces. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel. We show how our approach can be used to assess the quality of cortical parcellations with respect to connectivity. We further present empirical results that suggest that “discrete” connectomes derived from our model have substantially higher test-retest reliability compared to standard methods. In this, the expanded form of this paper for journal publication, we also explore parcellation free analysis techniques that avoid the use of explicit partitions of the cortical surface altogether. We provide an analysis of sex effects on our proposed continuous representation, demonstrating the utility of this approach.

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

Cited by 372 publications

References 111 publications

Comparison of template registration methods for multi-site meta-analysis of brain morphometry

Comparison of template registration methods for multi-site meta-analysis of brain morphometry

Tracking brain arousal fluctuations with fMRI

Continuous representations of brain connectivity using spatial point processes

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