A Baseline for the Multivariate Comparison of Resting-State Networks

By Elena A. Allen, Erik B. Erhardt, Eswar Damaraju, W. Gruner, J. Segall, Rogers F. Silva, Martin Havlicek, Srinivas Rachakonda, J. Fries, Ravi Kalyanam and 24 more. Frontiers in Systems Neuroscience, 2011

1,327 citations recorded by the source corpus.

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/7ef16f997257c30bd5a36b1dec6a3329afd35c4f

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Citations in the corpus

CLASSIFICATION OF SCHIZOPHRENIA AND BIPOLAR PATIENTS USING STATIC AND DYNAMIC RESTING-STATE FMRI BRAIN CONNECTIVITY (2016, result): “FC can also be investigated at the network level using spatial independent component analysis (ICA), and connectivity among spatial components is referred to as functional network connectivity (FNC) (Jafri et al., 2008).”
Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia (2014, methodology): “Following our earlier work, we used a MANCOVA framework (Allen et al., 2011)with backward selection to assess group differences in sFNC between ICNs.”
The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry (2012, methodology): “…normative assessments of brain development, maturation, and aging can be obtained (Evans and Brain Development Cooperative Group, 2006; Fair et al., 2007, 2008, 2009; Church et al., 2009a,b; Kelly et al., 2009; Dosenbach et al., 2010; Giedd and Rapoport, 2010; Zuo et al., 2010; Allen et al., 2011).”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, neutral): “One approach to address this is to use a multivariate testing framework for testing multiple intrinsic networks and multiple aspects of each network while also controlling the false positive rate associated with the multiple testing [15].”
Patterns of hemodynamic low-frequency oscillations in the brain are modulated by the nature of free thought during rest (2012, background): “In this context, the present findings suggest that unimodal associative areas increase their synchronization to implement a common “idling” state.”
Decreased Functional Connectivity by Aging Is Associated with Cognitive Decline (2012, background): “Recently, remarkable evidence was reported (Allen et al., 2011).”
Behavioral Interpretations of Intrinsic Connectivity Networks (2011, methodology): “Despite this minor variation in results, all of the components identified in the present data set have been observed in other ICA-based studies of resting state and intrinsic connectivity (Allen et al., 2011; Zuo et al., 2010; Robinson et al., 2009; Smith et al., 2009; van den Heuvel et al., 2009; C”
Hand classification of fMRI ICA noise components (2017, neutral): “…subject the power spectrum for each component (e.g., performing the first step of dual-regression (Filippini et al., 2009)) and average these across subjects to obtain an average spectrum per component (Allen et al., 2011; Griffanti et al., 2014; Kullmann et al., 2013; Mattiaccio et al., 2016).”
Tracking whole-brain connectivity dynamics in the resting state. (2014, neutral): “The current dataset represents a subset of the 603 subjects used in Allen et al. 2011, where more stringent inclusion criteria have been incorporated to limit the influence of motion (subject data with a maximum translation of >1.5 mm were excluded) and poor spatial normalization (subject data with…”