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      Resting-State Functional Connectivity in the Infant Brain: Methods, Pitfalls, and Potentiality

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          Abstract

          Early brain development is characterized by rapid growth and perpetual reconfiguration, driven by a dynamic milieu of heterogeneous processes. Postnatal brain plasticity is associated with increased vulnerability to environmental stimuli. However, little is known regarding the ontogeny and temporal manifestations of inter- and intra-regional functional connectivity that comprise functional brain networks. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a promising non-invasive neuroinvestigative tool, measuring spontaneous fluctuations in blood oxygen level dependent (BOLD) signal at rest that reflect baseline neuronal activity. Over the past decade, its application has expanded to infant populations providing unprecedented insight into functional organization of the developing brain, as well as early biomarkers of abnormal states. However, many methodological issues of rs-fMRI analysis need to be resolved prior to standardization of the technique to infant populations. As a primary goal, this methodological manuscript will (1) present a robust methodological protocol to extract and assess resting-state networks in early infancy using independent component analysis (ICA), such that investigators without previous knowledge in the field can implement the analysis and reliably obtain viable results consistent with previous literature; (2) review the current methodological challenges and ethical considerations associated with emerging field of infant rs-fMRI analysis; and (3) discuss the significance of rs-fMRI application in infants for future investigations of neurodevelopment in the context of early life stressors and pathological processes. The overarching goal is to catalyze efforts toward development of robust, infant-specific acquisition, and preprocessing pipelines, as well as promote greater transparency by researchers regarding methods used.

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          Most cited references98

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          An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.

          Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Network modelling methods for FMRI.

            There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.
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              Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization.

              Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measurement of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of distinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical-thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics-high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n = 98) to provide recommendations for optimization. Run length (2-12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moderate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fixation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correlations including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics.
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                Author and article information

                Contributors
                URI : http://frontiersin.org/people/u/432779
                URI : http://frontiersin.org/people/u/281172
                URI : http://frontiersin.org/people/u/360038
                Journal
                Front Pediatr
                Front Pediatr
                Front. Pediatr.
                Frontiers in Pediatrics
                Frontiers Media S.A.
                2296-2360
                14 August 2017
                2017
                : 5
                : 159
                Affiliations
                [1] 1Center for Pain and the Brain, Boston Children’s Hospital , Boston, MA, United States
                [2] 2Department of Anesthesiology, Perioperative and Pain Medicine, Boston Children’s Hospital , Boston, MA, United States
                [3] 3Department of Surgery, Boston Children’s Hospital , Boston, MA, United States
                [4] 4Department of Surgery, Harvard Medical School , Boston, MA, United States
                [5] 5Department of Anaesthesia, Harvard Medical School , Boston, MA, United States
                Author notes

                Edited by: Brigitte Vollmer, University of Southampton, United Kingdom

                Reviewed by: Peter Fransson, Karolinska Institutet, Sweden; Finn Lennartsson, Lund University, Sweden

                *Correspondence: Dusica Bajic, dusica.bajic@ 123456childrens.harvard.edu

                Specialty section: This article was submitted to Neuropediatrics, a section of the journal Frontiers in Pediatrics

                Article
                10.3389/fped.2017.00159
                5557740
                a37ce392-a4c6-423a-90dc-5d800984145b
                Copyright © 2017 Mongerson, Jennings, Borsook, Becerra and Bajic.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 April 2017
                : 04 July 2017
                Page count
                Figures: 8, Tables: 0, Equations: 0, References: 149, Pages: 26, Words: 17731
                Funding
                Funded by: Office of Extramural Research, National Institutes of Health 10.13039/100006955
                Award ID: NIDA K08 DA035972-01, Trailblazer Award 2013
                Categories
                Pediatrics
                Methods

                blood oxygen level dependent,functional magnetic resonance imaging,independent component analysis,magnetic resonance imaging,resting-state functional magnetic resonance imaging,neurodevelopment,pediatric,resting-state networks

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