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      Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls

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          Abstract

          Objective:

          Electroconvulsive therapy (ECT) is effective for major depressive episodes. Understanding of underlying mechanisms has been increased by examining changes of brain connectivity but studies often do not correct for test-retest variability in healthy controls (HC). In this study, we investigated changes in resting-state networks after ECT in a multicenter study.

          Methods:

          Functional resting-state magnetic resonance imaging data, acquired before start and within one week after ECT, from 90 depressed patients were analyzed, as well as longitudinal data of 24 HC. Group-information guided independent component analysis (GIG-ICA) was used to spatially restrict decomposition to twelve canonical resting-state networks. Selected networks of interest were the default mode network (DMN), salience network (SN), and left and right frontoparietal network (LFPN, and RFPN). Whole-brain voxel-wise analyses were used to assess group differences at baseline, group by time interactions, and correlations with treatment effectiveness. In addition, between-network connectivity and within-network strengths were computed.

          Results:

          Within-network strength of the DMN was lower at baseline in ECT patients which increased after ECT compared to HC, after which no differences were detected. At baseline, ECT patients showed lower whole-brain voxel-wise DMN connectivity in the precuneus. Increase of within-network strength of the LFPN was correlated with treatment effectiveness. We did not find whole-brain voxel-wise or between-network changes.

          Conclusion:

          DMN within-network connectivity normalized after ECT. Within-network increase of the LFPN in ECT patients was correlated with higher treatment effectiveness. In contrast to earlier studies, we found no whole-brain voxel-wise changes, which highlights the necessity to account for test-retest effects.

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          Most cited references50

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          Advances in functional and structural MR image analysis and implementation as FSL.

          The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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            A reproducible evaluation of ANTs similarity metric performance in brain image registration.

            The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669±0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling. Copyright © 2010 Elsevier Inc. All rights reserved.
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              Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.

              Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and yet its exact choice can have a large impact on the results, particularly at the lower (e.g., t, z < 4) cluster-forming thresholds frequently used. Furthermore, the amount of spatial pre-smoothing is also arbitrary (given that the expected signal extent is very rarely known in advance of the analysis). In the light of such problems, we propose a new method which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems. The method takes a raw statistic image and produces an output image in which the voxel-wise values represent the amount of cluster-like local spatial support. The method is thus referred to as "threshold-free cluster enhancement" (TFCE). We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-based and voxel-based thresholding. We find that TFCE gives generally better sensitivity than other methods over a wide range of test signal shapes and SNR values. We also show an example on a real imaging dataset, suggesting that TFCE does indeed provide not just improved sensitivity, but richer and more interpretable output than cluster-based thresholding.
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                Author and article information

                Journal
                101465726
                35618
                Brain Stimul
                Brain Stimul
                Brain stimulation
                1935-861X
                1876-4754
                29 May 2024
                Jan-Feb 2024
                13 December 2023
                03 June 2024
                : 17
                : 1
                : 140-147
                Affiliations
                [a ]Rijnstate Hospital, Department of Psychiatry, P.O. Box 9555, 6800 TA Arnhem, the Netherlands
                [b ]University of Twente, Department of Clinical Neurophysiology, Enschede, the Netherlands
                [c ]Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Amsterdam, the Netherlands
                [d ]Amsterdam Neuroscience, Amsterdam, the Netherlands
                [e ]Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, USA
                [f ]Center for Psychiatric Neuroscience at the Feinstein Institute for Medical Research, New York, NY, USA
                [g ]Department of Biomedicine, University of Bergen, Bergen, Norway
                [h ]Department of Radiology, Haukeland University Hospital, Bergen, Norway
                [i ]Department of Computer Science, University of Bergen, Bergen, Norway
                [j ]Mohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen, Norway
                [k ]Departments of Neurology, Psychiatry, and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
                [l ]Donders Institute for Brain, Cognition and Behavior, Department of Psychiatry, Nijmegen, the Netherlands
                [m ]Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Emory University, USA
                [n ]Emory Center for Biomedical Imaging Statistics, Emory University, USA
                Author notes
                [* ]Corresponding author. Rijnstate Hospital Department of Psychiatry, P.O. Box 9555, 6800 TA Arnhem, the Netherlands. jverdijk@ 123456rijnstate.nl (J.P.A.J. Verdijk).
                Article
                NIHMS1997977
                10.1016/j.brs.2023.12.005
                11145948
                38101469
                3365dbe6-14ba-4fc6-b1af-1f28dab11d21

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Categories
                Article

                Neurosciences
                major depressive disorder,bold imaging,functional networks
                Neurosciences
                major depressive disorder, bold imaging, functional networks

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