0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Individual prediction of trauma-focused psychotherapy response in youth with posttraumatic stress disorder using resting-state functional connectivity

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Highlights

          • ML and rs-fMRI have shown promise in predicting treatment-response in adults with PTSD.

          • Currently, no biomarkers for treatment-response are available in youth with PTSD.

          • FC between the FPN and SMN was stronger in treatment non-responders on the group-level.

          • A network within the bilateral STG predicted response for individual youth with 76% accuracy.

          • Future studies should test generalizability of these findings and test if larger cohorts increase accuracy.

          Abstract

          Randomized controlled trials have shown efficacy of trauma-focused psychotherapies in youth with posttraumatic stress disorder (PTSD). However, response varies considerably among individuals. Currently, no biomarkers are available to assist clinicians in identifying youth who are most likely to benefit from treatment. In this study, we investigated whether resting-state functional magnetic resonance imaging (rs-fMRI) could distinguish between responders and non-responders on the group- and individual patient level. Pre-treatment rs-fMRI was recorded in 40 youth (ages 8–17 years) with (partial) PTSD before trauma-focused psychotherapy. Change in symptom severity from pre- to post-treatment was assessed using the Clinician-Administered PTSD scale for Children and Adolescents to divide participants into responders (≥30% symptom reduction) and non-responders. Functional networks were identified using meta-independent component analysis. Group-differences within- and between-network connectivity between responders and non-responders were tested using permutation testing. Individual predictions were made using multivariate, cross-validated support vector machine classification. A network centered on the bilateral superior temporal gyrus predicted treatment response for individual patients with 76% accuracy (p FWE = 0.02, 87% sensitivity, 65% specificity, area-under-receiver-operator-curve of 0.82). Functional connectivity between the frontoparietal and sensorimotor network was significantly stronger in non-responders ( t = 5.35, p FWE = 0.01) on the group-level. Within-network connectivity was not significantly different between groups. This study provides proof-of-concept evidence for the feasibility to predict trauma-focused psychotherapy response in youth with PTSD at an individual-level. Future studies are required to test if larger cohorts could increase accuracy and to test further generalizability of the prediction models.

          Related collections

          Most cited references47

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Methods to detect, characterize, and remove motion artifact in resting state fMRI.

            Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects. © 2013 Elsevier Inc. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Large-scale brain networks and psychopathology: a unifying triple network model.

              The science of large-scale brain networks offers a powerful paradigm for investigating cognitive and affective dysfunction in psychiatric and neurological disorders. This review examines recent conceptual and methodological developments which are contributing to a paradigm shift in the study of psychopathology. I summarize methods for characterizing aberrant brain networks and demonstrate how network analysis provides novel insights into dysfunctional brain architecture. Deficits in access, engagement and disengagement of large-scale neurocognitive networks are shown to play a prominent role in several disorders including schizophrenia, depression, anxiety, dementia and autism. Synthesizing recent research, I propose a triple network model of aberrant saliency mapping and cognitive dysfunction in psychopathology, emphasizing the surprising parallels that are beginning to emerge across psychiatric and neurological disorders. Copyright © 2011 Elsevier Ltd. All rights reserved.
                Bookmark

                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                26 November 2021
                2021
                26 November 2021
                : 32
                : 102898
                Affiliations
                [a ]Amsterdam UMC, University of Amsterdam, Department of Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
                [b ]Amsterdam UMC, University of Amsterdam, Department of Child and Adolescent Psychiatry, Amsterdam Neuroscience, Amsterdam, The Netherlands
                [c ]Levvel, Academic Centre for Child and Adolescent Psychiatry, Amsterdam, The Netherlands
                [d ]Research Institute of Child Development and Education, University of Amsterdam, Amsterdam, The Netherlands
                Author notes
                [* ]Corresponding author at: Department of Psychiatry Amsterdam University Medical Center, Meibergdreef 5, room PA0-226, 1105 AZ, Amsterdam, The Netherlands. j.b.zantvoord@ 123456amsterdamumc.nl
                [1]

                These authors contributed equally to this work.

                [2]

                These author share senior authorship.

                Article
                S2213-1582(21)00342-9 102898
                10.1016/j.nicl.2021.102898
                8645516
                34911201
                d4f4619d-1903-41eb-8f0f-9ac2bad80ce7
                © 2021 The Authors. Published by Elsevier Inc.

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

                History
                : 21 September 2021
                : 15 November 2021
                : 23 November 2021
                Categories
                Regular Article

                ptsd,psychotherapy,adolescent,resting-state fmri,machine learning,clinical outcome

                Comments

                Comment on this article