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      Increased top-down control of emotions during symptom provocation working memory tasks following a RCT of alpha-down neurofeedback in PTSD

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          Highlights

          • Clinically significant reduction in PTSD symptom severity with alpha-down NFB.

          • Intervention increased activation in PFC areas involved in top-down emotion control.

          • Intervention improved task-irrelevant decoupling with executive areas (dlPFC)

          • Intervention improved DMN integration with posterior nodes.

          • Better NFB performance linked with increased activity in regions of bodily self-consciousness processing.

          Abstract

          Background

          Posttraumatic stress disorder (PTSD) has been found to be associated with emotion under-modulation from the prefrontal cortex and a breakdown of the top-down control of cognition and emotion. Novel adjunct therapies such as neurofeedback (NFB) have been shown to normalize aberrant neural circuits that underlie PTSD psychopathology at rest. However, little evidence exists for NFB-linked neural improvements under emotionally relevant cognitive load. The current study sought to address this gap by examining the effects of alpha-down NFB in the context of an emotional n-back task.

          Methods

          We conducted a 20-week double-blind randomized, sham-controlled trial of alpha-down NFB and collected neuroimaging data before and after the NFB protocol. Participants performed an emotional 1-back and 2-back working memory task, with interleaved trauma-neutral and trauma-relevant cues in the fMRI scanner. Data from 35 participants with a primary diagnosis of PTSD were analyzed in this study ( n = 18 in the experimental group undergoing alpha-down NFB, n = 17 in the sham-control group).

          Results

          Firstly, within-group analyses showed clinically significant reductions in PTSD symptom severity scores at the post-intervention timepoint and 3-month follow-up for the experimental group, and not for the sham-control group. The neuroimaging analyses revealed that alpha-down NFB enhanced engagement of top-down cognitive and emotional control centers, such as the dorsolateral prefrontal cortex (dlPFC), and improved integration of the anterior and posterior parts of the default mode network (DMN). Finally, our results also indicate that increased alpha-down NFB performance correlated with increased activity in brain regions involved in top-down control and bodily consciousness/embodied processing of self (TPJ and posterior insula).

          Conclusion

          This is the first study to provide mechanistic insights into how NFB may normalize dysfunctional brain activity and connectivity in PTSD under cognitive load with simultaneous symptom provocation, adding to a growing body of evidence supporting the therapeutic neuromodulatory effects of NFB. This preliminary study highlights the benefits of alpha-down NFB training as an adjunctive therapy for PTSD and warrants further investigation into its therapeutic effects on cognitive and emotion control in those with PTSD.

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          Most cited references94

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.

            Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.
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              Unified segmentation.

              A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                03 January 2023
                2023
                03 January 2023
                : 37
                : 103313
                Affiliations
                [a ]Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
                [b ]Vector Institute, Toronto, Ontario, Canada
                [c ]Homewood Research Institute (HRI), Guelph, Ontario, Canada
                [d ]School of Psychology, University of Ottawa, Canada
                [e ]Atlas Institute for Veterans and Families, Royal Ottawa Hospital, Canada
                [f ]Department of Psychiatry and Behavioral Neuroscience, McMaster University, Hamilton, Ontario, Canada
                [g ]Department of Medical Biophysics, Western University, London, Ontario, Canada
                [h ]Departments of Neuroscience and Psychiatry, University of Geneva, Geneva, Switzerland
                [i ]St. Joseph’s Healthcare, Hamilton, Ontario, Canada
                [j ]Lawson Health Research Institute, London, Ontario, Canada
                [k ]Department of Diagnostic Imaging, St. Joseph’s Healthcare, London, Ontario, Canada
                Author notes
                [* ]Corresponding author. Ruth.Lanius@ 123456lhsc.on.ca
                [1]

                Shared first authorship.

                Article
                S2213-1582(23)00002-5 103313
                10.1016/j.nicl.2023.103313
                9868881
                36669352
                83fd3822-b86c-435a-9f62-0ba0d50f7101
                © 2023 The Authors

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

                History
                : 25 July 2022
                : 31 December 2022
                : 2 January 2023
                Categories
                Regular Article

                post-traumatic stress disorder (ptsd),neurofeedback (nfb),functional magnetic resonance imaging (fmri),dlpfc,top-down control,cognition,emotion regulation

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