Attention training modulates resting-state neurophysiological abnormalities in posttraumatic stress disorder

Badura-Brack, Amy S.; McDermott, Timothy J.; Becker, Katherine M.; Ryan, Tara J.; Khanna, Maya M.; Pine, Daniel S.; Bar‐Haim, Yair; Heinrichs‐Graham, Elizabeth; Wilson, Tony W.

doi:10.1016/j.pscychresns.2017.11.008

Cited by 13 publications

(6 citation statements)

References 81 publications

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“…Indeed, MEG has been used to understand human neurophysiology, including a wide range of neurodegenerative and neuropsychological disorders, such as mTBI, Alzheimer's disease, depression and bipolar disorder [9][10][11] . Neural oscillations recovered using MEG are known to be dysfunctional in PTSD and correlate with primary symptoms and secondary complaints in the disorder [12][13][14][15]22 . Building on this, we explored the utility of a feature selection (FS) and machine learning-based classification modelling pipeline on PTSD biomarker discovery and classification.…”

Section: Discussionmentioning

confidence: 99%

“…A mechanism that mediates communication and information processing within and between brain circuits is neural oscillations and synchrony 7 . Magnetoencephalography (MEG) can image these phenomena non-invasively, and has been used as an effective research tool for exploring the neural activity associated with various neurodegenerative and neuropsychological disorders, including depression, bipolar disorder, mild traumatic brain injury (mTBI) and Alzheimer's disease [8][9][10][11] as well as PTSD-related functional circuitry [12][13][14][15] . At the group level, neural synchrony can stratify those with PTSD from a heavily traumatised, but otherwise matched, control group 15 , with hippocampal synchrony directly related to symptom severity across individuals 14 .…”

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confidence: 99%

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Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

Zhang

Richardson

Dunkley

2020

Sci Rep

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Given the subjective nature of conventional diagnostic methods for post-traumatic stress disorder (PTSD), an objectively measurable biomarker is highly desirable; especially to clinicians and researchers. Macroscopic neural circuits measured using magnetoencephalography (MEG) has previously been shown to be indicative of the PTSD phenotype and severity. In the present study, we employed a machine learning-based classification framework using MEG neural synchrony to distinguish combat-related PTSD from trauma-exposed controls. Support vector machine (SVM) was used as the core classification algorithm. A recursive random forest feature selection step was directly incorporated in the nested SVM cross validation process (CV-SVM-rRF-FS) for identifying the most important features for PTSD classification. For the five frequency bands tested, the CV-SVM-rRF-FS analysis selected the minimum numbers of edges per frequency that could serve as a PTSD signature and be used as the basis for SVM modelling. Many of the selected edges have been reported previously to be core in PTSD pathophysiology, with frequency-specific patterns also observed. Furthermore, the independent partial least squares discriminant analysis suggested low bias in the machine learning process. The final SVM models built with selected features showed excellent PTSD classification performance (area-under-curve value up to 0.9). Testament to its robustness when distinguishing individuals from a heavily traumatised control group, these developments for a classification model for PTSD also provide a comprehensive machine learning-based computational framework for classifying other mental health challenges using MEG connectome profiles.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

Zhang

Richardson

Dunkley

2020

Sci Rep

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“…The areas involved here have been identified as functionally (Badura-Brack et al, 2017; Boccia et al, 2016; Dunkley et al, 2014; Pitman et al, 2012; Rabinak et al, 2011) or structurally (Morey et al, 2012) altered when comparing PTSD patients with healthy controls. Furthermore, treatment studies in PTSD reveal a normalization within these regions following exposure-based therapy (Felmingham et al, 2007; Fonzo et al, 2017; Peres et al, 2007) or training in attentional control (Badura-Brack et al, 2018). Thus, our results may assist to develop a potential biomarker for the diagnosis of trauma-related and dissociative disorders and the monitoring of treatment efficacy.…”

Section: Discussionmentioning

confidence: 99%

Functional reorganization of neural networks involved in emotion regulation following trauma therapy for complex trauma disorders

Schlumpf

Nijenhuis²,

Klein

et al. 2019

NeuroImage: Clinical

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Objectives We investigated whether patients with complex interpersonal trauma engage neural networks that are commonly activated during cognitive reappraisal and responding naturally to affect-laden images. In this naturalistic study, we examined whether trauma treatment not only reduces symptoms but also changes neural networks involved in emotional control. Methods Before and after eight weeks of phase-oriented inpatient trauma treatment, patients ( n = 28) with complex posttraumatic stress disorder (cPTSD) and complex dissociative disorders (CDD) performed a cognitive reappraisal task while electroencephalography (EEG) was registered. Patients were measured as a prototypical dissociative part that aims to fulfill daily life goals while avoiding traumatic memories and associated dissociative parts. Matched healthy controls ( n = 38) were measured twice as well. We examined task-related functional connectivity and assessed self-reports of clinical symptoms and emotion regulation skills. Results Prior to treatment and compared to controls, patients showed hypoconnectivity within neural networks involved in emotional downregulation while reappraising affect-eliciting pictures as well as viewing neutral and affect-eliciting pictures. Following treatment, connectivity became normalized in these networks comprising regions associated with cognitive control and memory. Additionally, patients showed a treatment-related reduction of negative but not of positive dissociative symptoms. Conclusions This is the first study demonstrating that trauma-focused treatment was associated with favorable changes in neural networks involved in emotional control. Emotional overregulation manifesting as negative dissociative symptoms was reduced but not emotional underregulation, manifesting as positive dissociative symptoms.

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“…Indeed, MEG has been used to understand human neurophysiology, including a wide range of neurodegenerative and neuropsychological disorders, such as mTBI, Alzheimer's disease, depression and bipolar disorder (Vakorin et al, 2016;Alamian et al, 2017;Koelewijn et al, 2019). Neural oscillations recovered using MEG are known to be dysfunctional in PTSD and correlate with primary symptoms and secondary complaints in the disorder (Kolassa et al, 2007;Dunkley et al, 2014;Mišić et al, 2016;Badura-Brack et al, 2018a, 2018b. Building on this, we developed a comprehensive machine learning-based pipeline for downstream mining and modelling of MEG data.…”

Section: Discussionmentioning

confidence: 99%

“…A mechanism that mediates communication and information processing within and between brain circuits is neural oscillations and synchrony (Fries, 2015). Magnetoencephalography (MEG) can image these phenomena non-invasively, and has been used as an effective research tool for exploring the neural activity associated with various neurodegenerative and neuropsychological disorders, including depression, bipolar disorder, mild traumatic brain injury (mTBI) and Alzheimer’s disease (Stam 2010; Vakorin et al, 2016; Alamian et al, 2017; Koelewijn et al, 2019) as well as PTSD-related functional circuitry (Badura-Brack et al, 2018a, 2018b; Dunkley et al, 2014; Mišič et al, 2016). At the group level, neural synchrony can stratify those with PTSD from a heavily-traumatised, but otherwise matched, control group (Misic et al, 2016), with hippocampal synchrony directly related to symptom severity across individuals (Dunkley et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

Zhang

Richardson²,

Dunkley

2019

Preprint

View full text Add to dashboard Cite

Given the subjective nature of conventional diagnostic methods for post-traumatic stress disorder (PTSD), an objectively measurable biomarker is highly desirable. Macroscopic neural circuits measured using magnetoencephalography (MEG) has previously been shown to be indicative of the PTSD phenotype and severity. In the present study, we employed a machine learning-based classification framework using MEG neural synchrony to distinguish combat-related PTSD from trauma-exposed controls. Support vector machine (SVM) was used as the core classification algorithm. A recursive random forest feature selection step was directly incorporated in the nested SVM cross validation process (CV-SVM-rRF-FS) for identifying the most important features for PTSD classification. For the five frequency bands tested, the nested CV-SVM-rRF-FS analysis selected the minimum numbers of edges per frequency that could serve as a PTSD signature and be used as the basis for SVM modelling. Many of the selected edges have been reported previously to be core in PTSD pathophysiology, with frequency-specific patterns also observed. Furthermore, the independent partial least squares discriminant analysis suggested low bias in the nested CV-SVM-rRF-FS process. The final SVM models built with selected features showed excellent PTSD classification performance (area-under-curve value up to 0.9). Testament to its robustness when distinguishing individuals from a heavily-traumatised control group, these developments for a classification model for PTSD also provide a comprehensive machine learning-based computational framework for classifying other mental health challenges using MEG connectome profiles.

show abstract

Attention training modulates resting-state neurophysiological abnormalities in posttraumatic stress disorder

Cited by 13 publications

References 81 publications

Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

Functional reorganization of neural networks involved in emotion regulation following trauma therapy for complex trauma disorders

Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning

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