PTSD and its dissociative subtype through the lens of the insula: Anterior and posterior insula resting‐state functional connectivity and its predictive validity using machine learning

2019 ◽  
Vol 57 (1) ◽  
Author(s):  
Sherain Harricharan ◽  
Andrew A. Nicholson ◽  
Janine Thome ◽  
Maria Densmore ◽  
Margaret C. McKinnon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Connectivity ◽  
Predictive Validity ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Posterior Insula ◽  
Dissociative Subtype

scholarly journals Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Bidhan Lamichhane ◽  
Andy G. S. Daniel ◽  
John J. Lee ◽  
Daniel S. Marcus ◽  
Joshua S. Shimony ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glioblastoma Multiforme ◽  
Functional Connectivity ◽  
Resting State ◽  
Learning Analytics ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Term Survival ◽  
Long Term Survival

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

scholarly journals Back to the Basics: Resting State Functional Connectivity of the Reticular Activation System in PTSD and its Dissociative Subtype

2019 ◽  
Vol 3 ◽  
pp. 247054701987366
Author(s):  
Janine Thome ◽  
Maria Densmore ◽  
Georgia Koppe ◽  
Braeden Terpou ◽  
Jean Théberge ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
Critical Role ◽  
Ventromedial Prefrontal Cortex ◽  
Anterior Cingulate ◽  
Parahippocampal Gyrus ◽  
Resting State Functional Connectivity ◽  
Activation System ◽  
Dissociative Subtype

Background Brainstem and midbrain neuronal circuits that control innate, reflexive responses and arousal are increasingly recognized as central to the neurobiological framework of post-traumatic stress disorder (PTSD). The reticular activation system represents a fundamental neuronal circuit that plays a critical role not only in generating arousal but also in coordinating innate, reflexive responding. Accordingly, the present investigation aims to characterize the resting state functional connectivity of the reticular activation system in PTSD and its dissociative subtype. Methods We investigated patterns of resting state functional connectivity of a central node of the reticular activation system, namely, the pedunculopontine nuclei, among individuals with PTSD (n = 77), its dissociative subtype (PTSD+DS; n = 48), and healthy controls (n = 51). Results Participants with PTSD and PTSD+DS were characterized by within-group pedunculopontine nuclei resting state functional connectivity to brain regions involved in innate threat processing and arousal modulation (i.e., midbrain, amygdala, ventromedial prefrontal cortex). Critically, this pattern was most pronounced in individuals with PTSD+DS, as compared to both control and PTSD groups. As compared to participants with PTSD and controls, individuals with PTSD+DS showed enhanced pedunculopontine nuclei resting state functional connectivity to the amygdala and the parahippocampal gyrus as well as to the anterior cingulate and the ventromedial prefrontal cortex. No group differences emerged between PTSD and control groups. In individuals with PTSD+DS, state derealization/depersonalization was associated with reduced resting state functional connectivity between the left pedunculopontine nuclei and the anterior nucleus of the thalamus. Altered connectivity in these regions may restrict the thalamo-cortical transmission necessary to integrate internal and external signals at a cortical level and underlie, in part, experiences of depersonalization and derealization. Conclusions The present findings extend the current neurobiological model of PTSD and provide emerging evidence for the need to incorporate brainstem structures, including the reticular activation system, into current conceptualizations of PTSD and its dissociative subtype.

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