FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response

Brain Imaging and Behavior - The hemodynamic response function (HRF) represents the transfer function linking neural activity with the functional MRI (fMRI) signal, modeling neurovascular coupling....     

Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets

Brain Imaging and Behavior - There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large...     

Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma

Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region‐based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time‐varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting‐state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine‐learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal‐subcortical‐parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal‐variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less‐variable connectivity in subcortical regions, followed by a similar effect on parietal memory‐related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re‐experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264–287, 2018. © 2017 Wiley Periodicals, Inc.     

Dynamic brain connectivity is a better predictor of PTSD than static connectivity

Using resting‐state functional magnetic resonance imaging, we test the hypothesis that subjects with post‐traumatic stress disorder (PTSD) are characterized by reduced temporal variability of brain connectivity compared to matched healthy controls. Specifically, we test whether PTSD is characterized by elevated static connectivity, coupled with decreased temporal variability of those connections, with the latter providing greater sensitivity toward the pathology than the former. Static functional connectivity (FC; nondirectional zero‐lag correlation) and static effective connectivity (EC; directional time‐lagged relationships) were obtained over the entire brain using conventional models. Dynamic FC and dynamic EC were estimated by letting the conventional models to vary as a function of time. Statistical separation and discriminability of these metrics between the groups and their ability to accurately predict the diagnostic label of a novel subject were ascertained using separate support vector machine classifiers. Our findings support our hypothesis that PTSD subjects have stronger static connectivity, but reduced temporal variability of connectivity. Further, machine learning classification accuracy obtained with dynamic FC and dynamic EC was significantly higher than that obtained with static FC and static EC, respectively. Furthermore, results also indicate that the ease with which brain regions engage or disengage with other regions may be more sensitive to underlying pathology than the strength with which they are engaged. Future studies must examine whether this is true only in the case of PTSD or is a general organizing principle in the human brain. Hum Brain Mapp 38:4479–4496, 2017. © 2017 Wiley Periodicals, Inc.     

A translational neuroscience approach to body image disturbance and its remediation in anorexia nervosa

Deviant perception of the body is a fundamental component of anorexia nervosa. Here we offer a potential mechanistic explanation that involves perturbations within the visual system and the brain circuits that modulate perceptual organization. Based on the model proposed, we also suggest a mechanistic strategy for altering neuronal activity in the visual system to normalize perception of the body, and set out a strategy for empirically testing its clinical application.