Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here, we used a novel machine learning method – evolving partitions to improve classification (EPIC) – to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed military veterans. METHODS: We used EPIC with repeated cross-validation (CV) to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n = 40) versus controls (n = 57), and classification of ELS within the PTSD (ELS+ n = 16; ELS− n = 24) and control groups (ELS+ n = 16; ELS− n = 41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression. RESULTS: On average, EPIC classified PTSD with 69% accuracy (SD = 5%), and ELS with 64% accuracy in the PTSD group (SD = 10%), and 62% accuracy in controls (SD = 6%). EPIC selected unique sets of individual features that classified each group with 75–85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas-defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS. CONCLUSIONS: EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat-exposed military veterans, which may represent distinct biotypes of stress-related neuropathology

A Tutorial in Connectome Analysis: Topological and Spatial Features of Brain Networks

High-throughput methods for yielding the set of connections in a neural system, the connectome, are now being developed. This tutorial describes ways to analyze the topological and spatial organization of the connectome at the macroscopic level of connectivity between brain regions as well as the microscopic level of connectivity between neurons. We will describe topological features at three different levels: the local scale of individual nodes, the regional scale of sets of nodes, and the global scale of the complete set of nodes in a network. Such features can be used to characterize components of a network and to compare different networks, e.g. the connectome of patients and control subjects for clinical studies. At the global scale, different types of networks can be distinguished and we will describe Erd\"os-R\'enyi random, scale-free, small-world, modular, and hierarchical archetypes of networks. Finally, the connectome also has a spatial organization and we describe methods for analyzing wiring lengths of neural systems. As an introduction for new researchers in the field of connectome analysis, we discuss the benefits and limitations of each analysis approach.Comment: Neuroimage, in pres

Pediatric Blood Calculator

This paper outlines an expert system based solution for calculating the optimal amount of blood draw from infants to carry out critical tests requested by the attending clinicians. The solution is a hand-held device with a user-friendly interface that allows a meaningful two-way conversation between the clinician and the pathology office. Based on the tests being requested, the calculator determines the minimum amount of blood required in the different vials based on a smart expert system. This removes the uncertainty that is prevalent today in the amount of blood required to do all the tests, since in some cases there is not enough blood for all the requested tests by the attending clinicians. The expert-based solution would be a stand-alone hand-held device, but have the ability to interface with the hospital electronic record systems to ensure all compliances and easy transference of the information

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

Creating a new tool for Post-Traumatic Disorder treatment

The first article on real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback was published in 2003 (Weiskopf et al., 2003) with the aim to enable the subject to learn to control activation in rostral-ventral and dorsal anterior cingulate cortex (ACC). Rt-fMRI neurofeedback involves data collection of neural activity, real-time data preprocessing, online statistical analysis, providing the results back to the participant, and active effort of participant in order to either up- and/or down-regulate the target region’s activation. In the last 16 years the topic attracted great attention from different labs around the world and many different brain regions were regulated with the help of rt-fMRI neurofeedback. Nevertheless it had the most distinct impact in the clinical research as it could be used with clinical population in order to normalize their abnormal neural activity. The dissertation focused on the implementation of the rt-fMRI neurofeedback to the Post-Traumatic Stress Disorder (PTSD) patients. PTSD is developed as a result of experiencing a traumatic event in first hand or hearing that a close one experienced it. PTSD has a high prevalence (Kessler et al., 2005) and also high impact on the patient’s life quality (Warshaw et al., 1993). Unfortunately the response rate to the therapy is around 50% (Bradley et al., 2005; Stein et al., 2006). Hence, there is a need for a new treatment tool for PTSD. The neurocircuitry model of PTSD indicate that there is increased activity in amygdala, decreased activity in ventromedial prefrontral cortex (vmPFC)/rostral ACC (rACC) and hippocampus (Rauch et al., 2006). Animal model of PTSD revealed that stimulating rACC led to increase in extinction learning and rats exhibited less PTSD symptoms (Milad & Quirk, 2002). Following these findings, we decided to implement rACC rt-fMRI neurofeedback to PTSD patients. The first study focused to develop a new paradigm to target rACC and tested it with healthy population. We used Ekman faces as functional localizer in order to locate the rACC. Experimental design constituted of four functional runs in one session. The main aim was to assess the methods effectiveness in one session. Surprisingly eight out of sixteen female participants learned to regulate their rACC, whereas only four out of sixteen male participants were able to regulate their rACC at will. Interestingly the learner/non-learners are not widely reported in the rt-fMRI literature and no gender difference has been reported so far. As a result we decided to implement it with only one sex in PTSD group. In the second study we tested the paradigm with the female PTSD patients. Eight out of sixteen PTSD patients gained control over their rACC. We also found that PTSD patients recruited more brain regions, especially multi-sensory brain regions for the upregulation of rACC in comparison to healthy subjects. We failed to find a single factor to predict rACC control success across groups. There is a need for further study to identify the predictor factors. As a result we concluded that the best practice of rt-fMRI with PTSD patients would be to use it as a supportive tool to psychotherapy in order to identify the best working strategy for their treatment. Further research recommendations are discussed below

Micro-, Meso- and Macro-Connectomics of the Brain

Neurosciences, Neurolog