Reduced cortical thickness with increased lifetime burden of PTSD in OEF/OIF Veterans and the impact of comorbid TBI☆

Posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) in military personnel is increasing dramatically following the OEF/OIF conflicts and is associated with alterations to brain structure. The present study examined the relationship between PTSD and cortical thickness, and its possible modification by mTBI, in a 104-subject OEF/OIF veteran cohort ranging in age from 20 to 62 years. For each participant, two T1-weighted scans were averaged to create high-resolution images for calculation of regional cortical thickness. PTSD symptoms were assessed using the Clinician Administered PTSD Scale (CAPS) and scores were derived based on the previous month's symptoms (“current”) and a Cumulative Lifetime Burden of PTSD (CLB-P) reflecting the integral of CAPS scores across the lifetime. Mild TBI was diagnosed using the Boston Assessment of TBI-Lifetime (BAT-L). Results demonstrated a clear negative relationship between current PTSD severity and thickness in both postcentral gyri and middle temporal gyri. This relationship was stronger and more extensive when considering lifetime burden (CLB-P), demonstrating the importance of looking at trauma in the context of an individual's lifetime, rather than only at their current symptoms. Finally, interactions with current PTSD only and comorbid current PTSD and mTBI were found in several regions, implying an additive effect of lifetime PTSD and mTBI on cortical thickness

Regional staging of white matter signal abnormalities in aging and Alzheimer's disease

White matter lesions, quantified as ‘white matter signal abnormalities’ (WMSA) on neuroimaging, are common incidental findings on brain images of older adults. This tissue damage is linked to cerebrovascular dysfunction and is associated with cognitive decline. The regional distribution of WMSA throughout the cerebral white matter has been described at a gross scale; however, to date no prior study has described regional patterns relative to cortical gyral landmarks which may be important for understanding functional impact. Additionally, no prior study has described how regional WMSA volume scales with total global WMSA. Such information could be used in the creation of a pathologic ‘staging’ of WMSA through a detailed regional characterization at the individual level. Magnetic resonance imaging data from 97 cognitively-healthy older individuals (OC) aged 52–90 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were processed using a novel WMSA labeling procedure described in our prior work. WMSA were quantified regionally using a procedure that segments the cerebral white matter into 35 bilateral units based on proximity to landmarks in the cerebral cortex. An initial staging was performed by quantifying the regional WMSA volume in four groups based on quartiles of total WMSA volume (quartiles I–IV). A consistent spatial pattern of WMSA accumulation was observed with increasing quartile. A clustering procedure was then used to distinguish regions based on patterns of scaling of regional WMSA to global WMSA. Three patterns were extracted that showed high, medium, and non-scaling with global WMSA. Regions in the high-scaling cluster included periventricular, caudal and rostral middle frontal, inferior and superior parietal, supramarginal, and precuneus white matter. A data-driven staging procedure was then created based on patterns of WMSA scaling and specific regional cut-off values from the quartile analyses. Individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI) were then additionally staged, and significant differences in the percent of each diagnostic group in Stages I and IV were observed, with more AD individuals residing in Stage IV and more OC and MCI individuals residing in Stage I. These data demonstrate a consistent regional scaling relationship between global and regional WMSA that can be used to classify individuals into one of four stages of white matter disease. White matter staging could play an important role in a better understanding and the treatment of cerebrovascular contributions to brain aging and dementia

Data‐driven classification of cognitively normal and mild cognitive impairment subtypes predicts progression in the NACC dataset

IntroductionData-driven neuropsychological methods can identify mild cognitive impairment (MCI) subtypes with stronger associations to dementia risk factors than conventional diagnostic methods.MethodsCluster analysis used neuropsychological data from participants without dementia (mean age = 71.6 years) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (n = 26,255) and the "normal cognition" subsample (n = 16,005). Survival analyses examined MCI or dementia progression.ResultsFive clusters were identified: "Optimal" cognitively normal (oCN; 13.2%), "Typical" CN (tCN; 28.0%), Amnestic MCI (aMCI; 25.3%), Mixed MCI-Mild (mMCI-Mild; 20.4%), and Mixed MCI-Severe (mMCI-Severe; 13.0%). Progression to dementia differed across clusters (oCN < tCN < aMCI < mMCI-Mild < mMCI-Severe). Cluster analysis identified more MCI cases than consensus diagnosis. In the "normal cognition" subsample, five clusters emerged: High-All Domains (High-All; 16.7%), Low-Attention/Working Memory (Low-WM; 22.1%), Low-Memory (36.3%), Amnestic MCI (16.7%), and Non-amnestic MCI (naMCI; 8.3%), with differing progression rates (High-All < Low-WM = Low-Memory < aMCI < naMCI).DiscussionOur data-driven methods outperformed consensus diagnosis by providing more precise information about progression risk and revealing heterogeneity in cognition and progression risk within the NACC "normal cognition" group