Citations

Abstract Introduction Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) have previously been reported to be associated with increased risk of Alzheimer's disease (AD). We are using biomarkers to study Vietnam Veterans with/without mild cognitive impairment with a history of at least one TBI and/or ongoing PTSD to determine whether these contribute to the development of AD. Methods Potential subjects identified by Veterans Administration records underwent an initial telephone screen. Consented subjects underwent clinical evaluation, lumbar puncture, structural magnetic resonance imaging, and amyloid positron emission tomography (PET) scans. Results We observed worse cognitive functioning in PTSD and TBI + PTSD groups, worse global cognitive functioning in the PTSD group, lower superior parietal volume in the TBI + PTSD group, and lower amyloid positivity in the PTSD group, but not the TBI group compared to controls without TBI/PTSD. Medial temporal lobe atrophy was not increased in the PTSD and/or TBI groups. Discussion Preliminary results do not indicate that TBI or PTSD increase the risk for AD measured by amyloid PET. Additional recruitment, longitudinal follow‐up, and tau‐PET scans will provide more information in the future.

PurposeIn diffusion MRI (dMRI), fractional anisotropy derived from the single‐tensor model (FADTI) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors.MethodsWe compared the ability of standard FADTIand TDF‐derived FA (FATDF), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume.ResultsAcross angular resolutions and statistical tests, FATDFshowed larger effect sizes than FADTI, particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies.ConclusionThe TDF “corrected” form of FA may be a more sensitive and accurate alternative to the commonly used FADTI, even in clinical quality dMRI data. Magn Reson Med 78:2322–2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Abstract Aims The presence of cancer can complicate treatment choices for patients with atrial fibrillation (AF) increasing both the risk of thrombotic and bleeding events. Methods and results Using data from Outcomes Registry for Better Informed Treatment of Atrial Fibrillation, we aimed to characterize AF patients with cancer, to describe their management and to assess the association between cancer and cardiovascular (CV) outcomes. Among 9749 patients, 23.8% had history of cancer (57% solid malignancy, 1.3% leukaemia, 3.3% lymphoma, 40% other type, and 2.2% metastatic cancer). Patients with history of cancer were older, more likely to have CV disease, CV risk factors, and prior gastrointestinal bleeding. No difference in antiarrhythmic and antithrombotic therapy was observed between those with and without cancer. Patients with history of cancer had a significantly higher risk of death (7.8 vs. 4.9 deaths per 100 patient-years follow-up, P = 0.0003) mainly driven by non-CV death (4.2 vs. 2.4 per 100 patient-years follow-up; P = 0.0004) and higher risk of major bleeding (5.1 vs. 3.5 per 100 patient-years follow-up; P = 0.02) compared with non-cancer patients; no differences were observed in risks of strokes/non-central nervous system embolism (1.96 vs. 1.48, P = 0.74) and CV death (2.89 vs. 2.07, P = 0.35) between the two groups. Conclusion A history of cancer is common among AF patients with up to one in four patients having both. Antithrombotic therapy, rates of cerebrovascular accident, other thrombotic events and cardiac death were similar in AF patients with or without a history of cancer. Patients with cancer, however, were at higher risk of major bleeding and non-CV death.

AbstractIntroductionBiomarkers for Alzheimer's disease (AD) pathophysiology have been developed that focus on various levels of brain organization. However, no robust biomarker of large‐scale network failure has been developed. Using the recently introduced cascading network failure model of AD, we developed the network failure quotient (NFQ) as a biomarker of this process.MethodsWe developed and optimized the NFQ using our recently published analyses of task‐free functional magnetic resonance imaging data in clinically normal (n = 43) and AD dementia participants (n = 28) from the Alzheimer's Disease Neuroimaging Initiative. The optimized NFQ (oNFQ) was then validated in a cohort spanning the AD spectrum from the Mayo Clinic (n = 218).ResultsThe oNFQ (d = 1.25, 95% confidence interval [1.25, 1.26]) had the highest effect size for differentiating persons with AD dementia from clinically normal participants. The oNFQ measure performed similarly well on the validation Mayo Clinic sample (d = 1.44, 95% confidence interval [1.43, 1.44]). The oNFQ was also associated with other available key biomarkers in the Mayo cohort.DiscussionThis study demonstrates a measure of functional connectivity, based on a cascading network failure model of AD, and was highly successful in identifying AD dementia. A robust biomarker of the large‐scale effects of AD pathophysiology will allow for richer descriptions of the disease process and its modifiers, but is not currently suitable for discriminating clinical diagnostic categories. The large‐scale network level may be one of the earliest manifestations of AD, making this an attractive target for continued biomarker development to be used in prevention trials.