Screening for C9ORF72 repeat expansion in FTLD

In the present study we aimed to determine the prevalence of {C9ORF72} {GGGGCC} hexanucleotide expansion in our cohort of 53 frontotemporal lobar degeneration (FTLD) patients and 174 neurologically normal controls. We identified the hexanucleotide repeat, in the pathogenic range, in 4 (2 bv-frontotemporal dementia (FTD) and 2 FTD-amyotrophic lateral sclerosis ALS) out of 53 patients and 1 neurologically normal control. Interestingly, 2 of the \{C9ORF72\} expansion carriers also carried 2 novel missense mutations in \{GRN\} (Y294C) and in PSEN-2(I146V). Further, 1 of the \{C9ORF72\} expansion carriers, for whom pathology was available, showed amyloid plaques and tangles in addition to \{TAR\} (trans-activation response) DNA-binding protein (TDP)-43 pathology. In summary, our findings suggest that the hexanucleotide expansion is probably associated with ALS, FTD, or FTD-ALS and occasional comorbid conditions such as Alzheimer's disease. These findings are novel and need to be cautiously interpreted and most importantly replicated in larger numbers of samples

Brain lesions disrupting addiction map to a common human brain circuit

Drug addiction is a public health crisis for which new treatments are urgently needed. In rare cases, regional brain damage can lead to addiction remission. These cases may be used to identify therapeutic targets for neuromodulation. We analyzed two cohorts of patients addicted to smoking at the time of focal brain damage (cohort 1 n = 67; cohort 2 n = 62). Lesion locations were mapped to a brain atlas and the brain network functionally connected to each lesion location was computed using human connectome data (n = 1,000). Associations with addiction remission were identified. Generalizability was assessed using an independent cohort of patients with focal brain damage and alcohol addiction risk scores (n = 186). Specificity was assessed through comparison to 37 other neuropsychological variables. Lesions disrupting smoking addiction occurred in many different brain locations but were characterized by a specific pattern of brain connectivity. This pattern involved positive connectivity to the dorsal cingulate, lateral prefrontal cortex, and insula and negative connectivity to the medial prefrontal and temporal cortex. This circuit was reproducible across independent lesion cohorts, associated with reduced alcohol addiction risk, and specific to addiction metrics. Hubs that best matched the connectivity profile for addiction remission were the paracingulate gyrus, left frontal operculum, and medial fronto-polar cortex. We conclude that brain lesions disrupting addiction map to a specific human brain circuit and that hubs in this circuit provide testable targets for therapeutic neuromodulation.Lesions resulting in addiction remission occur in multiple different brain locations but map to a specific brain circuit and that hubs in this circuit provide testable targets for therapeutic neuromodulation.</p

A Human Depression Circuit Derived From Focal Brain Lesions

Background: Focal brain lesions can lend insight into the causal neuroanatomical substrate of depression in the human brain. However, studies of lesion location have led to inconsistent results.Methods: Five independent datasets with different lesion etiologies and measures of postlesion depression were collated (N = 461). Each 3-dimensional lesion location was mapped to a common brain atlas. We used voxel lesion symptom mapping to test for associations between depression and lesion locations. Next, we computed the network of regions functionally connected to each lesion location using a large normative connectome dataset (N = 1000). We used these lesion network maps to test for associations between depression and connected brain circuits. Reproducibility was assessed using a rigorous leave-one-dataset-out validation. Finally, we tested whether lesion locations associated with depression fell within the same circuit as brain stimulation sites that were effective for improving poststroke depression.Results: Lesion locations associated with depression were highly heterogeneous, and no single brain region was consistently implicated. However, these same lesion locations mapped to a connected brain circuit, centered on the left dorsolateral prefrontal cortex. Results were robust to leave-one-dataset-out cross-validation. Finally, our depression circuit derived from brain lesions aligned with brain stimulation sites that were effective for improving poststroke depression.Conclusions: Lesion locations associated with depression fail to map to a specific brain region but do map to a specific brain circuit. This circuit may have prognostic utility in identifying patients at risk for poststroke depression and therapeutic utility in refining brain stimulation targets.</p

Effects of traumatic brain injury and posttraumatic stress disorder on Alzheimer's disease in veterans, using the Alzheimer's Disease Neuroimaging Initiative

Both traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are common problems resulting from military service, and both have been associated with increased risk of cognitive decline and dementia resulting from Alzheimer's disease (AD) or other causes. This study aims to use imaging techniques and biomarker analysis to determine whether traumatic brain injury (TBI) and/or PTSD resulting from combat or other traumas increase the risk for AD and decrease cognitive reserve in Veteran subjects, after accounting for age. Using military and Department of Veterans Affairs records, 65 Vietnam War veterans with a history of moderate or severe TBI with or without PTSD, 65 with ongoing PTSD without TBI, and 65 control subjects are being enrolled in this study at 19 sites. The study aims to select subject groups that are comparable in age, gender, ethnicity, and education. Subjects with mild cognitive impairment (MCI) or dementia are being excluded. However, a new study just beginning, and similar in size, will study subjects with TBI, subjects with PTSD, and control subjects with MCI. Baseline measurements of cognition, function, blood, and cerebrospinal fluid biomarkers; magnetic resonance images (structural, diffusion tensor, and resting state blood-level oxygen dependent (BOLD) functional magnetic resonance imaging); and amyloid positron emission tomographic (PET) images with florbetapir are being obtained. One-year follow-up measurements will be collected for most of the baseline procedures, with the exception of the lumbar puncture, the PET imaging, and apolipoprotein E genotyping. To date, 19 subjects with TBI only, 46 with PTSD only, and 15 with TBI and PTSD have been recruited and referred to 13 clinics to undergo the study protocol. It is expected that cohorts will be fully recruited by October 2014. This study is a first step toward the design and statistical powering of an AD prevention trial using at-risk veterans as subjects, and provides the basis for a larger, more comprehensive study of dementia risk factors in veterans

Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment

TMEM106B is a genetic modifier of frontotemporal lobar degeneration with C9orf72 hexanucleotide repeat expansions

Hexanucleotide repeat expansions in chromosome 9 open reading frame 72 (C9orf72) have recently been linked to frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis, and may be the most common genetic cause of both neurodegenerative diseases. Genetic variants at TMEM106B influence risk for the most common neuropathological subtype of FTLD, characterized by inclusions of TAR DNA-binding protein of 43 kDa (FTLD-TDP). Previous reports have shown that TMEM106B is a genetic modifier of FTLD-TDP caused by progranulin (GRN) mutations, with the major (risk) allele of rs1990622 associating with earlier age at onset of disease. Here, we report that rs1990622 genotype affects age at death in a single-site discovery cohort of FTLD patients with C9orf72 expansions (n = 14), with the major allele correlated with later age at death (p = 0.024). We replicate this modifier effect in a 30-site international neuropathological cohort of FTLD-TDP patients with C9orf72 expansions (n = 75), again finding that the major allele associates with later age at death (p = 0.016), as well as later age at onset (p = 0.019). In contrast, TMEM106B genotype does not affect age at onset or death in 241 FTLD-TDP cases negative for GRN mutations or C9orf72 expansions. Thus, TMEM106B is a genetic modifier of FTLD with C9orf72 expansions. Intriguingly, the genotype that confers increased risk for developing FTLD-TDP (major, or T, allele of rs1990622) is associated with later age at onset and death in C9orf72 expansion carriers, providing an example of sign epistasis in human neurodegenerative disease

Genome-wide analyses as part of the international FTLD-TDP whole-genome sequencing consortium reveals novel disease risk factors and increases support for immune dysfunction in FTLD

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) represents the most common pathological subtype of FTLD. We established the international FTLD-TDP whole genome sequencing consortium to thoroughly characterize the known genetic causes of FTLD-TDP and identify novel genetic risk factors. Through the study of 1,131 unrelated Caucasian patients, we estimated that C9orf72 repeat expansions and GRN loss-of-function mutations account for 25.5% and 13.9% of FTLD-TDP patients, respectively. Mutations in TBK1 (1.5%) and other known FTLD genes (1.4%) were rare, and the disease in 57.7% of FTLD-TDP patients was unexplained by the known FTLD genes. To unravel the contribution of common genetic factors to the FTLD-TDP etiology in these patients, we conducted a two-stage association study comprising the analysis of whole-genome sequencing data from 517 FTLD-TDP patients and 838 controls, followed by targeted genotyping of the most associated genomic loci in 119 additional FTLD-TDP patients and 1653 controls. We identified three genome-wide significant FTLD-TDP risk loci: one new locus at chromosome 7q36 within the DPP6 gene led by rs118113626 (pvalue=4.82e-08, OR=2.12), and two known loci: UNC13A, led by rs1297319 (pvalue=1.27e-08, OR=1.50) and HLA-DQA2 led by rs17219281 (pvalue=3.22e-08, OR=1.98). While HLA represents a locus previously implicated in clinical FTLD and related neurodegenerative disorders, the association signal in our study is independent from previously reported associations. Through inspection of our whole genome sequence data for genes with an excess of rare loss-of-function variants in FTLD-TDP patients (n≥3) as compared to controls (n=0), we further discovered a possible role for genes functioning within the TBK1-related immune pathway (e.g. DHX58, TRIM21, IRF7) in the genetic etiology of FTLD-TDP. Together, our study based on the largest cohort of unrelated FTLD-TDP patients assembled to date provides a comprehensive view of the genetic landscape of FTLD-TDP, nominates novel FTLD-TDP risk loci, and strongly implicates the immune pathway in FTLD-TDP pathogenesis

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease