Rapid advances in neuroimaging techniques provide great potentials for study of Alzheimer’s disease (AD). Existing findings have shown that AD is closely related to alteration in the functional brain network, i.e., the functional connectivity between different brain regions. In this paper, we propose a method based on sparse inverse covariance estimation (SICE) to identify functional brain connectivity networks from PET data. Our method is able to identify both the connectivity network structure and strength for a large number o f brain regions with small sample sizes. We apply the proposed method to the PET data of AD, mild cognitive impairment (MCI), and normal control (NC) subjects. Compared with NC, AD shows decrease in the amount of inter-region functional connectivity within the temporal lobe especially between the area around hippocampus and other regions and increase in the amount of connectivity within the frontal lobe as well as between the parietal and occipital lobes. Also, AD shows weaker between-lobe connectivity than within-lobe connectivity and weaker between-hemisphere connectivity, compared with NC. In addition to being a method for knowledge discovery about AD, the proposed SICE method can also be used for classifying new subjects, which makes it a suitable approach for novel connectivity-based AD biomarker identification. Our experiments show that the best sensitivity and specificity our method can achieve in AD vs. NC classification are 88% and 88%, respectively.

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Background. Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods. We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results. We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A,

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Primary progressive aphasia is a neurodegenerative clinical syndrome that presents in adulthood with an isolated, progressive language disorder. Three main clinical/anatomical variants have been described, each associated with distinctive pathology. A high frequency of neurodevelopmental learning disability in primary progressive aphasia has been reported. Because the disorder is heterogeneous with different patterns of cognitive, anatomical and biological involvement, we sought to identify whether learning disability had a predilection for one or more of the primary progressive aphasia subtypes. We screened the University of California San Francisco Memory and Aging Center's primary progressive aphasia cohort (n = 198) for history of language-related learning disability as well as hand preference, which has associations with learning disability. The study included logopenic (n = 48), non-fluent (n = 54) and semantic (n = 96) variant primary progressive aphasias. We investigated whether the presence of learning disability or non-right-handedness was associated with differential effects on demographic, neuropsychological and neuroimaging features of primary progressive aphasia. We showed that a high frequency of learning disability was present only in the logopenic group (χ(2) = 15.17, P < 0.001) and (χ(2) = 11.51, P < 0.001) compared with semantic and non-fluent populations. In this group, learning disability was associated with earlier onset of disease, more isolated language symptoms, and more focal pattern of left posterior temporoparietal atrophy. Non-right-handedness was instead over-represented in the semantic group, at nearly twice the prevalence of the general population (χ(2) = 6.34, P = 0.01). Within semantic variant primary progressive aphasia the right-handed and non-right-handed cohorts appeared homogeneous on imaging, cognitive profile, and structural analysis of brain symmetry. Lastly, the non-fluent group showed no increase in learning disability or non-right-handedness. Logopenic variant primary progressive aphasia and developmental dyslexia both manifest with phonological disturbances and posterior temporal involvement. Learning disability might confer vulnerability of this network to early-onset, focal Alzheimer's pathology. Left-handedness has been described as a proxy for atypical brain hemispheric lateralization. As non-right-handedness was increased only in the semantic group, anomalous lateralization mechanisms might instead be related to frontotemporal lobar degeneration with abnormal TARDBP. Taken together, this study suggests that neurodevelopmental signatures impart differential trajectories towards neurodegenerative disease.

BackgroundGenetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.MethodsWe recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.ResultsWe collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).ConclusionMulti-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Background Some patients meeting behavioral variant frontotemporal dementia (bvFTD) diagnostic criteria progress slowly and plateau at mild symptom severity. Such patients have mild neuropsychological and functional impairments, lack characteristic bvFTD brain atrophy, and have thus been referred to as bvFTD “phenocopies” or slowly progressive (bvFTD-SP). The few patients with bvFTD-SP that have been studied at autopsy have found no evidence of FTD pathology, suggesting that bvFTD-SP is neuropathologically distinct from other forms of FTD. Here, we describe two patients with bvFTD-SP with chromosome 9 open reading frame 72 (C9ORF72) hexanucleotide expansions. Methods Three hundred and eighty-four patients with FTD clinical spectrum and Alzheimer’s disease diagnoses were screened for C9ORF72 expansion. Two bvFTD-SP mutation carriers were identified. Neuropsychological and functional data, as well as brain atrophy patterns assessed using voxel-based morphometry (VBM), were compared with 44 patients with sporadic bvFTD and 85 healthy controls. Results Both patients were age 48 at baseline and met possible bvFTD criteria. In the first patient, VBM revealed thalamic and posterior insula atrophy. Over seven years, his neuropsychological performance and brain atrophy remained stable. In the second patient, VBM revealed cortical atrophy with subtle frontal and insular volume loss. Over two years, her neuropsychological and functional scores as well as brain atrophy remained stable. Conclusions C9ORF72 mutations can present with a bvFTD-SP phenotype. Some bvFTD-SP patients may have neurodegenerative pathology, and C9ORF72 mutations should be considered in patients with bvFTD-SP and a family history of dementia or motor neuron disease.