Background and PurposeIn Alzheimer's continuum (a comprehensive of preclinical Alzheimer's disease [AD], mild cognitive impairment [MCI] due to AD, and AD dementia), cognitive dysfunctions are often related to cortical atrophy in specific brain regions. The purpose of this study was to investigate the association between anatomical pattern of cortical atrophy and specific neuropsychological deficits.MethodsA total of 249 participants with Alzheimer's continuum (125 AD dementia, 103 MCI due to AD, and 21 preclinical AD) who were confirmed to be positive for amyloid deposits were collected from the memory disorder clinic in the department of neurology at Samsung Medical Center in Korea between September 2013 and March 2018. To analyze neuropsychological test-specific neural correlates representing the relationship between cortical atrophy measured by cortical thickness and performance in specific neuropsychological tests, a linear regression analysis was performed. Two neural correlates acquired by 2 different standardized scores in neuropsychological tests were also compared.ResultsCortical atrophy in several specific brain regions was associated with most neuropsychological deficits, including digit span backward, naming, drawing-copying, verbal and visual recall, semantic fluency, phonemic fluency, and response inhibition. There were a few differences between 2 neural correlates obtained by different z-scores.ConclusionsThe poor performance of most neuropsychological tests is closely related to cortical thinning in specific brain areas in Alzheimer's continuum. Therefore, the brain atrophy pattern in patients with Alzheimer's continuum can be predict by an accurate analysis of neuropsychological tests in clinical practice.

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Background: The retina and the brain share anatomic, embryologic, and physiologic characteristics. Therefore, retinal imaging in patients with brain disorders has been of significant interest. Using optical coherence tomography angiography (OCTA), a novel quantitative method of measuring retinal vasculature, we aimed to evaluate radial peripapillary capillary (RPC) network density and retinal nerve fiber layer (RNFL) thickness in cognitively impaired patients and determine their association with brain imaging markers. Methods: In this prospective cross-sectional study, a total of 69 patients (138 eyes) including 29 patients with amyloid-positive Alzheimer's disease-related cognitive impairment (ADCI), 25 patients with subcortical vascular cognitive impairment (SVCI), and 15 amyloid-negative cognitively normal (CN) subjects were enrolled. After excluding eyes with an ophthalmologic disease or poor image quality, 117 eyes of 60 subjects were included in the final analyses. Retinal vascular [capillary density (CD) of the radial peripapillary capillary (RPC) network] and neurodegeneration markers [retinal nerve fiber layer (RNFL) thickness at four quadrants] were measured using OCTA and OCT imaging. Brain vascular (CSVD score) and neurodegeneration markers (cortical thickness) were assessed using 3D brain magnetic resonance imaging. The CD and RNFL thickness and their correlation with brain imaging markers were investigated. Results: The SVCI group showed lower CD in the temporal quadrant of the RPC network compared to the CN group (mean (SD), 42.34 (6.29) vs 48.45 (7.08); p = 0.001). When compared to the ADCI group, the SVCI showed lower CD in the superior quadrant (mean (SD), 60.14 (6.42) vs 64.15 (6.39); p = 0. 033) as well as in the temporal quadrant (ADCI 45.76, SVCI 42.34; p = 0.048) of the RPC network. The CD was negatively correlated with CSVD score in the superior (B (95%CI), − 0.059 (− 0.097 to − 0.021); p = 0.003) and temporal (B (95%CI), − 0.048 (− 0.080 to − 0.017); p = 0.003) quadrants of the RPC network. RNFL thickness did not differ among the groups nor did it correlate with cortical thickness.

A comparative physical map of the AA genome (Oryza sativa) and the BB genome (O. punctata) was constructed by aligning a physical map of O. punctata, deduced from 63,942 BAC end sequences (BESs) and 34,224 fingerprints, onto the O. sativa genome sequence. The level of conservation of each chromosome between the two species was determined by calculating a ratio of BES alignments. The alignment result suggests more divergence of intergenic and repeat regions in comparison to gene-rich regions. Further, this characteristic enabled localization of heterochromatic and euchromatic regions for each chromosome of both species. The alignment identified 16 locations containing expansions, contractions, inversions, and transpositions. By aligning 40% of the punctata BES on the map, 87% of the punctata FPC map covered 98% of the O. sativa genome sequence. The genome size of O. punctata was estimated to be 8% larger than that of O. sativa with individual chromosome differences of 1.5-16.5%. The sum of expansions and contractions observed in regions .500 kb were similar, suggesting that most of the contractions/ expansions contributing to the genome size difference between the two species are small, thus preserving the macro-collinearity between these species, which diverged $2 million years ago. C OMPARATIVE genome analysis is proving to be an excellent tool, not only to discover genes and understand their functions, but also to unravel the evolutionary relationships between species. Since related species are derived from recent common ancestors, it comes as no surprise that in both dicots and monocots extensive genetic collinearity was found when related species were mapped using common RFLP probe sets (Bonierbale et al. 1988;Hulbert et al. 1990;Ahn and Tanksley 1993;Jena et al. 1994). While extensive collinearity seems to be limited to the genus level among dicots (Tanksley et al.1988), sufficient collinearity exists to allow rough alignment of genetic maps across entire genomes throughout the entire cereal clade (Moore et al. 1995). The complete sequences of the model organisms Arabidopsis and rice (Arabi- Although rice is considered a model plant and placed at the center of the cereal crop syntenic circle (Moore et al. 1995; Gale and Devos 1998a,b;Devos 2005), only a few genomewide comparative analyses as yet have been performed using the rice genome sequence as a reference. The sorghum genome was compared to the rice genome sequence using two sorghum physical maps integrated with genetic markers and BAC hybridization data (Bowers et al. 2005). Various local rearrangements between other cereals and rice have been reported in sequence-level comparisons using the rice genome sequence as the reference (Chen et al. 1998;Goff et al. 2002;Bennetzen and Ma 2003;Sorrells Sequence data from this article have been deposited with the EMBL/GenBank Data Libraries under accession nos. CW502583-CW509125, CW514009-539178, CW620733-CW624836, CW628185-633039, CW672722-CW676096, CW691361-692844, CW748472-CW754418, CW775494-CW77843...

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Background In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method. Methods We recruited 143 patients with FTD, 50 patients with Alzheimer's disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD + AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability. Results The classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1–4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA. Conclusions In the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.

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