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Alzheimer's disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10-15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder's effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

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Diabetic retinopathy (DR) is a serious retinal disease and is considered as a leading cause of blindness in the world. Ophthalmologists use optical coherence tomography (OCT) and fundus photography for the purpose of assessing the retinal thickness, and structure, in addition to detecting edema, hemorrhage, and scars. Deep learning models are mainly used to analyze OCT or fundus images, extract unique features for each stage of DR and therefore classify images and stage the disease. Throughout this paper, a deep Convolutional Neural Network (CNN) with 18 convolutional layers and 3 fully connected layers is proposed to analyze fundus images and automatically distinguish between controls (i.e. no DR), moderate DR (i.e. a combination of mild and moderate Non Proliferative DR (NPDR)) and severe DR (i.e. a group of severe NPDR, and Proliferative DR (PDR)) with a validation accuracy of 88%-89%, a sensitivity of 87%-89%, a specificity of 94%-95%, and a Quadratic Weighted Kappa Score of 0.91-0.92 when both 5-fold, and 10-fold cross validation methods were used respectively. A prior pre-processing stage was deployed where image resizing and a class-specific data augmentation were used. The proposed approach is considerably accurate in objectively diagnosing and grading diabetic retinopathy, which obviates the need for a retina specialist and expands access to retinal care. This technology enables both early diagnosis and objective tracking of disease progression which may help optimize medical therapy to minimize vision loss.

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PURPOSE: Early assessment of renal allograft function post-transplantation is crucial to minimize and control allograft rejection. Biopsy - the gold standard - is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer-assisted diagnostic (Renal-CAD) system was developed to assess kidney transplant function. METHODS: The developed Renal-CAD system integrates data collected from two image-based sources and two clinical-based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion-weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm 2 ), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal-CAD system initially performed kidney segmentation using the level-set method, followed by estimation of the ADCs from DW-MRIs and the R2* from BOLD-MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning-based classifier, namely stacked autoencoders (SAEs) to differentiate non-rejection (NR) from acute rejection (AR) renal transplants. RESULTS: Using a leave-one-subject-out cross-validation approach along with SAEs, the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross-validation approach, the Renal-CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. CONCLUSION: The obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal-CAD system.

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Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans.

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Previous research on autism risk (ASD), developmental regulatory (DevReg), and central nervous system (CNS) genes suggests they tend to be large in size, enriched in nested repeats, and mutation intolerant. The relevance of these genomic features is intriguing yet poorly understood. In this study, we investigated the feature landscape of these gene groups to discover structural themes useful in interpreting their function, developmental patterns, and evolutionary history. ASD, DevReg, CNS, housekeeping, and whole genome control (WGC) groups were compiled using various resources. Multiple gene features of interest were extracted from NCBI/UCSC Bioinformatics. Residual variation intolerance scores, Exome Aggregation Consortium pLI scores, and copy number variation data from Decipher were used to estimate variation intolerance. Gene age and protein-protein interactions (PPI) were estimated using Ensembl and EBI Intact databases, respectively. Compared to WGC: ASD, DevReg, and CNS genes are longer, produce larger proteins, maintain greater numbers/density of conserved noncoding elements and transposable elements, produce more transcript variants, and are comparatively variation intolerant. After controlling for gene size, mutation tolerance, and clinical association, ASD genes still retain many of these same features. In addition, we also found that ASD genes that are extremely mutation intolerant have larger PPI networks. These data support many of the recent findings within the field of autism genetics but also expand our understanding of the evolution of these broad gene groups, their potential regulatory complexity, and the extent to which they interact with the cellular network. Autism Res 2019, 12: 860-869. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Autism risk genes are more ancient compared to other genes in the genome. As such, they exhibit physical features related to their age, including long gene and protein size and regulatory sequences that help to control gene expression. They share many of these same features with other genes that are expressed in the brain and/or are associated with prenatal development.

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This study was to demonstrate the feasibility of an automatic approach for early detection of diabetic retinopathy (DR) from SD-OCT images. These scans were prospectively collected from 200 subjects through the fovea then were automatically segmented, into 12 layers. Each layer was characterized by its thickness, tortuosity, and normalized reflectivity. 26 diabetic patients, without DR changes visible by funduscopic examination, were matched with 26 controls, according to age and sex, for purposes of statistical analysis using mixed effects ANOVA. The INL was narrower in diabetes (p = 0.14), while the NFL (p = 0.04) and IZ (p = 0.34) were thicker. Tortuosity of layers NFL through the OPL was greater in diabetes (all p < 0.1), while significantly greater normalized reflectivity was observed in the MZ and OPR (both p < 0.01) as well as ELM and IZ (both p < 0.5). A novel automated method enables to provide quantitative analysis of the changes in each layer of the retina that occur with diabetes. In turn, carries the promise to a reliable non-invasive diagnostic tool for early detection of DR.

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This paper introduces a new framework for the segmentation of different brain structures (white matter, gray matter, and cerebrospinal fluid) from 3D MR brain images at different life stages. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on first- and second-order visual appearance characteristics of MR images. These characteristics are described using voxel-wise image intensities and their spatial interaction features. To more accurately model the empirical grey level distribution of the brain signals, we use a linear combination of discrete Gaussians (LCDG) model having positive and negative components. To accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov-Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth- order families with a traditional second-order model is proposed. The proposed approach was tested and evaluated on 102 3D MR brain scans using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results show better segmentation of MR brain images compared to current open source segmentation tools.

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OBJECTIVE: The main goal of this study is to determine which parameters [e.g. clinical biomarkers, demographics and image-markers using 4D (3D + b-value) diffusion-weighted MRI (DW-MRI)] are more correlated with transplanted kidney status in patients who have undergone kidney transplantation, and can be used for early assessment of acute renal rejection. METHODS: The study included 16 patients with stable graft function and 37 patients with acute rejection (AR), determined by renal biopsy post-transplantation. 3D DW-MRI of each allograft had been acquired using a series of b-values 50 and 100-1000 in steps of 100 smm-2. The kidney was automatically segmented and co-aligned across series for motion correction using geometric deformable models. Volume-averaged apparent diffusion coefficients (ADCs) at each b-value were calculated. All possible subsets of ADC were used, along with patient age, sex, serum plasma creatinine (SPCr) and creatinine clearance (CrCl), as predictors in 211 logistic regression models where AR was the outcome variable. Predictive value of ADC at each b-value was assessed using its Akaike weight. RESULTS: ANOVA of the saturated model found that odds of AR depended significantly on SPCr, CrCl and ADC at b = 500, 600, 700 and 900 smm-2. The model incorporating ADC at b = 100 and700 smm-2 had the lowest value of the Akaike information criterion; the same two b-values also had the greatest Akaike weights. For comparison, the top 10 submodels and the full model were reported. CONCLUSION: Preliminary findings suggest that ADC provides improved detection of AR than lab values alone. At least two non-zero gradient strengths should be used for optimal results. Advances in knowledge: This paper investigated possible correlations between image-based and clinical biomarkers, and the fusion of both with respect to biopsy diagnosis of AR.

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Some people remember events more completely and accurately than other people, but the origins of individual differences in episodic memory are poorly understood. One way to advance understanding is by identifying characteristics of individuals that reliably covary with memory performance. Recent research suggests motor behavior is related to memory performance, with individuals who consistently use a single preferred hand for unimanual actions performing worse than individuals who make greater use of both hands. This research has relied on self-reports of behavior. It is unknown whether objective measures of motor behavior also predict memory performance. Here, we tested the predictive power of bimanual coordination, an important form of manual dexterity. Bimanual coordination, as measured objectively on the Purdue Pegboard Test, was positively related to correct recall on the California Verbal Learning Test-II and negatively related to false recall. Furthermore, MRI data revealed that cortical surface area in right lateral prefrontal regions was positively related to correct recall. In one of these regions, cortical thickness was negatively related to bimanual coordination. These results suggest that individual differences in episodic memory may partially reflect morphological variation in right lateral prefrontal cortex and suggest a relationship between neural correlates of episodic memory and motor behavior.

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It is possible that during long lasting chronic infections such as tuberculosis (TB) and leprosy individuals who generate a stronger immune response will produce a chronic shift in the systemic levels of inflammatory proteins. Consequently, the systemic immunological shift could affect inflammatory responses against other persistent pathogens such as Porphyromonas gingivalis associated with periodontal disease (PD). OBJECTIVE: To determine if in vitro exposure to Mycobacterium tuberculosis or M. leprae lysates impacts subsequent immune responses to P. gingivalis; and to propose a new dialogue between experimental immunology and paleopathology. MATERIAL AND METHODS: We sequentially (2 days protocol) exposed peripheral blood mononuclear cells (PBMCs) from healthy donors to bacterial lysates either from M. tuberculosis, or M. leprae, or P. gingivalis. After collecting all supernatants, we measured the expression of immune proteins TNFα and IFNγ using an enzyme-linked immunosorbent assay. RESULTS: Early exposure (day 1) of PBMCs to M. leprae or M. tuberculosis lysates induces an inflammatory shift detected by the increase of TNFα and IFNγ when the same cells are subsequently (day 2) exposed to oral pathogen P. gingivalis. DISCUSSION: By extrapolating these results, we suggest that chronic infections, such as TB and leprosy, could generate a systemic immunological shift that can affect other inflammatory processes such the one present in PD. We propose that the presence and severity of PD should be explored as a proxy for inflammatory status or competence when reconstructing the health profile in past populations.

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BACKGROUND: Previous reports indicate the presence of histological abnormalities in the brains of individuals with autism spectrum disorders (ASD) suggestive of a dysplastic process. In this study we identified areas of abnormal cortical thinning within the cerebral cortex of ASD individuals and examined the same for neuronal morphometric abnormalities by using computerized image analysis. RESULTS: The study analyzed celloidin-embedded and Nissl-stained serial full coronal brain sections of 7 autistic (ADI-R diagnosed) and 7 age/sex-matched neurotypicals. Sections were scanned and manually segmented before implementing an algorithm using Laplace's equation to measure cortical width. Identified areas were then subjected to analysis for neuronal morphometry. Results of our study indicate the presence within our ASD population of circumscribed foci of diminished cortical width that varied among affected individuals both in terms of location and overall size with the frontal lobes being particularly involved. Spatial statistic indicated a reduction in size of neurons within affected areas. Granulometry confirmed the presence of smaller pyramidal cells and suggested a concomitant reduction in the total number of interneurons. CONCLUSIONS: The neuropathology is consistent with a diagnosis of focal cortical dysplasia (FCD). Results from the medical literature (e.g., heterotopias) and our own study suggest that the genesis of this cortical malformation seemingly resides in the heterochronic divisions of periventricular germinal cells. The end result is that during corticogenesis radially migrating neuroblasts (future pyramidal cells) are desynchronized in their development from those that follow a tangential route (interneurons). The possible presence of a pathological mechanism in common among different conditions expressing an autism-like phenotype argue in favor of considering ASD a "sequence" rather than a syndrome. Focal cortical dysplasias in ASD may serve to explain the high prevalence of seizures and sensory abnormalities in this patient population.

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Alterations in gyral form and complexity have been consistently noted in both autism and dyslexia. In this present study, we apply spherical harmonics, an established technique which we have exapted to estimate surface complexity of the brain, in order to identify abnormalities in gyrification between autistics, dyslexics, and controls. On the order of absolute surface complexity, autism exhibits the most extreme phenotype, controls occupy the intermediate ranges, and dyslexics exhibit lesser surface complexity. Here, we synthesize our findings which demarcate these three groups and review how factors controlling neocortical proliferation and neuronal migration may lead to these distinctive phenotypes.

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Dyslexia severely impairs learning abilities; therefore, improved diagnostic methods are needed. Neuropathological studies have revealed an abnormal anatomy of the corpus callosum (CC) in dyslexic brains. We propose a new approach for the quantitative analysis of 3-D magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of dyslexic and control subjects. The proposed approach consists of three main processing steps: 1) segmenting the CC from a given 3-D MRI using the learned CC shape and visual appearance; 2) extracting the centerline of the CC; and 3) cylindrical mapping of the CC surface for its comparative analysis. Validation on 3-D simulated phantoms demonstrates the ability of the proposed approach to accurately detect the shape variability between two 3-D surfaces. Experimental results revealed significant differences (at the 95% confidence level) between 14 normal and 16 dyslexic subjects in all four anatomical divisions, i.e., splenium, rostrum, genu, and body of their CCs. Moreover, the initial classification results based on the centerline length and CC thickness suggest that the proposed shape analysis is a promising supplement to the current techniques for diagnosing dyslexia.

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Development, aging, and evolution offer different time scales regarding possible anatomical transformations of the brain. This article expands on the perspective that the cerebral cortex exhibits a modular architecture with invariant properties in regards to these time scales. These properties arise from morphometric relations of the ontogenetic minicolumn as expressed in Noether's first theorem, i.e., that for each continuous symmetry there is a conserved quantity. Whenever minicolumnar symmetry is disturbed by either developmental or aging processes the principle of least action limits the scope of morphometric alterations. Alternatively, local and global divergences from these laws apply to acquired processes when the system is no longer isolated from its environment. The underlying precepts to these physical laws can be expressed in terms of mathematical equations that are conservative of quantity. Invariant properties of the brain include the rotational symmetry of minicolumns, a scaling proportion or "even expansion" between pyramidal cells and core minicolumnar size, and the translation of neuronal elements from the main axis of the minicolumn. It is our belief that a significant portion of the architectural complexity of the cerebral cortex, its response to injury, and its evolutionary transformation, can all be captured by a small set of basic physical laws dictated by the symmetry of minicolumns. The putative preservations of parameters related to the symmetry of the minicolumn suggest that the development and final organization of the cortex follows a deterministic process.

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Multiple studies suggest that the corpus callosum in patients with autism is reduced in size. This study attempts to elucidate the nature of this morphometric abnormality by analyzing the shape of this structure in 17 high-functioning patients with autism and an equal number of comparison participants matched for age, sex, IQ, and handedness. The corpus callosum was segmented from T1 weighted images acquired with a Siemens 1.5 T scanner. Transformed coordinates of the curvilinear axis were aggregated into a parametric map and compared across series to derive regions of statistical significance. Our results indicate that a reduction in size of the corpus callosum occurs over all of its subdivisions (genu, body, splenium) in patients with autism. Since the commissural fibers that traverse the different anatomical compartments of the corpus callosum originate in disparate brain regions our results suggest the presence of widely distributed cortical abnormalities in people with autism.

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The importance of accurate early diagnostics of autism that severely affects personal behavior and communication skills cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in autistic brains. We propose a new approach to quantitative analysis of three-dimensional (3D) magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of autistic and normal subjects. It consists of three main processing steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting a centerline of the CC; and (iii) cylindrical mapping of the CC surface for its comparative analysis. Our experiments revealed significant differences (at the 95% confidence level) between 17 normal and 17 autistic subjects in four anatomical divisions, i.e. splenium, rostrum, genu and body of their CCs.

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Using the NIH Pediatric MRI Data Repository for normative developmental studies, white matter depth within the gyri of the frontal, temporal, parietal, and occipital lobes, and of the left and right hemisphere was identified for 312 typically developing children and young adults (168 male and 144 female) between 4 and 23 years of age. There was no significant age difference between male and female groups overall (F(1,867) = 0.0002; p = 0.99) or per-visit (F(2,867) = 2.18; p = 0.86). There was significant dependence of gyral window upon age (F(1,6544) = 115, p < 0.0001), lobe (F(3,6544) = 229, p < 0.0001), hemisphere (F(1,6544) = 5.23, p = 0.022), age*sex (F(1,6544) = 13.8, p = 0.0002), age*lobe (F(3,6544) = 120, p = 0.0001), and age*hemisphere (F(1,6544) = 4.41, p = 0.036). Gyrification increased with age in both males and females in the frontal, temporal and parietal lobes with opposite effects observed in the occipital lobe. Relative gyral depth, as measured in this study, was significantly (p < 0.0001) inversely correlated with gyrification index. Previous studies relate gyral window measurements to the differential expression of short and long corticocortical projections. Our results therefore suggest that the pattern of corticocortical connections is malleable during the first two decades of development.

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Recent studies provide credence to the minicolumnar origin of several developmental conditions, including dyslexia. Characteristics of minicolumnopathies include abnormalities in how the cortex expands and folds. This study examines the depth of the gyral white matter measured in an MRI series of 15 dyslexic adult men and eleven age-matched comparison subjects. Measurements were based upon the 3D Euclidean distance map inside the segmented cerebral white matter surface. Mean gyral white matter depth was 3.05 mm (SD +/- 0.30 mm) in dyslexic subjects and 1.63 mm (SD +/- 0.15 mm) in the controls. The results add credence to the growing literature suggesting that the attained reading circuit in dyslexia is abnormal because it is inefficient. Otherwise the anatomical substratum (i.e., corticocortical connectivity) underlying this inefficient circuit is normal. A deficit in very short-range connectivity (e.g., angular gyrus, striate cortex), consistent with results of a larger gyral window, could help explain reading difficulties in patients with dyslexia. The structural findings hereby reported are diametrically opposed to those reported for autism.

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Radial cell minicolumns are basic cytoarchitectonic motifs of the mammalian neocortex. Recent studies reveal that autism is associated with a "minicolumnopathy" defined by decreased columnar width and both a diminished and disrupted peripheral neuropil compartment. This study further characterizes this cortical deficit by comparing minicolumnar widths across layers. Brains from seven autistic patients and an equal number of age-matched controls were celloidin embedded, serially sectioned at 200 microm and Nissl stained with gallocyanin. Photomicrograph mosaics of the cortex were analyzed with computerized imaging methods to determine minicolumnar width at nine separate neocortical areas: Brodmann Area's (BA) 3b, 4, 9, 10, 11, 17, 24, 43 and 44. Each area was assessed at supragranular, granular and infragranular levels. Autistic subjects had smaller minicolumns whose dimensions varied according to neocortical area. The greatest difference between autistic and control groups was observed in area 44. The interaction of diagnosis x cortical area x lamina (F(16,316) = 1.33; P = 0.175) was not significant. Diminished minicolumnar width across deep and superficial neocortical layers most probably reflects involvement of shared constituents among the different layers. In this article we discuss the possible role of double bouquet and pyramidal cells in the translaminar minicolumnar width narrowing observed in autistic subjects.

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The brain of the bottlenose dolphin exhibits patterns of isocortical parcellation and cytoarchitecture distinct from those seen in primates, yet cell clusters in anterior insula are comparable in scale to module-like cell arrangements found throughout isocortex in other placental mammalian species with long divergent evolutionary histories. This similarity may be due to common ancestry, or to convergence as a result of selective constraints on organization of connections within such modules. Differences reflect alternate arrangements of minicolumns, an elemental cytoarchitectonic motif of isocortex defined by radially oriented pyramidal cell arrays. In contrast with larger modular structures incorporating them, minicolumns have been highly conserved in mammalian evolution. In this study a previously validated imaging method was employed to assess verticality, D, a parameter indicating radial bias of isocortex. Photomicrographs of coronal Nissl-stained sections of dolphin anterior insular cortex were compared with sections from human brains of putatively homologous areas as well as other isocortical areas differing in modular organization. Dolphin insula exhibited a high degree of verticality consistent with conserved minicolumnar organization. Our findings indicate that a basic structural motif of isocortex is synapomorphic in a species of marine mammal exhibiting unique phylogenetically derived isocortical characteristics.