Neuroanatomical subtypes of schizophrenia and relationship with illness duration and deficit status.

BACKGROUND: The heterogeneity of schizophrenia (SCZ) regarding psychopathology, illness trajectory and their inter-relationships with underlying neural substrates remain incompletely understood. In a bid to reduce illness heterogeneity using neural substrates, our study aimed to replicate the findings of an earlier study by Chand et al. (2020). We employed brain structural measures for subtyping SCZ patients, and evaluate each subtype's relationship with clinical features such as illness duration, psychotic psychopathology, and additionally deficit status. METHODS: Overall, 240 subjects (160 SCZ patients, 80 healthy controls) were recruited for this study. The participants underwent brain structural magnetic resonance imaging scans and clinical rating using the Positive and Negative Syndrome Scale. Neuroanatomical subtypes of SCZ were identified using "Heterogeneity through discriminative analysis" (HYDRA), a clustering technique which accounted for relevant covariates and the inter-group normalized percentage changes in brain volume were also calculated. RESULTS: As replicated, two neuroanatomical subtypes (SG-1 and SG-2) were found amongst our patients with SCZ. The subtype SG-1 was associated with enlargements in the third and lateral ventricles, volume increase in the basal ganglia (putamen, caudate, pallidum), longer illness duration, and deficit status. The subtype SG-2 was associated with reductions of cortical and subcortical structures (hippocampus, thalamus, basal ganglia). CONCLUSIONS: These replicated findings have clinical implications in the early intervention, response monitoring, and prognostication of SCZ. Future studies may adopt a multi-modal neuroimaging approach to enhance insights into the neurobiological composition of relevant subtypes.

Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods.

Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. Determining classification biomarkers can complement clinical measures and improve understanding of the neural basis underlying schizophrenia. Using neuroanatomical features, several machine learning based investigations have attempted to classify schizophrenia from healthy controls but the range of neuroanatomical measures employed have been limited in range to date. In this study, we sought to classify schizophrenia and healthy control cohorts using a diverse set of neuroanatomical measures (cortical and subcortical volumes, cortical areas and thickness, cortical mean curvature) and adopted Ensemble methods for better performance. Additionally, we correlated such neuroanatomical features with Quality of Life (QoL) assessment scores within the schizophrenia cohort. With Ensemble methods and diverse neuroanatomical measures, we achieved classification accuracies ranging from 83 to 87%, sensitivities and specificities varying between 90-98% and 65-70% respectively. In addition to lower QoL scores within schizophrenia cohort, significant correlations were found between specific neuroanatomical measures and psychological health, social relationship subscale domains of QoL. Our results suggest the utility of inclusion of subcortical and cortical measures and Ensemble methods to achieve better classification performance and their potential impact of parsing out neurobiological correlates of quality of life in schizophrenia.

Adipocyte Ceramides Regulate Subcutaneous Adipose Browning, Inflammation, and Metabolism.

Adipocytes package incoming fatty acids into triglycerides and other glycerolipids, with only a fraction spilling into a parallel biosynthetic pathway that produces sphingolipids. Herein, we demonstrate that subcutaneous adipose tissue of type 2 diabetics contains considerably more sphingolipids than non-diabetic, BMI-matched counterparts. Whole-body and adipose tissue-specific inhibition/deletion of serine palmitoyltransferase (Sptlc), the first enzyme in the sphingolipid biosynthesis cascade, in mice markedly altered adipose morphology and metabolism, particularly in subcutaneous adipose tissue. The reduction in adipose sphingolipids increased brown and beige/brite adipocyte numbers, mitochondrial activity, and insulin sensitivity. The manipulation also increased numbers of anti-inflammatory M2 macrophages in the adipose bed and induced secretion of insulin-sensitizing adipokines. By comparison, deletion of serine palmitoyltransferase from macrophages had no discernible effects on metabolic homeostasis or adipose function. These data indicate that newly synthesized adipocyte sphingolipids are nutrient signals that drive changes in the adipose phenotype to influence whole-body energy expenditure and nutrient metabolism.

Segmentation and quantification of intra-ventricular/cerebral hemorrhage in CT scans by modified distance regularized level set evolution technique.

PURPOSE: An automatic, accurate and fast segmentation of hemorrhage in brain Computed Tomography (CT) images is necessary for quantification and treatment planning when assessing a large number of data sets. Though manual segmentation is accurate, it is time consuming and tedious. Semi-automatic methods need user interactions and might introduce variability in results. Our study proposes a modified distance regularized level set evolution (MDRLSE) algorithm for hemorrhage segmentation. METHODS: Study data set (from the ongoing CLEAR-IVH phase III clinical trial) is comprised of 200 sequential CT scans of 40 patients collected at 10 different hospitals using different machines/vendors. Data set contained both constant and variable slice thickness scans. Our study included pre-processing (filtering and skull removal), segmentation (MDRLSE which is a two-stage method with shrinking and expansion) with modified parameters for faster convergence and higher accuracy and post-processing (reduction in false positives and false negatives). RESULTS: Results are validated against the gold standard marked manually by a trained CT reader and neurologist. Data sets are grouped as small, medium and large based on the volume of blood. Statistical analysis is performed for both training and test data sets in each group. The median Dice statistical indices (DSI) for the 3 groups are 0.8971, 0.8580 and 0.9173 respectively. Pre- and post-processing enhanced the DSI by 8 and 4% respectively. CONCLUSIONS: The MDRLSE improved the accuracy and speed for segmentation and calculation of the hemorrhage volume compared to the original DRLSE method. The method generates quantitative information, which is useful for specific decision making and reduces the time needed for the clinicians to localize and segment the hemorrhagic regions.

Identification, segmentation, and image property study of acute infarcts in diffusion-weighted images by using a probabilistic neural network and adaptive Gaussian mixture model.

RATIONALE AND OBJECTIVES: Accurate identification of infarcted regions of the brain is critical in management of stroke patients. An efficient and fast method for identification and segmentation of infarcts in the diffusion-weighted images (DWI) is proposed. MATERIALS AND METHODS: Thirteen stroke patients were studied. DWI scans were acquired with a slice thickness of 5 mm. We have used a probabilistic neural network for selecting infarct slices and an adaptive (two-level) Gaussian mixture model for segmentation of the infarcts. Statistical analysis, such as identification of distribution, first-order statistics calculation, and receiver operating characteristic curve analysis, was performed. RESULTS: The average dice index is about 0.6, and average sensitivity and specificity are about 81% and 99%, respectively. The value of sensitivity and dice index are influenced by the number of false positives and false negatives. Because artifacts and infarcts have similar imaging characteristics, it is difficult to completely eliminate the artifacts. The accuracy of localization is nearly 100% as there were only two false-positive and three false-negative slices of all 381 slices. The algorithm takes about 1 minute in the Matlab computing environment to process a volume. CONCLUSION: A method to localize and segment the acute brain infarcts is proposed. The method aids the clinician in reducing the time needed to localize and segment the infarcts. The speed of localization and segmentation can be enhanced further by implementing the algorithm in VC++ and using fast algorithms for selection of Gaussian mixture model parameters.

Morphologic relationship among the corpus callosum, fornix, anterior commissure, and posterior commissure MRI-based variability study.

RATIONALE AND OBJECTIVES: This study explores morphological relationships and structural variability of the corpus callosum (CC), fornix (Fo), anterior (AC), and posterior commissures (PC). MATERIALS AND METHODS: These structures are extracted automatically on the midsagittal plane. The CC and Fo are modeled using best-fit ellipses. The parameters characterizing these structures and relationships among them are points, distances, angles, and eccentricities. The minimum, maximum and mean values, standard deviations, and coefficients of variation for all parameters are calculated for 62 diversified MRI datasets. Subsequently, the regression analysis and parameter distribution study are performed. RESULTS: The parameters have at least 10% variations. The major axis of CC and eccentricities of CC and Fo vary much less than the other parameters The major axis of CC is approximately parallel to the AC-PC line. The mean eccentricity of each of CC and Fo is greater than 0.95. The most significant correlation (P < .05) is observed between various angles and the angle between the major axes of CC and Fo. The correlation is also significant between other angles and distances. The Weibull distribution characterizes the major axis of CC, and distance between the AC and the most superior point of CC. Distribution of angle between the major axes of CC and Fo is log (logistic), and normal for the AC-PC distance. CONCLUSIONS: The AC-PC distance, used prevalently for brain normalization, is not correlated with any parameters except with the distance between the AC and the most superior point on the body of the CC with P < .05.

Dorsoventral extension of the talairach transformation and its automatic calculation for magnetic resonance neuroimages.

The Talairach transformation (TT), the most prevalent method for brain normalization and atlas-to-data warping, is conceptually simple, fast and can be automated. Two problems with the TT in the clinical setting that are addressed in this article are reduced accuracy at the orbitofrontal cortex and upper corpus callosum (CC) and unsuitability for functional neurosurgery because of incomplete scanning. To increase dorsoventral accuracy, we introduce 2 additional landmarks: the top of the CC (SM) and the most ventral point of the orbitofrontal cortex on the midsagittal slab (IM). A method for their automatic calculation is proposed and validated against 55 diversified magnetic resonance (MR) imaging cases. The SM and IM landmarks are identified accurately and robustly in an automatic way. The average error of SM localization is 0.69 mm, and 91% of all cases have an error not greater than 1 mm. The average error of IM localization is 0.98 mm, approximately three quarters of cases have an error not greater than 1 mm, and 95% of all cases have an error not larger than 2 mm. The SM is correlated (R(2) = 0.72) with the most superior cortical landmark, whereas the IM is only loosely correlated (R(2) = 0.22) with the most inferior cortical landmark. On average, the original TT overlays the atlas axial plate at -24 on the orbitofrontal cortex as opposed to the correct plate at -28. Therefore, 1-dimensional ventral scaling in the original TT is insufficient to cope with variability in the orbitofrontal cortex. The key advantages of our approach are the preserved conceptual simplicity of the TT, fully automatic identification of the new landmarks, improved accuracy of the atlas-to-data match without compromising performance, and enabled TT use in functional neurosurgery when a dorsal part of the brain is not available in the scan.

Fetal lung maturity analysis using ultrasound image features.

This pilot study was carried out to find the feasibility of analyzing the maturity of the fetal lung using ultrasound images. Data were collected from normal pregnant women at intervals of two weeks from the gestation age of 24 to 38 weeks. Images were acquired at two centers located at different geographical locations. The total data acquired consisted of 750 images of immature and 250 images of mature class. A region of interest of 64 x 64 pixels was used for extracting the features. Various textural features were computed from the fetal lung and liver images. The ratios of fetal lung to liver feature values were investigated as possible indexes for classifying the images into those from mature (reduced pulmonary risk) and immature (possible pulmonary risk) lung. The features used are fractal dimension, lacunarity, and features derived from the histogram of the images. The following classifiers were used to classify the fetal lung images as belonging to mature or immature lung: nearest neighbor, k-nearest neighbor, modified k-nearest neighbor, multilayer perceptron, radial basis function network, and support vector machines. The classification accuracy obtained for the testing set ranges from 73% to 96%.