Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR Images.

Lesion studies are crucial in establishing brain-behavior relationships, and accurately segmenting the lesion represents the first step in achieving this. Manual lesion segmentation is the gold standard for chronic strokes. However, it is labor-intensive, subject to bias, and limits sample size. Therefore, our objective is to develop an automatic segmentation algorithm for chronic stroke lesions on T1-weighted MR images. Methods: To train our model, we utilized an open-source dataset: ATLAS v2.0 (Anatomical Tracings of Lesions After Stroke). We partitioned the dataset of 655 T1 images with manual segmentation labels into five subsets and performed a 5-fold cross-validation to avoid overfitting of the model. We used a deep neural network (DNN) architecture for model training. Results: To evaluate the model performance, we used three metrics that pertain to diverse aspects of volumetric segmentation, including shape, location, and size. The Dice similarity coefficient (DSC) compares the spatial overlap between manual and machine segmentation. The average DSC was 0.65 (0.61−0.67; 95% bootstrapped CI). Average symmetric surface distance (ASSD) measures contour distances between the two segmentations. ASSD between manual and automatic segmentation was 12 mm. Finally, we compared the total lesion volumes and the Pearson correlation coefficient (ρ) between the manual and automatically segmented lesion volumes, which was 0.97 (p-value < 0.001). Conclusions: We present the first automated segmentation model trained on a large multicentric dataset. This model will enable automated on-demand processing of MRI scans and quantitative chronic stroke lesion assessment.

It is not just the category: behavioral effects of fMRI-guided electrical microstimulation result from a complex interplay of factors.

Functional imaging and electrophysiological studies in primates revealed the existence of patches selective for visual categories in the inferior temporal cortex. Understanding the contribution of these patches to perception requires causal techniques that assess the effect of neural activity manipulations on perception. We used electrical microstimulation (EM) to determine the role of body patch activity in visual categorization in macaques. We tested the hypothesis that EM in a body patch would affect the categorization of bodies versus objects but not of other visual categories. We employed low-current EM of an anterior body patch (ASB) in the superior temporal sulcus, which was defined by functional magnetic resonance imaging and verified with electrophysiological recordings in each session. EM of ASB affected body categorization, but the EM effects were more complex than the expected increase of body-related choices: EM affected the categorization of both body and inanimate images and showed interaction with the choice target location, but its effect was location-specific (tested in 1 subject) on a millimeter scale. Our findings suggest that the behavioral effects of EM in a category-selective patch are not merely a manifestation of the category selectivity of the underlying neuronal population but reflect a complex interplay of multiple factors.

Similar neural and perceptual masking effects of low-power optogenetic stimulation in primate V1.

Can direct stimulation of primate V1 substitute for a visual stimulus and mimic its perceptual effect? To address this question, we developed an optical-genetic toolkit to 'read' neural population responses using widefield calcium imaging, while simultaneously using optogenetics to 'write' neural responses into V1 of behaving macaques. We focused on the phenomenon of visual masking, where detection of a dim target is significantly reduced by a co-localized medium-brightness mask (Cornsweet and Pinsker, 1965; Whittle and Swanston, 1974). Using our toolkit, we tested whether V1 optogenetic stimulation can recapitulate the perceptual masking effect of a visual mask. We find that, similar to a visual mask, low-power optostimulation can significantly reduce visual detection sensitivity, that a sublinear interaction between visual- and optogenetic-evoked V1 responses could account for this perceptual effect, and that these neural and behavioral effects are spatially selective. Our toolkit and results open the door for further exploration of perceptual substitutions by direct stimulation of sensory cortex.

Body Patches in Inferior Temporal Cortex Encode Categories with Different Temporal Dynamics.

An unresolved question in cognitive neuroscience is how representations of object categories at different levels (basic and superordinate) develop during the course of the neural response within an area. To address this, we decoded categories of different levels from the spiking responses of populations of neurons recorded in two fMRI-defined body patches in the macaque STS. Recordings of the two patches were made in the same animals with the same stimuli. Support vector machine classifiers were trained at brief response epochs and tested at the same or different epochs, thus assessing whether category representations change during the course of the response. In agreement with hierarchical processing within the body patch network, the posterior body patch mid STS body (MSB) showed an earlier onset of categorization compared with the anterior body patch anterior STS body (ASB), irrespective of the categorization level. Decoding of the superordinate body versus nonbody categories was less dynamic in MSB than in ASB, with ASB showing a biphasic temporal pattern. Decoding of the ordinate-level category human versus monkey bodies showed similar temporal patterns in both patches. The decoding onset of superordinate categorizations involving bodies was as early as for basic-level categorization, suggesting that previously reported differences between the onset of basic and superordinate categorizations may depend on the area. The qualitative difference between areas in their dynamics of category representation may hinder the interpretation of decoding dynamics based on EEG or MEG, methods that may mix signals of different areas.

Transformation of Visual Representations Across Ventral Stream Body-selective Patches.

Although the neural processing of visual images of bodies is critical for survival, it is much less well understood than face processing. Functional imaging studies demonstrated body selective regions in primate inferior temporal cortex. To advance our understanding of how the visual brain represents bodies, we compared the representation of animate and inanimate objects in two such body patches with fMRI-guided single unit recordings in rhesus monkeys. We found that the middle Superior Temporal Sulcus body patch (MSB) distinguishes to a greater extent bodies from non-bodies than the anterior Superior Temporal Sulcus body patch (ASB). Importantly, ASB carried more viewpoint-tolerant information about body posture and body identity than MSB, while MSB showed greater orientation selectivity. Combined with previous work on faces, this suggests that an increase in view-tolerant representations, coupled with a refined individuation, along the visual hierarchy is a general property of information processing within the inferior temporal cortex.

Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons.

Functional MRI studies in primates have demonstrated cortical regions that are strongly activated by visual images of bodies. The presence of such body patches in macaques allows characterization of the stimulus selectivity of their single neurons. Middle superior temporal sulcus body (MSB) patch neurons showed similar stimulus selectivity for natural, shaded, and textured images compared with their silhouettes, suggesting that shape is an important determinant of MSB responses. Here, we examined and modeled the shape selectivity of single MSB neurons. We measured the responses of single MSB neurons to a variety of shapes producing a wide range of responses. We used an adaptive stimulus sampling procedure, selecting and modifying shapes based on the responses of the neuron. Forty percent of shapes that produced the maximal response were rated by humans as animal-like, but the top shape of many MSB neurons was not judged as resembling a body. We fitted the shape selectivity of MSB neurons with a model that parameterizes shapes in terms of curvature and orientation of contour segments, with a pixel-based model, and with layers of units of convolutional neural networks (CNNs). The deep convolutional layers of CNNs provided the best goodness-of-fit, with a median explained explainable variance of the neurons' responses of 77%. The goodness-of-fit increased along the convolutional layers' hierarchy but was lower for the fully connected layers. Together with demonstrating the successful modeling of single unit shape selectivity with deep CNNs, the data suggest that semantic or category knowledge determines only slightly the single MSB neuron's shape selectivity.

Characterization of host response to Cryptococcus neoformans through quantitative proteomic analysis of cryptococcal meningitis co-infected with HIV.

Cryptococcal meningitis is the most common opportunistic fungal infection causing morbidity and mortality (>60%) in HIV-associated immunocompromised individuals caused by Cryptococcus neoformans. Molecular mechanisms of cryptococcal infection in brain have been studied using experimental animal models and cell lines. There are limited studies for the molecular understanding of cryptococcal meningitis in human brain. The proteins involved in the process of invasion and infection in human brain still remains obscure. To this end we carried out mass spectrometry-based quantitative proteomics of frontal lobe brain tissues from cryptococcal meningitis patients and controls to identify host proteins that are associated with the pathogenesis of cryptococcal meningitis. We identified 317 proteins to be differentially expressed (≥2-fold) from a total of 3423 human proteins. We found proteins involved in immune response and signal transduction to be differentially expressed in response to cryptococcal infection in human brain. Immune response proteins including complement factors, major histocompatibility proteins, proteins previously known to be involved in fungal invasion to brain such as caveolin 1 and actin were identified to be differentially expressed in cryptococcal meningitis brain tissues co-infected with HIV. We also validated the expression status of 5 proteins using immunohistochemistry. Overexpression of major histocompatibility complexes, class I, B (HLA-B), actin alpha 2 smooth muscle aorta (ACTA2) and caveolin 1 (CAV1) and downregulation of peripheral myelin protein 2 (PMP2) and alpha crystallin B chain (CRYAB) in cryptococcal meningitis were confirmed by IHC-based validation experiments. This study provides the brain proteome profile of cryptococcal meningitis co-infected with HIV for a better understanding of the host response associated with the disease.

Host response profile of human brain proteome in toxoplasma encephalitis co-infected with HIV.

BACKGROUND: Toxoplasma encephalitis is caused by the opportunistic protozoan parasite Toxoplasma gondii. Primary infection with T. gondii in immunocompetent individuals remains largely asymptomatic. In contrast, in immunocompromised individuals, reactivation of the parasite results in severe complications and mortality. Molecular changes at the protein level in the host central nervous system and proteins associated with pathogenesis of toxoplasma encephalitis are largely unexplored. We used a global quantitative proteomic strategy to identify differentially regulated proteins and affected molecular networks in the human host during T. gondii infection with HIV co-infection. RESULTS: We identified 3,496 proteins out of which 607 proteins were differentially expressed (≥1.5-fold) when frontal lobe of the brain from patients diagnosed with toxoplasma encephalitis was compared to control brain tissues. We validated differential expression of 3 proteins through immunohistochemistry, which was confirmed to be consistent with mass spectrometry analysis. Pathway analysis of differentially expressed proteins indicated deregulation of several pathways involved in antigen processing, immune response, neuronal growth, neurotransmitter transport and energy metabolism. CONCLUSIONS: Global quantitative proteomic approach adopted in this study generated a comparative proteome profile of brain tissues from toxoplasma encephalitis patients co-infected with HIV. Differentially expressed proteins include previously reported and several new proteins in the context of T. gondii and HIV infection, which can be further investigated. Molecular pathways identified to be associated with the disease should enhance our understanding of pathogenesis in toxoplasma encephalitis.

Effect of preeclampsia on insulin sensitivity.

OBJECTIVE: The objective of this study is to investigate whether preeclampsia is associated with exacerbation of insulin resistance. MATERIALS AND METHODS: The study was conducted over a period of 7 months from November 2011 to May 2012, in a tertiary care hospital attached to a medical college. A total of 14 pregnant women in the third trimester with preeclampsia were recruited for this study and 14 well-matched normotensive women in the third trimester were taken as control. 15 g, 50% dextrose load was given intravenously and blood sampling was carried out for glucose and insulin levels up to 3 h afterward. Minimal model analysis of glucose and insulin levels was performed to arrive at results. RESULTS: No significant changes in mean age, body mass index, gestation, serum lipid and progesterone, cortisol and androgen concentrations were recognized. No significant difference was found between the glucose decay curves and between the glucose clearance rate K, in the two groups (preeclamptic vs. normotensive: 2.1 ± 0.2 vs. 2.2 ± 0.3; P = 0.48). Therefore, there was a small but prolonged decrease in the insulin response of women with preeclampsia compared with women in the normotensive group. CONCLUSION: Preeclampsia per se is not a risk factor for development of insulin resistance.

PrediQt-Cx: post treatment health related quality of life prediction model for cervical cancer patients.

BACKGROUND: Cervical cancer is the third largest cause of cancer mortality in India. The objectives of the study were to compare the pre and the post treatment quality of life in cervical cancer patients and to develop a prediction model to provide an insight into the possibilities in the treatment modules. METHODOLOGY/PRINCIPAL FINDINGS: A total of 198 patients were assessed with two structured questionnaires of Health Related Quality of Life (The European Organisation for Research and Treatment of Cancer, EORTC QLQ C-30 and CX-24). The baseline observations were recorded when the patients first reported (T1) and second evaluation was done at 6 months post treatment (T2). The mean age of detection was 50.9 years with the literacy level being non-educated or less than high school. Majority of them were married/cohabiting 179 (90.4%). On histopathological examination (HPE) squamous cell carcinoma was found to be the most common cell type carcinoma 147 (74.2%) followed by Adenocarcinoma 31 (15.7%). Radical hysterectomy was the most common treatment modality 76 (38.4%), followed by Wertheims Hysterectomy 46 (23.2%) and Radiochemotherapy 59 (29.8%). The mean score of global health of cervical cancer patients post treatment was 77.90, which was significantly higher than the pre - treatment score (54.32). Mean "symptoms score" post treatment was 21.69 with an aggravation of 7.32 compared to pre treatment scores. Patients experienced substantial decrease in sexual activity post treatment. CONCLUSIONS/SIGNIFICANCE: The prediction model(PrediQt-Cx), based on Support Vector Machine(SVM) for predicting post treatment HRQoL in cervical cancer patients was developed and internally cross validated. After external validation PrediQt-Cx can be easily employed to support decision making by clinicians and patients from north India region, through openly made available for access at http://prediqt.org.

Plasma Proteome Database as a resource for proteomics research: 2014 update.

Plasma Proteome Database (PPD; http://www.plasmaproteomedatabase.org/) was initially described in the year 2005 as a part of Human Proteome Organization's (HUPO's) pilot initiative on Human Plasma Proteome Project. Since then, improvements in proteomic technologies and increased throughput have led to identification of a large number of novel plasma proteins. To keep up with this increase in data, we have significantly enriched the proteomic information in PPD. This database currently contains information on 10,546 proteins detected in serum/plasma of which 3784 have been reported in two or more studies. The latest version of the database also incorporates mass spectrometry-derived data including experimentally verified proteotypic peptides used for multiple reaction monitoring assays. Other novel features include published plasma/serum concentrations for 1278 proteins along with a separate category of plasma-derived extracellular vesicle proteins. As plasma proteins have become a major thrust in the field of biomarkers, we have enabled a batch-based query designated Plasma Proteome Explorer, which will permit the users in screening a list of proteins or peptides against known plasma proteins to assess novelty of their data set. We believe that PPD will facilitate both clinical and basic research by serving as a comprehensive reference of plasma proteins in humans and accelerate biomarker discovery and translation efforts.