Zika virus damages the human placental barrier and presents marked fetal neurotropism

An unusually high incidence of microcephaly in newborns has recently been observed in Brazil. There is a temporal association between the increase in cases of microcephaly and the Zika virus (ZIKV) epidemic. Viral RNA has been detected in amniotic fluid samples, placental tissues and newborn and fetal brain tissues. However, much remains to be determined concerning the association between ZIKV infection and fetal malformations. In this study, we provide evidence of the transplacental transmission of ZIKV through the detection of viral proteins and viral RNA in placental tissue samples from expectant mothers infected at different stages of gestation. We observed chronic placentitis (TORCH type) with viral protein detection by immunohistochemistry in Hofbauer cells and some histiocytes in the intervillous spaces. We also demonstrated the neurotropism of the virus via the detection of viral proteins in glial cells and in some endothelial cells and the observation of scattered foci of microcalcifications in the brain tissues. Lesions were mainly located in the white matter. ZIKV RNA was also detected in these tissues by real-time-polymerase chain reaction. We believe that these findings will contribute to the body of knowledge of the mechanisms of ZIKV transmission, interactions between the virus and host cells and viral tropism.

Fiber Pathways for Language in the Developing Brain: A Diffusion Tensor Imaging (DTI) Study

The present study characterized two fiber pathways important for language, the superior longitudinal fasciculus/arcuate fasciculus (SLF/AF) and the frontal aslant tract (FAT), and related these tracts to speech, language, and literacy skill in children five to eight years old. We used Diffusion Tensor Imaging (DTI) to characterize the fiber pathways and administered several language assessments. The FAT was identified for the first time in children. Results showed no age-related change in integrity of the FAT, but did show age-related change in the left (but not right) SLF/AF. Moreover, only the integrity of the right FAT was related to phonology but not audiovisual speech perception, articulation, language, or literacy. Both the left and right SLF/AF related to language measures, specifically receptive and expressive language, and language content. These findings are important for understanding the neurobiology of language in the developing brain, and can be incorporated within contemporary dorsal-ventral-motor models for language.

Human congenital cytomegalovirus infection: characteristics and pathogenesis of fetal brain damage

Human cytomegalovirus (HCMV) causes congenital neurological lifelong disabilities. The study analyzed 10 HCMV-infected human fetuses at 21 weeks of gestation to evaluate the characteristics and pathogenesis of brain injury related to congenital human CMV (cCMV) infection. Specifically, tissues from cortical and white matter areas, subventricular zone, thalamus, hypothalamus, hippocampus, basal ganglia and cerebellum were analysed by: i) immunohistochemistry (IHC) to detect HCMV-infected cell distribution, ii) hematoxylin-eosin staining to evaluate histological damage and iii) real-time PCR to quantify tissue viral load (HCMV-DNA). Viral tropism was assessed by double IHC to detect HCMV-antigens and neural/neuronal markers: nestin (expressed in early differentiation stage), doublecortin (DCX, identifying neuronal precursor cells) and neuronal nuclei (NeuN, identifying mature neurons). HCMV-positive cells and viral DNA were found in the brain of 8/10 (80%) fetuses. For these cases, brain damage was classified in mild (n=4, 50%), moderate (n=3, 37.5%) and severe (n=1, 12.5%) based on presence of i) diffuse astrocytosis, microglial activation and vascular changes; ii) occasional (in mild) or multiple (in moderate/severe) microglial nodules and iii) necrosis (in severe). The highest median HCMV-DNA level was found in the hippocampus (212 copies/5ng of humanDNA [hDNA], range: 10-7,505) as well as the highest mean HCMV-infected cell value (2.9 cells, range: 0-23), followed by that detected in subventricular zone (1.8 cells, range: 0-19). This suggests a preferential HCMV tropism for immature neuronal cells, residing in these regions, confirmed by the detection of DCX and nestin in 94% and 63.3% of HCMV-positive cells, respectively. NeuN was not found among HCMV-positive cells and was nearly absent in the brain with severe damage, suggesting HCMV does not infect mature neurons and immature HCMV-infected neuronal cells do not differentiate into neurons. HCMV preferential tropism in immature neural/neuronal cells delays/inhibits their differentiation interfering with brain development processes that lead to structural and functional brain defects.

Fractal descriptors for quantifying brain complexity in MRI

Magnetic Resonance Imaging (MRI) is the in vivo technique most commonly employed to characterize changes in brain structures. The conventional MRI-derived morphological indices are able to capture only partial aspects of brain structural complexity. Fractal geometry and its most popular index, the fractal dimension (FD), can characterize self-similar structures including grey matter (GM) and white matter (WM). Previous literature shows the need for a definition of the so-called fractal scaling window, within which each structure manifests self-similarity. This justifies the existence of fractal properties and confirms Mandelbrot’s assertion that "fractals are not a panacea; they are not everywhere". In this work, we propose a new approach to automatically determine the fractal scaling window, computing two new fractal descriptors, i.e., the minimal and maximal fractal scales (mfs and Mfs). Our method was implemented in a software package, validated on phantoms and applied on large datasets of structural MR images. We demonstrated that the FD is a useful marker of morphological complexity changes that occurred during brain development and aging and, using ultra-high magnetic field (7T) examinations, we showed that the cerebral GM has fractal properties also below the spatial scale of 1 mm. We applied our methodology in two neurological diseases. We observed the reduction of the brain structural complexity in SCA2 patients and, using a machine learning approach, proved that the cerebral WM FD is a consistent feature in predicting cognitive decline in patients with small vessel disease and mild cognitive impairment. Finally, we showed that the FD of the WM skeletons derived from diffusion MRI provides complementary information to those obtained from the FD of the WM general structure in T1-weighted images. In conclusion, the fractal descriptors of structural brain complexity are candidate biomarkers to detect subtle morphological changes during development, aging and in neurological diseases.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.