Occipital Proton Magnetic Resonance Spectroscopy ((1)H-MRS) Reveals Normal Metabolite Concentrations in Retinal Visual Field Defects

BACKGROUND: Progressive visual field defects, such as age-related macular degeneration and glaucoma, prevent normal stimulation of visual cortex. We investigated whether in the case of visual field defects, concentrations of metabolites such as N-acetylaspartate (NAA), a marker for degenerative processes, are reduced in the occipital brain region. METHODOLOGY/PRINCIPAL FINDINGS: Participants known with glaucoma, age-related macular degeneration (the two leading causes of visual impairment in the developed world), and controls were examined by proton MR spectroscopic ((1)H-MRS) imaging. Absolute NAA, Creatine and Choline concentrations were derived from a single-voxel in the occipital region of each brain hemisphere. No significant differences in metabolites concentrations were found between the three groups. CONCLUSIONS/SIGNIFICANCE: We conclude that progressive retinal visual field defects do not affect metabolite concentration in visual brain areas suggesting that there is no ongoing occipital degeneration. We discuss the possibility that metabolite change is too slow to be detectable

Homosexual Women Have Less Grey Matter in Perirhinal Cortex than Heterosexual Women

Is sexual orientation associated with structural differences in the brain? To address this question, 80 homosexual and heterosexual men and women (16 homosexual men and 15 homosexual women) underwent structural MRI. We used voxel-based morphometry to test for differences in grey matter concentration associated with gender and sexual orientation. Compared with heterosexual women, homosexual women displayed less grey matter bilaterally in the temporo-basal cortex, ventral cerebellum, and left ventral premotor cortex. The relative decrease in grey matter was most prominent in the left perirhinal cortex. The left perirhinal area also showed less grey matter in heterosexual men than in heterosexual women. Thus, in homosexual women, the perirhinal cortex grey matter displayed a more male-like structural pattern. This is in accordance with previous research that revealed signs of sex-atypical prenatal androgenization in homosexual women, but not in homosexual men. The relevance of the perirhinal area for high order multimodal (olfactory and visual) object, social, and sexual processing is discussed

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data

Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively (p  <  0.00007). The DL method achieved a mean Dice coefficient of 0.87 on the test cases. Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its “human teachers” and produced output that was better, on average, than its training data