Automatic segmentation, classification, and follow-up of optic pathway gliomas using deep learning and fuzzy c-means clustering based on MRI.

PURPOSE: Optic pathway gliomas (OPG) are low-grade pilocytic astrocytomas accounting for 3-5% of pediatric intracranial tumors. Accurate and quantitative follow-up of OPG using magnetic resonance imaging (MRI) is crucial for therapeutic decision making, yet is challenging due to the complex shape and heterogeneous tissue pattern which characterizes these tumors. The aim of this study was to implement automatic methods for segmentation and classification of OPG and its components, based on MRI. METHODS: A total of 202 MRI scans from 29 patients with chiasmatic OPG scanned longitudinally were retrospectively collected and included in this study. Data included T2 and post-contrast T1 weighted images. The entire tumor volume and its components were manually annotated by a senior neuro-radiologist, and inter- and intra-rater variability of the entire tumor volume was assessed in a subset of scans. Automatic tumor segmentation was performed using deep-learning method with U-Net+ResNet architecture. A fivefold cross-validation scheme was used to evaluate the automatic results relative to manual segmentation. Voxel-based classification of the tumor into enhanced, non-enhanced, and cystic components was performed using fuzzy c-means clustering. RESULTS: The results of the automatic tumor segmentation were: mean dice score = 0.736 ± 0.025, precision = 0.918 ± 0.014, and recall = 0.635 ± 0.039 for the validation data, and dice score = 0.761 ± 0.011, precision = 0.794 ± 0.028, and recall = 0.742 ± 0.012 for the test data. The accuracy of the voxel-based classification of tumor components was 0.94, with precision = 0.89, 0.97, and 0.85, and recall = 1.00, 0.79, and 0.94 for the non-enhanced, enhanced, and cystic components, respectively. CONCLUSION: This study presents methods for automatic segmentation of chiasmatic OPG tumors and classification into the different components of the tumor, based on conventional MRI. Automatic quantitative longitudinal assessment of these tumors may improve radiological monitoring, facilitate early detection of disease progression and optimize therapy management.

Functional MRI (fMRI) human mapping for indicating hippocampal content-specific responsivity and laterality.

OBJECTIVE: Presurgical memory functional MRI (fMRI) mapping for temporal lobe epilepsy surgery is important because of the excision of structures in the temporal lobe (e.g., hippocampus) that are relevant for intact memory. Although the American Academy of Neurology recommends the use of fMRI for presurgical mapping of epilepsy of verbal and nonverbal memory to predict memory outcome, there are still no specific recommendations about which tests to use. In the current study, we evaluate the potential for clinical utility of two established neuropsychological tests of memory adapted into the fMRI setting. METHOD: We used the Verbal Paired Associates (VPA) for assessment of verbal memory and the Object Learning and Location (OLL) task for assessment of visuospatial memory. To confirm that these tasks engage the hippocampus, we examined their neural underpinning and patterns of laterality in 20 healthy volunteers (mean age = 26.35). RESULTS: During fMRI of the VPA task of verbal memory, we found a strong left-lateralized posterior hippocampal activation. Remembering the location of objects in the OLL task of visuospatial memory elicited right-lateralized hippocampal activation. CONCLUSIONS: These findings demonstrate the utility of the VPA and OLL tests to delineate domain-specific activity and laterality and, as such, may provide supportive evidence to strengthen links between presurgical neuropsychological assessment and memory fMRI mapping for epilepsy surgery. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Beyond production: Brain responses during speech perception in adults who stutter.

Developmental stuttering is a speech disorder that disrupts the ability to produce speech fluently. While stuttering is typically diagnosed based on one's behavior during speech production, some models suggest that it involves more central representations of language, and thus may affect language perception as well. Here we tested the hypothesis that developmental stuttering implicates neural systems involved in language perception, in a task that manipulates comprehensibility without an overt speech production component. We used functional magnetic resonance imaging to measure blood oxygenation level dependent (BOLD) signals in adults who do and do not stutter, while they were engaged in an incidental speech perception task. We found that speech perception evokes stronger activation in adults who stutter (AWS) compared to controls, specifically in the right inferior frontal gyrus (RIFG) and in left Heschl's gyrus (LHG). Significant differences were additionally found in the lateralization of response in the inferior frontal cortex: AWS showed bilateral inferior frontal activity, while controls showed a left lateralized pattern of activation. These findings suggest that developmental stuttering is associated with an imbalanced neural network for speech processing, which is not limited to speech production, but also affects cortical responses during speech perception.