Unique Brain Network Identification Number for Parkinson's Individuals Using Structural MRI

We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding brain networks of individual subject. To realize this objective, we employed T1-weighted structural MRI of 180 Parkinson's disease (PD) patients from National Institute of Mental Health and Neurosciences, India. We parcellated each subject's brain volume and constructed individual adjacency matrix using correlation between grey matter (GM) volume of every pair of regions. The unique code is derived from values representing connections of every node (i), weighted by a factor of 2^-(i-1). The numerical representation UBNIN was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. This model may be implemented as neural signature of a person's unique brain connectivity, thereby useful for brainprinting applications. Additionally, we segregated the above dataset into five age-cohorts: A:22-32years, B:33-42years, C:43-52years, D:53-62years and E:63-72years to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox-based MATLAB functions. For each age-cohort, a decreasing trend was observed in mean clustering coefficient with increasing sparsity. Significantly different clustering coefficient was noted between age-cohort B and C (sparsity: 0.63,0.66), C and E (sparsity: 0.66,0.69). Our findings suggest network connectivity patterns change with age, indicating network disruption due to the underlying neuropathology. Varying clustering coefficient for different cohorts indicate that information transfer between neighboring nodes change with age. This provides evidence on age-related brain shrinkage and network degeneration.Comment: 9 pages, 5 figures,1 algorithm, 1 main table, 1 appendix tabl

Interpretable simultaneous localization of MRI corpus callosum and classification of atypical Parkinsonian disorders using YOLOv5

Structural MRI(S-MRI) is one of the most versatile imaging modality that revolutionized the anatomical study of brain in past decades. The corpus callosum (CC) is the principal white matter fibre tract, enabling all kinds of inter-hemispheric communication. Thus, subtle changes in CC might be associated with various neurological disorders. The present work proposes the potential of YOLOv5-based CC detection framework to differentiate atypical Parkinsonian disorders (PD) from healthy controls (HC). With 3 rounds of hold-out validation, mean classification accuracy of 92% is obtained using the proposed method on a proprietary dataset consisting of 20 healthy subjects and 20 cases of APDs, with an improvement of 5% over SOTA methods (CC morphometry and visual texture analysis) that used the same dataset. Subsequently, in order to incorporate the explainability of YOLO predictions, Eigen CAM based heatmap is generated for identifying the most important sub-region in CC that leads to the classification. The result of Eigen CAM showed CC mid-body as the most distinguishable sub-region in classifying APDs and HC, which is in-line with SOTA methodologies and the current prevalent understanding in medicine

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Neuroimaging features of fatal high-altitude cerebral edema

Acute high-altitude cerebral edema can occur in an unacclimatised individual on exposure to high altitudes and sometimes it can be fatal. Here we have described the neuroimaging features of a patient who suffered from fatal high altitude cerebral edema. Available literature is reviewed. Probable pathogenesis is discussed. The risk of acute mountain sickness is reported up to 25% in individuals who ascend to an altitude of 3500 meter and in more than 50% subjects at an altitude of 6000 meter. The lack of availability of advanced imaging facilities at such a higher altitude makes imaging of such condition a less described entity

Unusual occurrence of supratentorial medulloepithelioma in a young female

Medulloepithelioma is an extremely rare PNET in late adolescence and adults with only two cases noted in literature. These are WHO grade IV tumors with dismal prognosis. Only few cases survived beyond 5 months. We report a rare case of supratentorial medulloepithelioma in a 17 year old girl. She had presented with right sided weakness, headache and vomiting. Imaging showed an enhancing mass lesion in left parietal region which undergone gross total resection. After surgery, her headache, vomiting and right sided weakness improved. On histopathology, the tumor had characteristic trabecular, ribbon and palisaded arrangement with brisk mitotic activity, necrosis and calcification. Immuno-histochemistry revealed positivity for Synaptophysin, Vimentin and EMA while GFAP was negative. MIB-1 labeling was very high. Patient received postoperative radiotherapy. On follow up after 14 months, she was clinically asymptomatic with no recurrence on imaging

Decolourization of dyes by <em>Alcaligenes faecalis </em>and <em>Bacillus flexus</em> <em> </em>isolated from textile effluent

820-826In textile industry, untreated effluents pollute aquatic systems, almost irreversibly. Synthetic dyes not only change the colour of water resources but also make them toxic. In this study, we evaluated decolourizing potential of microbial isolates so as to use them as bioremediation agents. Two bacterial isolates, Bacillus flexus and Alcaligenes faecalis were isolated from the textile effluent samples collected from Nahar textile industry, Lalru (Punjab). Both these isolates have high decolourization potential and take only 24 h for complete decolourization. Different parameters, such as carbon source, nitrogen source, temperature, pH, concentration of dyes and inoculum size were optimized for decolourization of remazol black, direct blue and acid orange which are azo dyes that are most widely used and are highly toxic. Bacillus flexus showed 100% decolourization after 20 h with acid orange and at 24 h for remazol black and direct blue. Alcaligenes faecalis showed the best incubation time for all the three dyes to be 24 h and the extent of decolourization was found to be 89, 98 and 100% for remazol black, direct blue and acid, respectively. Such decolourizing potential of the isolates is quite high as compared to the earlier reports and can be effectively used as a tool for bioremediation of various textile effluents