An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets.

The increasing use of neuroimaging in clinical research has driven the creation of many large imaging datasets. However, these datasets often rely on inconsistent naming conventions in image file headers to describe acquisition, and time-consuming manual curation is necessary. Therefore, we sought to automate the process of classifying and organizing magnetic resonance imaging (MRI) data according to acquisition types common to the clinical routine, as well as automate the transformation of raw, unstructured images into Brain Imaging Data Structure (BIDS) datasets. To do this, we trained an XGBoost model to classify MRI acquisition types using relatively few acquisition parameters that are automatically stored by the MRI scanner in image file metadata, which are then mapped to the naming conventions prescribed by BIDS to transform the input images to the BIDS structure. The model recognizes MRI types with 99.475% accuracy, as well as a micro/macro-averaged precision of 0.9995/0.994, a micro/macro-averaged recall of 0.9995/0.989, and a micro/macro-averaged F1 of 0.9995/0.991. Our approach accurately and quickly classifies MRI types and transforms unstructured data into standardized structures with little-to-no user intervention, reducing the barrier of entry for clinical scientists and increasing the accessibility of existing neuroimaging data.

Nicotinamide Mononucleotide Administration Prevents Experimental Diabetes-Induced Cognitive Impairment and Loss of Hippocampal Neurons.

Diabetes predisposes to cognitive decline leading to dementia and is associated with decreased brain NAD+ levels. This has triggered an intense interest in boosting nicotinamide adenine dinucleotide (NAD+) levels to prevent dementia. We tested if the administration of the precursor of NAD+, nicotinamide mononucleotide (NMN), can prevent diabetes-induced memory deficits. Diabetes was induced in Sprague-Dawley rats by the administration of streptozotocin (STZ). After 3 months of diabetes, hippocampal NAD+ levels were decreased (p = 0.011). In vivo localized high-resolution proton magnetic resonance spectroscopy (MRS) of the hippocampus showed an increase in the levels of glucose (p < 0.001), glutamate (p < 0.001), gamma aminobutyric acid (p = 0.018), myo-inositol (p = 0.018), and taurine (p < 0.001) and decreased levels of N-acetyl aspartate (p = 0.002) and glutathione (p < 0.001). There was a significant decrease in hippocampal CA1 neuronal volume (p < 0.001) and neuronal number (p < 0.001) in the Diabetic rats. Diabetic rats showed hippocampal related memory deficits. Intraperitoneal NMN (100 mg/kg) was given after induction and confirmation of diabetes and was provided on alternate days for 3 months. NMN increased brain NAD+ levels, normalized the levels of glutamate, taurine, N-acetyl aspartate (NAA), and glutathione. NMN-treatment prevented the loss of CA1 neurons and rescued the memory deficits despite having no significant effect on hyperglycemic or lipidemic control. In hippocampal protein extracts from Diabetic rats, SIRT1 and PGC-1α protein levels were decreased, and acetylation of proteins increased. NMN treatment prevented the diabetes-induced decrease in both SIRT1 and PGC-1α and promoted deacetylation of proteins. Our results indicate that NMN increased brain NAD+, activated the SIRT1 pathway, preserved mitochondrial oxidative phosphorylation (OXPHOS) function, prevented neuronal loss, and preserved cognition in Diabetic rats.

Clinical neuropathy scales in neuropathy associated with impaired glucose tolerance.

AIMS: Disagreement exists on effective and sensitive outcome measures in neuropathy associated with impaired glucose tolerance (IGT). Nerve conduction studies and skin biopsies are costly, invasive and may have their problems with reproducibility and clinical applicability. A clinical measure of neuropathy that has sufficient sensitivity and correlates to invasive measures would enable significant future research. METHODS: Data was collected prospectively on patients with IGT and symptomatic early neuropathy (neuropathy symptoms <2years) and normal controls. The seven scales that were examined were the Neuropathy Impairment Score of the Lower Limb (NIS-LL), Michigan Diabetic Neuropathy Score (MNDS), modified Toronto Clinical Neuropathy Scale (mTCNS), Total Neuropathy Score (Clinical) (TNSc), The Utah Early Neuropathy Scale (UENS), the Early Neuropathy Score (ENS), and the Neuropathy Disability Score (NDS). RESULTS: All seven clinical scales were determined to be excellent in discriminating between patients with neuropathy from controls without neuropathy. The strongest discrimination was seen with the mTCNS. The best sensitivity and specificity for the range of scores obtained, as determined by using receiver operating characteristic curves, was seen for the mTCNS followed by the TNSc. Most scales show a stronger correlation with measures of large rather than small fiber neuropathy. CONCLUSIONS: All seven scales identify patients with neuropathy. For the purpose of screening potential patients for a clinical study, the mTCNS followed by the TNSc would be most helpful to select patients with neuropathy.