An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI.

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients' lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widely to predict MCI. This study proposes optimized deep learning models for differentiating between MCI and normal control samples. In previous studies, the hippocampus region located in the brain is used extensively to diagnose MCI. The entorhinal cortex is a promising area for diagnosing MCI since severe atrophy is observed when diagnosing the disease before the shrinkage of the hippocampus. Due to the small size of the entorhinal cortex area relative to the hippocampus, limited research has been conducted on the entorhinal cortex brain region for predicting MCI. This study involves the construction of a dataset containing only the entorhinal cortex area to implement the classification system. To extract the features of the entorhinal cortex area, three different neural network architectures are optimized independently: VGG16, Inception-V3, and ResNet50. The best outcomes were achieved utilizing the convolution neural network classifier and the Inception-V3 architecture for feature extraction, with accuracy, sensitivity, specificity, and area under the curve scores of 70%, 90%, 54%, and 69%, respectively. Furthermore, the model has an acceptable balance between precision and recall, achieving an F1 score of 73%. The results of this study validate the effectiveness of our approach in predicting MCI and may contribute to diagnosing MCI through MRI.

Developing fMRI protocol for clinical use Comparison of 6 Arabic paradigms for brain language mapping in native Arabic speakers.

OBJECTIVES: To assess a baseline assessment using developed functional magnetic resonance imaging (fMRI) language paradigms for Arabic-speakers. METHODS: 24-healthy right-handed volunteers scanned on a 3.0 Tesla MRI machine. For fMRI, a BOLD-sensitive sequence used to measure signals over time across 6 language paradigms: rhyming (RH), semantic category generations (SCG), silent word generation (SWG), verb generation picture (VGp), verb generation word (VGw), and verb generation audio (VGa). fMRI data was analyzed using FMRIB Software Library (FSL). RESULTS: We found that VGa, SWG, VGw and VGp robustly activated language-related regions in the dominant hemisphere. RH and SCG failed to adequately define these activation regions but this may be related to the study's preliminary nature and limitations. After assessment of their validity, considerable activation of the inferior frontal gyrus during VGa, SWG, VGw and VGp suggests that these paradigms have the potential for localizing of Broca's area in native Arabic speakers. CONCLUSION: Set of well adapted, and evidence-based, fMRI paradigms were established for Arabic-speakers to enable accurate and sufficient localization and lateralization of the language area. After validation, these paradigms may provide sequences for accurate localization of brain language areas, and could be used as a presurgical evaluation tool.