Imagine your brain is a complex city, filled with different regions and bustling with activity. Now, imagine using advanced technology to analyze the structures and chemicals within this city to understand and classify Alzheimer’s disease (AD). Researchers have developed a powerful method that combines magnetic resonance imaging (MRI) to examine the structures of 279 brain regions, along with the levels of 12 metabolites in the frontal and parietal regions. By reducing the data dimensionality and using a neural network, they were able to accurately classify AD patients and healthy controls. Interestingly, they found that gamma-aminobutyric acid (GABA) + levels in the parietal region played a crucial role in improving model performance. These findings support the idea that dysfunction in the GABAergic system is involved in the development of AD. This groundbreaking study provides valuable insights into how we can better diagnose and understand this devastating disease. Explore the full research article to dive deeper into this fascinating exploration of the brain!

To improve the diagnosis and classification of Alzheimer’s disease (AD), a modeling method is proposed based on the combining magnetic resonance images (MRI) brain structural data with metabolite levels of the frontal and parietal regions. First, multi-atlas brain segmentation technology based on T1-weighted images and edited magnetic resonance spectroscopy (MRS) were used to extract data of 279 brain regions and levels of 12 metabolites from regions of interest (ROIs) in the frontal and parietal regions. The t-test combined with false discovery rate (FDR) correction was used to reduce the dimensionality in the data, and MRI structural data of 54 brain regions and levels of 4 metabolites that obviously correlated with AD were screened out. Lastly, the stacked auto-encoder neural network (SAE) was used to classify AD and healthy controls (HCs), which judged the effect of classification method by fivefold cross validation. The results indicated that the mean accuracy of the five experimental model increased from 96 to 100%, the AUC value increased from 0.97 to 1, specificity increased from 90 to 100%, and F1 value increased from 0.97 to 1. Comparing the effect of each metabolite on model performance revealed that the gamma-aminobutyric acid (GABA) + levels in the parietal region resulted in the most significant improvement in model performance, with the accuracy rate increasing from 96 to 98%, the AUC value increased from 0.97 to 0.99 and the specificity increasing from 90 to 95%. Moreover, the GABA + levels in the parietal region was significantly correlated with Mini Mental State Examination (MMSE) scores of patients with AD (r = 0.627), and the F statistics were largest (F = 25.538), which supports the hypothesis that dysfunctional GABAergic system play an important role in the pathogenesis of AD. Overall, our findings support that a comprehensive method that combines MRI structural and metabolic data of brain regions can improve model classification efficiency of AD.

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