RESUMO
Background: Alzheimer's disease (AD) is one of the most common causes of neurodegenerative disease affecting over 50 million people worldwide. However, most AD diagnosis occurs in the moderate to late stage, which means that the optimal time for treatment has already passed. Mild cognitive impairment (MCI) is an intermediate state between cognitively normal people and AD patients. Therefore, the accurate prediction in the conversion process of MCI to AD may allow patients to start preventive intervention to slow the progression of the disease. Nowadays, neuroimaging techniques have been developed and are used to determine AD-related structural biomarkers. Deep learning approaches have rapidly become a key methodology applied to these techniques to find biomarkers. Methods: In this study, we aimed to investigate an MCI-to-AD prediction method using Vision Transformers (ViT) to structural magnetic resonance images (sMRI). The Alzheimer's Disease Neuroimaging Initiative (ADNI) database containing 598 MCI subjects was used to predict MCI subjects' progression to AD. There are three main objectives in our study: (i) to propose an MRI-based Vision Transformers approach for MCI to AD progression classification, (ii) to evaluate the performance of different ViT architectures to obtain the most advisable one, and (iii) to visualize the brain region mostly affect the prediction of deep learning approach to MCI progression. Results: Our method achieved state-of-the-art classification performance in terms of accuracy (83.27%), specificity (85.07%), and sensitivity (81.48%) compared with a set of conventional methods. Next, we visualized the brain regions that mostly contribute to the prediction of MCI progression for interpretability of the proposed model. The discriminative pathological locations include the thalamus, medial frontal, and occipital-corroborating the reliability of our model. Conclusion: In conclusion, our methods provide an effective and accurate technique for the prediction of MCI conversion to AD. The results obtained in this study outperform previous reports using the ADNI collection, and it suggests that sMRI-based ViT could be efficiently applied with a considerable potential benefit for AD patient management. The brain regions mostly contributing to prediction, in conjunction with the identified anatomical features, will support the building of a robust solution for other neurodegenerative diseases in future.
RESUMO
BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia, and is characterized by amyloid-ß (Aß) plaques and tauopathy. Reducing Aß has been considered a major AD treatment strategy in pharmacological and non-pharmacological approaches. Impairment of gamma oscillations, which play an important role in perception and cognitive function, has been shown in mouse AD models and human patients. Recently, the therapeutic effect of gamma entrainment in AD mouse models has been reported. Given that ultrasound is an emerging neuromodulation modality, we investigated the effect of ultrasound stimulation pulsed at gamma frequency (40 Hz) in an AD mouse model. METHODS: We implanted electroencephalogram (EEG) electrodes and a piezo-ceramic disc ultrasound transducer on the skull surface of 6-month-old 5×FAD and wild-type control mice (n = 12 and 6, respectively). Six 5×FAD mice were treated with two-hour ultrasound stimulation at 40 Hz daily for two weeks, and the other six mice received sham treatment. Soluble and insoluble Aß levels in the brain were measured by enzyme-linked immunosorbent assay. Spontaneous EEG gamma power was computed by wavelet analysis, and the brain connectivity was examined with phase-locking value and cross-frequency phase-amplitude coupling. RESULTS: We found that the total Aß42 levels, especially insoluble Aß42, in the treatment group decreased in pre- and infra-limbic cortex (PIL) compared to that of the sham treatment group. A reduction in the number of Aß plaques was also observed in the hippocampus. There was no increase in microbleeding in the transcranial ultrasound stimulation (tUS) group. In addition, the length and number of microglial processes decreased in PIL and hippocampus. Encelphalographic spontaneous gamma power was increased, and cross-frequency coupling was normalized, implying functional improvement after tUS stimulation. CONCLUSION: These results suggest that the transcranial ultrasound-based gamma-band entrainment technique can be an effective therapy for AD by reducing the Aß load and improving brain connectivity.
