Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging DOI Creative Commons
Saurabh Bhattacharya, Sashikanta Prusty,

Sanjay P. Pande

et al.

Frontiers in Human Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: March 21, 2025

Introduction Combining many types of imaging data—especially structural MRI (sMRI) and functional (fMRI)—may greatly assist in the diagnosis treatment brain disorders like Alzheimer’s. Current approaches are less helpful for forecasting, however, as they do not always blend spatial temporal patterns from different sources properly. This work presents a novel mixed deep learning (DL) method combining data using CNN, GRU, attention techniques. introduces hybrid Dynamic Cross-Modality Attention Module to help more efficiently data. Through working around issues with current multimodal fusion techniques, our approach increases accuracy readability diagnoses. Methods Utilizing CNNs models dynamics fMRI connection measures utilizing GRUs, proposed extracts characteristics sMRI. Strong integration is made possible by including an mechanism give diagnostically important features top priority. Training evaluation model took place Human Connectome Project (HCP) dataset behavioral data, fMRI, Measures include accuracy, recall, precision F1-score used evaluate performance. Results It was correct 96.79% time combined structure. Regarding identification disorders, successful than existing ones. Discussion These findings indicate that strategy makes sense complimentary information several kinds photos. detail helped one choose which aspects concentrate on, thereby enhancing diagnostic accuracy. Conclusion The offers fresh benchmark neuroimaging analysis has great potential use real-world assessment prediction. Researchers will investigate future applications this technique new picture clinical

Language: Английский

Synthesis, preclinical evaluation and pilot clinical study of a P2Y12 receptor targeting radiotracer [18F]QTFT for imaging brain disorders by visualizing anti-inflammatory microglia DOI Creative Commons

Bolin Yao,

Yanyan Kong, Jianing Li

et al.

Acta Pharmaceutica Sinica B, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

As the brain's resident immune cells, microglia perform crucial functions such as phagocytosis, neuronal network maintenance, and injury restoration by adopting various phenotypes. Dynamic imaging of these phenotypes is essential for accessing brain diseases therapeutic responses. Although numerous probes are available pro-inflammatory microglia, no PET tracers have been developed specifically to visualize anti-inflammatory microglia. In this study, we present an 18F-labeled tracer (QTFT) that targets P2Y12, a receptor highly expressed on [18F]QTFT exhibited high binding affinity P2Y12 (14.43 nmol/L) superior blood-brain barrier permeability compared other candidates. Micro-PET in IL-4-induced neuroinflammation models showed higher uptake lesions contralateral normal tissues. Importantly, specific could be blocked QTFT or antagonist. Furthermore, visualized mouse epilepsy, glioma, aging targeting aberrantly pilot clinical successfully located epileptic foci, showing enhanced radioactive signals patient with epilepsy. Collectively, studies suggest serve valuable diagnostic tool disorders overexpressed

Language: Английский

Citations

1

Emerging TSPO-PET Radiotracers for Imaging Neuroinflammation: A Critical Analysis DOI Creative Commons
Grace A. Cumbers,

Edward D. Harvey-Latham,

Michael Kassiou

et al.

Seminars in Nuclear Medicine, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

Citations

5

Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging DOI Creative Commons
Saurabh Bhattacharya, Sashikanta Prusty,

Sanjay P. Pande

et al.

Frontiers in Human Neuroscience, Journal Year: 2025, Volume and Issue: 19

Published: March 21, 2025

Introduction Combining many types of imaging data—especially structural MRI (sMRI) and functional (fMRI)—may greatly assist in the diagnosis treatment brain disorders like Alzheimer’s. Current approaches are less helpful for forecasting, however, as they do not always blend spatial temporal patterns from different sources properly. This work presents a novel mixed deep learning (DL) method combining data using CNN, GRU, attention techniques. introduces hybrid Dynamic Cross-Modality Attention Module to help more efficiently data. Through working around issues with current multimodal fusion techniques, our approach increases accuracy readability diagnoses. Methods Utilizing CNNs models dynamics fMRI connection measures utilizing GRUs, proposed extracts characteristics sMRI. Strong integration is made possible by including an mechanism give diagnostically important features top priority. Training evaluation model took place Human Connectome Project (HCP) dataset behavioral data, fMRI, Measures include accuracy, recall, precision F1-score used evaluate performance. Results It was correct 96.79% time combined structure. Regarding identification disorders, successful than existing ones. Discussion These findings indicate that strategy makes sense complimentary information several kinds photos. detail helped one choose which aspects concentrate on, thereby enhancing diagnostic accuracy. Conclusion The offers fresh benchmark neuroimaging analysis has great potential use real-world assessment prediction. Researchers will investigate future applications this technique new picture clinical

Language: Английский

Citations

0