Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within thebrain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detectdisease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis usinga single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3Dwhole-brain structure using standard post-processing methods. A deep learning model was then developed,optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia.Our proposed model outperformed the benchmark model, which was also trained with structural MR imagesusing a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987)distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regionalanalysis localized subcortical regions and ventricles as the most predictive brain regions. Subcorticalstructures serve a pivotal role in cognitive, affective, and social functions in humans, and structuralabnormalities of these regions have been associated with schizophrenia. Our finding corroborates thatschizophrenia is associated with widespread alterations in subcortical brain structure and the subcorticalstructural information provides prominent features in diagnostic classification. Together, these results furtherdemonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structuralneuroimaging signatures from a single, standard T1-weighted brain MRI.
<p>The development of high-performance photoacoustic (PA) probes that can monitor disease biomarkers in deep-tissue has the potential to replace invasive medical procedures such as a biopsy. However, such probes must be highly optimized for <i>in vivo</i> performance and exhibit an exceptional safety profile. In this study, we have developed PACu-1, the first PA probe designed for biopsy-free assessment (BFA) of hepatic Cu via photoacoustic imaging. PACu-1 features a Cu(I)-responsive trigger appended to an aza-BODIPY dye platform that has been optimized for ratiometric sensing. Owing to its excellent performance, we were able to detect basal levels of Cu in healthy wildtype mice, as well as elevated Cu in a Wilson’s disease model and in a liver metastasis model. To showcase the potential impact of PACu-1 for BFA, we conducted a blind study where we were able to successfully identify a Wilson’s disease animal from a group of healthy control mice with greater than 99.7% confidence.</p>
Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.
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