This Topic has been realised in collaboration with Dr
Yi-han Sheu, Researcher at Harvard Medical School.
Despite substantial efforts, the causes of most psychiatric disorders remain unclear; even categorizing such disorders precisely has been difficult. The diagnostic systems in psychiatry have mostly relied on descriptive phenomenology that does not fully consider the heterogeneous symptoms or their biological mechanisms and etiology. Recent approaches to psychiatric classification such as Research Domain Criteria (RDoC) have moved toward the characterization of biomarkers that cut across symptom-based diagnoses, but map on to translational domains from cellular to circuitry and behavioral levels. An increasing amount of epidemiology and neuroimaging data has been established in recent years to understand the complex phenotypes of mental illness.
To quantify the complex bid data in psychiatry, an approach that integrates mathematics, physics, and computational neuroscience is required. Therefore, this Research Topic aims to illustrate the applications of deep learning to relevant studies in psychiatric illness and develop the RDoC strategy with machine learning-based computational algorithms to phenotype mental disorders. We anticipate that these approaches will provide a better characterization of the heterogeneity of mental illness and will highlight deep learning applications that will lead to the translational research utilizing these computational models in the clinic.
This Topic has been realised in collaboration with Dr
Yi-han Sheu, Researcher at Harvard Medical School.
Despite substantial efforts, the causes of most psychiatric disorders remain unclear; even categorizing such disorders precisely has been difficult. The diagnostic systems in psychiatry have mostly relied on descriptive phenomenology that does not fully consider the heterogeneous symptoms or their biological mechanisms and etiology. Recent approaches to psychiatric classification such as Research Domain Criteria (RDoC) have moved toward the characterization of biomarkers that cut across symptom-based diagnoses, but map on to translational domains from cellular to circuitry and behavioral levels. An increasing amount of epidemiology and neuroimaging data has been established in recent years to understand the complex phenotypes of mental illness.
To quantify the complex bid data in psychiatry, an approach that integrates mathematics, physics, and computational neuroscience is required. Therefore, this Research Topic aims to illustrate the applications of deep learning to relevant studies in psychiatric illness and develop the RDoC strategy with machine learning-based computational algorithms to phenotype mental disorders. We anticipate that these approaches will provide a better characterization of the heterogeneity of mental illness and will highlight deep learning applications that will lead to the translational research utilizing these computational models in the clinic.
