Aging is a major risk factor for most common neurodegenerative diseases, including mild cognitive impairment, dementias including Alzheimer's disease, cerebrovascular disease and Parkinson's disease. There are also other age-related diseases that commonly affect brain functioning, such as type 2 diabetes, hypertension, chronic obstructive pulmonary disease (COPD) and hearing loss. Their effects on the brain are widespread and have multiple aetiologies, with changes at all levels from molecules to gross morphology and function. Early detection of the effects of these age-related diseases offers the best hope for optimal management of troubling behavioral and psychological symptoms as well as accompanying physical illness. Identifying the feature of the disease and developing an appropriate clinical strategy as soon as possible helps alleviate the health and economic burden globally. Therefore, accelerating the discovery of novel diagnostics, from bench to bedside, that can advance the treatment and prognosis of multiple age-related diseases is foremost in the field of neuroscience.
Artificial intelligence (AI), with its subdivisions (machine and deep learning), is a new branch of computer science that has shown impressive results across various domains. The applications of AI to medicine and biology are being widely investigated. Neuroscience, which heavily relies on medical images, explores the frontiers of artificial intelligence methods in analysis and diagnosis. The applications of modern AI algorithms offer tremendous opportunities for formerly incompatible dynamic and static data types. With the fast development of novel AI techniques such as deep convolutional neural networks, AI technology plays more critical roles in the early detection, prognosis prediction and evaluation of brain dysfunction. The current Topic aims to bring together some of the latest research applying advanced AI techniques to add clinical significance in age-related brain disorders. Development and validation of AI-based methods are expected, including segmentation, feature extraction, quantitative analysis, diagnosis model construction. Furthermore, applications of AI-based methods are most welcomed to conduct risk factor screening, subtype diagnosis, treatment efficiency prediction in age-related brain disorders. We hope that the current topic will make optimal use of the power of AI and provide new personalized care to fight against age-related brain disorders.
We welcome articles focused on, but not restricted to:
1. Construction and validation of novel models to aid early recognition, as well as predict prognosis and guide interventions
2. Identification of various phenotypes of cognitive impairment and their common features
3. Improvement of imaging quality and identification of core/at-risk tissue of cerebrovascular disease
4. Identification of imaging markers and progression of age-related brain disorders
5. Prediction of brain age
6. Comorbidity characteristics of cognitive impairment
7. Cellular, molecular and pathological signatures of age-related brain disorders
8. System review of AI application in age-related brain disorders
Aging is a major risk factor for most common neurodegenerative diseases, including mild cognitive impairment, dementias including Alzheimer's disease, cerebrovascular disease and Parkinson's disease. There are also other age-related diseases that commonly affect brain functioning, such as type 2 diabetes, hypertension, chronic obstructive pulmonary disease (COPD) and hearing loss. Their effects on the brain are widespread and have multiple aetiologies, with changes at all levels from molecules to gross morphology and function. Early detection of the effects of these age-related diseases offers the best hope for optimal management of troubling behavioral and psychological symptoms as well as accompanying physical illness. Identifying the feature of the disease and developing an appropriate clinical strategy as soon as possible helps alleviate the health and economic burden globally. Therefore, accelerating the discovery of novel diagnostics, from bench to bedside, that can advance the treatment and prognosis of multiple age-related diseases is foremost in the field of neuroscience.
Artificial intelligence (AI), with its subdivisions (machine and deep learning), is a new branch of computer science that has shown impressive results across various domains. The applications of AI to medicine and biology are being widely investigated. Neuroscience, which heavily relies on medical images, explores the frontiers of artificial intelligence methods in analysis and diagnosis. The applications of modern AI algorithms offer tremendous opportunities for formerly incompatible dynamic and static data types. With the fast development of novel AI techniques such as deep convolutional neural networks, AI technology plays more critical roles in the early detection, prognosis prediction and evaluation of brain dysfunction. The current Topic aims to bring together some of the latest research applying advanced AI techniques to add clinical significance in age-related brain disorders. Development and validation of AI-based methods are expected, including segmentation, feature extraction, quantitative analysis, diagnosis model construction. Furthermore, applications of AI-based methods are most welcomed to conduct risk factor screening, subtype diagnosis, treatment efficiency prediction in age-related brain disorders. We hope that the current topic will make optimal use of the power of AI and provide new personalized care to fight against age-related brain disorders.
We welcome articles focused on, but not restricted to:
1. Construction and validation of novel models to aid early recognition, as well as predict prognosis and guide interventions
2. Identification of various phenotypes of cognitive impairment and their common features
3. Improvement of imaging quality and identification of core/at-risk tissue of cerebrovascular disease
4. Identification of imaging markers and progression of age-related brain disorders
5. Prediction of brain age
6. Comorbidity characteristics of cognitive impairment
7. Cellular, molecular and pathological signatures of age-related brain disorders
8. System review of AI application in age-related brain disorders
