Cognitive impairment is a common symptom in the elderly which significantly impacts the quality of patients' lives and leads to a substantial economic burden on society. Yet, diagnosing cognitive impairment tends to be missed or delayed due to its laborious assessment. Functional and structural disconnection of the brain is a prevailing hypothesis to explain cognitive impairment in many diseases. Specifically, interneuron dysfunction and structural network abnormalities have emerged as potential mechanisms of cognitive dysfunction, where network hypersynchrony and altered oscillatory rhythm may contribute to cognitive impairment. Such mechanisms are potential targets for the prevention or treatment of modulation of the brain networks organization during prodromal stage.
We aim to explore the mechanisms of brain functional and structural network advances including the alterations in intra- and inter-network connectivity or disconnection regarding cognitive decline to investigate whether the altered network properties could serve as an underlying neuropathological mechanism associated with the progression of cognitive decline in associated diseases, hoping to reveal insight into the unclear areas of the diagnosis in cognitive impairment. These may help to pave the way for earlier diagnosis of cognitive impairment in non-clinical settings without professional help, which can facilitate more timely intervention to mitigate or delay cognitive decline, particularly in early disease stages.
We welcome any types of manuscripts supported by the Journal – comprised of original research article, brief research article, case report, review, mini-review, and meta-analysis - pertaining, but not limited to, the following themes:
- Studies of advanced mechanisms of aberrant brain functional and structural network of diseases associated cognitive impairment, including Alzheimer's disease (AD) spectrum, vascular cognitive impairment, post-stroke cognitive impairment, cerebral small vessel disease with cognitive impairment, cognitive impairment in multiple sclerosis, Parkinson's disease with cognitive impairment, acute mild traumatic brain injury with cognitive impairment, and SARS-CoV-2/COVID-19-associated neurocognitive disorders.
- Methods describing either novel or existing state-of-art functional connectivity analysis methods that investigate brain networks and cognitive mechanisms to detect abnormal brain connectivity of cognitive impairment associated diseases.
- Machine learning methods including but not limited to deep learning algorithms, optimization methods and probabilistic graphical models used to model clinical detection, classification and evaluation of disease severity/progression and/or response to treatment outcomes related to cognitive impairment.
Cognitive impairment is a common symptom in the elderly which significantly impacts the quality of patients' lives and leads to a substantial economic burden on society. Yet, diagnosing cognitive impairment tends to be missed or delayed due to its laborious assessment. Functional and structural disconnection of the brain is a prevailing hypothesis to explain cognitive impairment in many diseases. Specifically, interneuron dysfunction and structural network abnormalities have emerged as potential mechanisms of cognitive dysfunction, where network hypersynchrony and altered oscillatory rhythm may contribute to cognitive impairment. Such mechanisms are potential targets for the prevention or treatment of modulation of the brain networks organization during prodromal stage.
We aim to explore the mechanisms of brain functional and structural network advances including the alterations in intra- and inter-network connectivity or disconnection regarding cognitive decline to investigate whether the altered network properties could serve as an underlying neuropathological mechanism associated with the progression of cognitive decline in associated diseases, hoping to reveal insight into the unclear areas of the diagnosis in cognitive impairment. These may help to pave the way for earlier diagnosis of cognitive impairment in non-clinical settings without professional help, which can facilitate more timely intervention to mitigate or delay cognitive decline, particularly in early disease stages.
We welcome any types of manuscripts supported by the Journal – comprised of original research article, brief research article, case report, review, mini-review, and meta-analysis - pertaining, but not limited to, the following themes:
- Studies of advanced mechanisms of aberrant brain functional and structural network of diseases associated cognitive impairment, including Alzheimer's disease (AD) spectrum, vascular cognitive impairment, post-stroke cognitive impairment, cerebral small vessel disease with cognitive impairment, cognitive impairment in multiple sclerosis, Parkinson's disease with cognitive impairment, acute mild traumatic brain injury with cognitive impairment, and SARS-CoV-2/COVID-19-associated neurocognitive disorders.
- Methods describing either novel or existing state-of-art functional connectivity analysis methods that investigate brain networks and cognitive mechanisms to detect abnormal brain connectivity of cognitive impairment associated diseases.
- Machine learning methods including but not limited to deep learning algorithms, optimization methods and probabilistic graphical models used to model clinical detection, classification and evaluation of disease severity/progression and/or response to treatment outcomes related to cognitive impairment.