About this Research Topic
Dementia is a syndrome that causes a deterioration in memory, thinking, behaviour and the ability to perform everyday activities. According to the World Health Organization it affects 47.5 million people, with 7.7 million new cases every year. The total number of people with dementia is projected to be 75.6 million in 2030 and almost triple that number by 2050, with 135.5 million affected people. Dementia is not a normal part of aging, representing one of the major causes of disability and dependency among older people worldwide.
There are many different forms of dementia. Alzheimer’s disease (AD) contributes to 60-70% of the cases. Other major forms include vascular dementia, dementia with Lewy bodies, Parkinson’s disease, frontotemporal dementia, etc.
Although new treatments are being investigated in clinical trials, no treatment to cure dementia or to alter its progressive course exists. Today, we understand that dementia appears only after a decade or more of brain degeneration (preclinical dementia) and current consensus has established the need for early recognition.
An intensive research effort is being devoted to the development of novel neuroimaging biomarkers that can provide an alert even before the cognitive decline appears. Structural magnetic resonance imaging (MRI), functional nuclear medicine neuroimaging techniques including single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are widely used in combination with other blood, cerebrospinal fluid (CSF) and genetic biomarkers for early diagnosis of dementia.
Large multicenter studies are currently investigating the value of existing and novel multimodal and longitudinal neurodegeneration biomarkers. The vast amount of data available represents an opportunity for the development of more accurate statistical models of neurodegeneration enabling the early recognition as well as the characterization of the progressive course of dementia.
The aim of this Research Topic in Frontiers in Aging Neuroscience is to present the current state of the art in the theory and practice of multimodal and longitudinal neuroimaging approaches for characterizing the progressive course of dementia. Potential fields of research covered in this Topic include, but are not limited to, novel pattern recognition techniques enabling to elucidate normal and abnormal brain function:
- Learning from neuroimaging data
- Bayesian analysis of neuroimaging data
- Network and connectivity models (the connectome)
- Dynamic and time-varying models
- Longitudinal data analysis techniques
- Statistical models of neurodegeneration
- Early diagnosis and prognosis
- Single modal and multimodal structural and functional data analysis methods
- Advanced data classification methods
- Algorithms for large-scale data analysis
There are many different forms of dementia. Alzheimer’s disease (AD) contributes to 60-70% of the cases. Other major forms include vascular dementia, dementia with Lewy bodies, Parkinson’s disease, frontotemporal dementia, etc.
Although new treatments are being investigated in clinical trials, no treatment to cure dementia or to alter its progressive course exists. Today, we understand that dementia appears only after a decade or more of brain degeneration (preclinical dementia) and current consensus has established the need for early recognition.
An intensive research effort is being devoted to the development of novel neuroimaging biomarkers that can provide an alert even before the cognitive decline appears. Structural magnetic resonance imaging (MRI), functional nuclear medicine neuroimaging techniques including single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are widely used in combination with other blood, cerebrospinal fluid (CSF) and genetic biomarkers for early diagnosis of dementia.
Large multicenter studies are currently investigating the value of existing and novel multimodal and longitudinal neurodegeneration biomarkers. The vast amount of data available represents an opportunity for the development of more accurate statistical models of neurodegeneration enabling the early recognition as well as the characterization of the progressive course of dementia.
The aim of this Research Topic in Frontiers in Aging Neuroscience is to present the current state of the art in the theory and practice of multimodal and longitudinal neuroimaging approaches for characterizing the progressive course of dementia. Potential fields of research covered in this Topic include, but are not limited to, novel pattern recognition techniques enabling to elucidate normal and abnormal brain function:
- Learning from neuroimaging data
- Bayesian analysis of neuroimaging data
- Network and connectivity models (the connectome)
- Dynamic and time-varying models
- Longitudinal data analysis techniques
- Statistical models of neurodegeneration
- Early diagnosis and prognosis
- Single modal and multimodal structural and functional data analysis methods
- Advanced data classification methods
- Algorithms for large-scale data analysis
Keywords: Dementia, Bioimaging, Multimodal analysis, Large-scale data analysis, Early diagnosis and prognosis, Statistical models of neurodegeneration, Advanced data analysis, Network and connectivity models
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