Subjective cognitive decline (SCD) with self-reported concerns and mild cognitive impairment (MCI) are well-established to be at increased risk of developing Alzheimer’s disease (AD) dementia and a clinical continuum of dementia progression as a spectrum of AD. AD may develop from SCD to MCI (early MCI and late MCI) and eventually to AD. Nevertheless, until recently little was known about their pathophysiology associated with cognitive-behavioral syndrome. Although for researchers, scientists and clinicians, the pathophysiology of AD spectrum is an intriguing issue, delineating it in a clear way is far from easy. Taken together, in-depth understanding of neuroimaging-based pathology behind cognitive impairments across AD spectrum may help to develop new strategy for the early diagnosis and treatment of AD.
Neuroimaging has been thought to potentially reveal the pathological mechanisms of AD progression. Individuals across AD spectrum are often associated with anatomical and functional brain alterations and cognitive impairment, most of the pathophysiology will focus primarily on the brain. To investigate brain structures and functions associated with cognition, neuroimaging will be the most appropriate tool.
In this Research Topic, we aim to collect worldwide researchers, scientists and clinicians findings on AD spectrum, especially concerning neuroimaging biomarkers and cognition. A first focus is on functional and structural neuroimaging to reveal the underlying neuronal architecture behind cognitive impairments across AD spectrum. This Research Topic could cast new light on commonalities across AD spectrum, that in turn could say something about a continuum of clinical symptoms. A second focus is on network connectivity and connectome mapping in AD spectrum using novel state-of-the-art neuroimaging analysis tools. AD has long been considered a disconnection syndrome and thus special emphasis is given to work which addresses the AD spectrum connectome including both functional and structural aspects. A third focus is on classification and prediction in AD spectrum using machine learning, pattern recognition, logistic regression, and deep learning based on neuroimaging biomarkers and cognition. Our aim for this Research Topic is to help to identify biomarkers in AD spectrum to develop new strategy for the early diagnosis and treatment of AD.
This Research Topic welcomes Original Research, Review, and Clinical Trial articles regarding neuroimaging biomarkers and cognition across AD or preclinical AD spectrum (including SCD, MCI, and AD). Studies using the following techniques and analytical methods are particularly encouraged:
• Magnetic resonance imaging (MRI) (functional and structural MRI);
• Diffusion Tensor Imaging (DTI);
• Regional homogeneity;
• Amplitude of low frequency fluctuation;
• Functional connectivity;
• Dynamic network connectivity;
• Graph theory analysis;
• Deterministic and probabilistic fiber tracking;
• Machine learning, pattern recognition, logistic regression, and deep learning in neuroimaging
biomarkers and cognition;
• The neuroimaging biomarkers for cognitive training and transcranial magnetic stimulation.
Subjective cognitive decline (SCD) with self-reported concerns and mild cognitive impairment (MCI) are well-established to be at increased risk of developing Alzheimer’s disease (AD) dementia and a clinical continuum of dementia progression as a spectrum of AD. AD may develop from SCD to MCI (early MCI and late MCI) and eventually to AD. Nevertheless, until recently little was known about their pathophysiology associated with cognitive-behavioral syndrome. Although for researchers, scientists and clinicians, the pathophysiology of AD spectrum is an intriguing issue, delineating it in a clear way is far from easy. Taken together, in-depth understanding of neuroimaging-based pathology behind cognitive impairments across AD spectrum may help to develop new strategy for the early diagnosis and treatment of AD.
Neuroimaging has been thought to potentially reveal the pathological mechanisms of AD progression. Individuals across AD spectrum are often associated with anatomical and functional brain alterations and cognitive impairment, most of the pathophysiology will focus primarily on the brain. To investigate brain structures and functions associated with cognition, neuroimaging will be the most appropriate tool.
In this Research Topic, we aim to collect worldwide researchers, scientists and clinicians findings on AD spectrum, especially concerning neuroimaging biomarkers and cognition. A first focus is on functional and structural neuroimaging to reveal the underlying neuronal architecture behind cognitive impairments across AD spectrum. This Research Topic could cast new light on commonalities across AD spectrum, that in turn could say something about a continuum of clinical symptoms. A second focus is on network connectivity and connectome mapping in AD spectrum using novel state-of-the-art neuroimaging analysis tools. AD has long been considered a disconnection syndrome and thus special emphasis is given to work which addresses the AD spectrum connectome including both functional and structural aspects. A third focus is on classification and prediction in AD spectrum using machine learning, pattern recognition, logistic regression, and deep learning based on neuroimaging biomarkers and cognition. Our aim for this Research Topic is to help to identify biomarkers in AD spectrum to develop new strategy for the early diagnosis and treatment of AD.
This Research Topic welcomes Original Research, Review, and Clinical Trial articles regarding neuroimaging biomarkers and cognition across AD or preclinical AD spectrum (including SCD, MCI, and AD). Studies using the following techniques and analytical methods are particularly encouraged:
• Magnetic resonance imaging (MRI) (functional and structural MRI);
• Diffusion Tensor Imaging (DTI);
• Regional homogeneity;
• Amplitude of low frequency fluctuation;
• Functional connectivity;
• Dynamic network connectivity;
• Graph theory analysis;
• Deterministic and probabilistic fiber tracking;
• Machine learning, pattern recognition, logistic regression, and deep learning in neuroimaging
biomarkers and cognition;
• The neuroimaging biomarkers for cognitive training and transcranial magnetic stimulation.