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Artificial Intelligence in Dementia Research

Alzheimer's Research & Therapy presents a thematic series focusing on the use of artificial intelligence in dementia research.

Guest Editors: James Cole1, Kerstin Ritter2 & Bin Zhang3
1 University College London; 2 Charité - Universitätsmedizin Berlin; 3 Icahn School of Medicine at Mount Sinai

Submission Deadline: 15 February 2021

Artificial Intelligence in Dementia

The aim of this series is to showcase cutting edge research using Artificial Intelligence (AI), machine (deep) learning and related techniques to study dementia.

Often inspired by models of human neural networks, AI has recently transformed the fields of computer vision, image analysis, natural language processing, automation and robotics. By harnessing recent developments in computing infrastructure and processing power, AI has set new state-of-the-art benchmarks for performance, generated new innovations in engineering and steadily become part of the mainstream of science and technology.

Amidst the hype, there is genuine excitement about the power of AI to benefit healthcare across the board, and dementia is no exception. Already AI and machine learning have improved neuroimage analysis, led to discoveries of new biological pathways and systems implicated in dementia and related diseases.

However, many critical research questions remain open, for example:

  • How to discover intrinsic patterns and mechanisms underlying massive, highly heterogeneous biological data? 
  • How to best integrate data from different modalities or different cohort studies? 
  • How to accelerate development of novel therapeutics for dementia by leveraging all existing knowledge and data? 
  • How to interpret results from deep learning? 
  • How to embed ethical considerations in intelligent machine decisions?

Answering such questions will be fundamental in the success of AI for dementia, and we hope to attract research that contributes towards this goal.

The series is organised into three broad thematic categories, Basic Mechanisms, Translational Research and Clinical Applications. We invite original research submissions from scientists using AI, machine learning or related advanced statistical methods to analyse dementia related data across all levels of biology including molecular, cellular, organismal, behavioural and population levels. 

This covers research at any scale; from microscopic, through mesoscopic to macroscopic and beyond to population level epidemiology. These can include but not limit to genetics, 'omics, cell imaging, brain imaging, cognitive assessments, smart home, wearable monitoring devices, target identification, drug discovery, and clinical decision-making for dementia and related diseases. 

For this series Alzheimer's Research & Therapy (Impact Factor: 6.116) will publish both commissioned and non-commissioned content. All submissions will be undergo peer review. Submissions of both original research and reviews will be considered, and accepted manuscripts will be published on a timely, ongoing basis.

The submission deadline for this series has now passed. Articles will continue to publish as part of this series as they are available throughout 2021.

  1. Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clini...

    Authors: Martin Dyrba, Moritz Hanzig, Slawek Altenstein, Sebastian Bader, Tommaso Ballarini, Frederic Brosseron, Katharina Buerger, Daniel Cantré, Peter Dechent, Laura Dobisch, Emrah Düzel, Michael Ewers, Klaus Fliessbach, Wenzel Glanz, John-Dylan Haynes, Michael T. Heneka…

    Citation: Alzheimer's Research & Therapy 2021 13:191

    Content type: Research

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  2. An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging...

    Authors: Sergio Grueso and Raquel Viejo-Sobera

    Citation: Alzheimer's Research & Therapy 2021 13:162

    Content type: Research

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  3. For the recruitment and monitoring of subjects for therapy studies, it is important to predict whether mild cognitive impaired (MCI) subjects will prospectively develop Alzheimer’s disease (AD). Machine learni...

    Authors: Louise Bloch and Christoph M. Friedrich

    Citation: Alzheimer's Research & Therapy 2021 13:155

    Content type: Research

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  4. In Alzheimer’s disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively ...

    Authors: Hákon Valur Dansson, Lena Stempfle, Hildur Egilsdóttir, Alexander Schliep, Erik Portelius, Kaj Blennow, Henrik Zetterberg and Fredrik D. Johansson

    Citation: Alzheimer's Research & Therapy 2021 13:151

    Content type: Research

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  5. Identification of reliable, affordable, and easy-to-use strategies for detection of dementia is sorely needed. Digital technologies, such as individual voice recordings, offer an attractive modality to assess ...

    Authors: Chonghua Xue, Cody Karjadi, Ioannis Ch. Paschalidis, Rhoda Au and Vijaya B. Kolachalama

    Citation: Alzheimer's Research & Therapy 2021 13:146

    Content type: Research

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  6. Blood circulating microRNAs that are specific for Alzheimer’s disease (AD) can be identified from differentially expressed microRNAs (DEmiRNAs). However, non-reproducible and inconsistent reports of DEmiRNAs h...

    Authors: Sze Chung Yuen, Xiaonan Liang, Hongmei Zhu, Yongliang Jia and Siu-wai Leung

    Citation: Alzheimer's Research & Therapy 2021 13:126

    Content type: Research

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  7. Language impairment is an important marker of neurodegenerative disorders. Despite this, there is no universal system of terminology used to describe these impairments and large inter-rater variability can exi...

    Authors: Anthony Yeung, Andrea Iaboni, Elizabeth Rochon, Monica Lavoie, Calvin Santiago, Maria Yancheva, Jekaterina Novikova, Mengdan Xu, Jessica Robin, Liam D. Kaufman and Fariya Mostafa

    Citation: Alzheimer's Research & Therapy 2021 13:109

    Content type: Research

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  8. Alzheimer’s disease (AD) is a progressive and irreversible brain disorder. Hippocampus is one of the involved regions and its atrophy is a widely used biomarker for AD diagnosis. We have recently developed Den...

    Authors: Sreevani Katabathula, Qinyong Wang and Rong Xu

    Citation: Alzheimer's Research & Therapy 2021 13:104

    Content type: Research

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  9. Identifying novel therapeutic targets is crucial for the successful development of drugs. However, the cost to experimentally identify therapeutic targets is huge and only approximately 400 genes are targets f...

    Authors: Shingo Tsuji, Takeshi Hase, Ayako Yachie-Kinoshita, Taiko Nishino, Samik Ghosh, Masataka Kikuchi, Kazuro Shimokawa, Hiroyuki Aburatani, Hiroaki Kitano and Hiroshi Tanaka

    Citation: Alzheimer's Research & Therapy 2021 13:92

    Content type: Research

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  10. Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magneti...

    Authors: Xiao Zhou, Shangran Qiu, Prajakta S. Joshi, Chonghua Xue, Ronald J. Killiany, Asim Z. Mian, Sang P. Chin, Rhoda Au and Vijaya B. Kolachalama

    Citation: Alzheimer's Research & Therapy 2021 13:60

    Content type: Research

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  11. Recent DNA/RNA sequencing and other multi-omics technologies have advanced the understanding of the biology and pathophysiology of AD, yet there is still a lack of disease-modifying treatments for AD. A new ap...

    Authors: Yadi Zhou, Jiansong Fang, Lynn M. Bekris, Young Heon Kim, Andrew A. Pieper, James B. Leverenz, Jeffrey Cummings and Feixiong Cheng

    Citation: Alzheimer's Research & Therapy 2021 13:24

    Content type: Research

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