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Artikel: Heterogeneity in Alzheimer's Disease and Related Dementias.

Au, Rhoda

Advances in geriatric medicine and research

2019 Band 1

Sprache	Englisch
Erscheinungsdatum	2019-08-19
Erscheinungsland	England
Dokumenttyp	Journal Article
DOI	10.20900/agmr20190010
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: When wrong answers lead us down the right path.

Thomas, Kelsey R / Au, Rhoda

International psychogeriatrics

2021 Band 34, Heft 11, Seite(n) 959–961

Sprache	Englisch
Erscheinungsdatum	2021-10-01
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	1038825-4
ISSN	1741-203X ; 1041-6102
ISSN (online)	1741-203X
ISSN	1041-6102
DOI	10.1017/S1041610221002581
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.

Lteif, Diala / Sreerama, Sandeep / Bargal, Sarah A / Plummer, Bryan A / Au, Rhoda / Kolachalama, Vijaya B

Human brain mapping

2024 Band 45, Heft 8, Seite(n) e26707

Abstract: Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from ... ...

Abstract	Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from different data generation protocols, imaging equipment, radiological artifacts, and shifts in demographic distributions. Domain generalization (DG) techniques show promise in addressing these challenges by enabling the model to learn from one or more source domains and apply this knowledge to new, unseen target domains. Here we present a framework that utilizes model interpretability to enhance the generalizability of classification models across various cohorts. We used MRI scans and clinical diagnoses from four independent cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 1821), the Framingham Heart Study (FHS, n = 304), the Australian Imaging Biomarkers & Lifestyle Study of Ageing (AIBL, n = 661), and the National Alzheimer's Coordinating Center (NACC, n = 4647). With this data, we trained a deep neural network to focus on areas of the brain identified as relevant to the disease for model training. Our approach involved training a classifier to differentiate between structural neurodegeneration in individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer's disease (AD). This was achieved by aligning class-wise attention with a unified visual saliency prior, which was computed offline for each class using all the training data. Our method not only competes with state-of-the-art approaches but also shows improved correlation with postmortem histology. This alignment with the gold standard evidence is a significant step towards validating the effectiveness of DG frameworks, paving the way for their broader application in the field.
Mesh-Begriff(e)	Humans ; Alzheimer Disease/diagnostic imaging ; Alzheimer Disease/pathology ; Aged ; Magnetic Resonance Imaging/methods ; Magnetic Resonance Imaging/standards ; Female ; Male ; Neuroimaging/methods ; Neuroimaging/standards ; Aged, 80 and over ; Cognitive Dysfunction/diagnostic imaging ; Cognitive Dysfunction/pathology ; Deep Learning ; Cohort Studies
Sprache	Englisch
Erscheinungsdatum	2024-05-27
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	1197207-5
ISSN	1097-0193 ; 1065-9471
ISSN (online)	1097-0193
ISSN	1065-9471
DOI	10.1002/hbm.26707
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Redefining and Validating Digital Biomarkers as Fluid, Dynamic Multi-Dimensional Digital Signal Patterns.

Au, Rhoda / Kolachalama, Vijaya B / Paschalidis, Ioannis C

Frontiers in digital health

2022 Band 3, Seite(n) 751629

Abstract: Digital biomarker" is a term broadly and indiscriminately applied and often limited in its conceptualization to mimic well-established biomarkers as defined and approved by regulatory agencies such as the United States Food and Drug Administration (FDA). ...

Abstract	"Digital biomarker" is a term broadly and indiscriminately applied and often limited in its conceptualization to mimic well-established biomarkers as defined and approved by regulatory agencies such as the United States Food and Drug Administration (FDA). There is a practical urgency to revisit the definition of a digital biomarker and expand it beyond current methods of identification and validation. Restricting the promise of digital technologies within the realm of currently defined biomarkers creates a missed opportunity. A whole new field of prognostic and early diagnostic digital biomarkers driven by data science and artificial intelligence can break the current cycle of high healthcare costs and low health quality that is being driven by today's chronic disease detection and treatment approaches. This new class of digital biomarkers will be dynamic and require developing new FDA approval pathways and next-generation gold standards.
Sprache	Englisch
Erscheinungsdatum	2022-01-13
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ISSN	2673-253X
ISSN (online)	2673-253X
DOI	10.3389/fdgth.2021.751629
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Au et al. Respond to "Body Mass Index and Risk of Dementia".

Au, Rhoda / Li, Jinlei / Liu, Chunyu

American journal of epidemiology

2021 Band 190, Heft 12, Seite(n) 2515–2516

Mesh-Begriff(e)	Body Mass Index ; Dementia/epidemiology ; Humans
Sprache	Englisch
Erscheinungsdatum	2021-04-08
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	2937-3
ISSN	1476-6256 ; 0002-9262
ISSN (online)	1476-6256
ISSN	0002-9262
DOI	10.1093/aje/kwab097
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: BMI decline patterns and relation to dementia risk across four decades of follow-up in the Framingham Study.

Li, Jinlei / Liu, Chunyu / Ang, Ting Fang Alvin / Au, Rhoda

Alzheimer's & dementia : the journal of the Alzheimer's Association

2022 Band 19, Heft 6, Seite(n) 2520–2527

Abstract: Background: Obesity has been associated with increased risk of dementia with several studies reporting a reverse causality, with weight loss preceding the onset of dementia.: Methods: Two thousand forty-five non-demented Framingham Offspring ... ...

Abstract	Background: Obesity has been associated with increased risk of dementia with several studies reporting a reverse causality, with weight loss preceding the onset of dementia. Methods: Two thousand forty-five non-demented Framingham Offspring participants, aged 30 to 50 years, were included to determine effect of body mass index (BMI) decline patterns from mid- to late life over a 39-year follow-up. Group-based trajectory models were used to create BMI trajectories. Results: Decreasing BMI trends were associated with higher risk of developing dementia in late life. Decliners with first early mid-life increasing and then later mid-life declining patterns of BMI were at greater increased risk of dementia compared to non-decliners (hazard ratio 3.84, 95% confidence interval 1.39-10.60). Conclusion: While patterns of decline in BMI were associated with dementia, a subgroup with a pattern of initial increasing BMI followed by declining BMI, both occurring within mid-life, appeared to be central to declining BMI-dementia association. Further validations are needed to provide robust conclusions.
Mesh-Begriff(e)	Humans ; Body Mass Index ; Dementia/etiology ; Follow-Up Studies ; Risk Factors ; Obesity/epidemiology ; Obesity/complications
Sprache	Englisch
Erscheinungsdatum	2022-12-15
Erscheinungsland	United States
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2211627-8
ISSN	1552-5279 ; 1552-5260
ISSN (online)	1552-5279
ISSN	1552-5260
DOI	10.1002/alz.12839
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.

Lteif, Diala / Sreerama, Sandeep / Bargal, Sarah A / Plummer, Bryan A / Au, Rhoda / Kolachalama, Vijaya B

medRxiv : the preprint server for health sciences

2023

Abstract: Development of deep learning models to assess the degree of cognitive impairment on magnetic resonance imaging (MRI) scans has high translational significance. Performance of such models is often affected by potential variabilities stemming from ... ...

Abstract	Development of deep learning models to assess the degree of cognitive impairment on magnetic resonance imaging (MRI) scans has high translational significance. Performance of such models is often affected by potential variabilities stemming from independent protocols for data generation, imaging equipment, radiology artifacts, and demographic distributional shifts. Domain generalization (DG) frameworks have the potential to overcome these issues by learning signal from one or more source domains that can be transferable to unseen target domains. We developed an approach that leverages model interpretability as a means to improve generalizability of classification models across multiple cohorts. Using MRI scans and clinical diagnosis obtained from four independent cohorts (Alzheimer's Disease Neuroimaging Initiative (ADNI,
Sprache	Englisch
Erscheinungsdatum	2023-09-25
Erscheinungsland	United States
Dokumenttyp	Preprint
DOI	10.1101/2023.09.22.23295984
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: DREAMER: a computational framework to evaluate readiness of datasets for machine learning.

Ahangaran, Meysam / Zhu, Hanzhi / Li, Ruihui / Yin, Lingkai / Jang, Joseph / Chaudhry, Arnav P / Farrer, Lindsay A / Au, Rhoda / Kolachalama, Vijaya B

BMC medical informatics and decision making

2024 Band 24, Heft 1, Seite(n) 152

Abstract: Background: Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the successful deployment of ML models. ...

Abstract	Background: Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the successful deployment of ML models. In this study, we introduce DREAMER (Data REAdiness for MachinE learning Research), an algorithmic framework leveraging supervised and unsupervised machine learning techniques to autonomously evaluate the suitability of tabular datasets for ML model development. DREAMER is openly accessible as a tool on GitHub and Docker, facilitating its adoption and further refinement within the research community.. Results: The proposed model in this study was applied to three distinct tabular datasets, resulting in notable enhancements in their quality with respect to readiness for ML tasks, as assessed through established data quality metrics. Our findings demonstrate the efficacy of the framework in substantially augmenting the original dataset quality, achieved through the elimination of extraneous features and rows. This refinement yielded improved accuracy across both supervised and unsupervised learning methodologies. Conclusion: Our software presents an automated framework for data readiness, aimed at enhancing the integrity of raw datasets to facilitate robust utilization within ML pipelines. Through our proposed framework, we streamline the original dataset, resulting in enhanced accuracy and efficiency within the associated ML algorithms.
Mesh-Begriff(e)	Humans ; Machine Learning ; Datasets as Topic ; Unsupervised Machine Learning ; Algorithms ; Supervised Machine Learning ; Software
Sprache	Englisch
Erscheinungsdatum	2024-06-04
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2046490-3
ISSN	1472-6947 ; 1472-6947
ISSN (online)	1472-6947
ISSN	1472-6947
DOI	10.1186/s12911-024-02544-w
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Large Language Models in Neurology Research and Future Practice.

Romano, Michael F / Shih, Ludy C / Paschalidis, Ioannis C / Au, Rhoda / Kolachalama, Vijaya B

Neurology

2023 Band 101, Heft 23, Seite(n) 1058–1067

Abstract: Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records ...

Abstract	Recent advancements in generative artificial intelligence, particularly using large language models (LLMs), are gaining increased public attention. We provide a perspective on the potential of LLMs to analyze enormous amounts of data from medical records and gain insights on specific topics in neurology. In addition, we explore use cases for LLMs, such as early diagnosis, supporting patient and caregivers, and acting as an assistant for clinicians. We point to the potential ethical and technical challenges raised by LLMs, such as concerns about privacy and data security, potential biases in the data for model training, and the need for careful validation of results. Researchers must consider these challenges and take steps to address them to ensure that their work is conducted in a safe and responsible manner. Despite these challenges, LLMs offer promising opportunities for improving care and treatment of various neurologic disorders
Mesh-Begriff(e)	Humans ; Artificial Intelligence ; Neurology ; Language ; Medical Records ; Research Personnel
Sprache	Englisch
Erscheinungsdatum	2023-12-04
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	207147-2
ISSN	1526-632X ; 0028-3878
ISSN (online)	1526-632X
ISSN	0028-3878
DOI	10.1212/WNL.0000000000207967
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Exploring cognitive progression subtypes in the Framingham Heart Study.

Ding, Huitong / Wang, Biqi / Hamel, Alexander P / Karjadi, Cody / Ang, Ting F A / Au, Rhoda / Lin, Honghuang

Alzheimer's & dementia (Amsterdam, Netherlands)

2024 Band 16, Heft 1, Seite(n) e12574

Abstract: Introduction: Alzheimer's disease (AD) is a heterogeneous disorder characterized by complex underlying neuropathology that is not fully understood. This study aimed to identify cognitive progression subtypes and examine their correlation with clinical ... ...

Abstract	Introduction: Alzheimer's disease (AD) is a heterogeneous disorder characterized by complex underlying neuropathology that is not fully understood. This study aimed to identify cognitive progression subtypes and examine their correlation with clinical outcomes. Methods: Participants of this study were recruited from the Framingham Heart Study. The Subtype and Stage Inference (SuStaIn) method was used to identify cognitive progression subtypes based on eight cognitive domains. Results: Three cognitive progression subtypes were identified, including verbal learning (Subtype 1), abstract reasoning (Subtype 2), and visual memory (Subtype 3). These subtypes represent different domains of cognitive decline during the progression of AD. Significant differences in age of onset among the different subtypes were also observed. A higher SuStaIn stage was significantly associated with increased mortality risk. Discussion: This study provides a characterization of AD heterogeneity in cognitive progression, emphasizing the importance of developing personalized approaches for risk stratification and intervention. Highlights: We used the Subtype and Stage Inference (SuStaIn) method to identify three cognitive progression subtypes.Different subtypes have significant variations in age of onset.Higher stages of progression are associated with increased mortality risk.
Sprache	Englisch
Erscheinungsdatum	2024-03-21
Erscheinungsland	United States
Dokumenttyp	Journal Article
ZDB-ID	2832898-X
ISSN	2352-8729
ISSN	2352-8729
DOI	10.1002/dad2.12574
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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