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  • 1
    Online Resource
    Online Resource
    Circuit Performance Classification With Active Learning Guided Sampling for Support Vector Machines
    Institute of Electrical and Electronics Engineers (IEEE) ; 2015
    In:  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems Vol. 34, No. 9 ( 2015-9), p. 1467-1480
    Details
    In: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Institute of Electrical and Electronics Engineers (IEEE), Vol. 34, No. 9 ( 2015-9), p. 1467-1480
    Type of Medium: Online Resource
    ISSN: 0278-0070 , 1937-4151
    URL: Article
    DOI: 10.1109/TCAD.2015.2413840
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2015
    detail.hit.zdb_id: 627344-0
    detail.hit.zdb_id: 2034328-0
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  • 2
    Online Resource
    Online Resource
    Identification of digital voice biomarkers for cognitive health
    Open Exploration Publishing ; 2020
    In:  Exploration of Medicine Vol. 1, No. 6 ( 2020-12-31), p. 406-417
    Details
    In: Exploration of Medicine, Open Exploration Publishing, Vol. 1, No. 6 ( 2020-12-31), p. 406-417
    Abstract: Aim: Human voice contains rich information. Few longitudinal studies have been conducted to investigate the potential of voice to monitor cognitive health. The objective of this study is to identify voice biomarkers that are predictive of future dementia. Methods: Participants were recruited from the Framingham Heart Study. The vocal responses to neuropsychological tests were recorded, which were then diarized to identify participant voice segments. Acoustic features were extracted with the OpenSMILE toolkit (v2.1). The association of each acoustic feature with incident dementia was assessed by Cox proportional hazards models. Results: Our study included 6, 528 voice recordings from 4, 849 participants (mean age 63 ± 15 years old, 54.6% women). The majority of participants (71.2%) had one voice recording, 23.9% had two voice recordings, and the remaining participants (4.9%) had three or more voice recordings. Although all asymptomatic at the time of examination, participants who developed dementia tended to have shorter segments than those who were dementia free (P 〈 0.001). Additionally, 14 acoustic features were significantly associated with dementia after adjusting for multiple testing (P 〈 0.05/48 = 1 × 10–3). The most significant acoustic feature was jitterDDP_sma_de (P = 7.9 × 10–7), which represents the differential frame-to-frame Jitter. A voice based linear classifier was also built that was capable of predicting incident dementia with area under curve of 0.812. Conclusions: Multiple acoustic and linguistic features are identified that are associated with incident dementia among asymptomatic participants, which could be used to build better prediction models for passive cognitive health monitoring.
    Type of Medium: Online Resource
    ISSN: 2692-3106
    URL: Journal
    URL: Article
    DOI: 10.37349/emed
    DOI: 10.37349/emed.2020.00028
    Language: Unknown
    Publisher: Open Exploration Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 3075034-9
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  • 3
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    Table_1.DOCX
    Frontiers Media SA ; 2024
    In:  Frontiers in Neurology Vol. 15 ( 2024-2-15)
    Details
    In: Frontiers in Neurology, Frontiers Media SA, Vol. 15 ( 2024-2-15)
    Abstract: Although the growth of digital tools for cognitive health assessment, there’s a lack of known reference values and clinical implications for these digital methods. This study aims to establish reference values for digital neuropsychological measures obtained through the smartphone-based cognitive assessment application, Defense Automated Neurocognitive Assessment (DANA), and to identify clinical risk factors associated with these measures. Methods The sample included 932 cognitively intact participants from the Framingham Heart Study, who completed at least one DANA task. Participants were stratified into subgroups based on sex and three age groups. Reference values were established for digital cognitive assessments within each age group, divided by sex, at the 2.5th, 25th, 50th, 75th, and 97.5th percentile thresholds. To validate these values, 57 cognitively intact participants from Boston University Alzheimer’s Disease Research Center were included. Associations between 19 clinical risk factors and these digital neuropsychological measures were examined by a backward elimination strategy. Results Age- and sex-specific reference values were generated for three DANA tasks. Participants below 60 had median response times for the Go-No-Go task of 796 ms (men) and 823 ms (women), with age-related increases in both sexes. Validation cohort results mostly aligned with these references. Different tasks showed unique clinical correlations. For instance, response time in the Code Substitution task correlated positively with total cholesterol and diabetes, but negatively with high-density lipoprotein and low-density lipoprotein cholesterol levels, and triglycerides. Discussion This study established and validated reference values for digital neuropsychological measures of DANA in cognitively intact white participants, potentially improving their use in future clinical studies and practice.
    Type of Medium: Online Resource
    ISSN: 1664-2295
    URL: Article
    URL: Article
    DOI: 10.3389/fneur.2024.1340710
    DOI: 10.3389/fneur.2024.1340710.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2024
    detail.hit.zdb_id: 2564214-5
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  • 4
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    Datasheet1.pdf
    Frontiers Media SA ; 2023
    In:  Frontiers in Cardiovascular Medicine Vol. 10 ( 2023-4-24)
    Details
    In: Frontiers in Cardiovascular Medicine, Frontiers Media SA, Vol. 10 ( 2023-4-24)
    Abstract: People age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear. Methods This study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Δage. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Δage, and the models were adjusted for sex and chronological age. Results This study included 44,094 individuals (mean age 64 ± 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P   & lt; 0.001) and the mean Δage (absolute error of biological age and chronological age) was 9.8 ± 7.4 years. Δage was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 ± 0.11 for the healthy diet to 2.37 ± 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 ± 0.29 years of older predicted ECG-age. Conclusion In this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.
    Type of Medium: Online Resource
    ISSN: 2297-055X
    URL: Article
    URL: Article
    DOI: 10.3389/fcvm.2023.1160091
    DOI: 10.3389/fcvm.2023.1160091.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2781496-8
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  • 5
    Online Resource
    Online Resource
    Cerebral Gray and White Matter Monogalactosyl Diglyceride Levels Rise with the Progression of Alzheimer’s Disease
    IOS Press ; 2023
    In:  Journal of Alzheimer's Disease Vol. 95, No. 4 ( 2023-10-10), p. 1623-1634
    Details
    In: Journal of Alzheimer's Disease, IOS Press, Vol. 95, No. 4 ( 2023-10-10), p. 1623-1634
    Abstract: Background: Multiple studies have reported brain lipidomic abnormalities in Alzheimer’s disease (AD) that affect glycerophospholipids, sphingolipids, and fatty acids. However, there is no consensus regarding the nature of these abnormalities, and it is unclear if they relate to disease progression. Objective: Monogalactosyl diglycerides (MGDGs) are a class of lipids which have been recently detected in the human brain. We sought to measure their levels in postmortem human brain and determine if these levels correlate with the progression of the AD-related traits. Methods: We measured MGDGs by ultrahigh performance liquid chromatography tandem mass spectrometry in postmortem dorsolateral prefrontal cortex gray matter and subcortical corona radiata white matter samples derived from three cohorts of participants: the Framingham Heart Study, the Boston University Alzheimer’s Disease Research Center, and the Arizona Study of Aging and Neurodegenerative Disorders/Brain and Body Donation Program (total n = 288). Results: We detected 40 molecular species of MGDGs (including diacyl and alkyl/acyl compounds) and found that the levels of 29 of them, as well as total MGDG levels, are positively associated with AD-related traits including pathologically confirmed AD diagnosis, clinical dementia rating, Braak and Braak stage, neuritic plaque score, phospho-Tau AT8 immunostaining density, levels of phospho-Tau396 and levels of Aβ40. Increased MGDG levels were present in both gray and white matter, indicating that they are widespread and likely associated with myelin-producing oligodendrocytes—the principal cell type of white matter. Conclusions: Our data implicate the MGDG metabolic defect as a central correlate of clinical and pathological progression in AD.
    Type of Medium: Online Resource
    ISSN: 1387-2877 , 1875-8908
    URL: Article
    DOI: 10.3233/JAD-230543
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2023
    detail.hit.zdb_id: 2070772-1
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  • 6
    Online Resource
    Online Resource
    Assessment of the Mid-Life Demographic and Lifestyle Risk Factors of Dementia Using Data from the Framingham Heart Study Offspring Cohort
    IOS Press ; 2018
    In:  Journal of Alzheimer's Disease Vol. 63, No. 3 ( 2018-05-08), p. 1119-1127
    Details
    In: Journal of Alzheimer's Disease, IOS Press, Vol. 63, No. 3 ( 2018-05-08), p. 1119-1127
    Type of Medium: Online Resource
    ISSN: 1387-2877 , 1875-8908
    URL: Article
    DOI: 10.3233/JAD-170917
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2018
    detail.hit.zdb_id: 2070772-1
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  • 7
    Online Resource
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    Image_1.pdf
    Frontiers Media SA ; 2018
    In:  Frontiers in Pharmacology Vol. 9 ( 2018-4-24)
    Details
    In: Frontiers in Pharmacology, Frontiers Media SA, Vol. 9 ( 2018-4-24)
    Type of Medium: Online Resource
    ISSN: 1663-9812
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fphar.2018.00207
    DOI: 10.3389/fphar.2018.00207.s001
    DOI: 10.3389/fphar.2018.00207.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2018
    detail.hit.zdb_id: 2587355-6
    SSG: 15,3
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  • 8
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    Online Resource
    An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test
    IOS Press ; 2021
    In:  Journal of Alzheimer's Disease Vol. 83, No. 2 ( 2021-09-14), p. 581-589
    Details
    In: Journal of Alzheimer's Disease, IOS Press, Vol. 83, No. 2 ( 2021-09-14), p. 581-589
    Abstract: Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.
    Type of Medium: Online Resource
    ISSN: 1387-2877 , 1875-8908
    URL: Article
    DOI: 10.3233/JAD-210299
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2021
    detail.hit.zdb_id: 2070772-1
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  • 9
    Online Resource
    Online Resource
    Multimodal Machine Learning for 10-Year Dementia Risk Prediction: The Framingham Heart Study
    IOS Press ; 2023
    In:  Journal of Alzheimer's Disease ( 2023-09-21), p. 1-10
    Details
    In: Journal of Alzheimer's Disease, IOS Press, ( 2023-09-21), p. 1-10
    Abstract: Background: Early prediction of dementia risk is crucial for effective interventions. Given the known etiologic heterogeneity, machine learning methods leveraging multimodal data, such as clinical manifestations, neuroimaging biomarkers, and well-documented risk factors, could predict dementia more accurately than single modal data. Objective: This study aims to develop machine learning models that capitalize on neuropsychological (NP) tests, magnetic resonance imaging (MRI) measures, and clinical risk factors for 10-year dementia prediction. Methods: This study included participants from the Framingham Heart Study, and various data modalities such as NP tests, MRI measures, and demographic variables were collected. CatBoost was used with Optuna hyperparameter optimization to create prediction models for 10-year dementia risk using different combinations of data modalities. The contribution of each modality and feature for the prediction task was also quantified using Shapley values. Results: This study included 1,031 participants with normal cognitive status at baseline (age 75±5 years, 55.3% women), of whom 205 were diagnosed with dementia during the 10-year follow-up. The model built on three modalities demonstrated the best dementia prediction performance (AUC 0.90±0.01) compared to single modality models (AUC range: 0.82–0.84). MRI measures contributed most to dementia prediction (mean absolute Shapley value: 3.19), suggesting the necessity of multimodal inputs. Conclusion: This study shows that a multimodal machine learning framework had a superior performance for 10-year dementia risk prediction. The model can be used to increase vigilance for cognitive deterioration and select high-risk individuals for early intervention and risk management.
    Type of Medium: Online Resource
    ISSN: 1387-2877 , 1875-8908
    URL: Article
    DOI: 10.3233/JAD-230496
    Language: Unknown
    Publisher: IOS Press
    Publication Date: 2023
    detail.hit.zdb_id: 2070772-1
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  • 10
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    Online Resource
    Matching science to reality: how to deploy a participant-driven digital brain health platform
    Frontiers Media SA ; 2023
    In:  Frontiers in Dementia Vol. 2 ( 2023-5-5)
    Details
    In: Frontiers in Dementia, Frontiers Media SA, Vol. 2 ( 2023-5-5)
    Abstract: Advances in digital technologies for health research enable opportunities for digital phenotyping of individuals in research and clinical settings. Beyond providing opportunities for advanced data analytics with data science and machine learning approaches, digital technologies offer solutions to several of the existing barriers in research practice that have resulted in biased samples. Methods A participant-driven, precision brain health monitoring digital platform has been introduced to two longitudinal cohort studies, the Boston University Alzheimer's Disease Research Center (BU ADRC) and the Bogalusa Heart Study (BHS). The platform was developed with prioritization of digital data in native format, multiple OS, validity of derived metrics, feasibility and usability. A platform including nine remote technologies and three staff-guided digital assessments has been introduced in the BU ADRC population, including a multimodal smartphone application also introduced to the BHS population. Participants select which technologies they would like to use and can manipulate their personal platform and schedule over time. Results Participants from the BU ADRC are using an average of 5.9 technologies to date, providing strong evidence for the usability of numerous digital technologies in older adult populations. Broad phenotyping of both cohorts is ongoing, with the collection of data spanning cognitive testing, sleep, physical activity, speech, motor activity, cardiovascular health, mood, gait, balance, and more. Several challenges in digital phenotyping implementation in the BU ADRC and the BHS have arisen, and the protocol has been revised and optimized to minimize participant burden while sustaining participant contact and support. Discussion The importance of digital data in its native format, near real-time data access, passive participant engagement, and availability of technologies across OS has been supported by the pattern of participant technology use and adherence across cohorts. The precision brain health monitoring platform will be iteratively adjusted and improved over time. The pragmatic study design enables multimodal digital phenotyping of distinct clinically characterized cohorts in both rural and urban U.S. settings.
    Type of Medium: Online Resource
    ISSN: 2813-3919
    URL: Article
    DOI: 10.3389/frdem.2023.1135451
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
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