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  • 1
    Online Resource
    Online Resource
    Identification of cognitive resilience through neuropsychological tests in the Framingham Heart Study
    Wiley ; 2022
    In:  Alzheimer's & Dementia Vol. 18, No. S7 ( 2022-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S7 ( 2022-12)
    Abstract: To examine the predictive abilities of 1) traditional neuropsychological (NP) test summary scores, and 2) error and process scores in separating cognitively resilient individuals from those who exhibit cognitive decline. Method A total of 69 participants from the Framingham Heart Study (FHS) Offspring and Omni 1 cohorts who had ≥1 NP assessment from 2011–2014 and at high risk for dementia were included in the sample. Participants also had to be ≥60 years of age and dementia‐free at the time of NP assessment. Being at high risk for dementia was determined using a cutoff score of ≥23 based on a published FHS dementia risk score algorithm. Dementia diagnosis was adjudicated based on DSM‐IV criteria by consensus. Participants completed 10 NP tests, which were scored using 11 traditional scores and 75 error and process scores. Supervised machine learning algorithms were used to classify individuals who were cognitively resilient, defined as not progressing to dementia within ≥5 years after the NP evaluation. Result Among the 69 participants in our sample, 10 were cognitively resilient while 59 developed dementia. Average area under the curve when using traditional NP metrics to predict cognitive resilience ranged from 90.5% to 95.7% across each supervised machine learning algorithm (logistic regression, random forest, support vector machines, and light gradient boosting machine) (Figure 1). Average accuracy ranged from 85.3% to 92.6%, while average sensitivity was 100% across all models and average specificity ranged from 82.7% to 91.4% (Table 1). Average area under the curve when using the error and process scores to predict cognitive resilience ranged from 93.2% to 98.3% across each supervised machine learning algorithm (Figure 2). Average accuracy ranged from 89.9% to 97.0%, while average sensitivity was also 100% across all models and average specificity ranged from 88.2% to 96.5% (Table 2). All performance metrics were calculated based on five‐fold cross‐validation. Conclusion Results from this pilot study suggest that use of NP tests may serve as an accurate method to identify individuals who are cognitive resilient, allowing for the study of genetic, lifestyle, and other factors that confer resilience among individuals with an elevated risk for dementia.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v18.S7
    DOI: 10.1002/alz.059082
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2201940-6
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  • 2
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    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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  • 3
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    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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  • 4
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    Remote diagnosis of dementia using AI methods on clock drawing images
    Wiley ; 2021
    In:  Alzheimer's & Dementia Vol. 17, No. S6 ( 2021-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 17, No. S6 ( 2021-12)
    Abstract: Detection of any form of cognitive impairment is challenging and the subjects have to undergo numerous evaluations and clinical tests. Hence, it would be of great importance to design a reliable and accessible procedure by which patients may get diagnosed for dementia remotely. The capability of the Clock Drawing Test (CDT)as an effective cognitive assessment tool has motivated us to develop an online diagnostic tool by leveraging artificial intelligence techniques. Method Digital pen recordings of 3,263 normal subjects and 160 with dementia in the Framingham Heart Study (FHS) were collected, where all subjects have completed two analog clock drawings, one drawn on command and the other by copying. Using the idea of transfer learning, we first modified and trained a Convolutional Neural Network (CNN) pre‐trained on the ImageNet dataset to extract high level features of the CDT images, which generated a score associated with the likelihood of dementia for each patient. The proposed method integrates the scores of the CDT images and other demographic information. Therefore, the generated scores for both command and copy CDTs along with age were used to train a logistic regression model to classify individuals as demented or normal. Result We have evaluated the performance of the developed models by applying 5‐fold cross validation on the FHS dataset. On the test dataset, the model (modified pre‐trained CNN) based on command CDT images yielded an AUC of 0.81±0.043. The logistic regression model using age and the generated scores of command and copy CDTs, yielded an average AUC and average F1 score of 0.92±0.008 and 0.94±0.008, respectively. Conclusion Our method need not necessarily have access to digital biomarkers or clinical tests since the CDT can be completed using pen and paper, capturing the image using a smartphone. Hence, our method offers a cost‐effective and accurate screening tool to diagnose dementia and related diseases remotely.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v17.S6
    DOI: 10.1002/alz.051911
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2201940-6
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  • 5
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    Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification
    Oxford University Press (OUP) ; 2020
    In:  Brain Vol. 143, No. 6 ( 2020-06-01), p. 1920-1933
    Details
    In: Brain, Oxford University Press (OUP), Vol. 143, No. 6 ( 2020-06-01), p. 1920-1933
    Abstract: Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer’s disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer’s disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer’s disease and cognitively normal subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer’s Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer’s disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.
    Type of Medium: Online Resource
    ISSN: 0006-8950 , 1460-2156
    URL: Article
    DOI: 10.1093/brain/awaa137
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1474117-9
    SSG: 12
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  • 6
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    Global challenges in cognitive assessment: How technology will drive neuropsychologists to the forefront : Using digital technology to drive clinical innovation in brain‐behavior relationships: A new era in neuropsychology
    Wiley ; 2020
    In:  Alzheimer's & Dementia Vol. 16, No. S6 ( 2020-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 16, No. S6 ( 2020-12)
    Abstract: Age remains the strongest risk factor for Alzheimer’s disease (AD). With the number of people over 65 now outnumbering those under the age of 5, the global burden of AD is rapidly increasing. Cognitive decline is the hallmark behavioral symptom of AD. However, accurate cognitive assessment requires consideration of a myriad of factors, including education, language, and culture. In fact, in a majority of low‐to‐middle income countries, as well as many emerging economies, there is no formalized doctoral training in neuropsychology. These circumstances necessitate embracing novel data collection methods in order to preserve the voice of the neuropsychologist. Technological advances can provide the tools to address the global realities of AD and other related disorders. Method We have developed a brain health monitoring platform that leverages the relatively ubiquitous use of smartphones across virtually all countries and collects behavioral measures that are inextricably linked to cognitive skills. We are applying machine learning approaches to quantify digital indices into cognitive ones. Importantly, we have strategically selected mobile applications that measure behaviors that are 1) common regardless of age, education, language and culture and 2) can be flexibly customized to circumstances. Result We have begun deploying versions of this high tech‐driven platform in the U.S. Further, we are starting to have an impact internationally, particularly in Asia. The health and age demographics in China make its population particularly vulnerable to AD. We present findings that speak to this risk and how our platform, and others like it, will help fill the neuropsychological expertise gap that such countries are currently experiencing. Conclusion AD is a rising epidemic. Early and accurate detection is the first step to finding disease modifying interventions and providing the context necessary for maximized efficacy. The coupling of professional neuropsychology skills and digital technology will generate solutions that can sufficiently address the impending challenge of conducting cognitive assessments universally. This potentially includes resolving longstanding health disparities by extending neuropsychologists’ reach into regions with significant economic and/or geographical constraints.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v16.S6
    DOI: 10.1002/alz.038142
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 2201940-6
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  • 7
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    Detection of Dementia on Raw Voice Recordings Using Deep Learning: A Framingham Heart Study
    Elsevier BV ; 2021
    In:  SSRN Electronic Journal
    Details
    In: SSRN Electronic Journal, Elsevier BV
    Type of Medium: Online Resource
    ISSN: 1556-5068
    URL: Article
    DOI: 10.2139/ssrn.3788945
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
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  • 8
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    Association Between the Digital Clock Drawing Test and Neuropsychological Test Performance: Large Community-Based Prospective Cohort (Framingham Heart Study)
    JMIR Publications Inc. ; 2021
    In:  Journal of Medical Internet Research Vol. 23, No. 6 ( 2021-6-8), p. e27407-
    Details
    In: Journal of Medical Internet Research, JMIR Publications Inc., Vol. 23, No. 6 ( 2021-6-8), p. e27407-
    Abstract: The Clock Drawing Test (CDT) has been widely used in clinic for cognitive assessment. Recently, a digital Clock Drawing Text (dCDT) that is able to capture the entire sequence of clock drawing behaviors was introduced. While a variety of domain-specific features can be derived from the dCDT, it has not yet been evaluated in a large community-based population whether the features derived from the dCDT correlate with cognitive function. Objective We aimed to investigate the association between dCDT features and cognitive performance across multiple domains. Methods Participants from the Framingham Heart Study, a large community-based cohort with longitudinal cognitive surveillance, who did not have dementia were included. Participants were administered both the dCDT and a standard protocol of neuropsychological tests that measured a wide range of cognitive functions. A total of 105 features were derived from the dCDT, and their associations with 18 neuropsychological tests were assessed with linear regression models adjusted for age and sex. Associations between a composite score from dCDT features were also assessed for associations with each neuropsychological test and cognitive status (clinically diagnosed mild cognitive impairment compared to normal cognition). Results The study included 2062 participants (age: mean 62, SD 13 years, 51.6% women), among whom 36 were diagnosed with mild cognitive impairment. Each neuropsychological test was associated with an average of 50 dCDT features. The composite scores derived from dCDT features were significantly associated with both neuropsychological tests and mild cognitive impairment. Conclusions The dCDT can potentially be used as a tool for cognitive assessment in large community-based populations.
    Type of Medium: Online Resource
    ISSN: 1438-8871
    URL: Article
    DOI: 10.2196/27407
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2021
    detail.hit.zdb_id: 2028830-X
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  • 9
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    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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  • 10
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    An AI‐based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments
    Wiley ; 2022
    In:  Alzheimer's & Dementia Vol. 18, No. S7 ( 2022-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S7 ( 2022-12)
    Abstract: Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide‐spread screening for dementia and accelerate preventative efforts. Method We used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub‐tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI. Result We evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held‐out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub‐tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task. Conclusion The proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develop a remote screening tool that could be easily adapted to any language.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v18.S7
    DOI: 10.1002/alz.064029
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2201940-6
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