Russel Banks, Connor Higgins, Barry R. Greene, Ali Jannati, J. Gomes-Osman, Sean E Tobyne, David Bates, Alvaro Pascual‐Leone
Abstract INTRODUCTION Early detection of Alzheimer's disease and cognitive impairment is critical to improving the healthcare trajectories of aging adults, enabling early intervention and potential prevention of decline. METHODS To evaluate multi‐modal feature sets for assessing memory and cognitive impairment, feature selection and subsequent logistic regressions were used to identify the most salient features in classifying Rey Auditory Verbal Learning Test‐determined memory impairment. RESULTS Multimodal models incorporating graphomotor, memory, and speech and voice features provided the stronger classification performance (area under the curve = 0.83; sensitivity = 0.81, specificity = 0.80). Multimodal models were superior to all other single modality and demographics models. DISCUSSION The current research contributes to the prevailing multimodal profile of those with cognitive impairment, suggesting that it is associated with slower speech with a particular effect on the duration, frequency, and percentage of pauses compared to normal healthy speech.
{"title":"Clinical classification of memory and cognitive impairment with multimodal digital biomarkers","authors":"Russel Banks, Connor Higgins, Barry R. Greene, Ali Jannati, J. Gomes-Osman, Sean E Tobyne, David Bates, Alvaro Pascual‐Leone","doi":"10.1002/dad2.12557","DOIUrl":"https://doi.org/10.1002/dad2.12557","url":null,"abstract":"Abstract INTRODUCTION Early detection of Alzheimer's disease and cognitive impairment is critical to improving the healthcare trajectories of aging adults, enabling early intervention and potential prevention of decline. METHODS To evaluate multi‐modal feature sets for assessing memory and cognitive impairment, feature selection and subsequent logistic regressions were used to identify the most salient features in classifying Rey Auditory Verbal Learning Test‐determined memory impairment. RESULTS Multimodal models incorporating graphomotor, memory, and speech and voice features provided the stronger classification performance (area under the curve = 0.83; sensitivity = 0.81, specificity = 0.80). Multimodal models were superior to all other single modality and demographics models. DISCUSSION The current research contributes to the prevailing multimodal profile of those with cognitive impairment, suggesting that it is associated with slower speech with a particular effect on the duration, frequency, and percentage of pauses compared to normal healthy speech.","PeriodicalId":516929,"journal":{"name":"Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring","volume":"25 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dave Evenden, A. Prosser, S. Michopoulou, Christopher Kipps
Abstract BACKGROUND The Alzheimer's Disease COMposite Score (ADCOMS) is more sensitive in clinical trials than conventional measures when assessing pre‐dementia. This study compares ADCOMS trajectories using clustered progression characteristics to better understand different patterns of decline. METHODS Post‐baseline ADCOMS values were analyzed for sensitivity using mean‐to‐standard deviation ratio (MSDR), partitioned by baseline diagnosis, comparing with the original scales upon which ADCOMS is based. Because baseline diagnosis was not a particularly reliable predictor of progression, individuals were also grouped into similar ADCOMS progression trajectories using clustering methods and the MSDR compared for each progression group. RESULTS ADCOMS demonstrated increased sensitivity for clinically important progression groups. ADCOMS did not show statistically significant sensitivity or clinical relevance for the less‐severe baseline diagnoses and marginal progression groups. CONCLUSIONS This analysis complements and extends previous work validating the sensitivity of ADCOMS. The large data set permitted evaluation–in a novel approach–by the clustered progression group.
{"title":"ADCOMS sensitivity versus baseline diagnosis and progression phenotypes","authors":"Dave Evenden, A. Prosser, S. Michopoulou, Christopher Kipps","doi":"10.1002/dad2.12540","DOIUrl":"https://doi.org/10.1002/dad2.12540","url":null,"abstract":"Abstract BACKGROUND The Alzheimer's Disease COMposite Score (ADCOMS) is more sensitive in clinical trials than conventional measures when assessing pre‐dementia. This study compares ADCOMS trajectories using clustered progression characteristics to better understand different patterns of decline. METHODS Post‐baseline ADCOMS values were analyzed for sensitivity using mean‐to‐standard deviation ratio (MSDR), partitioned by baseline diagnosis, comparing with the original scales upon which ADCOMS is based. Because baseline diagnosis was not a particularly reliable predictor of progression, individuals were also grouped into similar ADCOMS progression trajectories using clustering methods and the MSDR compared for each progression group. RESULTS ADCOMS demonstrated increased sensitivity for clinically important progression groups. ADCOMS did not show statistically significant sensitivity or clinical relevance for the less‐severe baseline diagnoses and marginal progression groups. CONCLUSIONS This analysis complements and extends previous work validating the sensitivity of ADCOMS. The large data set permitted evaluation–in a novel approach–by the clustered progression group.","PeriodicalId":516929,"journal":{"name":"Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring","volume":"31 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nazib Seidu, S. Kern, S. Sacuiu, T. R. Sterner, K. Blennow, Henrik Zetterberg, O. Lindberg, D. Ferreira, Eric Westman, A. Zettergren, Ingmar Skoog
Abstract The relation between cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) and magnetic resonance imaging (MRI) measures is poorly understood in cognitively healthy individuals from the general population. Participants’ (n = 226) mean age was 70.9 years (SD = 0.4). CSF concentrations of amyloid beta (Aβ)1‐42, total tau (t‐tau), phosphorylated tau (p‐tau), neurogranin, and neurofilament light, and volumes of hippocampus, amygdala, total basal forebrain (TBF), and cortical thickness were measured. Linear associations between CSF biomarkers and MRI measures were investigated. In Aβ1‐42 positives, higher t‐tau and p‐tau were associated with smaller hippocampus (P = 0.001 and P = 0.003) and amygdala (P = 0.005 and P = 0.01). In Aβ1‐42 negatives, higher t‐tau, p‐tau, and neurogranin were associated with larger TBF volume (P = 0.001, P = 0.001, and P = 0.01). No associations were observed between the CSF biomarkers and an AD signature score of cortical thickness. AD‐specific biomarkers in cognitively healthy 70‐year‐olds may be related to TBF, hippocampus, and amygdala. Lack of association with cortical thickness might be due to early stage of disease.
{"title":"Association of CSF biomarkers with MRI brain changes in Alzheimer's disease","authors":"Nazib Seidu, S. Kern, S. Sacuiu, T. R. Sterner, K. Blennow, Henrik Zetterberg, O. Lindberg, D. Ferreira, Eric Westman, A. Zettergren, Ingmar Skoog","doi":"10.1002/dad2.12556","DOIUrl":"https://doi.org/10.1002/dad2.12556","url":null,"abstract":"Abstract The relation between cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) and magnetic resonance imaging (MRI) measures is poorly understood in cognitively healthy individuals from the general population. Participants’ (n = 226) mean age was 70.9 years (SD = 0.4). CSF concentrations of amyloid beta (Aβ)1‐42, total tau (t‐tau), phosphorylated tau (p‐tau), neurogranin, and neurofilament light, and volumes of hippocampus, amygdala, total basal forebrain (TBF), and cortical thickness were measured. Linear associations between CSF biomarkers and MRI measures were investigated. In Aβ1‐42 positives, higher t‐tau and p‐tau were associated with smaller hippocampus (P = 0.001 and P = 0.003) and amygdala (P = 0.005 and P = 0.01). In Aβ1‐42 negatives, higher t‐tau, p‐tau, and neurogranin were associated with larger TBF volume (P = 0.001, P = 0.001, and P = 0.01). No associations were observed between the CSF biomarkers and an AD signature score of cortical thickness. AD‐specific biomarkers in cognitively healthy 70‐year‐olds may be related to TBF, hippocampus, and amygdala. Lack of association with cortical thickness might be due to early stage of disease.","PeriodicalId":516929,"journal":{"name":"Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring","volume":"12 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Katharina Bolsewig, Hester Blok, E. Willemse, Rob B. M. Groot Zwaaftink, Minke Kooistra, Ellen M. A. Smets, Charlotte E. Teunissen, Leonie N. C. Visser
Abstract INTRODUCTION We aimed to evaluate informal caregivers’ attitudes toward undergoing and future implementation of blood‐based biomarkers (BBBM) testing for Alzheimer's disease (AD). METHODS We explored caregivers’ perspectives, by combining an online survey (n = 107) with a subsequent focus group (n = 7). We used descriptive statistics and thematic content analysis to identify common themes in answers to open‐ended survey questions and focus group data. RESULTS Most caregivers (72.0%) favored BBBM for AD diagnosis. Provided with hypothetical scenarios, confidence in a normal result decreased significantly if experienced symptoms were more severe (mild: 78.5% vs. severe: 48.6%). Caregivers’ attitudes toward BBBM for screening purposes significantly improved with prospect of treatment (53.3% vs. 92.5%). Concerns toward BBBM testing included treatment unavailability, increased/prolonged distress, and AD‐related stigma. Potential benefits were actionability, explanation for symptoms, and opportunities for better care and future treatment. DISCUSSION Emerging AD treatment and reduction of AD‐related stigma could profoundly increase public interest in BBBM testing for AD. Highlights Most informal caregivers would want blood‐based biomarker (BBBM) testing for Alzheimer's disease (AD) diagnosis. Perceived (dis)advantages were related to diagnosing AD early. With severe symptoms, there was less confidence in normal BBBM results. Treatment availability would significantly increase interest in BBBM testing for AD. Informal caregivers showed uncertainty regarding the meaning of the term “AD.”
{"title":"Caregivers’ attitudes toward blood‐based biomarker testing for Alzheimer's disease","authors":"Katharina Bolsewig, Hester Blok, E. Willemse, Rob B. M. Groot Zwaaftink, Minke Kooistra, Ellen M. A. Smets, Charlotte E. Teunissen, Leonie N. C. Visser","doi":"10.1002/dad2.12549","DOIUrl":"https://doi.org/10.1002/dad2.12549","url":null,"abstract":"Abstract INTRODUCTION We aimed to evaluate informal caregivers’ attitudes toward undergoing and future implementation of blood‐based biomarkers (BBBM) testing for Alzheimer's disease (AD). METHODS We explored caregivers’ perspectives, by combining an online survey (n = 107) with a subsequent focus group (n = 7). We used descriptive statistics and thematic content analysis to identify common themes in answers to open‐ended survey questions and focus group data. RESULTS Most caregivers (72.0%) favored BBBM for AD diagnosis. Provided with hypothetical scenarios, confidence in a normal result decreased significantly if experienced symptoms were more severe (mild: 78.5% vs. severe: 48.6%). Caregivers’ attitudes toward BBBM for screening purposes significantly improved with prospect of treatment (53.3% vs. 92.5%). Concerns toward BBBM testing included treatment unavailability, increased/prolonged distress, and AD‐related stigma. Potential benefits were actionability, explanation for symptoms, and opportunities for better care and future treatment. DISCUSSION Emerging AD treatment and reduction of AD‐related stigma could profoundly increase public interest in BBBM testing for AD. Highlights Most informal caregivers would want blood‐based biomarker (BBBM) testing for Alzheimer's disease (AD) diagnosis. Perceived (dis)advantages were related to diagnosing AD early. With severe symptoms, there was less confidence in normal BBBM results. Treatment availability would significantly increase interest in BBBM testing for AD. Informal caregivers showed uncertainty regarding the meaning of the term “AD.”","PeriodicalId":516929,"journal":{"name":"Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring","volume":"321 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. G. Amato, A. A. Vergani, M. Lassi, C. Fabbiani, S. Mazzeo, R. Burali, B. Nacmias, S. Sorbi, R. Mannella, A. Grippo, V. Bessi, A. Mazzoni
Abstract INTRODUCTION Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern. METHODS We developed a cortical model of AD‐related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. RESULTS Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50). DISCUSSION Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non‐invasive EEG recordings. Highlights Personalized cortical model estimating structural alterations from EEG recordings. Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%) Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%) Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y
摘要 引言 早期识别阿尔茨海默病（AD）对于及时开始治疗非常必要。然而，与阿尔茨海默病相关的皮质结构改变却很难辨别。方法 我们建立了一个与阿兹海默病相关的皮层神经变性模型，该模型考虑了局部动力学减缓和全局连通性退化。在一项单中心研究中，我们收集了健康（HC，n = 17）、主观认知能力下降（SCD，n = 58）和轻度认知障碍（MCI，n = 44）患者静息状态下的脑电图（EEG）记录。我们根据每个患者的脑电图记录估算了神经变性模型参数。结果 我们的模型不仅在区分HC和MCI情况（F1得分0.95 vs 0.75）方面优于标准脑电图分析，而且在识别脑脊液中具有AD生物学特征的SCD患者（召回率0.87 vs 0.50）方面也优于标准脑电图分析。讨论 个性化模型可（1）支持 MCI 分类，（2）评估是否存在 AD 病理，以及（3）仅根据经济和无创的脑电图记录来估计认知能力下降的风险。亮点 通过脑电图记录估计结构改变的个性化皮质模型。鉴别轻度认知功能障碍（MCI）和健康（HC）受试者（95%） 预测主观衰退（SCD）受试者的阿尔茨海默氏症生物标记物（87%） 1 年后从 SCD 转为 MCI 的 3/3 受试者的转归预测正确
{"title":"Personalized modeling of Alzheimer's disease progression estimates neurodegeneration severity from EEG recordings","authors":"L. G. Amato, A. A. Vergani, M. Lassi, C. Fabbiani, S. Mazzeo, R. Burali, B. Nacmias, S. Sorbi, R. Mannella, A. Grippo, V. Bessi, A. Mazzoni","doi":"10.1002/dad2.12526","DOIUrl":"https://doi.org/10.1002/dad2.12526","url":null,"abstract":"Abstract INTRODUCTION Early identification of Alzheimer's disease (AD) is necessary for a timely onset of therapeutic care. However, cortical structural alterations associated with AD are difficult to discern. METHODS We developed a cortical model of AD‐related neurodegeneration accounting for slowing of local dynamics and global connectivity degradation. In a monocentric study we collected electroencephalography (EEG) recordings at rest from participants in healthy (HC, n = 17), subjective cognitive decline (SCD, n = 58), and mild cognitive impairment (MCI, n = 44) conditions. For each patient, we estimated neurodegeneration model parameters based on individual EEG recordings. RESULTS Our model outperformed standard EEG analysis not only in discriminating between HC and MCI conditions (F1 score 0.95 vs 0.75) but also in identifying SCD patients with biological hallmarks of AD in the cerebrospinal fluid (recall 0.87 vs 0.50). DISCUSSION Personalized models could (1) support classification of MCI, (2) assess the presence of AD pathology, and (3) estimate the risk of cognitive decline progression, based only on economical and non‐invasive EEG recordings. Highlights Personalized cortical model estimating structural alterations from EEG recordings. Discrimination of Mild Cognitive Impairment (MCI) and Healthy (HC) subjects (95%) Prediction of biological markers of Alzheimer's in Subjective Decline (SCD) Subjects (87%) Transition correctly predicted for 3/3 subjects that converted from SCD to MCI after 1y","PeriodicalId":516929,"journal":{"name":"Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring","volume":"310 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139894226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Baril, C. Picard, A. Labonté, Erlan Sanchez, Catherine Duclos, Béry Mohammediyan, N. Ashton, Henrik Zetterberg, K. Blennow, John C. S. Breitner, S. Villeneuve, Judes Poirier
Abstract INTRODUCTION Measuring day‐to‐day sleep variability might reveal unstable sleep‐wake cycles reflecting neurodegenerative processes. We evaluated the association between Alzheimer's disease (AD) fluid biomarkers with day‐to‐day sleep variability. METHODS In the PREVENT‐AD cohort, 203 dementia‐free participants (age: 68.3 ± 5.4; 78 males) with a parental history of sporadic AD were tested with actigraphy and fluid biomarkers. Day‐to‐day variability (standard deviations over a week) was assessed for sleep midpoint, duration, efficiency, and nighttime activity count. RESULTS Lower cerebrospinal fluid (CSF) ApoE, higher CSF p‐tau181/amyloid‐β (Aβ)42, and higher plasma p‐tau231/Aβ42 were associated with higher variability of sleep midpoint, sleep duration, and/or activity count. The associations between fluid biomarkers with greater sleep duration variability were especially observed in those that carried the APOE4 allele, mild cognitive impairment converters, or those with gray matter atrophy. DISCUSSION Day‐to‐day sleep variability were associated with biomarkers of AD in at‐risk individuals, suggesting that unstable sleep promotes neurodegeneration or, conversely, that AD neuropathology disrupts sleep‐wake cycles.
{"title":"Day‐to‐day sleep variability with Alzheimer's biomarkers in at‐risk elderly","authors":"A. Baril, C. Picard, A. Labonté, Erlan Sanchez, Catherine Duclos, Béry Mohammediyan, N. Ashton, Henrik Zetterberg, K. Blennow, John C. S. Breitner, S. Villeneuve, Judes Poirier","doi":"10.1002/dad2.12521","DOIUrl":"https://doi.org/10.1002/dad2.12521","url":null,"abstract":"Abstract INTRODUCTION Measuring day‐to‐day sleep variability might reveal unstable sleep‐wake cycles reflecting neurodegenerative processes. We evaluated the association between Alzheimer's disease (AD) fluid biomarkers with day‐to‐day sleep variability. METHODS In the PREVENT‐AD cohort, 203 dementia‐free participants (age: 68.3 ± 5.4; 78 males) with a parental history of sporadic AD were tested with actigraphy and fluid biomarkers. Day‐to‐day variability (standard deviations over a week) was assessed for sleep midpoint, duration, efficiency, and nighttime activity count. RESULTS Lower cerebrospinal fluid (CSF) ApoE, higher CSF p‐tau181/amyloid‐β (Aβ)42, and higher plasma p‐tau231/Aβ42 were associated with higher variability of sleep midpoint, sleep duration, and/or activity count. The associations between fluid biomarkers with greater sleep duration variability were especially observed in those that carried the APOE4 allele, mild cognitive impairment converters, or those with gray matter atrophy. DISCUSSION Day‐to‐day sleep variability were associated with biomarkers of AD in at‐risk individuals, suggesting that unstable sleep promotes neurodegeneration or, conversely, that AD neuropathology disrupts sleep‐wake cycles.","PeriodicalId":516929,"journal":{"name":"Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring","volume":"299 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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