Salient Cognitive Paradigms to Assess Preclinical Alzheimer's Disease

Despite the growing emphasis to identify early biological markers that can detect the progressive accumulation of brain pathology in the complex pathophysiologic cascade that occurs in Alzheimer's disease (AD), we continue to employ the same neuropsychological paradigms that were developed to detect dementia or frank cognitive impairment. It has become increasingly clear that we cannot expect to measure clinically meaningful change in relationship to these emerging preclinical biomarkers using these traditional cognitive assessment paradigms, nor will we advance the efforts to identify the earliest cognitive changes that emerge in AD. Over the last decade, a few novel promising cognitive assessment paradigms have emerged that have shown promise in identifying subtle cognitive deficits in AD which aids in early detection and monitoring of meaningful cognitive change over time. Some of these cognitive assessment paradigms are reviewed here, including semantic interference, semantic intrusion errors, memory binding, and binding of face and name associations. These paradigms may be useful for AD clinical trials focused on secondary prevention if there is sufficient rigor to suggest that they correlate with AD biomarkers, having robust sensitivity, specificity, and predictive utility among culturally and linguistically diverse populations at-risk for AD

Patient values in healthcare decision making among diverse older adults

To provide high-quality healthcare, it is essential to understand values that guide the healthcare decisions of older adults. We investigated the types of values that culturally diverse older adults incorporate in medical decision making. Focus groups were held with older adults who varied in cognitive status (mildly impaired versus those with normal cognition) and ethnicity (Hispanic and non-Hispanic). Investigators used a qualitative descriptive approach to analyze transcripts and identify themes. Forty-nine individuals (49% with cognitive impairment; 51% Hispanic) participated. Participants expressed a wide range of values relating to individual factors, familial/cultural beliefs and expectations, balancing risks and benefits, receiving decisional support, and considering values other than their own. Participants emphasized that values are individual-specific, influenced by aging, and change throughout life course. Participants described barriers and facilitators that interfere with or promote value solicitation and incorporation during medical encounters. Study findings highlight that in older adults with various health experiences, cognitive and physical health status, and sociocultural backgrounds, medical decisions are influenced by a variety of values. Clinicians should take time to elicit, understand, and reassess the different types of values of older adults. •Older adults incorporate unique and multifaceted values in health decisions.•Values that influence medical decisions change with aging.•Values include consideration of familial roles and decisional support.•Older adults value quality of life and balance risks versus benefits

A Review of Novel Cognitive Challenge Tests for the Assessment of Preclinical Alzheimer's Disease

Objectives: There is currently a lack of consensus among neuropsychologists about which cognitive assessment paradigms hold the most promise in identifying subtle cognitive deficits in preclinical Alzheimer's Disease (AD) and which are most useful for monitoring risk of cognitive deterioration. Many widely used instruments are older versions of tests originally developed for the assessment of dementia or traumatic brain injury. Current efforts to digitize these measures provides more uniform and remote assessment, which is an advancement, but does not reflect significant changes in paradigmatic underpinnings or recent advances in cognitive neuroscience. Method: This work provides an overview of novel Cognitive Challenge Tests (CCTs) that employ semantic interference paradigms that uniquely measure the failure to recover from proactive semantic interference (frPSI). Other salient methods to measure meaningful cognitive change in early stage AD are also presented, as well as how they compare with traditional neuropsychological assessments. Finally, future directions for the development of more effective assessment paradigms are discussed. Results: frPSI is a cognitive marker which measures the persistent inability to learn new semantically competing stimuli despite multiple opportunities to do so. frPSI and deficits in semantic inhibitory control have repeatedly shown utility for the early detection of AD during its preclinical stages. These novel cognitive markers have been related to various biomarkers of AD and neurodegeneration among culturally diverse older adults. Conclusions: To meet the critical needs of a rapidly evolving field, cognitive assessment instruments must show sufficient scientific rigor including robust sensitivity, specificity, and predictive utility among culturally and linguistically diverse populations and importantly, be correlated to AD biomarkers

Value Assessment in Healthcare Decision-Making of Ethnically and Cognitively Diverse Older Adults

Abstract Objective Little is known regarding the values that patients with mild cognitive impairment (MCI) incorporate into healthcare decision-making or how culture may affect such values. Even if values overlap across cultures, cultural groups may emphasize the importance of specific values differently since values emanate, at least in part, from cultural and life-long learning. The aim of this study was to explore and compare values that older adults of different ethnicities and cognitive statuses incorporate in their medical decisions. Participants and Method Four focus groups were established by identifying older adults as, a) Hispanic or non-Hispanic, and with b) normal cognition or MCI. Participants were recruited from the 1Florida Alzheimer’s Disease Research Center. Focus groups were audio-recorded and transcribed using a professional transcription service. Results There were a total of 23 participants (Age: M = 70.9, SD = 6.4). MCI groups had briefer discussions (Time M = 44 minutes) than the normal cognition groups (Time M = 62 minutes). Qualitative analysis of discussions was used to explore the values identified across the focus groups. The MCI groups valued spirituality, doctor recommendations, and family involvement when facing medical decisions. Normal cognition groups valued the necessity of proactive involvement as healthcare consumers and the relationship between the quality of patient-clinician interaction and their health care related decisions. Cultural themes involving perceptions of gender and generational differences emerged from the Hispanic normal cognition group. Conclusions This study identified many determinants influencing the medical decision-making process of diverse older adults: including past experiences, family involvement, healthcare barriers, and cultural background. These results have the potential to impact patient-clinician discussions, decisions made by surrogates, and the development of decision aids with a broader range of relevant patient values

A Deep-Learning Approach for the Prediction of Mini-Mental State Examination Scores in a Multimodal Longitudinal Study

This study introduces a new multimodal deep regression method to predict cognitive test score in a 5-year longitudinal study on Alzheimer's disease (AD). The proposed model takes advantage of multimodal data that includes cerebrospinal fluid (CSF) levels of tau and beta-amyloid, structural measures from magnetic resonance imaging (MRI), functional and metabolic measures from positron emission tomography (PET), and cognitive scores from neuropsychological tests (Cog), all with the aim of achieving highly accurate predictions of future Mini-Mental State Examination (MMSE) test scores up to five years after baseline biomarker collection. A novel data augmentation technique is leveraged to increase the numbers of training samples without relying on synthetic data. With the proposed method, the best and most encompassing regressor is shown to achieve better than state-of-the-art correlations of 85.07%(SD=1.59) for 6 months in the future, 87.39% (SD =1.48) for 12 months, 84.78% (SD=2.66) for 18 months, 85.13% (SD=2.19) for 24 months, 81.15% (SD=5.48) for 30 months, 81.17% (SD=4.44) for 36 months, 79.25% (SD=5.85) for 42 months, 78.98% (SD=5.79) for 48 months, 78.93%(SD=5.76) for 54 months, and 74.96% (SD=7.54) for 60 months

Semantic intrusion errors as a function of age, amyloid, and volumetric loss: a confirmatory path analysis

To examine the direct and indirect effects of age, APOE ϵ4 genotype, amyloid positivity, and volumetric reductions in AD-prone brain regions as it relates to semantic intrusion errors reflecting proactive semantic interference (PSI) and the failure to recover from proactive semantic interference (frPSI) on the Loewenstein-Acevedo Scales of Semantic Interference and Learning (LASSI-L), a cognitive stress test that has been consistently more predictive of preclinical and prodromal Alzheimer's disease (AD) than traditional list-learning tests. Cross-sectional study. 1Florida Alzheimer's Disease Research Center baseline study. Two-hundred and twelve participants with Mini-Mental State Examination (MMSE) score above 16 and a broad array of cognitive diagnoses ranging from cognitively normal (CN) to dementia, of whom 58% were female, mean age of 72.1 (SD 7.9). Participants underwent extensive clinical and neuropsychological evaluations, MR and amyloid Positron Emission Tomography/Computer/Computer Tomography (PET/CT) imaging, and analyses of APOE ϵ4 genotype. Confirmatory path analyses were conducted in the structural equation modeling framework that estimated multiple equations simultaneously while controlling for important covariates such as sex, education, language of evaluation, and global cognitive impairment. Both amyloid positivity and decreased brain volumes in AD-prone regions were directly related to LASSI-L Cued B1 and Cued B2 intrusions (sensitive to PSI and frPSI effects) even after controlling for covariates. APOE ϵ4 status did not evidence direct effects on these LASSI-L cognitive markers, but rather exerted their effects on amyloid positivity, which in turn related to PSI and frPSI. Similarly, age did not have a direct relationship with LASSI-L scores, but exerted its effects indirectly through amyloid positivity and volumes of AD-prone brain regions. Our study provides insight into the relationships among age, APOE ϵ4, amyloid, and brain volumetric reductions as it relates to semantic intrusion errors. The investigation expands our understanding of the underpinnings of PSI and frPSI intrusions in a large cohort

A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer’s Disease

With the advances in machine learning for the diagnosis of Alzheimer’s disease (AD), most studies have focused on either identifying the subject’s status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these two tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this study proposes a deep neural network based on modality fusion, kernelization, and tensorization that perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. This relationship between multiclass classification and longitudinal regression is found to boost the efficacy of the final model in dealing with both tasks. Different multimodality scenarios are investigated, and complementary aspects of the multimodal features are exploited to simultaneously delineate the subject’s label and predict related cognitive scores at future timepoints using baseline data. The main intent in this multitask framework is to consolidate the highest accuracy possible in terms of precision, sensitivity, F1 score, and area under the curve (AUC) in the multiclass classification task while maintaining the highest similarity in the MMSE score as measured through the correlation coefficient and the RMSE for all time points under the prediction task, with both tasks, run simultaneously under the same set of hyperparameters. The overall accuracy for multiclass classification of the proposed KTMnet method is 66.85 ± 3.77. The prediction results show an average RMSE of 2.32 ± 0.52 and a correlation of 0.71 ± 5.98 for predicting MMSE throughout the time points. These results are compared to state-of-the-art techniques reported in the literature. A discovery from the multitasking of this consolidated machine learning framework is that a set of hyperparameters that optimize the prediction results may not necessarily be the same as those that would optimize the multiclass classification. In other words, there is a breakpoint beyond which enhancing further the results of one process could lead to the downgrading in accuracy for the other

A Brief Version of the LASSI-L Detects Prodromal Alzheimer's Disease States

The Loewenstein-Acevedo Scales for Semantic Interference and Learning (LASSI-L) is an increasingly utilized cognitive stress test designed to identify early cognitive changes associated with incipient neurodegenerative disease. To examine previously derived cut-points for cognitively unimpaired older adults that were suggestive of performance impairment on multiple subscales of the LASSI-L. These cut-points were applied to a new sample of older adults who were cognitive healthy controls (HC: n = 26) and those on the Alzheimer's disease (AD) continuum from early stage mild cognitive impairment (EMCI: n = 28), late stage MCI (LMCI: n = 18) to mild AD (AD: n = 27). All participants were administered the LASSI-L. All cognitively impaired participants were PET amyloid positive which likely reflects underlying AD neuropathology, while cognitively normal counterparts were deemed to have amyloid negative scans. There was a monotonic relationship between the number of deficits on LASSI-L subscales and independent classification of study groups with greater severity of cognitive impairment. Importantly, taken together, impairment on maximum learning ability and measures of proactive semantic interference (both reflected by cued recall and intrusion errors) correctly classified 74.1% of EMCI, 94.4% of LMCI, and 96.3% of AD. Only 7.7% of HC were incorrectly classified as having impairments. A modest number of LASSI-L subscales taking approximately 8 minutes to administer, had excellent discriminative ability using established cut-offs among individuals with presumptive stages of AD. This has potential implications for both clinical practice and clinical research settings targeting AD during early prodromal stages

Prediction Modeling of Alzheimer's Disease and Its Prodromal Stages from Multimodal Data with Missing Values

A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification

Barriers and Best Practices in Disclosing a Dementia Diagnosis: A Clinician Interview Study

The vast majority of individuals with dementia want to receive a diagnosis. Research suggests, however, that only a fraction of individuals with dementia receive a diagnosis and patients and families often feel the information is poorly explained. We thus aimed to assess clinician-reported barriers to dementia disclosure and recommendations for giving a dementia diagnosis. To accomplish this, we performed telephone interviews with 15 clinicians from different specialties using a semi-structured interview guide. Transcripts were analyzed thematically. Clinician-reported barriers fit 3 categories: patient and caregiver-related barriers, clinician-related barriers, and barriers related to the triadic interaction. Patient and caregiver-related barriers included lack of social support, misunderstanding the diagnosis, and denial. Clinician barriers included difficulty giving bad news, difficulty communicating uncertainty, and lack of time. Triadic interaction barriers included challenges meeting multiple goals or needs and family requests for non-disclosure. Recommendations for best practice included for clinicians to foster relationships, educate patients and family, and take a family-centered approach. Clinicians described recommendations for fostering relationships such as using empathic communication and developing and maintaining connection. Educating patients and families included tailoring communication, explaining how the diagnosis was reached, and following up. Family approaches included meeting with family members prior to delivering the diagnosis and involving the caregiver in the discussion. Findings may inform updated recommendations for best practices when communicating a dementia diagnosis