Digital cognitive assessments as low-burden markers for predicting future cognitive decline and tau accumulation across the Alzheimer's spectrum.

BACKGROUND: Digital cognitive assessments, particularly those that can be done at home, present as low-burden biomarkers for participants and patients alike, but their effectiveness in the diagnosis of Alzheimer's disease (AD) or predicting its trajectory is still unclear. Here, we assessed what utility or added value these digital cognitive assessments provide for identifying those at high risk of cognitive decline. METHODS: We analyzed >500 Alzheimer's Disease Neuroimaging Initiative participants who underwent a brief digital cognitive assessment and amyloid beta (Aß)/tau positron emission tomography scans, examining their ability to distinguish cognitive status and predict cognitive decline. RESULTS: Performance on the digital cognitive assessment was superior to both cortical Aß and entorhinal tau in detecting mild cognitive impairment and future cognitive decline, with mnemonic discrimination deficits emerging as the most critical measure for predicting decline and future tau accumulation. DISCUSSION: Digital assessments are effective at identifying at-risk individuals, supporting their utility as low-burden tools for early AD detection and monitoring. HIGHLIGHTS: Performance on digital cognitive assessments predicts progression to mild cognitive impairment at a higher proficiency compared to amyloid beta and tau. Deficits in mnemonic discrimination are indicative of future cognitive decline. Impaired mnemonic discrimination predicts future entorhinal and inferior temporal tau.

Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer's disease.

BACKGROUND: The Mnemonic Similarity Task (MST) is a popular memory task designed to assess hippocampal integrity. We assessed whether analyzing MST performance using a multinomial processing tree (MPT) cognitive model could detect individuals with elevated Alzheimer's disease (AD) biomarker status prior to cognitive decline. METHOD: We analyzed MST data from >200 individuals (young, cognitively healthy older adults and individuals with mild cognitive impairment [MCI]), a subset of which also had existing cerebrospinal fluid (CSF) amyloid beta (Aß) and phosphorylated tau (pTau) data using both traditional and model-derived approaches. We assessed how well each could predict age group, memory ability, MCI status, Aß, and pTau status using receiver operating characteristic analyses. RESULTS: Both approaches predicted age group membership equally, but MPT-derived metrics exceeded traditional metrics in all other comparisons. DISCUSSION: A MPT model of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for screening and monitoring older adults during the asymptomatic phase of AD. HIGHLIGHTS: The MST, along with cognitive modeling, identifies individuals with memory deficits and cognitive impairment. Cognitive modeling of the MST identifies individuals with increased AD biomarkers prior to changes in cognitive function. The MST is a digital biomarker that identifies individuals at high risk of AD.

A response time model of the three-choice Mnemonic Similarity Task provides stable, mechanistically interpretable individual-difference measures.

Introduction: The Mnemonic Similarity Task (MST) is a widely used measure of individual tendency to discern small differences between remembered and presently presented stimuli. Significant work has established this measure as a reliable index of neurological and cognitive dysfunction and decline. However, questions remain about the neural and psychological mechanisms that support performance in the task. Methods: Here, we provide new insights into these questions by fitting seven previously-collected MST datasets (total N = 519), adapting a three-choice evidence accumulation model (the Linear Ballistic Accumulator). The model decomposes choices into automatic and deliberative components. Results: We show that these decomposed processes both contribute to the standard measure of behavior in this task, as well as capturing individual variation in this measure across the lifespan. We also exploit a delayed test/re-test manipulation in one of the experiments to show that model parameters exhibit improved stability, relative to the standard metric, across a 1 week delay. Finally, we apply the model to a resting-state fMRI dataset, finding that only the deliberative component corresponds to off-task co-activation in networks associated with long-term, episodic memory. Discussion: Taken together, these findings establish a novel mechanistic decomposition of MST behavior and help to constrain theories about the cognitive processes that support performance in the task.

Resilience to AD pathology in Top Cognitive Performers.

Successful cognitive aging is often thought to result from resistance to the accumulation of pathology, resilience to the effects of pathological accumulation, or some combination of the two. While evidence for resilience has been found in typical aging populations, the oldest-old provide us with a unique window into the role of pathological accumulation in impacting cognition. Here, we aimed to assess group differences in measures of amyloid and tau across older age groups using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI age: 60-89) and The 90+ Study (age: 90-101). Additionally, using the ADNI dataset, we performed exploratory analyses of regional cingulate AV-45 SUVRs to assess if amyloid load in particular areas was associated with Top Cognitive Performance (TCP). Consistent with the literature, results showed no group differences in amyloid SUVRs both regionally and in the whole cortex. For tau with AV-1451, we also observed no differences in Braak composite SUVRs. Interestingly, these relationships persisted in the oldest-old. This indicates that Top Cognitive Performance throughout aging does not reflect resistance to amyloid and tau burden, but that other mechanisms may be associated with protection against amyloid and tau related neurodegeneration.

Digital cognitive assessments as low-burden markers for predicting future cognitive decline and tau accumulation across the Alzheimer's spectrum.

Digital cognitive assessments, particularly those that can be done at home, present as low burden biomarkers for participants and patients alike, but their effectiveness in diagnosis of Alzheimer's or predicting its trajectory is still unclear. Here, we assessed what utility or added value these digital cognitive assessments provide for identifying those at high risk for cognitive decline. We analyzed >500 ADNI participants who underwent a brief digital cognitive assessment and Aß/tau PET scans, examining their ability to distinguish cognitive status and predict cognitive decline. Performance on the digital cognitive assessment were superior to both cortical Aß and entorhinal tau in detecting mild cognitive impairment and future cognitive decline, with mnemonic discrimination deficits emerging as the most critical measure for predicting decline and future tau accumulation. Digital assessments are effective in identifying at-risk individuals, supporting their utility as low-burden tools for early Alzheimer's detection and monitoring.

Cognitive modeling of the Mnemonic Similarity Task as a digital biomarker for Alzheimer's Disease.

AD related pathologies, such as beta-amyloid (Aß) and phosphorylated tau (pTau), are evident decades before any noticeable decline in memory occurs. Identifying individuals during this asymptomatic phase is crucial for timely intervention. The Mnemonic Similarity Task (MST), a modified recognition memory task, is especially relevant for early AD screening, as it assesses hippocampal integrity, a region affected (both directly and indirectly) early in the progression of the disease. Further, strong inferences on the underlying cognitive mechanisms that support performance on this task can be made using Bayesian cognitive modeling. We assessed whether analyzing MST performance using a cognitive model could detect subtle changes in cognitive function and AD biomarker status prior to overt cognitive decline. We analyzed MST data from >200 individuals (young, cognitively healthy older adults, and individuals with MCI), a subset of which also had existing CSF Aß and pTau data. Traditional performance scores and cognitive modeling using multinomial processing trees was applied to each participants MST data using Bayesian approaches. We assessed how well each could predict age group, memory ability, MCI status, Aß/pTau status using ROC analyses. Both approaches predicted age group membership equally, but cognitive modeling approaches exceeded traditional metrics in all other comparisons. This work establishes that cognitive modeling of the MST can detect individuals with AD prior to cognitive decline, making it a potentially useful tool for both screening and monitoring older adults during the asymptomatic phase of AD.

Age-Related Brain Atrophy and the Positive Effects of Behavioral Enrichment in Middle-Aged Beagles.

Aging dogs serve as a valuable preclinical model for Alzheimer's disease (AD) due to their natural age-related development of ß-amyloid (Aß) plaques, human-like metabolism, and large brains that are ideal for studying structural brain aging trajectories from serial neuroimaging. Here we examined the effects of chronic treatment with the calcineurin inhibitor (CNI) tacrolimus or the nuclear factor of activated T cells (NFAT)-inhibiting compound Q134R on age-related canine brain atrophy from a longitudinal study in middle-aged beagles (36 females, 7 males) undergoing behavioral enrichment. Annual MRI was analyzed using modern, automated techniques for region-of-interest-based and voxel-based volumetric assessments. We found that the frontal lobe showed accelerated atrophy with age, while the caudate nucleus remained relatively stable. Remarkably, the hippocampus increased in volume in all dogs. None of these changes were influenced by tacrolimus or Q134R treatment. Our results suggest that behavioral enrichment can prevent atrophy and increase the volume of the hippocampus but does not prevent aging-associated prefrontal cortex atrophy.

Improving clinical efficiency in screening for cognitive impairment due to Alzheimer's.

INTRODUCTION: To reduce demands on expert time and improve clinical efficiency, we developed a framework to evaluate whether inexpensive, accessible data could accurately classify Alzheimer's disease (AD) clinical diagnosis and predict the likelihood of progression. METHODS: We stratified relevant data into three tiers: obtainable at primary care (low-cost), mostly available at specialty visits (medium-cost), and research-only (high-cost). We trained several machine learning models, including a hierarchical model, an ensemble model, and a clustering model, to distinguish between diagnoses of cognitively unimpaired, mild cognitive impairment, and dementia due to AD. RESULTS: All models showed viable classification, but the hierarchical and ensemble models outperformed the conventional model. Classifier "error" was predictive of progression rates, and cluster membership identified subgroups with high and low risk of progression within 1.5 to 3 years. DISCUSSION: Accessible, inexpensive clinical data can be used to guide AD diagnosis and are predictive of current and future disease states. HIGHLIGHTS: Classification performance using cost-effective features was accurate and robustHierarchical classification outperformed conventional multinomial classificationClassification labels indicated significant changes in conversion risk at follow-upA clustering-classification method identified subgroups at high risk of decline.

Acute cycling exercise and hippocampal subfield function and microstructure in healthy older adults.

Aging is associated with deterioration in dentate gyrus (DG) and CA3, both crucial hippocampal subfields for age susceptible memory processes such as mnemonic discrimination (MD). Meanwhile, a single aerobic exercise session alters DG/CA3 function and neural activity in both rats and younger adults and can elicit short-term microstructural alterations in the hippocampus of older adults. However, our understanding of the effects of acute exercise on hippocampal subfield integrity via function and microstructure in older adults is limited. Thus, a within subject-design was employed to determine if 20-min of moderate to vigorous aerobic exercise alters bilateral hippocampal subfield function and microstructure using high-resolution functional magnetic resonance imaging (fMRI) during an MD task (n = 35) and high angular resolution multi-shell diffusion imaging (n = 31), in healthy older adults, compared to seated rest. Following the exercise condition, participants exhibited poorer MD performance, particularly when their perception of effort was higher. Exercise was also related to lower MD-related activity within the DG/CA3 but not CA1 subfield. Finally, after controlling for whole brain gray matter diffusion, exercise was associated with lower neurite density index (NDI) within the DG/CA3. However, exercise-related differences in DG/CA3 activity and NDI were not associated with differences in MD performance. Our results suggest moderate to vigorous aerobic exercise may temporarily inhibit MD performance, and suppress DG/CA3 MD-related activity and NDI, potentially through neuroinflammatory/glial processes. However, additional studies are needed to confirm whether these short-term changes in behavior and hippocampal subfield neurophysiology are beneficial and how they might relate to long-term exercise habits.

Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease.

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts.

Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.

Optimizing the mnemonic similarity task for efficient, widespread use.

Introduction: The Mnemonic Similarity Task (MST) has become a popular test of memory and, in particular, of hippocampal function. It has been heavily used in research settings and is currently included as an alternate outcome measure on a number of clinical trials. However, as it typically requires ~15 min to administer and benefits substantially from an experienced test administrator to ensure the instructions are well-understood, its use in trials and in other settings is somewhat restricted. Several different variants of the MST are in common use that alter the task format (study-test vs. continuous) and the response prompt given to participants (old/similar/new vs. old/new). Methods: In eight online experiments, we sought to address three main goals: (1) To determine whether a robust version of the task could be created that could be conducted in half the traditional time; (2) To determine whether the test format or response prompt choice significantly impacted the MST's results; and (3) To determine how robust the MST is to repeat testing. In Experiments 1-7, participants received both the traditional and alternate forms of the MST to determine how well the alternate version captured the traditional task's performance. In Experiment 8, participants were given the MST four times over approximately 4 weeks. Results: In Experiments 1-7, we found that test format had no effect on the reliability of the MST, but that shifting to the two-choice response format significantly reduced its ability to reflect the traditional MST's score. We also found that the full running time could be cut it half or less without appreciable reduction in reliability. We confirmed the efficacy of this reduced task in older adults as well. Here, and in Experiment 8, we found that while there often are no effects of repeat-testing, small effects are possible, but appear limited to the initial testing session. Discussion: The optimized version of the task developed here (oMST) is freely available for web-based experiment delivery and provides an accurate estimate of the same memory ability as the classic MST in less than half the time.

Effects of acute aerobic exercise on mnemonic discrimination performance in older adults.

OBJECTIVES: Ample evidence suggests exercise is beneficial for hippocampal function. Furthermore, a single session of aerobic exercise provides immediate benefits to mnemonic discrimination performance, a highly hippocampal-specific memory process, in healthy younger adults. However, it is unknown if a single session of aerobic exercise alters mnemonic discrimination in older adults, who generally exhibit greater hippocampal deterioration and deficits in mnemonic discrimination performance. METHODS: We conducted a within subject acute exercise study in 30 cognitively healthy and physically active older adults who underwent baseline testing and then completed two experimental visits in which they performed a mnemonic discrimination task before and after either 30 min of cycling exercise or 30 min of seated rest. Linear mixed-effects analyses were conducted in which condition order and age were controlled, time (pre vs. post) and condition (exercise vs. rest) were modeled as fixed effects, and subject as a random effect. RESULTS: No significant time by condition interaction effect was found for object recognition (p = .254, η2=.01), while a significant reduction in interference was found for mnemonic discrimination performance following the exercise condition (p = .012, η2=.07). A post-intervention only analysis indicated that there was no difference between condition for object recognition (p = .186, η2=.06), but that participants had better mnemonic discrimination performance (p < .001, η2=.22) following the exercise. CONCLUSIONS: Our results suggest a single session of moderate-intensity aerobic exercise may reduce interference and elicit better mnemonic discrimination performance in healthy older adults, suggesting benefits for hippocampal-specific memory function.

Bayesian Modeling of the Mnemonic Similarity Task Using Multinomial Processing Trees.

The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings. We develop new cognitive models, based on the multinomial processing tree framework, for two versions of the MST. The models are implemented as generative probabilistic models and applied to behavioral data using Bayesian graphical modeling methods. We demonstrate how the combination of cognitive modeling and Bayesian methods allows for flexible and powerful inferences about performance on the MST. These demonstrations include latent-mixture extensions for identifying individual differences in decision strategies, and hierarchical extensions that measure fine-grained differences in the ability to detect lures. One key finding is that the availability of a "similar" response in the MST reduces individual differences in decision strategies and allows for more direct measurement of recognition memory.

Adaptive Design Optimization for a Mnemonic Similarity Task.

The Mnemonic Similarity Task (MST: Stark et al., 2019) is a modified recognition memory task designed to place strong demand on pattern separation. The sensitivity and reliability of the MST make it an extremely valuable tool in clinical settings, where it has been used to identify hippocampal dysfunction associated with healthy aging, dementia, schizophrenia, depression, and other disorders. As with any test used in a clinical setting, it is especially important for the MST to be administered as efficiently as possible. We apply adaptive design optimization methods (Lesmes et al., 2015; Myung et al., 2013) to optimize the presentation of test stimuli in accordance with previous responses. This optimization is based on a signal-detection model of an individual's memory capabilities and decision-making processes. We demonstrate that the adaptive design optimization approach generally reduces the number of test stimuli needed to provide these measures.

Using Advanced Diffusion-Weighted Imaging to Predict Cell Counts in Gray Matter: Potential and Pitfalls.

Recent advances in diffusion imaging have given it the potential to non-invasively detect explicit neurobiological properties, beyond what was previously possible with conventional structural imaging. However, there is very little known about what cytoarchitectural properties these metrics, especially those derived from newer multi-shell models like Neurite Orientation Dispersion and Density Imaging (NODDI) correspond to. While these diffusion metrics do not promise any inherent cell type specificity, different brain cells have varying morphologies, which could influence the diffusion signal in distinct ways. This relationship is currently not well-characterized. Understanding the possible cytoarchitectural signatures of diffusion measures could allow them to estimate important neurobiological properties like cell counts, potentially resulting in a powerful clinical diagnostic tool. Here, using advanced diffusion imaging (NODDI) in the mouse brain, we demonstrate that different regions have unique relationships between cell counts and diffusion metrics. We take advantage of this exclusivity to introduce a framework to predict cell counts of different types of cells from the diffusion metrics alone, in a region-specific manner. We also outline the challenges of reliably developing such a model and discuss the precautions the field must take when trying to tie together medical imaging modalities and histology.

Impact of spaceflight stressors on behavior and cognition: A molecular, neurochemical, and neurobiological perspective.

The response of the human body to multiple spaceflight stressors is complex, but mounting evidence implicate risks to CNS functionality as significant, able to threaten metrics of mission success and longer-term behavioral and neurocognitive health. Prolonged exposure to microgravity, sleep disruption, social isolation, fluid shifts, and ionizing radiation have been shown to disrupt mechanisms of homeostasis and neurobiological well-being. The overarching goal of this review is to document the existing evidence of how the major spaceflight stressors, including radiation, microgravity, isolation/confinement, and sleep deprivation, alone or in combination alter molecular, neurochemical, neurobiological, and plasma metabolite/lipid signatures that may be linked to operationally-relevant behavioral and cognitive performance. While certain brain region-specific and/or systemic alterations titrated in part with neurobiological outcome, variations across model systems, study design, and the conspicuous absence of targeted studies implementing combinations of spaceflight stressors, confounded the identification of specific signatures having direct relevance to human activities in space. Summaries are provided for formulating new research directives and more predictive readouts of portending change in neurobiological function.

Dissecting Differential Complex Behavioral Responses to Simulated Space Radiation Exposures.

This study has established the impact that space radiation exposure has on the capability of rats to successfully negotiate behavioral tasks of increasing complexity. Rats previously exposed to a low dose (10 cGy) of either 4He ions or a cocktail of 6 ions that simulates the galactic cosmic ray spectrum (GCRSim) were screened initially on an attentional set shifting (ATSET) task that provides a measure of executive function. Rats that exhibited superior ATSET performance were then selected for follow up behavioral assessments designed to evaluate how the cohort of "good performers" would fare when presented with a novel behavioral paradigm termed the Associative Recognition Memory and Interference Touchscreen (ARMIT) task. Central to this approach was to discriminate if/how adaptive problem solving would be impacted by changing the options of associative cues presented over several learning sessions to obtain a reward under time constraints using this newly designed touch screen-based task. Data from these studies indicated that when faced with an increased cognitive load, possibly due to interference from prior associative recognition memories, rats exhibited impairments in their capability to negotiate task dynamics and efficiently engage abstract reasoning. Interestingly, while exposure to the GCRSim adversely impacted problem-solving capabilities, single ion exposure did not, pointing to the nuances of space radiation exposure on CNS functionality. Since the selected behavioral paradigms exhibit strong cross-species correlates, data suggest that rodents succumb to increased task rigor as observed in humans, and make similar mistakes when challenged with the interference of overlapping associative memories. Furthermore, data clearly points to the limitations of over-reliance on a single cognitive endpoint that may underestimate global neurocognitive risk due to space radiation exposure.

Impaired Behavioral Pattern Separation in Refractory Temporal Lobe Epilepsy and Mild Cognitive Impairment.

OBJECTIVE: Episodic memory impairment and hippocampal pathology are hallmark features of both temporal lobe epilepsy (TLE) and amnestic mild cognitive impairment (aMCI). Pattern separation (PS), which enables the distinction between similar but unique experiences, is thought to contribute to successful encoding and retrieval of episodic memories. Impaired PS has been proposed as a potential mechanism underling episodic memory impairment in aMCI, but this association is less established in TLE. In this study, we examined behavioral PS in patients with TLE and explored whether profiles of performance in TLE are similar to aMCI. METHOD: Patients with TLE, aMCI, and age-matched, healthy controls (HCs) completed a modified recognition task that relies on PS for the discrimination of highly similar lure items, the Mnemonic Similarity Task (MST). Group differences were evaluated and relationships between clinical characteristics, California Verbal Learning Test-Second Edition scores, and MST performance were tested in the TLE group. RESULTS: Patients with TLE and aMCI demonstrated poorer PS performance relative to the HCs, but performance did not differ between the two patient groups. Neither the side of seizure focus nor having hippocampal sclerosis affected performance in TLE. However, TLE patients with clinically defined memory impairment showed the poorest performance. CONCLUSION: Memory performance on a task that relies on PS was disrupted to a similar extent in TLE and aMCI. The MST could provide a clinically useful tool for measuring hippocampus-dependent memory impairments in TLE and other neurological disorders associated with hippocampal damage.

Regional Cortical Thickness Predicts Top Cognitive Performance in the Elderly.

While aging is typically associated with cognitive decline, some individuals are able to diverge from the characteristic downward slope and maintain very high levels of cognitive performance. Prior studies have found that cortical thickness in the cingulate cortex, a region involved in information processing, memory, and attention, distinguish those with exceptional cognitive abilities when compared to their cognitively more typical elderly peers. Others major areas outside of the cingulate, such as the prefrontal cortex and insula, are also key in successful aging well into late age, suggesting that structural properties across a wide range of areas may better explain differences in cognitive abilities. Here, we aim to assess the role of regional cortical thickness, both in the cingulate and the whole brain, in modeling Top Cognitive Performance (TCP), measured by performance in the top 50th percentile of memory and executive function. Using data from National Alzheimer's Coordinating Center and The 90 + Study, we examined healthy subjects aged 70-100 years old. We found that, while thickness in cingulate regions can model TCP status with some degree of accuracy, a whole-brain, network-level approach out-performed the localist, cingulate models. These findings suggests a need for more network-style approaches and furthers our understanding of neurobiological factors contributing to preserved cognition.