Cognitive reserve and clinical progression in Alzheimer disease: A paradoxical relationship

Sex and gender differences in cognitive resilience to aging and Alzheimer's disease

Sex and gender-biological and social constructs-significantly impact the prevalence of protective and risk factors, influencing the burden of Alzheimer's disease (AD; amyloid beta and tau) and other pathologies (e.g., cerebrovascular disease) which ultimately shape cognitive trajectories. Understanding the interplay of these factors is central to understanding resilience and resistance mechanisms explaining maintained cognitive function and reduced pathology accumulation in aging and AD. In this narrative review, the ADDRESS! Special Interest Group (Alzheimer's Association) adopted a multidisciplinary approach to provide the foundations and recommendations for future research into sex- and gender-specific drivers of resilience, including a sex/gender-oriented review of risk factors, genetics, AD and non-AD pathologies, brain structure and function, and animal research. We urge the field to adopt a sex/gender-aware approach to resilience to advance our understanding of the intricate interplay of biological and social determinants and consider sex/gender-specific resilience throughout disease stages. HIGHLIGHTS: Sex differences in resilience to cognitive decline vary by age and cognitive status. Initial evidence supports sex-specific distinctions in brain pathology. Findings suggest sex differences in the impact of pathology on cognition. There is a sex-specific change in resilience in the transition to clinical stages. Gender and sex factors warrant study: modifiable, immune, inflammatory, and vascular.

Cognitive reserve, cortisol, and Alzheimer's disease biomarkers: A memory clinic study

INTRODUCTION

Cognitive reserve might mitigate the risk of Alzheimer's dementia among memory clinic patients. No study has examined the potential modifying role of stress on this relation.

METHODS

We examined cross-sectional associations of the cognitive reserve index (CRI; education, occupational complexity, physical and leisure activities, and social health) with cognitive performance and AD-related biomarkers among 113 memory clinic patients. The longitudinal association between CRI and cognition over a 3-year follow-up was assessed. We examined whether associations were influenced by perceived stress and five measures of diurnal salivary cortisol.

RESULTS

Higher CRI scores were associated with better cognition. Adjusting for cortisol measures reduced the beneficial association of CRI on cognition. A higher CRI score was associated with better working memory in individuals with higher (favorable) cortisol AM/PM ratio, but not among individuals with low cortisol AM/PM ratio. No association was found between CRI and AD-related biomarkers.

DISCUSSION

Physiological stress reduces the neurocognitive benefits of cognitive reserve among memory clinic patients.

HIGHLIGHTS

Physiological stress may reduce the neurocognitive benefits accrued from cognitively stimulating and enriching life experiences (cognitive reserve [CR]) in memory clinic patients. Cortisol awakening response modified the relation between CR and P-tau181, a marker of Alzheimer's disease (AD). Effective stress management techniques for AD and related dementia prevention are warranted.

Instruments to Assess Cognitive Reserve Among Older Adults: a Systematic Review of Measurement Properties

Cognitive reserve explains the differences in the susceptibility to cognitive impairment related to brain aging, pathology, or insult. Given that cognitive reserve has important implications for the cognitive health of typically and pathologically aging older adults, research needs to identify valid and reliable instruments for measuring cognitive reserve. However, the measurement properties of current cognitive reserve instruments used in older adults have not been evaluated according to the most up-to-date COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN). This systematic review aimed to critically appraise, compare, and summarize the quality of the measurement properties of all existing cognitive reserve instruments for older adults. A systematic literature search was performed to identify relevant studies published up to December 2021, which was conducted by three of four researchers using 13 electronic databases and snowballing method. The COSMIN was used to assess the methodological quality of the studies and the quality of measurement properties. Out of the 11,338 retrieved studies, only seven studies that concerned five instruments were eventually included. The methodological quality of one-fourth of the included studies was doubtful and three-seventh was very good, while only four measurement properties from two instruments were supported by high-quality evidence. Overall, current studies and evidence for selecting cognitive reserve instruments suitable for older adults were insufficient. All included instruments have the potential to be recommended, while none of the identified cognitive reserve instruments for older adults appears to be generally superior to the others. Therefore, further studies are recommended to validate the measurement properties of existing cognitive reserve instruments for older adults, especially the content validity as guided by COSMIN.Systematic Review Registration numbers: CRD42022309399 (PROSPERO).

Structural underpinnings and long-term effects of resilience in Parkinson's disease

Resilience in neuroscience generally refers to an individual's capacity to counteract the adverse effects of a neuropathological condition. While resilience mechanisms in Alzheimer's disease are well-investigated, knowledge regarding its quantification, neurobiological underpinnings, network adaptations, and long-term effects in Parkinson's disease is limited. Our study involved 151 Parkinson's patients from the Parkinson's Progression Marker Initiative Database with available Magnetic Resonance Imaging, Dopamine Transporter Single-Photon Emission Computed Tomography scans, and clinical information. We used an improved prediction model linking neuropathology to symptom severity to estimate individual resilience levels. Higher resilience levels were associated with a more active lifestyle, increased grey matter volume in motor-associated regions, a distinct structural connectivity network and maintenance of relative motor functioning for up to a decade. Overall, the results indicate that relative maintenance of motor function in Parkinson's patients may be associated with greater neuronal substrate, allowing higher tolerance against neurodegenerative processes through dynamic network restructuring.

Real-word application of the AT(N) classification and disease-modifying treatment eligibility in a hospital-based cohort

BACKGROUND AND OBJECTIVES

The AT(N) classification system stratifies patients based on biomarker profiles, including amyloid-beta deposition (A), tau pathology (T), and neurodegeneration (N). This study aims to apply the AT(N) classification to a hospital-based cohort of patients with cognitive decline and/or dementia, within and outside the Alzheimer's disease (AD) continuum, to enhance our understanding of the multidimensional aspects of AD and related disorders. Furthermore, we wish to investigate how many cases from our cohort would be eligible for the available disease modifying treatments, such as aducanemab and lecanemab.

METHODS

We conducted a retrospective evaluation of 429 patients referred to the Memory Center of IRCCS San Raffaele Hospital in Milan. Patients underwent clinical/neuropsychological assessments, lumbar puncture, structural brain imaging, and positron emission tomography (FDG-PET). Patients were stratified according to AT(N) classification, group comparisons were performed and the number of eligible cases for anti-β amyloid monoclonal antibodies was calculated.

RESULTS

Sociodemographic and clinical features were similar across groups. The most represented group was A + T + N + accounting for 38% of cases, followed by A + T - N + (21%) and A - T - N + (20%). Although the clinical presentation was similar, the A + T + N + group showed more severe cognitive impairment in memory, language, attention, executive, and visuospatial functions compared to other AT(N) groups. Notably, T + patients demonstrated greater memory complaints compared to T -  cases. FDG-PET outperformed MRI and CT in distinguishing A + from A -  patients. Although 61% of the observed cases were A + , only 17% of them were eligible for amyloid-targeting treatments.

DISCUSSION

The AT(N) classification is applicable in a real-world clinical setting. The classification system provided insights into clinical management and treatment strategies. Low cognitive performance and specific regional FDG-PET hypometabolism at diagnosis are highly suggestive for A + T + or A - T + profiles. This work provides also a realistic picture of the proportion of AD patients eligible for disease modifying treatments emphasizing the need for early detection.

Cognitive trajectories in older adults and the role of depressive symptoms: A 7-year follow-up study

OBJECTIVES

To examine different trajectories of cognitive changes in elderly adults and explore the mediating role of depressive symptoms.

DESIGN

A 7-year, community-based, prospective cohort study.

SETTING

The downtown neighborhood of Shanghai, China.

PARTICIPANTS

A cohort of 394 older adults, with an average age of 71.8 years, was recruited in 2015 and has been reassessed every two years until 2021.

METHODS

Latent Class Growth Analysis was used to model aging trajectories and Linear Mixed-Effect Models for Repeated Measures were used to estimate the least squares mean changes of cognition between subjects with depression (DEP+) and without (DEP-) across all visits.

RESULTS

Three cognitive trajectories were identified: the "successful aging" (SA) trajectory had the best and most consistent performance (n=229, 55.9%); the "normal aging" (NA) trajectory showed lower but stable cognition (n=141, 37.3%); while the "cognitive decline" (CD) trajectory displayed poor and declining cognition (n=24, 6.8%). Depressive symptoms were found to be influential across all trajectories. In the CD trajectory, the MoCA scores of the DEP+ group increased in within-group comparisons and were significantly higher than those of the DEP- group at visits 1 and 3 in between-group comparisons. A similar trend was observed in the NA trajectory, though it did not reach statistical significance.

CONCLUSIONS

Our research suggests that mild and decreasing depressive symptoms can be a reversible factor that might slow down the irreversible cognitive decline in the elderly. Therefore, we suggest that even mild depressive symptoms in the elderly should be monitored and detected.

Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease

Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and symptoms, despite the fact that the multiplicity of clinical symptoms renders various neuropsychological assessments inadequate to reflect the underlying pathophysiological mechanisms. Since putative neuroimaging biomarkers play a crucial role in understanding the etiology of AD, we sought to stratify the diverse relationships between AD biomarkers and cognitive decline in the aging population and uncover risk factors contributing to the diversities in AD. To do so, we capitalized on a large amount of neuroimaging data from the ADNI study to examine the inflection points along the dynamic relationship between cognitive decline trajectories and whole-brain neuroimaging biomarkers, using a state-of-the-art statistical model of change point detection. Our findings indicated that the temporal relationship between AD biomarkers and cognitive decline may differ depending on the synergistic effect of genetic risk and biological sex. Specifically, tauopathy-PET biomarkers exhibit a more dynamic and age-dependent association with Mini-Mental State Examination scores (p<0.05), with inflection points at 72, 78, and 83 years old, compared with amyloid-PET and neurodegeneration (cortical thickness from MRI) biomarkers. In the landscape of health disparities in AD, our analysis indicated that biological sex moderates the rate of cognitive decline associated with APOE4 genotype. Meanwhile, we found that higher education levels may moderate the effect of APOE4, acting as a marker of cognitive reserve.

Cognitive reserve in ALS: the role of occupational skills and requirements

OBJECTIVE

Amyotrophic Lateral Sclerosis (ALS) is a heterogeneous neurodegenerative condition featuring variable degrees of motor and cognitive impairment. We assessed the impact of specific, empirically derived occupational skills and requirements on cognitive and motor functioning in ALS.

METHODS

Individuals with ALS (n = 150) were recruited from the University of Pennsylvania's Comprehensive ALS Clinic. The Edinburgh Cognitive and Behavioral ALS Screen (ECAS) measured cognition, and the Penn Upper Motor Neuron (PUMNS) and ALS Functional Rating Scales (ALSFRS-R) measured motor symptoms. We derived 17 factors representing distinct occupational skills and requirements from the Occupational Information Network (O*NET), which were related to cognitive and motor scores using multiple linear regression.

RESULTS

Occupational roles involving greater reasoning ability (β = 2.12, p < .05), social ability (β = 1.73, p < .05), analytic skills, (β = 3.12, p < .01) and humanities knowledge (β = 1.83, p<.01) were associated with better performance on the ECAS, while jobs involving more exposure to environmental hazards (β=-2.57, p < .01) and technical skills (β=-2.16, p<.01) were associated with lower ECAS scores. Jobs requiring more precision skills (β = 1.91, p < .05) were associated with greater motor dysfunction on the PUMNS.

CONCLUSIONS

Occupational histories involving more cognitively complex skills and activities were related to preserved cognitive functioning in ALS consistent with the cognitive reserve hypothesis, while jobs with greater exposure to environmental hazards and technical demands were linked to poorer cognitive functioning. Jobs involving more repetitive movements were associated with worse motor functioning, possibly due to overuse. Occupational history provides insight into protective and risk factors for variable degrees of cognitive and motor dysfunction in ALS.

Longitudinal trajectories of cognitive reserve in hypometabolic subtypes of Alzheimer's disease

Previous studies have demonstrated resilience to AD-related neuropathology in a form of cognitive reserve (CR). In this study we investigated a relationship between CR and hypometabolic subtypes of AD, specifically the typical and the limbic-predominant subtypes. We analyzed data from 59 Aβ-positive cognitively normal (CN), 221 prodromal Alzheimer's disease (AD) and 174 AD dementia participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) from ADNI and ADNIGO/2 phases. For replication, we analyzed data from 5 Aβ-positive CN, 89 prodromal AD and 43 AD dementia participants from ADNI3. CR was estimated as standardized residuals in a model predicting cognition from temporoparietal grey matter volumes and covariates. Higher CR estimates predicted slower cognitive decline. Typical and limbic-predominant hypometabolic subtypes demonstrated similar baseline CR, but the results suggested a faster decline of CR in the typical subtype. These findings support the relationship between subtypes and CR, specifically longitudinal trajectories of CR. Results also underline the importance of longitudinal analyses in research on CR.

Modeling the development of cognitive reserve in children: A residual index approach

OBJECTIVE

To model cognitive reserve (CR) longitudinally in a neurodiverse pediatric sample using a residual index approach, and to test the criterion and construct validity of this index.

METHOD

Participants were N = 115 children aged 9.5-13 years at baseline (MAge = 10.48 years, SDAge = 0.61), and n = 43 (37.4%) met criteria for ADHD. The CR index represented variance in Matrix Reasoning scores from the WASI that was unexplained by MRI-based brain variables (bilateral hippocampal volumes, total gray matter volumes, and total white matter hypointensity volumes) or demographics (age and sex).

RESULTS

At baseline, the CR index predicted math computation ability (estimate = 0.50, SE = 0.07, p < .001), and word reading ability (estimate = 0.26, SE = 0.10, p = .012). Longitudinally, change in CR over time was not associated with change in math computation ability (estimate = -0.02, SE = 0.03, p < .513), but did predict change in word reading ability (estimate = 0.10, SE = 0.03, p < .001). Change in CR was also found to moderate the relationship between change in word reading ability and white matter hypointensity volume (estimate = 0.10, SE = 0.05, p = .045).

CONCLUSIONS

Evidence for the criterion validity of this CR index is encouraging, but somewhat mixed, while construct validity was evidenced through interaction between CR, brain, and word reading ability. Future research would benefit from optimization of the CR index through careful selection of brain variables for a pediatric sample.

Epilepsy and epileptiform activity in late-onset Alzheimer disease: clinical and pathophysiological advances, gaps and conundrums

A growing body of evidence has demonstrated a link between Alzheimer disease (AD) and epilepsy. Late-onset epilepsy and epileptiform activity can precede cognitive deterioration in AD by years, and its presence has been shown to predict a faster disease course. In animal models of AD, amyloid and tau pathology are linked to cortical network hyperexcitability that precedes the first signs of memory decline. Thus, detection of epileptiform activity in AD has substantial clinical importance as a potential novel modifiable risk factor for dementia. In this Review, we summarize the epidemiological evidence for the complex bidirectional relationship between AD and epilepsy, examine the effect of epileptiform activity and seizures on cognition in people with AD, and discuss the precision medicine treatment strategies based on the latest research in human and animal models. Finally, we outline some of the unresolved questions of the field that should be addressed by rigorous research, including whether particular clinicopathological subtypes of AD have a stronger association with epilepsy, and the sequence of events between epileptiform activity and amyloid and tau pathology.

A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning

The challenge of making flexible, standard, and early medical diagnoses is significant. However, some limitations are not fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress and avoids isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemistry, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently, which have a great relationship with semantics, vary widely, and have linguistic information. This negatively affects early diagnosis decision-making. Depending on the circumstances, the diagnosis is made solo on imaging and some medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer's disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN respectively.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach

Background

The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

Objective

To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

Methods

We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline.

Results

We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways.

Conclusions

Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

Lower mental health related quality of life precedes dementia diagnosis: findings from the EPIC-Norfolk prospective population-based study

Lower Health Related Quality of Life (HRQoL) precedes dementia in older adults in the USA. We explore prospective associations between HRQoL and dementia in British adults in mid and late-life, when interventions to optimise cognitive ageing may provide benefit. 7,452 community-dwelling participants (57% women; mean age 69.3 ± 8.3 years) attended the European Prospective Investigation of Cancer-Norfolk study's third health check (3HC) and reported their HRQoL using Short-Form 36 (SF-36). Cox Proportional Hazard regression models explored associations between standard deviation differences in baseline Physical Component (PCS) and Mental Component Summary (MCS) scores, as well as eight SF-36 sub-scales (physical functioning, role-physical, bodily pain, general health, vitality, social functioning, role-emotional, mental health), and incident dementia over ten years. Logistic regression models explored cross-sectional relationships at the 3HC between HRQoL and objective global cognitive function (n = 4435; poor cognition = lowest performance decile). The cohort was examined as a whole and by age-group (50-69, ≥ 70), considering socio-demographics and co-morbidity. Higher MCS scores were associated with lower chance of incident dementia (Hazard Ratio [HR] = 0.74, 95% CI 0.68-0.81) and lower odds of poor cognition (Odds Ratio [OR] = 0.82, 0.76-0.89), with findings similar by age-group. Higher PCS scores were not associated with dementia in the whole cohort (HR = 0.93, 0.84-1.04) or considering age-groups; and were only associated with poor cognition in younger participants (OR = 0.81, 0.72-0.92). Similarly, associations between higher scores on subscales pertaining to mental, but not physical, HRQoL and lower dementia incidence were observed. Lower mental HRQoL precedes dementia diagnosis in middle-aged and older British adults.

Comparing machine learning-derived MRI-based and blood-based neurodegeneration biomarkers in predicting syndromal conversion in early AD

INTRODUCTION

We compared the machine learning-derived, MRI-based Alzheimer's disease (AD) resemblance atrophy index (AD-RAI) with plasma neurofilament light chain (NfL) level in predicting conversion of early AD among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects.

METHODS

We recruited participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had the following data: clinical features (age, gender, education, Montreal Cognitive Assessment [MoCA]), structural MRI, plasma biomarkers (p-tau181 , NfL), cerebrospinal fluid biomarkers (CSF) (Aβ42, p-tau181 ), and apolipoprotein E (APOE) ε4 genotype. We defined AD using CSF Aβ42 (A+) and p-tau181 (T+). We defined conversion (C+) if a subject progressed to the next syndromal stage within 4 years.

RESULTS

Of 589 participants, 96 (16.3%) were A+T+C+. AD-RAI performed better than plasma NfL when added on top of clinical features, plasma p-tau181 , and APOE ε4 genotype (area under the curve [AUC] = 0.832 vs. AUC = 0.650 among CU, AUC = 0.853 vs. AUC = 0.805 among MCI) in predicting A+T+C+.

DISCUSSION

AD-RAI outperformed plasma NfL in predicting syndromal conversion of early AD.

HIGHLIGHTS

AD-RAI outperformed plasma NfL in predicting syndromal conversion among early AD. AD-RAI showed better metrics than volumetric hippocampal measures in predicting syndromal conversion. Combining clinical features, plasma p-tau181 and apolipoprotein E (APOE) with AD-RAI is the best model for predicting syndromal conversion.

Education, Social Engagement, and Cognitive Function: A Cross-Lagged Panel Analysis

OBJECTIVES

Although education and social engagement are considered cognitive reserves, the pathway of both reserves on cognitive function has been rarely studied. This study aimed to examine the underlying mechanism between education, social engagement, and cognitive function.

METHODS

This study used 2-wave data (2010 and 2014) from Health and Retirement Study in the United States (N = 3,201). Education was measured by years of schooling. Social engagement was evaluated by 20 items including volunteering, physical activities, social activities, and cognitive activities. Cognitive function was assessed by a modified Telephone Interview for Cognitive Status. A cross-lagged panel model was fitted to test the mediating mechanism between education, social engagement, and cognitive function.

RESULTS

Controlling for covariates, higher education in early life was associated with better cognitive function in old age (b = 0.211, 95% confidence interval [CI] = [0.163, 0.259], p < .01). Late-life social engagement partially mediated the association between education and cognitive function (indirect effect = 0.021, 95% CI = [0.010, 0.033], p < .01). The indirect path between education and social engagement via cognition also existed (b = 0.009, 95% CI = [0.005, 0.012], p < .001).

DISCUSSION

Education in earlier life stage may exert a lifelong effect on cognitive function as well as an indirect effect via enhancing late-life cognitive reserve such as social engagement. The cross-lagged effect of social engagement on cognitive function is significant and vice versa. Future research may explore other cognitive reserves over the life course and its underlying mechanism to achieve healthy cognitive aging.

Modulation of associations between education years and cortical volume in Alzheimer's disease vulnerable brain regions by Aβ deposition and APOE ε4 carrier status in cognitively normal older adults

Background

Education years, as a measure of cognitive reserve, have been shown to affect the progression of Alzheimer's disease (AD), both pathologically and clinically. However, inconsistent results have been reported regarding the association between years of education and intermediate structural changes in AD-vulnerable brain regions, particularly when AD risk factors were not considered during the preclinical phase.

Objective

This study aimed to examine how Aβ deposition and APOE ε4 carrier status moderate the relationship between years of education and cortical volume in AD-vulnerable regions among cognitively normal older adults.

Methods

A total of 121 participants underwent structural MRI, [18F] flutemetamol PET-CT imaging, and neuropsychological battery assessment. Multiple regression analysis was conducted to examine the interaction between years of education and the effects of potential modifiers on cortical volume. The associations between cortical volume and neuropsychological performance were further explored in subgroups categorized based on AD risk factors.

Results

The cortical volume of the left lateral occipital cortex and bilateral fusiform gyrus demonstrated a significant differential association with years of education, depending on the presence of Aβ deposition and APOE ε4 carrier status. Furthermore, a significant relationship between the cortical volume of the bilateral fusiform gyrus and AD-nonspecific cognitive function was predominantly observed in individuals without AD risk factors.

Conclusion

AD risk factors exerted varying influences on the association between years of education and cortical volume during the preclinical phase. Further investigations into the long-term implications of these findings would enhance our understanding of cognitive reserves in the preclinical stages of AD.

Disentangling accelerated cognitive decline from the normal aging process and unraveling its genetic components: A neuroimaging-based deep learning approach

Background

The progressive cognitive decline that is an integral component of AD unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and Alzheimer's disease between different chronological points.

Methods

We developed a deep-learning framework based on dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline.

Results

We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G>T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neuron and plays a role in controlling cell growth and differentiation. In addition, MUC7 and PROL1/OPRPNon chromosome 4 were significant at the gene level. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Furthermore, we found that the cognitive decline slope GWAS was positively correlated with previous AD GWAS.

Conclusion

Our deep learning model was demonstrated effective on extracting relevant neuroimaging features and predicting individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene. Our approach has the potential to disentangle accelerated cognitive decline from the normal aging process and to determine its related genetic factors, leveraging opportunities for early intervention.

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With machine learning: the PREVIEW study protocol

BACKGROUND

As disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia.

METHODS

We will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of Aβ42, t-tau, and p-tau concentration and Aβ42/Aβ40 ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD.

DISCUSSION

This is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD.

TRIAL REGISTRATION NUMBER (TRN)

NCT05569083.

Association of Lifelong Cognitive Reserve with Dementia and Mild Cognitive Impairment among Older Adults with Limited Formal Education: A Population-Based Cohort Study

INTRODUCTION

Early-life educational attainment contributes to cognitive reserve (CR). We investigated the associations of lifelong CR with dementia and mild cognitive impairment (MCI) among older people with limited formal education.

METHODS

This population-based cohort study included 2,127 dementia-free participants (≥60 years; 59.4% women; 81.5% with no or elementary school) who were examined at baseline (August-December 2014) and follow-up (March-September 2018). Lifelong CR score at baseline was generated from six lifespan intellectual factors. Dementia, MCI, and their subtypes were defined according to the international criteria. Data were analyzed using Cox proportional-hazards models.

RESULTS

During the total of 8,330.6 person-years of follow-up, 101 persons were diagnosed with dementia, including 74 with Alzheimer's disease (AD) and 26 with vascular dementia (VaD). The high (vs. low) tertile of lifelong CR score was associated with multivariable-adjusted hazards ratios (95% confidence interval) of 0.28 (0.14-0.55) for dementia and 0.18 (0.07-0.48) for AD. The association between higher CR and reduced AD risk was significant in people aged 60-74 but not in those aged ≥75 years (p for interaction = 0.011). Similarly, among MCI-free people at baseline (n = 1,635), the high (vs. low) tertile of lifelong CR score was associated with multivariable-adjusted hazard ratios of 0.51 (0.38-0.69) for MCI and 0.46 (0.33-0.64) for amnestic MCI. Lifelong CR was not related to VaD or non-amnestic MCI.

DISCUSSION

High lifelong CR is associated with reduced risks of dementia and MCI, especially AD and amnestic MCI. It highlights the importance of lifelong CR in maintaining late-life cognitive health even among people with no or limited education.

Greater cognitive reserve is related to lower cortical excitability in healthy cognitive aging, but not in early clinical Alzheimer's disease

Objective

To investigate the relationship between cortico-motor excitability and cognitive reserve (CR) in cognitively unimpaired older adults (CU) and in older adults with mild cognitive impairment or mild dementia due to Alzheimer's disease (AD).

Methods

Data were collected and analyzed from 15 CU and 24 amyloid-positive AD participants aged 50-90 years. A cognitive reserve questionnaire score (CRQ) assessed education, occupation, leisure activities, physical activities, and social engagement. Cortical excitability was quantified as the average amplitude of motor evoked potentials (MEP amplitude) elicited with single-pulse transcranial magnetic stimulation delivered to primary motor cortex. A linear model compared MEP amplitudes between groups. A linear model tested for an effect of CRQ on MEP amplitude across all participants. Finally, separate linear models tested for an effect of CRQ on MEP amplitude within each group. Exploratory analyses tested for effect modification of demographics, cognitive scores, atrophy measures, and CSF measures within each group using nested regression analysis.

Results

There was no between-group difference in MEP amplitude after accounting for covariates. The primary model showed a significant interaction term of group*CRQ (R2adj = 0.18, p = 0.013), but no main effect of CRQ. Within the CU group, higher CRQ was significantly associated with lower MEP amplitude (R2adj = 0.45, p = 0.004). There was no association in the AD group.

Conclusion

Lower cortico-motor excitability is related to greater CRQ in CU, but not in AD. Lower MEP amplitudes may reflect greater neural efficiency in cognitively unimpaired older adults. The lack of association seen in AD participants may reflect disruption of the protective effects of CR. Future work is needed to better understand the neurophysiologic mechanisms leading to the protective effects of CR in older adults with and without neurodegenerative disorders.

A unique color-coded visualization system with multimodal information fusion and deep learning in a longitudinal study of Alzheimer's disease

PURPOSE

Automated diagnosis and prognosis of Alzheimer's Disease remain a challenging problem that machine learning (ML) techniques have attempted to resolve in the last decade. This study introduces a first-of-its-kind color-coded visualization mechanism driven by an integrated ML model to predict disease trajectory in a 2-year longitudinal study. The main aim of this study is to help capture visually in 2D and 3D renderings the diagnosis and prognosis of AD, therefore augmenting our understanding of the processes of multiclass classification and regression analysis.

METHOD

The proposed method, Machine Learning for Visualizing AD (ML4VisAD), is designed to predict disease progression through a visual output. This newly developed model takes baseline measurements as input to generate a color-coded visual image that reflects disease progression at different time points. The architecture of the network relies on convolutional neural networks. With 1123 subjects selected from the ADNI QT-PAD dataset, we use a 10-fold cross-validation process to evaluate the method. Multimodal inputs* include neuroimaging data (MRI, PET), neuropsychological test scores (excluding MMSE, CDR-SB, and ADAS to avoid bias), cerebrospinal fluid (CSF) biomarkers with measures of amyloid beta (ABETA), phosphorylated tau protein (PTAU), total tau protein (TAU), and risk factors that include age, gender, years of education, and ApoE4 gene.

FINDINGS/RESULTS

Based on subjective scores reached by three raters, the results showed an accuracy of 0.82 ± 0.03 for a 3-way classification and 0.68 ± 0.05 for a 5-way classification. The visual renderings were generated in 0.08 msec for a 23 × 23 output image and in 0.17 ms for a 45 × 45 output image. Through visualization, this study (1) demonstrates that the ML visual output augments the prospects for a more accurate diagnosis and (2) highlights why multiclass classification and regression analysis are incredibly challenging. An online survey was conducted to gauge this visualization platform's merits and obtain valuable feedback from users. All implementation codes are shared online on GitHub.

CONCLUSION

This approach makes it possible to visualize the many nuances that lead to a specific classification or prediction in the disease trajectory, all in context to multimodal measurements taken at baseline. This ML model can serve as a multiclass classification and prediction model while reinforcing the diagnosis and prognosis capabilities by including a visualization platform.

A Residual Marker of Cognitive Reserve Is Associated with Resting-State Intrinsic Functional Connectivity Along the Alzheimer's Disease Continuum

BACKGROUND

Cognitive reserve (CR) explains inter-individual differences in the impact of the neurodegenerative burden on cognitive functioning. A residual model was proposed to estimate CR more accurately than previous measures. However, associations between residual CR markers (CRM) and functional connectivity (FC) remain unexplored.

OBJECTIVE

To explore the associations between the CRM and intrinsic network connectivity (INC) in resting-state networks along the neuropathological-continuum of Alzheimer's disease (ADN).

METHODS

Three hundred eighteen participants from the DELCODE cohort were stratified using cerebrospinal fluid biomarkers according to the A(myloid-β)/T(au)/N(eurodegeneration) classification. CRM was calculated utilizing residuals obtained from a multilinear regression model predicting cognition from markers of disease burden. Using an independent component analysis in resting-state fMRI data, we measured INC of resting-state networks, i.e., default mode network (DMN), frontoparietal network (FPN), salience network (SAL), and dorsal attention network. The associations of INC with a composite memory score and CRM and the associations of CRM with the seed-to-voxel functional connectivity of memory-related were tested in general linear models.

RESULTS

CRM was positively associated with INC in the DMN in the entire cohort. The A+T+N+ group revealed an anti-correlation between the SAL and the DMN. Furthermore, CRM was positively associated with anti-correlation between memory-related regions in FPN and DMN in ADN and A+T/N+.

CONCLUSION

Our results provide evidence that INC is associated with CRM in ADN defined as participants with amyloid pathology with or without cognitive symptoms, suggesting that the neural correlates of CR are mirrored in network FC in resting-state.

Clear-headed into old age: Resilience and resistance against brain aging-A PET imaging perspective

With the advances in modern medicine and the adaptation towards healthier lifestyles, the average life expectancy has doubled since the 1930s, with individuals born in the millennium years now carrying an estimated life expectancy of around 100 years. And even though many individuals around the globe manage to age successfully, the prevalence of aging-associated neurodegenerative diseases such as sporadic Alzheimer's disease has never been as high as nowadays. The prevalence of Alzheimer's disease is anticipated to triple by 2050, increasing the societal and economic burden tremendously. Despite all efforts, there is still no available treatment defeating the accelerated aging process as seen in this disease. Yet, given the advances in neuroimaging techniques that are discussed in the current Review article, such as in positron emission tomography (PET) or magnetic resonance imaging (MRI), pivotal insights into the heterogenous effects of aging-associated processes and the contribution of distinct lifestyle and risk factors already have and are still being gathered. In particular, the concepts of resilience (i.e. coping with brain pathology) and resistance (i.e. avoiding brain pathology) have more recently been discussed as they relate to mechanisms that are associated with the prolongation and/or even stop of the progressive brain aging process. Better understanding of the underlying mechanisms of resilience and resistance may one day, hopefully, support the identification of defeating mechanism against accelerating aging.

Early Life Stress Enhances Cognitive Decline and Alters Synapse Function and Interneuron Numbers in Young Male APP/PS1 Mice

BACKGROUND

Exposure to stress early in life increases the susceptibility to Alzheimer's disease (AD) pathology in aged AD mouse models. So far, the underlying mechanisms have remained elusive.

OBJECTIVE

To investigate 1) effects of early life stress (ELS) on early functional signs that precede the advanced neuropathological changes, and 2) correlate synaptosomal protein content with cognition to identify neural correlates of AD.

METHODS

APPswe/PS1dE9 mice and littermates were subjected to ELS by housing dams and pups with limited bedding and nesting material from postnatal days 2-9. At 3 months of age, an age where no cognitive loss or amyloid-β (Aβ) pathology is typically reported in this model, we assessed hippocampal Aβ pathology, synaptic strength and synapse composition and interneuron populations. Moreover, cognitive flexibility was assessed and correlated with synaptosomal protein content.

RESULTS

While ELS did not affect Aβ pathology, it increased synaptic strength and decreased the number of calretinin+ interneurons in the hippocampal dentate gyrus. Both genotype and condition further affected the level of postsynaptic glutamatergic protein content. Finally, APP/PS1 mice were significantly impaired in cognitive flexibility at 3 months of age, and ELS exacerbated this impairment, but only at relatively high learning criteria.

CONCLUSIONS

ELS reduced cognitive flexibility in young APP/PS1 mice and altered markers for synapse and network function. These findings at an early disease stage provide novel insights in AD etiology and in how ELS could increase AD susceptibility.

Hippocampal Volume and Episodic Associative Memory Identify Memory Risk in Subjective Cognitive Decline Individuals in the CIMA-Q Cohort, Regardless of Cognitive Reserve Level and APOE4 Status

BACKGROUND

Subjective cognitive decline (SCD) was proposed to identify older adults who complain about their memory but perform within a normal range on standard neuropsychological tests. Persons with SCD are at increased risk of dementia meaning that some SCD individuals experience subthreshold memory decline due to an underlying progression of Alzheimer's disease (AD).

OBJECTIVE

Our main goal was to determine whether hippocampal volume and APOE4, which represent typical AD markers, predict inter-individual differences in memory performance among SCD individuals and can be used to identify a meaningful clinical subgroup.

METHODS

Neuropsychological assessment, structural MRI, and genetic testing for APOE4 were administered to one hundred and twenty-five older adults over the age of 65 from the CIMAQ cohort: 66 SCD, 29 individuals with mild cognitive impairment (MCI), and 30 cognitively intact controls (CTRLS). Multiple regression models were first used to identify which factor (hippocampal volume, APOE4 allele, or cognitive reserve) best predicted inter-individual differences in a Face-name association memory task within the SCD group.

RESULTS

Hippocampal volume was found to be the only and best predictor of memory performance. We then compared the demographic, clinical and cognitive characteristics of two SCD subgroups, one with small hippocampal volume (SCD/SH) and another with normal hippocampal volume (SCD/NH), with MCI and CTRLS. SCD/SH were comparable to MCI on neuropsychological tasks evaluating memory (i.e., test of delayed word recall), whereas SCD/NH were comparable to CTRLS.

CONCLUSION

Thus, using hippocampal volume allows identification of an SCD subgroup with a cognitive profile consistent with a higher risk of conversion to AD.

Local striatal volume and motor reserve in drug-naïve Parkinson's disease

Motor reserve (MR) may explain why individuals with similar pathological changes show marked differences in motor deficits in Parkinson's disease (PD). In this study, we investigated whether estimated individual MR was linked to local striatal volume (LSV) in PD. We analyzed data obtained from 333 patients with drug naïve PD who underwent dopamine transporter scans and high-resolution 3-tesla T1-weighted structural magnetic resonance images. Using a residual model, we estimated individual MRs on the basis of initial UPDRS-III score and striatal dopamine depletion. We performed a correlation analysis between MR estimates and LSV. Furthermore, we assessed the effect of LSV, which is correlated with MR estimates, on the longitudinal increase in the levodopa-equivalent dose (LED) during the 4-year follow-up period using a linear mixed model. After controlling for intracranial volume, there was a significant positive correlation between LSV and MR estimates in the bilateral caudate, anterior putamen, and ventro-posterior putamen. The linear mixed model showed that the large local volume of anterior and ventro-posterior putamen was associated with the low requirement of LED initially and accelerated LED increment thereafter. The present study demonstrated that LSV is crucial to MR in early-stage PD, suggesting LSV as a neural correlate of MR in PD.

Occupational complexity and cognition in the FINGER multidomain intervention trial

INTRODUCTION

Lifetime exposure to occupational complexity is linked to late-life cognition, and may affect benefits of preventive interventions.

METHODS

In the 2-year multidomain Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), we investigated, through post hoc analyses (N = 1026), the association of occupational complexity with cognition. Occupational complexity with data, people, and substantive complexity were classified through the Dictionary of Occupational Titles.

RESULTS

Higher levels of occupational complexity were associated with better baseline cognition. Measures of occupational complexity had no association with intervention effects on cognition, except for occupational complexity with data, which was associated with the degree of intervention-related gains for executive function.

DISCUSSION

In older adults at increased risk for dementia, higher occupational complexity is associated with better cognition. The cognitive benefit of the FINGER intervention did not vary significantly among participants with different levels of occupational complexity. These exploratory findings require further testing in larger studies.

Effects of multi-component non-pharmacological interventions on cognition in participants with mild cognitive impairment: A systematic review and meta-analysis

BACKGROUND AND PURPOSE

Mild cognitive impairment (MCI) describes a stage of intermediate cognitive dysfunction where the risk of conversion to dementia is elevated. Given the absence of effective pharmacological treatments for MCI, increasing numbers of studies are attempting to understand how multicomponent non-pharmacological interventions (MNPI) could benefit MCI. The purpose of this systematic review and meta-analysis were to assess the effects of two-component MNPI (simultaneous cognitive intervention based on cognitive stimulation, cognitive training and/or cognitive rehabilitation or combined cognitive and physical interventions) on global cognition and cognitive functions in older adults with MCI and to compare the degree of efficacy between the two interventions.

METHODS

After searching electronic databases (PubMed, Web of Science, Scopus and Cochrane Central) for randomized controlled trials and clinical trials published from 2010 to 18 January 2021, 562 studies were found. 8 studies were included in this review, with a fair to good quality according to the PEDro scale.

RESULTS

From a random-effects model meta-analysis, the pooled standardized MMSE mean difference between the intervention and control groups showed a significant small-to-medium effect in global cognition in MMSE score (0.249; 95% CI = [0.067, 0.431]), which seemed to be greater for combined physical and cognitive interventions. However, the meta-analyses did not show any effects regarding specific cognitive functions.

CONCLUSION

Our analyses support that MNPI could improve the global cognition in older adults with MCI. However, more studies are needed to analyze the potential benefits of MNPI on older adults with MCI.

Depression, cognitive reserve markers, and dementia risk in the general population

OBJECTIVES

We investigated depression, cognitive reserve, and their interaction as risk factors for incident dementia among community-dwelling older adults.

METHODS

In total, 2099 participants, aged ≥65 years with no dementia during baseline assessment, who completed the follow-up two years later were included from the Cognitive Function and Ageing Study Wales. Baseline depression and dementia and dementia at follow-up were evaluated using the Geriatric Mental State Examination and the Automated Geriatric Examination for Computer Assisted Taxonomy. Cognitive reserve was measured by combining overall education, mid-life occupational complexity, and later-life social and cognitive activities. Risk of dementia in relation to depression and cognitive reserve was estimated using penalized maximum likelihood logistic regression. Interactions between cognitive reserve and depression were assessed using both multiplicative and additive scales.

RESULTS

Baseline depression and low cognitive reserve significantly increased the risk of subsequent dementia at follow-up. No multiplicative interaction between cognitive reserve and depression existed. We observed an additive interaction between case-level depression and cognitive reserve. A significant association between depression and dementia was only found among people with low cognitive-reserve levels.

CONCLUSIONS

Greater cognitive reserve attenuated the depression-associated risk of developing dementia. This suggests the need to emphasize prodromal dementia detection among older adults with lower cognitive reserve and depression.

Longitudinal cognitive change and duration of Alzheimer's disease stages in relation to cognitive reserve

The relationship of cognitive reserve and measures of reserve with longitudinal cognitive change and the duration of preclinical, prodromal, and mild Alzheimer disease (AD) dementia remains to be fully characterized. In our study, 660 β-amyloid-positive participants staged with preclinical AD, prodromal AD, and dementia due to AD from the Alzheimer's Disease Neuroimaging Initiative were selected. Cognitive reserve and brain reserve were defined by conventional proxies or the residual method at baseline. We evaluated the utility of these reserves in predicting longitudinal cognitive change by mixed effects models and used a multi-state model to estimate stage duration stratified by reserve groups. Corrected for age, sex, and APOE-ε4 status, reserve was associated with cognitive decline, and the effects changed depending on the specific measures of reserve and the stage of AD. Reserves defined by the residual method were stronger predictors of cognitive change than those defined by conventional proxies. The estimated time from preclinical to mild AD dementia varied from 15-24 years based on the different reserve groups, and we observed a linear trend for the longest duration in individuals with high cognitive reserve/high brain reserve, followed by those with high cognitive reserve/low brain reserve, low cognitive reserve/high brain reserve, and low cognitive reserve/low brain reserve. This study showed a reduced risk of cognitive decline for individuals with higher level of reserve regardless of methods for measuring reserve. Interindividual differences in reserve may be important for clinical practice and trial design.

Association of Structural Measurements of Brain Reserve With Motor Progression in Patients With Parkinson Disease

BACKGROUND AND OBJECTIVES

To investigate the relationship between baseline structural measurements of brain reserve and clinical progression in Parkinson disease (PD). To further provide a possible underlying mechanism for structural measurements of brain reserve in PD, we combined functional and transcriptional data and investigated their relationship with progression-associated patterns derived from structural measurements and longitudinal clinical scores.

METHODS

This longitudinal study collected data from June 2010 to March 2019 from 2 datasets. The Parkinson's Progression Markers Initiative (PPMI) included controls and patients with newly diagnosed PD from 24 participating sites worldwide. Results were confirmed using data from the Huashan dataset (Shanghai, China), which included controls and patients with PD. Clinical symptoms were assessed with Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores and Schwab & England activities of daily living (ADL). Both datasets were followed up to 5 years. Linear mixed-effects (LME) models were performed to examine whether changes in clinical scores over time differed as a function of brain structural measurements at baseline.

RESULTS

A total of 389 patients with PD (n = 346, age 61.3 ± 10.03, 35% female, PPMI dataset; n = 43, age 59.4 ± 7.3, 38.7% female, Huashan dataset) with T1-MRI and follow-up clinical assessments were included in this study. Results of LME models revealed significant interactions between baseline structural measurements of subcortical regions and time on longitudinal deterioration of clinical scores (MDS-UPDRS Part II, absolute β > 0.27; total MDS-UPDRS scores, absolute β > 1.05; postural instability-gait difficulty (PIGD) score, absolute β > 0.03; Schwab & England ADL, absolute β > 0.59; all p < 0.05, false discovery rate corrected). The interaction of baseline structural measurements of subcortical regions and time on longitudinal deterioration of the PIGD score was replicated using data from Huashan Hospital. Furthermore, the β-coefficients of these interactions recapitulated the spatial distribution of dopaminergic, metabolic, and structural changes between patients with PD and normal controls and the spatial distribution of expression of the α-synuclein gene (SNCA).

DISCUSSION

Patients with PD with greater brain resources (that is, higher deformation-based morphometry values) had greater compensatory capacity, which was associated with slower rates of clinical progression. This knowledge could be used to stratify and monitor patients for clinical trials.

Identifying the white matter structural network of motor reserve in early Parkinson's disease

INTRODUCTION

Motor reserve refers to the individual capacity to cope with nigrostriatal dopamine depletion in Parkinson's disease (PD). This study aimed to explore the white matter structural network associated with motor reserve in patients with newly diagnosed PD.

METHODS

A total of 238 patients with early-stage drug-naïve PD who underwent ¹⁸F-FP-CIT PET and brain MRI scans at initial assessment were enrolled. We estimated individual motor reserve based on the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) scores and dopamine transporter availability in the posterior putamen using a residual model. Then, we performed threshold-free network-based statistics (TFNBS) analysis to identify the structural brain network associated with the estimated motor reserve. We also assessed the effect of the network connectivity strength on the longitudinal increase in levodopa-equivalent dose (LED).

RESULTS

The mean age at PD symptom onset was 69.10 ± 9.03 years and the mean UPDRS-III score at the time of PD diagnosis was 22.44 ± 9.72. TFNBS analysis identified a motor reserve-associated structural network whose nodes were mainly in the frontal region and cerebellum. Higher network strength (i.e., greater motor reserve) was associated with a slower longitudinal increase in LED during a 3-year follow-up period.

CONCLUSION

The structural brain network is associated with motor reserve in patients with PD. Connectivity strength within the identified network indicates the individual's capacity to tolerate PD-related pathologies, which is maintained with disease progression and affects the long-term motor prognosis of PD.

Effect of cognitive reserve on amnestic mild cognitive impairment due to Alzheimer’s disease defined by fluorodeoxyglucose-positron emission tomography

This study aimed to investigate the effect of cognitive reserve (CR) on the rate of cognitive decline and cerebral glucose metabolism in amnestic mild cognitive impairment (MCI) using the Study on Diagnosis of Early Alzheimer’s Disease-Japan (SEAD-J) dataset. The patients in SEAD-J underwent cognitive tests and fluorodeoxyglucose-positron emission tomography (FDG-PET). MCI to be studied was classified as amnestic MCI due to Alzheimer’s disease (AD) with neurodegeneration. A total of 57 patients were visually interpreted as having an AD pattern (P1 pattern, Silverman’s classification). The 57 individuals showing the P1 pattern were divided into a high-education group (years of school education ≥13, N = 18) and a low-education group (years of school education ≤12, N = 39). Voxel-based statistical parametric mapping revealed more severe hypometabolism in the high-education group than in the low-education group. Glucose metabolism in the hippocampus and temporoparietal area was inversely associated with the years of school education in the high- and low-education groups (N = 57). General linear mixed model analyses demonstrated that cognitive decline was more rapid in the high-education group during 3-year follow-up. These results suggest that the cerebral glucose metabolism is lower and cognitive function declines faster in patients with high CR of amnestic MCI due to AD defined by FDG-PET.

Effects of cognitive reserve proxies on cognitive function and frontoparietal control network in subjects with white matter hyperintensities: A cross-sectional functional magnetic resonance imaging study

Aims

This study aimed to analyze the potential association between cognition reserve (CR) components, including education, working activity, and leisure time activity, and cognitive function in subjects with white matter hyperintensities (WMH). The study also explored the role of the frontoparietal control network (FPCN) in such association.

Methods

White matter hyperintensities subjects with and without cognitive impairment (CI) were evaluated with multimodal magnetic resonance imaging, neuropsychological testing, and CR survey. FPCN patterns were assessed with dorsolateral prefrontal cortex seed‐based functional connectivity analysis.

Results

Education was positively associated with cognitive function in WMH subjects with or without CI, whereas working activity and leisure time activity were positively associated with cognitive function only in those without CI. Similarly, education was associated with bilateral FPCN in both WMH groups, whereas working activity and leisure time activity were associated with bilateral FPCN mainly in the group without CI. Furthermore, FPCN partially mediated the association between education and cognitive function in both WMH groups.

Conclusion

Education showed a positive impact on cognitive function in WMH subjects regardless of their cognitive status, whereas working activity and leisure time activity exhibited beneficial effects only in those without CI. The FPCN mediated the beneficial effect of education on cognitive function.

Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer’s Disease and Mild Cognitive Impairment

Objective

Alzheimer’s Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD.

Methods

Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests.

Results

11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment.

Conclusion

Approaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD.

Computerized Cognitive Training in Amnestic Mild Cognitive Impairment: A Randomized Clinical Trial

OBJECTIVE

Computerized cognitive training has been successful in healthy older adults, but its efficacy has been mixed in patients with amnestic Mild Cognitive Impairment (MCI).

METHODS

In a randomized, placebo-controlled, double-blind, parallel clinical trial, we examined the short- and long-term efficacy of a brain-plasticity computerized cognitive training in 113 participants with amnestic MCI.

RESULTS

Immediately after 40-hours of training, participants in the active control group who played computer games performed better than those in the experimental group on the primary cognitive outcome (p = 0.02), which was an auditory memory/attention composite score. There were no group differences on 2 secondary outcomes (global cognitive composite and rating of daily functioning). After 1 year, there was no difference between the 2 groups on primary or secondary outcomes. No adverse events were noted.

CONCLUSIONS

Although the experimental cognitive training program did not improve outcomes in those with MCI, the short-term effects of the control group should not be dismissed, which may alter treatment recommendations for these patients.

AD Resemblance Atrophy Index of Brain Magnetic Resonance Imaging in Predicting the Progression of Mild Cognitive Impairment Carrying Apolipoprotein E-ε4 Allele

Background and Objective

Early identification is important for timely Alzheimer’s disease (AD) treatment. Apolipoprotein E ε4 allele (APOE-ε4) is an important genetic risk factor for sporadic AD. The AD-Resemblance Atrophy Index (RAI)—a structural magnetic resonance imaging-derived composite index—was found to predict the risk of progression from mild cognitive impairment (MCI) to AD. Therefore, we investigated whether the AD-RAI can predict cognitive decline and progression to AD in patients with MCI carrying APOE ε4.

Methods

We included 733 participants with MCI from the Alzheimer’s Disease Neuroimaging Initiative Database (ADNI). Their APOE genotypes, cognitive performance, and levels of AD-RAI were assessed at baseline and follow-up. Linear regression models were used to test the correlations between the AD-RAI and baseline cognitive measures, and linear mixed models with random intercepts and slopes were applied to investigate whether AD-RAI and APOE-ε4 can predict the level of cognitive decline. Cox proportional risk regression models were used to test the association of AD-RAI and APOE status with the progression from MCI to AD.

Results

The baseline AD-RAI was higher in the MCI converted to AD group than in the MCI stable group (P < 0.001). The AD-RAI was significantly correlated with cognition, and had a synergistic effect with APOE-ε4 to predict the rate of cognitive decline. The AD-RAI predicted the risk and timing of MCI progression to AD. Based on the MCI population carrying APOE-ε4, the median time to progression from MCI to AD was 24 months if the AD-RAI > 0.5, while the median time to progression from MCI to AD was 96 months for patients with an AD-RAI ≤ 0.5.

Conclusion

The AD-RAI can predict the risk of progression to AD in people with MCI carrying APOE ε4, is strongly correlated with cognition, and can predict cognitive decline.

Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease

INTRODUCTION

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.

METHODS

We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.

RESULTS

Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.

DISCUSSION

ADNI has had a profound impact in improving clinical trials for AD.

Association of Education and Intracranial Volume With Cognitive Trajectories and Mortality Rates Across the Alzheimer Disease Continuum

Objective

To investigate relationships of education and intracranial volume (ICV) (factors related to cognitive and brain reserve, respectively) with cognitive trajectories and mortality in individuals with biomarker-defined Alzheimer disease (AD).

Methods

We selected 1,298 β-amyloid–positive memory clinic patients with subjective cognitive decline (SCD, n = 142), mild cognitive impairment (MCI, n = 274), or AD dementia (n = 882) from the Amsterdam Dementia Cohort. All participants underwent baseline MRI and neuropsychological assessment, and 68% received cognitive follow-up (median 2.3 years, interquartile range 2.4). Mortality data were collected from the Central Public Administration. In the total sample and stratified by disease stage (i.e., SCD/MCI vs dementia), we examined education and ICV as predictors of baseline and longitudinal cognitive performance on 5 cognitive domains (memory, attention, executive, language, and visuospatial functions; linear mixed models) and time to death (Cox proportional hazard models). Analyses were adjusted for age, sex, whole brain gray matter atrophy, and MRI field strength.

Results

Education and ICV showed consistent positive associations with baseline cognition across disease stages. Longitudinally, we observed a relationship between higher education and faster cognitive decline among patients with dementia on global cognition, memory, executive function, and language (range β = −0.06 to −0.13; all p < 0.05). Furthermore, in the total sample, both higher education and larger ICV were related to lower mortality risk (hazard ratio 0.84 and 0.82, respectively; p < 0.05).

Discussion

In this β-amyloid–positive memory clinic sample, both cognitive and brain reserve were positively associated with baseline cognition, whereas only education was related to longitudinal cognition (i.e., accelerated decline among more highly educated patients with dementia). Higher education and ICV both moderately attenuated overall mortality risk in AD.

Effects of Cognitive Reserve in Alzheimer's Disease and Cognitively Unimpaired Individuals

The concept of cognitive reserve (CR) has been proposed as a protective factor that modifies the effect of brain pathology on cognitive performance. It has been characterized through CR proxies; however, they have intrinsic limitations. In this study, we utilized two different datasets containing tau, amyloid PET, and T1 magnetic resonance imaging. First, 91 Alzheimer's disease (AD) continuum subjects were included from Alzheimer's Disease Neuroimaging Initiative 3. CR was conceptualized as the residual between actual cognition and estimated cognition based on amyloid, tau, and neurodegeneration. The proposed marker was tested by the correlation with CR proxy and modulation of brain pathology effects on cognitive function. Second, longitudinal data of baseline 53 AD spectrum and 34 cognitively unimpaired (CU) participants in the MEMORI dataset were analyzed. CR marker was evaluated for the association with disease conversion rate and clinical progression. Applying our multimodal CR model, this study demonstrates the differential effect of CR on clinical progression according to the disease status and the modulating effect on the relationship between brain pathology and cognition. The proposed marker was associated with years of education and modulated the effect of pathological burden on cognitive performance in the AD spectrum. Longitudinally, higher CR marker was associated with lower disease conversion rate among prodromal AD and CU individuals. Higher CR marker was related to exacerbated cognitive decline in the AD spectrum; however, it was associated with a mitigated decline in CU individuals. These results provide evidence that CR may affect the clinical progression differentially depending on the disease status.

Breakdown of specific functional brain networks in clinical variants of Alzheimer's disease

Alzheimer's disease (AD) is characterized by different clinical entities. Although AD phenotypes share a common molecular substrate (i.e., amyloid beta and tau accumulation), several clinicopathological differences exist. Brain functional networks might provide a macro-scale scaffolding to explain this heterogeneity. In this review, we summarize the evidence linking different large-scale functional network abnormalities to distinct AD phenotypes. Specifically, executive deficits in early-onset AD link with the dysfunction of networks that support sustained attention and executive functions. Posterior cortical atrophy relates to the breakdown of visual and dorsal attentional circuits, while the primary progressive aphasia variant of AD may be associated with the dysfunction of the left-lateralized language network. Additionally, network abnormalities might provide in vivo signatures for distinguishing proteinopathies that mimic AD, such as TAR DNA binding protein 43 related pathologies. These network differences vis-a-vis clinical syndromes are more evident in the earliest stage of AD. Finally, we discuss how these findings might pave the way for new tailored interventions targeting the most vulnerable brain circuit at the optimal time window to maximize clinical benefits.

Longitudinal Analysis of Brain-Predicted Age in Amnestic and Non-amnestic Sporadic Early-Onset Alzheimer's Disease

Objective: Predicted age difference (PAD) is a score computed by subtracting chronological age from "brain" age, which is estimated using neuroimaging data. The goal of this study was to evaluate the PAD as a marker of phenotypic heterogeneity and severity among early-onset Alzheimer's disease (EOAD) patients. Methods: We first used 3D T1-weighted (3D-T1) magnetic resonance images (MRI) of 3,227 healthy subjects aged between 18 and 85 years to train, optimize, and evaluate the brain age model. A total of 123 participants who met the criteria for early-onset (<65 years) sporadic form of probable Alzheimer's disease (AD) and presented with two distinctive clinical presentations [an amnestic form (n = 74) and a non-amnestic form (n = 49)] were included at baseline and followed-up for a maximum period of 4 years. All the participants underwent a work-up at baseline and every year during the follow-up period, which included clinical examination, neuropsychological testing and genotyping, and structural MRI. In addition, cerebrospinal fluid biomarker assay was recorded at baseline. PAD score was calculated by applying brain age model to 3D-T1 images of the EOAD patients and healthy controls, who were matched based on age and sex. At baseline, between-group differences for neuropsychological and PAD scores were assessed using linear models. Regarding longitudinal analysis of neuropsychological and PAD scores, differences between amnestic and non-amnestic participants were analyzed using linear mixed-effects modeling. Results: PAD score was significantly higher for non-amnestic patients (2.35 ± 0.91) when compared to amnestic patients (2.09 ± 0.74) and controls (0.00 ± 1). Moreover, PAD score was linearly correlated with the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating Sum of Boxes (CDR-SB), for both amnestic and non-amnestic sporadic forms. Longitudinal analyses showed that the gradual development of the disease in patients was accompanied by a significant increase in PAD score over time, for both amnestic and non-amnestic patients. Conclusion: PAD score was able to separate amnestic and non-amnestic sporadic forms. Regardless of the clinical presentation, as PAD score was a way of quantifying an early brain age acceleration, it was an appropriate method to detect the development of AD and follow the evolution of the disease as a marker of severity as MMSE and CDR-SB.

When Does Alzheimer's Disease Really Start? The Role of Biomarkers

(Appeared originally in Int J Mol Sci 2019, 20 5536).

Characterizing the Resilience Effect of Neurodegeneration for the Mechanistic Pathway of Alzheimer's Disease

BACKGROUND

With the rapid development of neurobiology and neuroimaging technologies, mounting evidence shows that Alzheimer's disease (AD) is caused by the build-up of two abnormal proteins, amyloid-β plaques (A) and neurofibrillary tangles (T). Over time, these AD-related neuropathological burdens begin to spread throughout the brain, which results in the characteristic progression of symptoms in AD.

OBJECTIVE

Although tremendous efforts have been made to link biological indicators to the progression of AD, limited attention has been paid to investigate the multi-factorial role of socioeconomic status (SES) in the prevalence or incidence of AD. There is high demand to explore the synergetic effect of sex and SES factors in moderating the neurodegeneration process caused by the accumulation of A and T biomarkers.

METHODS

We carry out a meta-data analysis on the longitudinal neuroimaging data, clinical outcomes, genotypes, and demographic data in Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu).

RESULTS

Our major findings include 1) education and occupation show resilience effects at the angular gyrus, superior parietal lobule, lateral occipital-temporal sulcus, and posterior transverse collateral sulcus where we found significant slowdown of neurodegeneration due to higher education level or more advanced occupation rank; 2) A and T biomarkers manifest different spatial patterns of brain resilience; 3) BDNF (brain-derived neurotrophic factor) single nucleotide polymorphism (SNP) rs10835211 shows strong association to the identified resilience effect; 4) the identified resilience effect is associated with the clinical manifestation in memory, learning, and organization performance.

CONCLUSION

Several brain regions manifest resilience from SES to A and T biomarkers. BDNF SNPs have a potential association with the resilience effect from SES. In addition, cognitive measures of learning and memory demonstrate the resilience effect.

Education, APOE ε4, and Cognition in Individuals with Subjective Cognitive Decline with Worry in the SILCODE Study

BACKGROUND

Education could offer a protective effect on cognition in individuals with Subjective Cognitive Decline (SCD), which is considered to be the early stage of Alzheimer's Disease (AD). However, the effect of education on cognition in SCD individuals with SCD-plus features is not clear.

OBJECTIVE

The aim of the study was to explore the effect of education on cognition in SCD individuals with SCD-plus features.

METHODS

A total of 234 individuals with SCD were included from the Sino Longitudinal Study on Cognitive Decline (SILCODE). Cognition was assessed across 4 domains (memory, executive, language, and general cognitive functions). Multiple linear regression models were constructed to examine the effect of education on cognitive scores in individuals without worry (n=91) and with worry (n=143). Furthermore, we assessed differences in effects between APOE ε4 noncarriers and APOE ε4 carriers in both groups.

RESULTS

Multiple linear regression analysis showed a positive effect of education on memory, executive, and language cognition in individuals without worry and all cognitive domains in individuals with worry. Furthermore, we found a positive effect of education on executive cognition in APOE ε4 noncarriers without worry and language and general cognition in APOE ε4 carriers without worry. Meanwhile, education had a positive effect on all cognitive domains in APOE ε4 noncarriers with worry and executive, language, and general cognition in APOE ε4 carriers with worry.

CONCLUSION

This study indicates that education has the potential to delay or reduce cognitive decline in SCD individuals with SCD-plus features.

Cognitive Reserve Characteristics and Occupational Performance Implications in People with Mild Cognitive Impairment

The Cognitive Reserve hypothesis suggests that there are individual differences in the ability to cope with the pathologic changes in Alzheimer's Disease. The proportion of elderly individuals has increased in recent years; this increase emphasizes the importance of early detection of mild cognitive impairment and the promotion of healthy ageing. The purpose of our study is to characterize cognitive reserve and occupational performance implications in people with mild cognitive impairment. 125 patients with mild cognitive impairment were enrolled. The Montreal Cognitive Assessments (MoCA) was used to evaluate cognitive status and the Cognitive Reserve Index Questionnaire (CRIq) as an indicator of cognitive reserve. Higher level of education was associated with higher MoCA scores (r = 0.290, p = 0.001). Positive significant correlations were observed between MoCA and total CRIq (r = 0.385, p < 0.001) as well as its three sub-domains, education (r = 0.231, p = 0.010), working activity (r = 0.237, p = 0.008) and leisure time (r = 0.319, p < 0.001). This study findings provide the importance of considering socio-behavioral factors in cognitive status. This research helps to describe the importance of engaging occupationally along the whole life-course as a potential protective factor in ageing, and includes a perspective of occupational therapy regarding the hypothesis of cognitive reserve.

Positive Effects of Education on Cognitive Functioning Depend on Clinical Status and Neuropathological Severity

Background: Variability in cognitive functions in healthy and pathological aging is often explained by educational attainment. However, it remains unclear to which extent different disease states alter protective effects of education. We aimed to investigate whether protective effects of education on cognition depend on (1) clinical diagnosis severity, and (2) the neuropathological burden within a diagnosis in a memory clinic setting. Methods: In this cross-sectional study, we included 108 patients with subjective cognitive decline [SCD, median age 71, IQR (66-78), 43% men], 190 with mild cognitive impairment [MCI, median age 78, IQR (73-82), 44% men], and 245 with Alzheimer's disease dementia (AD) [median age 80, IQR (76-84), 35% men]. We combined visual ratings of hippocampal atrophy, global atrophy, and white matter hyperintensities on MRI into a single neuropathology score. To investigate whether the contribution of education to cognitive performance differed across SCD, MCI, and AD, we employed several multiple linear regression models, stratified by diagnosis and adjusted for age, sex, and neurodegeneration. We re-ran each model with an additional interaction term to investigate whether these effects were influenced by neuropathological burden for each diagnostic group separately. False discovery rate (FDR) corrections for multiple comparisons were applied. Results: We observed significant positive associations between education and performance for global cognition and executive functions (all adjusted p-values < 0.05). As diagnosis became more severe, however, the strength of these associations decreased (all adjusted p-values < 0.05). Education related to episodic memory only at relatively lower levels of neuropathology in SCD (β = -0.23, uncorrected p = 0.02), whereas education related to episodic memory in those with higher levels of neuropathology in MCI (β = 0.15, uncorrected p = 0.04). However, these interaction effects did not survive FDR-corrections. Conclusions: Altogether, our results demonstrated that positive effects of education on cognitive functioning reduce with diagnosis severity, but the role of neuropathological burden within a particular diagnosis was small and warrants further investigation. Future studies may further unravel the extent to which different dimensions of an individual's disease severity contribute to the waxing and waning of protective effects in cognitive aging.

Measuring Resilience and Resistance in Aging and Alzheimer Disease Using Residual Methods: A Systematic Review and Meta-analysis

BACKGROUND AND OBJECTIVE

There is a lack of consensus on how to optimally define and measure resistance and resilience in brain and cognitive aging. Residual methods use residuals from regression analysis to quantify the capacity to avoid (resistance) or cope (resilience) "better or worse than expected" given a certain level of risk or cerebral damage. We reviewed the rapidly growing literature on residual methods in the context of aging and Alzheimer disease (AD) and performed meta-analyses to investigate associations of residual method-based resilience and resistance measures with longitudinal cognitive and clinical outcomes.

METHODS

A systematic literature search of PubMed and Web of Science databases (consulted until March 2020) and subsequent screening led to 54 studies fulfilling eligibility criteria, including 10 studies suitable for the meta-analyses.

RESULTS

We identified articles using residual methods aimed at quantifying resistance (n = 33), cognitive resilience (n = 23), and brain resilience (n = 2). Critical examination of the literature revealed that there is considerable methodologic variability in how the residual measures were derived and validated. Despite methodologic differences across studies, meta-analytic assessments showed significant associations of levels of resistance (hazard ratio [HR] [95% confidence interval (CI)] 1.12 [1.07-1.17]; p < 0.0001) and levels of resilience (HR [95% CI] 0.46 [0.32-0.68]; p < 0.001) with risk of progression to dementia/AD. Resilience was also associated with rate of cognitive decline (β [95% CI] 0.05 [0.01-0.08]; p < 0.01).

DISCUSSION

This review and meta-analysis supports the usefulness of residual methods as appropriate measures of resilience and resistance, as they capture clinically meaningful information in aging and AD. More rigorous methodologic standardization is needed to increase comparability across studies and, ultimately, application in clinical practice.