The interactive effect of demographic and clinical factors on hippocampal volume: A multicohort study on 1958 cognitively normal individuals

Temporal atrophy together with verbal encoding impairment is highly predictive for cognitive decline in typical Alzheimer's dementia - a retrospective follow-up study

Introduction

The increasing prevalence of Alzheimer's disease (AD) has created an urgent need for rapid and cost-effective methods to diagnose and monitor people at all stages of the disease. Progressive memory impairment and hippocampal atrophy are key features of the most common so-called typical variant of AD. However, studies evaluating detailed cognitive measures combined with region of interest (ROI)-based imaging markers of progression over the long term in the AD dementia (ADD) stage are rare.

Method

We conducted a retrospective longitudinal follow-up study in patients with mild to moderate ADD (aged 60-92 years). They underwent magnetic resonance imaging (MRI; 3 Tesla, MPRAGE) as well as clinical and neuropsychological examination (Consortium to Establish a Registry for Alzheimer's Disease [CERAD] -Plus test battery) at baseline and at least one follow-up visit. ROI-based brain structural analysis of baseline MRIs was performed using the Computational Anatomy Toolbox (CAT) 12. Clinical dementia progression (progression index [PI]) was measured by the annual decline in the Mini Mental State Examination (MMSE) scores. MRI, demographic, and neuropsychological data were included in univariate and multiple linear regression models to predict the PI.

Results

104 ADD patients (age 63 to 90 years, 73% female, mean MMSE score 22.63 ± 3.77, mean follow-up 4.27 ± 2.15 years) and 32 age- and gender-matched cognitively intact controls were included. The pattern of gray matter (GM) atrophy and the cognitive profile were consistent with the amnestic/typical variant of ADD in all patients. Deficits in word list learning together with temporal lobe GM atrophy had the highest predictive value for rapid cognitive decline in the multiple linear regression model, accounting for 25.4% of the PI variance.

Discussion

Our results show that temporal atrophy together with deficits in the encoding of verbal material, rather than in immediate or delayed recall, is highly predictive for rapid cognitive decline in patients with mild to moderate amnestic/typical ADD. These findings point to the relevance of combining detailed cognitive and automated structural imaging analyses to predict clinical progression in patients with ADD.

The hippocampal sparing subtype of Alzheimer's disease assessed in neuropathology and in vivo tau positron emission tomography: a systematic review

Neuropathology and neuroimaging studies have identified several subtypes of Alzheimer's disease (AD): hippocampal sparing AD, typical AD, and limbic predominant AD. An unresolved question is whether hippocampal sparing AD cases can present with neurofibrillary tangles (NFT) in association cortices while completely sparing the hippocampus. To address that question, we conducted a systematic review and performed original analyses on tau positron emission tomography (PET) data. We searched EMBASE, PubMed, and Web of Science databases until October 2022. We also implemented several methods for AD subtyping on tau PET to identify hippocampal sparing AD cases. Our findings show that seven out of the eight reviewed neuropathologic studies included cases at Braak stages IV or higher and therefore, could not identify hippocampal sparing cases with NFT completely sparing the hippocampus. In contrast, tau PET did identify AD participants with tracer retention in the association cortex while completely sparing the hippocampus. We conclude that tau PET can identify hippocampal sparing AD cases with NFT completely sparing the hippocampus. Based on the accumulating data, we suggest two possible pathways of tau spread: (1) a canonical pathway with early involvement of transentorhinal cortex and subsequent involvement of limbic regions and association cortices, and (2) a less common pathway that affects association cortices with limbic involvement observed at end stages of the disease or not at all.

Unraveling the heterogeneity in Alzheimer's disease progression across multiple cohorts and the implications for data-driven disease modeling

INTRODUCTION

Given study-specific inclusion and exclusion criteria, Alzheimer's disease (AD) cohort studies effectively sample from different statistical distributions. This heterogeneity can propagate into cohort-specific signals and subsequently bias data-driven investigations of disease progression patterns.

METHODS

We built multi-state models for six independent AD cohort datasets to statistically compare disease progression patterns across them. Additionally, we propose a novel method for clustering cohorts with regard to their progression signals.

RESULTS

We identified significant differences in progression patterns across cohorts. Models trained on cohort data learned cohort-specific effects that bias their estimations. We demonstrated how six cohorts relate to each other regarding their disease progression.

DISCUSSION

Heterogeneity in cohort datasets impedes the reproducibility of data-driven results and validation of progression models generated on single cohorts. To ensure robust scientific insights, it is advisable to externally validate results in independent cohort datasets. The proposed clustering assesses the comparability of cohorts in an unbiased, data-driven manner.

A guidance of model selection for genomic prediction based on linear mixed models for complex traits

Brain imaging outcomes are important for Alzheimer's disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to make the best use of them for prediction analyses remains unknown. In this research, we first explored the relationship between genetic architecture and prediction accuracy of linear mixed models via visualizing the Manhattan plots generated based on the data obtained from the Wellcome Trust Case Control Consortium, and then constructed prediction models for eleven AD-related brain imaging outcomes using data from United Kingdom Biobank and Alzheimer's Disease Neuroimaging Initiative studies. We found that the simple Manhattan plots can be informative for the selection of prediction models. For traits that do not exhibit any significant signals from the Manhattan plots, the simple genomic best linear unbiased prediction (gBLUP) model is recommended due to its robust and accurate prediction performance as well as its computational efficiency. For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. For the prediction of AD-related traits, the Manhattan plots suggest their polygenic nature, and gBLUP has achieved robust performance for all these traits. We found that for these AD-related traits, genetic factors themselves only explain a very small proportion of the heritability, and the well-known AD risk factors can substantially improve the prediction model.

Contributions of sex, depression, and cognition on brain connectivity dynamics in Parkinson's disease

Alterations in time-varying functional connectivity (FC) have been found in Parkinson's disease (PD) patients. To date, very little is known about the influence of sex on brain FC in PD patients and how this could be related to disease severity. The first objective was to evaluate the influence of sex on dynamic FC characteristics in PD patients and healthy controls (HC), while the second aim was to investigate the temporal patterns of dynamic connectivity related to PD motor and non-motor symptoms. Ninety-nine PD patients and sixty-two HC underwent a neuropsychological and clinical assessment. Rs-fMRI and T1-weighted MRI were also acquired. Dynamic FC analyses were performed in the GIFT toolbox. Dynamic FC analyses identified two States: State I, characterized by within-network positive coupling; and State II that showed between-network connectivity, mostly involving somatomotor and visual networks. Sex differences were found in dynamic indexes in HC but these differences were not observed in PD. Hierarchical clustering analysis identified three phenotypically distinct PD subgroups: (1) Subgroup A was characterized by mild motor symptoms; (2) Subgroup B was characterized by depressive and motor symptoms; (3) Subgroup C was characterized by cognitive and motor symptoms. Results revealed that changes in the temporal properties of connectivity were related to the motor/non-motor outcomes of PD severity. Findings suggest that while in HC sex differences may play a certain role in dynamic connectivity patterns, in PD patients, these effects may be overcome by the neurodegenerative process. Changes in the temporal properties of connectivity in PD were mainly related to the clinical markers of PD severity.

The protective gene dose effect of the APOE ε2 allele on gray matter volume in cognitively unimpaired individuals

INTRODUCTION

Harboring two copies of the apolipoprotein E (APOE) ε2 allele strongly protects against Alzheimer's disease (AD). However, the effect of this genotype on gray matter (GM) volume in cognitively unimpaired individuals has not yet been described.

METHODS

Multicenter brain magnetic resonance images (MRIs) from cognitively unimpaired ε2 homozygotes were matched (1:1) against all other APOE genotypes for relevant confounders (n = 223). GM volumes of ε2 genotypic groups were compared to each other and to the reference group (APOE ε3/ε3).

RESULTS

Carrying at least one ε2 allele was associated with larger GM volumes in brain areas typically affected by AD and also in areas associated with cognitive resilience. APOE ε2 homozygotes, but not APOE ε2 heterozygotes, showed larger GM volumes in areas related to successful aging.

DISCUSSION

In addition to the known resistance against amyloid-β deposition, the larger GM volumes in key brain regions may confer APOE ε2 homozygotes additional protection against AD-related cognitive decline.

Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β_1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.

Brain Atrophy Subtypes and the ATN Classification Scheme in Alzheimer's Disease

INTRODUCTION

We investigated the association between atrophy subtypes of Alzheimer's disease (AD), the ATN classification scheme, and key demographic and clinical factors in 2 cohorts with different source characteristics (a highly selective research-oriented cohort, the Alzheimer's Disease Neuroimaging Initiative [ADNI]; and a naturalistic heterogeneous clinically oriented cohort, Karolinska Imaging Dementia Study [KIDS]).

METHODS

A total of 382 AD patients were included. Factorial analysis of mixed data was used to investigate associations between AD subtypes based on brain atrophy patterns, ATN profiles based on cerebrospinal fluid biomarkers, and age, sex, Mini Mental State Examination (MMSE), cerebrovascular disease (burden of white matter signal abnormalities, WMSAs), and APOE genotype.

RESULTS

Older patients with high WMSA burden, belonging to the typical AD subtype and showing A+T+N+ or A+T+N- profiles clustered together and were mainly from ADNI. Younger patients with low WMSA burden, limbic-predominant or minimal atrophy AD subtypes, and A+T-N- or A+T-N+ profiles clustered together and were mainly from KIDS. APOE ε4 carriers more frequently showed the A+T-N- and A+T+N- profiles.

CONCLUSIONS

Our findings align with the recent framework for biological subtypes of AD: the combination of risk factors, protective factors, and brain pathologies determines belonging of AD patients to distinct subtypes.

Evaluating the Alzheimer's disease data landscape

INTRODUCTION

Numerous studies have collected Alzheimer's disease (AD) cohort data sets. To achieve reproducible, robust results in data-driven approaches, an evaluation of the present data landscape is vital.

METHODS

Previous efforts relied exclusively on metadata and literature. Here, we evaluate the data landscape by directly investigating nine patient-level data sets generated in major clinical cohort studies.

RESULTS

The investigated cohorts differ in key characteristics, such as demographics and distributions of AD biomarkers. Analyzing the ethnoracial diversity revealed a strong bias toward White/Caucasian individuals. We described and compared the measured data modalities. Finally, the available longitudinal data for important AD biomarkers was evaluated. All results are explorable through our web application ADataViewer (https://adata.scai.fraunhofer.de).

DISCUSSION

Our evaluation exposed critical limitations in the AD data landscape that impede comparative approaches across multiple data sets. Comparison of our results to those gained by metadata-based approaches highlights that thorough investigation of real patient-level data is imperative to assess a data landscape.

Cholinergic dysfunction, neurodegeneration, and amyloid-beta pathology in neurodegenerative diseases

Cholinergic dysfunction is central in Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). The electroencephalography-based acetylcholine index (EEG-Ach index) has been proposed as a biomarker of cholinergic dysfunction. However, it is unclear how the EEG-Ach index relates to amyloid-beta pathology and neurodegeneration. We investigated the association between the EEG-Ach index and cerebrospinal fluid (CSF) amyloid-beta, CSF total tau, cortical thickness, and hippocampal volume from magnetic resonance imaging (MRI), and cognition. A total of 127 patients with different neurodegenerative diseases were studied. The EEG-Ach index was calculated from quantitative EEG using statistical pattern recognition. The EEG-Ach index was associated with hippocampal volume and cortical thickness in frontal, temporal, and occipital cortices. Cross-sectional sub-analyses based on a small sample suggests that the EEG-Ach index increases the closest to AD dementia, downstream to amyloid-beta pathology, CSF total tau, and hippocampal volume. We conclude that cholinergic dysfunction correlates with atrophy in brain areas important for AD pathogenesis, and this association is more prominent in the dementia stage. These results together with previous studies from this project suggest that the EEG-Ach index may be a useful biomarker for cholinergic dysfunction, with value for differential diagnosis of dementia and monitoring patients at the dementia stage.

A comparison of Freesurfer and multi-atlas MUSE for brain anatomy segmentation: Findings about size and age bias, and inter-scanner stability in multi-site aging studies

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.

Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice

Artificial intelligence (AI) approaches pose a great opportunity for individualized, pre-symptomatic disease diagnosis which plays a key role in the context of personalized, predictive, and finally preventive medicine (PPPM). However, to translate PPPM into clinical practice, it is of utmost importance that AI-based models are carefully validated. The validation process comprises several steps, one of which is testing the model on patient-level data from an independent clinical cohort study. However, recruitment criteria can bias statistical analysis of cohort study data and impede model application beyond the training data. To evaluate whether and how data from independent clinical cohort studies differ from each other, this study systematically compares the datasets collected from two major dementia cohorts, namely, the Alzheimer's Disease Neuroimaging Initiative (ADNI) and AddNeuroMed. The presented comparison was conducted on individual feature level and revealed significant differences among both cohorts. Such systematic deviations can potentially hamper the generalizability of results which were based on a single cohort dataset. Despite identified differences, validation of a previously published, ADNI trained model for prediction of personalized dementia risk scores on 244 AddNeuroMed subjects was successful: External validation resulted in a high prediction performance of above 80% area under receiver operator characteristic curve up to 6 years before dementia diagnosis. Propensity score matching identified a subset of patients from AddNeuroMed, which showed significantly smaller demographic differences to ADNI. For these patients, an even higher prediction performance was achieved, which demonstrates the influence systematic differences between cohorts can have on validation results. In conclusion, this study exposes challenges in external validation of AI models on cohort study data and is one of the rare cases in the neurology field in which such external validation was performed. The presented model represents a proof of concept that reliable models for personalized predictive diagnostics are feasible, which, in turn, could lead to adequate disease prevention and hereby enable the PPPM paradigm in the dementia field.

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical datasets-collected with different scanners, protocols and disease populations-and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens' scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to datasets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment.

Challenges of Integrative Disease Modeling in Alzheimer's Disease

Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.

Low Hippocampal Dentate Gyrus Volume Associated With Hypertension-Related Cognitive Impairment

Hypertension increases the risk of cognitive impairment independent of detectable stroke or cerebral lesions. However, the principal pathophysiological basis of this increase has not been fully elucidated. The present study investigates the relationships among blood pressure, hippocampal subfields volume, and cognitive function in a relatively young non-stroke population. A total of 59 non-stroke non-dementia subjects (mean age, 57.2 ± 4.9 years) were enrolled. All subjects were subjected to complete assessment of vascular risk factors including 24-hour blood pressure monitoring, various neuropsychological tests, and 3D-T1 MR scan. Freesurfer V6.0 was used for segmentation of hippocampal subfields. Our analyses revealed that both 24-hour and daytime mean systolic blood pressure (SBP) were significantly associated with the low volume of the left DG. Higher coefficient of variation (CV) of daytime SBP was significantly associated with lower volume of the left Cornu Ammonis 4 and dentate gyrus (DG) region. Both higher CV of 24-hour mean SBP and daytime SBP were significantly associated with lower performance in both executive and linguistic function. The low volume of the left DG was significantly associated with the low performance in linguistic function. Our findings support that reduced DG volume and increased SBP variability associated with hypertension-related cognitive impairment.

The effect of white matter hyperintensities on regional brain volumes and white matter microstructure, a population-based study in HUNT

Even though age-related white matter hyperintensities (WMH) begin to emerge in middle age, their effect on brain micro- and macrostructure in this age group is not fully elucidated. We have examined how presence of WMH and load of WMH affect regional brain volumes and microstructure in a validated, representative general population sample of 873 individuals between 50 and 66 years. Presence of WMH was determined as Fazakas grade ≥1. WMH load was WMH volume from manual tracing of WMHs divided on intracranial volume. The impact of age appropriate WMH (Fazakas grade 1) on the brain was also investigated. Major novel findings were that even the age appropriate WMH group had widespread macro- and microstructural changes in gray and white matter, showing that the mere presence of WMH, not just WMH load is an important clinical indicator of brain health. With increasing WMH load, structural changes spread centrifugally. Further, we found three major patterns of FA and MD changes related to increasing WMH load, demonstrating a heterogeneous effect on white matter microstructure, where distinct patterns were found in the proximity of the lesions, in deep white matter and in white matter near the cortex. This study also raises several questions about the onset of WMH related pathology, in particular, whether some of the aberrant brain structural and microstructural findings are present before the emergence of WMH. We also found, similar to other studies, that WMH risk factors had low explanatory power for WMH, making it unclear which factors lead to WMH.

Understanding disease progression and improving Alzheimer's disease clinical trials: Recent highlights from the Alzheimer's Disease Neuroimaging Initiative

INTRODUCTION

The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI is a multisite, longitudinal, observational study that has collected many biomarkers since 2004. Recent publications highlight the multifactorial nature of late-onset AD. We discuss selected topics that provide insights into AD progression and outline how this knowledge may improve clinical trials.

METHODS

We used standard methods to identify nearly 600 publications using ADNI data from 2016 and 2017 (listed in Supplementary Material and searchable at http://adni.loni.usc.edu/news-publications/publications/).

RESULTS

(1) Data-driven AD progression models supported multifactorial interactions rather than a linear cascade of events. (2) β-Amyloid (Aβ) deposition occurred concurrently with functional connectivity changes within the default mode network in preclinical subjects and was followed by specific and progressive disconnection of functional and anatomical networks. (3) Changes in functional connectivity, volumetric measures, regional hypometabolism, and cognition were detectable at subthreshold levels of Aβ deposition. 4. Tau positron emission tomography imaging studies detailed a specific temporal and spatial pattern of tau pathology dependent on prior Aβ deposition, and related to subsequent cognitive decline. 5. Clustering studies using a wide range of modalities consistently identified a "typical AD" subgroup and a second subgroup characterized by executive impairment and widespread cortical atrophy in preclinical and prodromal subjects. 6. Vascular pathology burden may act through both Aβ dependent and independent mechanisms to exacerbate AD progression. 7. The APOE ε4 allele interacted with cerebrovascular disease to impede Aβ clearance mechanisms. 8. Genetic approaches identified novel genetic risk factors involving a wide range of processes, and demonstrated shared genetic risk for AD and vascular disorders, as well as the temporal and regional pathological associations of established AD risk alleles. 9. Knowledge of early pathological changes guided the development of novel prognostic biomarkers for preclinical subjects. 10. Placebo populations of randomized controlled clinical trials had highly variable trajectories of cognitive change, underscoring the importance of subject selection and monitoring. 11. Selection criteria based on Aβ positivity, hippocampal volume, baseline cognitive/functional measures, and APOE ε4 status in combination with improved cognitive outcome measures were projected to decrease clinical trial duration and cost. 12. Multiple concurrent therapies targeting vascular health and other AD pathology in addition to Aβ may be more effective than single therapies.

DISCUSSION

ADNI publications from 2016 and 2017 supported the idea of AD as a multifactorial disease and provided insights into the complexities of AD disease progression. These findings guided the development of novel biomarkers and suggested that subject selection on the basis of multiple factors may lower AD clinical trial costs and duration. The use of multiple concurrent therapies in these trials may prove more effective in reversing AD disease progression.

Cognitive Variability during Middle-Age: Possible Association with Neurodegeneration and Cognitive Reserve

Objective: Increased variability in cognition with age has been argued as an indication of pathological processes. Focusing on early detection of neurodegenerative disorders, we investigated variability in cognition in healthy middle-aged adults. In order to understand possible determinants of this variability, we also investigated associations with cognitive reserve, neuroimaging markers, subjective memory complaints, depressive symptomatology, and gender.

Method: Thirty-one 50 ± 2 years old individuals were investigated as target group and deviation was studied in comparison to a reference younger group of 30 individuals 40 ± 2 years old. Comprehensive neuropsychological and structural imaging protocols were collected. Brain regional volumes and cortical thickness were calculated with FreeSurfer, white matter hyperintensities with CASCADE, and mean diffusivity with FSL.

Results: Across-individuals variability showed greater dispersion in lexical access, processing speed, executive functions, and memory. Variability in global cognition correlated with, reduced cortical thickness in the right parietal-temporal-occipital association cortex, and increased mean diffusivity in the cingulum bundle and right inferior fronto-occipital fasciculus. A trend was also observed for the correlation between global cognition and hippocampal volume and female gender. All these associations were influenced by cognitive reserve. No correlations were found with subjective memory complaints, white matter hyperintensities and depressive symptomatology. Across-domains and across-tasks variability was greater in several executive components and cognitive processing speed.

Conclusion: Variability in cognition during middle-age is associated with neurodegeneration in the parietal–temporal–occipital association cortex and white matter tracts connecting this to the prefrontal dorsolateral cortex and the hippocampus. Moreover, this effect is influenced by cognitive reserve. Studying variability offers valuable information showing that differences do not occur in the same magnitude and direction across individuals, cognitive domains and tasks. These findings may have important implications for early detection of subtle cognitive impairment and clinical interpretation of deviation from normality.