Presymptomatic atrophy in autosomal dominant Alzheimer's disease: A serial magnetic resonance imaging study

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.

Cognitive Fitness: Harnessing the Strength of Exerkines for Aging and Metabolic Challenges

Addressing cognitive impairment (CI) represents a significant global challenge in health and social care. Evidence suggests that aging and metabolic disorders increase the risk of CI, yet promisingly, physical exercise has been identified as a potential ameliorative factor. Specifically, there is a growing understanding that exercise-induced cognitive improvement may be mediated by molecules known as exerkines. This review delves into the potential impact of aging and metabolic disorders on CI, elucidating the mechanisms through which various exerkines may bolster cognitive function in this context. Additionally, the discussion extends to the role of exerkines in facilitating stem cell mobilization, offering a potential avenue for improving cognitive impairment.

Tipping points in neurodegeneration

In Alzheimer's disease (AD), Aβ deposits form slowly, several decades before further pathological events trigger neurodegeneration and dementia. However, a substantial proportion of affected individuals remains non-demented despite AD pathology, raising questions about the underlying factors that determine the transition to clinical disease. Here, we emphasize the critical function of resilience and resistance factors, which we extend beyond the concept of cognitive reserve to include the glial, immune, and vascular system. We review the evidence and use the metaphor of "tipping points" to illustrate how gradually forming AD neuropathology in the preclinical stage can transition to dementia once adaptive functions of the glial, immune, and vascular system are lost and self-reinforcing pathological cascades are unleashed. Thus, we propose an expanded framework for pathomechanistic research that focuses on tipping points and non-neuronal resilience mechanisms, which may represent previously untapped therapeutic targets in preclinical AD.

Frontal and temporal lobe correlates of verbal learning and memory in aMCI and suspected Alzheimer's disease dementia

Alzheimer's disease is primarily known for deficits in learning and retaining new information. This has long been associated with pathological changes in the mesial temporal lobes. The role of the frontal lobes in memory in Alzheimer's disease is less well understood. In this study, we examined the role of the frontal lobes in learning, recognition, and retention of new verbal information, as well as the presence of specific errors (i.e., intrusions and false-positive errors). Participants included one hundred sixty-seven patients clinically diagnosed with amnestic mild cognitive impairment or suspected Alzheimer's disease dementia who were administered the California Verbal Learning Test and completed high-resolution MRI. We confirmed the role of the mesial temporal lobes in learning and retention, including the volumes of the hippocampus, entorhinal cortex, and parahippocampal gyrus. In addition, false-positive errors were associated with all volumes of the mesial temporal lobes and widespread areas within the frontal lobes. Errors of intrusion were related to the supplementary motor cortex and hippocampus. Most importantly, the mesial temporal lobes interacted with the frontal lobes for learning, recognition, and memory errors. Lower volumes in both regions explained more performance variance than any single structure. This study supports the interaction of the frontal lobes with the temporal lobes in many aspects of memory in Alzheimer's disease.

Identifying healthy individuals with Alzheimer's disease neuroimaging phenotypes in the UK Biobank

BACKGROUND

Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease-modifying trials. Evidence from genetic and other studies suggests the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be used to reliably detect prediagnostic sporadic disease.

METHODS

We trained a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real-world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80) and demonstrate the correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease.

RESULTS

We show that the cohort with a neuroimaging Alzheimer's phenotype has a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and some evidence of poorer numeric memory, reaction time, working memory, and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking.

CONCLUSIONS

This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

Longitudinal Brain Atrophy Rates in Presymptomatic Carriers of Genetic Frontotemporal Dementia

BACKGROUND AND OBJECTIVES

It is important to identify at what age brain atrophy rates in genetic frontotemporal dementia (FTD) start to accelerate and deviate from normal aging effects to find the optimal starting point for treatment. We investigated longitudinal brain atrophy rates in the presymptomatic stage of genetic FTD using normative brain volumetry software.

METHODS

Presymptomatic GRN, MAPT, and C9orf72 pathogenic variant carriers underwent longitudinal volumetric T1-weighted magnetic resonance imaging of the brain as part of a prospective cohort study. Images were automatically analyzed with Quantib® ND, which consisted of volume measurements (CSF and sum of gray and white matter) of lobes, cerebellum, and hippocampus. All volumes were compared with reference centile curves based on a large population-derived sample of nondemented individuals (n = 4,951). Mixed-effects models were fitted to analyze atrophy rates of the different gene groups as a function of age.

RESULTS

Thirty-four GRN, 8 MAPT, and 14 C9orf72 pathogenic variant carriers were included (mean age = 52.1, standard deviation = 7.2; 66% female). The mean follow-up duration of the study was 64 ± 33 months (median = 52; range 13-108). GRN pathogenic variant carriers showed a faster decline than the reference centile curves for all brain areas, though relative volumes remained between the 5th and 75th percentiles between the ages of 45 and 70 years. In MAPT pathogenic variant carriers, frontal lobe volume was already at the 5th percentile at age 45 years and showed a further decline between the ages 50 and 60 years. Temporal lobe volume started in the 50th percentile at age 45 years but showed fastest decline over time compared with other brain structures. Frontal, temporal, parietal, and cerebellar volume already started below the 5th percentile compared with the reference centile curves at age 45 years for C9orf72 pathogenic variant carriers, but there was minimal decline over time until the age of 60 years.

DISCUSSION

We provide evidence for longitudinal brain atrophy in the presymptomatic stage of genetic FTD. The affected brain areas and the age after which atrophy rates start to accelerate and diverge from normal aging slopes differed between gene groups. These results highlight the value of normative volumetry software for disease tracking and staging biomarkers in genetic FTD. These techniques could help in identifying the optimal time window for starting treatment and monitoring treatment response.

Herpes simplex virus and rates of cognitive decline or whole brain atrophy in the Dominantly Inherited Alzheimer Network

OBJECTIVE

To investigate whether herpes simplex virus type 1 (HSV-1) infection was associated with rates of cognitive decline or whole brain atrophy among individuals from the Dominantly Inherited Alzheimer Network (DIAN).

METHODS

Among two subsets of the DIAN cohort (age range 19.6-66.6 years; median follow-up 3.0 years) we examined (i) rate of cognitive decline (N = 164) using change in mini-mental state examination (MMSE) score, (ii) rate of whole brain atrophy (N = 149), derived from serial MR imaging, calculated using the boundary shift integral (BSI) method. HSV-1 antibodies were assayed in baseline sera collected from 2009-2015. Linear mixed-effects models were used to compare outcomes by HSV-1 seropositivity and high HSV-1 IgG titres/IgM status.

RESULTS

There was no association between baseline HSV-1 seropositivity and rates of cognitive decline or whole brain atrophy. Having high HSV-1 IgG titres/IgM was associated with a slightly greater decline in MMSE points per year (difference in slope - 0.365, 95% CI: -0.958 to -0.072), but not with rate of whole brain atrophy. Symptomatic mutation carriers declined fastest on both MMSE and BSI measures, however, this was not influenced by HSV-1. Among asymptomatic mutation carriers, rates of decline on MMSE and BSI were slightly greater among those who were HSV-1 seronegative. Among mutation-negative individuals, no differences were seen by HSV-1. Stratifying by APOE4 status yielded inconsistent results.

INTERPRETATION

We found no evidence for a major role of HSV-1, measured by serum antibodies, in cognitive decline or whole brain atrophy among individuals at high risk of early-onset AD.

Pre-diagnostic cognitive and functional impairment in multiple sporadic neurodegenerative diseases

INTRODUCTION

The pathophysiological processes of neurodegenerative diseases begin years before diagnosis. However, pre-diagnostic changes in cognition and physical function are poorly understood, especially in sporadic neurodegenerative disease.

METHODS

UK Biobank data were extracted. Cognitive and functional measures in individuals who subsequently developed Alzheimer's disease (AD), Parkinson disease, frontotemporal dementia, progressive supranuclear palsy, dementia with Lewy bodies, or multiple system atrophy were compared against individuals without neurodegenerative diagnoses. The same measures were regressed against time to diagnosis, after adjusting for the effects of age.

RESULTS

There was evidence for pre-diagnostic cognitive impairment and decline with time, particularly in AD. Pre-diagnostic functional impairment and decline were observed in multiple diseases.

DISCUSSION

The scale and longitudinal follow-up of UK Biobank participants provides evidence for cognitive and functional decline years before symptoms become obvious in multiple neurodegenerative diseases. Identifying pre-diagnostic functional and cognitive changes could improve selection for preventive and early disease-modifying treatment trials.

Longitudinal Cognitive Changes in Genetic Frontotemporal Dementia Within the GENFI Cohort

BACKGROUND AND OBJECTIVES

Disease-modifying therapeutic trials for genetic frontotemporal dementia (FTD) are underway, but sensitive cognitive outcome measures are lacking. The aim of this study was to identify such cognitive tests in early stage FTD by investigating cognitive decline in a large cohort of genetic FTD pathogenic variant carriers and by investigating whether gene-specific differences are moderated by disease stage (asymptomatic, prodromal, and symptomatic).

METHODS

C9orf72, GRN, and MAPT pathogenic variant carriers as well as controls underwent a yearly neuropsychological assessment covering 8 cognitive domains as part of the Genetic FTD Initiative, a prospective multicenter cohort study. Pathogenic variant carriers were stratified according to disease stage using the global Clinical Dementia Rating (CDR) plus National Alzheimer's Coordinating Center (NACC) FTLD score (0, 0.5, or ≥1). Linear mixed-effects models were used to investigate differences between genetic groups and disease stages as well as the 3-way interaction between time, genetic group, and disease stage.

RESULTS

A total of 207 C9orf72, 206 GRN, and 86 MAPT pathogenic variant carriers and 255 controls were included. C9orf72 pathogenic variant carriers performed lower on attention, executive function, and verbal fluency from CDR plus NACC FTLD 0 onwards, with relatively minimal decline over time regardless of the CDR plus NACC FTLD score (i.e., disease progression). The cognitive profile in MAPT pathogenic variant carriers was characterized by lower memory performance at CDR plus NACC FTLD 0.5, with decline over time in language from the CDR plus NACC FTLD 0.5 stage onwards, and executive dysfunction rapidly developing at CDR plus NACC FTLD ≥1. GRN pathogenic variant carriers declined on verbal fluency and visuoconstruction in the CDR plus NACC FTLD 0.5 stage, with progressive decline in other cognitive domains starting at CDR plus NACC FTLD ≥1.

DISCUSSION

We confirmed cognitive decline in the asymptomatic and prodromal stage of genetic FTD. Specifically, tests for attention, executive function, language, and memory showed clear differences between genetic groups and controls at baseline, but the speed of change over time differed depending on genetic group and disease stage. This confirms the value of neuropsychological assessment in tracking clinical onset and progression and could inform clinical trials in selecting sensitive end points for measuring treatment effects as well as characterizing the best time window for starting treatment.

What contribution can genetics make to predict the risk of Alzheimer's disease?

Alzheimer's disease (AD) is the most common neurodegenerative disorder. Although its etiology remains incompletely understood, genetic variants are important contributors. The prediction of AD risk through individual genetic variants is an important topic of research that may have individual and societal consequences when preventive treatments will become available. However, the genetic substratum of AD is heterogeneous. In addition to the extremely rare and fully penetrant pathogenic variants of the PSEN1, PSEN2 or APP genes causing autosomal dominant AD, a large spectrum of risk factors have been identified in complex forms, including the common risk factor APOEɛ4, which is associated with a moderate-to-high risk, common polymorphisms associated with a modest individual risk, and a plethora of rare variants in genes like SORL1, TREM2 or ABCA7 with moderate to high-magnitude effect. Understanding how these genetic factors contribute to AD risk in a given individual, in additional to non-genetic factors, remains a challenge. Over the last 10 years, age-related penetrance curves have progressively incorporated advances in the knowledge of AD genetics, from APOE to common polygenic components and, currently, SORL1 rare variants, which represents an important step towards precision medicine in AD. In this review, we present the complex genetic architecture of AD and we expose the prediction of AD risk according to its underlying genetic component.

Clinical applicability of quantitative atrophy measures on MRI in patients suspected of Alzheimer's disease

OBJECTIVES

Neurodegeneration in suspected Alzheimer's disease can be determined using visual rating or quantitative volumetric assessments. We examined the feasibility of volumetric measurements of gray matter (GMV) and hippocampal volume (HCV) and compared their diagnostic performance with visual rating scales in academic and non-academic memory clinics.

MATERIALS AND METHODS

We included 231 patients attending local memory clinics (LMC) in the Netherlands and 501 of the academic Amsterdam Dementia Cohort (ADC). MRI scans were acquired using local protocols, including a T1-weighted sequence. Quantification of GMV and HCV was performed using FSL and FreeSurfer. Medial temporal atrophy and global atrophy were assessed with visual rating scales. ROC curves were derived to determine which measure discriminated best between cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's dementia (AD).

RESULTS

Patients attending LMC (age 70.9 ± 8.9 years; 47% females; 19% CN; 34% MCI; 47% AD) were older, had more cerebrovascular pathology, and had lower GMV and HCV compared to those of the ADC (age 64.9 ± 8.2 years; 42% females; 35% CN, 43% MCI, 22% AD). While visual ratings were feasible in > 95% of scans in both cohorts, quantification was achieved in 94-98% of ADC, but only 68-85% of LMC scans, depending on the software. Visual ratings and volumetric outcomes performed similarly in discriminating CN vs AD in both cohorts.

CONCLUSION

In clinical settings, quantification of GM and hippocampal atrophy currently fails in up to one-third of scans, probably due to lack of standardized acquisition protocols. Diagnostic accuracy is similar for volumetric measures and visual rating scales, making the latter suited for clinical practice. In a real-life clinical setting, volumetric assessment of MRI scans in dementia patients may require acquisition protocol optimization and does not outperform visual rating scales.

KEY POINTS

• In a real-life clinical setting, the diagnostic performance of visual rating scales is similar to that of automatic volumetric quantification and may be sufficient to distinguish Alzheimer's disease groups. • Volumetric assessment of gray matter and hippocampal volumes from MRI scans of patients attending non-academic memory clinics fails in up to 32% of cases. • Clinical MR acquisition protocols should be optimized to improve the output of quantitative software for segmentation of Alzheimer's disease-specific outcomes.

Functional Connectivity Alterations Based on the Weighted Phase Lag Index: An Exploratory Electroencephalography Study on Alzheimer's Disease

OBJECTIVE

Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer's Disease (AD), but there are no consistent results especially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD.

METHODS

We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores.

RESULTS

In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores.

CONCLUSION

Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.

The Clinical and Neuropathological Features of Sporadic (Late-Onset) and Genetic Forms of Alzheimer's Disease

The purpose of this review is to compare and highlight the clinical and pathological aspects of genetic versus acquired Alzheimer's disease: Down syndrome-associated Alzheimer's disease in (DSAD) and Autosomal Dominant Alzheimer's disease (ADAD) are compared with the late-onset form of the disease (LOAD). DSAD and ADAD present in a younger population and are more likely to manifest with non-amnestic (such as dysexecutive function features) in the prodromal phase or neurological features (such as seizures and paralysis) especially in ADAD. The very large variety of mutations associated with ADAD explains the wider range of phenotypes. In the LOAD, age-associated comorbidities explain many of the phenotypic differences.

Pattern and degree of individual brain atrophy predicts dementia onset in dominantly inherited Alzheimer's disease

INTRODUCTION

Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD-MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD-MC and could help predict risk for dementia during trial enrollment.

METHODS

We created a dementia risk score by entering standardized gray-matter volumes from 231 DIAD-MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD-MC followed longitudinally.

RESULTS

Our risk score separated asymptomatic versus demented DIAD-MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%-98.2%]) and improved prediction beyond established methods based on familial age of onset.

DISCUSSION

Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD-MC participants for prevention trials.

Brain Shape Changes Associated With Cerebral Atrophy in Healthy Aging and Alzheimer's Disease

Both healthy and pathological brain aging are characterized by various degrees of cognitive decline that strongly correlate with morphological changes referred to as cerebral atrophy. These hallmark morphological changes include cortical thinning, white and gray matter volume loss, ventricular enlargement, and loss of gyrification all caused by a myriad of subcellular and cellular aging processes. While the biology of brain aging has been investigated extensively, the mechanics of brain aging remains vastly understudied. Here, we propose a multiphysics model that couples tissue atrophy and Alzheimer's disease biomarker progression. We adopt the multiplicative split of the deformation gradient into a shrinking and an elastic part. We model atrophy as region-specific isotropic shrinking and differentiate between a constant, tissue-dependent atrophy rate in healthy aging, and an atrophy rate in Alzheimer's disease that is proportional to the local biomarker concentration. Our finite element modeling approach delivers a computational framework to systematically study the spatiotemporal progression of cerebral atrophy and its regional effect on brain shape. We verify our results via comparison with cross-sectional medical imaging studies that reveal persistent age-related atrophy patterns. Our long-term goal is to develop a diagnostic tool able to differentiate between healthy and accelerated aging, typically observed in Alzheimer's disease and related dementias, in order to allow for earlier and more effective interventions.

Biphasic cortical macro- and microstructural changes in autosomal dominant Alzheimer's disease

INTRODUCTION

A biphasic model for brain structural changes in preclinical Alzheimer's disease (AD) could reconcile some conflicting and paradoxical findings in observational studies and anti-amyloid clinical trials.

METHODS

In this study we tested this model fitting linear versus quadratic trajectories and computed the timing of the inflection points vertexwise of cortical thickness and cortical diffusivity-a novel marker of cortical microstructure-changes in 389 participants from the Dominantly Inherited Alzheimer Network.

RESULTS

In early preclinical AD, between 20 and 15 years before estimated symptom onset, we found increases in cortical thickness and decreases in cortical diffusivity followed by cortical thinning and cortical diffusivity increases in later preclinical and symptomatic stages. The inflection points 16 to 19 years before estimated symptom onset are in agreement with the start of tau biomarker alterations.

DISCUSSION

These findings confirm a biphasic trajectory for brain structural changes and have direct implications when interpreting magnetic resonance imaging measures in preventive AD clinical trials.

Resting-State Functional Connectivity Disruption as a Pathological Biomarker in Autosomal Dominant Alzheimer Disease

Aim: Identify a global resting-state functional connectivity (gFC) signature in mutation carriers (MC) from the Dominantly Inherited Alzheimer Network (DIAN). Assess the gFC with regard to amyloid (A), tau (T), and neurodegeneration (N) biomarkers, and estimated years to symptom onset (EYO). Introduction: Cross-sectional measures were assessed in MC (n = 171) and mutation noncarrier (NC) (n = 70) participants. A functional connectivity (FC) matrix that encompassed multiple resting-state networks was computed for each participant. Methods: A global FC was compiled as a single index indicating FC strength. The gFC signature was modeled as a nonlinear function of EYO. The gFC was linearly associated with other biomarkers used for assessing the AT(N) framework, including cerebrospinal fluid (CSF), positron emission tomography (PET) molecular biomarkers, and structural magnetic resonance imaging. Results: The gFC was reduced in MC compared with NC participants. When MC participants were differentiated by clinical dementia rating (CDR), the gFC was significantly decreased in MC CDR >0 (demented) compared with either MC CDR 0 (cognitively normal) or NC participants. The gFC varied nonlinearly with EYO and initially decreased at EYO = -24 years, followed by a stable period followed by a further decline near EYO = 0 years. Irrespective of EYO, a lower gFC associated with values of amyloid PET, CSF Aβ_1-42, CSF p-tau, CSF t-tau, 18F-fluorodeoxyglucose, and hippocampal volume. Conclusions: The gFC correlated with biomarkers used for defining the AT(N) framework. A biphasic change in the gFC suggested early changes associated with CSF amyloid and later changes associated with hippocampal volume.

Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia

INTRODUCTION

The presymptomatic phase of neurodegenerative disease can last many years, with sustained cognitive function despite progressive atrophy. We investigate this phenomenon in familial frontotemporal dementia (FTD).

METHODS

We studied 121 presymptomatic FTD mutation carriers and 134 family members without mutations, using multivariate data-driven approach to link cognitive performance with both structural and functional magnetic resonance imaging. Atrophy and brain network connectivity were compared between groups, in relation to the time from expected symptom onset.

RESULTS

There were group differences in brain structure and function, in the absence of differences in cognitive performance. Specifically, we identified behaviorally relevant structural and functional network differences. Structure-function relationships were similar in both groups, but coupling between functional connectivity and cognition was stronger for carriers than for non-carriers, and increased with proximity to the expected onset of disease.

DISCUSSION

Our findings suggest that the maintenance of functional network connectivity enables carriers to maintain cognitive performance.

The presymptomatic phase of amyotrophic lateral sclerosis: are we merely scratching the surface?

Presymptomatic studies in ALS have consistently captured considerable disease burden long before symptom manifestation and contributed important academic insights. With the emergence of genotype-specific therapies, however, there is a pressing need to address practical objectives such as the estimation of age of symptom onset, phenotypic prediction, informing the optimal timing of pharmacological intervention, and identifying a core panel of biomarkers which may detect response to therapy. Existing presymptomatic studies in ALS have adopted striking different study designs, relied on a variety of control groups, used divergent imaging and electrophysiology methods, and focused on different genotypes and demographic groups. We have performed a systematic review of existing presymptomatic studies in ALS to identify common themes, stereotyped shortcomings, and key learning points for future studies. Existing presymptomatic studies in ALS often suffer from sample size limitations, lack of disease controls and rarely follow their cohort until symptom manifestation. As the characterisation of presymptomatic processes in ALS serves a multitude of academic and clinical purposes, the careful review of existing studies offers important lessons for future initiatives.

Quantitative detection and staging of presymptomatic cognitive decline in familial Alzheimer's disease: a retrospective cohort analysis

BACKGROUND

Understanding the earliest manifestations of Alzheimer's disease (AD) is key to realising disease-modifying treatments. Advances in neuroimaging and fluid biomarkers have improved our ability to identify AD pathology in vivo. The critical next step is improved detection and staging of early cognitive change. We studied an asymptomatic familial Alzheimer's disease (FAD) cohort to characterise preclinical cognitive change.

METHODS

Data included 35 asymptomatic participants at 50% risk of carrying a pathogenic FAD mutation. Participants completed a multi-domain neuropsychology battery. After accounting for sex, age and education, we used event-based modelling to estimate the sequence of cognitive decline in presymptomatic FAD, and uncertainty in the sequence. We assigned individuals to their most likely model stage of cumulative cognitive decline, given their data. Linear regression of estimated years to symptom onset against model stage was used to estimate the timing of preclinical cognitive decline.

RESULTS

Cognitive change in mutation carriers was first detected in measures of accelerated long-term forgetting, up to 10 years before estimated symptom onset. Measures of subjective cognitive decline also revealed early abnormalities. Our data-driven model demonstrated subtle cognitive impairment across multiple cognitive domains in clinically normal individuals on the AD continuum.

CONCLUSIONS

Data-driven modelling of neuropsychological test scores has potential to differentiate cognitive decline from cognitive stability and to estimate a fine-grained sequence of decline across cognitive domains and functions, in the preclinical phase of Alzheimer's disease. This can improve the design of future presymptomatic trials by informing enrichment strategies and guiding the selection of outcome measures.

The pathway to secondary prevention of Alzheimer's disease

Alzheimer's disease (AD) is a continuum consisting of a preclinical stage that occurs decades before symptoms appear. As researchers make advances in investigating the continuum, the importance of developing drugs for secondary prevention is garnering increased discussion. For efficacious drug development for secondary prevention it is important to define what are the earliest biological stages of AD. The Alzheimer's Association Research Roundtable convened November 27 to 28, 2018 to focus on pre-clinical AD. This review will address the biological approach to defining pre-clinical AD, detection, identification of at-risk individuals, and lessons learned from trials such as A4 and TOMMORROW.

Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease

Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer's disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset -9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer's disease, which is alike sporadic Alzheimer's disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer's disease.

Association of Longitudinal Changes in Cerebrospinal Fluid Total Tau and Phosphorylated Tau 181 and Brain Atrophy With Disease Progression in Patients With Alzheimer Disease

IMPORTANCE

The amyloid/tau/neurodegeneration (A/T/N) framework uses cerebrospinal fluid (CSF) levels of total tau (tTau) as a marker of neurodegeneration and CSF levels of phosphorylated tau 181 (pTau181) as a marker of tau tangles. However, it is unclear whether CSF levels of tTau and pTau181 have similar or different trajectories over the course of Alzheimer disease.

OBJECTIVES

To examine the rates of change in CSF levels of tTau and pTau181 across the Alzheimer disease course and how the rates of change are associated with brain atrophy as measured by magnetic resonance imaging.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study was set in tertiary research clinics. Each participant was a member of a pedigree with a known mutation for dominantly inherited Alzheimer disease. Participants were divided into 3 groups on the basis of the presence of a mutation and their Clinical Dementia Rating score. Data analysis was performed in June 2019.

MAIN OUTCOMES AND MEASURES

Rates of change of CSF tTau and pTau181 levels and their association with the rate of change of brain volume.

RESULTS

Data from 465 participants (283 mutation carriers and 182 noncarriers) were analyzed. The mean (SD) age of the cohort was 37.8 (11.3) years, and 262 (56.3%) were women. The mean (SD) follow-up duration was 2.7 (1.5) years. Two or more longitudinal CSF and magnetic resonance imaging assessments were available for 160 and 247 participants, respectively. Sixty-five percent of mutation carriers (183) did not have symptoms at baseline (Clinical Dementia Rating score, 0). For mutation carriers, the annual rates of change for CSF tTau and pTau181 became significantly different from 0 approximately 10 years before the estimated year of onset (mean [SE] rates of change, 5.5 [2.8] for tTau [P = .05] and 0.7 [0.3] for pTau 181 [P = .04]) and 15 years before onset (mean [SE] rates of change, 5.4 [3.9] for tTau [P = .17] and 1.1 [0.5] for pTau181 [P = .03]), respectively. The rate of change of pTau181 was positive and increased at the early stages of the disease, showing a positive rate of change starting at 15 estimated years before onset until 5 estimated years before onset (mean [SE], 0.4 [0.3]), followed by a positive but decreasing rate of change at year 0 (mean [SE], 0.1 [0.3]) and then negative rates of change at 5 years (mean [SE], -0.3 [0.4]) and 10 years (mean [SE], -0.6 [0.6]) after symptom onset. In individuals without symptoms (Clinical Dementia Rating score, 0), the rates of change of CSF tTau and pTau181 were negatively associated with brain atrophy (high rates of change in CSF measures were associated with low rates of change in brain volume in asymptomatic stages). After symptom onset (Clinical Dementia Rating score, >0), an increased rate of brain atrophy was not associated with rates of change of levels of both CSF tTau and pTau181.

CONCLUSIONS AND RELEVANCE

These findings suggest that CSF tTau and pTau181 may have different associations with brain atrophy across the disease time course. These results have implications for understanding the dynamics of disease pathobiology and interpreting neuronal injury biomarker concentrations in response to Alzheimer disease progression and disease-modifying therapies.

Brain MR Spectroscopy Changes Precede Frontotemporal Lobar Degeneration Phenoconversion in Mapt Mutation Carriers

BACKGROUND AND PURPOSE

The objective of this study was to longitudinally investigate the trajectory of change in 1 H MRS measurements in asymptomatic MAPT mutation carriers who became symptomatic during follow-up, and to determine the time at which the neurochemical alterations accelerated during disease progression.

METHODS

We identified eight MAPT mutations carriers who transitioned from asymptomatic to symptomatic disease during follow-up. All participants were longitudinally followed with an average of 7.75 years (range 4-11 years) and underwent two or more single voxel 1 H MRS examinations from the posterior cingulate voxel, with a total of 60 examinations. The rate of longitudinal change for each metabolite was estimated using linear mixed models. A flex point model was used to estimate the flex time point of the change in slope.

RESULTS

The decrease in the NAA/mI ratio accelerated 2.09 years prior to symptom onset, and continued to decline. A similar trajectory was observed in the presumed glial marker mI/Cr ratio accelerating 1.86 years prior to symptom onset.

CONCLUSIONS

Our findings support the potential use of longitudinal 1 H MRS for monitoring the neurodegenerative progression in MAPT mutation carriers starting from the asymptomatic stage.

Tau PET in autosomal dominant Alzheimer's disease: relationship with cognition, dementia and other biomarkers

Tauopathy is a hallmark pathology of Alzheimer's disease with a strong relationship with cognitive impairment. As such, understanding tau may be a key to clinical interventions. In vivo tauopathy has been measured using cerebrospinal fluid assays, but these do not provide information about where pathology is in the brain. The introduction of PET ligands that bind to paired helical filaments provides the ability to measure the amount and distribution of tau pathology. The heritability of the age of dementia onset tied to the specific mutations found in autosomal dominant Alzheimer's disease families provides an elegant model to study the spread of tau across the course of the disease as well as the cross-modal relationship between tau and other biomarkers. To better understand the pathobiology of Alzheimer's disease we measured levels of tau PET binding in individuals with dominantly inherited Alzheimer's disease using data from the Dominantly Inherited Alzheimer Network (DIAN). We examined cross-sectional measures of amyloid-β, tau, glucose metabolism, and grey matter degeneration in 15 cognitively normal mutation non-carriers, 20 asymptomatic carriers, and 15 symptomatic mutation carriers. Linear models examined the association of pathology with group, estimated years to symptom onset, as well as cross-modal relationships. For comparison, tau PET was acquired on 17 older adults with sporadic, late onset Alzheimer disease. Tau PET binding was starkly elevated in symptomatic DIAN individuals throughout the cortex. The brain areas demonstrating elevated tau PET binding overlapped with those seen in sporadic Alzheimer's disease, but with a greater cortical involvement and greater levels of binding despite similar cognitive impairment. Tau PET binding was elevated in the temporal lobe, but the most prominent loci of pathology were in the precuneus and lateral parietal regions. Symptomatic mutation carriers also demonstrated elevated tau PET binding in the basal ganglia, consistent with prior work with amyloid-β. The degree of tau tracer binding in symptomatic individuals was correlated to other biomarkers, particularly markers of neurodegeneration. In addition to the differences seen with tau, amyloid-β was increased in both asymptomatic and symptomatic groups relative to non-carriers. Glucose metabolism showed decline primarily in the symptomatic group. MRI indicated structural degeneration in both asymptomatic and symptomatic cohorts. We demonstrate that tau PET binding is elevated in symptomatic individuals with dominantly inherited Alzheimer's disease. Tau PET uptake was tied to the onset of cognitive dysfunction, and there was a higher amount, and different regional pattern of binding compared to late onset, non-familial Alzheimer's disease.

Alzheimer's disease neuroimaging

PURPOSE OF REVIEW

The aim of this study was to discuss the contribution of neuroimaging studies to our understanding of Alzheimer's disease. We now have the capability of measuring both tau and beta-amyloid (Aβ) proteins in the brain, which together with more traditional neuroimaging modalities, has led the field to focus on using neuroimaging to better characterize disease mechanisms underlying Alzheimer's disease.

RECENT FINDINGS

Studies have utilized tau and Aβ PET, as well as [18F]fluorodeoxyglucose PET, and structural and functional MRI, to investigate the following topics: phenotypic variability in Alzheimer's disease , including how neuroimaging findings are related to clinical phenotype and age; multimodality analyses to investigate the relationships between different neuroimaging modalities and what that teaches us about disease mechanisms; disease staging by assessing neuroimaging changes in the very earliest phases of the disease in cognitively normal individuals and individuals carrying an autosomal dominant Alzheimer's disease mutation; and influence of other comorbidities and proteins to the disease process.

SUMMARY

The findings shed light on the role of tau and Aβ, as well as age and other comorbidities, in the neurodegenerative process in Alzheimer's disease. This knowledge will be crucial in the development of better disease biomarkers and targeted therapeutic approaches.

The role of de novo mutations in adult-onset neurodegenerative disorders

The genetic underpinnings of the most common adult-onset neurodegenerative disorders (AOND) are complex in majority of the cases. In some families, however, the disease can be inherited in a Mendelian fashion as an autosomal-dominant trait. Next to that, patients carrying mutations in the same disease genes have been reported despite a negative family history. Although challenging to demonstrate due to the late onset of the disease in most cases, the occurrence of de novo mutations can explain this sporadic presentation, as demonstrated for severe neurodevelopmental disorders. Exome or genome sequencing of patient-parent trios allows a hypothesis-free study of the role of de novo mutations in AOND and the discovery of novel disease genes. Another hypothesis that may explain a proportion of sporadic AOND cases is the occurrence of a de novo mutation after the fertilization of the oocyte (post-zygotic mutation) or even as a late-somatic mutation, restricted to the brain. Such somatic mutation hypothesis, that can be tested with the use of novel sequencing technologies, is fully compatible with the seeding and spreading mechanisms of the pathological proteins identified in most of these disorders. We review here the current knowledge and future perspectives on de novo mutations in known and novel candidate genes identified in the most common AONDs such as Alzheimer's disease, Parkinson's disease, the frontotemporal lobar degeneration spectrum and Prion disorders. Also, we review the first lessons learned from recent genomic studies of control and diseased brains and the challenges which remain to be addressed.

Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease

Neurofilament light chain (NfL) is a promising fluid biomarker of disease progression for various cerebral proteopathies. Here we leverage the unique characteristics of the Dominantly Inherited Alzheimer Network and ultrasensitive immunoassay technology to demonstrate that NfL levels in the cerebrospinal fluid (n = 187) and serum (n = 405) are correlated with one another and are elevated at the presymptomatic stages of familial Alzheimer's disease. Longitudinal, within-person analysis of serum NfL dynamics (n = 196) confirmed this elevation and further revealed that the rate of change of serum NfL could discriminate mutation carriers from non-mutation carriers almost a decade earlier than cross-sectional absolute NfL levels (that is, 16.2 versus 6.8 years before the estimated symptom onset). Serum NfL rate of change peaked in participants converting from the presymptomatic to the symptomatic stage and was associated with cortical thinning assessed by magnetic resonance imaging, but less so with amyloid-β deposition or glucose metabolism (assessed by positron emission tomography). Serum NfL was predictive for both the rate of cortical thinning and cognitive changes assessed by the Mini-Mental State Examination and Logical Memory test. Thus, NfL dynamics in serum predict disease progression and brain neurodegeneration at the early presymptomatic stages of familial Alzheimer's disease, which supports its potential utility as a clinically useful biomarker.

Gray and white matter changes in presymptomatic genetic frontotemporal dementia: a longitudinal MRI study

In genetic frontotemporal dementia, cross-sectional studies have identified profiles of presymptomatic neuroanatomical loss for C9orf72 repeat expansion, MAPT, and GRN mutations. In this study, we characterize longitudinal gray matter (GM) and white matter (WM) brain changes in presymptomatic frontotemporal dementia. We included healthy carriers of C9orf72 repeat expansion (n = 12), MAPT (n = 15), GRN (n = 33) mutations, and related noncarriers (n = 53), that underwent magnetic resonance imaging at baseline and 2-year follow-up. We analyzed cross-sectional baseline, follow-up, and longitudinal GM and WM changes using voxel-based morphometry and cortical thickness analysis in SPM and tract-based spatial statistics in FSL. Compared with noncarriers, C9orf72 repeat expansion carriers showed lower GM volume in the cerebellum and insula, and WM differences in the anterior thalamic radiation, at baseline and follow-up. MAPT mutation carriers showed emerging GM temporal lobe changes and longitudinal WM degeneration of the uncinate fasciculus. GRN mutation carriers did not show presymptomatic neurodegeneration. This study shows distinct presymptomatic cross-sectional and longitudinal patterns of GM and WM changes across C9orf72 repeat expansion, MAPT, and GRN mutation carriers compared with noncarriers.

Longitudinal multimodal MRI as prognostic and diagnostic biomarker in presymptomatic familial frontotemporal dementia

Developing and validating sensitive biomarkers for the presymptomatic stage of familial frontotemporal dementia is an important step in early diagnosis and for the design of future therapeutic trials. In the longitudinal Frontotemporal Dementia Risk Cohort, presymptomatic mutation carriers and non-carriers from families with familial frontotemporal dementia due to microtubule-associated protein tau (MAPT) and progranulin (GRN) mutations underwent a clinical assessment and multimodal MRI at baseline, 2-, and 4-year follow-up. Of the cohort of 73 participants, eight mutation carriers (three GRN, five MAPT) developed clinical features of frontotemporal dementia ('converters'). Longitudinal whole-brain measures of white matter integrity (fractional anisotropy) and grey matter volume in these converters (n = 8) were compared with healthy mutation carriers ('non-converters'; n = 35) and non-carriers (n = 30) from the same families. We also assessed the prognostic performance of decline within white matter and grey matter regions of interest by means of receiver operating characteristic analyses followed by stepwise logistic regression. Longitudinal whole-brain analyses demonstrated lower fractional anisotropy values in extensive white matter regions (genu corpus callosum, forceps minor, uncinate fasciculus, and superior longitudinal fasciculus) and smaller grey matter volumes (prefrontal, temporal, cingulate, and insular cortex) over time in converters, present from 2 years before symptom onset. White matter integrity loss of the right uncinate fasciculus and genu corpus callosum provided significant classifiers between converters, non-converters, and non-carriers. Converters' within-individual disease trajectories showed a relatively gradual onset of clinical features in MAPT, whereas GRN mutations had more rapid changes around symptom onset. MAPT converters showed more decline in the uncinate fasciculus than GRN converters, and more decline in the genu corpus callosum in GRN than MAPT converters. Our study confirms the presence of spreading predominant frontotemporal pathology towards symptom onset and highlights the value of multimodal MRI as a prognostic biomarker in familial frontotemporal dementia.

An Algorithm for Preclinical Diagnosis of Alzheimer's Disease

Almost all Alzheimer's disease (AD) therapeutic trials have failed in recent years. One of the main reasons for failure is due to designing the disease-modifying clinical trials at the advanced stage of the disease when irreversible brain damage has already occurred. Diagnosis of the preclinical stage of AD and therapeutic intervention at this phase, with a perfect target, are key points to slowing the progression of the disease. Various AD biomarkers hold enormous promise for identifying individuals with preclinical AD and predicting the development of AD dementia in the future, but no single AD biomarker has the capability to distinguish the AD preclinical stage. A combination of complimentary AD biomarkers in cerebrospinal fluid (Aβ₄₂, tau, and phosphor-tau), non-invasive neuroimaging, and genetic evidence of AD can detect preclinical AD in the in-vivo ante mortem brain. Neuroimaging studies have examined region-specific cerebral blood flow (CBF) and microstructural changes in the preclinical AD brain. Functional MRI (fMRI), diffusion tensor imaging (DTI) MRI, arterial spin labeling (ASL) MRI, and advanced PET have potential application in preclinical AD diagnosis. A well-validated simple framework for diagnosis of preclinical AD is urgently needed. This article proposes a comprehensive preclinical AD diagnostic algorithm based on neuroimaging, CSF biomarkers, and genetic markers.

Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study

BACKGROUND

Models of Alzheimer's disease propose a sequence of amyloid β (Aβ) accumulation, hypometabolism, and structural decline that precedes the onset of clinical dementia. These pathological features evolve both temporally and spatially in the brain. In this study, we aimed to characterise where in the brain and when in the course of the disease neuroimaging biomarkers become abnormal.

METHODS

Between Jan 1, 2009, and Dec 31, 2015, we analysed data from mutation non-carriers, asymptomatic carriers, and symptomatic carriers from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer's Network. We analysed 11C-Pittsburgh Compound B (11C-PiB) PET, 18F-Fluorodeoxyglucose (18F-FDG) PET, and structural MRI data using regions of interest to assess change throughout the brain. We estimated rates of biomarker change as a function of estimated years to symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories differed between mutation carriers and non-carriers. This study is registered at ClinicalTrials.gov (number NCT00869817) FINDINGS: 11C-PiB PET was available for 346 individuals (162 with longitudinal imaging), 18F-FDG PET was available for 352 individuals (175 with longitudinal imaging), and MRI data were available for 377 individuals (201 with longitudinal imaging). We found a sequence to pathological changes, with rates of Aβ deposition in mutation carriers being significantly different from those in non-carriers first (across regions that showed a significant difference, at a mean of 18·9 years [SD 3·3] before expected onset), followed by hypometabolism (14·1 years [5·1] before expected onset), and lastly structural decline (4·7 years [4·2] before expected onset). This biomarker ordering was preserved in most, but not all, regions. The temporal emergence within a biomarker varied across the brain, with the precuneus being the first cortical region for each method to show divergence between groups (22·2 years before expected onset for Aβ accumulation, 18·8 years before expected onset for hypometabolism, and 13·0 years before expected onset for cortical thinning).

INTERPRETATION

Mutation carriers had elevations in Aβ deposition, reduced glucose metabolism, and cortical thinning compared with non-carriers which preceded the expected onset of dementia. Accrual of these pathologies varied throughout the brain, suggesting differential regional and temporal vulnerabilities to Aβ, metabolic decline, and structural atrophy, which should be taken into account when using biomarkers in a clinical setting as well as designing and evaluating clinical trials.

FUNDING

US National Institutes of Health, the German Center for Neurodegenerative Diseases, and the Medical Research Council Dementias Platform UK.