Long-term clinical outcomes in patients with CIS treated with interferon beta-1b: results from the 15-year follow up of the BENEFIT trial.

Multiple sclerosis (MS) treatment intervention with immunomodulating therapy at early disease stage improves short term clinical outcomes. The objective of this study is to describe the long-term outcomes and healthcare utilization of patients with clinically isolated syndrome (CIS) included in the Betaferon®/Betaseron® in Newly Emerging MS for Initial Treatment (BENEFIT) randomized, parallel group trial. In BENEFIT patients were assigned to "early" IFNB-1b treatment or placebo ("delayed" treatment). After 2 years or conversion to clinically definite multiple sclerosis (CDMS), all patients were offered IFNB-1b and were reassessed 15 years later. Of 468 patients, 261 (55.8%) were enrolled into BENEFIT 15 (161 [55.1%] from the early, 100 [56.8%] from the delayed treatment arm). In the full BENEFIT analysis set, risk of conversion to CDMS remained lower in the early treatment group ( - 30.5%; hazard ratio 0.695 [95% CI, 0.547-0.883]; p = 0.0029) with a 15.7% lower risk of relapse than in the delayed treatment group (p = 0.1008). Overall, 25 patients (9.6%; 9.9% early, 9.0% delayed) converted to secondary progressive multiple sclerosis. Disability remained low and stable with no significant difference between groups in Expanded Disability Status Scale score or MRI metrics. Paced Auditory Serial Addition Task-3 scores were better in the early treatment group (p = 0.0036 for treatment effect over 15 years). 66.3% of patients were still employed at Year 15 versus 74.7% at baseline. In conclusion, results 15 years from initial randomization support long-term benefits of early treatment with IFNB-1b.

Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients.

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. Methods & results: We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Conclusion: Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

Optic chiasm involvement in multiple sclerosis, aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder and myelin oligodendrocyte glycoprotein-associated disease.

BACKGROUND: Optic neuritis (ON) is a common feature of inflammatory demyelinating diseases (IDDs) such as multiple sclerosis (MS), aquaporin 4-antibody neuromyelitis optica spectrum disorder (AQP4 + NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). However, the involvement of the optic chiasm (OC) in IDD has not been fully investigated. AIMS: To examine OC differences in non-acute IDD patients with (ON+) and without ON (ON-) using magnetisation transfer ratio (MTR), to compare differences between MS, AQP4 + NMOSD and MOGAD and understand their associations with other neuro-ophthalmological markers. METHODS: Twenty-eight relapsing-remitting multiple sclerosis (RRMS), 24 AQP4 + NMOSD, 28 MOGAD patients and 32 healthy controls (HCs) underwent clinical evaluation, MRI and optical coherence tomography (OCT) scan. Multivariable linear regression models were applied. RESULTS: ON + IDD patients showed lower OC MTR than HCs (28.87 ± 4.58 vs 31.65 ± 4.93; p = 0.004). When compared with HCs, lower OC MTR was found in ON + AQP4 + NMOSD (28.55 ± 4.18 vs 31.65 ± 4.93; p = 0.020) and MOGAD (28.73 ± 4.99 vs 31.65 ± 4.93; p = 0.007) and in ON- AQP4 + NMOSD (28.37 ± 7.27 vs 31.65 ± 4.93; p = 0.035). ON+ RRMS had lower MTR than ON- RRMS (28.87 ± 4.58 vs 30.99 ± 4.76; p = 0.038). Lower OC MTR was associated with higher number of ON (regression coefficient (RC) = -1.15, 95% confidence interval (CI) = -1.819 to -0.490, p = 0.001), worse visual acuity (RC = -0.026, 95% CI = -0.041 to -0.011, p = 0.001) and lower peripapillary retinal nerve fibre layer (pRNFL) thickness (RC = 1.129, 95% CI = 0.199 to 2.059, p = 0.018) when considering the whole IDD group. CONCLUSION: OC microstructural damage indicates prior ON in IDD and is linked to reduced vision and thinner pRNFL.

Investigating reliable amyloid accumulation in Centiloids: Results from the AMYPAD Prognostic and Natural History Study.

INTRODUCTION: To support clinical trial designs focused on early interventions, our study determined reliable early amyloid-ß (Aß) accumulation based on Centiloids (CL) in pre-dementia populations. METHODS: A total of 1032 participants from the Amyloid Imaging to Prevent Alzheimer's Disease-Prognostic and Natural History Study (AMYPAD-PNHS) and Insight46 who underwent [18F]flutemetamol, [18F]florbetaben or [18F]florbetapir amyloid-PET were included. A normative strategy was used to define reliable accumulation by estimating the 95th percentile of longitudinal measurements in sub-populations (NPNHS = 101/750, NInsight46 = 35/382) expected to remain stable over time. The baseline CL threshold that optimally predicts future accumulation was investigated using precision-recall analyses. Accumulation rates were examined using linear mixed-effect models. RESULTS: Reliable accumulation in the PNHS was estimated to occur at >3.0 CL/year. Baseline CL of 16 [12,19] best predicted future Aß-accumulators. Rates of amyloid accumulation were tracer-independent, lower for APOE ε4 non-carriers, and for subjects with higher levels of education. DISCUSSION: Our results support a 12-20 CL window for inclusion into early secondary prevention studies. Reliable accumulation definition warrants further investigations.

Amyloid pathology and vascular risk are associated with distinct patterns of cerebral white matter hyperintensities: A multicenter study in 3132 memory clinic patients.

INTRODUCTION: White matter hyperintensities (WMH) are associated with key dementia etiologies, in particular arteriolosclerosis and amyloid pathology. We aimed to identify WMH locations associated with vascular risk or cerebral amyloid-ß1-42 (Aß42)-positive status. METHODS: Individual patient data (n = 3,132; mean age 71.5 ± 9 years; 49.3% female) from 11 memory clinic cohorts were harmonized. WMH volumes in 28 regions were related to a vascular risk compound score (VRCS) and Aß42 status (based on cerebrospinal fluid or amyloid positron emission tomography), correcting for age, sex, study site, and total WMH volume. RESULTS: VRCS was associated with WMH in anterior/superior corona radiata (B = 0.034/0.038, p < 0.001), external capsule (B = 0.052, p < 0.001), and middle cerebellar peduncle (B = 0.067, p < 0.001), and Aß42-positive status with WMH in posterior thalamic radiation (B = 0.097, p < 0.001) and splenium (B = 0.103, p < 0.001). DISCUSSION: Vascular risk factors and Aß42 pathology have distinct signature WMH patterns. This regional vulnerability may incite future studies into how arteriolosclerosis and Aß42 pathology affect the brain's white matter. HIGHLIGHTS: Key dementia etiologies may be associated with specific patterns of white matter hyperintensities (WMH). We related WMH locations to vascular risk and cerebral Aß42 status in 11 memory clinic cohorts. Aß42 positive status was associated with posterior WMH in splenium and posterior thalamic radiation. Vascular risk was associated with anterior and infratentorial WMH. Amyloid pathology and vascular risk have distinct signature WMH patterns.

Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm.

While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients' brains appear older than normal and to validate brain-predicted age difference (brain-PAD) as a possible disease severity biomarker. MRI scans of FD patients and healthy controls (HCs) from a single Institution were, retrospectively, studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1-weighted brain scans of healthy subjects from eight publicly available sources (N = 2160; mean age = 33 years [range 4-86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain-age predictions in the internal cohort. Within a linear modeling framework, brain-PAD was tested for age/sex-adjusted associations with diagnostic group (FD vs. HC), FASTEX score, and both global and voxel-level neuroimaging measures. We studied 52 FD patients (40.6 ± 12.6 years; 28F) and 58 HC (38.4 ± 13.4 years; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error = 4.01 years, R2 = .90). FD patients had significantly higher brain-PAD than HC (estimated marginal means: 3.1 vs. -0.1, p = .01). Brain-PAD was associated with FASTEX score (B = 0.10, p = .02), brain parenchymal fraction (B = -153.50, p = .001), white matter hyperintensities load (B = 0.85, p = .01), and tissue volume reduction throughout the brain. We demonstrated that FD patients' brains appear older than normal. Brain-PAD correlates with FD-related multi-organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.

The cognitive relevance of non-lesional damage to cortical networks in people with multiple sclerosis.

BACKGROUND: Cognitive impairment, a common and debilitating symptom in people with multiple sclerosis (MS), is especially related to cortical damage. However, the impact of regional cortical damage remains poorly understood. Our aim was to evaluate structural (network) integrity in lesional and non-lesional cortex in people with MS, and its relationship with cognitive dysfunction. METHODS: In this cross-sectional study, 176 people with MS and 48 healthy controls underwent MRI, including double inversion recovery and diffusion-weighted scans, and neuropsychological assessment. Cortical integrity was assessed based on fractional anisotropy (FA) and mean diffusivity (MD) within 212 regions split into lesional or non-lesional cortex, and grouped into seven cortical networks. Integrity was compared between people with MS and controls, and across cognitive groups: cognitively-impaired (CI; ≥ two domains at Z ≤ - 2 below controls), mildly CI (≥ two at - 2 < Z ≤ - 1.5), or cognitively-preserved (CP). RESULTS: Cortical lesions were observed in 87.5% of people with MS, mainly in ventral attention network, followed by limbic and default mode networks. Compared to controls, in non-lesional cortex, MD was increased in people with MS, but mean FA did not differ. Within the same individual, MD and FA were increased in lesional compared to non-lesional cortex. CI-MS exhibited higher MD than CP-MS in non-lesional cortex of default mode, frontoparietal and sensorimotor networks, of which the default mode network could best explain cognitive performance. CONCLUSION: Diffusion differences in lesional cortex were more severe than in non-lesional cortex. However, while most people with MS had cortical lesions, diffusion differences in CI-MS were more prominent in non-lesional cortex than lesional cortex, especially within default mode, frontoparietal and sensorimotor networks.

Validation of image-derived input function using a long axial field of view PET/CT scanner for two different tracers.

BACKGROUND: Accurate image-derived input function (IDIF) from highly sensitive large axial field of view (LAFOV) PET/CT scanners could avoid the need of invasive blood sampling for kinetic modelling. The aim is to validate the use of IDIF for two kinds of tracers, 3 different IDIF locations and 9 different reconstruction settings. METHODS: Eight [18F]FDG and 10 [18F]DPA-714 scans were acquired respectively during 70 and 60 min on the Vision Quadra PET/CT system. PET images were reconstructed using various reconstruction settings. IDIFs were taken from ascending aorta (AA), descending aorta (DA), and left ventricular cavity (LV). The calibration factor (CF) extracted from the comparison between the IDIFs and the manual blood samples as reference was used for IDIFs accuracy and precision assessment. To illustrate the effect of various calibrated-IDIFs on Patlak linearization for [18F]FDG and Logan linearization for [18F]DPA-714, the same target time-activity curves were applied for each calibrated-IDIF. RESULTS: For [18F]FDG, the accuracy and precision of the IDIFs were high (mean CF ≥ 0.82, SD ≤ 0.06). Compared to the striatum influx (Ki) extracted using calibrated AA IDIF with the updated European Association of Nuclear Medicine Research Ltd. standard reconstruction (EARL2), Ki mean differences were < 2% using the other calibrated IDIFs. For [18F]DPA714, high accuracy of the IDIFs was observed (mean CF ≥ 0.86) except using absolute scatter correction, DA and LV (respectively mean CF = 0.68, 0.47 and 0.44). However, the precision of the AA IDIFs was low (SD ≥ 0.10). Compared to the distribution volume (VT) in a frontal region obtained using calibrated continuous arterial sampler input function as reference, VT mean differences were small using calibrated AA IDIFs (for example VT mean difference = -5.3% using EARL2), but higher using calibrated DA and LV IDIFs (respectively + 12.5% and + 19.1%). CONCLUSIONS: For [18F]FDG, IDIF do not need calibration against manual blood samples. For [18F]DPA-714, AA IDIF can replace continuous arterial sampling for simplified kinetic quantification but only with calibration against arterial blood samples. The accuracy and precision of IDIF from LAFOV PET/CT system depend on tracer, reconstruction settings and IDIF VOI locations, warranting careful optimization.

Developing blood-brain barrier arterial spin labelling as a non-invasive early biomarker of Alzheimer's disease (DEBBIE-AD): a prospective observational multicohort study protocol.

INTRODUCTION: Loss of blood-brain barrier (BBB) integrity is hypothesised to be one of the earliest microvascular signs of Alzheimer's disease (AD). Existing BBB integrity imaging methods involve contrast agents or ionising radiation, and pose limitations in terms of cost and logistics. Arterial spin labelling (ASL) perfusion MRI has been recently adapted to map the BBB permeability non-invasively. The DEveloping BBB-ASL as a non-Invasive Early biomarker (DEBBIE) consortium aims to develop this modified ASL-MRI technique for patient-specific and robust BBB permeability assessments. This article outlines the study design of the DEBBIE cohorts focused on investigating the potential of BBB-ASL as an early biomarker for AD (DEBBIE-AD). METHODS AND ANALYSIS: DEBBIE-AD consists of a multicohort study enrolling participants with subjective cognitive decline, mild cognitive impairment and AD, as well as age-matched healthy controls, from 13 cohorts. The precision and accuracy of BBB-ASL will be evaluated in healthy participants. The clinical value of BBB-ASL will be evaluated by comparing results with both established and novel AD biomarkers. The DEBBIE-AD study aims to provide evidence of the ability of BBB-ASL to measure BBB permeability and demonstrate its utility in AD and AD-related pathologies. ETHICS AND DISSEMINATION: Ethics approval was obtained for 10 cohorts, and is pending for 3 cohorts. The results of the main trial and each of the secondary endpoints will be submitted for publication in a peer-reviewed journal.

Association Between Years of Education and Amyloid Burden in Patients With Subjective Cognitive Decline, MCI, and Alzheimer Disease.

OBJECTIVES: Higher-educated patients with Alzheimer disease (AD) can harbor greater neuropathologic burden than those with less education despite similar symptom severity. In this study, we assessed whether this observation is also present in potential preclinical AD stages, namely in individuals with subjective cognitive decline and clinical features increasing AD likelihood (SCD+). METHODS: Amyloid-PET information ([18F]Flutemetamol or [18F]Florbetaben) of individuals with SCD+, mild cognitive impairment (MCI), and AD were retrieved from the AMYPAD-DPMS cohort, a multicenter randomized controlled study. Group classification was based on the recommendations by the SCD-I and NIA-AA working groups. Amyloid PET images were acquired within 8 months after initial screening and processed with AMYPYPE. Amyloid load was based on global Centiloid (CL) values. Educational level was indexed by formal schooling and subsequent higher education in years. Using linear regression analysis, the main effect of education on CL values was tested across the entire cohort, followed by the assessment of an education-by-diagnostic-group interaction (covariates: age, sex, and recruiting memory clinic). To account for influences of non-AD pathology and comorbidities concerning the tested amyloid-education association, we compared white matter hyperintensity (WMH) severity, cardiovascular events, depression, and anxiety history between lower-educated and higher-educated groups within each diagnostic category using the Fisher exact test or χ2 test. Education groups were defined using a median split on education (Md = 13 years) in a subsample of the initial cohort, for whom this information was available. RESULTS: Across the cohort of 212 individuals with SCD+ (M(Age) = 69.17 years, F 42.45%), 258 individuals with MCI (M(Age) = 72.93, F 43.80%), and 195 individuals with dementia (M(Age) = 74.07, F 48.72%), no main effect of education (ß = 0.52, 95% CI -0.30 to 1.58), but a significant education-by-group interaction on CL values, was found (p = 0.024) using linear regression modeling. This interaction was driven by a negative association of education and CL values in the SCD+ group (ß = -0.11, 95% CI -4.85 to -0.21) and a positive association in the MCI group (ß = 0.15, 95% CI 0.79-5.22). No education-dependent differences in terms of WMH severity and comorbidities were found in the subsample (100 cases with SCD+, 97 cases with MCI, 72 cases with dementia). DISCUSSION: Education may represent a factor oppositely modulating subjective awareness in preclinical stages and objective severity of ongoing neuropathologic processes in clinical stages.

Brain Tumor Imaging without Gadolinium-based Contrast Agents: Feasible or Fantasy?

Gadolinium-based contrast agents (GBCAs) form the cornerstone of current primary brain tumor MRI protocols at all stages of the patient journey. Though an imperfect measure of tumor grade, GBCAs are repeatedly used for diagnosis and monitoring. In practice, however, radiologists will encounter situations where GBCA injection is not needed or of doubtful benefit. Reducing GBCA administration could improve the patient burden of (repeated) imaging (especially in vulnerable patient groups, such as children), minimize risks of putative side effects, and benefit costs, logistics, and the environmental footprint. On the basis of the current literature, imaging strategies to reduce GBCA exposure for pediatric and adult patients with primary brain tumors will be reviewed. Early postoperative MRI and fixed-interval imaging of gliomas are examples of GBCA exposure with uncertain survival benefits. Half-dose GBCAs for gliomas and T2-weighted imaging alone for meningiomas are among options to reduce GBCA use. While most imaging guidelines recommend using GBCAs at all stages of diagnosis and treatment, non-contrast-enhanced sequences, such as the arterial spin labeling, have shown a great potential. Artificial intelligence methods to generate synthetic postcontrast images from decreased-dose or non-GBCA scans have shown promise to replace GBCA-dependent approaches. This review is focused on pediatric and adult gliomas and meningiomas. Special attention is paid to the quality and real-life applicability of the reviewed literature.

Longitudinal fibre-specific white matter damage predicts cognitive decline in multiple sclerosis.

During the course of multiple sclerosis, many patients experience cognitive deficits which are not simply driven by lesion number or location. By considering the full complexity of white matter structure at macro- and microstructural levels, our understanding of cognitive impairment in multiple sclerosis may increase substantially. Accordingly, this study aimed to investigate specific patterns of white matter degeneration, the evolution over time, the manifestation across different stages of the disease and their role in cognitive impairment using a novel fixel-based approach. Neuropsychological test scores and MRI scans including 30-direction diffusion-weighted images were collected from 327 multiple sclerosis patients (mean age = 48.34 years, 221 female) and 95 healthy controls (mean age = 45.70 years, 55 female). Of those, 233 patients and 61 healthy controls had similar follow-up assessments 5 years after. Patients scoring 1.5 or 2 standard deviations below healthy controls on at least two out of seven cognitive domains (from the Brief Repeatable Battery of Neuropsychological Tests, BRB-N) were classified as mildly cognitively impaired or cognitively impaired, respectively, or otherwise cognitively preserved. Fixel-based analysis of diffusion data was used to calculate fibre-specific measures (fibre density, reflecting microstructural diffuse axonal damage; fibre cross-section, reflecting macrostructural tract atrophy) within atlas-based white matter tracts at each visit. At baseline, all fixel-based measures were significantly worse in multiple sclerosis compared with healthy controls (P < 0.05). For both fibre density and fibre cross-section, a similar pattern was observed, with secondary progressive multiple sclerosis patients having the most severe damage, followed by primary progressive and relapsing-remitting multiple sclerosis. Similarly, damage was least severe in cognitively preserved (n = 177), more severe in mildly cognitively impaired (n = 63) and worst in cognitively impaired (n = 87; P < 0.05). Microstructural damage was most pronounced in the cingulum, while macrostructural alterations were most pronounced in the corticospinal tract, cingulum and superior longitudinal fasciculus. Over time, white matter alterations worsened most severely in progressive multiple sclerosis (P < 0.05), with white matter atrophy progression mainly seen in the corticospinal tract and microstructural axonal damage worsening in cingulum and superior longitudinal fasciculus. Cognitive decline at follow-up could be predicted by baseline fixel-based measures (R2 = 0.45, P < 0.001). Fixel-based approaches are sensitive to white matter degeneration patterns in multiple sclerosis and can have strong predictive value for cognitive impairment. Longitudinal deterioration was most marked in progressive multiple sclerosis, indicating that degeneration in white matter remains important to characterize further in this phenotype.

Visual associative learning to detect early episodic memory deficits and distinguish Alzheimer's disease from other types of dementia.

OBJECTIVE: We investigated how well a visual associative learning task discriminates Alzheimer's disease (AD) dementia from other types of dementia and how it relates to AD pathology. METHODS: 3,599 patients (63.9 ± 8.9 years old, 41% female) from the Amsterdam Dementia Cohort completed two sets of the Visual Association Test (VAT) in a single test session and underwent magnetic resonance imaging. We performed receiver operating curve analysis to investigate the VAT's discriminatory ability between AD dementia and other diagnoses and compared it to that of other episodic memory tests. We tested associations between VAT performance and medial temporal lobe atrophy (MTA), and amyloid status (n = 2,769, 77%). RESULTS: Patients with AD dementia performed worse on the VAT than all other patients. The VAT discriminated well between AD and other types of dementia (area under the curve range 0.70-0.86), better than other episodic memory tests. Six-hundred forty patients (17.8%) learned all associations on VAT-A, but not on VAT-B, and they were more likely to have higher MTA scores (odds ratios range 1.63 (MTA 0.5) through 5.13 for MTA ≥ 3, all p < .001) and to be amyloid positive (odds ratio = 3.38, 95%CI = [2.71, 4.22], p < .001) than patients who learned all associations on both sets. CONCLUSIONS: Performance on the VAT, especially on a second set administered immediately after the first, discriminates AD from other types of dementia and is associated with MTA and amyloid positivity. The VAT might be a useful, simple tool to assess early episodic memory deficits in the presence of AD pathology.

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box.

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

Rates of cortical thinning in Alzheimer's disease signature regions associate with vascular burden but not with ß-amyloid status in cognitively normal adults at age 70.

BACKGROUND: Consistent patterns of reduced cortical thickness have been identified in early Alzheimer's disease (AD). However, the pathological factors that influence rates of cortical thinning within these AD signature regions remain unclear. METHODS: Participants were from the Insight 46 substudy of the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort), a prospective longitudinal cohort study. Linear regression was used to examine associations of baseline cerebral ß-amyloid (Aß) deposition, measured using florbetapir positron emission tomography, and baseline white matter hyperintensity volume (WMHV) on MRI, a marker of cerebral small vessel disease, with subsequent longitudinal changes in AD signature cortical thickness quantified from baseline and repeat MRI (mean [SD] interval 2.4 [0.2] years). RESULTS: In a population-based sample of 337 cognitively normal older white adults (mean [SD] age at baseline 70.5 [0.6] years; 48.1% female), higher global WMHV at baseline related to faster subsequent rates of cortical thinning in both AD signature regions (~0.15%/year faster per 10 mL additional WMHV), whereas baseline Aß status did not. Among Aß positive participants (n=56), there was some evidence that greater global Aß standardised uptake value ratio at baseline related to faster cortical thinning in the AD signature Mayo region, but this did not reach statistical significance (p=0.08). CONCLUSIONS: Cortical thinning within AD signature regions may develop via cerebrovascular pathways. Perhaps reflecting the age of the cohort and relatively low prevalence of Aß-positivity, robust Aß-related differences were not detected. Longitudinal follow-up incorporating additional biomarkers will allow assessment of how these relationships evolve closer to expected dementia onset.

Updating the study protocol: Insight 46 - a longitudinal neuroscience sub-study of the MRC National Survey of Health and Development - phases 2 and 3.

BACKGROUND: Although age is the biggest known risk factor for dementia, there remains uncertainty about other factors over the life course that contribute to a person's risk for cognitive decline later in life. Furthermore, the pathological processes leading to dementia are not fully understood. The main goals of Insight 46-a multi-phase longitudinal observational study-are to collect detailed cognitive, neurological, physical, cardiovascular, and sensory data; to combine those data with genetic and life-course information collected from the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort); and thereby contribute to a better understanding of healthy ageing and dementia. METHODS/DESIGN: Phase 1 of Insight 46 (2015-2018) involved the recruitment of 502 members of the NSHD (median age = 70.7 years; 49% female) and has been described in detail by Lane and Parker et al. 2017. The present paper describes phase 2 (2018-2021) and phase 3 (2021-ongoing). Of the 502 phase 1 study members who were invited to a phase 2 research visit, 413 were willing to return for a clinic visit in London and 29 participated in a remote research assessment due to COVID-19 restrictions. Phase 3 aims to recruit 250 study members who previously participated in both phases 1 and 2 of Insight 46 (providing a third data time point) and 500 additional members of the NSHD who have not previously participated in Insight 46. DISCUSSION: The NSHD is the oldest and longest continuously running British birth cohort. Members of the NSHD are now at a critical point in their lives for us to investigate successful ageing and key age-related brain morbidities. Data collected from Insight 46 have the potential to greatly contribute to and impact the field of healthy ageing and dementia by combining unique life course data with longitudinal multiparametric clinical, imaging, and biomarker measurements. Further protocol enhancements are planned, including in-home sleep measurements and the engagement of participants through remote online cognitive testing. Data collected are and will continue to be made available to the scientific community.