Frederic St‐Onge

Alzheimer's disease is associated with pre-symptomatic changes in brain morphometry and accumulation of abnormal tau and amyloid-beta pathology. Studying the development of brain changes prior to symptoms onset may lead to early diagnostic biomarkers and a better understanding of Alzheimer's disease pathophysiology. Alzheimer's disease pathology is thought to arise from a combination of protein accumulation and spreading via neural connections, but how these processes influence brain atrophy progression in the pre-symptomatic phases remains unclear. Individuals with a family history of Alzheimer's disease (FHAD) have an elevated risk of Alzheimer's disease, providing an opportunity to study the pre-symptomatic phase. Here, we used structural MRI from three databases (Alzheimer's Disease Neuroimaging Initiative, Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer Disease and Montreal Adult Lifespan Study) to map atrophy progression in FHAD and Alzheimer's disease and assess the constraining effects of structural connectivity on atrophy progression. Cross-sectional and longitudinal data up to 4 years were used to perform atrophy progression analysis in FHAD and Alzheimer's disease compared with controls. PET radiotracers were also used to quantify the distribution of abnormal tau and amyloid-beta protein isoforms at baseline. We first derived cortical atrophy progression maps using deformation-based morphometry from 153 FHAD, 156 Alzheimer's disease and 116 controls with similar age, education and sex at baseline. We next examined the spatial relationship between atrophy progression and spatial patterns of tau aggregates and amyloid-beta plaques deposition, structural connectivity and neurotransmitter receptor and transporter distributions. Our results show that there were similar patterns of atrophy progression in FHAD and Alzheimer's disease, notably in the cingulate, temporal and parietal cortices, with more widespread and severe atrophy in Alzheimer's disease. Both tau and amyloid-beta pathology tended to accumulate in regions that were structurally connected in FHAD and Alzheimer's disease. The pattern of atrophy and its progression also aligned with existing structural connectivity in FHAD. In Alzheimer's disease, our findings suggest that atrophy progression results from pathology propagation that occurred earlier, on a previously intact connectome. Moreover, a relationship was found between serotonin receptor spatial distribution and atrophy progression in Alzheimer's disease. The current study demonstrates that regions showing atrophy progression in FHAD and Alzheimer's disease present with specific connectivity and cellular characteristics, uncovering some of the mechanisms involved in pre-clinical and clinical neurodegeneration.

Early detection of atypical dementia remains difficult partly because of the absence of specific cognitive screening tools. This creates undue delays in diagnosis and management. The Dépistage Cognitif de Québec (DCQ; dcqtest.org) was developed in French and later validated in participants with atypical syndromes. We report the validation of the English version. This multicentre prospective validation study was conducted in 10 centers across Canada and the United States on 260 English-speaking participants aged over 50. We translated and modified the original French DCQ to add targeted stimuli to the Visusopatial Index and social cognition vignettes to the Behavioral Index. A backward translation was performed and equivalence between languages was assessed by administering both tests to 30 bilingual participants. Mean DCQ total score (out of 100) was 95.0 (SD = 3.6). Spearman’s correlation coefficient showed a strong and significant correlation (r = 0.49, p < .001) with the Montreal Cognitive Assessment. Test–retest reliability was good (Spearman’s coefficient = 0.72, p < .001) and interrater reliability, excellent (intraclass correlation = 0.97, p < .001). Normative data shown in percentiles were stratified by age and education for a population-based sample of 260 English-speaking controls aged between 50 and 87 years old. Similar to the French version, the English DCQ proved to be a valid cognitive screening test. The original version was very sensitive to detect atypical dementias such as primary progressive aphasias, Alzheimer’s disease’ variants and syndromes along the frontotemporolobar degeneration spectrum. This 20-min test can be administered à la carte and offers an alternative to detailed comprehensive neuropsychological evaluations.

Importance: Positron emission tomography (PET) biomarkers are the gold standard for detection of Alzheimer amyloid and tau in vivo . Such imaging can identify cognitively unimpaired (CU) individuals who will subsequently develop cognitive impartment (CI). Plasma biomarkers would be more practical than PET or even cerebrospinal fluid (CSF) assays in clinical settings. Objective: Assess the prognostic accuracy of plasma p-tau217 in comparison to CSF and PET biomarkers for predicting the clinical progression from CU to CI. Design: In a cohort of elderly at high risk of developing Alzheimer’s dementia (AD), we measured the proportion of CU individuals who developed CI, as predicted by Aβ (A+) and/or tau (T+) biomarker assessment from plasma, CSF, and PET. Results from each method were compared with (A-T-) reference individuals. Data were analyzed from June 2023 to April 2024. Setting: Longitudinal observational cohort. Participants: Some 228 participants from the PREVENT-AD cohort were CU at the time of biomarker assessment and had 1 - 10 years of follow-up. Plasma was available from 215 participants, CSF from 159, and amyloid- and tau-PET from 155. Ninety-three participants had assessment using all three methods (main group of interest). Progression to CI was determined by clinical consensus among physicians and neuropsychologists who were blind to plasma, CSF, PET, and MRI findings, as well as APOE genotype. Exposures: Plasma Aβ _42/40 was measured using IP-MS; CSF Aβ _42/40 using Lumipulse; plasma and CSF p-tau217 using UGOT assay. Aβ-PET employed the ¹⁸ F-NAV4694 ligand, and tau-PET used ¹⁸ F-flortaucipir. Main Outcome: Prognostic accuracy of plasma, CSF, and PET biomarkers for predicting the development of CI in CU individuals. Results: Cox proportional hazard models indicated a greater progression rate in all A+T+ groups compared to A-T-groups (HR = 6.61 [95% CI = 2.06 – 21.17] for plasma, 3.62 [1.49 – 8.81] for CSF and 9.24 [2.34 – 36.43] for PET). The A-T+ groups were small, but also characterized with individuals who developed CI. Plasma biomarkers identified about five times more T+ than PET. Conclusion and relevance: Plasma p-tau217 assessment is a practical method for identification of persons who will develop cognitive impairment up to 10 years later. Key Points: Question: Can plasma p-tau217 serve as a prognostic indicator for identifying cognitively unimpaired (CU) individuals at risk of developing cognitive impairments (CI)? Findings: In a longitudinal cohort of CU individuals with a family history of sporadic AD, almost all individuals with abnormal plasma p-tau217 concentrations developed CI within 10 years, regardless of plasma amyloid levels. Similar findings were obtained with CSF p-tau217 and tau-PET. Fluid p-tau217 biomarkers had the main advantage over PET of identifying five times more participants with elevated tau. Meaning: Elevated plasma p-tau217 levels in CU individuals strongly indicate future clinical progression

The accumulation of tau abnormality in sporadic Alzheimer’s disease is believed typically to follow neuropathologically defined Braak staging. Recent in-vivo PET evidence challenges this belief, however, as accumulation patterns for tau appear heterogeneous among individuals with varying clinical expressions of Alzheimer’s disease. We, therefore, sought a better understanding of the spatial distribution of tau in the preclinical and clinical phases of sporadic Alzheimer’s disease and its association with cognitive decline. Longitudinal tau-PET data (1,370 scans) from 832 participants (463 cognitively unimpaired, 277 with mild cognitive impairment and 92 with Alzheimer’s disease dementia) were obtained from the Alzheimer’s Disease Neuroimaging Initiative. Among these, we defined thresholds of abnormal tau deposition in 70 brain regions from the Desikan atlas, and for each group of regions characteristic of Braak staging. We summed each scan’s number of regions with abnormal tau deposition to form a spatial extent index. We then examined patterns of tau pathology cross-sectionally and longitudinally and assessed their heterogeneity. Finally, we compared our spatial extent index of tau uptake with a temporal meta region of interest—a commonly used proxy of tau burden—assessing their association with cognitive scores and clinical progression. More than 80% of amyloid-beta positive participants across diagnostic groups followed typical Braak staging, both cross-sectionally and longitudinally. Within each Braak stage, however, the pattern of abnormality demonstrated significant heterogeneity such that the overlap of abnormal regions across participants averaged less than 50%, particularly in persons with mild cognitive impairment. Accumulation of tau progressed more rapidly among cognitively unimpaired and participants with mild cognitive impairment (1.2 newly abnormal regions per year) compared to participants with Alzheimer’s disease dementia (less than one newly abnormal region per year). Comparing the association of tau pathology and cognitive performance our spatial extent index was superior to the temporal meta-region of interest for identifying associations with memory in cognitively unimpaired individuals and explained more variance for measures of executive function in patients with mild cognitive impairments and Alzheimer’s disease dementia. Thus, while participants broadly followed Braak stages, significant individual regional heterogeneity of tau binding was observed at each clinical stage. Progression of the spatial extent of tau pathology appears to be fastest in cognitively unimpaired and persons with mild cognitive impairment. Exploring the spatial distribution of tau deposits throughout the entire brain may uncover further pathological variations and their correlation with cognitive impairments.

Background: Neuropathological studies defined the paradigm that tau—the pathological hallmark of Alzheimer’s disease (AD)—spreads homogenously across individuals from the medial temporal lobe to the neocortex. This staging system is referred to as Braak stages. However, neuroimaging studies have since highlighted significant inter‐regional and inter‐individual differences in tau spreading patterns. We investigated the spatial extent of cortical tau over time in the amnestic AD spectrum.Method: We included 195 amyloid‐positive ADNI participants that had at least two tau‐PET scans (90 cognitively unimpaired [CU], 66 with mild cognitive impairment [MCI], 39 with AD dementia; average follow‐up 2.6 years). We derived regional thresholds of tau positivity for 64 cortical regions and the amygdalae using Gaussian mixture modeling and applied them to all tau scans. The same approach was used to obtain thresholds for composite regions corresponding to the Braak stages. For each region, we calculated the rate of change in the amount (i.e., standardized uptake value ratio [SUVR] change) of tau pathology and the annual change in the positivity status (i.e., spatial extent change). These rates were compared between diagnostic groups using linear mixed models.Result: Across diagnostic groups, change in tau positivity followed Braak staging; over 90% of participants positive at follow‐up either progressed or were already positive on a previous Braak stage at baseline (Fig‐1AI) and greater SUVR change was observed in temporal regions (Fig‐1B). However, across the brain, there was heterogeneity between individual patterns of tau positivity progression (Fig‐1AII). This heterogeneity was most striking in MCI patients, where many regions progressed from low to high levels of tau (Fig‐1C). Regional SUVR change did not differ between participants with AD or with MCI (Fig‐2A), but participants with MCI showed the fastest change in regional positivity compared to participants with AD and CU participants. (Fig‐2B)Conclusion: While tau propagation follows Braak staging, inter‐individual differences can be found by considering a fine‐grained approach of regional progression to positivity. Furthermore, participants with MCI have the highest number of regions becoming abnormal annually. Our research pushes the importance of considering the entire brain when studying change in tau pathology, particularly before AD diagnosis.

Background: Increasing evidence suggests a link between sleep and late‐life Alzheimer disease’s (AD) pathology. We investigated whether sleep degradation might be accompanied by faster AD pathology accumulation and/or whether early AD pathologic changes might be accompanied by sleep changes before the onset of cognitive symptoms.Method: We investigated 228 PREVENT‐AD participants with available sleep data who underwent Aβ, [¹⁸F] NAV‐4694, and tau,[¹⁸F] Flortaucipir, positron emission tomography (PET). Longitudinal subjective sleep quality data were available for 191 participants (follow‐up:1.03±0.19y), longitudinal objective sleep data measured with actigraphy was available for 152 participants (follow‐up:2.5±1.24y), and longitudinal PET scans were available for 104 participants (follow‐up:4.3±0.24y). We assessed sleep quality using the Pittsburgh sleep quality index (PSQI) global score along with 7‐day actigraphy to indicate sleep duration, efficiency, and fragmentation index. Daily variation in sleep was measured using cross‐sectional and longitudinal standard deviations across the 7 days of actigraphy. Linear mixed effect models tested for longitudinal association of baseline sleep measures with PET outcomes over time. We also examined whether an increase in PET AD tracer uptake predicted changes in sleep quality and day‐to‐day variability. Every model controlled for age and sex.Result: We found no association between sleep measures and Aβ PET.Higher baseline PSQI scores were associated with higher levels of tau over time (p = 0.004, R² = 0.034, 𝛽 = 0.007, Fig.1, A). Higher baseline variability of sleep duration and fragmentation index were also associated with higher levels of tau longitudinally (p = 0.001, R² = 0.05, 𝛽 = 0.001, Fig.1, B; p = 0.029, R² = 0.022, 𝛽 = 0.01; Fig.1, C). Furthermore, increased tau at baseline was associated with lower sleep quality (p = 0.006, R² = 0.026, 𝛽 = 4.12, Fig.2, A), and with higher sleep duration variability over time (p = 0.004, R² = 0.06, 𝛽 = 61.56, Fig.1, B.)Conclusion: Poor sleep quality and higher day‐to‐day sleep variability in older age could contribute to faster rate of tau pathology accumulation, which could in turn disrupt sleep further. Targeting sleep disturbances may therefore serve in delaying tau burden and slowing down disease progression.

Background: Patients presenting with distinct clinical variants of Alzheimer’s disease (AD) present differential patterns of tau pathology measured with positron emission tomography (PET). Yet, a single set of brain regions like a temporal meta‐ROI is often chosen to study the association between cognition and tau. Instead, we aimed to map the associations between regional tau and multiple cognitive domains, and to test whether using whole brain information yields stronger associations with cognition compared to a singular composite region.Method: We included 372 amyloid‐positive ADNI participants that had at least one tau‐PET scan (163 cognitively unimpaired [CU], 132 with mild cognitive impairment [MCI], 77 with AD dementia). Composite cognitive scores for memory, executive functioning, visuospatial and language were used for cross‐sectional (closest score to tau‐PET) and longitudinal analyses. Individual rate of cognitive decline was computed using linear mixed effect models, leveraging all visits available for each participant. We did region‐wise analyses, relating tau in the 64 regions of the Desikan‐Killiany atlas plus the amygdalae with cognitive performance and rate of cognitive decline. We also derived a “spatial extent index” where we computed the number of abnormal tau regions and compared the performance of this index to detect cognitive impairment to what would have been found with a classical temporal meta‐ROI SUVR value.Result: No regional association between tau and cognition survived FDR correction in CU participants. Cross‐sectionally and longitudinally, participants with MCI and with AD dementia, associations between tau and memory were predominant in the temporal lobe, associations with language were restricted mostly to the left hemisphere and associations with executive functioning spanned the entire cortex. (Fig 1) Comparing the sum of tau positive regions (spatial extent index) to tau SUVR in the temporal meta‐ROI, both measures were similarly associated with cognition, except for executive functioning at baseline where the spatial extent index was more strongly associated with cognition than the meta‐ROI. (Fig 2)Conclusion: While temporal meta‐ROI tau SUVR may be sufficient to track non‐executive cognitive performance in individual with cognitive impairments, a whole brain measure may provide additional information regarding executive impairment occurring in AD.

Background: Females and individuals with a family history (FH) of Alzheimer’s disease (AD) are considered at higher risk of sporadic AD. However, how sex and maternal/paternal heredity affects in vivo AD biomarkers is not well understood. Our objectives were to 1) assess if females or individuals with a first‐degree maternal FH of AD accumulate amyloid‐beta (Aβ) and/or tau faster than males or individuals with a first‐degree paternal FH of AD and 2) assess if both could influence the rate of Aβ driving tau in preclinical AD.Method: Two hundred and thirty‐five older adults (age 68.3 ± 5.1 years, 69.8% female) from the PREVENT‐AD cohort, cognitively unimpaired at baseline, underwent [¹⁸F]‐NAV4694 and [¹⁸F]‐AV1451 positron emission tomography (PET) scans. Longitudinal PET scans were available for 106 individuals (4.3 ± 0.4 years follow‐up). We extracted and averaged the standard uptake value ratio (SUVr) in fronto‐parietal regions for Aβ and in temporal regions for tau. We performed t‐tests and ANOVA models to assess sex or maternal/paternal FH (FH_sex) differences in Aβ and tau burden or annual change. We then performed linear regression models with an interaction term between Aβ and sex (or Aβ and FH_sex) on tau burden and annual change, plus additional models to adjust for age and education.Result: Sex and FH_sex differences on Aβ and tau burden or annual rate of change did not reach significance although females tended to have higher tau SUVr at baseline (p = 0.053) (Figure 1 & 2). We found an interaction between Aβ and sex (𝛽 = ‐0.13, p = 0.008), and between Aβ and FH_sex (𝛽 = 0.11, p = 0.03), on tau at baseline (Figure 3) suggesting that, for the same level of Aβ burden, females and individuals with paternal FH of AD had higher tau burden than males and individuals with maternal FH of AD. Results were similar when adjusted for age and education.Conclusion: Females and individuals with a paternal FH of AD might need less Aβ before developing AD‐related tau, but did not accumulate tau faster. Identifying individuals more vulnerable to AD pathology, by disentangling sex‐specific effects, would help to design personalized interventions for AD.

Background: Increasing evidence suggests a link between sleep and late‐life Alzheimer disease’s (AD) pathology. We investigated whether sleep degradation might be accompanied by faster AD pathology accumulation and/or whether early AD pathologic changes might be accompanied by sleep changes before the onset of cognitive symptoms.Method: We investigated 228 PREVENT‐AD participants with available sleep data who underwent Aβ, [18F] NAV‐4694, and tau,[18F] Flortaucipir, positron emission tomography (PET). Longitudinal subjective sleep quality data were available for 191 participants (follow‐up:1.03±0.19y), longitudinal objective sleep data measured with actigraphy was available for 152 participants (follow‐up:2.5±1.24y), and longitudinal PET scans were available for 104 participants (follow‐up:4.3±0.24y). We assessed sleep quality using the Pittsburgh sleep quality index (PSQI) global score along with 7‐day actigraphy to indicate sleep duration, efficiency, and fragmentation index. Daily variation in sleep was measured using cross‐sectional and longitudinal standard deviations across the 7 days of actigraphy. Linear mixed effect models tested for longitudinal association of baseline sleep measures with PET outcomes over time. We also examined whether an increase in PET AD tracer uptake predicted changes in sleep quality and day‐to‐day variability. Every model controlled for age and sex.Result: We found no association between sleep measures and Aβ PET. Higher baseline PSQI scores were associated with higher levels of tau over time (p = 0.004, R2 = 0.034, 𝛽 = 0.007, Fig.1, A). Higher baseline variability of sleep duration and fragmentation index were also associated with higher levels of tau longitudinally (p = 0.001, R2 = 0.05, 𝛽 = 0.001, Fig.1, B; p = 0.029, R2 = 0.022, 𝛽 = 0.01; Fig.1, C). Furthermore, increased tau at baseline was associated with lower sleep quality (p = 0.006, R2 = 0.026, 𝛽 = 4.12, Fig.2, A), and with higher sleep duration variability over time (p = 0.004, R2 = 0.06, 𝛽 = 61.56, Fig.1, B.)Conclusion: Poor sleep quality and higher day‐to‐day sleep variability in older age could contribute to faster rate of tau pathology accumulation, which could in turn disrupt sleep further. Targeting sleep disturbances may therefore serve in delaying tau burden and slowing down disease progression.

Background: Females and individuals with a family history (FH) of Alzheimer’s disease (AD) are considered at higher risk of sporadic AD. However, how sex and maternal/paternal heredity affects in vivo AD biomarkers is not well understood. Our objectives were to 1) assess if females or individuals with a first‐degree maternal FH of AD accumulate amyloid‐beta (Aβ) and/or tau faster than males or individuals with a first‐degree paternal FH of AD and 2) assess if both could influence the rate of Aβ driving tau in preclinical AD.Method: Two hundred and thirty‐five older adults (age 68.3 ± 5.1 years, 69.8% female) from the PREVENT‐AD cohort, cognitively unimpaired at baseline, underwent [18F]‐NAV4694 and [18F]‐AV1451 positron emission tomography (PET) scans. Longitudinal PET scans were available for 106 individuals (4.3 ± 0.4 years follow‐up). We extracted and averaged the standard uptake value ratio (SUVr) in fronto‐parietal regions for Aβ and in temporal regions for tau. We performed t‐tests and ANOVA models to assess sex or maternal/paternal FH (FHsex) differences in Aβ and tau burden or annual change. We then performed linear regression models with an interaction term between Aβ and sex (or Aβ and FHsex) on tau burden and annual change, plus additional models to adjust for age and education.Result: Sex and FHsex differences on Aβ and tau burden or annual rate of change did not reach significance although females tended to have higher tau SUVr at baseline (p = 0.053) (Figure 1 & 2). We found an interaction between Aβ and sex (𝛽 = ‐0.13, p = 0.008), and between Aβ and FHsex (𝛽 = 0.11, p = 0.03), on tau at baseline (Figure 3) suggesting that, for the same level of Aβ burden, females and individuals with paternal FH of AD had higher tau burden than males and individuals with maternal FH of AD. Results were similar when adjusted for age and education.Conclusion: Females and individuals with a paternal FH of AD might need less Aβ before developing AD‐related tau, but did not accumulate tau faster. Identifying individuals more vulnerable to AD pathology, by disentangling sex‐specific effects, would help to design personalized interventions for AD.