Spatial-Temporal Patterns of β-Amyloid Accumulation: A Subtype and Stage Inference Model Analysis

Quantification of baseline amyloid PET in individuals with subjective cognitive decline can identify risk of amyloid accumulation and cognitive worsening: the FACEHBI study

PURPOSE

Amyloid PET imaging is capable of measuring brain amyloid load in vivo. The aim of this study is to assess the relationship of the baseline amyloid with its accumulation over time and with cognition in individuals with subjective cognitive decline (SCD), giving a focus on those below Aβ positivity thresholds.

METHODS

118 of 197 individuals with SCD from the Fundació ACE Healthy Brain Initiative underwent three [18F]florbetaben scans and the remaining 79 underwent two scans in a 5-year span. Individuals were categorised based on baseline Centiloid values (CL) into amyloid positive (Aβ+; CL > 35.7), Grey Zone (GZ; 20 < CL ≤ 35.7), and amyloid negative (Aβ-; CL ≤ 20). Relationship between conversion to mild cognitive decline (MCI) and baseline amyloid levels was assessed. Then, to focus on sub-threshold individuals with amyloid accumulation, the Aβ- group was split into two groups (N1 (CL ≤ 13.5) and N2 (13.5 < CL ≤ 20)), Aβ accumulation was determined, and a parametric image analysis of the Aβ accumulators in the N1 group was performed.

RESULTS

At baseline, 20 individuals were Aβ+, 8 GZ, 160 N1, and 9 N2. Higher Aβ load, older and less educated individuals presented increased risk of MCI-conversion. Longitudinally, 19% of N1 individuals were accumulators despite very low Aβ burden at baseline. Meanwhile, 89% of the N2 group accumulated Aβ as well as all GZ individuals (which had the highest rate of amyloid accumulation, 5.1 CL/year). In the parametric image analysis of N1 accumulators, a region within the precuneus was linked to increased Aβ over time.

CONCLUSION

Baseline amyloid levels differentiate individuals who accumulate amyloid over time and that are at risk for cognitive decline, including those at sub-threshold levels of Aβ. This can be valuable to identify pre-clinical AD in a SCD population.

Neuroimaging-based data-driven subtypes of spatiotemporal atrophy due to Parkinson's disease

Parkinson's disease is the second most common neurodegenerative disease. Despite this, there are no robust biomarkers to predict progression, and understanding of disease mechanisms is limited. We used the Subtype and Stage Inference algorithm to characterize Parkinson's disease heterogeneity in terms of spatiotemporal subtypes of macroscopic atrophy detectable on T1-weighted MRI-a successful approach used in other neurodegenerative diseases. We trained the model on covariate-adjusted cortical thicknesses and subcortical volumes from the largest known T1-weighted MRI dataset in Parkinson's disease, Enhancing Neuroimaging through Meta-Analysis consortium Parkinson's Disease dataset (n = 1100 cases). We tested the model by analyzing clinical progression over up to 9 years in openly-available data from people with Parkinson's disease from the Parkinson's Progression Markers Initiative (n = 584 cases). Under cross-validation, our analysis supported three spatiotemporal atrophy subtypes, named for the location of the earliest affected regions as: 'Subcortical' (n = 359, 33%), 'Limbic' (n = 237, 22%) and 'Cortical' (n = 187, 17%). A fourth subgroup having sub-threshold/no atrophy was named 'Sub-threshold atrophy' (n = 317, 29%). Statistical differences in clinical scores existed between the no-atrophy subgroup and the atrophy subtypes, but not among the atrophy subtypes. This suggests that the prime T1-weighted MRI delineator of clinical differences in Parkinson's disease is atrophy severity, rather than atrophy location. Future work on unravelling the biological and clinical heterogeneity of Parkinson's disease should leverage more sensitive neuroimaging modalities and multimodal data.

Spatial-temporal interactions between white matter hyperintensities and multiple pathologies across the Alzheimer's disease continuum

INTRODUCTION

The interactive relationships between Alzheimer's disease (AD) and white matter hyperintensities (WMHs) in multiscale brain structural networks still need to be clarified.

METHODS

Based on subjects enrolled from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, regional WMHs, amyloid beta (Aβ) accumulation, and microstructural changes detected by diffusion weighted imaging (DWI) in multiscale brain networks were modeled by time-evolving graphs; their interactive relationships were further investigated using Granger causality after constructing pseudo-time subject sequences.

RESULTS

In up to 86% of the extracted pseudo-time subject sequences, Aβ was determined to be the Granger cause of WMHs in the structural connectivity of the inferior longitudinal fasciculus (ILF). Meanwhile WMHs were significantly correlated with microstructural changes measured by reduced fractional anisotropy in the inferior fronto-occipital fasciculus, ILF, and cingulum, which Granger causality pathways detected in 91%, 94%, and 93% of pseudo-time subject sequences, respectively.

DISCUSSION

These findings provide novel insights for understanding the multiscale space-time interactions between WMHs and AD pathologies.

HIGHLIGHTS

This study proposed time-evolving graph modeling of heterogeneous disease markers (amyloid beta [Aβ], white matter hyperintensities [WMHs], and microstructural changes of white matter tracts) across the Alzheimer's disease (AD) continuum to investigate their complex interactions in multiscale brain structural networks. Regional accumulation of Aβ promoted WMH progression in subnetworks connected by the inferior longitudinal fasciculus (ILF). Regional WMHs were strongly associated with bundle-specific microstructural changes in the ILF, inferior fronto-occipital fasciculus, and cingulum. These results might provide novel insights for understanding the interactive relationship between cerebral small vessel disease and AD.

Alzheimer's disease neuropathology and its estimation with fluid and imaging biomarkers

Alzheimer's disease (AD) is neuropathologically characterized by the extracellular deposition of the amyloid-β peptide (Aβ) and the intraneuronal accumulation of abnormal phosphorylated tau (τ)-protein (p-τ). Most frequently, these hallmark lesions are accompanied by other co-pathologies in the brain that may contribute to cognitive impairment, such as vascular lesions, intraneuronal accumulation of phosphorylated transactive-response DNA-binding protein 43 (TDP-43), and/or α-synuclein (αSyn) aggregates. To estimate the extent of these AD and co-pathologies in patients, several biomarkers have been developed. Specific tracers target and visualize Aβ plaques, p-τ and αSyn pathology or inflammation by positron emission tomography. In addition to these imaging biomarkers, cerebrospinal fluid, and blood-based biomarker assays reflecting AD-specific or non-specific processes are either already in clinical use or in development. In this review, we will introduce the pathological lesions of the AD brain, the related biomarkers, and discuss to what extent the respective biomarkers estimate the pathology determined at post-mortem histopathological analysis. It became evident that initial stages of Aβ plaque and p-τ pathology are not detected with the currently available biomarkers. Interestingly, p-τ pathology precedes Aβ deposition, especially in the beginning of the disease when biomarkers are unable to detect it. Later, Aβ takes the lead and accelerates p-τ pathology, fitting well with the known evolution of biomarker measures over time. Some co-pathologies still lack clinically established biomarkers today, such as TDP-43 pathology or cortical microinfarcts. In summary, specific biomarkers for AD-related pathologies allow accurate clinical diagnosis of AD based on pathobiological parameters. Although current biomarkers are excellent measures for the respective pathologies, they fail to detect initial stages of the disease for which post-mortem analysis of the brain is still required. Accordingly, neuropathological studies remain essential to understand disease development especially in early stages. Moreover, there is an urgent need for biomarkers reflecting co-pathologies, such as limbic predominant, age-related TDP-43 encephalopathy-related pathology, which is known to modify the disease by interacting with p-τ. Novel biomarker approaches such as extracellular vesicle-based assays and cryptic RNA/peptides may help to better detect these co-pathologies in the future.

Connectome-based predictive modeling of brain pathology and cognition in autosomal dominant Alzheimer's disease

INTRODUCTION

Autosomal dominant Alzheimer's disease (ADAD) through genetic mutations can result in near complete expression of the disease. Tracking AD pathology development in an ADAD cohort of Presenilin-1 (PSEN1) E280A carriers' mutation has allowed us to observe incipient tau tangles accumulation as early as 6 years prior to symptom onset.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) scans were acquired in a group of PSEN1 carriers (n = 32) and non-carrier family members (n = 35). We applied connectome-based predictive modeling (CPM) to examine the relationship between the participant's functional connectome and their respective tau/amyloid-β levels and cognitive scores (word list recall).

RESULTS

CPM models strongly predicted tau concentrations and cognitive scores within the carrier group. The connectivity patterns between the temporal cortex, default mode network, and other memory networks were the most informative of tau burden.

DISCUSSION

These results indicate that resting-state functional magnetic resonance imaging (fMRI) methods can complement PET methods in early detection and monitoring of disease progression in ADAD.

HIGHLIGHTS

Connectivity-based predictive modeling of tau and amyloid-β in ADAD carriers. Strong predictions for tau deposition; weaker predictions for amyloid-β. Cognitive scores for memory and mental state are predicted strongly. Connectivity between IPL, DAN, DMN, temporal cortex most predictive.

Identification of metabolic progression and subtypes in progressive supranuclear palsy by PET molecular imaging

INTRODUCTION

Progressive supranuclear palsy (PSP) is a neurodegenerative disorder with diverse clinical presentations that are linked to tau pathology. Recently, Subtype and Stage Inference (SuStaIn) algorithm, an innovative data-driven method, has been developed to model both the spatial-temporal progression and subtypes of disease. This study explores PSP progression using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging and the SuStaIn algorithm to identify PSP metabolic progression subtypes and understand disease mechanisms.

METHODS

The study included 72 PSP patients and 70 controls, with an additional 24 PSP patients enrolled as a test set, undergoing FDG-PET, dopamine transporter (DAT) PET, and neuropsychological assessments. The SuStaIn algorithm was employed to analyze the FDG-PET data, identifying progression subtypes and sequences.

RESULTS

Two PSP subtypes were identified: the cortical subtype with early prefrontal hypometabolism and the brainstem subtype with initial midbrain alterations. The cortical subtype displayed greater cognitive impairment and DAT reduction than the brainstem subtype. The test set demonstrates the robustness and reproducibility of the findings. Pathway analysis indicated that disruptions in dopaminergic cortico-basal ganglia pathways are crucial for elucidating the mechanisms of cognitive and behavioral impairment in PSP, leading to the two metabolic progression subtypes.

CONCLUSION

This study identified two spatiotemporal progression subtypes of PSP based on FDG-PET imaging, revealing significant differences in metabolic patterns, striatal dopaminergic uptake, and clinical profiles, particularly cognitive impairments. The findings highlight the crucial role of dopaminergic cortico-basal ganglia pathways in PSP pathophysiology, especially in the cortical subtype, providing insights into PSP heterogeneity and potential avenues for personalized treatments.

Mapping amyloid beta predictors of entorhinal tau in preclinical Alzheimer's disease

INTRODUCTION

Amyloid beta (Aβ) plaques and hyperphosphorylated tau in the entorhinal regions are key Alzheimer's disease (AD) markers, but the spatial Aβ pathways influencing tau pathology remain unclear.

METHODS

We applied predictive modeling to identify Aβ standardized uptake value ratio (SUVR) spatial patterns that predict entorhinal tau levels, future hippocampal volume, and Preclinical Alzheimer's Cognitive Composite (PACC) scores at 5-year follow-up. The model was trained on Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 237), incorporating amyloid-PET (positron emission tomography), tau-PET, magnetic resonance imaging (MRI), and cognitive data, and validated on Harvard Aging Brain Study (HABS) (N = 276).

RESULTS

The model accurately predicted entorhinal tau levels (r = 0.48, p < 0.0001), future hippocampal volume (r = 0.24, p = 0.002), and PACC scores (r = 0.35, p < 0.0001) based on regional Aβ.

DISCUSSION

Aβ in the rostral middle frontal, medial orbitofrontal, and striatal regions predict entorhinal tau levels, future hippocampal volume, and PACC scores, indicating their potential as early biomarkers in AD prediction models.

HIGHLIGHTS

Positron emission tomography (PET) imaging reveals amyloid beta (Aβ) patterns predicting entorhinal tau levels in preclinical Alzheimer's disease (AD). Aβ in medial orbitofrontal, rostral middle frontal, and nucleus accumbens best predicts tau. Aβ distribution in these regions predicts future hippocampal neurodegeneration and cognitive decline. Model validated with Alzheimer's Disease Neuroimaging Initiative (ADNI) and Harvard Aging Brain Study (HABS) data sets, showing robustness and reproducibility. Findings suggest early Aβ patterns can aid in diagnosing AD and guide anti-Aβ therapies.

Interrater agreement and variability in visual reading of [18F] flutemetamol PET images

OBJECTIVE

The purpose of this study was to validate the concordance of visual ratings of [18F] flutemetamol amyloid positron emission tomography (PET) images and to investigate the correlation between the agreement of each rater and the Centiloid (CL) scale.

METHODS

A total of 192 participants, clinically classified as cognitively normal (CN) (n = 59), mild cognitive impairment (MCI) (n = 65), Alzheimer's disease (AD) (n = 55), or non-AD dementia (n = 13), participated in this study. Three experts conducted visual ratings of the amyloid PET images for all 192 patients, assigning a confidence level to each rating on a three-point scale (certain, probable, or neither). The positive or negative determination of amyloid PET results was made by majority vote. The CL value was calculated using the CapAIBL pipeline.

RESULTS

Overall, 101 images were determined to be positive, and 91 images were negative. Of the 101 positive images, the three raters were in complete agreement for 92 images and in disagreement for 9 images. Of the 91 negative images, the three raters were in complete agreement for 75 images and in disagreement for 16 images. Interrater reliability among the three experts was particularly high, with both Fleiss' kappa and Conger's kappa measuring 0.83 (0.76-0.89). The CL values of the unanimous positive group were significantly greater than those of the other groups, whereas the CL values of the unanimous negative group were significantly lower than those of the other groups. Images with rater disagreement had intermediate CLs. In cases with a high confidence level, the positive or negative visual ratings were in almost complete agreement. However, as confidence levels decreased, experts' visual ratings became more variable. The lower the confidence level was, the greater the number of cases with disagreement in the visual ratings.

CONCLUSION

Three experts independently rated 192 amyloid PET images, achieving a high level of interrater agreement. However, in patients with intermediate amyloid accumulation, visual ratings varied. Therefore, determining positive and negative decisions in these patients should be performed with caution.

Longitudinal relationships between Aβ and tau to executive function and memory in cognitively normal older adults

The early accumulation of AD pathology such as Aβ and tau in cognitively normal older people is predictive of cognitive decline, but it has been difficult to dissociate the cognitive effects of these two proteins. Early Aβ and tau target distinct brain regions that have different functional roles. Here, we assessed specific longitudinal pathology-cognition associations in seventy-six cognitively normal older adults from the Berkeley Aging Cohort Study who underwent longitudinal PiB PET, FTP PET, and cognitive assessments. Using linear mixed-effects models to estimate longitudinal changes and residual approach to characterizing cognitive domain-specific associations, we found that Aβ accumulation, especially in frontal/parietal regions, was associated with faster decline in executive function, not memory, whereas tau accumulation, especially in left entorhinal/parahippocampal regions, was associated with faster decline in memory, not executive function, supporting an "Aβ-executive function, tau-memory" double-dissociation in cognitively normal older people. These specific relationships between accumulating pathology and domain-specific cognitive decline may be due to the particular vulnerabilities of the frontal-parietal executive network to Aβ and temporal memory network to tau.

A Multiform Heterogeneity Framework for Alzheimer's Disease Based on Multimodal Neuroimaging

Understanding the heterogeneity of Alzheimer's disease (AD) is crucial for advancing precision medicine specifically tailored to this disorder. Recent research has deepened our understanding of AD heterogeneity; however, translating these insights from bench to bedside via neuroimaging heterogeneity frameworks presents significant challenges. In this review, we systematically revisit prior studies and summarize the existing methodology of data-driven neuroimaging studies for AD heterogeneity. We organized the current methodology into 1) a subtyping clustering strategy for patients with AD, and we also subdivided it into subtyping analysis based on cross-sectional multimodal neuroimaging profiles and the identification of long-term disease progression from short-term datasets; 2) a stratified strategy that integrates neuroimaging measures with biomarkers; and 3) individual-specific abnormal patterns based on the normative model. Then, we evaluated the characteristics of these studies along 2 dimensions: 1) the understanding of pathology and 2) clinical application. We systematically address the limitations, challenges, and future directions of research into AD heterogeneity. Our goal is to enhance the neuroimaging heterogeneity framework for AD, thereby facilitating its transition from bench to bedside.

Association of glial fibrillary acid protein, Alzheimer's disease pathology and cognitive decline

Increasing evidence shows that neuroinflammation is a possible modulator of tau spread effects on cognitive impairment in Alzheimer's disease. In this context, plasma levels of the glial fibrillary acidic protein (GFAP) have been suggested to have a robust association with Alzheimer's disease pathophysiology. This study aims to assess the correlation between plasma GFAP and Alzheimer's disease pathology, and their synergistic effect on cognitive performance and decline. A cohort of 122 memory clinic subjects with amyloid and tau PET, MRI scans, plasma GFAP and Mini-Mental State Examination (MMSE) was included in the study. A subsample of 94 subjects had a follow-up MMSE score at ≥1 year after baseline. Regional and voxel-based correlations between Alzheimer's disease biomarkers and plasma GFAP were assessed. Mediation analyses were performed to evaluate the effects of plasma GFAP on the association between amyloid and tau PET and between tau PET and cognitive impairment and decline. GFAP was associated with increased tau PET ligand uptake in the lateral temporal and inferior temporal lobes in a strong left-sided pattern independently of age, sex, education, amyloid and APOE status (β = 0.001, P < 0.01). The annual rate of MMSE change was significantly and independently correlated with both GFAP (β = 0.006, P < 0.01) and global tau standardized uptake value ratio (β = 4.33, P < 0.01), but not with amyloid burden. Partial mediation effects of GFAP were found on the association between amyloid and tau pathology (13.7%) and between tau pathology and cognitive decline (17.4%), but not on global cognition at baseline. Neuroinflammation measured by circulating GFAP is independently associated with tau Alzheimer's disease pathology and with cognitive decline, suggesting neuroinflammation as a potential target for future disease-modifying trials targeting tau pathology.

Centiloid recommendations for clinical context-of-use from the AMYPAD consortium

Amyloid-PET quantification through the tracer-independent Centiloid (CL) scale has emerged as an essential tool for the accurate measurement of amyloid-β (Aβ) pathology in Alzheimer's disease (AD) patients. The AMYPAD consortium set out to integrate existing literature and recent work from the consortium to provide clinical context-of-use recommendations for the CL scale. Compared to histopathology, visual reads, and cerebrospinal fluid, CL quantification accurately reflects the amount of AD pathology. With high certainty, a CL value below 10 excludes the presence of Aβ pathology, while a value above 30 corresponds well with pathological amounts. Values falling in between these two cutoffs ("intermediate range") are related to an increased risk of disease progression. Together, CL quantification is a valuable adjunct to visual assessments of amyloid-PET images. An abnormal amyloid biomarker assessment is a key criterion to determine eligibility for anti-amyloid disease-modifying therapies, and amyloid-PET quantification can add further value by precisely monitoring amyloid clearance, and hence guiding patient management decisions. HIGHLIGHTS: Centiloid (CL) quantification robustly reflects of the amount of Aβ pathology. CL < 10/CL > 30 reflects Aβ-negativity/positivity thresholds with high certainty. CL quantification is a valuable adjunct to visual assessments of amyloid-PET. CL quantification can support trial design and treatment management. CL quantification could support the identification of early or emerging Aβ pathology.

Timeline to symptomatic Alzheimer's disease in people with Down syndrome as assessed by amyloid-PET and tau-PET: a longitudinal cohort study

BACKGROUND

Adults with Down syndrome are at risk for Alzheimer's disease. Natural history cohort studies have characterised the progression of Alzheimer's disease biomarkers in people with Down syndrome, with a focus on amyloid β-PET and tau-PET. In this study, we aimed to leverage these well characterised imaging biomarkers in a large cohort of individuals with Down syndrome, to examine the timeline to symptomatic Alzheimer's disease based on estimated years since the detection on PET of amyloid β-positivity, referred to here as amyloid age, and in relation to tau burden as assessed by PET.

METHODS

In this prospective, longitudinal, observational cohort study, data were collected at four university research sites in the UK and USA as part of the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study. Eligible participants were aged 25 years or older with Down syndrome, had a mental age of at least 3 years (based on a standardised intelligence quotient test), and had trisomy 21 (full, mosaic, or translocation) confirmed through karyotyping. Participants were assessed twice between 2017 and 2022, with approximately 32 months between visits. Participants had amyloid-PET and tau-PET scans, and underwent cognitive assessment with the modified Cued Recall Test (mCRT) and the Down Syndrome Mental Status Examination (DSMSE) to assess cognitive functioning. Study partners completed the National Task Group-Early Detection Screen for Dementia (NTG-EDSD). Generalised linear models were used to assess the association between amyloid age (whereby 0 years equated to 18 centiloids) and mCRT, DSMSE, NTG-EDSD, and tau PET at baseline and the 32-month follow-up. Broken stick regression was used to identify the amyloid age that corresponded to decreases in cognitive performance and increases in tau PET after the onset of amyloid β positivity.

FINDINGS

167 adults with Down syndrome, of whom 92 had longitudinal data, were included in our analyses. Generalised linear regressions showed significant quadratic associations between amyloid age and cognitive performance and cubic associations between amyloid age and tau, both at baseline and at the 32-month follow-up. Using broken stick regression models, differences in mCRT total scores were detected beginning 2·7 years (95% credible interval [CrI] 0·2 to 5·4; equating to 29·8 centiloids) after the onset of amyloid β positivity in cross-sectional models. Based on cross-sectional data, increases in tau deposition started a mean of 2·7-6·1 years (equating to 29·8-47·9 centiloids) after the onset of amyloid β positivity. Mild cognitive impairment was observed at a mean amyloid age of 7·4 years (SD 6·6; equating to 56·8 centiloids) and dementia was observed at a mean amyloid age of 12·7 years (5·6; equating to 97·4 centiloids).

INTERPRETATION

There is a short timeline to initial cognitive decline and dementia from onset of amyloid β positivity and tau deposition in people with Down syndrome. This newly established timeline based on amyloid age (or equivalent centiloid values) is important for clinical practice and informing the design of Alzheimer's disease clinical trials, and it avoids the limitations of timelines based on chronological age.

FUNDING

National Institute on Aging and the National Institute for Child Health and Human Development.

Divergent neurodegenerative patterns: Comparison of [18F] fluorodeoxyglucose-PET- and MRI-based Alzheimer's disease subtypes

[18F] fluorodeoxyglucose (FDG)-PET and MRI are key imaging markers for neurodegeneration in Alzheimer's disease. It has been well established that parieto-temporal hypometabolism on FDG-PET is closely associated with medial temporal atrophy on MRI in Alzheimer's disease. Substantial biological heterogeneity, expressed as distinct subtypes of hypometabolism or atrophy patterns, has been previously described in Alzheimer's disease using data-driven and hypothesis-driven methods. However, the link between these two imaging modalities has not yet been explored in the context of Alzheimer's disease subtypes. To investigate this link, the current study utilized FDG-PET and MRI scans from 180 amyloid-beta positive Alzheimer's disease dementia patients, 339 amyloid-beta positive mild cognitive impairment and 176 amyloid-beta negative cognitively normal controls from the Alzheimer's Disease Neuroimaging Initiative. Random forest hierarchical clustering, a data-driven model for identifying subtypes, was implemented in the two modalities: one with standard uptake value ratios and the other with grey matter volumes. Five hypometabolism- and atrophy-based subtypes were identified, exhibiting both cortical-predominant and limbic-predominant patterns although with differing percentages and clinical presentations. Three cortical-predominant hypometabolism subtypes found were Cortical Predominant (32%), Cortical Predominant+ (11%) and Cortical Predominant posterior (8%), and two limbic-predominant hypometabolism subtypes found were Limbic Predominant (36%) and Limbic Predominant frontal (13%). In addition, little atrophy (minimal) and widespread (diffuse) neurodegeneration subtypes were observed from the MRI data. The five atrophy subtypes found were Cortical Predominant (19%), Limbic Predominant (27%), Diffuse (29%), Diffuse+ (6%) and Minimal (19%). Inter-modality comparisons showed that all FDG-PET subtypes displayed medial temporal atrophy, whereas the distinct MRI subtypes showed topographically similar hypometabolic patterns. Further, allocations of FDG-PET and MRI subtypes were not consistent when compared at an individual level. Additional analysis comparing the data-driven clustering model with prior hypothesis-driven methods showed only partial agreement between these subtyping methods. FDG-PET subtypes had greater differences between limbic-predominant and cortical-predominant patterns, and MRI subtypes had greater differences in severity of atrophy. In conclusion, this study highlighted that Alzheimer's disease subtypes identified using both FDG-PET and MRI capture distinct pathways showing cortical versus limbic predominance of neurodegeneration. However, the subtypes do not share a bidirectional relationship between modalities and are thus not interchangeable.

Amyloid PET detects the deposition of brain Aβ earlier than CSF fluid biomarkers

INTRODUCTION

Understanding the relationship between amyloid beta (Aβ) positron emission tomography (PET) and Aβ cerebrospinal fluid (CSF) biomarkers will define their potential utility in Aβ treatment. Few population-based or neuropathologic comparisons have been reported.

METHODS

Participants 50+ years with Aβ PET and Aβ CSF biomarkers (phosphorylated tau [p-tau]181/Aβ42, n = 505, and Aβ42/40, n = 54) were included from the Mayo Clinic Study on Aging. From these participants, an autopsy subgroup was identified (n = 47). The relationships of Aβ PET and Aβ CSF biomarkers were assessed cross-sectionally in all participants and longitudinally in autopsy data.

RESULTS

Cross-sectionally, more participants were Aβ PET+ versus Aβ CSF- than Aβ PET- versus Aβ CSF+ with an incremental effect when using Aβ PET regions selected for early Aβ deposition. The sensitivity for the first detection of Thal phase ≥ 1 in longitudinal data was higher for Aβ PET (89%) than p-tau181/Aβ42 (64%).

DISCUSSION

Aβ PET can detect earlier cortical Aβ deposition than Aβ CSF biomarkers. Aβ PET+ versus Aβ CSF- findings are several-fold greater using regional Aβ PET analyses and in peri-threshold-standardized uptake value ratio participants.

HIGHLIGHTS

Amyloid beta (Aβ) positron emission tomography (PET) has greater sensitivity for Aβ deposition than Aβ cerebrospinal fluid (CSF) in early Aβ development. A population-based sample of participants (n = 505) with PET and CSF tests was used. Cortical regions showing early Aβ on Aβ PET were also used in these analyses. Neuropathology was used to validate detection of Aβ by Aβ PET and Aβ CSF biomarkers.

Association between striatal amyloid deposition and motor prognosis in Parkinson's disease

BACKGROUND AND PURPOSE

The co-occurrence of amyloid-β pathology in Parkinson's disease (PD) is common; however, the role of amyloid-β deposition in motor prognosis remains elusive. This study aimed to investigate the association between striatal amyloid deposition, motor complications and motor prognosis in patients with PD.

METHODS

Ninety-six patients with PD who underwent 18F florbetaben (FBB) positron emission tomography were retrospectively assessed. The ratio of the striatum to global (STG) FBB uptake was obtained for each individual, and patients were allotted into low and high STG groups according to the median value. The effect of STG group on regional amyloid deposition, the occurrence of motor complications and longitudinal change in levodopa equivalent dose (LED) requirement were investigated after controlling for age, sex, LED and disease duration at FBB scan.

RESULTS

The high STG group was associated with lower cortical FBB uptake in the parietal, occipital and posterior cingulate cortices and higher striatal FBB uptake compared to the low STG group. Patients in the high STG group had a higher risk of developing wearing off and levodopa-induced dyskinesia than those in the low STG group, whereas the risk for freezing of gait was comparable between the two groups. The high STG group showed a more rapid increase in LED requirements over time than the low STG group.

CONCLUSIONS

These findings suggest that relatively high striatal amyloid deposition is associated with poor motor outcomes in patients with PD.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Amyloid age and tau PET timeline to symptomatic Alzheimer's disease in Down syndrome

Background

Adults with Down syndrome (DS) are at risk for Alzheimer's disease (AD). Recent natural history cohort studies have characterized AD biomarkers, with a focus on PET amyloid-beta (Aβ) and PET tau. Leveraging these well-characterized biomarkers, the present study examined the timeline to symptomatic AD based on estimated years since reaching Aβ+, referred to as "amyloid age", and in relation to tau in a large cohort of individuals with DS.

Methods

In this multicenter cohort study, 25 - 57-year-old adults with DS (n = 167) were assessed twice from 2017 to 2022, with approximately 32 months between visits as part of the Alzheimer Biomarker Consortium - Down Syndrome. Adults with DS completed amyloid and tau PET scans, and were administered the modified Cued Recall Test and the Down Syndrome Mental Status Examination. Study partners completed the National Task Group-Early Detection Screen for Dementia.

Findings

Mixed linear regressions showed significant quadratic associations between amyloid age and cognitive performance and cubic associations between amyloid age and tau, both at baseline and across 32 months. Using broken stick regression models, differences in mCRT scores were detected beginning 2.7 years following Aβ+ in cross-sectional models, with an estimated decline of 1.3 points per year. Increases in tau began, on average, 2.7 - 6.1 years following Aβ+. On average, participants with mild cognitive impairment were 7.4 years post Aβ+ and those with dementia were 12.7 years post Aβ+.

Interpretation

There is a short timeline to initial cognitive decline and dementia from Aβ+ (Centiloid = 18) and tau deposition in DS relative to late onset AD. The established timeline based on amyloid age (or equivalent Centiloid values) is important for clinical practice and informing AD clinical trials, and avoids limitations of timelines based on chronological age. Funding. National Institute on Aging and the National Institute for Child Health and Human Development.

Research in Context

Evidence before this study: We searched PubMed for articles published involving the progression of Aβ and tau deposition in adults with Down syndrome from database inception to March 1, 2024. Terms included "amyloid", "Down syndrome", "tau", "Alzheimer's disease", "cognitive decline", and "amyloid chronicity," with no language restrictions. One previous study outlined the progression of tau in adults with Down syndrome without consideration of cognitive decline or clinical status. Other studies reported cognitive decline associated with Aβ burden and estimated years to AD symptom onset in Down syndrome. Amyloid age estimates have also been created for older neurotypical adults and compared to cognitive performance, but this has not been investigated in Down syndrome.Added value of this study: The timeline to symptomatic Alzheimer's disease in relation to amyloid, expressed as duration of Aβ+, and tau has yet to be described in adults with Down syndrome. Our longitudinal study is the first to provide a timeline of cognitive decline and transition to mild cognitive impairment and dementia in relation to Aβ+.Implications of all the available evidence: In a cohort study of 167 adults with Down syndrome, cognitive decline began 2.7 - 5.4 years and tau deposition began 2.7 - 6.1 years following Aβ+ (Centiloid = 18). Adults with Down syndrome converted to MCI after ~7 years and dementia after ~12-13 years of Aβ+. This shortened timeline to AD symptomology from Aβ+ and tau deposition in DS based on amyloid age (or corresponding Centiloid values) can inform clinical AD intervention trials and is of use in clinical settings.

Spatial extent as a sensitive amyloid-PET metric in preclinical Alzheimer's disease

INTRODUCTION

Spatial extent-based measures of how far amyloid beta (Aβ) has spread throughout the neocortex may be more sensitive than traditional Aβ-positron emission tomography (PET) measures of Aβ level for detecting early Aβ deposits in preclinical Alzheimer's disease (AD) and improve understanding of Aβ's association with tau proliferation and cognitive decline.

METHODS

Pittsburgh Compound-B (PIB)-PET scans from 261 cognitively unimpaired older adults from the Harvard Aging Brain Study were used to measure Aβ level (LVL; neocortical PIB DVR) and spatial extent (EXT), calculated as the proportion of the neocortex that is PIB+.

RESULTS

EXT enabled earlier detection of Aβ deposits longitudinally confirmed to reach a traditional LVL-based threshold for Aβ+ within 5 years. EXT improved prediction of cognitive decline (Preclinical Alzheimer Cognitive Composite) and tau proliferation (flortaucipir-PET) over LVL.

DISCUSSION

These findings indicate EXT may be more sensitive to Aβ's role in preclinical AD than level and improve targeting of individuals for AD prevention trials.

HIGHLIGHTS

Aβ spatial extent (EXT) was measured as the percentage of the neocortex with elevated Pittsburgh Compound-B. Aβ EXT improved detection of Aβ below traditional PET thresholds. Early regional Aβ deposits were spatially heterogeneous. Cognition and tau were more closely tied to Aβ EXT than Aβ level. Neocortical tau onset aligned with reaching widespread neocortical Aβ.

A Comparative Analysis of Two Automated Quantification Methods for Regional Cerebral Amyloid Retention: PET-Only and PET-and-MRI-Based Methods

Accurate quantification of amyloid positron emission tomography (PET) is essential for early detection of and intervention in Alzheimer's disease (AD) but there is still a lack of studies comparing the performance of various automated methods. This study compared the PET-only method and PET-and-MRI-based method with a pre-trained deep learning segmentation model. A large sample of 1180 participants in the Catholic Aging Brain Imaging (CABI) database was analyzed to calculate the regional standardized uptake value ratio (SUVR) using both methods. The logistic regression models were employed to assess the discriminability of amyloid-positive and negative groups through 10-fold cross-validation and area under the receiver operating characteristics (AUROC) metrics. The two methods showed a high correlation in calculating SUVRs but the PET-MRI method, incorporating MRI data for anatomical accuracy, demonstrated superior performance in predicting amyloid-positivity. The parietal, frontal, and cingulate importantly contributed to the prediction. The PET-MRI method with a pre-trained deep learning model approach provides an efficient and precise method for earlier diagnosis and intervention in the AD continuum.

Patterns of Early Neocortical Amyloid-β Accumulation: A PET Population-Based Study

The widespread deposition of amyloid-β (Aβ) plaques in late-stage Alzheimer disease is well defined and confirmed by in vivo PET. However, there are discrepancies between which regions contribute to the earliest topographic Aβ deposition within the neocortex. Methods: This study investigated Aβ signals in the perithreshold SUV ratio range using Pittsburgh compound B (PiB) PET in a population-based study cross-sectionally and longitudinally. PiB PET scans from 1,088 participants determined the early patterns of PiB loading in the neocortex. Results: Early-stage Aβ loading is seen first in the temporal, cingulate, and occipital regions. Regional early deposition patterns are similar in both apolipoprotein ε4 carriers and noncarriers. Clustering analysis shows groups with different patterns of early amyloid deposition. Conclusion: These findings of initial Aβ deposition patterns may be of significance for diagnostics and understanding the development of Alzheimer disease phenotypes.

Predicting Progression From Mild Cognitive Impairment to Alzheimer's Dementia With Adversarial Attacks

Early diagnosis of Alzheimer's disease plays a crucial role in treatment planning that might slow down the disease's progression. This problem is commonly posed as a classification task performed by machine learning and deep learning techniques. Although data-driven techniques set the state-of-the-art in many domains, the scale of the available datasets in Alzheimer's research is not sufficient to learn complex models from patient data. This study proposes a simple yet promising framework to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). The proposed framework comprises a shallow neural network for binary classification and a single-step gradient-based adversarial attack to find an adversarial progression direction in the input space. The step size required for the adversarial attack to change a patient's diagnosis from MCI to AD indicates the distance to the decision boundary. The patient's diagnosis at the next visit is predicted by employing this notion of distance to the decision boundary. We also present a potential application of the proposed framework to patient subtyping. Experiments with two publicly available datasets for Alzheimer's disease research imply that the proposed framework can predict MCI-to-AD conversions and assist in subtyping by only training a shallow neural network.

Awareness of episodic memory and meta-cognitive profiles: associations with cerebrospinal fluid biomarkers at the preclinical stage of the Alzheimer's continuum

Introduction

The lack of cognitive awareness, anosognosia, is a clinical deficit in Alzheimer's disease (AD) dementia. However, an increased awareness of cognitive function, hypernosognosia, may serve as a marker in the preclinical stage. Subjective cognitive decline (SCD) might correspond to the initial symptom in the dynamic trajectory of awareness, but SCD might be absent along with low awareness of actual cognitive performance in the preclinical stage. We hypothesized that distinct meta-cognitive profiles, both hypernosognosia and anosognosia, might be identified in preclinical-AD. This research evaluated the association between cerebrospinal fluid (CSF) AD biomarkers and the awareness of episodic memory, further exploring dyadic (participant-partner) SCD reports, in the preclinical Alzheimer's continuum.

Methods

We analyzed 314 cognitively unimpaired (CU) middle-aged individuals (mean age: 60, SD: 4) from the ALFA+ cohort study. Episodic memory was evaluated with the delayed recall from the Memory Binding Test (MBT). Awareness of episodic memory, meta-memory, was defined as the normalized discrepancy between objective and subjective performance. SCD was defined using self-report, and dyadic SCD profiles incorporated the study partner's report using parallel SCD-Questionnaires. The relationship between CSF Aβ42/40 and CSF p-tau181 with meta-memory was evaluated with multivariable regression models. The role of SCD and the dyadic contingency was explored with the corresponding stratified analysis.

Results

CSF Aβ42/40 was non-linearly associated with meta-memory, showing an increased awareness up to Aβ-positivity and a decreased awareness beyond this threshold. In the non-SCD subset, the non-linear association between CSF Aβ42/40 and meta-memory persisted. In the SCD subset, higher Aβ-pathology was linearly associated with increased awareness. Individuals presenting only study partner's SCD, defined as unaware decliners, exhibited higher levels of CSF p-tau181 correlated with lower meta-memory performance.

Discussion

These results suggested that distinct meta-cognitive profiles can be identified in preclinical-AD. While most individuals might experience an increased awareness associated with the entrance in the AD continuum, hypernosognosia, some might be already losing insight and stepping into the anosognosic trajectory. This research reinforced that an early anosognosic profile, although at increased risk of AD-related decline, might be currently overlooked considering actual diagnostic criteria, and therefore its medical attention delayed.

The spatial extent of tauopathy on [18F]MK-6240 tau PET shows stronger association with cognitive performances than the standard uptake value ratio in Alzheimer's disease

PURPOSE

[18F]MK-6240, a second-generation tau PET tracer, is increasingly used for the detection and the quantification of in vivo cerebral tauopathy in Alzheimer's disease (AD). Given that neurological symptoms are better explained by the topography rather than by the nature of brain lesions, our study aimed to evaluate whether cognitive impairment would be more closely associated with the spatial extent than with the intensity of tau-PET signal, as measured by the standard uptake value ratio (SUVr).

METHODS

[18F]MK6240 tau-PET data from 82 participants in the AD spectrum were quantified in three different brain regions (Braak ≤ 2, Braak ≤ 4, and Braak ≤ 6) using SUVr and the extent of tauopathy (EOT, percentage of voxels with SUVr ≥ 1.3). PET data were first compared between diagnostic categories, and ROC curves were computed to evaluate sensitivity and specificity. PET data were then correlated to cognitive performances and cerebrospinal fluid (CSF) tau values.

RESULTS

The EOT in the Braak ≤ 2 region provided the highest diagnostic accuracies, distinguishing between amyloid-negative and positive clinically unimpaired individuals (threshold = 9%, sensitivity = 79%, specificity = 82%) as well as between prodromal AD and preclinical AD (threshold = 38%, sensitivity = 81%, specificity = 93%). The EOT better correlated with cognition than SUVr (∆R2 + 0.08-0.09) with the best correlation observed for EOT in the Braak ≤ 4 region (R2 = 0.64). Cognitive performances were more closely associated with PET metrics than with CSF values.

CONCLUSIONS

Quantifying [18F]MK-6240 tau PET in terms of EOT rather than SUVr significantly increases the correlation with cognitive performances. Quantification in the mesiotemporal lobe is the most useful to diagnose preclinical AD or prodromal AD.

Positive rate and quantification of amyloid pathology with [18F]florbetapir in the urban Chinese population

OBJECTIVES

Amyloid deposition is considered the initial pathology in Alzheimer's disease (AD). Personalized management requires investigation of amyloid pathology and the risk factors for both amyloid pathology and cognitive decline in the Chinese population. We aimed to investigate amyloid positivity and deposition in AD patients, as well as factors related to amyloid pathology in Chinese cities.

METHODS

This cross-sectional multicenter study was conducted in Shanghai and Zhengzhou, China. All participants were recruited from urban communities and memory clinics. Amyloid positivity and deposition were analyzed based on amyloid positron emission tomography (PET). We used partial least squares (PLS) models to investigate how related factors contributed to amyloid deposition and cognitive decline.

RESULTS

In total, 1026 participants were included: 768 participants from the community-based cohort (COMC) and 258 participants from the clinic-based cohort (CLIC). The overall amyloid-positive rates in individuals with clinically diagnosed AD, mild cognitive impairment (MCI), and normal cognition (NC) were 85.8%, 44.5%, and 26.9%, respectively. The global amyloid deposition standardized uptake value ratios (SUVr) (reference: cerebellar crus) were 1.44 ± 0.24, 1.30 ± 0.22, and 1.24 ± 0.14, respectively. CLIC status, apolipoprotein E (ApoE) ε4, and older age were strongly associated with amyloid pathology by PLS modeling.

CONCLUSION

The overall amyloid-positive rates accompanying AD, MCI, and NC in the Chinese population were similar to those in published cohorts of other populations. ApoE ε4 and CLIC status were risk factors for amyloid pathology across the AD continuum. Education was a risk factor for amyloid pathology in MCI. Female sex and age were risk factors for amyloid pathology in NC.

CLINICAL RELEVANCE STATEMENT

This study provides new details about amyloid pathology in the Chinese population. Factors related to amyloid deposition and cognitive decline can help to assess patients' AD risk.

KEY POINTS

• We studied amyloid pathology and related risk factors in the Chinese population. •·The overall amyloid-positive rates in individuals with clinically diagnosed AD, MCI, and NC were 85.8%, 44.5%, and 26.9%, respectively. • These overall amyloid-positive rates were in close agreement with the corresponding prevalence for other populations.

Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.

Presenilin-1 mutation position influences amyloidosis, small vessel disease, and dementia with disease stage

INTRODUCTION

Amyloidosis, including cerebral amyloid angiopathy, and markers of small vessel disease (SVD) vary across dominantly inherited Alzheimer's disease (DIAD) presenilin-1 (PSEN1) mutation carriers. We investigated how mutation position relative to codon 200 (pre-/postcodon 200) influences these pathologic features and dementia at different stages.

METHODS

Individuals from families with known PSEN1 mutations (n = 393) underwent neuroimaging and clinical assessments. We cross-sectionally evaluated regional Pittsburgh compound B-positron emission tomography uptake, magnetic resonance imaging markers of SVD (diffusion tensor imaging-based white matter injury, white matter hyperintensity volumes, and microhemorrhages), and cognition.

RESULTS

Postcodon 200 carriers had lower amyloid burden in all regions but worse markers of SVD and worse Clinical Dementia Rating® scores compared to precodon 200 carriers as a function of estimated years to symptom onset. Markers of SVD partially mediated the mutation position effects on clinical measures.

DISCUSSION

We demonstrated the genotypic variability behind spatiotemporal amyloidosis, SVD, and clinical presentation in DIAD, which may inform patient prognosis and clinical trials.

HIGHLIGHTS

Mutation position influences Aβ burden, SVD, and dementia. PSEN1 pre-200 group had stronger associations between Aβ burden and disease stage. PSEN1 post-200 group had stronger associations between SVD markers and disease stage. PSEN1 post-200 group had worse dementia score than pre-200 in late disease stage. Diffusion tensor imaging-based SVD markers mediated mutation position effects on dementia in the late stage.

Distinct spatiotemporal atrophy patterns in corticobasal syndrome are associated with different underlying pathologies

Objective

To identify imaging subtypes of the cortico-basal syndrome (CBS) based solely on a data-driven assessment of MRI atrophy patterns, and investigate whether these subtypes provide information on the underlying pathology.

Methods

We applied Subtype and Stage Inference (SuStaIn), a machine learning algorithm that identifies groups of individuals with distinct biomarker progression patterns, to a large cohort of 135 CBS cases (52 had a pathological or biomarker defined diagnosis) and 252 controls. The model was fit using volumetric features extracted from baseline T1-weighted MRI scans and validated using follow-up MRI. We compared the clinical phenotypes of each subtype and investigated whether there were differences in associated pathology between the subtypes.

Results

SuStaIn identified two subtypes with distinct sequences of atrophy progression; four-repeat-tauopathy confirmed cases were most commonly assigned to the Subcortical subtype (83% of CBS-PSP and 75% of CBS-CBD), while CBS-AD was most commonly assigned to the Fronto-parieto-occipital subtype (81% of CBS-AD). Subtype assignment was stable at follow-up (98% of cases), and individuals consistently progressed to higher stages (100% stayed at the same stage or progressed), supporting the model's ability to stage progression.

Interpretation

By jointly modelling disease stage and subtype, we provide data-driven evidence for at least two distinct and longitudinally stable spatiotemporal subtypes of atrophy in CBS that are associated with different underlying pathologies. In the absence of sensitive and specific biomarkers, accurately subtyping and staging individuals with CBS at baseline has important implications for screening on entry into clinical trials, as well as for tracking disease progression.

Clinical application of MAO-B PET using 18F-THK5351 in neurological disorders

Monoamine oxidase B (MAO-B) is an enzyme localized to the outer mitochondrial membrane and highly concentrated in astrocytes. Temporal changes in regional MAO-B levels can be used as an index of astrocytic proliferation, known as activated astrocytes or astrogliosis. MAO-B is a marker to evaluate the degree of astrogliosis. Therefore, MAO-B positron emission tomography (PET) is a powerful imaging technique for visualizing and quantifying ongoing astrogliosis through the estimate of regional MAO-B levels. Each neurodegenerative disorder generally has a characteristic distribution pattern of astrogliosis secondary to neuronal loss and pathological protein aggregation. Therefore, by imaging astrogliosis, MAO-B PET can be used as a neurodegeneration marker for identifying degenerative lesions. Any inflammation in the brain usually accompanies astrogliosis starting from an acute phase to a chronic phase. Therefore, by imaging astrogliosis, MAO-B PET can be used as a neuroinflammation marker for identifying inflammatory lesions. MAO-B levels are high in gliomas originating from astrocytes but low in lymphoid tumors. Therefore, MAO-B PET can be used as a brain tumor marker for identifying astrocytic gliomas by imaging MAO-B levels and distinguishing between astrocytic and lymphoid tumors. This review summarizes the clinical application of MAO-B PET using 18F-THK5351 as markers for neurodegeneration, neuroinflammation, and brain tumors in neurological disorders. Because we assume that MAO-B PET is clinically applied to an individual patient, we focus on visual inspection of MAO-B images at the individual patient level. Geriatr Gerontol Int 2024; 24: 31-43.

A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis

Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.

Distinct spatiotemporal patterns of cortical thinning in Alzheimer's disease-type cognitive impairment and subcortical vascular cognitive impairment

Previous studies on Alzheimer's disease-type cognitive impairment (ADCI) and subcortical vascular cognitive impairment (SVCI) has rarely explored spatiotemporal heterogeneity. This study aims to identify distinct spatiotemporal cortical atrophy patterns in ADCI and SVCI. 1,338 participants (713 ADCI, 208 SVCI, and 417 cognitively unimpaired elders) underwent brain magnetic resonance imaging (MRI), amyloid positron emission tomography, and neuropsychological tests. Using MRI, this study measures cortical thickness in five brain regions (medial temporal, inferior temporal, posterior medial parietal, lateral parietal, and frontal areas) and utilizes the Subtype and Stage Inference (SuStaIn) model to predict the most probable subtype and stage for each participant. SuStaIn identifies two distinct cortical thinning patterns in ADCI (medial temporal: 65.8%, diffuse: 34.2%) and SVCI (frontotemporal: 47.1%, parietal: 52.9%) patients. The medial temporal subtype of ADCI shows a faster decline in attention, visuospatial, visual memory, and frontal/executive domains than the diffuse subtype (p-value < 0.01). However, there are no significant differences in longitudinal cognitive outcomes between the two subtypes of SVCI. Our study provides valuable insights into the distinct spatiotemporal patterns of cortical thinning in patients with ADCI and SVCI, suggesting the potential for individualized therapeutic and preventive strategies to improve clinical outcomes.

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.

Combined Connectomics, MAPT Gene Expression, and Amyloid Deposition to Explain Regional Tau Deposition in Alzheimer Disease

OBJECTIVE

We aimed to test whether region-specific factors, including spatial expression patterns of the tau-encoding gene MAPT and regional levels of amyloid positron emission tomography (PET), enhance connectivity-based modeling of the spatial variability in tau-PET deposition in the Alzheimer disease (AD) spectrum.

METHODS

We included 685 participants (395 amyloid-positive participants within AD spectrum and 290 amyloid-negative controls) with tau-PET and amyloid-PET from 3 studies (Alzheimer's Disease Neuroimaging Initiative, 18 F-AV-1451-A05, and BioFINDER-1). Resting-state functional magnetic resonance imaging was obtained in healthy controls (n = 1,000) from the Human Connectome Project, and MAPT gene expression from the Allen Human Brain Atlas. Based on a brain-parcellation atlas superimposed onto all modalities, we obtained region of interest (ROI)-to-ROI functional connectivity, ROI-level PET values, and MAPT gene expression. In stepwise regression analyses, we tested connectivity, MAPT gene expression, and amyloid-PET as predictors of group-averaged and individual tau-PET ROI values in amyloid-positive participants.

RESULTS

Connectivity alone explained 21.8 to 39.2% (range across 3 studies) of the variance in tau-PET ROI values averaged across amyloid-positive participants. Stepwise addition of MAPT gene expression and amyloid-PET increased the proportion of explained variance to 30.2 to 46.0% and 45.0 to 49.9%, respectively. Similarly, for the prediction of patient-level tau-PET ROI values, combining all 3 predictors significantly improved the variability explained (mean adjusted R2 range across studies = 0.118-0.148, 0.156-0.196, and 0.251-0.333 for connectivity alone, connectivity plus MAPT expression, and all 3 modalities combined, respectively).

INTERPRETATION

Across 3 study samples, combining the functional connectome and molecular properties substantially enhanced the explanatory power compared to single modalities, providing a valuable tool to explain regional susceptibility to tau deposition in AD. ANN NEUROL 2024;95:274-287.

Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review

Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

Exploring cognitive progression subtypes in the Framingham Heart Study

INTRODUCTION

Alzheimer's disease (AD) is a heterogeneous disorder characterized by complex underlying neuropathology that is not fully understood. This study aimed to identify cognitive progression subtypes and examine their correlation with clinical outcomes.

METHODS

Participants of this study were recruited from the Framingham Heart Study. The Subtype and Stage Inference (SuStaIn) method was used to identify cognitive progression subtypes based on eight cognitive domains.

RESULTS

Three cognitive progression subtypes were identified, including verbal learning (Subtype 1), abstract reasoning (Subtype 2), and visual memory (Subtype 3). These subtypes represent different domains of cognitive decline during the progression of AD. Significant differences in age of onset among the different subtypes were also observed. A higher SuStaIn stage was significantly associated with increased mortality risk.

DISCUSSION

This study provides a characterization of AD heterogeneity in cognitive progression, emphasizing the importance of developing personalized approaches for risk stratification and intervention.

Highlights

We used the Subtype and Stage Inference (SuStaIn) method to identify three cognitive progression subtypes.Different subtypes have significant variations in age of onset.Higher stages of progression are associated with increased mortality risk.

High Contrast PET Imaging of Subcortical and Allocortical Amyloid-β in Early Alzheimer's Disease Using [11C]AZD2184

Background

Deposits of amyloid-β (Aβ) appear early in Alzheimer's disease (AD).

Objective

The aim of the present study was to compare the presence of cortical and subcortical Aβ in early AD using positron emission tomography (PET).

Methods

Eight cognitively unimpaired (CU) subjects, 8 with mild cognitive impairment (MCI) and 8 with mild AD were examined with PET and [11C]AZD2184. A data driven cut-point for Aβ positivity was defined by Gaussian mixture model of isocortex binding potential (BPND) values.

Results

Sixteen subjects (3 CU, 5 MCI and 8 AD) were Aβ-positive. BPND was lower in subcortical and allocortical regions compared to isocortex. Fifteen of the 16 Aβ-positive subjects displayed Aβ binding in striatum, 14 in thalamus and 10 in allocortical regions.

Conclusions

Aβ deposits appear to be widespread in early AD. It cannot be excluded that deposits appear simultaneously throughout the whole brain which has implications for improved diagnostics and disease monitoring.

PET/CT/MRI in Clinical Trials of Alzheimer's Disease

With the advent of PET imaging in 1976, 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-PET became the preferred method for in vivo investigation of cerebral processes, including regional hypometabolism in Alzheimer's disease. With the emergence of amyloid-PET tracers, [11C]Pittsburgh Compound-B in 2004 and later [18F]florbetapir, [18F]florbetaben, and [18F]flumetamol, amyloid-PET has replaced FDG-PET in Alzheimer's disease anti-amyloid clinical trial treatments to ensure "amyloid positivity" as an entry criterion, and to measure treatment-related decline in cerebral amyloid deposits. MRI has been used to rule out other brain diseases and screen for 'amyloid-related imaging abnormalities' (ARIAs) of two kinds, ARIA-E and ARIA-H, characterized by edema and micro-hemorrhage, respectively, and, to a lesser extent, to measure changes in cerebral volumes. While early immunotherapy trials of Alzheimer's disease showed no clinical effects, newer monoclonal antibody trials reported decreases of 27% to 85% in the cerebral amyloid-PET signal, interpreted by the Food and Drug Administration as amyloid removal expected to result in a reduction in clinical decline. However, due to the lack of diagnostic specificity of amyloid-PET tracers, amyloid positivity cannot prevent the inclusion of non-Alzheimer's patients and even healthy subjects in these clinical trials. Moreover, the "decreasing amyloid accumulation" assessed by amyloid-PET imaging has questionable quantitative value in the presence of treatment-related brain damage (ARIAs). Therefore, future Alzheimer's clinical trials should disregard amyloid-PET imaging and focus instead on assessment of regional brain function by FDG-PET and MRI monitoring of ARIAs and brain volume loss in all trial patients.

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models

The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.

A data-driven study of Alzheimer's disease related amyloid and tau pathology progression

Amyloid-β is thought to facilitate the spread of tau throughout the neocortex in Alzheimer's disease, though how this occurs is not well understood. This is because of the spatial discordance between amyloid-β, which accumulates in the neocortex, and tau, which accumulates in the medial temporal lobe during ageing. There is evidence that in some cases amyloid-β-independent tau spreads beyond the medial temporal lobe where it may interact with neocortical amyloid-β. This suggests that there may be multiple distinct spatiotemporal subtypes of Alzheimer's-related protein aggregation, with potentially different demographic and genetic risk profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post-mortem neuropathology and in vivo PET-based measures from two large observational studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). We consistently identified 'amyloid-first' and 'tau-first' subtypes using cross-sectional information from both studies. In the amyloid-first subtype, extensive neocortical amyloid-β precedes the spread of tau beyond the medial temporal lobe, while in the tau-first subtype, mild tau accumulates in medial temporal and neocortical areas prior to interacting with amyloid-β. As expected, we found a higher prevalence of the amyloid-first subtype among apolipoprotein E (APOE) ε4 allele carriers while the tau-first subtype was more common among APOE ε4 non-carriers. Within tau-first APOE ε4 carriers, we found an increased rate of amyloid-β accumulation (via longitudinal amyloid PET), suggesting that this rare group may belong within the Alzheimer's disease continuum. We also found that tau-first APOE ε4 carriers had several fewer years of education than other groups, suggesting a role for modifiable risk factors in facilitating amyloid-β-independent tau. Tau-first APOE ε4 non-carriers, in contrast, recapitulated many of the features of primary age-related tauopathy. The rate of longitudinal amyloid-β and tau accumulation (both measured via PET) within this group did not differ from normal ageing, supporting the distinction of primary age-related tauopathy from Alzheimer's disease. We also found reduced longitudinal subtype consistency within tau-first APOE ε4 non-carriers, suggesting additional heterogeneity within this group. Our findings support the idea that amyloid-β and tau may begin as independent processes in spatially disconnected regions, with widespread neocortical tau resulting from the local interaction of amyloid-β and tau. The site of this interaction may be subtype-dependent: medial temporal lobe in amyloid-first, neocortex in tau-first. These insights into the dynamics of amyloid-β and tau may inform research and clinical trials that target these pathologies.

Artificial intelligence for dementia drug discovery and trials optimization

Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.

Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates

The pair-wise observation of the input and target values obtained from the same sample is mandatory in any prediction problem. In the biomarker discovery of Alzheimer's disease (AD), however, obtaining such paired data is laborious and often avoided. Accumulation of amyloid-beta (Aβ) in the brain precedes neurodegeneration in AD, and the quantitative accumulation level may reflect disease progression in the very early phase. Nevertheless, the direct observation of Aβ is rarely paired with the observation of other biomarker candidates. To this end, we established a method that quantitatively predicts Aβ accumulation from biomarker candidates by integrating the mostly unpaired observations via a few-shot learning approach. When applied to 5xFAD mouse behavioral data, the proposed method predicted the accumulation level that conformed to the observed amount of Aβ in the samples with paired data. The results suggest that the proposed model can contribute to discovering Aβ predictability-based biomarkers.

Uncovering distinct progression patterns of tau deposition in progressive supranuclear palsy using [18F]Florzolotau PET imaging and subtype/stage inference algorithm

BACKGROUND

Progressive supranuclear palsy (PSP) is a primary 4-repeat tauopathy with diverse clinical phenotypes. Previous post-mortem studies examined tau deposition sequences in PSP, but in vivo scrutiny is lacking.

METHODS

We conducted [18F]Florzolotau tau positron emission tomography (PET) scans on 148 patients who were clinically diagnosed with PSP and 20 healthy controls. We employed the Subtype and Stage Inference (SuStaIn) algorithm to identify PSP subtype/stage and related tau patterns, comparing clinical features across subtypes and assessing PSP stage-clinical severity association. We also evaluated functional connectivity differences among subtypes through resting-state functional magnetic resonance imaging.

FINDINGS

We identified two distinct subtypes of PSP: Subtype1 and Subtype2. Subtype1 typically exhibits a sequential progression of the disease, starting from subcortical and gradually moving to cortical regions. Conversely, Subtype2 is characterized by an early, simultaneous onset in both regions. Interestingly, once the disease is initiated, Subtype1 tends to spread more rapidly within each region compared to Subtype2. Individuals categorized as Subtype2 are generally older and exhibit less severe dysfunctions in areas such as cognition, bulbar, limb motor, and general motor functions compared to those with Subtype1. Moreover, they have a more favorable prognosis in terms of limb motor function. We found significant correlations between several clinical variables and the identified PSP SuStaIn stages. Furthermore, Subtype2 displayed a remarkable reduction in functional connectivity compared to Subtype1.

INTERPRETATION

We present the evidence of distinct in vivo spatiotemporal tau trajectories in PSP. Our findings can contribute to precision medicine advancements for PSP.

FUNDING

This work was supported by grants from the National Natural Science Foundation of China (number 82272039, 81971641, 82021002, and 92249302); Swiss National Science Foundation (number 188350); the STI2030-Major Project of China (number 2022ZD0211600); the Clinical Research Plan of Shanghai Hospital Development Center of China (number SHDC2020CR1038B); and the National Key R&D Program of China (number 2022YFC2009902, 2022YFC2009900), the China Scholarship Council (number 202006100181); the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198).

Prognostic value of imaging-based ATN profiles in a memory clinic cohort

PURPOSE

The ATN model represents a research framework used to classify subjects based on the presence or absence of Alzheimer's disease (AD) pathology through biomarkers for amyloid (A), tau (T), and neurodegeneration (N). The aim of this study was to assess the relationship between ATN profiles defined through imaging and cognitive decline in a memory clinic cohort.

METHODS

One hundred-eight patients from the memory clinic of Geneva University Hospitals underwent complete clinical and neuropsychological evaluation at baseline and 23 ± 5 months after inclusion, magnetic resonance imaging, amyloid and tau PET scans. ATN profiles were divided into four groups: normal, AD pathological change (AD-PC: A + T-N-, A + T-N +), AD pathology (AD-P: A + T + N-, A + T + N +), and suspected non-AD pathology (SNAP: A-T + N-, A-T-N + , A-T + N +).

RESULTS

Mini-Mental State Examination (MMSE) scores were significantly different among groups, both at baseline and follow-up, with the normal group having higher average MMSE scores than the other groups. MMSE scores changed significantly after 2 years only in AD-PC and AD-P groups. AD-P profile classification also had the largest number of decliners at follow-up (55%) and the steepest global cognitive decline compared to the normal group. Cox regression showed that participants within the AD-P group had a higher risk of cognitive decline (HR = 6.15, CI = 2.59-14.59), followed by AD-PC (HR = 3.16, CI = 1.17-8.52).

CONCLUSION

Of the different group classifications, AD-P was found to have the most significant effect on cognitive decline over a period of 2 years, highlighting the value of both amyloid and tau PET molecular imaging as prognostic imaging biomarkers in clinical practice.

Neuroimaging in Dementia: More than Typical Alzheimer Disease

Alzheimer disease (AD) is the most common cause of dementia. The prevailing theory of the underlying pathology assumes amyloid accumulation followed by tau protein aggregation and neurodegeneration. However, the current antiamyloid and antitau treatments show only variable clinical efficacy. Three relevant points are important for the radiologic assessment of dementia. First, besides various dementing disorders (including AD, frontotemporal dementia, and dementia with Lewy bodies), clinical variants and imaging subtypes of AD include both typical and atypical AD. Second, atypical AD has overlapping radiologic and clinical findings with other disorders. Third, the diagnostic process should consider mixed pathologies in neurodegeneration, especially concurrent cerebrovascular disease, which is frequent in older age. Neuronal loss is often present at, or even before, the onset of cognitive decline. Thus, for effective emerging treatments, early diagnosis before the onset of clinical symptoms is essential to slow down or stop subsequent neuronal loss, requiring molecular imaging or plasma biomarkers. Neuroimaging, particularly MRI, provides multiple imaging parameters for neurodegenerative and cerebrovascular disease. With emerging treatments for AD, it is increasingly important to recognize AD variants and other disorders that mimic AD. Describing the individual composition of neurodegenerative and cerebrovascular disease markers while considering overlapping and mixed diseases is necessary to better understand AD and develop efficient individualized therapies.

Two distinct trajectories of clinical and neurodegeneration events in Parkinson's disease

Increasing evidence suggests that Parkinson's disease (PD) exhibits disparate spatial and temporal patterns of progression. Here we used a machine-learning technique-Subtype and Stage Inference (SuStaIn) - to uncover PD subtypes with distinct trajectories of clinical and neurodegeneration events. We enrolled 228 PD patients and 119 healthy controls with comprehensive assessments of olfactory, autonomic, cognitive, sleep, and emotional function. The integrity of substantia nigra (SN), locus coeruleus (LC), amygdala, hippocampus, entorhinal cortex, and basal forebrain were assessed using diffusion and neuromelanin-sensitive MRI. SuStaIn model with above clinical and neuroimaging variables as input was conducted to identify PD subtypes. An independent dataset consisting of 153 PD patients and 67 healthy controls was utilized to validate our findings. We identified two distinct PD subtypes: subtype 1 with rapid eye movement sleep behavior disorder (RBD), autonomic dysfunction, and degeneration of the SN and LC as early manifestations, and cognitive impairment and limbic degeneration as advanced manifestations, while subtype 2 with hyposmia, cognitive impairment, and limbic degeneration as early manifestations, followed later by RBD and degeneration of the LC in advanced disease. Similar subtypes were shown in the validation dataset. Moreover, we found that subtype 1 had weaker levodopa response, more GBA mutations, and poorer prognosis than subtype 2. These findings provide new insights into the underlying disease biology and might be useful for personalized treatment for patients based on their subtype.

Occipital Amyloid Deposition Is Associated with Rapid Cognitive Decline in the Alzheimer's Disease Continuum

BACKGROUND

Clinical significance of additional occipital amyloid-β (Aβ) plaques in Alzheimer's disease (AD) remains unclear.

OBJECTIVE

In this study, we investigated the effect of regional Aβ deposition on cognition in patients on the AD continuum, especially in the occipital region.

METHODS

We retrospectively reviewed the medical record of 208 patients with AD across the cognitive continuum (non-dementia and dementia). Multivariable linear regression analyses were performed to determine the effect of regional Aβ deposition on cognitive function. A linear mixed model was used to assess the effect of regional deposition on longitudinal changes in Mini-Mental State Examination (MMSE) scores. Additionally, the patients were dichotomized according to the occipital-to-global Aβ deposition ratio (ratio ≤1, Aβ-OCC- group; ratio >1, Aβ-OCC+ group), and the same statistical analyses were applied for between-group comparisons.

RESULTS

Regional Aβ burden itself was not associated with baseline cognitive function. In terms of Aβ-OCC group effect, the Aβ-OCC+ group exhibited a poorer cognitive performance on language function compared to the Aβ-OCC- group. High Aβ retention in each region was associated with a rapid decline in MMSE scores, only in the dementia subgroup. Additionally, Aβ-OCC+ individuals exhibited a faster annual decline in MMSE scores than Aβ-OCC- individuals in the non-dementia subgroup (β= -0.77, standard error [SE] = 0.31, p = 0.013).

CONCLUSION

The present study demonstrated that additional occipital Aβ deposition was associated with poor baseline language function and rapid cognitive deterioration in patients on the AD continuum.

Multimodal comparisons of QSM and PET in neurodegeneration and aging

Quantitative susceptibility mapping (QSM) has been used to study susceptibility changes that may occur based on tissue composition and mineral deposition. Iron is a primary contributor to changes in magnetic susceptibility and of particular interest in applications of QSM to neurodegeneration and aging. Iron can contribute to neurodegeneration through inflammatory processes and via interaction with aggregation of disease-related proteins. To better understand the local susceptibility changes observed on QSM, its signal has been studied in association with other imaging metrics such as positron emission tomography (PET). The associations of QSM and PET may provide insight into the pathophysiology of disease processes, such as the role of iron in aging and neurodegeneration, and help to determine the diagnostic utility of QSM as an indirect indicator of disease processes typically evaluated with PET. In this review we discuss the proposed mechanisms and summarize prior studies of the associations of QSM and amyloid PET, tau PET, TSPO PET, FDG-PET, 15O-PET, and F-DOPA PET in evaluation of neurologic diseases with a focus on aging and neurodegeneration.

Evidence against a temporal association between cerebrovascular disease and Alzheimer's disease imaging biomarkers

Whether a relationship exists between cerebrovascular disease and Alzheimer's disease has been a source of controversy. Evaluation of the temporal progression of imaging biomarkers of these disease processes may inform mechanistic associations. We investigate the relationship of disease trajectories of cerebrovascular disease (white matter hyperintensity, WMH, and fractional anisotropy, FA) and Alzheimer's disease (amyloid and tau PET) biomarkers in 2406 Mayo Clinic Study of Aging and Mayo Alzheimer's Disease Research Center participants using accelerated failure time models. The model assumes a common pattern of progression for each biomarker that is shifted earlier or later in time for each individual and represented by a per participant age adjustment. An individual's amyloid and tau PET adjustments show very weak temporal association with WMH and FA adjustments (R = -0.07 to 0.07); early/late amyloid or tau timing explains <1% of the variation in WMH and FA adjustment. Earlier onset of amyloid is associated with earlier onset of tau (R = 0.57, R2 = 32%). These findings support a strong mechanistic relationship between amyloid and tau aggregation, but not between WMH or FA and amyloid or tau PET.