INTRODUCTION:Network attack tolerance (NAT) measures the brain's ability to sustain information flow despite the loss of critical brain regions. In Parkinson's disease (PD), NAT has been linked to cognitive and motor function. However, it remains unclear how dopaminergic degeneration shapes NAT, and whether it relates to motor symptom progression. OBJECTIVE:To examine dopamine deficiency-related changes in NAT across the early PD disease continuum and its association with longitudinal motor outcomes. METHODS:We used cross-sectional resting-state functional magnetic resonance imaging of 28 healthy controls, 60 prodromal, and 94 clinical PD patients to create graph-theoretical networks. NAT was assessed at the global and subnetwork levels by calculating the global efficiency upon iterative node removal at different network densities. Using linear mixed-effects models, we assessed how putaminal dopamine transporter (DaT) binding or disease status affected NAT, controlling for network density, age, sex, and education. Baseline somatomotor (SMN) NAT was examined as a predictor of motor symptom progression in 145 patients. RESULTS:Lower putaminal DaT signal was associated with higher SMN NAT across groups. PD patients exhibited elevated SMN NAT relative to controls. Neither global nor other subnetworks showed effects. While the effect of baseline SMN NAT on motor progression was not significant, an exploratory analysis (Johnson-Neyman) suggested that higher SMN NAT may associate with slower motor decline across most of the observed NAT range. CONCLUSIONS:Dopaminergic depletion is associated with a targeted SMN reorganization, potentially maintaining network information flow despite progressive hub loss. Whether this reorganization represents compensation or a consequence of progressive pathology requires further longitudinal assessment. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

INTRODUCTION Sleep disturbances have been associated with Alzheimer's disease (AD), but their relevance in preclinical stages, such as subjective cognitive decline (SCD), and their relationship with brain pathology remain unclear.METHODS We used a portable sleep-monitoring headband over four consecutive nights to assess sleep in 19 cognitively unimpaired (CU), 15 SCD, and 20 mild cognitive impairment (MCI) participants with available amyloid positron emission tomography (PET). Linear-mixed-effects models compared sleep parameters across groups, accounting for amyloid burden, age, sex, education, and recording. Regional and voxel-wise analyses examined regional associations between sleep parameters and amyloid burden.RESULTS MCI patients presented reduced N3 (i.e., deep sleep), while SCD individuals showed longer N1 (i.e., light sleep) duration compared to CU. Regional amyloid burden was associated with longer light and deep sleep in amyloid-positive individuals. Higher education was linked to better sleep efficiency.DISCUSSION Sleep changes may serve as early indicators of cognitive dysfunction and regional amyloid accumulation.

Atypical Parkinsonian Disorders (APD), also known as Parkinson-plus syndrome, are a group of neurodegenerative diseases that include progressive supranuclear palsy (PSP) and multiple system atrophy (MSA). In the early stages, overlapping clinical features often lead to misdiagnosis as Parkinson's disease (PD). Identifying reliable imaging biomarkers for early differential diagnosis remains a critical challenge. In this study, we propose a hybrid framework combining convolutional neural networks (CNNs) with machine learning (ML) techniques to classify APD subtypes versus PD and distinguish between the subtypes themselves: PSP vs. PD, MSA vs. PD, and PSP vs. MSA. The model leverages multi-modal input data, including T1-weighted magnetic resonance imaging (MRI), segmentation masks of 12 deep brain structures associated with APD, and their corresponding volumetric measurements. By integrating these complementary modalities, including image data, structural segmentation masks, and quantitative volume features, the hybrid approach achieved promising classification performance with area under the curve (AUC) scores of 0.95 for PSP vs. PD, 0.86 for MSA vs. PD, and 0.92 for PSP vs. MSA. These results highlight the potential of combining spatial and structural information for robust subtype differentiation. In conclusion, this study demonstrates that fusing CNN-based image features with volume-based ML inputs improves classification accuracy for APD subtypes. The proposed approach may contribute to more reliable early-stage diagnosis, facilitating timely and targeted interventions in clinical practice.

Summary:This procedure guideline for SPECT examinations of striatal dopamine transporter availability is intended to support the planning, execution, quality control, interpretation and reporting of cerebral SPECT scans with [123I]ioflupane. It is an update and expansion built on the 2019 version of the procedure guideline. It was developed through an informal process as a consensus of the Neuroimaging Working Group of the German Society of Nuclear Medicine and in consultation with the German Neurological Society. It is intended for use by physicians and technical staff working in the field of nuclear medicine.

Dopamine (DA) has been implicated in exploration-exploitation behaviour, i.e., exploring novel, potentiallybetter options vs. exploiting known, previously rewarding options. Impairments in this trade-off occur inpsychiatric disorders involving DAergic dysfunction, including addiction and schizophrenia. Pharmacologicalstudies revealed a contribution of DA to exploration, but inconsistent findings suggest that interindividualvariability in baseline DA may modulate effects. To address this, we investigated the effects of the DAprecursor L-DOPA on exploration-exploitation during reinforcement learning in a sample of N = 75 healthyparticipants (n = 32 women), following a randomised, double-blind, placebo-controlled, pre-registered design(https://osf.io/p2r7u). We assessed whether putative baseline DA markers, including spontaneouseye blink rate, working memory (WM) capacity, and impulsivity, modulated drug effects and probed visualfixation patterns and pupil dilation as markers of exploration. L-DOPA had no overall effect on computationalmodel parameters of random exploration, directed exploration or choice perseveration. WM capacitymoderated drug effects on random exploration, with stronger effects at higher WM capacity. Remaining DAproxies showed no credible effects. Pooling the data from male participants with that from an earlier male-only study (Chakroun et al., 2020; total N = 74), L-DOPA increased uncertainty-dependent value weightingand perseveration strength, while decreasing habit updating, indicating a stronger tendency to repeatprevious choices and slower decay of their influence over time. No credible drug effects were observed infemale participants. Pupil dilation was tonically increased under L-DOPA and scaled with explorationbehaviour and prediction error, confirming that pupillometry can index exploration-exploitation dynamics.Visual exploration patterns reflected uncertainty-driven sampling, but were unaffected by L-DOPA. Takentogether, results suggest that DAergic modulation of exploration and perseveration behaviour may becontingent on cognitive capacity and sex, rather than exerting uniform effects across individuals. ### Competing Interest Statement The authors have declared no competing interest. German Research Foundation (DFG), PE1627/5-1 Cologne Clinician Scientist Program (CCSP) of the Faculty of Medicine at the University of Cologne, funded by the DFG, 413543196

The link between regional tau load and clinical manifestation of Alzheimer's disease (AD) highlights the importance of characterizing spatial tau distribution. In typical (memory-predominant) AD, the spatial progression of tau pathology mirrors the functional connections from temporal lobe epicenters. However, atypical (non-amnestic-predominant) AD variants with heterogeneous tau patterns provide a key opportunity to assess the universality of connectivity as a scaffold for tau progression. We included tau-PET data from 320 subjects with atypical AD, characterized by highly heterogeneous tau patterns ( n  = 139 posterior cortical atrophy/PCA-AD; n  = 103 logopenic variant primary progressive aphasia/lvPPA-AD; n  = 35 behavioural variant AD/bvAD; n  = 43 corticobasal syndrome/CBS-AD) from 14 sites, with a subset of patients ( n  = 78) having longitudinal tau-PET data. As an independent sample, we further included regional post-mortem tau stainings from 93 atypical AD patients from two sites ( n  = 19 PCA-AD, n  = 32 lvPPA-AD, n  = 23 bvAD, n  = 19 CBS-AD). Gaussian mixture modeling was used to harmonize different tau-PET tracers by transforming tau-PET standardized uptake value ratios to tau positivity probabilities (a uniform scale ranging from 0% to 100%). Using linear regression, we assessed whether 1) brain regions with stronger functional connectivity showed greater covariance in cross-sectional and longitudinal tau-PET and post-mortem tau pathology, and 2) functional connectivity of tau-PET epicenters and tau-PET accumulation epicenters was associated with cross-sectional and longitudinal tau patterns. Tau-PET epicenters—defined as the 5% brain regions with the highest tau load—aligned with clinical variants, e.g. a posterior pattern in PCA-AD (“visual AD”) and left-hemispheric temporal predominance in lvPPA-AD (“language AD”) (Figure 1). More strongly functionally connected regions showed correlated concurrent tau-PET levels, which was confirmed with post-mortem data (Figure 2). Moreover, the connectivity profile of tau-PET epicenters and accumulation epicenters corresponded to tau-PET progression patterns (Figure 3). Our data are consistent with the hypothesis that tau propagation occurs along functional connections originating from local epicenters, across all AD clinical variants. Since tau proteinopathy is a key driver of neurodegeneration and cognitive decline, this finding may advance personalized medicine and participant-specific endpoints in clinical trials.

Background Artificial intelligence is a valuable tool in medical imaging and diagnostics. This study assesses the agreement between ChatGPT-4o and ChatGPT-5, two large language models, and expert nuclear medicine physicians in interpreting [18F]FDG-PET/CT findings in neurodegenerative diseases. Methods 100 anonymized cases were analyzed, comparing AI-generated differential diagnoses with expert reports. Models received only textual descriptions of imaging findings and clinical history extracted from the reports, with patient age provided as an additional input variable. In a 20-case reproducibility subset per model, we re-queried the same cases in a new chat session with the conversation history cleared, using the identical prompt, and assessed run-to-run agreement on a five-level ordinal scale (0, 0.25, 0.5, 0.75, 1). Results Median agreement scores were 1.00 [IQR 0.50–1.00] with ChatGPT-4o and 1.00 [0.75–1.00] with ChatGPT-5. The main diagnosis was correctly identified in 86% (ChatGPT-4o) and 89% (ChatGPT-5) of cases, respectively. Both models performed best in cases with well-defined metabolic patterns but were less accurate for complex metabolic patterns associated with broad differentials. In the reproducibility subset, exact run-to-run agreement was 75% for ChatGPT-4o (quadratic-weighted κ = 0.48) and 55% for ChatGPT-5 (κ = 0.65). The higher κ for ChatGPT-5 reflects greater consistency on the ordinal scale despite fewer exact matches, with most differences representing one-step shifts. Conclusions While ChatGPT is not approved for clinical diagnostics, it demonstrated substantial diagnostic alignment with expert physicians when interpreting textual case information. Reproducible outputs highlight its potential as a supportive diagnostic tool.

There are 14 modifiable factors that are associated with a significantly lower risk of dementia. We tested the interactive effect of modifiable factors, genetic determinants, and initial pathologic burden on the spatial progression and local amplification of tau pathology. Methods: In total, 162 amyloid-positive individuals were included, for whom longitudinal [18F]AV-1451 PET scans, baseline information on global amyloid burden, ApoE4 status, body mass index (BMI), hypertension, education, neuropsychiatric symptom severity, and demographic information were available in the Alzheimer Disease Neuroimaging Initiative. All [18F]AV-1451 scans were intensity-standardized (reference: inferior cerebellum), z-transformed (control sample: 147 amyloid-negative subjects), thresholded (z score, >1.96), and converted to volume maps. Longitudinal tau changes were then assessed in terms of tau spatial extent (i.e., newly affected volume at follow-up) and tau level rise (i.e., tau increase in previously affected volume). These 2 measures were entered as dependent variables in linear mixed-effects models, including baseline modifiable risk factors (BMI, education, hypertension, neuropsychiatric symptom severity), global amyloid, tau volume or tau burden, ApoE4 status, clinical stage, sex, and age as predictors. Next, we tested the interactive effects between baseline amyloid or tau burden with the 4 modifiable factors on tau extent or tau level rise, respectively. Results: Greater tau extent was linked to higher BMI (β = 0.002; 95% CI, 0.0003-0.003), ApoE4 status (β = 0.024; 95% CI, 0.001-0.046), and baseline tau volume (β = 0.207; 95% CI, 0.107-0.308) across groups. In terms of tau level rise, we observed that absence of hypertension (β = 0.295; 95% CI, -0.477 to -0.114), dementia group (β = 0.305; 95% CI, 0.088-0.522), and BMI (β = 0.011; 95% CI, 0.00004-0.022) were linked to increased tau burden. A load-dependent effect of baseline amyloid and tau volume/burden was found for both tau extent (β = -0.005; 95% CI, -0.008 to -0.002) and tau level rise (β = -0.003; 95% CI, -0.005 to -0.001). Higher amyloid and BMI (β = 0.001; 95% CI, 0.0004-0.001) and lower education and higher tau burden (β = -0.035; 95% CI, -0.064 to -0.006) were linked to greater tau level rise. Conclusion: Education, BMI, and hypertension differentially influence tau's spatial extent and increase by its interaction with initial pathologic burden. Timely modification of these factors may overall slow tau progression.

FDG PET is a powerful adjunct for the differential diagnosis of parkinsonian syndromes because it reveals disease specific regional and network level metabolic signatures. Compared with dopamine transporter imaging, it better distinguishes Parkinson disease from progressive supranuclear palsy, corticobasal degeneration or corticobasal syndrome, multiple system atrophy, and Lewy body dementia such as Parkinson disease dementia and dementia with Lewy bodies. When combined with expert clinical assessment and multimodal imaging, FDG PET substantially improves diagnostic confidence, especially in early or atypical presentations.

PURPOSE:Attenuation correction is critical for accurate SPECT brain imaging and to quantify uptake ratios in neurological and psychiatric disorders. This prospective study aimed to validate the use of deep-learning-based magnetic resonance to synthetic computed tomography (DL-MRAC) attenuation correction by quantifying striatal and extrastriatal binding of [123I]I-FP-CIT to dopamine and serotonin transporters in Parkinson's disease patients. METHODS:Synthetic CTs were generated from T1-weighted MRIs acquired in 12 Parkinson's disease patients using a validated 3D residual U-Net for attenuation correction of [123I]I-FP-CIT SPECT scans. We tested for equivalence of DL-MRAC versus CT-based attenuation correction (CTAC). We further compared uniform correction using Chang's method (UAC) and no attenuation correction (NAC) for regional analysis of specific binding ratios (SBRs) in striatal and extrastriatal areas. Data from the Parkinson's Progression Markers Initiative were used for external validation (n = 18). RESULTS:As compared to CTAC, mean bias of DL-MRAC SBRs was -0.4% (95% confidence interval, CI -1.2 to 0.4) in the striatum and - 0.1% (95% CI, -0.8 to 0.6) in extrastriatal areas. UAC overestimated SBRs with mean bias ranging from 7.5% to 12.4%, whereas NAC underestimated SBRs with mean bias ranging from -6.5% to -24.0%, for striatal and extrastriatal binding estimates in all cohorts. CONCLUSION:DL-MRAC is a valid, radiation-free method for attenuation correction and quantification of [123I]I-FP-CIT binding to dopamine and serotonin transporters in subjects undergoing DaTSPECT examinations. It outperforms UAC and NAC and may therefore serve as a valuable alternative for patients for whom MRI is available and for data acquired on SPECT-only cameras.

The “Neuroimaging and Pathology Biomarkers in Parkinson’s Disease” course held on 12–13 September 2025 in Milan, Italy, convened an international faculty to review state-of-the-art biomarkers spanning neurotransmitter dysfunction, protein pathology and clinical translation. Here, we synthesize the four themed sessions and highlights convergent messages for diagnosis, stratification and trial design. The first session focused on neuroimaging markers of neurotransmitter dysfunction, highlighting how positron emission tomography (PET), single photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI) provided complementary insights into dopaminergic, noradrenergic, cholinergic and serotonergic dysfunction. The second session addressed in vivo imaging of protein pathology, presenting recent advances in PET ligands targeting α-synuclein, progress in four-repeat tau imaging for progressive supranuclear palsy and corticobasal syndromes, and the prognostic relevance of amyloid imaging in the context of mixed pathologies. Imaging of neuroinflammation captures inflammatory processes in vivo and helps study pathophysiological effects. The third session bridged pathology and disease mechanisms, covering the biology of α-synuclein and emerging therapeutic strategies, the clinical potential of seed amplification assays and skin biopsy, the impact of co-pathologies on disease expression, and the “brain-first” versus “body-first” model of pathological spread. Finally, the fourth session addressed disease progression and clinical translation, focusing on imaging predictors of phenoconversion from prodromal to clinically overt stages of synucleinopathies, concepts of neural reserve and compensation, imaging correlates of cognitive impairment, and MRI approaches for atypical parkinsonism. Biomarker-informed pharmacological, infusion-based, and surgical strategies, including network-guided and adaptive deep brain stimulation, were discussed as examples of how multimodal biomarkers may inform personalized management. Across all sessions, the need for harmonization, longitudinal validation, and pathology-confirmed outcome measures was consistently emphasized as essential for advancing biomarker qualification in multicentre research and clinical practice.

The global burden of age-associated diseases continues to grow. In particular, the accelerating impact of neurodegenerative diseases on individuals, communities and societies necessitates more effective approaches to diagnosis, prognosis and treatment of such disorders. Hence, the establishment of imaging biomarkers for early detection of disease, progression monitoring, and therapeutic evaluation is of utmost importance. Yet, despite the scientific consensus on the benefits of scientific collaboration and consequently medical innovation including biomarker development, substantial barriers for sharing neuroimaging data remain, demanding a transformation in how scientific data are generated, made accessible, re-used and valued. These barriers range from technical and infrastructural limitations to legal and motivational challenges that hinder widespread adoption of open science practices. Here, we present a comprehensive overview of the current landscape of brain imaging data sharing in neurodegenerative disease research. We explore the status of preregistration, data harmonization and storage standardization, legal compliance, and researcher incentives. We highlight best practices before, during and after data generation and the pressing need for a coordinated strategy regarding simplified and unified legal frameworks compliant with the General Data Protection Regulation of the European Union. Finally, we advocate for the establishment of an academic credit system designed to reward data stewardship. Only with a combined effort from researchers, stakeholders and funding agencies including a sustained infrastructure investment and community education, the field can fully overcome inertia and move towards much-desired open science, thereby fully leveraging shared data to improve patient outcomes and scientific discovery.

The early diagnosis of age-related neurodegenerative diseases, which often progress to dementia, poses significant clinical challenges due to subtle and overlapping symptoms of these diseases at early stage. Automated MRI segmentation is important for early detection, as it offers consistent measurements and the ability to detect subtle structural changes in the brain. Manual segmentation is impractical for large datasets or clinical use. Deep learning approaches provide fast processing, however, they often encounter graphics processing unit (GPU) memory constraints when handling large datasets. Here we introduce a deep learning-based approach using region-based U-nets specifically designed to segment 12 deep-brain structures relevant to Parkinson Plus Syndromes. By dividing the brain image into targeted regions around the brainstem, ventricular system, and striatum, our method optimizes GPU usage and significantly reduces training times, while maintaining high accuracy. Validating the proposed method on three datasets, including a 660-subject clinical dataset comprising both healthy controls and patients with various movement disorders, we demonstrate robustness and practical applicability in separating different diseases. The method achieves superior segmentation performance compared to state-of-the-art methods, with a mean Dice Similarity Coefficient (DSC) of 0.90, a 95% Hausdorff Distance (HD95) of 1.35 mm, and an Average Symmetric Surface Distance (ASSD) of 0.45 mm, showcasing its segmentation accuracy and robustness. Furthermore, our method outperforms these methods by reducing training time from several days to a few hours while providing a processing time of less than a second per subject. The source code and trained model will be made publicly available on GitHub.

The link between regional tau load and clinical manifestation of Alzheimer's disease (AD) highlights the importance of characterizing spatial tau distribution across disease variants. In typical (memory-predominant) AD, the spatial progression of tau pathology mirrors the functional connections from temporal lobe epicentres. However, given the limited spatial heterogeneity of tau in typical AD, atypical (non-amnestic-predominant) AD variants with distinct tau patterns provide a key opportunity to investigate the universality of connectivity as a scaffold for tau progression. In this large-scale, multicentre study across 14 international sites, we included cross-sectional tau-PET data from 320 individuals with atypical AD (n = 139 posterior cortical atrophy/PCA-AD; n = 103 logopenic variant primary progressive aphasia/lvPPA-AD; n = 35 behavioural variant AD/bvAD; n = 43 corticobasal syndrome/CBS-AD), with a subset of individuals (n = 78) having longitudinal tau-PET data. Additionally, as an independent sample, we included regional post-mortem tau stainings from 93 atypical AD patients from two sites (n = 19 PCA-AD, n = 32 lvPPA-AD, n = 23 bvAD, n = 19 CBS-AD). Gaussian mixture modelling was used to harmonize different tau-PET tracers by transforming tau-PET standardized uptake value ratios to tau positivity probabilities (a uniform scale ranging from 0% to 100%). Using linear regression, we assessed whether brain regions with stronger resting-state functional MRI-based functional connectivity, derived from healthy elderly controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI), showed greater covariance in cross-sectional and longitudinal tau-PET and post-mortem tau pathology. Furthermore, we examined whether functional connectivity of tau-PET epicentres (i.e. the top 5% of regions with the highest baseline tau load) and tau-PET accumulation epicentres (i.e. the top 5% of regions with the highest tau accumulation rates) was associated with cross-sectional and longitudinal tau patterns. Our findings show that tau-PET epicentres aligned with clinical variants, e.g. a visual network predominant pattern in PCA-AD ('visual AD') and left-hemispheric temporal predominance, particularly within the language network, in lvPPA-AD ('language AD'). Moreover, more strongly functionally connected regions showed correlated concurrent tau-PET levels (confirmed with post-mortem data) and tau-PET accumulation rates. The functional connectivity profile of tau-PET epicentres and accumulation epicentres corresponded to tau-PET progression patterns, with higher tau-PET levels and accumulation rates in functionally close regions, and lower tau-PET levels and accumulation rates in functionally distant regions. Our data are consistent with the hypothesis that tau propagation occurs along functional connections originating from local epicentres, across all AD clinical variants. Since tau proteinopathy is a major driver of neurodegeneration and cognitive decline, this finding may advance personalized medicine and participant-specific end points in clinical trials.

AbstractPatients with Alzheimer’s disease (AD) and clinically overlapping neurodegenerative diseases are classified molecularly using the A/T/N classification system. Apart from fluid biomarkers and structural MRI, the three-dimensional A/T/N system incorporates characteristic features from β-amyloid-PET (A), tau-PET (T), and FDG-PET (N). We evaluated if dynamic features of tau-PET with [18F]PI-2620 allow assessment of A/T/N in individual patients using a single imaging session. Cortical tissue clearance (K2a) of [18F]PI-2620 was validated as a surrogate of the β-amyloid status against β-amyloid-PET and cerebrospinal fluid (CSF) Aβ42/40ratio, demonstrating remarkable positive (91.5%) and negative (95.1%) predictive values at an AUC of 0.99 (P<0.0001). K2a outperformed cortical tau burden as a surrogate for β-amyloid status in 47 participants with a clinical diagnosis of probable AD (3/4-repeat(R)-tauopathy) and 82 β-amyloid-negative patients with primary 4R-tauopathies. Perfusion-like [18F]PI-2620 images (R1) were validated as a surrogate marker for neuronal injury, exhibiting strong quantitative and visual correlations with FDG-PET and early-phase β-amyloid-PET, as well as with volumetric MRI and CSF total tau levels. Composite quantitative A/T/N indices facilitated personalized staging along temporal disease trajectories. Our results suggest that [18F]PI-2620 imaging has the potential to facilitate the assessment of region and stage dependent PET-based A/T/N during a single dynamic PET session.Graphical Abstract

There is a strong link between tau and progression of Alzheimer’s disease (AD), necessitating an understanding of tau spreading mechanisms. Prior research, predominantly in typical AD, suggested that tau propagates from epicenters (regions with earliest tau) to functionally connected regions. However, given the constrained spatial heterogeneity of tau in typical AD, validating this connectivity-based tau spreading model in AD variants with distinct tau deposition patterns is crucial. We included 269 amyloid-β-positive (PET/CSF) individuals with clinically diagnosed atypical AD (113 posterior cortical atrophy, PCA-AD; 83 logopenic variant primary progressive aphasia, lvPPA-AD; 33 behavioural variant AD, bvAD; 40 corticobasal syndrome, CBS-AD) and 68 with typical AD from 12 international cohorts, who underwent tau-PET (54% [ 18 F]AV1451/[ 18 F]flortaucipir/Tauvid, 27% [ 18 F]MK6240, 19% [ 18 F]PI2620). Using Gaussian mixture modeling including amyloid-β-negative controls, cross-sectional tau-PET standardized uptake value ratios within Schaefer-200 atlas regions were transformed to tau positivity probabilities. Tau epicenters were defined as the 5% regions with highest tau positivity probabilities. For each variant, the association between functional connectivity-based distance (using the 30% strongest positive region-to-region connections of a group-average connectivity matrix from ADNI elderly controls) and tau-PET covariance (group-average correlation per region pair) was assessed through linear regression, adjusting for age, sex, site, and Euclidean distance. Regions were categorized based on functional proximity to the epicenter (quartiles 1-4) and tau positivity probabilities were assessed accordingly. Tau positivity probabilities matched clinical variants, with a posterior pattern in PCA-AD, left-hemispheric dominant pattern in lvPPA-AD, widespread pattern in bvAD, sensorimotor cortex involvement in CBS-AD, and temporo-parietal predominance in typical AD (Figure 1). In line with this, tau epicenters were highly heterogeneous across variants (Figure 1). In all variants, greater tau-PET covariance was associated with shorter functional connectivity-based distance (Figure 2). We observed that regions in closer functional proximity to the epicenter exhibited higher tau positivity probabilities than regions functionally further away (p<0.05, Figure 3). This multi-center study shows that the brain’s functional architecture serves as a universal predictor of tau spreading in AD. Since tau is a key driver of neurodegeneration and cognitive decline in AD, this finding holds potential for personalized medicine and defining participant-specific endpoints in clinical trials.

Reliable progression models are essential for clinical decision-making and trial design in Parkinson’s disease. We discuss linear, exponential, and sigmoidal patterns in PET and SPECT data, emphasizing the mismatch between biomarker and clinical trajectories. We propose more adaptable modeling strategies to improve patient stratification, support trial outcomes, and align imaging biomarkers with real-world disease complexity.

Aim Standardized evaluation of [18F]PI-2620 tau-PET scans in 4R-tauopathies represents an unmet need in clinical practice. This study aims to investigate the effectiveness of visual evaluation of [18F]PI-2620 images for diagnosing 4R-tauopathies and to develop a straight-forward reading algorithm to improve objectivity and data reproducibility. Methods A total of 83 individuals with [18F]PI-2620 PET scans were included. Participants were classified as probable 4R-tauopathies (n=29), Alzheimer's disease (AD) (n=20), α-synucleinopathies (n=15), and healthy controls (n=19) based on clinical criteria. Visual assessment of tau-PET scans (choice: 4R-tauopathy, AD-tauopathy, no-tauopathy) was conducted using either 20-40-minute or 40-60-minute intervals, with raw (common) and cerebellar grey matter scaled standardized reading settings (intensity-scaled). Two readers evaluated scans independently and blinded, with a third reader providing consensus in case of discrepant primary evaluation. A regional analysis was performed using the cortex, basal ganglia, midbrain, and dentate nucleus. Sensitivity, specificity, and interrater agreement were calculated for all settings and compared against the visual reads of parametric images (0-60-minutes, distribution volume ratios, DVR). Results Patients with 4R-tauopathies in contrast to non-4R-tauopathies were detected at higher sensitivity in the 20-40-minute frame (common: 79%, scaled: 76%) compared to the 40-60-minute frame (common: 55%, scaled: 62%), albeit with reduced specificity in the common setting (20-40-min: 78%, 40-60-min: 95%), which was ameliorated in the intensity-scaled setting (20-40-min: 91%, 40-60-min: 96%). Combined assessment of multiple brain regions did not significantly improve diagnostic sensitivity, compared to assessing the basal ganglia alone (76% each). Evaluation of intensity-scaled parametric images resulted in higher sensitivity compared to intensity-scaled static scans (86% vs. 76%) at similar specificity (89% vs. 91%). Conclusion Visual reading of [18F]PI-2620 tau-PET scans demonstrated reliable detection of 4R-tauopathies, particularly when standardized processing methods and early imaging windows were employed. Parametric images should be preferred for visual assessment of 4R-tauopathies.

Objective: To examine interactive effects of modifiable factors, genetic determinants and load-dependent pathology effects on tau pathology progression. Methods: Data of 162 amyloid-positive individuals were included, for whom longitudinal [18F]AV-1451-PET scans, baseline information on global amyloid load, ApoE4 status, body-mass-index (BMI), hypertension, education, neuropsychiatric symptom severity and demographic information were available in ADNI. All [18F]AV-1451 PETs were intensity-standardized (reference: inferior cerebellum), z-transformed (control sample: 147 amyloid-negative subjects) and subsequently thresholded (z-score > 1.96) and converted to volume-maps. Based on these volume-maps, tau-changes over time were assessed in terms of 1) tau-speed (i.e. newly affected volume at follow-up), and 2) tau-level-rise (i.e. tau increase in previously affected volume). These two measures were entered as dependent variables in separate linear mixed effects models including four baseline risk factors (BMI, education, hypertension, neuropsychiatric symptom severity), baseline amyloid, tau-volume or tau burden, ApoE4 status, clinical stage, sex, and age as predictors. Next, we tested the interactive effects between baseline amyloid or tau burden with the four modifiable factors on either tau-speed or tau-level-rise, respectively. Results: Faster tau-speed was linked to higher BMI, female sex, ApoE4-status, and baseline tau-volume. The effect of baseline tau-volume on tau-speed was driven by greater global amyloid burden. In terms of tau-level-rise, we observed that lower hypertension and BMI were linked to a slower increase in tau burden. A load-dependent effect of baseline amyloid and tau burden was found. Higher amyloid and BMI as well as lower education and higher tau burden were linked to greater tau-level-rise. Conclusion: Education, BMI and hypertension differentially influence tau speed and level rise by its interaction with initial pathological burden. Timely modification of these factors may overall slow tau's progression. ### Competing Interest Statement MCH, VD, ED and GNB report no competing interests. TvE reports having received consulting and lecture fees from Lundbeck A/S, Lilly Germany, Shire Germany and research funding from the German Research Foundation (DFG), the Leibniz Association and the EU-joint program for neurodegenerative disease research (JPND). AD reports: Research support by Siemens Healthineers, Life Molecular Imaging, GE Healthcare, AVID Radiopharmaceuticals, Sofie, Eisai, Novartis/AAA, Ariceum Therapeutics; Speaker Honorary/Advisory Boards: Siemens Healthineers, Sanofi, GE Healthcare, Biogen, Novo Nordisk, Invicro, Novartis/AAA, Bayer Vital, Lilly; Stock: Siemens Healthineers, Lantheus Holding, Structured therapeutics, Lilly: Patents: Patent for 18F-JK-PSMA- 7 (Patent No.: EP3765097A1; Date of patent: Jan. 20, 2021).

Disorders characterized by changes in dopamine (DA) neurotransmission are often linked to changes in the temporal discounting of future rewards. Likewise, pharmacological manipulations of DA neurotransmission in healthy individuals modulate temporal discounting, but there is considerable variability in the directionality of reported pharmacological effects, as enhancements and reductions of DA signaling have been linked to both increases and reductions of temporal discounting. This may be due to meaningful individual differences in drug effects and/or false-positive findings in small samples. To resolve these inconsistencies, we (1) revisited pharmacological effects of the DA precursor l-DOPA on temporal discounting in a large sample of N = 76 healthy participants (n = 44 male) and (2) examined several putative proxy measures for DA to revisit the role of individual differences in a randomized, double-blind placebo-controlled preregistered study (https://osf.io/a4k9j/). Replicating previous findings, higher rewards were discounted less (magnitude effect). Computational modeling using hierarchical Bayesian parameter estimation confirmed that the data in both drug conditions were best accounted for by a nonlinear temporal discounting drift diffusion model. In line with recent animal and human work, l-DOPA reliably reduced the discount rate with a small effect size, challenging earlier findings in substantially smaller samples. We found no credible evidence for effects of putative DA proxy measures on model parameters, calling into question the role of these measures in accounting for individual differences in DA drug effects.