Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network.

OBJECTIVE: The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. METHODS: The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. RESULTS: The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796-0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. INTERPRETATION: The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.

Characterization of tau propagation pattern and cascading hypometabolism from functional connectivity in Alzheimer's disease.

Tau pathology and its spatial propagation in Alzheimer's disease (AD) play crucial roles in the neurodegenerative cascade leading to dementia. However, the underlying mechanisms linking tau spreading to glucose metabolism remain elusive. To address this, we aimed to examine the association between pathologic tau aggregation, functional connectivity, and cascading glucose metabolism and further explore the underlying interplay mechanisms. In this prospective cohort study, we enrolled 79 participants with 18F-Florzolotau positron emission tomography (PET), 18F-fluorodeoxyglucose PET, resting-state functional, and anatomical magnetic resonance imaging (MRI) images in the hospital-based Shanghai Memory Study. We employed generalized linear regression and correlation analyses to assess the associations between Florzolotau accumulation, functional connectivity, and glucose metabolism in whole-brain and network-specific manners. Causal mediation analysis was used to evaluate whether functional connectivity mediates the association between pathologic tau and cascading glucose metabolism. We examined 22 normal controls and 57 patients with AD. In the AD group, functional connectivity was associated with Florzolotau covariance (ß = .837, r = 0.472, p < .001) and glucose covariance (ß = 1.01, r = 0.499, p < .001). Brain regions with higher tau accumulation tend to be connected to other regions with high tau accumulation through functional connectivity or metabolic connectivity. Mediation analyses further suggest that functional connectivity partially modulates the influence of tau accumulation on downstream glucose metabolism (mediation proportion: 49.9%). Pathologic tau may affect functionally connected neurons directly, triggering downstream glucose metabolism changes. This study sheds light on the intricate relationship between tau pathology, functional connectivity, and downstream glucose metabolism, providing critical insights into AD pathophysiology and potential therapeutic targets.

International consensus on clinical use of presynaptic dopaminergic positron emission tomography imaging in parkinsonism.

PURPOSE: Presynaptic dopaminergic positron emission tomography (PET) imaging serves as an essential tool in diagnosing and differentiating patients with suspected parkinsonism, including idiopathic Parkinson's disease (PD) and other neurodegenerative and non-neurodegenerative diseases. The PET tracers most commonly used at the present time mainly target dopamine transporters (DAT), aromatic amino acid decarboxylase (AADC), and vesicular monoamine type 2 (VMAT2). However, established standards for the imaging procedure and interpretation of presynaptic dopaminergic PET imaging are still lacking. The goal of this international consensus is to help nuclear medicine practitioners procedurally perform presynaptic dopaminergic PET imaging. METHOD: A multidisciplinary task group formed by experts from various countries discussed and approved the consensus for presynaptic dopaminergic PET imaging in parkinsonism, focusing on standardized recommendations, procedures, interpretation, and reporting. CONCLUSION: This international consensus and practice guideline will help to promote the standardized use of presynaptic dopaminergic PET imaging in parkinsonism. It will become an international standard for this purpose in clinical practice.

Visual reading for [18F]Florzolotau Tau PET scans in progressive supranuclear palsy.

PURPOSE: The identification of tau accumulation within living brains holds significant potential in facilitating accurate diagnosis of progressive supranuclear palsy (PSP). While visual assessment is frequently employed, standardized methods for tau positron emission tomography (PET) specifically in PSP are absent. We aimed to develop a visual reading algorithm dedicated to the evaluation of [18F]Florzolotau PET in PSP. METHODS: 148 PSP and 30 healthy volunteers were divided into a development set (for the establishment of the reading rules; n = 89) and a testing set (for the validation of the reading rules; n = 89). For differential diagnosis, 55 α-synucleinopathies were additionally included into the testing set. The visual reading method was established by an experienced assessor (Reader 0) and was then validated by Reader 0 and two additional readers on regional and overall binary manners. A positive binding in both midbrain and globus pallidus/putamen regions was characterized as a PSP-like pattern, whereas any other pattern was classified as non-PSP-like. RESULTS: Reader 1 (94.4%) and Reader 2 (93.8%) showed excellent agreement for the overall binary determination against Reader 0. The regional binary determinations of midbrain and globus pallidus/putamen showed excellent agreement among readers (kappa > 0.80). The overall binary evaluation demonstrated reproducibility of 86.1%, 94.4% and 77.8% for three readers. The visual reading algorithm showed high agreement with regional standardized uptake value ratios and clinical diagnoses. CONCLUSION: Through the application of the suggested visual reading algorithm, [18F]Florzorotau PET imaging demonstrated a robust performance for the imaging diagnosis of PSP.

Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [18F]FDG PET study.

OBJECTIVES: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [18F]FDG PET images to diagnose the clinical spectrum of Alzheimer's disease (AD) and predict the progression from mild cognitive impairment (MCI) to AD. METHODS: This multicentre study included 1962 subjects from two ethnically diverse, independent cohorts (a Caucasian cohort from ADNI and an Asian cohort merged from two hospitals in China). The IDLR model involved feature extraction, feature selection, and classification/prediction. We evaluated the IDLR model's ability to distinguish between subjects with different cognitive statuses and MCI trajectories (sMCI and pMCI) and compared results with radiomic and deep learning (DL) models. A Cox model tested the IDLR signature's predictive capability for MCI to AD progression. Correlation analyses identified critical IDLR features and verified their clinical diagnostic value. RESULTS: The IDLR model achieved the best classification results for subjects with different cognitive statuses as well as in those with MCI with distinct trajectories, with an accuracy of 76.51% [72.88%, 79.60%], (95% confidence interval, CI) while those of radiomic and DL models were 69.13% [66.28%, 73.12%] and 73.89% [68.99%, 77.89%], respectively. According to the Cox model, the hazard ratio (HR) of the IDLR model was 1.465 (95% CI: 1.236-1.737, p < 0.001). Moreover, three crucial IDLR features were significantly different across cognitive stages and were significantly correlated with cognitive scale scores (p < 0.01). CONCLUSIONS: Preliminary results demonstrated that the IDLR model based on [18F]FDG PET images enhanced accuracy in diagnosing the clinical spectrum of AD. KEY POINTS: Question The study addresses the lack of interpretability in existing DL classification models for diagnosing the AD spectrum. Findings The proposed interpretable DL radiomics model, using radiomics-supervised DL features, enhances interpretability from traditional DL models and improves classification accuracy. Clinical relevance The IDLR model interprets DL features through radiomics supervision, potentially advancing the application of DL in clinical classification tasks.

Impaired glymphatic function as a biomarker for subjective cognitive decline: An exploratory dual cohort study.

BACKGROUND: Subjective cognitive decline (SCD) has been recognized as a potential risk stage for progression to Alzheimer's disease (AD), while glymphatic dysfunction is considered an important characteristic of AD. We hypothesize that glymphatic dysfunction occurs during the SCD stage, aiming to discover potential biomarkers for SCD. METHODS: Participants from two independent studies, Sino Longitudinal Study on Cognitive Decline (SILCODE, n = 654) and the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 650), representing different ethnicities and disease stages, were included to assess glymphatic function using diffusion tensor image analysis along the perivascular space (DTI-ALPS). RESULTS: Abnormal glymphatic function occurs during the SCD stage, with the ALPS index demonstrating excellent classification performance for SCD and normal controls (area under the receiver operating characteristic curve [AUC]SILCODE = 0.816, AUCADNI = 0.797). Lower ALPS index indicates higher risk of cognitive progression, which is negatively correlated with Subjective Cognitive Decline Questionnaire 9 scores and amyloid positron emission tomography burden. DISSCUSION: Our study suggests the ALPS index has the potential to serve as a biomarker for SCD. HIGHLIGHTS: Glymphatic function characterized by the analysis along the perivascular space (ALPS) index becomes abnormal in subjective cognitive decline (SCD), the earliest symptomatic manifestation and preclinical stage of Alzheimer's disease (AD). The ALPS index demonstrates excellent classification performance for SCD and normal controls in the East Asian and Western cohorts. Participants with a lower ALPS index show a higher risk of clinical progression. The ALPS index is closely associated with serval cognitive scales and amyloid beta burden.

Pilot implementation of the revised criteria for staging of Alzheimer's disease by the Alzheimer's Association Workgroup in a tertiary memory clinic.

INTRODUCTION: We aimed to evaluate the feasibility of the 2024 Alzheimer's Association Workgroup's integrated clinical-biological staging scheme in outpatient settings within a tertiary memory clinic. METHODS: The 2018 syndromal cognitive staging system, coupled with a binary biomarker classification, was implemented for 236 outpatients with cognitive concerns. The 2024 numeric clinical staging framework, incorporating biomarker staging, was specifically applied to 154 individuals within the Alzheimer's disease (AD) continuum. RESULTS: The 2024 staging scheme accurately classified 95.5% AD. Among these, 56.5% exhibited concordant clinical and biological stages (canonical), 34.7% demonstrated more advanced clinical stages than biologically expected (susceptible), and 8.8% displayed the inverse pattern (resilient). The susceptible group was characterized by a higher burden of neurodegeneration and inflammation than anticipated from tau, whereas the resilient group showed the opposite. DISCUSSION: The 2024 staging scheme is generally feasible. A discrepancy between clinical and biological stages is relatively frequent among symptomatic patients with AD. HIGHLIGHTS: The 2024 AA staging scheme is generally feasible in a tertiary memory clinic. A discrepancy between clinical and biological stages is relatively frequent in AD. The mismatch may be influenced by a non-specific pathological process involved in AD. Individual profiles like aging and lifestyles may contribute to such a mismatch. Matched and mismatched cases converge toward similar clinical outcomes.

Incremental value of amyloid PET in a tertiary memory clinic setting in China.

INTRODUCTION: The objective of this study is to investigate the incremental value of amyloid positron emission tomography (Aß-PET) in a tertiary memory clinic setting in China. METHODS: A total of 1073 patients were offered Aß-PET using 18F-florbetapir. The neurologists determined a suspected etiology (Alzheimer's disease [AD] or non-AD) with a percentage estimate of their confidence and medication prescription both before and after receiving the Aß-PET results. RESULTS: After disclosure of the Aß-PET results, etiological diagnoses changed in 19.3% of patients, and diagnostic confidence increased from 69.3% to 85.6%. Amyloid PET results led to a change of treatment plan in 36.5% of patients. Compared to the late-onset group, the early-onset group had a more frequent change in diagnoses and a higher increase in diagnostic confidence. DISCUSSION: Aß-PET has significant impacts on the changes of diagnoses and management in Chinese population. Early-onset cases are more likely to benefit from Aß-PET than late-onset cases. HIGHLIGHTS: Amyloid PET contributes to diagnostic changes and its confidence in Chinese patients. Amyloid PET leads to a change of treatment plans in Chinese patients. Early-onset cases are more likely to benefit from amyloid PET than late-onset cases.

Multipredictor risk models for predicting individual risk of Alzheimer's disease.

BACKGROUND: Early prevention of Alzheimer's disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features. METHODS: A total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction. RESULTS: During the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer's continuum model was developed which could predict the Alzheimer's continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91). CONCLUSIONS: The risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.

Plasma Glial Fibrillary Acidic Protein in the Alzheimer Disease Continuum: Relationship to Other Biomarkers, Differential Diagnosis, and Prediction of Clinical Progression.

BACKGROUND: Plasma glial fibrillary acidic protein (GFAP) has emerged as a promising biomarker in neurological disorders, but further evidence is required in relation to its usefulness for diagnosis and prediction of Alzheimer disease (AD). METHODS: Plasma GFAP was measured in participants with AD, non-AD neurodegenerative disorders, and controls. Its diagnostic and predictive value were analyzed alone or combined with other indicators. RESULTS: A total of 818 participants were recruited (210 followed). Plasma GFAP was significantly higher in AD than in non-AD dementia and non-demented individuals. It increased in a stepwise pattern from preclinical AD, through prodromal AD to AD dementia. It effectively distinguished AD from controls [area under the curve (AUC) > 0.97] and non-AD dementia (AUC > 0.80) and distinguished preclinical (AUC > 0.89) and prodromal AD (AUC > 0.85) from Aß-normal controls. Adjusted or combined with other indicators, higher levels of plasma GFAP displayed predictive value for risk of AD progression (adjusted hazard radio= 4.49, 95%CI, 1.18-16.97, P = 0.027 based on the comparison of those above vs below average at baseline) and cognitive decline (standard-ß=0.34, P = 0.002). Additionally, it strongly correlated with AD-related cerebrospinal fluid (CSF)/neuroimaging markers. CONCLUSIONS: Plasma GFAP effectively distinguished AD dementia from multiple neurodegenerative diseases, gradually increased across the AD continuum, predicted the individual risk of AD progression, and strongly correlated with AD CSF/neuroimaging biomarkers. Plasma GFAP could serve as both a diagnostic and predictive biomarker for AD.

Characterization of neuroinflammation pattern in anti-LGI1 encephalitis based on TSPO PET and symptom clustering analysis.

PURPOSE: TSPO PET with radioligand [18F]DPA-714 is an emerging molecular imaging technique that reflects cerebral inflammation and microglial activation, and it has been recently used in central nervous system diseases. In this study, we aimed to investigate the neuroinflammation pattern of anti-leucine-rich glioma-inactivated 1 (LGI1) protein autoimmune encephalitis (AIE) and to evaluate its possible correlation with clinical phenotypes. METHODS: Twenty patients with anti-LGI1 encephalitis from the autoimmune encephalitis cohort in Huashan Hospital and ten with other AIE and non-inflammatory diseases that underwent TSPO PET imaging were included in the current study. Increased regional [18F]DPA-714 retention in anti-LGI1 encephalitis was detected on a voxel basis using statistic parametric mapping analysis. Multiple correspondence analysis and hierarchical clustering were conducted for discriminate subgroups in anti-LGI1 encephalitis. Standardized uptake value ratios normalized to the cerebellum (SUVRc) were calculated for semiquantitative analysis of TSPO PET features between different LGI1-AIE subgroups. RESULTS: Increased regional retention of [18F]DPA-714 was identified in the bilateral hippocampus, caudate nucleus, and frontal cortex in LGI1-AIE patients. Two subgroups of LGI1-AIE patients were distinguished based on the top seven common symptoms. Patients in cluster 1 had a high frequency of facio-brachial dystonic seizures than those in cluster 2 (p = 0.004), whereas patients in cluster 2 had a higher frequency of general tonic-clonic (GTC) seizures than those in cluster 1 (p < 0.001). Supplementary motor area and superior frontal gyrus showed higher [18F]DPA-714 retention in cluster 2 patients compared with those in cluster 1 (p = 0.024; p = 0.04, respectively). CONCLUSIONS: Anti-LGI1 encephalitis had a distinctive molecular imaging pattern presented by TSPO PET scan. LGI1-AIE patients with higher retention of [18F]DPA-714 in the frontal cortex were more prone to present with GTC seizures. Further studies are required for verifying its value in clinical application.

Dopaminergic damage pattern predicts phenoconversion time in isolated rapid eye movement sleep behavior disorder.

PURPOSE: The exact phenoconversion time from isolated rapid eye movement (REM) sleep behavior disorder (iRBD) to synucleinopathies remains unpredictable. This study investigated whole-brain dopaminergic damage pattern (DDP) with disease progression and predicted phenoconversion time in individual patients. METHODS: Age-matched 33 iRBD patients and 20 healthy controls with 11C-CFT-PET scans were enrolled. The patients were followed up 2-10 (6.7 ± 2.0) years. The phenoconversion year was defined as the base year, and every 2 years before conversion was defined as a stage. Support vector machine with leave-one-out cross-validation strategy was used to perform prediction. RESULTS: Dopaminergic degeneration of iRBD was found to occur about 6 years before conversion and then abnormal brain regions gradually expanded. Using DDP, area under curve (AUC) was 0.879 (90% sensitivity and 88.3% specificity) for predicting conversion in 0-2 years, 0.807 (72.7% sensitivity and 83.3% specificity) in 2-4 years, 0.940 (100% sensitivity and 84.6% specificity) in 4-6 years, and 0.879 (100% sensitivity and 80.7% specificity) over 6 years. In individual patients, predicted stages correlated with whole-brain dopaminergic levels (r = - 0.740, p < 0.001). CONCLUSION: Our findings suggest that DDP could accurately predict phenoconversion time of individual iRBD patients, which may help to screen patients for early intervention.

18F-Florzolotau PET imaging captures the distribution patterns and regional vulnerability of tau pathology in progressive supranuclear palsy.

PURPOSE: Human post mortem studies have described the topographical patterns of tau pathology in progressive supranuclear palsy (PSP). Recent advances in tau PET tracers are expected to herald the next era of PSP investigation for early detection of tau pathology in living brains. This study aimed to investigate whether 18F-Florzolotau PET imaging may capture the distribution patterns and regional vulnerability of tau pathology in PSP, and to devise a novel image-based staging system. METHODS: The study cohort consisted of 148 consecutive patients with PSP who had undergone 18F-Florzolotau PET imaging. The PSP rating scale (PSPrs) was used to measure disease severity. Similarities and differences of tau deposition among different clinical phenotypes were examined at the regional and voxel levels. An 18F-Florzolotau pathological staging system was devised according to the scheme originally developed for post mortem data. In light of conditional probabilities for the sequence of events, an 18F-Florzolotau modified staging system by integrating clusters at the regional level was further developed. The ability of 18F-Florzolotau staging systems to reflect disease severity in terms of PSPrs score was assessed by analysis of variance. RESULTS: The distribution patterns of 18F-Florzolotau accumulation in living brains of PSP showed a remarkable similarity to those reported in post mortem studies, with the binding intensity being markedly higher in Richardson's syndrome. Moreover, 18F-Florzolotau PET imaging allowed detecting regional vulnerability and tracking tau accumulation in an earlier fashion compared with post mortem immunostaining. The 18F-Florzolotau staging systems were positively correlated with clinical severity as reflected by PSPrs scores. CONCLUSIONS: 18F-Florzolotau PET imaging can effectively capture the distribution patterns and regional vulnerability of tau pathology in PSP. The 18F-Florzolotau modified staging system holds promise for early tracking of tau deposition in living brains.

In Vivo 18 F-Florzolotau Tau Positron Emission Tomography Imaging in Parkinson's Disease Dementia.

BACKGROUND: Tau pathology is observed during autopsy in many patients with Parkinson's disease dementia (PDD). Positron emission tomography (PET) imaging using the tracer 18 F-florzolotau has the potential to capture tau accumulation in the living brain. OBJECTIVE: The aim was to describe the results of 18 F-florzolotau PET/CT (computed tomography) imaging in patients with PDD. METHODS: Ten patients with PDD, 9 with Parkinson's disease with normal cognition (PD-NC), and 9 age-matched healthy controls (HCs) were enrolled. Clinical assessments and 18 F-florzolotau PET/CT imaging were performed. RESULTS: 18 F-Florzolotau uptake was significantly higher in the cortical regions of patients with PDD compared with both PD-NC and HCs, especially in the temporal lobe. Notably, 18 F-florzolotau uptake in the occipital lobe of patients with PDD showed a significant correlation with cognitive impairment as reflected by Mini-Mental State Examination (MMSE) scores. CONCLUSIONS: 18 F-Florzolotau PET imaging can effectively capture the occurrence of tau pathology in patients with PDD, which was also linked to MMSE scores. © 2022 International Parkinson and Movement Disorder Society.

Plasma Biomarkers and Positron Emission Tomography Tau Pathology in Progressive Supranuclear Palsy.

BACKGROUND: Development of disease-modifying therapeutic trials of progressive supranuclear palsy (PSP) urges the need for sensitive fluid biomarkers. OBJECTIVES: The objectives of this study were to explore the utility of plasma biomarkers in the diagnosis, differential diagnosis, and assessment of disease severity, brain atrophy, and tau deposition in PSP. METHODS: Plasma biomarkers were measured using a single-molecule array in a cohort composed of patients with PSP, Parkinson's disease (PD), multiple system atrophy with predominant parkinsonism (MSA-P), and healthy controls (HCs). RESULTS: Plasma neurofilament light chain (NfL) outperformed other plasma makers (ie, glial fibrillary acidic protein [GFAP], phosphorylated-tau 181 [p-tau181], amyloid-ß 1-40, amyloid-ß 1-42) in identifying PSP from HC (area under the curve [AUC] = 0.904) and from MSA-P (AUC = 0.711). Plasma GFAP aided in distinguishing PSP from HC (AUC = 0.774) and from MSA-P (AUC = 0.832). It correlated with brainstem atrophy and higher regional tau accumulation. However, plasma p-tau181 neither helped in diagnosis nor was it associated with clinical or neuroimaging measures. CONCLUSIONS: Plasma NfL and GFAP showed different values in differentiating PSP from HC or controls with other forms of neurodegenerative parkinsonism and detecting disease severity, brain atrophy, or tau deposition in PSP. © 2023 International Parkinson and Movement Disorder Society.

18 F-Florzolotau Positron Emission Tomography Imaging of Tau Pathology in the Living Brains of Patients with Corticobasal Syndrome.

BACKGROUND: Recent development in tau-sensitive tracers has sparkled significant interest in tracking tauopathies using positron emission tomography (PET) biomarkers. However, the ability of 18 F-florzolotau PET imaging to topographically characterize tau pathology in corticobasal syndrome (CBS) remains unclear. Further, the question as to whether disease-level differences exist with other neurodegenerative tauopathies is still unanswered. OBJECTIVE: To analyze the topographical patterns of tau pathology in the living brains of patients with CBS using 18 F-florzolotau PET imaging and to examine whether differences with other tauopathies exist. METHODS: 18 F-florzolotau PET imaging was performed in 20 consecutive patients with CBS, 20 cognitively healthy controls (HCs), 20 patients with Alzheimer's disease (AD), and 16 patients with progressive supranuclear palsy-Richardson's syndrome (PSP-RS). Cerebrospinal fluid (CSF) levels of ß-amyloid biomarkers were quantified in all patients with CBS. 18 F-florzolotau uptake was quantitatively assessed using standardized uptake value ratios. RESULTS: Of the 20 patients with CBS, 19 (95%) were negative for CSF biomarkers of amyloid pathology; of them, three had negative 18 F-florzolotau PET findings. Compared with HCs, patients with CBS showed increased 18 F-florzolotau signals in both cortical and subcortical regions. In addition, patients with CBS were characterized by higher tracer retentions in subcortical regions compared with those with AD and showed a trend toward higher signals in cortical areas compared with PSP-RS. An asymmetric pattern of 18 F-florzolotau uptake was associated with an asymmetry of motor severity in patients with CBS. CONCLUSIONS: In vivo 18 F-florzolotau PET imaging holds promise for distinguishing CBS in the spectrum of neurodegenerative tauopathies. © 2023 International Parkinson and Movement Disorder Society.

Feasibility of 18F-florzolotau quantification in patients with Alzheimer's disease based on an MRI-free tau PET template.

OBJECTIVES: Quantification of tau accumulation using positron emission tomography (PET) is critical for the diagnosis of Alzheimer's disease (AD). This study aimed to evaluate the feasibility of 18F-florzolotau quantification in patients with AD using a magnetic resonance imaging (MRI)-free tau PET template, since individual high-resolution MRI is costly and not always available in practice. METHODS: 18F-florzolotau PET and MRI scans were obtained in a discovery cohort including (1) patients within the AD continuum (n = 87), (2) cognitively impaired patients with non-AD (n = 32), and (3) cognitively unimpaired subjects (n = 26). The validation cohort comprised 24 patients with AD. Following MRI-dependent spatial normalization (standard approach) in randomly selected subjects (n = 40) to cover the entire spectrum of cognitive function, selected PET images were averaged to create the 18F-florzolotau-specific template. Standardized uptake value ratios (SUVRs) were calculated in five predefined regions of interest (ROIs). MRI-free and MRI-dependent methods were compared in terms of continuous and dichotomous agreement, diagnostic performances, and associations with specific cognitive domains. RESULTS: MRI-free SUVRs had a high continuous and dichotomous agreement with MRI-dependent measures for all ROIs (intraclass correlation coefficient ≥ 0.980; agreement ≥ 94.5%). Similar findings were observed for AD-related effect sizes, diagnostic performances with respect to categorization across the cognitive spectrum, and associations with cognitive domains. The robustness of the MRI-free approach was confirmed in the validation cohort. CONCLUSIONS: The use of an 18F-florzolotau-specific template is a valid alternative to MRI-dependent spatial normalization, improving the clinical generalizability of this second-generation tau tracer. KEY POINTS: • Regional 18F-florzolotau SUVRs reflecting tau accumulation in the living brains are reliable biomarkers for the diagnosis, differential diagnosis, and assessment of disease severity in patients with AD. • The 18F-florzolotau-specific template is a valid alternative to MRI-dependent spatial normalization, improving the clinical generalizability of this second-generation tau tracer.

Using radiomics-based modelling to predict individual progression from mild cognitive impairment to Alzheimer's disease.

BACKGROUND: Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aimed to provide a personalized MCI-to-AD conversion prediction via radiomics-based predictive modelling (RPM) with multicenter 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data. METHOD: FDG PET and neuropsychological data of 884 subjects were collected from Huashan Hospital, Xuanwu Hospital, and from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. First, 34,400 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection, and an RPM model was constructed and validated on the ADNI dataset. In addition, we used clinical data and the routine semiquantification index (standard uptake value ratio, SUVR) to establish clinical and SUVR Cox models for further comparison. FDG images from local hospitals were used to explore RPM performance in a separate cohort of individuals with healthy controls and different cognitive levels (a complete AD continuum). Finally, correlation analysis was conducted between the radiomic biomarkers and neuropsychological assessments. RESULTS: The experimental results showed that the predictive performance of the RPM Cox model was better than that of other Cox models. In the validation dataset, Harrell's consistency coefficient of the RPM model was 0.703 ± 0.002, while those of the clinical and SUVR models were 0.632 ± 0.006 and 0.683 ± 0.009, respectively. Moreover, most crucial imaging biomarkers were significantly different at different cognitive stages and significantly correlated with cognitive disease severity. CONCLUSION: The preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD.

International consensus on the use of tau PET imaging agent 18F-flortaucipir in Alzheimer's disease.

PURPOSE: Positron emission tomography (PET) with the first and only tau targeting radiotracer of 18F-flortaucipir approved by FDA has been increasingly used in depicting tau pathology deposition and distribution in patients with cognitive impairment. The goal of this international consensus is to help nuclear medicine practitioners procedurally perform 18F-flortaucipir PET imaging. METHOD: A multidisciplinary task group formed by experts from various countries discussed and approved the consensus for 18F-flortaucipir PET imaging in Alzheimer's disease (AD), focusing on clinical scenarios, patient preparation, and administered activities, as well as image acquisition, processing, interpretation, and reporting. CONCLUSION: This international consensus and practice guideline will help to promote the standardized use of 18F-flortaucipir PET in patients with AD. It will become an international standard for this purpose in clinical practice.

Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

PURPOSE: This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential diagnosis of parkinsonism. METHODS: This study involved 1017 subjects who underwent DAT PET imaging ([11C]CFT) including 43 healthy subjects and 974 parkinsonian patients with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA) or progressive supranuclear palsy (PSP). We developed a 3D deep convolutional neural network to learn distinguishable DAT features for the differential diagnosis of parkinsonism. A full-gradient saliency map approach was employed to investigate the functional basis related to the decision mechanism of the network. Furthermore, deep-learning-guided radiomics features and quantitative analysis were compared with their conventional counterparts to further interpret the performance of deep learning. RESULTS: The proposed network achieved area under the curve of 0.953 (sensitivity 87.7%, specificity 93.2%), 0.948 (sensitivity 93.7%, specificity 97.5%), and 0.900 (sensitivity 81.5%, specificity 93.7%) in the cross-validation, together with sensitivity of 90.7%, 84.1%, 78.6% and specificity of 88.4%, 97.5% 93.3% in the blind test for the differential diagnosis of IPD, MSA and PSP, respectively. The saliency map demonstrated the most contributed areas determining the diagnosis located at parkinsonism-related regions, e.g., putamen, caudate and midbrain. The deep-learning-guided binding ratios showed significant differences among IPD, MSA and PSP groups (P < 0.001), while the conventional putamen and caudate binding ratios had no significant difference between IPD and MSA (P = 0.24 and P = 0.30). Furthermore, compared to conventional radiomics features, there existed average above 78.1% more deep-learning-guided radiomics features that had significant differences among IPD, MSA and PSP. CONCLUSION: This study suggested the developed deep neural network can decode in-depth information from DAT and showed potential to assist the differential diagnosis of parkinsonism. The functional regions supporting the diagnosis decision were generally consistent with known parkinsonian pathology but provided more specific guidance for feature selection and quantitative analysis.