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Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

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. [ABSTRACT FROM AUTHOR]

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: • Regional18F-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. • The18F-florzolotau-specific template is a valid alternative to MRI-dependent spatial normalization, improving the clinical generalizability of this second-generation tau tracer. [ABSTRACT FROM AUTHOR]

Purpose: 18F-Florzolotau is a novel second-generation tau radiotracer that shows higher binding affinity and selectivity and no off-target binding. The proportion loss of functional connectivity strength (PLFCS) is a new indicator for representing brain functional connectivity (FC) alteration. This study aims to estimate the relationship between the regional tau accumulation and brain FC abnormality in Alzheimer's disease (AD) and mild cognitive impairment (MCI) patients based on Florzolotau PET and fMRI. Methods: 22 NC (normal control), 31 MCI and 42 AD patients who have already been scanned with 18F-Florzolotau PET were recruited in this study. (We calculated the PLFCS and standardized uptake value ratio (SUVR) of each node based on the Brainnetome atlas (BNA) template. The SUVR of 246 brain regions was calculated with the cerebellum as the reference region. Further functional connection strength (FCs), PLFCS and SUVR of each brain region were obtained in three groups for comparison.) For each patient, PLFCS and standardized uptake value ratio (SUVR) were calculated based on the Brainnetome atlas (BNA) template. These results, as well as functional connection strength (FCs), were then compared between different groups. Multiple permutation tests were used to determine the target nodes between NC and cognitive impairment (CI) groups (MCI and AD). The relationship between PLFCS and neuropsychological scores or cortical tau deposit was investigated via Pearson correlation analysis. Results: Higher PLFCS and FCs in AD and MCI groups were found compared to the NC group. The PLFCS of 129 brain regions were found to be different between NC and CI groups, and 8 of them were correlated with tau SUVR, including superior parietal lobule (MCI: r = 0.4360, p = 0.0260, AD: r = −0.3663, p = 0.0280), middle frontal gyrus (AD: MFG_R_7_2: r = 0.4106, p = 0.0129; MFG_R_7_5: r = 0.4239, p = 0.0100), inferior frontal gyrus (AD: IFG_R_6_2: r = 0.3589, p = 0.0316), precentral gyrus (AD: PrG_R_6_6: r = 0.3493, p = 0.0368), insular gyrus (AD: INS_R_6_3: r = 0.3496, p = 0.0366) and lateral occipital cortex (AD: LOcC _L_4_3: r = −0.3433, p = 0.0404). Noteworthily, the opposing relationship was found in the superior parietal lobule in the MCI and AD groups. Conclusions: Brain functional connectivity abnormality is correlated with tau pathology in AD and MCI. [ABSTRACT FROM AUTHOR]

Subthalamic nucleus (STN) deep brain stimulation (DBS) can improve motor symptoms in Parkinson's disease (PD), as well as potentially improving otherwise intractable comorbid depressive symptoms. To address the latter issue, we evaluated the severity of depressive symptoms along with the severity of motor symptoms in 18 PD patients (mean age, 58.4 ± 5.4 years; 9 males, 9 females; mean PD duration, 9.4 ± 4.4 years) with treatment-resistant depression (TRD) before and after approximately 1 year of STN-DBS treatment. Moreover, to gain more insight into the brain mechanism mediating the therapeutic action of STN-DBS, we utilized 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) to assess cerebral regional glucose metabolism in the patients at baseline and 1-year follow-up. Additionally, the baseline PET data from patients were compared with PET data from an age- and sex-matched control group of 16 healthy volunteers. Among them, 12 PD patients underwent post-operative follow-up PET scans. Results showed that the severity of both motor and depressive symptoms in patients with PD-TRD was reduced significantly at 1-year follow-up. Also, patients used significantly less antiparkinsonian medications and antidepressants at 1-year follow-up, as well as experiencing improved daily functioning and a better quality of life. Moreover, relative to the PET data from healthy controls, PD-TRD patients displayed widespread abnormalities in cerebral regional glucose metabolism before STN-DBS treatment, which were partially recovered at 1-year follow-up. Additionally, significant correlations were observed between the patients' improvements in depressive symptoms following STN-DBS and post-operative changes in glucose metabolism in brain regions implicated in emotion regulation. These results support the view that STN-DBS provides a promising treatment option for managing both motor and depressive symptoms in patients who suffer from PD with TRD. However, the results should be interpreted with caution due to the observational nature of the study, small sample size, and relatively short follow-up. [ABSTRACT FROM AUTHOR]

Background: Tau positron emission tomography (PET) imaging can reveal the pathophysiology and neurodegeneration that occurs in Alzheimer's disease (AD) in vivo. The standardized uptake value ratio (SUVR) is widely used for semi-quantification of tau deposition but is susceptible to disturbance from the reference region and the partial volume effect (PVE). To overcome this problem, we applied the parametric estimation of reference signal intensity (PERSI) method—which was previously evaluated for flortaucipir imaging—to two tau tracers, flortaucipir and [18F]-APN-1607. Methods: Two cohorts underwent tau PET scanning. Flortaucipir PET imaging data for cohort I (65 healthy controls [HCs], 60 patients with mild cognitive impairment [MCI], and 12 AD patients) were from the AD Neuroimaging Initiative database. [18F]-APN-1607 ([18F]-PM-PBB3) PET imaging data were for Cohort II, which included 21 patients with a clinical diagnosis of amyloid PET-positive AD and 15 HCs recruited at Huashan Hospital. We used white matter (WM) postprocessed by PERSI (PERSI-WM) as the reference region and compared this with the traditional semi-quantification method that uses the whole cerebellum as the reference. SUVRs were calculated for regions of interest including the frontal, parietal, temporal, and occipital lobes; anterior and posterior cingulate; precuneus; and Braak I/II (entorhinal cortex and hippocampus). Receiver operating characteristic (ROC) curve analysis and effect sizes were used to compare the two methods in terms of ability to discriminate between different clinical groups. Results: In both cohorts, regional SUVR determined using the PERSI-WM method was superior to using the cerebellum as reference region for measuring tau retention in AD patients (e.g., SUVR of the temporal lobe: flortaucipir, 1.08 ± 0.17 and [18F]-APN-1607, 1.57 ± 0.34); and estimates of the effect size and areas under the ROC curve (AUC) indicated that it also increased between-group differences (e.g., AUC of the temporal lobe for HC vs AD: flortaucipir, 0.893 and [18F]-APN-1607: 0.949). Conclusion: The PERSI-WM method significantly improves diagnostic discrimination compared to conventional approach of using the cerebellum as a reference region and can mitigate the PVE; it can thus enhance the efficacy of semi-quantification of multiple tau tracers in PET scanning, making it suitable for large-scale clinical application. [ABSTRACT FROM AUTHOR]

Purpose: Positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual's risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual's risk of conversion from MCI to AD. Methods: FDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual's metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell's concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics. Results: The KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77–4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model). Conclusion: The KLSE indicator identifies abnormal brain networks predicting an individual's risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker. [ABSTRACT FROM AUTHOR]