Fingerprints of brain disease: connectome identifiability in Alzheimer's disease.

Functional connectivity patterns in the human brain, like the friction ridges of a fingerprint, can uniquely identify individuals. Does this "brain fingerprint" remain distinct even during Alzheimer's disease (AD)? Using fMRI data from healthy and pathologically ageing subjects, we find that individual functional connectivity profiles remain unique and highly heterogeneous during mild cognitive impairment and AD. However, the patterns that make individuals identifiable change with disease progression, revealing a reconfiguration of the brain fingerprint. Notably, connectivity shifts towards functional system connections in AD and lower-order cognitive functions in early disease stages. These findings emphasize the importance of focusing on individual variability rather than group differences in AD studies. Individual functional connectomes could be instrumental in creating personalized models of AD progression, predicting disease course, and optimizing treatments, paving the way for personalized medicine in AD management.

[68Ga]Ga-PSMA-11 PET and Prostate Cancer Bone Metastases: Diagnostic Performance of Available Standardized Criteria.

In up to two thirds of prostate-specific membrane antigen (PSMA) PET scans, unspecific bone uptake has been described. The aim of this study was to estimate the diagnostic accuracy of [68Ga]Ga-PSMA-11 PET/CT for bone metastases and the occurrence of equivocal lesions. Methods: We analyzed retrospectively 118 patients who underwent a [68Ga]Ga-PSMA-11 PET/CT for initial staging or recurrence evaluation. Lesions were interpreted according to the PSMA reporting and data system (PSMA-RADS) and the prostate cancer molecular imaging standardized evaluation (PROMISE) criteria. The SUVmax and the localization of each lesion were recorded. A combination of prior or follow-up examinations was used as a reference standard to categorize benign and malignant lesions. Correlation between the final diagnosis and imaging or clinicobiochemical parameters was tested. The diagnostic accuracy was calculated for different cutoffs of PSMA-RADS criteria, for PROMISE criteria, and the sequential combination of both. Results: In total, 265 bone abnormalities were identified in 70 of 118 patients. Among these, 148 (55.8%) lesions in 50 (42.4%) patients were classified as PSMA-RADS-3B. There were no PSMA-RADS-3D lesions in our cohort. Equivocal lesions were more frequent on the ribs (30.6%) followed by the pelvis (26.5%), but in the ribs, such an uptake was malignant in 33.3% of cases versus 66.7% in the pelvis. A significant association was found between the final diagnosis and the SUVmax, prostate-specific antigen (PSA), PSA doubling time, International Society of Urological Pathology score, and the number of foci. The sensitivity and specificity were 100% and 63.6% for the PSMA-RADS-3B cutoff, respectively; 40.5% and 100% for the PSMA-RADS-4 cutoff, respectively; and 89.3% and 96.6% for both the PROMISE criteria and the sequential PSMA-RADS/PROMISE strategy, respectively. In the sequential method, the number of equivocal lesions was reduced from 147 to 2. We found that 53% of PSMA-RADS-3B lesions were malignant; 95.5% of lesions classified positive by the sequential method were true positives, whereas 32.6% were false negatives. Conclusion: [68Ga]Ga-PSMA-11 PET/CT has high accuracy for the diagnosis of bone metastases. Equivocal lesions constitute nearly half of the lesions seen on PSMA PET. The sequential combination of PSMA-RADS and PROMISE criteria reduces the number of lesions classified as equivocal. PSMA-RADS-3B lesions which are positive according to the PROMISE criteria should be considered highly suggestive of malignancy.

Automatic Classification of Conclusions from Multi-Tracer Reports of PET Brain Imaging in Cognitive Impairment.

The goal of this paper is to build an automatic way to interpret conclusions from brain molecular imaging reports performed for investigation of cognitive disturbances (FDG, Amyloid and Tau PET) by comparing several traditional machine learning (ML) techniques-based text classification methods. Two purposes are defined: to identify positive or negative results in all three modalities, and to extract diagnostic impressions for Alzheimer's Disease (AD), Fronto-Temporal Dementia (FTD), Lewy Bodies Dementia (LBD) based on metabolism of perfusion patterns. A dataset was created by manual parallel annotation of 1668 conclusions of reports from the Nuclear Medicine and Molecular Imaging Division of Geneva University Hospitals. The 6 Machine Learning (ML) algorithms (Support Vector Machine (Linear and Radial Basis function), Naive Bayes, Logistic Regression, Random Forrest, and K-Nearest Neighbors) were trained and evaluated with a 5-fold cross-validation scheme to assess their performance and generalizability. The best classifier was SVM showing the following accuracies: FDG (0.97), Tau (0.94), Amyloid (0.98), Oriented Diagnostic (0.87 for a diagnosis among AD, FTD, LBD, undetermined, other), paving the way for a paradigm shift in the field of data handling in nuclear medicine research.

The role of biomarkers and dosimetry parameters in overall and progression free survival prediction for patients treated with personalized 90Y glass microspheres SIRT: a preliminary machine learning study.

BACKGROUND: Overall Survival (OS) and Progression-Free Survival (PFS) analyses are crucial metrics for evaluating the efficacy and impact of treatment. This study evaluated the role of clinical biomarkers and dosimetry parameters on survival outcomes of patients undergoing 90Y selective internal radiation therapy (SIRT). MATERIALS/METHODS: This preliminary and retrospective analysis included 17 patients with hepatocellular carcinoma (HCC) treated with 90Y SIRT. The patients underwent personalized treatment planning and voxel-wise dosimetry. After the procedure, the OS and PFS were evaluated. Three structures were delineated including tumoral liver (TL), normal perfused liver (NPL), and whole normal liver (WNL). 289 dose-volume constraints (DVCs) were extracted from dose-volume histograms of physical and biological effective dose (BED) maps calculated on 99mTc-MAA and 90Y SPECT/CT images. Subsequently, the DVCs and 16 clinical biomarkers were used as features for univariate and multivariate analysis. Cox proportional hazard ratio (HR) was employed for univariate analysis. HR and the concordance index (C-Index) were calculated for each feature. Using eight different strategies, a cross-combination of various models and feature selection (FS) methods was applied for multivariate analysis. The performance of each model was assessed using an averaged C-Index on a three-fold nested cross-validation framework. The Kaplan-Meier (KM) curve was employed for univariate and machine learning (ML) model performance assessment. RESULTS: The median OS was 11 months [95% CI: 8.5, 13.09], whereas the PFS was seven months [95% CI: 5.6, 10.98]. Univariate analysis demonstrated the presence of Ascites (HR: 9.2[1.8,47]) and the aim of SIRT (segmentectomy, lobectomy, palliative) (HR: 0.066 [0.0057, 0.78]), Aspartate aminotransferase (AST) level (HR:0.1 [0.012-0.86]), and MAA-Dose-V205(%)-TL (HR:8.5[1,72]) as predictors for OS. 90Y-derived parameters were associated with PFS but not with OS. MAA-Dose-V205(%)-WNL, MAA-BED-V400(%)-WNL with (HR:13 [1.5-120]) and 90Y-Dose-mean-TL, 90Y-D50-TL-Gy, 90Y-Dose-V205(%)-TL, 90Y-Dose- D50-TL-Gy, and 90Y-BED-V400(%)-TL (HR:15 [1.8-120]) were highly associated with PFS among dosimetry parameters. The highest C-index observed in multivariate analysis using ML was 0.94 ± 0.13 obtained from Variable Hunting-variable-importance (VH.VIMP) FS and Cox Proportional Hazard model predicting OS, using clinical features. However, the combination of VH. VIMP FS method with a Generalized Linear Model Network model predicting OS using Therapy strategy features outperformed the other models in terms of both C-index and stratification of KM curves (C-Index: 0.93 ± 0.14 and log-rank p-value of 0.023 for KM curve stratification). CONCLUSION: This preliminary study confirmed the role played by baseline clinical biomarkers and dosimetry parameters in predicting the treatment outcome, paving the way for the establishment of a dose-effect relationship. In addition, the feasibility of using ML along with these features was demonstrated as a helpful tool in the clinical management of patients, both prior to and following 90Y-SIRT.

A deep learning model for generating [18F]FDG PET Images from early-phase [18F]Florbetapir and [18F]Flutemetamol PET images.

INTRODUCTION: Amyloid-ß (Aß) plaques is a significant hallmark of Alzheimer's disease (AD), detectable via amyloid-PET imaging. The Fluorine-18-Fluorodeoxyglucose ([18F]FDG) PET scan tracks cerebral glucose metabolism, correlated with synaptic dysfunction and disease progression and is complementary for AD diagnosis. Dual-scan acquisitions of amyloid PET allows the possibility to use early-phase amyloid-PET as a biomarker for neurodegeneration, proven to have a good correlation to [18F]FDG PET. The aim of this study was to evaluate the added value of synthesizing the later from the former through deep learning (DL), aiming at reducing the number of PET scans, radiation dose, and discomfort to patients. METHODS: A total of 166 subjects including cognitively unimpaired individuals (N = 72), subjects with mild cognitive impairment (N = 73) and dementia (N = 21) were included in this study. All underwent T1-weighted MRI, dual-phase amyloid PET scans using either Fluorine-18 Florbetapir ([18F]FBP) or Fluorine-18 Flutemetamol ([18F]FMM), and an [18F]FDG PET scan. Two transformer-based DL models called SwinUNETR were trained separately to synthesize the [18F]FDG from early phase [18F]FBP and [18F]FMM (eFBP/eFMM). A clinical similarity score (1: no similarity to 3: similar) was assessed to compare the imaging information obtained by synthesized [18F]FDG as well as eFBP/eFMM to actual [18F]FDG. Quantitative evaluations include region wise correlation and single-subject voxel-wise analyses in comparison with a reference [18F]FDG PET healthy control database. Dice coefficients were calculated to quantify the whole-brain spatial overlap between hypometabolic ([18F]FDG PET) and hypoperfused (eFBP/eFMM) binary maps at the single-subject level as well as between [18F]FDG PET and synthetic [18F]FDG PET hypometabolic binary maps. RESULTS: The clinical evaluation showed that, in comparison to eFBP/eFMM (average of clinical similarity score (CSS) = 1.53), the synthetic [18F]FDG images are quite similar to the actual [18F]FDG images (average of CSS = 2.7) in terms of preserving clinically relevant uptake patterns. The single-subject voxel-wise analyses showed that at the group level, the Dice scores improved by around 13% and 5% when using the DL approach for eFBP and eFMM, respectively. The correlation analysis results indicated a relatively strong correlation between eFBP/eFMM and [18F]FDG (eFBP: slope = 0.77, R2 = 0.61, P-value < 0.0001); eFMM: slope = 0.77, R2 = 0.61, P-value < 0.0001). This correlation improved for synthetic [18F]FDG (synthetic [18F]FDG generated from eFBP (slope = 1.00, R2 = 0.68, P-value < 0.0001), eFMM (slope = 0.93, R2 = 0.72, P-value < 0.0001)). CONCLUSION: We proposed a DL model for generating the [18F]FDG from eFBP/eFMM PET images. This method may be used as an alternative for multiple radiotracer scanning in research and clinical settings allowing to adopt the currently validated [18F]FDG PET normal reference databases for data analysis.

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

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

Tau Positron Emission Tomography for Predicting Dementia in Individuals With Mild Cognitive Impairment.

Importance: An accurate prognosis is especially pertinent in mild cognitive impairment (MCI), when individuals experience considerable uncertainty about future progression. Objective: To evaluate the prognostic value of tau positron emission tomography (PET) to predict clinical progression from MCI to dementia. Design, Setting, and Participants: This was a multicenter cohort study with external validation and a mean (SD) follow-up of 2.0 (1.1) years. Data were collected from centers in South Korea, Sweden, the US, and Switzerland from June 2014 to January 2024. Participant data were retrospectively collected and inclusion criteria were a baseline clinical diagnosis of MCI; longitudinal clinical follow-up; a Mini-Mental State Examination (MMSE) score greater than 22; and available tau PET, amyloid-ß (Aß) PET, and magnetic resonance imaging (MRI) scan less than 1 year from diagnosis. A total of 448 eligible individuals with MCI were included (331 in the discovery cohort and 117 in the validation cohort). None of these participants were excluded over the course of the study. Exposures: Tau PET, Aß PET, and MRI. Main Outcomes and Measures: Positive results on tau PET (temporal meta-region of interest), Aß PET (global; expressed in the standardized metric Centiloids), and MRI (Alzheimer disease [AD] signature region) was assessed using quantitative thresholds and visual reads. Clinical progression from MCI to all-cause dementia (regardless of suspected etiology) or to AD dementia (AD as suspected etiology) served as the primary outcomes. The primary analyses were receiver operating characteristics. Results: In the discovery cohort, the mean (SD) age was 70.9 (8.5) years, 191 (58%) were male, the mean (SD) MMSE score was 27.1 (1.9), and 110 individuals with MCI (33%) converted to dementia (71 to AD dementia). Only the model with tau PET predicted all-cause dementia (area under the receiver operating characteristic curve [AUC], 0.75; 95% CI, 0.70-0.80) better than a base model including age, sex, education, and MMSE score (AUC, 0.71; 95% CI, 0.65-0.77; P = .02), while the models assessing the other neuroimaging markers did not improve prediction. In the validation cohort, tau PET replicated in predicting all-cause dementia. Compared to the base model (AUC, 0.75; 95% CI, 0.69-0.82), prediction of AD dementia in the discovery cohort was significantly improved by including tau PET (AUC, 0.84; 95% CI, 0.79-0.89; P < .001), tau PET visual read (AUC, 0.83; 95% CI, 0.78-0.88; P = .001), and Aß PET Centiloids (AUC, 0.83; 95% CI, 0.78-0.88; P = .03). In the validation cohort, only the tau PET and the tau PET visual reads replicated in predicting AD dementia. Conclusions and Relevance: In this study, tau-PET showed the best performance as a stand-alone marker to predict progression to dementia among individuals with MCI. This suggests that, for prognostic purposes in MCI, a tau PET scan may be the best currently available neuroimaging marker.

Comparison of plasma and neuroimaging biomarkers to predict cognitive decline in non-demented memory clinic patients.

BACKGROUND: Plasma biomarkers of Alzheimer's disease (AD) pathology, neurodegeneration, and neuroinflammation are ideally suited for secondary prevention programs in self-sufficient persons at-risk of dementia. Plasma biomarkers have been shown to be highly correlated with traditional imaging biomarkers. However, their comparative predictive value versus traditional AD biomarkers is still unclear in cognitively unimpaired (CU) subjects and with mild cognitive impairment (MCI). METHODS: Plasma (Aß42/40, p-tau181, p-tau231, NfL, and GFAP) and neuroimaging (hippocampal volume, centiloid of amyloid-PET, and tau-SUVR of tau-PET) biomarkers were assessed at baseline in 218 non-demented subjects (CU = 140; MCI = 78) from the Geneva Memory Center. Global cognition (MMSE) was evaluated at baseline and at follow-ups up to 5.7 years. We used linear mixed-effects models and Cox proportional-hazards regression to assess the association between biomarkers and cognitive decline. Lastly, sample size calculations using the linear mixed-effects models were performed on subjects positive for amyloid-PET combined with tau-PET and plasma biomarker positivity. RESULTS: Cognitive decline was significantly predicted in MCI by baseline plasma NfL (ß=-0.55), GFAP (ß=-0.36), hippocampal volume (ß = 0.44), centiloid (ß=-0.38), and tau-SUVR (ß=-0.66) (all p < 0.05). Subgroup analysis with amyloid-positive MCI participants also showed that only NfL and GFAP were the only significant predictors of cognitive decline among plasma biomarkers. Overall, NfL and tau-SUVR showed the highest prognostic values (hazard ratios of 7.3 and 5.9). Lastly, we demonstrated that adding NfL to the inclusion criteria could reduce the sample sizes of future AD clinical trials by up to one-fourth in subjects with amyloid-PET positivity or by half in subjects with amyloid-PET and tau-PET positivity. CONCLUSIONS: Plasma NfL and GFAP predict cognitive decline in a similar manner to traditional imaging techniques in amyloid-positive MCI patients. Hence, even though they are non-specific biomarkers of AD, both can be implemented in memory clinic workups as important prognostic biomarkers. Likewise, future clinical trials might employ plasma biomarkers as additional inclusion criteria to stratify patients at higher risk of cognitive decline to reduce sample sizes and enhance effectiveness.

The Taxonomy of Subjective Cognitive Decline: Proposal and First Clinical Evidence from the Geneva Memory Clinic Cohort.

INTRODUCTION: Subjective cognitive decline (SCD) is characterized by subjective cognitive concerns without objective cognitive impairment and is considered a risk factor for cognitive decline and dementia. However, most SCD patients will not develop neurodegenerative disorders, yet they may suffer from minor psychiatric, neurological, or somatic comorbidities. The aim of the present study was to provide a taxonomy of the heterogeneous SCD entity and to conduct a preliminary validation using data from a memory clinic sample. METHODS: Participants were fifty-five SCD individuals consecutively recruited at the Geneva Memory Center. Based on clinical reports, they were classified into three clinically pre-defined subgroups: (i) those with psychological or psychiatric comorbidities (Psy), (ii) those with somatic comorbidities (SomCom), (iii) and those with no apparent cause (NAC). Baseline demographics, clinical, cognitive, and biomarker differences among the SCD subgroups were assessed. Longitudinal cognitive changes (average 3 years follow-up) were modeled using a linear mixed model. RESULTS: Out of the 55 SCD cases, 16 were SomCom, 18 Psy, and 21 NAC. 47% were female, mean age was 71 years. We observed higher frequency of APOE ε4 carriers in NAC (53%) compared to SomCom (14%) and Psy (0%, p = 0.023) and lower level of plasma Aß42 in NAC (6.8 ± 1.0) compared to SomCom (8.4 ± 1.1; p = 0.031). SomCom subjects were older (74 years) than Psy (67 years, p = 0.011), and had greater medial temporal lobe atrophy (1.0 ± 1.0) than Psy (0.2 ± 0.6) and NAC (0.4 ± 0.5, p = 0.005). SomCom has worse episodic memory performances (14.5 ± 3.5) than Psy (15.8 ± 0.4) and NAC (15.8 ± 0.7, p = 0.032). We observed a slightly steeper, yet not statistically significant, cognitive decline in NAC (ß = -0.48) compared to Psy (ß = -0.28) and SomCom (ß = -0.24). CONCLUSIONS: NAC features a higher proportion of APOE ε4 carriers, lower plasma Aß42 and a trend towards steeper cognitive decline than SomCom and Psy. Taken together, these findings suggest that NACs are at higher risk of cognitive decline due to AD. The proposed clinical taxonomy might be implemented in clinical practice to identify SCD at higher risk. However, such taxonomy should be tested on an independent cohort with a larger sample size.

Alterations in gamma frequency oscillations correlate with cortical tau deposition in Alzheimer's disease.

We assessed the relationship of gamma oscillations with tau deposition in Alzheimer's disease (AD) and other cognitive diseases, as both are altered during the disease course and relate to neurodegeneration. We retrospectively analyzed data from 7 AD, tau positive patients and 9 tau negative patients, who underwent cerebral amyloid PET and tau PET, and EEG within 12 months. Relative gamma power was higher in tau positive (AD) patients than in tau negative patients (p < .05). In tau positive AD patients, tau burden was associated with a linear increase in gamma power (p < .05), while no association was present in the tau negative group nor with amyloid-ß burden in either group. Thus, increase in the gamma power might represent a novel biomarker for tau driven neurodegeneration.

Neuroimaging-guided diagnosis of possible FTLD-FUS pathology: a case report.

BACKGROUND: This case report presents a patient with progressive memory loss and choreiform movements. CASE PRESENTATION: Neuropsychological tests indicated multi-domain amnestic mild cognitive impairment (aMCI), and neurological examination revealed asymmetrical involuntary hyperkinetic movements. Imaging studies showed severe left-sided atrophy and hypometabolism in the left frontal and temporoparietal cortex. [18F]Flortaucipir PET exhibited moderately increased tracer uptake in hypometabolic areas. The diagnosis initially considered Alzheimer's disease (AD), frontotemporal degeneration (FTD), and corticobasal degeneration (CBD), cerebral hemiatrophy syndrome, but imaging and cerebrospinal fluid analysis excluded AD and suggested fused-in-sarcoma-associated FTD (FTLD-FUS), a subtype of the behavioural variant of FTD. CONCLUSIONS: Our case highlights that despite the lack of specific FUS biomarkers the combination of clinical features and neuroimaging biomarkers can guide choosing the most likely differential diagnosis in a complex neurological case. Imaging in particular allowed an accurate measure of the topography and severity of neurodegeneration and the exclusion of AD-related pathology.

Mapping brain metabolism, connectivity and neurotransmitters topography in early and late onset dementia with lewy bodies.

INTRODUCTION: Early-onset dementia with Lewy bodies (EO-DLB) is associated with rapid cognitive decline and severe neuropsychiatric symptoms at onset. METHODS: Using FDG-PET imaging for 62 patients (21 EO-DLB, 41 LO (late-onset)-DLB), we explored brain hypometabolism, and metabolic connectivity in the whole-brain network and resting-state networks (RSNs). We also evaluated the spatial association between brain hypometabolism and neurotransmitter pathways topography. RESULTS: Direct comparisons between the two clinical subgroups showed that EO-DLB was characterized by a lower metabolism in posterior cingulate/precuneus and occipital cortex. Metabolic connectivity analysis revealed significant alterations in posterior regions in both EO-DLB and LO-DLB. The EO-DLB, however, showed more severe loss of connectivity between occipital and parietal nodes and hyperconnectivity between frontal and cerebellar nodes. Spatial topography association analysis indicated significant correlations between neurotransmitter maps (i.e. acetylcholine, GABA, serotonin, dopamine) and brain hypometabolism in both EO and LO-DLB, with significantly higher metabolic correlation in the presynaptic serotonergic system for EO-DLB, supporting its major dysfunction. CONCLUSIONS: Our study revealed greater brain hypometabolism and loss of connectivity in posterior brain region in EO- than LO-DLB. Serotonergic mapping emerges as a relevant factor for further investigation addressing clinical differences between DLB subtypes.

Distinctive clinical and imaging trajectories in SWEDD and Parkinson's disease patients.

A proportion of patients clinically diagnosed with Parkinson's disease (PD) can have a 123I-FP-CIT-SPECT scan without evidence of dopaminergic deficit (SWEDD), generating a debate about the underlying biological mechanisms. This study investigated differences in clinical features, 123I-FP-CIT binding, molecular connectivity, as well as clinical and imaging progression between SWEDD and PD patients. We included 36 SWEDD, 49 de novo idiopathic PD, and 49 healthy controls with 123I-FP-CIT-SPECT from the Parkinson's Progression Markers Initiative. Clinical and imaging 2-year follow-ups were available for 27 SWEDD and 40 PD. Regional-based and voxel-wise analysis assessed dopaminergic integrity in dorsal and ventral striatal, as well as extrastriatal regions, at baseline and follow-up. Molecular connectivity analyses evaluated dopaminergic pathways. Spatial correlation analyses tested whether 123I-FP-CIT-binding alterations would also pertain to the serotoninergic system. SWEDD and PD patients showed comparable symptoms at baseline, except for hyposmia, which was more severe for PD. PD showed significantly lower striatal and extrastriatal 123I-FP-CIT-binding compared to SWEDD and controls. SWEDD exhibited lower binding than controls in striatal regions, insula, and olfactory cortex. Both PD and SWEDD showed extensive altered connectivity of dopaminergic pathways, however, with major impairment in the mesocorticolimbic system for SWEDD. Motor symptoms and dopaminergic deficits worsened after 2 years for PD only. The limited dopaminergic impairment and its stability over time observed for SWEDD, as well as the presence of extrastriatal 123I-FP-CIT binding alterations and prevalent mesocorticolimbic connectivity impairment, suggest other mechanisms contributing to SWEDD pathophysiology.

The diagnostic and prognostic value of tau-PET in amnestic MCI with different FDG-PET subtypes.

OBJECTIVES: Mild cognitive impairment presenting with an amnestic syndrome (aMCI) and amyloid positivity is considered due to AD. Many subjects, however, can show an overall very slow progression relevant for differential diagnosis, prognosis, and treatment. This study assessed PET biomarkers, including brain glucose metabolism, tau, and amyloid load, in a series of comparable aMCI at baseline, clinically evaluated at follow-up. METHODS: We included 72 aMCI subjects from Geneva Memory Center (N = 31) and ADNI cohorts (N = 41), selected based on available FDG-PET, tau-PET, amyloid-PET, and clinical follow-up (2.3 years ± 1.2). A data-driven algorithm classified brain metabolic patterns into subtypes that were then compared for clinical and PET biomarker measures and cognitive decline. Voxel-wise comparisons were performed both with FDG-PET and tau-PET data. RESULTS: The algorithm classified three metabolic subtypes, namely "Hippocampal-sparing with cortical hypometabolism" (Type1; N = 27), "Hippocampal and cortical hypometabolism" (Type 2; N = 23), and "Medial temporal hypometabolism" (Type 3; N = 22). Amyloid positivity and tau accumulation in the medial temporal and neocortical regions characterized Type 1 and Type 2, whereas Type 3 showed no significant tau pathology, variable amyloid positivity, and stability at follow-up. All tau-positive patients, independently of the FDG-based subtype, showed faster cognitive decline. INTERPRETATION: aMCI subjects can differ in metabolic patterns, tau and amyloid pathology, and clinical progression. Here, we complemented with PET tau biomarker the specific brain hypometabolic patterns at the individual level in the prodromal phase, contributing to the patient's classification. Tau PET is the most accurate biomarker in supporting or excluding the AD diagnosis in aMCI across metabolic subtypes and also predicting the risk of decline.

Low-Dose Radiation Therapy Impacts Microglial Inflammatory Response without Modulating Amyloid Load in Female TgF344-AD Rats.

Background: Low-dose radiation therapy (LD-RT) has demonstrated in preclinical and clinical studies interesting properties in the perspective of targeting Alzheimer's disease (AD), including anti-amyloid and anti-inflammatory effects. Nevertheless, studies were highly heterogenous with respect to total doses, fractionation protocols, sex, age at the time of treatment and delay post treatment. Recently, we demonstrated that LD-RT reduced amyloid peptides and inflammatory markers in 9-month-old TgF344-AD (TgAD) males. Objective: As multiple studies demonstrated a sex effect in AD, we wanted to validate that LD-RT benefits are also observed in TgAD females analyzed at the same age. Methods: Females were bilaterally treated with 2 Gy×5 daily fractions, 2 Gy×5 weekly fractions, or 10 fractions of 1 Gy delivered twice a week. The effect of each treatment on amyloid load and inflammation was evaluated using immunohistology and biochemistry. Results: A daily treatment did not affect amyloid and reduced only microglial-mediated inflammation markers, the opposite of the results obtained in our previous male study. Moreover, altered fractionations (2 Gy×5 weekly fractions or 10 fractions of 1 Gy delivered twice a week) did not influence the amyloid load or neuroinflammatory response in females. Conclusions: A daily treatment consequently appears to be the most efficient for AD. This study also shows that the anti-amyloid and anti-inflammatory response to LD-RT are, at least partly, two distinct mechanisms. It also emphasizes the necessity to assess the sex impact when evaluating responses in ongoing pilot clinical trials testing LD-RT against AD.

PET Imaging in Dementia: Mini-Review and Canadian Perspective for Clinical Use.

PET imaging is increasingly recognized as an important diagnostic tool to investigate patients with cognitive disturbances of possible neurodegenerative origin. PET with 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG), assessing glucose metabolism, provides a measure of neurodegeneration and allows a precise differential diagnosis among the most common neurodegenerative diseases, such as Alzheimer's disease, frontotemporal dementia or dementia with Lewy bodies. PET tracers specific for the pathological deposits characteristic of different neurodegenerative processes, namely amyloid and tau deposits typical of Alzheimer's Disease, allow the visualization of these aggregates in vivo. [18F]FDG and amyloid PET imaging have reached a high level of clinical validity and are since 2022 investigations that can be offered to patients in standard clinical care in most of Canada.This article will briefly review and summarize the current knowledge on these diagnostic tools, their integration into diagnostic algorithms as well as perspectives for future developments.

Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review.

PURPOSE: Epidemiological and logistical reasons are slowing the clinical validation of the molecular imaging biomarkers in the initial stages of neurocognitive disorders. We provide an updated systematic review of the recent advances (2017-2022), highlighting methodological shortcomings. METHODS: Studies reporting the diagnostic accuracy values of the molecular imaging techniques (i.e., amyloid-, tau-, [18F]FDG-PETs, DaT-SPECT, and cardiac [123I]-MIBG scintigraphy) in predicting progression from mild cognitive impairment (MCI) to dementia were selected according to the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) method and evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Main eligibility criteria were as follows: (1) ≥ 50 subjects with MCI, (2) follow-up ≥ 3 years, (3) gold standard: progression to dementia or diagnosis on pathology, and (4) measures of prospective accuracy. RESULTS: Sensitivity (SE) and specificity (SP) in predicting progression to dementia, mainly to Alzheimer's dementia were 43-100% and 63-94% for [18F]FDG-PET and 64-94% and 48-93% for amyloid-PET. Longitudinal studies were lacking for less common disorders (Dementia with Lewy bodies-DLB and Frontotemporal lobe degeneration-FTLD) and for tau-PET, DaT-SPECT, and [123I]-MIBG scintigraphy. Therefore, the accuracy values from cross-sectional studies in a smaller sample of subjects (n > 20, also including mild dementia stage) were chosen as surrogate outcomes. DaT-SPECT showed 47-100% SE and 71-100% SP in differentiating Lewy body disease (LBD) from non-LBD conditions; tau-PET: 88% SE and 100% SP in differentiating DLB from Posterior Cortical Atrophy. [123I]-MIBG scintigraphy differentiated LBD from non-LBD conditions with 47-100% SE and 71-100% SP. CONCLUSION: Molecular imaging has a moderate-to-good accuracy in predicting the progression of MCI to Alzheimer's dementia. Longitudinal studies are sparse in non-AD conditions, requiring additional efforts in these settings.