Advanced brain aging in multiple system atrophy compared to Parkinson's disease

Investigating dopaminergic abnormalities in schizophrenia and first-episode psychosis with normative modelling and multisite molecular neuroimaging

Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brain's in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility, high costs, and high between-subject heterogeneity of neuroimaging measures. In this study, we explore the use of normative modelling (NM) to identify individual patient alterations by describing the physiological variability of molecular functions. NM potentially addresses challenges such as small sample sizes and diverse acquisition protocols typical of molecular neuroimaging studies. We applied NM to two PET radiotracers targeting the dopaminergic system ([11C]-(+)-PHNO and [18F]FDOPA) to create a reference-cohort model of healthy controls. The models were subsequently utilized on different independent cohorts of patients with psychosis in different disease stages and treatment outcomes. Our results showed that patients with psychosis exhibited a higher degree of extreme deviations (~3-fold increase) than controls, although this pattern was heterogeneous, with minimal overlap of extreme deviations topology (max 20%). We also confirmed that striatal [18F]FDOPA signal, when referenced to a normative distribution, can predict treatment response (striatal AUC ROC: 0.77-0.83). In conclusion, our results indicate that normative modelling can be effectively applied to molecular neuroimaging after proper harmonization, enabling insights into disease mechanisms and advancing precision medicine. In addition, the method is valuable in understanding the heterogeneity of patient populations and can contribute to maximising cost efficiency in studies aimed at comparing cases and controls.

Brain age in genetic and idiopathic Parkinson's disease

The brain-age gap, i.e. the difference between the brain age estimated from structural MRI data and the chronological age of an individual, has been proposed as a summary measure of brain integrity in neurodegenerative diseases. Here, we aimed to determine the brain-age gap in genetic and idiopathic Parkinson's disease and its association with surrogate markers of Alzheimer's disease and Parkinson's disease pathology and with rates of cognitive and motor function decline. We studied 1200 cases from the Parkinson's Progression Markers Initiative cohort, including idiopathic Parkinson's disease, asymptomatic and clinical mutation carriers in the leucine-rich repeat kinase 2 gene (LRRK2) and the glucocerebrosidase gene (GBA), and normal controls using a cohort study design. For comparison, we studied 187 Alzheimer's disease dementia cases and 254 controls from the Alzheimer's Disease Neuroimaging Initiative cohort. We used Bayesian ANOVA to determine associations of the brain-age gap with diagnosis, and baseline measures of motor and cognitive function, dopamine transporter activity and CSF markers of Alzheimer's disease type amyloid-β42 and phosphotau pathology. Associations of brain-age gap with rates of cognitive and motor function decline were determined using Bayesian generalized mixed effect models. The brain-age gap in idiopathic Parkinson's disease patients was 0.7 years compared to controls, but 5.9 years in Alzheimer's disease dementia cases. In contrast, asymptomatic LRRK2 individuals had a 1.1. year younger brain age than controls. Across all cases, the brain-age gap was associated with motor impairment and (in the clinically manifest PD cases) reduced dopamine transporter activity, but less with CSF amyloid-β42 and phosphotau. In idiopathic Parkinson's disease cases, however, the brain-age gap was associated with lower CSF amyloid-β42 levels. In sporadic and genetic Parkinson's disease cases, a higher brain-age gap was associated with faster decline in episodic memory, and executive and motor function, whereas in asymptomatic LRRK2 cases, a smaller brain-age gap was associated with faster cognitive decline. In conclusion, brain age was sensitive to Alzheimer's disease like rather than Parkinson's disease like brain atrophy. Once an individual had idiopathic Parkinson's disease, their brain age was associated with markers of Alzheimer's disease rather than Parkinson's disease. Asymptomatic LRRK2 cases had seemingly younger brains than controls, and in these cases, younger brain age was associated with poorer cognitive outcome. This suggests that the term brain age is misleading when applied to disease stages where reactive brain changes with apparent volume increases rather than atrophy may drive the calculation of the brain age.

Estimated Brain Age in Healthy Aging and Across Multiple Neurological Disorders

BACKGROUND

The brain aging in the general population and patients with neurological disorders is not well understood.

PURPOSE

To characterize brain aging in the above conditions and its clinical relevance.

STUDY TYPE

Retrospective.

POPULATION

A total of 2913 healthy controls (HC), with 1395 females; 331 multiple sclerosis (MS); 189 neuromyelitis optica spectrum disorder (NMOSD); 239 Alzheimer's disease (AD); 244 Parkinson's disease (PD); and 338 cerebral small vessel disease (cSVD).

FIELD STRENGTH/SEQUENCE

3.0 T/Three-dimensional (3D) T1-weighted images.

ASSESSMENT

The brain age was estimated by our previously developed model, using a 3D convolutional neural network trained on 9794 3D T1-weighted images of healthy individuals. Brain age gap (BAG), the difference between chronological age and estimated brain age, was calculated to represent accelerated and resilient brain conditions. We compared MRI metrics between individuals with accelerated (BAG ≥ 5 years) and resilient brain age (BAG ≤ -5 years) in HC, and correlated BAG with MRI metrics, and cognitive and physical measures across neurological disorders.

STATISTICAL TESTS

Student's t test, Wilcoxon test, chi-square test or Fisher's exact test, and correlation analysis. P < 0.05 was considered statistically significant.

RESULTS

In HC, individuals with accelerated brain age exhibited significantly higher white matter hyperintensity (WMH) and lower regional brain volumes than those with resilient brain age. BAG was significantly higher in MS (10.30 ± 12.6 years), NMOSD (2.96 ± 7.8 years), AD (6.50 ± 6.6 years), PD (4.24 ± 4.8 years), and cSVD (3.24 ± 5.9 years) compared to HC. Increased BAG was significantly associated with regional brain atrophy, WMH burden, and cognitive impairment across neurological disorders. Increased BAG was significantly correlated with physical disability in MS (r = 0.17).

DATA CONCLUSION

Healthy individuals with accelerated brain age show high WMH burden and regional volume reduction. Neurological disorders exhibit distinct accelerated brain aging, correlated with impaired cognitive and physical function.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Advanced Cognitive Patterns in Multiple System Atrophy Compared to Parkinson's Disease: An Individual Diffusion Tensor Imaging Study

RATIONALE AND OBJECTIVES

Although both Multiple system atrophy (MSA) and Parkinson's disease (PD) belong to alpha-synucleinopathy, they have divergent clinical courses and prognoses. The degeneration of white matter has a considerable impact on cognitive performance, yet it is uncertain how PD and MSA affect its functioning in a similar or different manner.

METHODS

In this study, a total of 116 individuals (37 PD with mild cognitive impairment (PD-MCI), 37 MSA (parkinsonian variant) with mild cognitive impairment (MSA-MCI), and 42 healthy controls) underwent diffusion tensor imaging (DTI) and cognitive assessment. Utilizing probabilistic fiber tracking, association fibers, projection fibers, and thalamic fibers were reconstructed. Subsequently, regression, support vector machine, and SHAP (Shapley Addictive exPlanations) analyzes were conducted to evaluate the association between microstructural diffusion metrics and multiple cognitive domains, thus determining the white matter predictors of MCI.

RESULTS

MSA-MCI patients exhibited distinct white matter impairment extending to the middle cerebellar peduncle, corticospinal tract, and cingulum bundle. Furthermore, the fractional anisotropy (FA) and mean diffusivity (MD)values of the right anterior thalamic radiation were significantly associated with global efficiency (FA: B = 0.69, P < 0.001, VIF = 1.31; MD: B = -0.53, P = 0.02, VIF = 2.50). The diffusion metrics of white matter between PD-MCI and MSA-MCI proved to be an effective predictor of the MCI, with an accuracy of 0.73 (P < 0.01), and the most predictive factor being the MD of the anterior thalamic radiation.

CONCLUSIONS

Our results demonstrated that MSA-MCI had a more noticeable deterioration in white matter, which potentially linked to various cognitive domain connections. Diffusion MRI could be a useful tool in comprehending the neurological basis of cognitive impairment in Parkinsonian disorders.

Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data

The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.

Advanced brain aging in Parkinson's disease with cognitive impairment

Patients with Parkinson's disease and cognitive impairment (PD-CI) deteriorate faster than those without cognitive impairment (PD-NCI), suggesting an underlying difference in the neurodegeneration process. We aimed to verify brain age differences in PD-CI and PD-NCI and their clinical significance. A total of 94 participants (PD-CI, n = 27; PD-NCI, n = 34; controls, n = 33) were recruited. Predicted age difference (PAD) based on gray matter (GM) and white matter (WM) features were estimated to represent the degree of brain aging. Patients with PD-CI showed greater GM-PAD (7.08 ± 6.64 years) and WM-PAD (8.82 ± 7.69 years) than those with PD-NCI (GM: 1.97 ± 7.13, Padjusted = 0.011; WM: 4.87 ± 7.88, Padjusted = 0.049) and controls (GM: -0.58 ± 7.04, Padjusted = 0.004; WM: 0.88 ± 7.45, Padjusted = 0.002) after adjusting demographic factors. In patients with PD, GM-PAD was negatively correlated with MMSE (Padjusted = 0.011) and MoCA (Padjusted = 0.013) and positively correlated with UPDRS Part II (Padjusted = 0.036). WM-PAD was negatively correlated with logical memory of immediate and delayed recalls (Padjusted = 0.003 and Padjusted < 0.001). Also, altered brain regions in PD-CI were identified and significantly correlated with brain age measures, implicating the neuroanatomical underpinning of neurodegeneration in PD-CI. Moreover, the brain age metrics can improve the classification between PD-CI and PD-NCI. The findings suggest that patients with PD-CI had advanced brain aging that was associated with poor cognitive functions. The identified neuroimaging features and brain age measures can serve as potential biomarkers of PD-CI.

Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset

Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases.

Brain Age Is Associated with Cognitive Outcomes of Cholinesterase Inhibitor Treatment in Patients with Mild Cognitive Impairment

Background

The effect of cholinesterase inhibitor (ChEI) on mild cognitive impairment (MCI) is controversial. Brain age has been shown to predict Alzheimer's disease conversion from MCI.

Objective

The study aimed to show that brain age is related to cognitive outcomes of ChEI treatment in MCI.

Methods

Brain MRI, the Clinical Dementia Rating (CDR) and Mini-Mental State Exam (MMSE) scores were retrospectively retrieved from a ChEI treatment database. Patients who presented baseline CDR of 0.5 and received ChEI treatment for at least 2 years were selected. Patients with stationary or improved cognition as verified by the CDR and MMSE were categorized to the ChEI-responsive group, and those with worsened cognition were assigned to the ChEI-unresponsive group. A gray matter brain age model was built with a machine learning algorithm by training T1-weighted MRI data of 362 healthy participants. The model was applied to each patient to compute predicted age difference (PAD), i.e. the difference between brain age and chronological age. The PADs were compared between the two groups.

Results

58 patients were found to fit the ChEI-responsive criteria in the patient data, and 58 matched patients that fit the ChEI-unresponsive criteria were compared. ChEI-unresponsive patients showed significantly larger PAD than ChEI-responsive patients (8.44±8.78 years versus 3.87±9.02 years, p = 0.0067).

Conclusions

Gray matter brain age is associated with cognitive outcomes after 2 years of ChEI treatment in patients with the CDR of 0.5. It might facilitate the clinical trials of novel therapeutics for MCI.

IFN-γ Triggered IFITM2 Expression to Induce Malignant Phenotype in Elderly GBM

Advanced age is an important risk factor for the worse clinical presentation of gliomas, especially glioblastoma (GBM). The tumor microenvironment (TME) in elderly GBM (eGBM) patients is considerably different from that in young ones, which causes the inferior clinical outcome. Based on the data from the Chinese Glioma Genome Atlas RNA sequence (CGGA RNA-seq), the Cancer Genome Atlas RNA array (TCGA RNA-array), and gene set enrichment (GSE) 16011 array sets, the differential genes and function between eGBM (≥ 60 years old) and young GBM (yGBM, 20-60 years old) groups were explored. Immunohistochemistry (IHC) was utilized to depict the abundance of CD8+ cells (the main resource of IFN-γ) and IFITM2 protein expression in GBM samples. Furthermore, reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting (WB) were performed to verify the link between IFN-γ and IFITM2. Moreover, the small-interfering RNA (siRNA) of IFITM2 was used to explore the function of IFITM2 in GBM. Characterized by inflammatory TME and higher IFITM2 expression, eGBM harbored a shorter survival time. Chemotaxis and inflammatory cytokine-related genes were enriched in the eGBM group, with more infiltrative CD8+ T cells. The IHC of CD8 and IFITM2-staining could demonstrate these results. In addition, the IFN-γ response pathway was activated in eGBM and resulted in a dismal outcome. Next, it was found that IFITM2 triggered by IFN-γ played a key role in IFN-γ-induced malignant phenotype in eGBM.

α-Synuclein oligomers and fibrils: partners in crime in synucleinopathies

The misfolding and aggregation of α-synuclein is the general hallmark of a group of devastating neurodegenerative pathologies referred to as synucleinopathies, such as Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. In such conditions, a range of different misfolded aggregates, including oligomers, protofibrils, and fibrils, are present both in neurons and glial cells. Growing experimental evidence supports the proposition that soluble oligomeric assemblies, formed during the early phases of the aggregation process, are the major culprits of neuronal toxicity; at the same time, fibrillar conformers appear to be the most efficient at propagating among interconnected neurons, thus contributing to the spreading of α-synuclein pathology. Moreover, α-synuclein fibrils have been recently reported to release soluble and highly toxic oligomeric species, responsible for an immediate dysfunction in the recipient neurons. In this review, we discuss the current knowledge about the plethora of mechanisms of cellular dysfunction caused by α-synuclein oligomers and fibrils, both contributing to neurodegeneration in synucleinopathies.