Brain age and Alzheimer's-like atrophy are domain-specific predictors of cognitive impairment in Parkinson's disease

Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study

OBJECTIVES

This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions.

METHODS

The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age-chronological age) for every subject.

RESULTS

MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers.

CONCLUSIONS

Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health.

Prediction of chronological age from resting-state EEG power in the first three years of life

The infant brain undergoes rapid and significant developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging data can provide insights into typical and atypical brain development. We utilized longitudinal resting-state EEG data from 457 typically developing infants, comprising 938 recordings, to develop age prediction models. The multilayer perceptron model demonstrated the highest accuracy with an R2 of 0.82 and a mean absolute error of 92.4 days. Aperiodic offset and periodic theta, alpha, and beta power were identified as key predictors of age via Shapley values. Application of the model to EEG data from infants later diagnosed with autism spectrum disorder or Down syndrome revealed significant underestimations of chronological age. This study establishes the feasibility of using EEG to assess brain maturation in early childhood and supports its potential as a clinical tool for early identification of alterations in brain development.

The influence of accelerated brain aging on coactivation pattern dynamics in Parkinson's disease

Aging is widely acknowledged as the primary risk factor for brain degeneration, with Parkinson's disease (PD) tending to follow accelerated aging trajectories. We aim to investigate the impact of structural brain aging on the temporal dynamics of a large-scale functional network in PD. We enrolled 62 PD patients and 32 healthy controls (HCs). The level of brain aging was determined by calculating global and local brain age gap estimates (G-brainAGE and L-brainAGE) from structural images. The neural network activity of the whole brain was captured by identifying coactivation patterns (CAPs) from resting-state functional images. Intergroup differences were assessed using the general linear model. Subsequently, a spatial correlation analysis between the L-brainAGE difference map and CAPs was conducted to uncover the anatomical underpinnings of functional alterations. Compared to HCs (-3.73 years), G-brainAGE was significantly higher in PD patients (+1.93 years), who also exhibited widespread elevation in L-brainAGE. G-brainAGE was correlated with disease severity and duration. PD patients spent less time in CAPs involving activated default mode and the fronto-parietal network (DMN-FPN), as well as the sensorimotor and salience network (SMN-SN), and had a reduced transition frequency from other CAPs to the DMN-FPN and SMN-SN CAPs. Furthermore, the pattern of localized brain age acceleration showed spatial similarities with the SMN-SN CAP. Accelerated structural brain aging in PD adversely affects brain function, manifesting as dysregulated brain network dynamics. These findings provide insights into the neuropathological mechanisms underlying neurodegenerative diseases and imply the possibility of interventions for modifying PD progression by slowing the brain aging process.

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.

Stumbling Blocks in the Investigation of the Relationship Between Age-Related Hearing Loss and Cognitive Impairment

The relationship between age-related hearing loss (ARHL) and cognitive impairment (CI) remains intricate. However, there is no robust evidence from experimental or clinical studies to elucidate their relationship. The key unaddressed questions are (a) whether there is a causal effect of ARHL on CI and (b) whether efficacious treatment of ARHL (such as hearing-aid use) ameliorates CI and dementia-related behavioral symptoms. Because of several methodological and systematic flaws/challenges, rigorous verification has not been conducted. Addressing these stumbling blocks is essential to unraveling the relationship between ARHL and CI, which motivated us to undertake this review. Here, we discuss the methodological problems from the perspectives of potential confounding bias, assessments of CI and ARHL, hearing-aid use, functional-imaging studies, and animal models based on the latest information and our experiences. We also identify potential solutions for each problem from the viewpoints of clinical epidemiology. We believe that "objectivity," specifically the use of more objective behavioral assessments and new computerized technologies, may be the key to improving experimental designs for studying the relationship between ARHL and CI.

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

The iron burden of cerebral microbleeds contributes to brain atrophy through the mediating effect of white matter hyperintensity

The goal of this work was to explore the total iron burden of cerebral microbleeds (CMBs) using a semi-automatic quantitative susceptibility mapping and to establish its effect on brain atrophy through the mediating effect of white matter hyperintensities (WMH). A total of 95 community-dwelling people were enrolled. Quantitative susceptibility mapping (QSM) combined with a dynamic programming algorithm (DPA) was used to measure the characteristics of 1309 CMBs. WMH were evaluated according to the Fazekas scale, and brain atrophy was assessed using a 2D linear measurement method. Histogram analysis was used to explore the distribution of CMBs susceptibility, volume, and total iron burden, while a correlation analysis was used to explore the relationship between volume and susceptibility. Stepwise regression analysis was used to analyze the risk factors for CMBs and their contribution to brain atrophy. Mediation analysis was used to explore the interrelationship between CMBs and brain atrophy. We found that the frequency distribution of susceptibility of the CMBs was Gaussian in nature with a mean of 201 ppb and a standard deviation of 84 ppb; however, the volume and total iron burden of CMBs were more Rician in nature. A weak but significant correlation between the susceptibility and volume of CMBs was found (r = -0.113, P < 0.001). The periventricular WMH (PVWMH) was a risk factor for the presence of CMBs (number: β = 0.251, P = 0.014; volume: β = 0.237, P = 0.042; total iron burden: β = 0.238, P = 0.020) and was a risk factor for brain atrophy (third ventricle width: β = 0.325, P = 0.001; Evans's index: β = 0.323, P = 0.001). PVWMH had a significant mediating effect on the correlation between CMBs and brain atrophy. In conclusion, QSM along with the DPA can measure the total iron burden of CMBs. PVWMH might be a risk factor for CMBs and may mediate the effect of CMBs on brain atrophy.

Genetic architecture of brain age and its causal relations with brain and mental disorders

The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.

Morphological characteristics differentiate dementia with Lewy bodies from Parkinson disease with and without dementia

Dementia with Lewy bodies (DLB) and Parkinson disease (PD) with and without dementia are entities of a spectrum of Lewy body diseases. About 26.3% of all PD patients develop dementia increasing up to 83%. Parkinson disease-dementia (PDD) and DLB share many clinical and morphological features that separate them from non-demented PD (PDND). Clinically distinguished by the temporal sequence of motor and cognitive symptoms, the pathology of PDD and DLB includes variable combinations of Lewy body (LB) and Alzheimer (AD) lesions, both being more severe in DLB, but much less frequent and less severe in PDND. The objective of this study was to investigate the morphological differences between these three groups. 290 patients with pathologically confirmed PD were reviewed. 190 of them had clinical dementia; 110 met the neuropathological criteria of PDD and 80 of DLB. The major demographic and clinical data were obtained from medical records. Neuropathology included semiquantitative assessment of LB and AD pathologies including cerebral amyloid angiopathy (CAA). PDD patients were significantly older than PDND and DLB ones (83.9 vs 77.9 years, p < 0.05); the age of DLB patients was between them (80.0 years), while the disease duration was shortest in DLB. Brain weight was lowest in DLB, which showed higher Braak LB scores (mean 5.2 vs 4.2) and highest Braak tau stages (mean 5.2 vs 4.4 and 2.3, respectively). Thal Aβ phases were also highest in DLB (mean 4.1 vs 3.0 and 1.8, respectively). Major findings were frequency and degree of CAA, being highest in DLB (95% vs 50% and 24%, with scores 2.9 vs 0.7 and 0.3, respectively), whereas other small vessel lesions showed no significant differences. Striatal Aβ deposits also differentiated DLB from the other groups. This and other studies of larger cohorts of PD patients indicate that the association of CAA and cortical tau-but less-LB pathologies are associated with more severe cognitive decline and worse prognosis that distinguish DLB from PDD and PDND. The particular impact of both CAA and tau pathology supports the concept of a pathogenic continuum ranging from PDND to DLB + AD within the spectrum of age-related synucleinopathies.

Cognitive heterogeneity in Parkinson's disease: A mechanistic view

Cognitive impairment occurs in most individuals with Parkinson's disease (PD), exacting a high toll on patients, their caregivers, and the healthcare system. In this review, we begin by summarizing the current clinical landscape surrounding cognition in PD. We then discuss how cognitive impairment and dementia may develop in PD based on the spread of the pathological protein alpha-synuclein (aSyn) from neurons in brainstem regions to those in the cortical regions of the brain responsible for higher cognitive functions, as first proposed in the Braak hypothesis. We appraise the Braak hypothesis from molecular (conformations of aSyn), cell biological (cell-to-cell spread of pathological aSyn), and organ-level (region-to-region spread of aSyn pathology at the whole brain level) viewpoints. Finally, we argue that individual host factors may be the most poorly understood aspect of this pathological process, accounting for substantial heterogeneity in the pattern and pace of cognitive decline in PD.

Visuospatial Deficits Are Associated with Pisa Syndrome and not Camptocormia in Parkinson's Disease

Background

Pisa syndrome (PS) and camptocormia (CC) are postural abnormalities frequently associated with Parkinson's disease (PD). Their pathophysiology remains unclear, but the role of cognitive deficits has been postulated.

Objectives

To identify differences in the neuropsychological functioning of patients with PD with PS or CC compared with matched patients with PD without postural abnormalities.

Methods

We performed a case-control study including 57 patients with PD with PS (PS+) or CC (CC+) and 57 PD controls without postural abnormalities matched for sex, age, PD duration, phenotype, and stage. Patients were divided into four groups: PS+ (n = 32), PS+ controls (PS-, n = 32), CC+ (n = 25), and CC+ controls (CC-, n = 25). We compared PS+ versus PS- and CC+ versus CC- using a neuropsychological battery assessing memory, attention, executive functions, visuospatial abilities, and language. Subjective visual vertical (SVV) perception was assessed by the Bucket test as a sign of vestibular function; the misperception of trunk position, defined as a mismatch between the objective versus subjective evaluation of the trunk bending angle >5°, was evaluated in PS+ and CC+.

Results

PS+ showed significantly worse visuospatial performances (P = 0.025) and SVV perception (P = 0.038) than their controls, whereas CC+ did not show significant differences compared with their control group. Reduced awareness of postural abnormality was observed in >60% of patients with PS or CC.

Conclusions

Low visuospatial performances and vestibular tone imbalance are significantly associated with PS but not with CC. These findings suggest different pathophysiology for the two main postural abnormalities associated with PD and can foster adequate therapeutic and prevention strategies.

Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry

It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.

Morphological basis of Parkinson disease-associated cognitive impairment: an update

Cognitive impairment is one of the most salient non-motor symptoms of Parkinson disease (PD) that poses a significant burden on the patients and carers as well as being a risk factor for early mortality. People with PD show a wide spectrum of cognitive dysfunctions ranging from subjective cognitive decline and mild cognitive impairment (MCI) to frank dementia. The mean frequency of PD with MCI (PD-MCI) is 25.8% and the pooled dementia frequency is 26.3% increasing up to 83% 20 years after diagnosis. A better understanding of the underlying pathological processes will aid in directing disease-specific treatment. Modern neuroimaging studies revealed considerable changes in gray and white matter in PD patients with cognitive impairment, cortical atrophy, hypometabolism, dopamine/cholinergic or other neurotransmitter dysfunction and increased amyloid burden, but multiple mechanism are likely involved. Combined analysis of imaging and fluid markers is the most promising method for identifying PD-MCI and Parkinson disease dementia (PDD). Morphological substrates are a combination of Lewy- and Alzheimer-associated and other concomitant pathologies with aggregation of α-synuclein, amyloid, tau and other pathological proteins in cortical and subcortical regions causing destruction of essential neuronal networks. Significant pathological heterogeneity within PD-MCI reflects deficits in various cognitive domains. This review highlights the essential neuroimaging data and neuropathological changes in PD with cognitive impairment, the amount and topographical distribution of pathological protein aggregates and their pathophysiological relevance. Large-scale clinicopathological correlative studies are warranted to further elucidate the exact neuropathological correlates of cognitive impairment in PD and related synucleinopathies as a basis for early diagnosis and future disease-modifying therapies.