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

Imaging biomarkers of cortical neurodegeneration underlying cognitive impairment in Parkinson's disease

PURPOSE

Imaging biomarkers bear great promise for improving the diagnosis and prognosis of cognitive impairment in Parkinson's disease (PD). We compared the ability of three commonly used neuroimaging modalities to detect cortical changes in PD patients with mild cognitive impairment (PD-MCI) and dementia (PDD).

METHODS

53 cognitively normal PD patients (PD-CN), 32 PD-MCI, and 35 PDD underwent concurrent structural MRI (sMRI), diffusion-weighted MRI (dMRI), and [18F]FDG PET. We extracted grey matter volumes (sMRI), mean diffusivity (MD, dMRI), and standardized uptake value ratios ([18F]FDG PET) for 52 cortical regions included in a neuroanatomical atlas. We assessed group differences using ANCOVA models and further applied a cross-validated machine learning approach to identify the modality-specific brain regions that are most indicative of dementia status and assessed their diagnostic accuracy for group separation using receiver operating characteristic analyses.

RESULTS

In sMRI, atrophy of temporal and posterior-parietal areas allowed separating PDD from PD-CN (AUC = 0.77 ± 0.07), but diagnostic accuracy was poor for separating PD-MCI from PD-CN (0.57 ± 0.10). dMRI showed most pronounced diffusivity changes in the medial temporal lobe, which provided excellent diagnostic performance for PDD (AUC = 0.87 ± 0.06), and a more modest but still significant performance for PD-MCI (AUC = 0.71 ± 0.09). Finally, [18F]FDG PET revealed pronounced hypometabolism in posterior-occipital regions, which provided the highest diagnostic accuracies for both PDD (AUC = 0.89 ± 0.05) and PD-MCI (AUC = 0.78 ± 0.05). In statistical comparisons, both [18F]FDG PET (p < 0.001) and dMRI (p < 0.031) outperformed sMRI for detecting PDD and PD-MCI.

CONCLUSION

Among the tested modalities, [18F]FDG PET was most accurate for detecting cortical changes associated with cognitive impairment in PD, especially at early stages. Diffusion measurements may represent a promising MRI-based alternative.

Neuropsychiatric symptoms and neuroimaging-based brain age in mild cognitive impairment and early dementia: A multicenter study

AIM

Despite the clinical importance and significant social burden of neuropsychiatric symptoms (NPS) in dementia, the underlying neurobiological mechanism remains poorly understood. Recently, neuroimaging-derived brain-age estimation by machine-learning analysis has shown promise as an individual-level biomarker. We investigated the relationship between NPS and brain-age in amnestic mild cognitive impairment (MCI) and early dementia.

METHODS

In this cross-sectional study, clinical data, including neuropsychiatric inventory (NPI), and structural brain MRI of 499 individuals with clinical diagnoses of amnestic MCI (n = 185), early Alzheimer's disease (AD) (n = 258) or dementia with Lewy bodies (DLB) (n = 56) were analyzed. We established a brain-age prediction model using 694 healthy brain MRIs and a support vector regression model and applied it to the participants' data. Finally, the brain-predicted age difference (brain-PAD: predicted age minus chronological age) was calculated.

RESULTS

All groups showed significantly increased brain-PAD, and the median (IQR) brain-PAD was 4.3 (5.4) years in MCI, 6.3 (6.2) years in AD, and 5.0 (6.5) years in DLB. The NPI scores were subdivided into the following four categories: (i) Agitation and Irritability, (ii) Depression and Apathy, (iii) Delusions and Hallucinations, and (iv) Euphoria and Disinhibition. We found a significantly positive correlation between brain-PAD and the depression/apathy factor (Spearman's rs = 0.156, FDR-corrected P = 0.002), whereas no significance was shown for the other NPS factors.

CONCLUSION

Higher brain-age may be associated with depression and apathy symptoms presented in MCI to early dementia stages, and brain-age analysis may be useful as a novel biomarker for the assessment or monitoring of NPS.

Age-disproportionate atrophy in Alzheimer's disease and Parkinson's disease spectra

INTRODUCTION

Brain age gap (BAG), defined as the difference between MRI-predicted 'brain age' and chronological age, can capture information underlying various neurological disorders. We investigated the pathophysiological significance of the BAG across neurodegenerative disorders.

METHODS

We developed a brain age estimator using structural MRIs of healthy-aged individuals from one cohort study. Subsequently, we applied this estimator to people with Alzheimer's disease spectra (AD) and Parkinson's disease (PD) from another cohort study. We investigated brain sources responsible for BAGs among these groups.

RESULTS

Both AD and PD exhibited a positive BAG. Brain sources showed overlapping, yet partially segregated, neuromorphological differences between these groups. Furthermore, employing with t-distributed stochastic neighbor embedding on the brain sources, we subclassified PD into two groups with and without cognitive impairment.

DISCUSSION

Our findings suggest that brain age estimation becomes a clinically relevant method for finely stratifying neurodegenerative disorders.

Highlights

Brain age estimated from structure MRI data was greater than chronological age in patients with Alzheimer's disease/mild cognitive impairment or Parkinson's disease.Brain regions attributed to brain age estimation were located mainly in the fronto-temporo-parietal cortices but not in the motor cortex or subcortical regions.Brain sources responsible for the brain age gaps revealed roughly overlapping, yet partially segregated, neuromorphological differences between participants with Alzheimer's disease/mild cognitive impairment and Parkinson's disease.Participants with Parkinson's disease were subclassified into two groups (with and without cognitive impairment) based on brain sources responsible for the brain age gaps.

EEG-based brain age prediction in infants-toddlers: Implications for early detection of neurodevelopmental disorders

The infant brain undergoes rapid developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging can provide insights into typical and atypical brain development. We utilized 938 resting-state EEG recordings from 457 typically developing infants, 2 to 38 months old, to develop age prediction models. The multilayer perceptron model demonstrated the highest accuracy with an R2 of 0.83 and a mean absolute error of 91.7 days. Feature importance analysis that combined hierarchical clustering and Shapley values identified two feature clusters describing periodic alpha and low beta activity as key predictors of age. Application of the model to EEG data from infants later diagnosed with autism or Down syndrome revealed significant underestimations of chronological age, supporting its potential as a clinical tool for early identification of alterations in brain development.

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.

Analysis of Brain Age Gap across Subject Cohorts and Prediction Model Architectures

Background: Brain age prediction from brain MRI scans and the resulting brain age gap (BAG)-the difference between predicted brain age and chronological age-is a general biomarker for a variety of neurological, psychiatric, and other diseases or disorders. Methods: This study examined the differences in BAG values derived from T1-weighted scans using five state-of-the-art deep learning model architectures previously used in the brain age literature: 2D/3D VGG, RelationNet, ResNet, and SFCN. The models were evaluated on healthy controls and cohorts with sleep apnea, diabetes, multiple sclerosis, Parkinson's disease, mild cognitive impairment, and Alzheimer's disease, employing rigorous statistical analysis, including repeated model training and linear mixed-effects models. Results: All five models consistently identified a statistically significant positive BAG for diabetes (ranging from 0.79 years with RelationNet to 2.13 years with SFCN), multiple sclerosis (2.67 years with 3D VGG to 4.24 years with 2D VGG), mild cognitive impairment (2.13 years with 2D VGG to 2.59 years with 3D VGG), and Alzheimer's dementia (5.54 years with ResNet to 6.48 years with SFCN). For Parkinson's disease, a statistically significant BAG increase was observed in all models except ResNet (1.30 years with 2D VGG to 2.59 years with 3D VGG). For sleep apnea, a statistically significant BAG increase was only detected with the SFCN model (1.59 years). Additionally, we observed a trend of decreasing BAG with increasing chronological age, which was more pronounced in diseased cohorts, particularly those with the largest BAG, such as multiple sclerosis (-0.34 to -0.2), mild cognitive impairment (-0.37 to -0.26), and Alzheimer's dementia (-0.66 to -0.47), compared to healthy controls (-0.18 to -0.1). Conclusions: Consistent with previous research, Alzheimer's dementia and multiple sclerosis exhibited the largest BAG across all models, with SFCN predicting the highest BAG overall. The negative BAG trend suggests a complex interplay of survival bias, disease progression, adaptation, and therapy that influences brain age prediction across the age spectrum.

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.