Longitudinal Associations of Magnetic Susceptibility with Clinical Severity in Parkinson's Disease

BACKGROUND

Dementia is common in Parkinson's disease (PD), but there is wide variation in its timing. A critical gap in PD research is the lack of quantifiable markers of progression, and methods to identify early stages of dementia. Atrophy-based magnetic resonance imaging (MRI) has limited sensitivity in detecting or tracking changes relating to PD dementia, but quantitative susceptibility mapping (QSM), sensitive to brain tissue iron, shows potential for these purposes.

OBJECTIVE

The objective of the paper is to study, for the first time, the longitudinal relationship between cognition and QSM in PD in detail.

METHODS

We present a longitudinal study of clinical severity in PD using QSM, including 59 PD patients (without dementia at study onset), and 22 controls over 3 years.

RESULTS

In PD, increased baseline susceptibility in the right temporal cortex, nucleus basalis of Meynert, and putamen was associated with greater cognitive severity after 3 years; and increased baseline susceptibility in basal ganglia, substantia nigra, red nucleus, insular cortex, and dentate nucleus was associated with greater motor severity after 3 years. Increased follow-up susceptibility in these regions was associated with increased follow-up cognitive and motor severity, with further involvement of hippocampus relating to cognitive severity. However, there were no consistent increases in susceptibility over 3 years.

CONCLUSIONS

Our study suggests that QSM may predict changes in cognitive severity many months prior to overt cognitive involvement in PD. However, we did not find robust longitudinal changes in QSM over the course of the study. Additional tissue metrics may be required together with QSM for it to monitor progression in clinical practice and therapeutic trials. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Changes in both top-down and bottom-up effective connectivity drive visual hallucinations in Parkinson's disease

Visual hallucinations are common in Parkinson's disease and are associated with a poorer quality of life and a higher risk of dementia. An important and influential model that is widely accepted as an explanation for the mechanism of visual hallucinations in Parkinson's disease and other Lewy body diseases is that these arise due to aberrant hierarchical processing, with impaired bottom-up integration of sensory information and overweighting of top-down perceptual priors within the visual system. This hypothesis has been driven by behavioural data and supported indirectly by observations derived from regional activation and correlational measures using neuroimaging. However, until now, there was no evidence from neuroimaging for differences in causal influences between brain regions measured in patients with Parkinson's hallucinations. This is in part because previous resting-state studies focused on functional connectivity, which is inherently undirected in nature and cannot test hypotheses about the directionality of connectivity. Spectral dynamic causal modelling is a Bayesian framework that allows the inference of effective connectivity-defined as the directed (causal) influence that one region exerts on another region-from resting-state functional MRI data. In the current study, we utilize spectral dynamic causal modelling to estimate effective connectivity within the resting-state visual network in our cohort of 15 Parkinson's disease visual hallucinators and 75 Parkinson's disease non-visual hallucinators. We find that visual hallucinators display decreased bottom-up effective connectivity from the lateral geniculate nucleus to primary visual cortex and increased top-down effective connectivity from the left prefrontal cortex to primary visual cortex and the medial thalamus, as compared with non-visual hallucinators. Importantly, we find that the pattern of effective connectivity is predictive of the presence of visual hallucinations and associated with their severity within the hallucinating group. This is the first study to provide evidence, using resting-state effective connectivity, to support a model of aberrant hierarchical predictive processing as the mechanism for visual hallucinations in Parkinson's disease.

Thalamic white matter macrostructure and subnuclei volumes in Parkinson's disease depression

Depression is a common non-motor feature of Parkinson's disease (PD) which confers significant morbidity and is challenging to treat. The thalamus is a key component in the basal ganglia-thalamocortical network critical to the pathogenesis of PD and depression but the precise thalamic subnuclei involved in PD depression have not been identified. We performed structural and diffusion-weighted imaging (DWI) on 76 participants with PD to evaluate the relationship between PD depression and grey and white matter thalamic subnuclear changes. We used a thalamic segmentation method to divide the thalamus into its 50 constituent subnuclei (25 each hemisphere). Fixel-based analysis was used to calculate mean fibre cross-section (FC) for white matter tracts connected to each subnucleus. We assessed volume and FC at baseline and 14-20 months follow-up. A generalised linear mixed model was used to evaluate the relationship between depression, subnuclei volume and mean FC for each thalamic subnucleus. We found that depression scores in PD were associated with lower right pulvinar anterior (PuA) subnucleus volume. Antidepressant use was associated with higher right PuA volume suggesting a possible protective effect of treatment. After follow-up, depression scores were associated with reduced white matter tract macrostructure across almost all tracts connected to thalamic subnuclei. In conclusion, our work implicates the right PuA as a relevant neural structure in PD depression and future work should evaluate its potential as a therapeutic target for PD depression.

Regional brain iron and gene expression provide insights into neurodegeneration in Parkinson's disease

The mechanisms responsible for the selective vulnerability of specific neuronal populations in Parkinson's disease are poorly understood. Oxidative stress secondary to brain iron accumulation is one postulated mechanism. We measured iron deposition in 180 cortical regions of 96 patients with Parkinson's disease and 35 control subjects using quantitative susceptibility mapping. We estimated the expression of 15 745 genes in the same regions using transcriptomic data from the Allen Human Brain Atlas. Using partial least squares regression, we then identified the profile of gene transcription in the healthy brain that underlies increased cortical iron in patients with Parkinson's disease relative to controls. Applying gene ontological tools, we investigated the biological processes and cell types associated with this transcriptomic profile and identified the sets of genes with spatial expression profiles in control brains that correlated significantly with the spatial pattern of cortical iron deposition in Parkinson's disease. Gene ontological analyses revealed that these genes were enriched for biological processes relating to heavy metal detoxification, synaptic function and nervous system development and were predominantly expressed in astrocytes and glutamatergic neurons. Furthermore, we demonstrated that the genes differentially expressed in Parkinson's disease are associated with the pattern of cortical expression identified in this study. Our findings provide mechanistic insights into regional selective vulnerabilities in Parkinson's disease, particularly the processes involving iron accumulation.

Sequence of clinical and neurodegeneration events in Parkinson's disease progression

Dementia is one of the most debilitating aspects of Parkinson's disease. There are no validated biomarkers that can track Parkinson's disease progression, nor accurately identify patients who will develop dementia and when. Understanding the sequence of observable changes in Parkinson's disease in people at elevated risk for developing dementia could provide an integrated biomarker for identifying and managing individuals who will develop Parkinson's dementia. We aimed to estimate the sequence of clinical and neurodegeneration events, and variability in this sequence, using data-driven statistical modelling in two separate Parkinson's cohorts, focusing on patients at elevated risk for dementia due to their age at symptom onset. We updated a novel version of an event-based model that has only recently been extended to cope naturally with clinical data, enabling its application in Parkinson's disease for the first time. The observational cohorts included healthy control subjects and patients with Parkinson's disease, of whom those diagnosed at age 65 or older were classified as having high risk of dementia. The model estimates that Parkinson's progression in patients at elevated risk for dementia starts with classic prodromal features of Parkinson's disease (olfaction, sleep), followed by early deficits in visual cognition and increased brain iron content, followed later by a less certain ordering of neurodegeneration in the substantia nigra and cortex, neuropsychological cognitive deficits, retinal thinning in dopamine layers, and further deficits in visual cognition. Importantly, we also characterize variation in the sequence. We found consistent, cross-validated results within cohorts, and agreement between cohorts on the subset of features available in both cohorts. Our sequencing results add powerful support to the increasing body of evidence suggesting that visual processing specifically is affected early in patients with Parkinson's disease at elevated risk of dementia. This opens a route to earlier and more precise detection, as well as a more detailed understanding of the pathological mechanisms underpinning Parkinson's dementia.