Identifying and validating subtypes of Parkinson's disease based on multimodal MRI data via hierarchical clustering analysis

Gray matter structural and functional brain abnormalities in Parkinson's disease: a meta-analysis of VBM and ALFF data

BACKGROUND

Previous studies based on resting-state functional imaging and voxel-based morphometry (VBM) have revealed structural and functional alterations in several brain regions in patients with Parkinson's disease (PD), but their results have been inconsistent. Furthermore, no studies have investigated specific and common functional and structural alterations in PD.

METHODS

The whole-brain voxel-wise meta-analyses on the VBM and amplitude of low-frequency fluctuation (ALFF) studies were conducted using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software, respectively, with multimodal overlapping to comprehensively identify the gray matter volume (GMV) and spontaneous functional activity changes in patients with PD.

RESULTS

A total of 30 independent studies for ALFF (1413 PD and 1424 HCs) and 27 independent studies for VBM (1236 PD and 1185 HCs) were included. Compared with HCs, patients with PD displayed significantly decreased spontaneous functional activity in the left striatum. For the VBM meta-analysis, patients with PD showed significantly decreased GMV in the right temporal pole: superior temporal gyrus (extending to the right hippocampus, parahippocampal gyrus, and amygdala), the left superior temporal gyrus (extending to the left insula, and temporal pole: superior temporal gyrus), and the left striatum. Furthermore, after overlapping functional and structural differences, patients with PD displayed a conjoint decrease of spontaneous functional activity and GMV in the left striatum.

CONCLUSION

The multimodal meta-analysis revealed that PD showed similar pattern of aberrant brain functional activity and structure in the striatum. In addition, some brain regions within the within the temporal lobe and limbic system displayed only structural deficits. These findings provide useful insights for understanding the underlying pathophysiology of PD.

Altered functional activity and connectivity in Parkinson's disease with chronic pain: a resting-state fMRI study

Background

Chronic pain is a common non-motor symptom of Parkinson's disease (PD) that significantly impacts patients' quality of life, but its neural mechanisms remain poorly understood. This study investigated changes in spontaneous neuronal activity and functional connectivity (FC) associated with chronic pain in PD patients.

Methods

The study included 41 PD patients with chronic pain (PDP), 41 PD patients without pain (nPDP), and 29 healthy controls. Pain severity was assessed using the visual analog scale (VAS). Resting-state fMRI images were used to measure the amplitude of low-frequency fluctuations (ALFF) as an indicator of regional brain activity. Subsequently, FC analysis was performed to evaluate synchronization between ALFF-identified regions and the entire brain.

Results

Compared to nPDP patients, PDP patients exhibited decreased ALFF in the right putamen, and increased ALFF in motor regions, including the right superior frontal gyrus/supplementary motor area and the left paracentral lobule/primary motor cortex. Additionally, PDP patients exhibited diminished right putamen-based FC in the midbrain, anterior cingulate cortex, orbitofrontal cortex, middle frontal gyrus, middle temporal gyrus, and posterior cerebellar lobe. The correlation analysis revealed that ALFF values in the right putamen were negatively associated with VAS scores in PDP patients.

Conclusion

This study demonstrates that chronic pain in PD is associated with reduced ALFF in the putamen and disrupted FC with brain regions involved in pain perception and modulation, highlighting the critical role of dopaminergic degeneration in the development and maintenance of pain in PD.

Amygdala-centered fusional connections characterized nonmotor symptoms in Parkinson's disease

The importance of nonmotor symptoms in understanding the pathogenesis of the heterogeneity of Parkinson's disease has been highlighted. However, the validation of specific brain network biomarkers in nonmotor symptom subtypes is currently lacking. By performing a new approach to compute functional connectivity with structural prior using magnetic resonance imaging, the present study computed both functional connectivity and fusional connectivity features in the nonmotor symptom subtypes of Parkinson's disease, one characterized by cognitive impairment with late onset and the other depression with early onset. The functional connectivity and fusional connectivity features centered at the left amygdala were both detected. The fusional features significantly enhanced the classification performance. The amygdala-postcentral and amygdala-orbital frontal features were critical for cognitive impairment with late onset detection, while the amygdala-temporooccipital features were crucial for depression with early onset detection. Additionally, the fusional connectivity features between the amygdala and the junction sulcus of parietooccipital and temporooccipital regions contributed significantly to differentiating cognitive impairment with late onset and depression with early onset. The within-subtype correlation analysis revealed that age at onset and cognitive scores were associated with features of amygdala-somatosensory/visual-motor processing areas in cognitive impairment with late onset, while related to features of amygdala-emotional processing areas in depression with early onset. Our findings highlighted distinct amygdala-centered fusional connectivity features related to diverse nonmotor symptoms in Parkinson's disease, offering new insights for pathogenesis-targeted treatments for specific Parkinson's disease subtypes.

Analysis of Two Neuroanatomical Subtypes of Parkinson's Disease and Their Motor Progression Based on Semi-Supervised Machine Learning

BACKGROUND

The high heterogeneity of Parkinson's disease (PD) hinders personalized interventions. Brain structure reflects damage and neuroplasticity and is one of the biological bases of symptomatology. Subtyping PD in the framework of brain structure helps in the prediction of disease trajectories and optimizes treatment strategies.

METHODS

The study included a total of 283 de novo PD and 141 healthy controls (HC). Structural heterogeneity between PD and HC was compared, and patients were classified using Heterogeneity through Discriminative Analysis. Gray matter volume (GMV), clinical symptoms, and substantia nigra free water (SNFW) among all subtypes were compared. These subtypes were followed for an average of 2.5 years to monitor motor impairment.

RESULTS

Early PD patients possessed higher GMV heterogeneity than HC, and two subtypes based on GMV patterns were identified. Subtype 1 showed widespread GMV reductions, while subtype 2 had an increased volume in the basal ganglia and parts of the cortex. Subtype 1 had more severe motor and non-motor symptoms, as well as higher posterior SNFW. The whole-brain GMV in the PD group was negatively correlated with posterior SNFW; basal ganglia volume in subtype 1 was negatively correlated with Unified Parkinson's Disease Rating Scale (UPDRS)-III scores, whereas no linear correlation was found in subtype 2. The UPDRS-III progression rate was higher in subtype 1 than in subtype 2 (2.52 vs 0.92 points/year).

CONCLUSION

The heterogeneity of PD patients reflected the changes in their brain structure. The identification of these changes helps the classification of patients into different subtypes, additionally supported by clinical manifestations and SNFW, with consequent benefits for clinical consultancy and precision medicine.

Grey matter volume differences across Parkinson's disease motor subtypes in the supplementary motor cortex

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disease worldwide due to loss of dopaminergic neurons projecting from the basal ganglia (BG). It is associated with various motor symptoms that are grouped into subtypes, each with different clinical presentations and disease progressions. Neuroimaging biomarkers focusing on regions a part of motor circuits projecting from the BG can distinguish and improve overall subtyping. The supplementary motor cortex (SMC) is well established in PD neuropathology and associated with freezing of gait and bradykinesia, but has not been thoroughly evaluated across subtypes. This study aims to identify volumetric differences of the SMC based on PD subtypes of tremor dominant (TD), postural instability with gait difficulty (PIGD), and akinetic rigid (AR) using data from Parkinson's Progression Markers Initiative. To segment grey matter volume and extract region of interest values, voxel-based processing was used. Multi-factor ANCOVAs, Tukey Honest Significance Test, and Kruskal-Wallis were utilized for volumetric analyses (α < 0.05). Subjects were classified and evaluated using TD, PIGD, and AR subtypes from the MDS-UPDRS rating scales. Inter-subtype differences in SMC GMV between TD and PIGD were significant in the right hemisphere for females (p = 0.01). No significant inter-subtype differences were found in the TD/AR system. These results support the use of broader motor networks, specifically the SMC in further understanding the neuropathological heterogeneity of PD. Furthermore, it reveals SMC differences across sexes, subtypes, and subtyping systems, calling for further evaluation of subtyping schemas, specifically regarding sex differences.

Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease

One of the key challenges in Big Data for clinical research and healthcare is how to integrate new sources of data, whose relation to disease processes are often not well understood, with multiple classical clinical measurements that have been used by clinicians for years to describe disease processes and interpret therapeutic outcomes. Without such integration, even the most promising data from emerging technologies may have limited, if any, clinical utility. This paper presents an approach to address this challenge, illustrated through an example in Parkinson's Disease (PD) management. We show how data from various sensing sources can be integrated with traditional clinical measurements used in PD; furthermore, we show how leveraging Big Data frameworks, augmented by Artificial Intelligence (AI) algorithms, can distinctively enrich the data resources available to clinicians. We showcase the potential of this approach in a cohort of 50 PD patients who underwent both evaluations with an Integrated Motion Analysis Suite (IMAS) composed of a battery of multimodal, portable, and wearable sensors and traditional Unified Parkinson's Disease Rating Scale (UPDRS)-III evaluations. Through techniques including Principal Component Analysis (PCA), elastic net regression, and clustering analysis we demonstrate how this combined approach can be used to improve clinical motor assessments and to develop personalized treatments. The scalability of our approach enables systematic data generation and analysis on increasingly larger datasets, confirming the integration potential of IMAS, whose use in PD assessments is validated herein, within Big Data paradigms. Compared to existing approaches, our solution offers a more comprehensive, multi-dimensional view of patient data, enabling deeper clinical insights and greater potential for personalized treatment strategies. Additionally, we show how IMAS can be integrated into established clinical practices, facilitating its adoption in routine care and complementing emerging methods, for instance, non-invasive brain stimulation. Future work will aim to augment our data repositories with additional clinical data, such as imaging and biospecimen data, to further broaden and enhance these foundational methodologies, leveraging the full potential of Big Data and AI.

MRI subtypes in Parkinson's disease across diverse populations and clustering approaches

Parkinson's disease (PD) is clinically heterogeneous, which suggests the existence of subtypes; however, there has been no consensus regarding their characteristics. This study included 633 PD individuals across distinct cohorts: unmedicated de novo PD, medicated PD, mild-moderate PD, and a cohort based on diagnostic work-up in clinical practice. Additionally, 233 controls were included. Clustering based on cortical and subcortical gray matter measures was conducted with and without adjusting for global atrophy in the entire PD sample and validated within each cohort. Subtypes were characterized using baseline and longitudinal demographic and clinical data. Unadjusted results identified three clusters showing a gradient of neurodegeneration and symptom severity across the entire sample and the individual cohorts. When adjusting for global atrophy eight clusters were identified in the entire sample, lacking consistency in individual cohorts. This study identified atrophy-based subtypes in PD, emphasizing the significant impact of global atrophy on subtype number, patterns, and interpretation in cross-sectional analyses.

Brain connectivity for subtypes of parkinson's disease using structural MRI

Objective. Delineating Parkinson's disease (PD) into distinct subtypes is a major challenge. Most studies use clinical symptoms to label PD subtypes while our work uses an imaging-based data-mining approach to subtype PD. Our study comprises two major objectives - firstly, subtyping Parkinson's patients based on grey matter information from structural magnetic resonance imaging scans of human brains; secondly, comparative structural brain connectivity analysis of PD subtypes derived from the former step.Approach. Source-based-morphometry decomposition was performed on 131 Parkinson's patients and 78 healthy controls from PPMI dataset, to derive at components (regions) with significance in disease and high effect size. The loading coefficients of significant components were thresholded for arriving at subtypes. Further, regional grey matter maps of subtype-specific subjects were separately parcellated and employed for construction of subtype-specific association matrices using Pearson correlation. These association matrices were binarized using sparsity threshold and leveraged for structural brain connectivity analysis using network metrics.Main results. Two distinct Parkinson's subtypes (namely A and B) were detected employing loadings of two components satisfying the selection criteria, and a third subtype (AB) was detected, common to these two components. Subtype A subjects were highly weighted in inferior, middle and superior frontal gyri while subtype B subjects in inferior, middle and superior temporal gyri. Network metrics analyses through permutation test revealed significant inter-subtype differences (p < 0.05) in clustering coefficient, local efficiency, participation coefficient and betweenness centrality. Moreover, hubs were obtained using betweenness centrality and mean network degree.Significance. MRI-based data-driven subtypes show frontal and temporal lobes playing a key role in PD. Graph theory-driven brain network analyses could untangle subtype-specific differences in structural brain connections showing differential network architecture. Replication of these initial results in other Parkinson's datasets may be explored in future. Clinical Relevance- Investigating structural brain connections in Parkinson's disease may provide subtype-specific treatment.

Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review

Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.

Olfactory functional covariance connectivity in Parkinson's disease: Evidence from a Chinese population

Introduction

Central anosmia is a potential marker of the prodrome and progression of Parkinson's disease (PD). Resting-state functional magnetic resonance imaging studies have shown that olfactory dysfunction is related to abnormal changes in central olfactory-related structures in patients with early PD.

Methods

This study, which was conducted at Guanyun People's Hospital, analyzed the resting-state functional magnetic resonance data using the functional covariance connection strength method to decode the functional connectivity between the white-gray matter in a Chinese population comprising 14 patients with PD and 13 controls.

Results

The following correlations were observed in patients with PD: specific gray matter areas related to smell (i.e., the brainstem, right cerebellum, right temporal fusiform cortex, bilateral superior temporal gyrus, right Insula, left frontal pole and right superior parietal lobule) had abnormal connections with white matter fiber bundles (i.e., the left posterior thalamic radiation, bilateral posterior corona radiata, bilateral superior corona radiata and right superior longitudinal fasciculus); the connection between the brainstem [region of interest (ROI) 1] and right cerebellum (ROI2) showed a strong correlation. Right posterior corona radiation (ROI11) showed a strong correlation with part 2 of the Unified Parkinson's Disease Rating Scale, and right superior longitudinal fasciculus (ROI14) showed a strong correlation with parts 1, 2, and 3 of the Unified Parkinson's Disease Rating Scale and Hoehn and Yahr Scale.

Discussion

The characteristics of olfactory-related brain networks can be potentially used as neuroimaging biomarkers for characterizing PD states. In the future, dynamic testing of olfactory function may help improve the accuracy and specificity of olfactory dysfunction in the diagnosis of neurodegenerative diseases.