Patterns of cortical thinning in nondemented Parkinson's disease patients

Convergent large-scale network and local vulnerabilities underlie brain atrophy across Parkinson's disease stages: a worldwide ENIGMA study

Parkinson's disease (PD) is associated with extensive structural brain changes. Recent work has proposed that the spatial pattern of disease pathology is shaped by both network spread and local vulnerability. However, only few studies assessed these biological frameworks in large patient samples across disease stages. Analyzing the largest imaging cohort in PD to date (N = 3,096 patients), we investigated the roles of network architecture and local brain features by relating regional abnormality maps to normative profiles of connectivity, intrinsic networks, cytoarchitectonics, neurotransmitter receptor densities, and gene expression. We found widespread cortical and subcortical atrophy in PD to be associated with advancing disease stage, longer time since diagnosis, and poorer global cognition. Structural brain connectivity best explained cortical atrophy patterns in PD and across disease stages. These patterns were robust among individual patients. The precuneus, lateral temporal cortex, and amygdala were identified as likely network-based epicentres, with high convergence across disease stages. Individual epicentres varied significantly among patients, yet they consistently localized to the default mode and limbic networks. Furthermore, we showed that regional overexpression of genes implicated in synaptic structure and signalling conferred increased susceptibility to brain atrophy in PD. In summary, this study demonstrates in a well-powered sample that structural brain abnormalities in PD across disease stages and within individual patients are influenced by both network spread and local vulnerability.

The impact of impulse control disorders on cognitive decline in de novo Parkinson's disease: a study based on structural MRI

Background

Impulse control disorders (ICDs) are common neuropsychiatric symptoms (NPS), which are prevalent among patients with Parkinson's disease (PD). Current research has not clarified the impact of ICDs on cognitive function nor provided sufficient objective evidence. This study aims to explore the effects of ICDs on cognitive functions in PD patients, and further investigate associated cerebral structural changes.

Methods

Two hundred PD patients with normal cognition (PDNC) and 69 healthy controls were included from the Parkinson's Progression Markers Initiative (PPMI), among these PDNC, 81 patients with "pure" ICDs (p-ICDs), 69 ICDs combined with other NPS (c-ICDs), and 50 patients without NPS. The cognitive status of each PD patient was obtained every year in four-year follow-up. The difference in conversion rates was obtained by chi-square test. Survival analysis was used to explore the conversion time difference among these groups. Further analysis was conducted on the potential structural difference. Finally, the correlation between significant brain structural changes and neuropsychological assessments were evaluated.

Results

The survival analysis suggested that the conversion time of p-ICDs from normal cognition to MCI was significantly delayed compared to NPS-negative, with no significant difference relative to the c-ICDs. There is no significant difference in conversion rates among them. Morphological analysis revealed that compared to the NPS-negative group, the p-ICDs and c-ICDs groups exhibited thickness changes in certain regions (Bonferroni-corrected, p < 0.05).

Conclusion

Our findings suggest that ICDs might exert a protective effect against cognitive decline, potentially delay the occurrence of MCI in PDNC, which could be associated with alterations in cortical thickness.

Identifying brain degeneration patterns in early-stage Parkinson's disease: a multimodal MRI study

Parkinson's disease (PD) is a highly heterogeneous neurodegenerative disorder. This study aimed to identify different patterns of early brain degeneration in PD patients and investigate their clinical relevance. 179 early-stage PD patients and 115 healthy controls were included. We assessed cortical morphology, white matter microstructure, and subcortical iron metabolism using multimodal magnetic resonance imaging and employed clustering techniques to identify subtypes. Two subtypes were identified: the early-deterioration subtype, characterized by fronto-temporal atrophy, parietal thickening, widespread reductions in fractional anisotropy (FA) values, and increased subcortical iron content, which exhibited more severe baseline symptoms and a trend of faster memory decline; and the early-compensatory subtype, characterized by rostral middle frontal atrophy, parietal-occipital thickening, increased FA values, and normal iron content, which exhibited milder symptoms initially but experienced faster progression of both motor and non-motor symptoms. These discoveries provided new insights into disease heterogeneity and facilitated the exploration of early neurodegenerative mechanisms.

Neuroimaging-based data-driven subtypes of spatiotemporal atrophy due to Parkinson's disease

Parkinson's disease is the second most common neurodegenerative disease. Despite this, there are no robust biomarkers to predict progression, and understanding of disease mechanisms is limited. We used the Subtype and Stage Inference algorithm to characterize Parkinson's disease heterogeneity in terms of spatiotemporal subtypes of macroscopic atrophy detectable on T1-weighted MRI-a successful approach used in other neurodegenerative diseases. We trained the model on covariate-adjusted cortical thicknesses and subcortical volumes from the largest known T1-weighted MRI dataset in Parkinson's disease, Enhancing Neuroimaging through Meta-Analysis consortium Parkinson's Disease dataset (n = 1100 cases). We tested the model by analyzing clinical progression over up to 9 years in openly-available data from people with Parkinson's disease from the Parkinson's Progression Markers Initiative (n = 584 cases). Under cross-validation, our analysis supported three spatiotemporal atrophy subtypes, named for the location of the earliest affected regions as: 'Subcortical' (n = 359, 33%), 'Limbic' (n = 237, 22%) and 'Cortical' (n = 187, 17%). A fourth subgroup having sub-threshold/no atrophy was named 'Sub-threshold atrophy' (n = 317, 29%). Statistical differences in clinical scores existed between the no-atrophy subgroup and the atrophy subtypes, but not among the atrophy subtypes. This suggests that the prime T1-weighted MRI delineator of clinical differences in Parkinson's disease is atrophy severity, rather than atrophy location. Future work on unravelling the biological and clinical heterogeneity of Parkinson's disease should leverage more sensitive neuroimaging modalities and multimodal data.

The disease-specific structural pattern in Parkinson's disease and its cortical characteristics associated with gene function: a 7-Tesla MRI study

Brain structure characteristics form the basis on regulating neuroplastic processes by genes, and structural alterations may contribute to the progression of Parkinson's disease (PD) and their divergent clinical manifestations. However, the neural mechanisms underlying the relations between the genetic signatures to structural alterations in PD patients are unclear. This study aimed to integrate alterations in cortical thickness and subcortical nuclei volume (thalamus, hippocampus, and amygdala) in PD, and to explore global cortical thickness differences associated with gene function. 7-Tesla magnetic resonance imaging scans were obtained for 98 patients with PD and 74 healthy controls (HC). Cortical thickness and subcortical nuclei volume were extracted based on FreeSurfer and were analyzed using general linear model to find significant differences between two groups. Regression model was used for cross-sectional the impact of structural alterations on motor signs as well as non-motor symptoms. Gene-imaging association analysis was used to characterize its gene signatures. Compared with HC, PD patients exhibited the disease-specific structural pattern, characterized by reduced cortical thickness in the right pars triangularis and altered volumes of specific nuclei subfields. Moreover, the Cornu Ammonis 1 head volume was significantly correlated with rigidity scores. Using human brain gene expression data, genes identified in this study were enriched for ribosome and synaptic organization and explain significant variation in global cortical thickness differences. Taken together, these findings may contribute to a better understanding of neural mechanisms in PD and the functional roles of genes that influence brain structure.

Neuroanatomical heterogeneity drives divergent cognitive and motor trajectories in Parkinson's disease subtypes

INTRODUCTION

Cognitive symptoms of Parkinson's disease (PD) may initially present subtly, often overshadowed by more noticeable motor symptoms. However, as PD progresses, predicting which individuals will experience significant cognitive decline becomes challenging due to variability, suggesting distinct PD subtypes with varying cognitive trajectories. This study aimed to identify early PD subtypes based on patterns of gray matter atrophy in brain regions associated with cognition and assess their distinct patterns of cognitive change over time. Recognizing PD primarily as a movement disorder, we also evaluated their motor symptoms.

METHODS

We analyzed T1-weighted MRI data, cognitive, and motor scores from 114 de novo PD patients and 120 healthy controls. Multivariate gray matter volumetric distances (MGMV) across frontal, subcortical, parietal, temporal, and occipital regions were computed, and K-means clustering was used to identify PD subtypes. Subsequently, cognitive assessments were compared between subtypes at baseline and 48 months using linear mixed-effects models and reliable change indices. Motor-symptom changes were assessed using linear mixed-effects models.

RESULTS

Two PD subtypes were identified from baseline MRI. Subtype 1 showed significantly higher MGMV in frontal (p < 0.001) and subcortical (p < 0.001) regions, indicating atrophy. At 48 months, subtype 1 had poorer global cognitive performance than subtype 2 (p = 0.005) and faster progression of postural instability and gait disturbance (p = 0.04).

CONCLUSIONS

PD subtypes identified early by distinct frontal and subcortical atrophy patterns exhibited divergent trajectories of cognitive decline and worsening motor symptoms over time, underscoring the neuroanatomical heterogeneity that drives clinical variability in PD.

Cortical microstructural alterations in different stages of Parkinson's disease

To explore the cortical microstructural alterations in Parkinson's disease (PD) at different stages. 149 PD patients and 76 healthy controls were included. PD patients were divided into early stage PD (EPD) (Hoehn-Yahr stage ≤ 2) and moderate-to-late stage PD (MLPD) (Hoehn-Yahr stage ≥ 2.5) according to their Hoehn-Yahr stages. All participants underwent two-shell diffusion MRI and the images were fitted to Neurite Orientation Dispersion and Density Imaging (NODDI) model to obtain the neurite density index (NDI) and orientation dispersion index (ODI) to reflect the cortical microstructure. We used gray matter-based spatial statistics method to compare the voxel-wise cortical NODDI metrics between groups. Partial correlation was used to correlate the NODDI metrics and global composite outcome in PD patients. Compared with healthy controls, EPD patients showed lower ODI in widespread regions, covering bilateral frontal, temporal, parietal and occipital cortices, as well as regional lower NDI in bilateral cingulate and frontal lobes. Compared with healthy controls, MLPD patients showed lower ODI and NDI in more widespread regions. Compared with EPD patients, MLPD patients showed lower ODI in bilateral temporal, parietal and occipital cortices, where the ODI values were negatively correlated with global composite outcome in PD patients. PD patients showed widespread cortical microstructural degeneration, characterized by reduced neurite density and orientation dispersion, and the cortical neuritic microstructure exhibit progressive degeneration during the progression of PD.

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.

The effect of dopamine replacement therapy on cortical structure in Parkinson's disease

AIMS

To explore the cortical structural reorganization in Parkinson's disease (PD) patients under chronic dopamine replacement therapy (DRT) in cross-sectional and longitudinal data and determine whether these changes were associated with clinical alterations.

METHODS

A total of 61 DRT-treated, 60 untreated PD patients, and 61 normal controls (NC) were retrospectively included. Structural MRI scans and neuropsychological tests were conducted. Cortical thickness and volume were extracted based on FreeSurfer and were analyzed using general linear model to find statistically significant differences among three groups. Correlation analyses were performed among significant cortical areas, medication treatment (duration and dosage), and neuropsychological tests. Longitudinal cortical structural changes of patients who initiated DRT were analyzed using linear mixed-effect model.

RESULTS

Significant cortical atrophy was primarily observed in the prefrontal cortex in treated patients, including the cortical thickness of right pars opercularis and the volume of bilateral superior frontal cortex (SFC), left rostral anterior cingulate cortex (rACC), right lateral orbital frontal cortex, right pars orbitalis, and right rostral middle frontal cortex. A negative correlation was detected between the left SFC volume and levodopa equivalent dose (LED) (r = -0.316, p = 0.016), as well as the left rACC volume and medication duration (r = -0.329, p = 0.013). In the patient group, the left SFC volume was positively associated with digit span forward score (r = 0.335, p = 0.017). The left SFC volume reduction was longitudinally correlated with increased LED (standardized coefficient = -0.077, p = 0.001).

CONCLUSION

This finding provided insights into the influence of DRT on cortical structure and highlighted the importance of drug dose titration in DRT.

A multi-site study on sex differences in cortical thickness in non-demented Parkinson's disease

Clinical, cognitive, and atrophy characteristics depending on sex have been previously reported in Parkinson's disease (PD). However, though sex differences in cortical gray matter measures in early drug naïve patients have been described, little is known about differences in cortical thickness (CTh) as the disease advances. Our multi-site sample comprised 211 non-demented PD patients (64.45% males; mean age 65.58 ± 8.44 years old; mean disease duration 6.42 ± 5.11 years) and 86 healthy controls (50% males; mean age 65.49 ± 9.33 years old) with available T1-weighted 3 T MRI data from four international research centers. Sex differences in regional mean CTh estimations were analyzed using generalized linear models. The relation of CTh in regions showing sex differences with age, disease duration, and age of onset was examined through multiple linear regression. PD males showed thinner cortex than PD females in six frontal (bilateral caudal middle frontal, bilateral superior frontal, left precentral and right pars orbitalis), three parietal (bilateral inferior parietal and left supramarginal), and one limbic region (right posterior cingulate). In PD males, lower CTh values in nine out of ten regions were associated with longer disease duration and older age, whereas in PD females, lower CTh was associated with older age but with longer disease duration only in one region. Overall, male patients show a more widespread pattern of reduced CTh compared with female patients. Disease duration seems more relevant to explain reduced CTh in male patients, suggesting worse prognostic over time. Further studies should explore sex-specific cortical atrophy trajectories using large longitudinal multi-site data.

Early-stage volume losses in the corpus callosum and thalamus predict the progression of brain atrophy in patients with multiple sclerosis

BACKGROUND

A method that can be used in the early stage of multiple sclerosis (MS) to predict the progression of brain volume loss (BVL) has not been fully established.

METHODS

To develop a method of predicting progressive BVL in patients with MS (pwMS), eighty-two consecutive Japanese pwMS-with either relapsing-remitting MS (86%) or secondary progressive MS (14%)-and 41 healthy controls were included in this longitudinal retrospective analysis over an observational period of approximately 3.5 years. Using a hierarchical cluster analysis with multivariate imaging data obtained by FreeSurfer analysis, we classified the pwMS into clusters.

RESULTS

At baseline and follow-up, pwMS were cross-sectionally classified into three major clusters (Clusters 1, 2, and 3) in ascending order by disability and BVL. Among the patients included in Cluster 1 at baseline, approximately one-third of patients (12/52) transitioned into Cluster 2 at follow-up. The volumes of the corpus callosum, the thalamus, and the whole brain excluding the ventricles were significantly decreased in the transition group compared with the nontransition group and were found to be the most important predictors of transition.

CONCLUSION

Decreased volumes of the corpus callosum and thalamus in the relatively early stage of MS may predict the development of BVL.

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.

Nuclear Imaging Data-Driven Classification of Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder characterized by motor and nonmotor symptoms. Several features have prognostic importance and have been used as key indicators for identifying clinical subtypes. However, the symptom-based classification approach has limitations with respect to the stability of the obtained subtypes.

OBJECTIVES

The purpose of this study was to identify subtypes of PD using nuclear imaging biomarkers targeting the cardiac sympathetic nervous and nigro-striatal systems and to compare patterns of cortical morphological change among obtained subtypes.

METHODS

We performed unbiased hierarchical cluster analysis using 123 I-metaiodobenzylguanidine cardiac scintigraphy and 123 I-N-(3-fluoropropyl)-2β-carbomethoxy-3β-(4-iodophenyl) nortropane single photon emission computed tomography data for 56 patients with PD. We compared clinical characteristics and the patterns of cortical atrophy in the obtained clusters.

RESULTS

Three clusters were identified and showed distinct characteristics in onset ages and dopamine-replacement therapy and deep brain stimulation requirements. According to the characteristics, clusters were classified into two subtypes, namely, "cardio-cortical impairment (CC)" and "dopaminergic-dominant dysfunction (DD)" subtype. The three clusters were named according to subtype and time since onset in which 14 patients were classified as "early DD," 25 as "advanced DD," and 17 as "early CC." Compared with the early DD subtype, the early CC subtype showed parietal-dominant diffuse cortical atrophy and the advanced DD subtype showed left-side predominant mild cortical atrophy.

CONCLUSIONS

Nuclear imaging biomarker-based classification can be used to identify clinically and pathologically relevant PD subtypes with distinct disease trajectories. © 2023 International Parkinson and Movement Disorder Society.

MRI-based multivariate gray matter volumetric distance for predicting motor symptom progression in Parkinson's disease

While Parkinson's disease (PD)-related neurodegeneration is associated with structural changes in the brain, conventional magnetic resonance imaging (MRI) has proven less effective for clinical diagnosis due to its inability to reliably identify subtle changes early in the disease course. In this study, we aimed to develop a structural MRI-based biomarker to predict the rate of progression of motor symptoms in the early stages of PD. The study included 88 patients with PD and 120 healthy controls from the Parkinson's Progression Markers Initiative database; MRI at baseline and motor symptom scores assessed using the MDS-UPDRS-III at two time points (baseline and 48 months) were selected. Group-level volumetric analyses revealed that the volumetric reductions in the left striatum were associated with the decline in motor functioning. Then, we developed a patient-specific multivariate gray matter volumetric distance and demonstrated that it could significantly predict changes in motor symptom scores (P < 0.05). Further, we classified patients as relatively slower and faster progressors with 89% accuracy using a support vector machine classifier. Thus, we identified a promising structural MRI-based biomarker for predicting the rate of progression of motor symptoms and classifying patients based on motor symptom severity.

Network connectivity and local transcriptomic vulnerability underpin cortical atrophy progression in Parkinson's disease

Parkinson's disease pathology is hypothesized to spread through the brain via axonal connections between regions and is further modulated by local vulnerabilities within those regions. The resulting changes to brain morphology have previously been demonstrated in both prodromal and de novo Parkinson's disease patients. However, it remains unclear whether the pattern of atrophy progression in Parkinson's disease over time is similarly explained by network-based spreading and local vulnerability. We address this gap by mapping the trajectory of cortical atrophy rates in a large, multi-centre cohort of Parkinson's disease patients and relate this atrophy progression pattern to network architecture and gene expression profiles. Across 4-year follow-up visits, increased atrophy rates were observed in posterior, temporal, and superior frontal cortices. We demonstrated that this progression pattern was shaped by network connectivity. Regional atrophy rates were strongly related to atrophy rates across structurally and functionally connected regions. We also found that atrophy progression was associated with specific gene expression profiles. The genes whose spatial distribution in the brain was most related to atrophy rate were those enriched for mitochondrial and metabolic function. Taken together, our findings demonstrate that both global and local brain features influence vulnerability to neurodegeneration in Parkinson's disease.

Altered brain network centrality in Parkinson's disease patients after deep brain stimulation: a functional MRI study using a voxel-wise degree centrality approach

OBJECTIVE

After deep brain stimulation (DBS), patients with Parkinson's disease (PD) show improved motor symptoms and decreased verbal fluency, an effect that occurs before the initiation of DBS in the subthalamic nucleus. However, the underlying mechanism remains unclear. This study aimed to evaluate the effects of DBS on whole-brain degree centrality (DC) and seed-based functional connectivity (FC) in PD patients.

METHODS

The authors obtained resting-state functional MRI data of 28 PD patients before and after DBS surgery. All patients underwent MRI scans in the off-stimulation state. The DC method was used to evaluate the effects of DBS on whole-brain FC at the voxel level. Seed-based FC analysis was used to examine network function changes after DBS.

RESULTS

After DBS surgery, PD patients showed significantly weaker DC values in the left middle temporal gyrus, left supramarginal gyrus, and left middle frontal gyrus, but significantly stronger DC values in the midbrain, left precuneus, and right precentral gyrus. FC analysis revealed decreased FC values within the default mode network (DMN).

CONCLUSIONS

This study demonstrated that the DC of DMN-related brain regions decreased in PD patients after DBS surgery, whereas the DC of the motor cortex increased. These findings provide new evidence for the neural effects of DBS on voxel-based whole-brain networks in PD patients.

Clinical Phenotype Imprints on Brain Atrophy Progression in Parkinson’s Disease

There is much controversy about the link between motor symptom progression and the plethora of reported brain atrophy patterns in idiopathic Parkinson’s disease (PD). The main goal of this study is to provide empirical evidence for unique and common contributions of clinical phenotype characteristics on the dynamic changes of brain structure over time. We analyzed the behavioral and magnetic resonance imaging (MRI) data of PD patients (n = 22) and healthy individuals (n = 21) acquired two years apart through the computational anatomy framework of longitudinal voxel-based morphometry (VBM). This analysis revealed a symmetrical bi-hemispheric pattern of accelerated grey matter decrease in PD extending through the insula, parahippocampal gyrus, medial temporal lobes and the precuneus. We observed a hemisphere-specific correlation between the established scores for motor symptoms severity and the rate of atrophy within motor regions, which was further differentiated by the clinical phenotype characteristics of PD patients. Baseline cerebellum anatomy differences between the tremor-dominant and akineto-rigid PD remained stable over time and can be regarded as trait rather than state-associated features. We interpret the observed pattern of progressive brain anatomy changes as mainly linked to insular areas that determine together with basal ganglia the motor and non-motor phenotype in PD. Our findings provide empirical evidence for the sensitivity of computational anatomy to dynamic changes in PD, offering additional opportunities to establish reliable models of disease progression.

MRI data-driven clustering reveals different subtypes of Dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. We used data-driven analysis of magnetic resonance imaging (MRI) data to investigate DLB subtypes. We included 165 DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid, tau, and cerebrovascular biomarkers at baseline, and cognitive decline over three years. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n = 49, 30%); a subtype with low GM volumes in fronto-occipital regions (n = 76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n = 40, 24%). This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.

Cerebral cortical thickness and cognitive decline in Parkinson's disease

In Parkinson's disease (PD), reduced cerebral cortical thickness may reflect network-based degeneration. This study performed cognitive assessment and brain MRI in 30 PD participants and 41 controls at baseline and 18 months later. We hypothesized that cerebral cortical thickness and volume, as well as change in these metrics, would differ between PD participants who remained cognitively stable and those who experienced cognitive decline. Dividing the participant sample into PD-stable, PD-decline, and control-stable groups, we compared mean cortical thickness and volume within segments that comprise the prefrontal cognitive-control, memory, dorsal spatial, and ventral object-based networks at baseline. We then compared the rate of change in cortical thickness and volume between the same groups using a vertex-wise approach. We found that the PD-decline group had lower cortical thickness within all 4 cognitive networks in comparison with controls, as well as lower cortical thickness within the prefrontal and medial temporal networks in comparison with the PD-stable group. The PD-decline group also experienced a greater rate of volume loss in the lateral temporal cortices in comparison with the control group. This study suggests that lower thickness and volume in prefrontal, medial, and lateral temporal regions may portend cognitive decline in PD.

Systematic review of data-driven cognitive subtypes in Parkinson disease

BACKGROUND AND PURPOSE

Recent application of the mild cognitive impairment concept to Parkinson disease (PD) has proven valuable in identifying patients at risk of dementia. However, it has sparked controversy regarding the existence of cognitive subtypes. The present review evaluates the current literature pertaining to data-driven subtypes of cognition in PD.

METHODS

Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, systematic literature searches for peer-reviewed articles on the topic of cognitive subtyping in PD were performed.

RESULTS

Twenty-two relevant articles were identified in the systematic search. Subtype structures showed either a spectrum of severity or specific domains of impairment. Domain-specific subtypes included amnestic/nonamnestic, memory/executive, and frontal/posterior dichotomies, as well as more complex structures with less definitive groupings. Preliminary longitudinal evidence showed some differences in cognitive progression among subtypes. Neuroimaging evidence provided insight into distinct patterns of brain alterations among subtypes.

CONCLUSIONS

Recurring phenotypes in the literature suggest strong clinical relevance of certain cognitive subtypes in PD. Although the current literature is limited, it raises critical questions about the utility of data-driven methods in cognitive research. The results encourage further integration of neuroimaging research to define the latent neural mechanisms behind divergent subtypes. Although there is no consensus, there appears to be growing consistency and inherent value in identifying cognitive subtypes in PD.

Topological patterns of motor networks in Parkinson’s disease with different sides of onset: A resting-state-informed structural connectome study

Parkinson’s disease (PD) has a characteristically unilateral pattern of symptoms at onset and in the early stages; this lateralization is considered a diagnostically important diagnosis feature. We aimed to compare the graph-theoretical properties of whole-brain networks generated by using resting-state functional MRI (rs-fMRI), diffusion tensor imaging (DTI), and the resting-state-informed structural connectome (rsSC) in patients with left-onset PD (LPD), right-onset PD (RPD), and healthy controls (HCs). We recruited 26 patients with PD (13 with LPD and 13 with RPD) as well as 13 age- and sex-matched HCs. Rs-fMRI and DTI were performed in all subjects. Graph-theoretical analysis was used to calculate the local and global efficiency of a whole-brain network generated by rs-fMRI, DTI, and rsSC. Two-sample t-tests and Pearson correlation analysis were conducted. Significantly decreased global and local efficiency were revealed specifically in LPD patients compared with HCs when the rsSC network was used; no significant intergroup difference was found by using rs-fMRI or DTI alone. For rsSC network analysis, multiple network metrics were found to be abnormal in LPD. The degree centrality of the left precuneus was significantly correlated with the Unified Parkinson’s Disease Rating Scale (UPDRS) score and disease duration (p = 0.030, r = 0.599; p = 0.037, r = 0.582). The topological properties of motor-related brain networks can differentiate LPD and RPD. Nodal metrics may serve as important structural features for PD diagnosis and monitoring of disease progression. Collectively, these findings may provide neurobiological insights into the lateralization of PD onset.

Stages of brain volume loss and performance in the Brief International Cognitive Assessment for Multiple Sclerosis

BACKGROUND

Cognitive dysfunction occurs in a substantial proportion of patients with multiple sclerosis (MS), negatively affects their daily activities, and is associated with poor prognosis. Cognitive dysfunction in MS can extend across multiple cognitive domains, depending on the patterns and extent of the brain regions affected. Therefore, a combination of tests, including the Brief International Cognitive Assessment for MS (BICAMS), that assess different aspects of cognition is recommended to capture the full picture of cognitive impairment in each patient. However, the temporal relationships between the progression of the MS brain pathology and the performances in different cognitive tests remain unclear.

METHODS

Global and regional brain volume data were obtained based on T1-weighted magnetic resonance imaging from 61 patients with MS, and hierarchical cluster analysis was performed using these brain volume data. Cognitive function was assessed using the three subcomponents of the BICAMS: the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test Second Edition (CVLT2), and Brief Visuospatial Memory Test-Revised (BVMTR). Clinical characteristics, patterns of regional brain volume loss, and cognitive test scores were compared among clusters.

RESULTS

Cluster analysis of the global and regional brain volume data classified patients into three clusters (Clusters 1, 2, and 3) in order of decreasing global brain volume. A comparison of the clinical profiles of the patients suggested that those in Clusters 1, 2, and 3 are in the early, intermediate, and advanced stages of MS, respectively. Pair-wise analysis of regional brain volume among the three clusters suggested brain regions where volume loss starts early and continues throughout the disease course, occurs preferentially at the early phase, or evolves relatively slowly. SDMT scores differed significantly among the three clusters, with a decrease from Clusters 1 to 3. BVMTR scores also declined in this order, whereas the CVLT2 was significantly impaired only in Cluster 3.

CONCLUSION

Our results suggest that SDMT performance declines in conjunction with brain volume loss throughout the disease course of MS. Performance in the BVMTR also declines in line with the brain volume loss, but impairment in the CVLT2 becomes particularly apparent at the late phase of MS.

Alcohol dependence inpatients classification with GLM and hierarchical clustering integration using fMRI data of alcohol multiple scenario cues

Alterations in brain reactions to alcohol-related cues are a neurobiological characteristic of alcohol dependence (AD) and a prospective target for achieving substantial treatment effects. However, a robust prediction of the differences in inpatients' brain responses to alcohol cues during the treatment process is still required. This study offers a data-driven approach for classifying AD inpatients undertaking alcohol treatment protocols based on their brain responses to alcohol imagery with and without drinking actions. The brain activity of thirty inpatients with AD undergoing treatment was scanned using functional magnetic resonance imaging (fMRI) while seeing alcohol and matched non-alcohol images. The mean values of brain regions of interest (ROI) for alcohol-related brain responses were obtained using general linear modeling (GLM) and subjected to hierarchical clustering analysis. The proposed classification technique identified two distinct subgroups of inpatients. For the two types of cues, subgroup one exhibited significant activation in a wide range of brain regions, while subgroup two showed mainly decreased activation. The proposed technique may aid in detecting the vulnerability of the classified inpatient subgroups, which can suggest allocating the inpatients in the classified subgroups to more effective therapies and developing prognostic future relapse markers in AD.

Brain aging differs with cognitive ability regardless of education

Higher general cognitive ability (GCA) is associated with lower risk of neurodegenerative disorders, but neural mechanisms are unknown. GCA could be associated with more cortical tissue, from young age, i.e. brain reserve, or less cortical atrophy in adulthood, i.e. brain maintenance. Controlling for education, we investigated the relative association of GCA with reserve and maintenance of cortical volume, -area and -thickness through the adult lifespan, using multiple longitudinal cognitively healthy brain imaging cohorts (n = 3327, 7002 MRI scans, baseline age 20-88 years, followed-up for up to 11 years). There were widespread positive relationships between GCA and cortical characteristics (level-level associations). In select regions, higher baseline GCA was associated with less atrophy over time (level-change associations). Relationships remained when controlling for polygenic scores for both GCA and education. Our findings suggest that higher GCA is associated with cortical volumes by both brain reserve and -maintenance mechanisms through the adult lifespan.

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

Objective

We wished to explore Parkinson's disease (PD) subtypes by clustering analysis based on the multimodal magnetic resonance imaging (MRI) indices amplitude of low-frequency fluctuation (ALFF) and gray matter volume (GMV). Then, we analyzed the differences between PD subtypes.

Methods

Eighty-six PD patients and 44 healthy controls (HCs) were recruited. We extracted ALFF and GMV according to the Anatomical Automatic Labeling (AAL) partition using Data Processing and Analysis for Brain Imaging (DPABI) software. The Ward linkage method was used for hierarchical clustering analysis. DPABI was employed to compare differences in ALFF and GMV between groups.

Results

Two subtypes of PD were identified. The “diffuse malignant subtype” was characterized by reduced ALFF in the visual-related cortex and extensive reduction of GMV with severe impairment in motor function and cognitive function. The “mild subtype” was characterized by increased ALFF in the frontal lobe, temporal lobe, and sensorimotor cortex, and a slight decrease in GMV with mild impairment of motor function and cognitive function.

Conclusion

Hierarchical clustering analysis based on multimodal MRI indices could be employed to identify two PD subtypes. These two PD subtypes showed different neurodegenerative patterns upon imaging.

Disrupted functional connectivity in PD with probable RBD and its cognitive correlates

Recent studies associated rapid eye movement sleep behavior disorder (RBD) in Parkinson’s disease (PD) with severe cognitive impairment and brain atrophy. However, whole-brain functional connectivity has never been explored in this group of PD patients. In this study, whole-brain network-based statistics and graph-theoretical approaches were used to characterize resting-state interregional functional connectivity in PD with probable RBD (PD-pRBD) and its relationship with cognition. Our sample consisted of 30 healthy controls, 32 PD without probable RBD (PD-non pRBD), and 27 PD-pRBD. The PD-pRBD group showed reduced functional connectivity compared with controls mainly involving cingulate areas with temporal, frontal, insular, and thalamic regions (p < 0.001). Also, the PD-pRBD group showed reduced functional connectivity between right ventral posterior cingulate and left medial precuneus compared with PD-non pRBD (p < 0.05). We found increased normalized characteristic path length in PD-pRBD compared with PD-non pRBD. In the PD-pRBD group, mean connectivity strength from reduced connections correlated with visuoperceptual task and normalized characteristic path length correlated with processing speed and verbal memory tasks. This work demonstrates the existence of disrupted functional connectivity in PD-pRBD, together with abnormal network integrity, that supports its consideration as a severe PD subtype.

Beyond the average patient: how neuroimaging models can address heterogeneity in dementia

Dementia is a highly heterogeneous condition, with pronounced individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic group. However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on comparisons of group average differences (e.g. patient versus control or treatment versus placebo), implicitly assuming within-group homogeneity. This one-size-fits-all approach potentially limits our understanding of dementia aetiology, hindering the identification of effective treatments. Neuroimaging has enabled the characterization of the average neuroanatomical substrates of dementias; however, the increasing availability of large open neuroimaging datasets provides the opportunity to examine patterns of neuroanatomical variability in individual patients. In this update, we outline the causes and consequences of heterogeneity in dementia and discuss recent research that aims to tackle heterogeneity directly, rather than assuming that dementia affects everyone in the same way. We introduce spatial normative modelling as an emerging data-driven technique, which can be applied to dementia data to model neuroanatomical variation, capturing individualized neurobiological 'fingerprints'. Such methods have the potential to detect clinically relevant subtypes, track an individual's disease progression or evaluate treatment responses, with the goal of moving towards precision medicine for dementia.

Analyses of Visuospatial and Visuoperceptual Errors as Predictors of Dementia in Parkinson's Disease Patients with Subjective Cognitive Decline and Mild Cognitive Impairment

OBJECTIVE

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in Parkinson's disease (PD) are considered as the risk factors for dementia (PDD). Posterior cortically based functions, such as visuospatial and visuoperceptual (VS-VP) processing, have been described as predictors of PDD. However, no investigations have focused on the qualitative analysis of the Judgment of Line Orientation Test (JLOT) and the Facial Recognition Test (FRT) in PD-SCD and PD-MCI. The aim of this work was to study the VS-VP errors in JLOT and FRT. Moreover, these variables are considered as predictors of PDD.

METHOD

Forty-two PD patients and 19 controls were evaluated with a neuropsychological protocol. Patients were classified as PD-SCD and PD-MCI. Analyses of errors were conducted following the procedure described by Ska, Poissant, and Joanette (1990). Follow-up assessment was conducted to a mean of 7.5 years after the baseline.

RESULTS

PD-MCI patients showed a poor performance in JLOT and FRT total score and made a greater proportion of severe intraquadrant (QO2) and interquadrant errors (IQO). PD-SCD showed a poor performance in FRT and made mild errors in JLOT. PD-MCI and QO2/IQO errors were independent risk factors for PDD during the follow-up. Moreover, the combination of both PD-MCI diagnosis and QO2/IQO errors was associated with a greater risk.

CONCLUSIONS

PD-MCI patients presented a greater alteration in VS-VP processing observable by the presence of severe misjudgments. PD-SCD patients also showed mild difficulties in VS-SP functions. Finally, QO2/IQO errors in PD-MCI are a useful predictor of PDD, more than PD-MCI diagnosis alone.

Altered Spontaneous Neural Activity and Functional Connectivity in Parkinson's Disease With Subthalamic Microlesion

Background

Transient improvement in motor symptoms are immediately observed in patients with Parkinson's disease (PD) after an electrode has been implanted into the subthalamic nucleus (STN) for deep brain stimulation (DBS). This phenomenon is known as the microlesion effect (MLE). However, the underlying mechanisms of MLE is poorly understood.

Purpose

We utilized resting state functional MRI (rs-fMRI) to evaluate changes in spontaneous brain activity and networks in PD patients during the microlesion period after DBS.

Method

Overall, 37 PD patients and 13 gender- and age-matched healthy controls (HCs) were recruited for this study. Rs-MRI information was collected from PD patients three days before DBS and one day after DBS, whereas the HCs group was scanned once. We utilized the amplitude of low-frequency fluctuation (ALFF) method in order to analyze differences in spontaneous whole-brain activity among all subjects. Furthermore, functional connectivity (FC) was applied to investigate connections between other brain regions and brain areas with significantly different ALFF before and after surgery in PD patients.

Result

Relative to the PD-Pre-DBS group, the PD-Post-DBS group had higher ALFF in the right putamen, right inferior frontal gyrus, right precentral gyrus and lower ALFF in right angular gyrus, right precuneus, right posterior cingulate gyrus (PCC), left insula, left middle temporal gyrus (MTG), bilateral middle frontal gyrus and bilateral superior frontal gyrus (dorsolateral). Functional connectivity analysis revealed that these brain regions with significantly different ALFF scores demonstrated abnormal FC, largely in the temporal, prefrontal cortices and default mode network (DMN).

Conclusion

The subthalamic microlesion caused by DBS in PD was found to not only improve the activity of the basal ganglia-thalamocortical circuit, but also reduce the activity of the DMN and executive control network (ECN) related brain regions. Results from this study provide new insights into the mechanism of MLE.

Impaired Structural Connectivity in Parkinson's Disease Patients with Mild Cognitive Impairment: A Study Based on Probabilistic Tractography

Background: Probabilistic tractography, in combination with graph theory, has been used to reconstruct the structural whole-brain connectome. Threshold-free network-based statistics (TFNBS) is a useful technique to study structural connectivity in neurodegenerative disorders; however, there are no previous studies using TFNBS in Parkinson's disease (PD) with and without mild cognitive impairment (MCI). Materials and Methods: Sixty-two PD patients, 27 of whom classified as PD-MCI, and 51 healthy controls (HC) underwent diffusion-weighted 3T magnetic resonance imaging. Probabilistic tractography, using FMRIB Software Library (FSL), was used to compute the number of streamlines (NOS) between regions. NOS matrices were used to find group differences with TFNBS, and to calculate global and local measures of network integrity using graph theory. A binominal logistic regression was then used to assess the discrimination between PD with and without MCI using non-overlapping significant tracts. Tract-based spatial statistics were also performed with FSL to study changes in fractional anisotropy (FA) and mean diffusivity. Results: PD-MCI showed 37 white matter connections with reduced connectivity strength compared with HC, mainly involving temporal/occipital regions. These were able to differentiate PD-MCI from PD without MCI with an area under the curve of 83-85%. PD without MCI showed disrupted connectivity in 18 connections involving frontal/temporal regions. No significant differences were found in graph measures. Only PD-MCI showed reduced FA compared with HC. Discussion: TFNBS based on whole-brain probabilistic tractography can detect structural connectivity alterations in PD with and without MCI. Reduced structural connectivity in fronto-striatal and posterior cortico-cortical connections is associated with PD-MCI.

Structural and functional magnetic resonance imaging in isolated REM sleep behavior disorder: A systematic review of studies using neuroimaging software

Isolated rapid eye movement sleep behavior disorder (iRBD) is a harbinger for developing clinical synucleinopathies. Magnetic resonance imaging (MRI) has been suggested as a tool for understanding the brain bases of iRBD and its evolution. This review systematically analyzed original full text articles on structural and functional MRI in patients with video-polysomnography-confirmed iRBD according to systematic procedures suggested by Reviews and Meta-analyses (PRISMA). The literature search was conducted via the PubMed database for articles related to structural and functional MRI in iRBD from 2000 to 2020. Investigations to date have been diverse in terms of methodology, but most agree that patients with iRBD have structural changes in deep gray matter nuclei, cortical gray matter atrophy, and disrupted functional connectivity within the basal ganglia, the cortico-striatal and cortico-cortical networks. Furthermore, there is evidence that MRI detects structural and functional brain changes associated with the motor and non-motor symptoms of iRBD. The current review highlights the need for larger multicenter and longitudinal studies, using complex approaches based on data-driven and unsupervised machine learning that will help to identify structural and functional patterns of brain degeneration. In turn, this may even allow for the prediction of subsequent phenoconversion from iRBD to the clinically defined synucleinopathies.

Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions

Head motion during MRI acquisition presents significant challenges for neuroimaging analyses. In this work, we present a retrospective motion correction framework built on a Fourier domain motion simulation model combined with established 3D convolutional neural network (CNN) architectures. Quantitative evaluation metrics were used to validate the method on three separate multi-site datasets. The 3D CNN was trained using motion-free images that were corrupted using simulated artifacts. CNN based correction successfully diminished the severity of artifacts on real motion affected data on a separate test dataset as measured by significant improvements in image quality metrics compared to a minimal motion reference image. On the test set of 13 image pairs, the mean peak signal-to-noise-ratio was improved from 31.7 to 33.3 dB. Furthermore, improvements in cortical surface reconstruction quality were demonstrated using a blinded manual quality assessment on the Parkinson's Progression Markers Initiative (PPMI) dataset. Upon applying the correction algorithm, out of a total of 617 images, the number of quality control failures was reduced from 61 to 38. On this same dataset, we investigated whether motion correction resulted in a more statistically significant relationship between cortical thickness and Parkinson's disease. Before correction, significant cortical thinning was found to be restricted to limited regions within the temporal and frontal lobes. After correction, there was found to be more widespread and significant cortical thinning bilaterally across the temporal lobes and frontal cortex. Our results highlight the utility of image domain motion correction for use in studies with a high prevalence of motion artifacts, such as studies of movement disorders as well as infant and pediatric subjects.

Patterns of cortical grey matter thickness reduction in multiple sclerosis

OBJECTIVE

To examine the patterns of cortical gray matter thickness in multiple sclerosis (MS) patients.

METHODS

Seventy-four MS patients-clinically isolated syndrome (4%), relapsing-remitting MS (79%), and progressive MS (17%)-and 21 healthy controls (HCs) underwent 1.5 Tesla T1-weighted 3D MRI examinations to measure brain cortical thickness in a total of 68 regions of interest. Using hierarchical cluster analysis with multivariate cortical thickness data, cortical thickness reduction patterns were cross-sectionally investigated in MS patients.

RESULTS

The MS patients were grouped into three major clusters (Clusters 1, 2, and 3). Most of the regional cortical thickness values were equivalent between the HCs and Cluster 1, but decreased in the order of Clusters 2 and 3. Only the thicknesses of the temporal lobe cortices (the bilateral superior and left middle temporal cortex, as well as the left fusiform cortex) were significantly different among Clusters 1, 2, and 3. In contrast, temporal pole thickness reduction was evident exclusively in Cluster 3, which was also characterized by increased lesion loads in the temporal pole and the adjacent juxtacortical white matter, dilatation of the inferior horn of the lateral ventricle, severe whole-brain volume reduction, and longer disease duration. Although cortical atrophy was significantly more common in the progressive phase, approximately half of the MS patients with the severe cortical atrophy pattern had relapsing-remitting disease.

CONCLUSION

Cortical thickness reduction patterns in MS are mostly characterized by the degree of temporal lobe cortical atrophy, which may start in the relapsing-remitting phase. Among the temporal lobe cortices, the neurodegenerative change may accelerate in the temporal pole in the progressive phase.

Meynert nucleus-related cortical thinning in Parkinson's disease with mild cognitive impairment

Background

Cognitive impairment in Parkinson's disease (PD) involves the cholinergic system and cholinergic neurons, especially the nucleus basalis of Meynert (NBM/Ch4) located in the basal forebrain (BF). We analyzed associations between NBM/Ch4 volume and cortical thickness to determine whether the NBM/Ch4-innervated neocortex shows parallel atrophy with the NBM/Ch4 as disease progresses in PD patients with cognitive impairment (PD-MCI).

Methods

We enrolled 35 PD-MCI patients, 48 PD patients with normal cognition (PD-NC), and 33 age- and education-matched healthy controls (HCs), with all participants undergoing neuropsychological assessment and structural magnetic resonance imaging (MRI). Correlation analyses between NBM/Ch4 volume and cortical thickness and correlation coefficient comparisons were conducted within and across groups.

Results

In the PD-MCI group, NBM/Ch4 volume was positively correlated with cortical thickness in the bilateral posterior cingulate, parietal, and frontal and left insular regions. Based on correlation coefficient comparisons, the atrophy of NBM/Ch4 was more correlated with the cortical thickness of right posterior cingulate and precuneus, anterior cingulate and medial orbitofrontal lobe in PD-MCI versus HC, and the right medial orbitofrontal lobe and anterior cingulate in PD-NC versus HC. Further partial correlations between cortical thickness and NBM/Ch4 volume were significant in the right medial orbitofrontal (PD-NC: r=0.3, P=0.045; PD-MCI: r=0.51, P=0.003) and anterior cingulate (PD-NC: r=0.41, P=0.006; PD-MCI: r=0.43, P=0.013) in the PD groups and in the right precuneus (r=0.37, P=0.04) and posterior cingulate (r=0.46, P=0.008) in the PD-MCI group.

Conclusions

The stronger correlation between NBM/Ch4 and cortical thinning in PD-MCI patients suggests that NBM/Ch4 volume loss may play an important role in PD cognitive impairment.

Differential functional connectivity of insular subdivisions in de novo Parkinson's disease with mild cognitive impairment

The insula, consisting of functionally diverse subdivisions, plays a significant role in Parkinson's disease (PD)-related cognitive disorders. However, the functional connectivity (FC) patterns of insular subdivisions in PD remain unclear. Our aim is to investigate the changes in FC patterns of insular subdivisions and their relationships with cognitive domains. Three groups of participants were recruited in this study, including PD patients with mild cognitive impairment (PD-MCI, n = 25), PD patients with normal cognition (PD-NC, n = 13), and healthy controls (HCs, n = 17). Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the FC in insular subdivisions of the three groups. Moreover, all participants underwent a neuropsychological battery to assess cognition so that the relationship between altered FC and cognitive performance could be elucidated. Compared with the PD-NC group, the PD-MCI group exhibited increased FC between the left dorsal anterior insular (dAI) and the right superior parietal gyrus (SPG), and altered FC was negatively correlated with memory and executive function. Compared with the HC group, the PD-MCI group showed significantly increased FC between the right dAI and the right median cingulate and paracingulate gyri (DCG), and altered FC was positively related to attention/working memory, visuospatial function, and language. Our findings highlighted the different abnormal FC patterns of insular subdivisions in PD patients with different cognitive abilities. Furthermore, dysfunction of the dAI may partly contribute to the decline in executive function and memory in early drug-naïve PD patients.

Distinct neural circuits are associated with subclinical neuropsychiatric symptoms in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) can present with neuropsychiatric symptoms (here, anxiety, depression, and apathy) at any stage of the disease. We investigated the neural correlates of subclinical neuropsychiatric symptoms in relation to motor and cognitive symptoms in a high-functioning PD cohort.

METHODS

Brain morphometry of the cognitively intact, early-stage (Hoehn & Yahr 2) PD group (n = 48) was compared to matched controls (n = 37). Whole-brain, pairwise, resting-state functional connectivity measures were correlated with neuropsychiatric symptom, motor exam, and global cognitive scores of the PD group.

RESULTS

Factor analysis of highly collinear anxiety, depression, and apathy scores revealed a single principal component (i.e., composite neuropsychiatric symptom score) explaining 71.6% of variance. There was no collinearity between the neuropsychiatric, motor, and cognitive scores. Compared to controls, PD group showed only subcortical changes including amygdala and nucleus accumbens atrophy, and greater pallidal volume. Reduced functional connectivity in the limbic cortical-striatal circuits and increased functional connectivity between the cerebellum and occipito-temporal regions were associated with a more impaired neuropsychiatric profile. This functional connectivity pattern was distinct from those associated with motor deficits and global cognitive functioning. The individual components of the neuropsychiatric symptoms also exhibited unique connectivity patterns.

LIMITATIONS

Patients were scanned in "on-medication" state only and a control group with similar neuropsychiatric symptoms was not included.

CONCLUSION

Abnormal functional connectivity of distinct neural circuits is present even at the subclinical stage of neuropsychiatric symptoms in PD. Neuropsychiatric phenotyping is important and may facilitate early interventions to "reorganize" these circuits and delay/prevent clinical symptom onset.

Cortical thickness in Parkinson's disease: a coordinate-based meta-analysis

Parkinson's disease (PD) is a common age-related neurodegenerative disease that affects the structural architecture of the cerebral cortex. Cortical thickness (CTh) via surface-based morphometry (SBM) analysis is a popular measure to assess brain structural alterations in the gray matter in PD. However, the results of CTh analysis in PD lack consistency and have not been systematically reviewed. We conducted a comprehensive coordinate-based meta-analysis (CBMA) of 38 CTh studies (57 comparison datasets) in 1,843 patients with PD using the latest seed-based d mapping software. Compared with 1,172 healthy controls, no significantly consistent CTh alterations were found in patients with PD, suggesting CTh as an unreliable neuroimaging marker for PD. The lack of consistent CTh alterations in PD could be ascribed to the heterogeneity in clinical populations, variations in imaging methods, and underpowered small sample sizes. These results highlight the need to control for potential confounding factors to produce robust and reproducible CTh results in PD.

Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions

BACKGROUND

Regional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases (CVD) can be estimated using specialized neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures.

PURPOSE

The main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer's (FS's) cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and CVD, with varying degrees of neurovascular lesions and brain atrophy.

METHODS

In 155 CVD participants enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FS outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures.

RESULTS

Corrected procedures increased "Acceptable" QC ratings from 18 to 76% for the cortical ribbon and from 38 to 92% for tissue segmentation. Corrected procedures reduced "Fail" ratings from 11 to 0% for the cortical ribbon and 62 to 8% for tissue segmentation. FS-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8 mL vs. 15.9 mL, p < 0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p = 0.018) and left insula (p = 0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p < 0.001).

CONCLUSION

These findings suggest that correction procedures which account for brain atrophy and neurovascular lesions can significantly improve FS's segmentation results and reduce failure rates, thus maximizing power by preventing the loss of our important study participants. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and cognitive dysfunction due to neurodegenerative disease in the ONDRI study.

Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults

Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

Cortical gray matter progression in idiopathic REM sleep behavior disorder and its relation to cognitive decline

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Cortical degeneration over time in IRBD patients is larger than in normal aging.

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IRBD patients have progressive parieto-occipital and orbitofrontal thinning.

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Visuospatial decline in IRBD is associated with degeneration in parietal regions.

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Increasing motor signs in IRBD are related to frontal and parietal degeneration.

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Cortical thinning in posterior regions is associated with late-onset IRBD.

Background

Idiopathic Rapid eye movement sleep behavior disorder (IRBD) is recognized as the prodromal stage of the alpha-Synucleinopathies. Although some studies have addressed the characterization of brain structure in IRBD, little is known about its progression.

Objective

The present work aims at further characterizing gray matter progression throughout IRBD relative to normal aging and investigating how these changes are associated with cognitive decline.

Methods

Fourteen patients with polysomnography-confirmed IRBD and 18 age-matched healthy controls (HC) underwent neuropsychological, olfactory, motor, and T1-weighted MRI evaluation at baseline and follow-up. We compared the evolution of cortical thickness (CTh), subcortical volumes, smell, motor and cognitive performance in IRBD and HC after a mean of 1.6 years. FreeSurfer was used for CTh and volumetry preprocessing and analyses. The symmetrized percent of change (SPC) of the CTh was correlated with the SPC of motor and neuropsychological performance.

Results

IRBD and HC differed significantly in the cortical thinning progression in regions encompassing bilateral superior parietal and precuneus, the right cuneus, the left occipital pole and lateral orbitofrontal gyri (FWE corrected, p < 0.05). The Visual form discrimination test showed worse progression in the IRBD relative to HC, that was associated with gray matter loss in the right superior parietal and the left precuneus. Increasing motor signs in IRBD were related to cortical thinning mainly involving frontal regions, and late-onset IRBD was associated with cortical thinning involving posterior areas (FWE corrected, p < 0.05). Despite finding olfactory identification deficits in IRBD, results did not show decline over the disease course.

Conclusion

Progression in IRBD patients is characterized by parieto-occipital and orbitofrontal thinning and visuospatial loss. The cognitive decline in IRBD is associated with degeneration in parietal regions.

Tracking Cortical Changes Throughout Cognitive Decline in Parkinson's Disease

BACKGROUND

The objectives of this study were to investigate progressive cortical thinning and volume loss in Parkinson's disease (PD) patients with different longitudinal patterns of cognitive decline: with stable normal cognition, with stable mild cognitive impairment, with conversion to mild cognitive impairment, and with conversion to dementia.

METHODS

We recruited 112 patients (37 Parkinson's disease with stable normal cognition, 20 Parkinson's disease with stable mild cognitive impairment, 36 Parkinson's disease with conversion to mild cognitive impairment, 19 Parkinson's disease with conversion to dementia) and 38 healthy controls. All patients underwent at least 2 visits within 4 years including clinical/cognitive assessments and structural MRI (total visits, 393). Baseline cortical thickness and gray matter volumetry were compared between groups. In PD, gray matter changes over time were investigated and compared between groups.

RESULTS

At baseline, compared with Parkinson's disease with stable normal cognition cases, Parkinson's disease with conversion to mild cognitive impairment patients showed cortical atrophy of the parietal and occipital lobes, similar to Parkinson's disease with stable mild cognitive impairment and Parkinson's disease with conversion to dementia patients. The latter groups (ie, patients with cognitive impairment from the study entry) showed additional involvement of the frontotemporal cortices. No baseline volumetric differences among groups were detected. The longitudinal analysis (group-by-time interaction) showed that, versus the other patient groups, Parkinson's disease with stable mild cognitive impairment and Parkinson's disease with conversion to dementia cases accumulated the least cortical damage, with Parkinson's disease with conversion to dementia showing unique progression of right thalamic and hippocampal volume loss; Parkinson's disease with conversion to mild cognitive impairment patients showing specific cortical thinning accumulation in the medial and superior frontal gyri, inferior temporal, precuneus, posterior cingulum, and supramarginal gyri bilaterally; and Parkinson's disease with stable normal cognition patients showing cortical thinning progression, mainly in the occipital and parietal regions bilaterally.

CONCLUSIONS

Cortical thinning progression is more prominent in the initial stages of PD cognitive decline. The involvement of frontotemporoparietal regions, the hippocampus, and the thalamus is associated with conversion to a more severe stage of cognitive impairment. In PD, gray matter alterations of critical brain regions may be an MRI signature for the identification of patients at risk of developing dementia. © 2020 International Parkinson and Movement Disorder Society.

High-resolution PET imaging reveals subtle impairment of the serotonin transporter in an early non-depressed Parkinson's disease cohort

PURPOSE

The serotonin transporter (SERT) is a biochemical marker for monoaminergic signaling in brain and has been suggested to be involved inthe pathophysiology of Parkinson's disease (PD). The aim of this PET study was to examine SERT availability in relevant brain regions in early stages ofnon-depressed PD patients.

METHODS

In a cross-sectional study, 18 PD patients (13 M/5F, 64 ± 7 years, range 46-74 years, disease duration 2.9 ± 2.6 years; UPDRS motor 21.9 ± 5.2) and 20 age- and gender-matched healthy control (HC) subjects (15 M/5F, 61 ± 7 years, range 50-72 years) were included. In a subsequent longitudinal phase, ten of the PD patients (7 M/3F, UPDRS motor 20.6 ± 6.9) underwent a second PET measurement after 18-24 months. After a 3-T MRI acquisition, baseline PET measurements were performed with [11C]MADAM using a high-resolution research tomograph. The non-displaceablebinding potential (BPND) was chosen as the outcome measure and was estimated at voxel level on wavelet-aided parametric images, by using the Logan graphical analysis and the cerebellum as reference region. A molecular template was generated to visualize and define different subdivisions of the raphe nuclei in the brainstem. Subortical and cortical regions of interest were segmented using FreeSurfer. Univariate analyses and multivariate network analyses were performed on the PET data.

RESULTS

The univariate region-based analysis showed no differences in SERT levels when the PD patients were compared with the HC neither at baseline or after 2 years of follow-up. The multivariate network analysis also showed no differences at baseline. However, prominent changes in integration and segregation measures were observed at follow-up, indicating a disconnection of the cortical and subcortical regions from the three nuclei of the raphe.

CONCLUSION

We conclude that the serotoninergic system in PD patients seems to become involved with a network dysregulation as the disease progresses, suggesting a disturbed serotonergic signaling from raphe nuclei to target subcortical and cortical regions.

Progressive brain atrophy and clinical evolution in Parkinson's disease

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Cortical and subcortical atrophy is accelerated early after the onset of PD.

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Brain atrophy in PD progressed with cognitive, non-motor and mood deficits.

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Structural MRI may be useful for predicting disease progression in PD.

Clinical manifestations and evolution are very heterogeneous among individuals with Parkinson’s disease (PD). The aims of this study were to investigate the pattern of progressive brain atrophy in PD according to disease stage and to elucidate to what extent cortical thinning and subcortical atrophy are related to clinical motor and non-motor evolution. 154 patients at different PD stages were assessed over time using motor, non-motor and structural MRI evaluations for a maximum of 4 years. Cluster analysis defined clinical subtypes. Cortical thinning and subcortical atrophy were assessed at baseline in patients relative to 60 healthy controls. Longitudinal trends of brain atrophy progression were compared between PD clusters. The contribution of brain atrophy in predicting motor, non-motor, cognitive and mood deterioration was explored. Two main PD clusters were defined: mild (N = 87) and moderate-to-severe (N = 67). Two mild subtypes were further identified: mild motor-predominant (N = 43) and mild-diffuse (N = 44), with the latter group being older and having more severe non-motor and cognitive symptoms. The initial pattern of brain atrophy was more severe in patients with moderate-to-severe PD. Over time, mild-diffuse PD patients had the greatest brain atrophy accumulation in the cortex and the left hippocampus, while less distributed atrophy progression was observed in moderate-to-severe and mild motor-predominant patients. Baseline and 1-year cortical thinning was associated with long-term progression of motor, cognitive, non-motor and mood symptoms. Cortical and subcortical atrophy is accelerated early after the onset of PD and becomes prominent in later stages of disease according to the development of cognitive, non-motor and mood dysfunctions. Structural MRI may be useful for monitoring and predicting disease progression in PD.

Disentangling Heterogeneity in Alzheimer's Disease and Related Dementias Using Data-Driven Methods

Brain aging is a complex process that includes atrophy, vascular injury, and a variety of age-associated neurodegenerative pathologies, together determining an individual's course of cognitive decline. While Alzheimer's disease and related dementias contribute to the heterogeneity of brain aging, these conditions themselves are also heterogeneous in their clinical presentation, progression, and pattern of neural injury. We reviewed studies that leveraged data-driven approaches to examining heterogeneity in Alzheimer's disease and related dementias, with a principal focus on neuroimaging studies exploring subtypes of regional neurodegeneration patterns. Over the past decade, the steadily increasing wealth of clinical, neuroimaging, and molecular biomarker information collected within large-scale observational cohort studies has allowed for a richer understanding of the variability of disease expression within the aging and Alzheimer's disease and related dementias continuum. Moreover, the availability of these large-scale datasets has supported the development and increasing application of clustering techniques for studying disease heterogeneity in a data-driven manner. In particular, data-driven studies have led to new discoveries of previously unappreciated disease subtypes characterized by distinct neuroimaging patterns of regional neurodegeneration, which are paralleled by heterogeneous profiles of pathological, clinical, and molecular biomarker characteristics. Incorporating these findings into novel frameworks for more differentiated disease stratification holds great promise for improving individualized diagnosis and prognosis of expected clinical progression, and provides opportunities for development of precision medicine approaches for therapeutic intervention. We conclude with an account of the principal challenges associated with data-driven heterogeneity analyses and outline avenues for future developments in the field.

Uncinate fasciculus and word selection processing in Parkinson's disease

We explored with Diffusion Tensor Imaging (DTI) technique whether the ability to select words among competitive alternatives during word production is related to the integrity of the left uncinate fasciculus (UF) in Parkinson's disease (PD). Nineteen PD patients (10 right-sided and 9 left-sided) and 17 matched healthy controls (HC) took part in the study. Participants were asked to derive nouns from verbs (reading from to read) or to generate verbs from nouns (to build from building). Noun and verb production, in this task, differ in the number of lexical entries among which the response is selected, as the noun must be selected from a larger number of alternatives compared to the verb, and thus is more demanding of processing resources. DTI evaluation was obtained for each subject. Fractional anisotropy (FA) and mean diffusivity (MD) maps were derived from DTI and median FA and MD values were computed within the left and right UF. Then, FA and MD of the left and right UF were correlated with noun and verb production. Both the left and right UF-FA correlated with the global (noun + verb) production and noun production in the whole PD group. In right-sided PD, correlations were found with the contralateral UF-FA; in left-sided PD the correlations emerged with both the left and right UF-FA. The most difficult task, noun production, significantly correlated with the right UF-FA in left-sided PD. The left UF is involved in word selection processes, and the right UF intervenes when the selection is particularly demanding of attentional resources.

Gray Matter Changes in Parkinson's and Alzheimer's Disease and Relation to Cognition

PURPOSE OF REVIEW

We summarize structural (s)MRI findings of gray matter (GM) atrophy related to cognitive impairment in Alzheimer's disease (AD) and Parkinson's disease (PD) in light of new analytical approaches and recent longitudinal studies results.

RECENT FINDINGS

The hippocampus-to-cortex ratio seems to be the best sMRI biomarker to discriminate between various AD subtypes, following the spatial distribution of tau pathology, and predict rate of cognitive decline. PD is clinically far more variable than AD, with heterogeneous underlying brain pathology. Novel multivariate approaches have been used to describe patterns of early subcortical and cortical changes that relate to more malignant courses of PD. New emerging analytical approaches that combine structural MRI data with clinical and other biomarker outcomes hold promise for detecting specific GM changes in the early stages of PD and preclinical AD that may predict mild cognitive impairment and dementia conversion.