Longitudinal Study of Gray Matter Changes in Parkinson Disease

Membrane remodeling by FAM92A1 during brain development regulates neuronal morphology, synaptic function, and cognition

The Bin/Amphiphysin/Rvs (BAR) domain protein FAM92A1 is a multifunctional protein engaged in regulating mitochondrial ultrastructure and ciliogenesis, but its physiological role in the brain remains unclear. Here, we show that FAM92A1 is expressed in neurons starting from embryonic development. FAM92A1 knockout in mice results in altered brain morphology and age-associated cognitive deficits, potentially due to neuronal degeneration and disrupted synaptic plasticity. Specifically, FAM92A1 deficiency impairs diverse neuronal membrane morphology, including the mitochondrial inner membrane, myelin sheath, and synapses, indicating its roles in membrane remodeling and maintenance. By determining the crystal structure of the FAM92A1 BAR domain, combined with atomistic molecular dynamics simulations, we uncover that FAM92A1 interacts with phosphoinositide- and cardiolipin-containing membranes to induce lipid-clustering and membrane curvature. Altogether, these findings reveal the physiological role of FAM92A1 in the brain, highlighting its impact on synaptic plasticity and neural function through the regulation of membrane remodeling and endocytic processes.

Learning Phenotypic Associations for Parkinson's Disease with Longitudinal Clinical Records

Parkinson's disease (PD) is associated with multiple clinical motor and non-motor manifestations. Understanding of PD etiologies has been informed by a growing number of genetic mutations and various fluid-based and brain imaging biomarkers. However, the mechanisms underlying its varied phenotypic features remain elusive. The present work introduces a data-driven approach for generating phenotypic association graphs for PD cohorts. Data collected by the Parkinson's Progression Markers Initiative (PPMI), the Parkinson's Disease Biomarkers Program (PDBP), and the Fox Investigation for New Discovery of Biomarkers (BioFIND) were analyzed by this approach to identify heterogeneous and longitudinal phenotypic associations that may provide insight into the pathology of this complex disease. Findings based on the phenotypic association graphs could improve understanding of longitudinal PD pathologies and how these relate to patient symptomology.

Decreased brain volume may be associated with the occurrence of peri-lead edema in Parkinson's disease patients with deep brain stimulation

BACKGROUND

Peri-lead edema (PLE) is a poorly understood complication of deep brain stimulation (DBS), which has been described in patients presenting occasionally with profound and often delayed symptoms with an incidence ranging from 0.4% up to even 100%. Therefore, our study aims to investigate the association of brain and brain compartment volumes on magnetic resonance imaging (MRI) with the occurrence of PLE in Parkinson's disease (PD) patients after DBS implantation in subthalamic nuclei (STN).

METHODS

This retrospective study included 125 consecutive PD patients who underwent STN DBS at the Department of Neurosurgery, Dubrava University Hospital from 2010 to 2022. Qualitative analysis was done on postoperative MRI T2-weighted sequence by two independent observers, marking PLE on midbrain, thalamus, and subcortical levels as mild, moderate, or severe. Quantitative volumetric analysis of brain and brain compartment volumes was conducted using an automated CIVET processing pipeline on preoperative MRI T1 MPRAGE sequences. In addition, observed PLE on individual hemispheres was delineated manually and measured using Analyze 14.0 software.

RESULTS

In our cohort, PLE was observed in 32.17%, mostly bilaterally. Mild PLE was observed in the majority of patients, regardless of the level observed. Age, sex, diabetes, hypertension, vascular disease, and the use of anticoagulant/antiplatelet therapy showed no significant association with the occurrence of PLE. Total grey matter volume showed a significant association with the PLE occurrence (r = -0.22, p = 0.04), as well as cortex volume (r = -0.32, p = 0.0005). Cortical volumes of hemispheres, overall hemisphere volumes, as well as hemisphere/total intracranial volume ratio showed significant association with the PLE occurrence. Furthermore, the volume of the cortex and total grey volume represent moderate indicators, while hemisphere volumes, cortical volumes of hemispheres, and hemisphere/total intracranial volume ratio represent mild to moderate indicators of possible PLE occurrence.

CONCLUSION

The results of our study suggest that the morphometric MRI measurements, as a useful tool, can provide relevant information about the structural status of the brain in patients with PD and represent moderate indicators of possible PLE occurrence. Identifying patients with greater brain atrophy, especially regarding grey matter before DBS implantation, will allow us to estimate the possible postoperative symptoms and intervene in a timely manner. Further studies are needed to confirm our findings and to investigate other potential predictors and risk factors of PLE occurrence.

Brain structure ages-A new biomarker for multi-disease classification

Age is an important variable to describe the expected brain's anatomy status across the normal aging trajectory. The deviation from that normative aging trajectory may provide some insights into neurological diseases. In neuroimaging, predicted brain age is widely used to analyze different diseases. However, using only the brain age gap information (i.e., the difference between the chronological age and the estimated age) can be not enough informative for disease classification problems. In this paper, we propose to extend the notion of global brain age by estimating brain structure ages using structural magnetic resonance imaging. To this end, an ensemble of deep learning models is first used to estimate a 3D aging map (i.e., voxel-wise age estimation). Then, a 3D segmentation mask is used to obtain the final brain structure ages. This biomarker can be used in several situations. First, it enables to accurately estimate the brain age for the purpose of anomaly detection at the population level. In this situation, our approach outperforms several state-of-the-art methods. Second, brain structure ages can be used to compute the deviation from the normal aging process of each brain structure. This feature can be used in a multi-disease classification task for an accurate differential diagnosis at the subject level. Finally, the brain structure age deviations of individuals can be visualized, providing some insights about brain abnormality and helping clinicians in real medical contexts.

Cortical gray matter and cerebral white matter atrophy and asymmetry in Parkinson's disease patients with normal cognitive precede

BACKGROUND

Parkinson's disease is the second most common neurodegenerative disorder with complex and distributed motor and non-motor symptoms. In this study, cortical gray matter (GM) and cerebral white matter (WM) overall atrophy, and asymmetry of atrophy are investigated in PD with normal cognitive function.

METHOD

Forty-eight male Parkinson's disease(PD) patients with normal cognitive precede (PD-NC), and thirty matched healthy control (HC) subjects were selected from the Parkinson's Progression Markers Initiative (PPMI) database. Brain structures volumes were extracted using Freesurfer software based on subject 3 tesla MRI images. The normalized volume of cortical GM and cerebral WM were compared in two study groups, and then the asymmetry index (AI) of GM and WM atrophy was also assessed in two groups. Statistical analysis was constructed using a t-test with p < 0.05 of significance.

RESULTS

No significant difference was observed in the volume of cortical GM and cerebral WM in the two study groups. The cortical GM asymmetry index in the PD-NC group was significantly (p = 0.01) higher than the HC group, however, no difference was observed for the cerebral WM asymmetry index.

CONCLUSION

Atrophy in cortical GM and WM was not observed between the PD-NC and the HC group, however, the asymmetry index in GM was significant between the two group. It seems that the brain's bilateral balance has ruptured in PD. Cortical GM asymmetry in PD-NC can be considered a potent biomarker and should be investigated more in the future. In future studies, construction of a longitudinal study on this issue could be useful.

Voxel-based morphometry of grey matter structures in Parkinson's Disease with wearing-off

Our study aimed to investigate the grey matter (GM) changes using voxel-based morphometry (VBM) in Parkinson's disease (PD) patients with wearing-off (WO). 3D-T1-weighted imaging was performed on 48 PD patients without wearing-off (PD-nWO), 39 PD patients with wearing-off (PD-WO) and 47 age and sex-matched healthy controls (HCs). 3D structural images were analyzed by VBM procedure with Statistical Parametric Mapping (SPM12) to detect grey matter volume. Widespread areas of grey matter changes were found in patients among three groups (in bilateral frontal, temporal lobes, lingual gyrus, inferior occipital gyrus, right precuneus, right superior parietal gyrus and right cerebellum). Grey matter reductions were found in frontal lobe (right middle frontal gyrus, superior frontal gyrus and precentral gyrus), right parietal lobe (precuneus, superior parietal gyrus, postcentral gyrus), right temporal lobe (superior temporal gyrus, middle temporal gyrus), bilateral lingual gyrus and inferior occipital gyrus in PD-WO group compared with the PD-nWO group. Our results suggesting that wearing-off may be associated with grey matter atrophy in the cortical areas. These findings may aid in a better understanding of the brain degeneration process in PD with wearing-off.

Volumetric changes and clinical trajectories in Parkinson's disease: a prospective multicentric study

BACKGROUND

Longitudinal measures of structural brain changes using MRI in relation to clinical features and progression patterns in PD have been assessed in previous studies, but few were conducted in well-defined and large cohorts, including prospective clinical assessments of both motor and non-motor symptoms.

OBJECTIVE

We aimed to identify brain volumetric changes characterizing PD patients, and determine whether regional brain volumetric characteristics at baseline can predict motor, psycho-behavioral and cognitive evolution at one year in a prospective cohort of PD patients.

METHODS

In this multicentric 1 year longitudinal study, PD patients and healthy controls from the MPI-R2* cohort were assessed for demographical, clinical and brain volumetric characteristics. Distinct subgroups of PD patients according to motor, cognitive and psycho-behavioral evolution were identified at the end of follow-up.

RESULTS

One hundred and fifty PD patients and 73 control subjects were included in our analysis. Over one year, there was no significant difference in volume variations between PD and control subjects, regardless of the brain region considered. However, we observed a reduction in posterior cingulate cortex volume at baseline in PD patients with motor deterioration at one year (p = 0.017). We also observed a bilateral reduction of the volume of the amygdala (p = 0.015 and p = 0.041) and hippocampus (p = 0.015 and p = 0.053) at baseline in patients with psycho-behavioral deterioration, regardless of age, dopaminergic treatment and center.

CONCLUSION

Brain volumetric characteristics at baseline may predict clinical trajectories at 1 year in PD as posterior cingulate cortex atrophy was associated with motor decline, while amygdala and hippocampus atrophy were associated with psycho-behavioral decline.

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.

A novel MRI-based volumetric index for monitoring the motor symptoms in Parkinson's disease

BACKGROUND

Conventional MRI scans have limited usefulness in monitoring Parkinson's disease as they typically do not show any disease-specific brain abnormalities. This study aimed to identify an imaging biomarker for tracking motor symptom progression by using a multivariate statistical approach that can combine gray matter volume information from multiple brain regions into a single score specific to each PD patient.

METHODS

A cohort of 150 patients underwent MRI at baseline and had their motor symptoms tracked for up to 10 years using MDS-UPDRS-III, with motor symptoms focused on total and subscores, including rigidity, bradykinesia, postural instability, and gait disturbances, resting tremor, and postural-kinetic tremor. Gray matter volume extracted from MRI data was summarized into a patient-specific summary score using Mahalanobis distance, MGMV. MDS-UPDRS-III's progression and its association with MGMV were modeled via linear mixed-effects models over 5- and 10-year follow-up periods.

RESULTS

Over the 5-year follow-up, there was a significant increase (P < 0.05) in MDS-UPDRS-III total and subscores, except for postural-kinetic tremor. Over the 10-year follow-up, all MDS-UPDRS-III scores increased significantly (P < 0.05). A higher baseline MGMV was associated with a significant increase in MDS-UPDRS-III total, bradykinesia, postural instability and gait disturbances, and resting tremor (P < 0.05) over the 5-year follow-up, but only with total, bradykinesia, and postural instability and gait disturbances during the 10-year follow-up (P < 0.05).

CONCLUSIONS

Higher MGMV scores were linked to faster motor symptom progression, suggesting it could be a valuable marker for clinicians monitoring Parkinson's disease over time.

A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis

Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

Effects of gait training on structural brain changes in Parkinson's disease

BACKGROUND

Gait training may lead to functional brain changes in Parkinson's disease (PD); however, there is a lack of studies investigating structural brain changes after gait training in PD.

OBJECTIVE

To investigate structural brain changes induced by 4 weeks of gait training in individuals with PD.

METHODS

Diffusion tensor imaging and structural T1 images were acquired in PD group before and after robot-assisted gait training or treadmill training, and in healthy control group. Tract-based spatial statistics and tensor-based morphometry were conducted to analyze the data. The outcome of gait training was assessed by gait speed and dual-task interference of cognitive or physical tests of the 10-meter walking test representing gait automaticity. The associations between structural brain changes and these outcomes were investigated using correlation analysis.

RESULTS

A total of 31 individuals with PD (68.5±8.7 years, the Hoehn & Yahr stage of 2.5 or 3) and 28 healthy controls (66.6±8.8 years) participated in this study. Compared to the controls, PD group at baseline showed a significant increased fractional anisotropy (FA) in the right forceps minor and bilateral brainstem and reduced radial diffusivity (RD) in the right superior longitudinal fasciculus, as well as the expanded structural volumes in the several brain areas. After gait training, FA increased in the left internal capsule and it decreased in the left cerebellar Crus I, while the structural volume did not change. The increased FA in the left internal capsule positively correlated with the baseline gait speed and negatively correlated with gait speed improvement; moreover, the decreased FA in the left cerebellum Crus I negatively correlated with the baseline gait speed during the cognitive task.

CONCLUSIONS

Gait training induces white matter changes in the brain of individuals with PD, which suggests the improvement of brain structural pathology to mitigate the impact of neurodegenerative consequences.

Evolutionary and genomic perspectives of brain aging and neurodegenerative diseases

This chapter utilizes genomic concepts and evolutionary perspectives to further understand the possible links between typical brain aging and neurodegenerative diseases, focusing on the two most prevalent of these: Alzheimer's disease and Parkinson's disease. Aging is the major risk factor for these neurodegenerative diseases. Researching the evolutionary and molecular underpinnings of aging helps to reveal elements of the typical aging process that leave individuals more vulnerable to neurodegenerative pathologies. Very little is known about the prevalence and susceptibility of neurodegenerative diseases in nonhuman species, as only a few individuals have been observed with these neuropathologies. However, several studies have investigated the evolution of lifespan, which is closely connected with brain size in mammals, and insights can be drawn from these to enrich our understanding of neurodegeneration. This chapter explores the relationship between the typical aging process and the events in neurodegeneration. First, we examined how age-related processes can increase susceptibility to neurodegenerative diseases. Second, we assessed to what extent neurodegeneration is an accelerated form of aging. We found that while at the phenotypic level both neurodegenerative diseases and the typical aging process share some characteristics, at the molecular level they show some distinctions in their profiles, such as variation in genes and gene expression. Furthermore, neurodegeneration of the brain is associated with an earlier onset of cellular, molecular, and structural age-related changes. In conclusion, a more integrative view of the aging process, both from a molecular and an evolutionary perspective, may increase our understanding of neurodegenerative diseases.

Morphologic brain network predicts levodopa responsiveness in Parkinson disease

Background

The levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson's Disease Rating Scale (UPDRS)-III score > 33% in the LCT receive DBS treatment. However, LCT results are affected by many factors, and only provide information on the immediate effectiveness of dopamine. The aim of the present study was to investigate the relationship between LCT outcome and brain imaging features of PD patients to determine whether the latter can be used to identify candidates for DBS.

Methods

A total of 38 PD patients were enrolled in the study. Based on improvement in UPDRS-III score in the LCT, patients were divided into low improvement (PD-LCT-L) and high improvement (PD-LCT-H) groups. Each patient's neural network was reconstructed based on T1-weighted magnetic resonance imaging data using the Jensen-Shannon divergence similarity estimation method. The network was established with the multiple kernel support vector machine technique. We analyzed differences in individual morphologic brain networks and their global and local metrics to determine whether there were differences in the connectomes of PD-LCT-L and PD-LCT-H groups.

Results

The 2 groups were similar in terms of demographic and clinical characteristics. Mean ± SD levodopa responsiveness was 26.52% ± 3.47% in the PD-LCT-L group (N = 13) and 58.66% ± 4.09% in the PD-LCT-H group (N = 25). There were no significant differences between groups in global and local metrics. There were 43 consensus connections that were affected in both groups; in PD-LCT-L patients, most of these connections were decreased whereas those related to the dorsolateral superior frontal gyrus and left cuneus were significantly increased.

Conclusion

Morphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS.

Sex Differences in Brain Structure in de novo Parkinson's Disease: A Cross-Sectional and Longitudinal Neuroimaging Study

BACKGROUND

Parkinson's disease (PD) varies in occurrence, presentation, and severity between males and females. However, the sex effects on the patterns of brain structure, cross-sectionally and longitudinally, are still unclear.

OBJECTIVE

We aimed to compare sex differences in brain features cross-sectionally and longitudinally using grey matter volume (GMV) and cortical thickness in a large sample of newly diagnosed drug-naive PD patients.

METHODS

Cognitive assessments and structural MR images of 262 PD patients (171 males) and 113 healthy controls (68 males) were selected from the Parkinson's Progression Markers Initiative. Of these, 97 PD patients (66 males) completed 12- and 24-month follow-up examinations. After regressing out the expected effects of age and sex, brain maps of GMV and cortical thickness were compared using two-sample t tests cross-sectionally and were compared using repeated measurement analyses of variance longitudinally.

RESULTS

At baseline, male PD patients exhibited a greater extent of brain atrophy and cortical thickness reduction than females, which mainly occurred in the cerebellum, frontal lobe, parietal lobe, and temporal lobe. At follow-up, female and male PD patients showed similar dynamics of disease progression, as both groups declined over time while the females maintained the advantage. The cortical thickness of the right precentral gyrus at baseline was negatively associated with the longitudinal changes of motor function in male PD patients.

CONCLUSION

The current findings might demonstrate sex effect in neuroanatomy during the course of PD, provide new insights into the neurodegenerative process, and facilitate the development of more effective sex-specific therapeutic strategies.

A bibliometric analysis of neuroimaging biomarkers in Parkinson disease based on Web of Science

BACKGROUND

This study aimed to analyze and summarize the research hotspots and trends in neuroimaging biomarkers (NMBM) in Parkinson disease (PD) based on the Web of Science core collection database and provide new references for future studies.

METHODS

Literature regarding NMBM in PD from 1998 to 2022 was analyzed using the Web of Science core collection database. We utilized CiteSpace software (6.1R2) for bibliometric analyses of countries/institutions/authors, keywords, keyword bursts, references, and their clusters.

RESULTS

A total of 339 studies were identified with a continually increasing annual trend. The most productive country and collaboration was the United States. The top research hotspot is PD cognitive disorder. NMBM and artificial intelligence medical imaging have been applied in the clinical diagnosis, differential diagnosis, treatment, and prognosis of PD. The trends in this field include research on T1 weighted structure magnetic resonance imaging in accordance with voxel-based morphometry, PD cognitive disorder, and neuroimaging features of Lewy body dementia and Alzheimer disease.

CONCLUSION

The development of NMBM in PD will be effectively promoted by drawing on international research hotspots and cutting-edge technologies, emphasizing international collaboration and institutional cooperation at the national level, and strengthening interdisciplinary research.

Subthalamic Beta Activity in Parkinson's Disease May Be Linked to Dorsal Striatum Gray Matter Volume and Prefrontal Cortical Thickness: A Pilot Study

Background

Excessive oscillations at beta frequencies (13–35 Hz) in the subthalamic nucleus (STN) represent a pathophysiological hallmark of Parkinson's disease (PD), which correlates well with parkinsonian symptoms and is reduced in response to standard disease treatments. However, the association of disease-specific regional gray matter (GM) atrophy or cortical thickness (CT) with the presence of STN beta oscillatory activity has been poorly investigated but is of relevance given the potential of these variables for extracting information about PD pathophysiology. This exploratory study investigated the involvement of regional GM volume and CT in the basal ganglia-cortical network and its potential association with the presence of STN beta oscillatory activity in PD.

Methods

We acquired preoperative GM densities on T1-weighted magnetic resonance imaging scans and we carried out regional estimation of GM volume and CT. LFP activities from the STN were recorded post-operatively in 7 cognitively preserved PD patients off dopaminergic medication undergoing deep-brain stimulation surgery. Oscillatory beta power was determined by power spectral density of 4-min resting state STN LFP activity. Spearman partial correlations and regression analysis were used to screen the presence of STN beta power for their relationship with GM volume and CT measurements.

Results

After controlling for the effects of age, educational level, and disease duration, and after correcting for multiple testing, enhanced STN beta power showed significant and negative correlations between, first, volume of the right putamen and left caudate nucleus, and second, smaller CT in frontal regions involving the left rostral middle frontal gyrus (MFG) and left medial orbitofrontal gyrus. A lower volume in the right putamen and a lower CT in the left MFG demonstrated the strongest associations with increased STN beta power.

Conclusions

These tentative results seem to suggest that STN LFP beta frequencies may be mainly linked to different but ongoing parallel neurodegenerative processes, on the one hand, to GM volume reduction in dorsal striatum, and on the other hand, to CT reduction of prefrontal-“associative” regions. These findings could further delineate the brain structural interactions underpinning the exaggerated STN beta activity commonly observed in PD patients.

Predict initial subthalamic nucleus stimulation outcome in Parkinson's disease with brain morphology

AIM

Subthalamic nucleus deep brain stimulation (STN-DBS) has been reported to be effective in treating motor symptoms in Parkinson's disease (PD), which may be attributed to changes in the brain network. However, the association between brain morphology and initial STN-DBS efficacy, as well as the performance of prediction using neuroimaging, has not been well illustrated. Therefore, we aim to investigate these issues.

METHODS

In the present study, 94 PD patients underwent bilateral STN-DBS, and the initial stimulation efficacy was evaluated. Brain morphology was examined by magnetic resonance imaging (MRI). The volume of tissue activated in the motor STN was measured with MRI and computed tomography. The prediction of stimulation efficacy was achieved with a support vector machine, using brain morphology and other features, after feature selection and hyperparameter optimization.

RESULTS

A higher stimulation efficacy was correlated with a thicker right precentral cortex. No association with subcortical gray or white matter volumes was observed. These morphological features could estimate the individual stimulation response with an r value of 0.5678, an R² of 0.3224, and an average error of 11.4%. The permutation test suggested these predictions were not based on chance.

CONCLUSION

Our results indicate that changes in morphology are associated with the initial stimulation motor response and could be used to predict individual initial stimulation-related motor responses.

Progressive Prefrontal Cortex Dysfunction in Parkinson's Disease With Probable REM Sleep Behavior Disorder: A 3-Year Longitudinal Study

This study aimed to explore the disrupted prefrontal cortex activity specific to patients with Parkinson's disease (PD) with rapid eye movement sleep behavior disorder (RBD) compared with those without and to further examine the associations between these alterations and neuropsychological measurements. Ninety-six patients with early PD underwent both structural and functional MRI, and also neuropsychological assessments in the Parkinson's Progression Markers Initiative (PPMI) database. Of these, 46 patients who completed 1- and 3-year fMRI follow-up examinations were categorized as PD with probable RBD (PD-pRBD+) and without (PD-pRBD−). The left dorsolateral prefrontal cortex (DLPFC) seed-to-voxel functional connectivity analysis was conducted to evaluate the progressive neural alterations specific to PD-pRBD+ compared with PD-pRBD− over time. Furthermore, relationships between these alterations and neuropsychological performance were examined. Compared with patients with PD-pRBD−, patients with PD-pRBD+ initially exhibited connectivity deficits between the left DLPFC and the medial frontopolar cortex. Moreover, these patients further exhibited disrupted DLPFC connectivity in the lateral frontopolar cortex at the 3-year follow-up evaluation. Correlation analysis revealed that connectivity between the left DLPFC and frontopolar cortex was positively related to executive function in PD-pRBD+ after adjusting for nuisance variables. Progressive prefrontal cortex dysfunction associated with RBD in early PD may provide an effective subtype-specific biomarker of neurodegenerative progression, which may shed light on the neuropathological mechanisms underlying the clinical heterogeneity of this disease.

Gray matter, white matter and cerebrospinal fluid abnormalities in Parkinson's disease: A voxel-based morphometry study

BACKGROUND

Parkinson's disease (PD) is a chronic neurodegenerative disorder characterized by bradykinesia, tremor, and rigidity among other symptoms. With a 70% cumulative prevalence of dementia in PD, cognitive impairment and neuropsychiatric symptoms are frequent.

MATERIALS AND METHODS

In this study, we looked at anatomical brain differences between groups of patients and controls. A total of 138 people with PD were compared to 64 age-matched healthy people using voxel-based morphometry (VBM). VBM is a fully automated technique that allows for the identification of regional differences in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) allowing for an objective comparison of brains of different groups of people. We used statistical parametric mapping for image processing and statistical analysis.

RESULTS

In comparison to controls, PD patients had lower GM volumes in the left middle cingulate, left lingual gyrus, right calcarine and left fusiform gyrus, also PD patients indicated lower WM volumes in the right middle cingulate, left lingual gyrus, right calcarine, and left inferior occipital gyrus. Moreover, PD patients group demonstrated higher CSF in the left caudate compared to the controls.

CONCLUSION

Physical fragility and cognitive impairments in PD may be detected more easily if anatomical abnormalities to the cingulate gyrus, occipital lobe and the level of CSF in the caudate are identified. Thus, our findings shed light on the role of the brain in PD and may aid in a better understanding of the events that occur in PD patients.

Neurodegeneration of the Globus Pallidus Internus as a Neural Correlate to Dopa-Response in Freezing of Gait

BACKGROUND

Background: Parkinson's disease (PD) patients who develop freezing of gait (FOG) have reduced mobility and independence. While some patients experience improvement in their FOG symptoms with dopaminergic therapies, a subset of patients have little to no response. To date, it is unknown what changes in brain structure underlie dopa-response and whether this can be measured using neuroimaging approaches.

OBJECTIVE

We tested the hypothesis that structural integrity of brain regions (subthalamic nucleus and globus pallidus internus, GPi) which link basal ganglia to the mesencephalic locomotor region (MLR), a region involved in automatic gait, would be associated with FOG response to dopaminergic therapy.

METHODS

In this observational study, thirty-six participants with PD and definite FOG were recruited to undergo diffusion kurtosis imaging (DKI) and multiple assessments of dopa responsiveness (UPDRS scores, gait times ON versus OFF medication).

RESULTS

The right GPi in participants with dopa-unresponsive FOG showed reduced fractional anisotropy, mean kurtosis (MK), and increased radial diffusivity relative to those with dopa-responsive FOG. Furthermore, using probabilistic tractography, we observed reduced MK and increased mean diffusivity along the right GPi-MLR tract in dopa-unresponsive FOG. MK in the right GPi was associated with a subjective dopa-response for FOG (r = -0.360, df = 30, p = 0.043) but not overall motor dopa-response.

CONCLUSION

These results support structural integrity of the GPi as a correlate to dopa-response in FOG. Additionally, this study suggests DKI metrics may be a sensitive biomarker for clinical studies targeting dopaminergic circuitry and improvements in FOG behavior.

Heterogeneous alterations in thalamic subfields in major depression disorder

BACKGROUND

It is well known that the thalamus is not a unitary and homogeneous entity but a complex and highly connected archeocortical structure. Although many neuroimaging studies have reported alterations in the thalamus in major depressive disorder (MDD), the structural alterations in thalamic subfields remain unclear. This study aimed to investigate changes in gray matter volume (GMV) in thalamic subfields in MDD patients.

METHODS

The present study included structural images of 848 MDD patients and 794 age-matched normal controls (NC) from 17 study sites of the REST-meta-MDD consortium. We performed voxel-based morphometric analyses to calculate the GMV in the entire thalamus and its subfields using three different automated anatomical labeling atlases and subsequently compared the differences between first-episode drug-naïve major depressive disorder (FEDN), recurrent major depressive disorder (RMDD), and NC groups. We also evaluated the relationships between thalamic GMV and clinical symptoms in MDD patients.

RESULTS

Compared to NC, the FEDN patients showed increased GMV in thalamic subfields but not in the entire thalamus, while RMDD patients showed no significant alterations in GMV in the entire thalamus and its subfields. Moreover, the mean GMV in the right anterior thalamus and left anteroventral thalamus in RMDD patients were mildly positively correlated with the Hamilton Anxiety Rating Scale scores.

LIMITATIONS

The main limitations are a single-modal analysis based on T1-weighted MR images and a cross-sectional design.

CONCLUSIONS

Our findings suggest that FEDN and RMDD patients show heterogeneous alterations across thalamic subfields, which may help us understand the pathophysiological mechanisms of MDD.

Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning

Structural MRI (sMRI) has been widely used to examine the cerebral changes that occur in Parkinson's disease (PD). However, previous studies have aimed for brain changes at the group level rather than at the individual level. Additionally, previous studies have been inconsistent regarding the changes they identified. It is difficult to identify which brain regions are the true biomarkers of PD. To overcome these two issues, we employed four different feature selection methods [ReliefF, graph-theory, recursive feature elimination (RFE), and stability selection] to obtain a minimal set of relevant features and nonredundant features from gray matter (GM) and white matter (WM). Then, a support vector machine (SVM) was utilized to learn decision models from selected features. Based on machine learning technique, this study has not only extended group level statistical analysis with identifying group difference to individual level with predicting patients with PD from healthy controls (HCs), but also identified most informative brain regions with feature selection methods. Furthermore, we conducted horizontal and vertical analyses to investigate the stability of the identified brain regions. On the one hand, we compared the brain changes found by different feature selection methods and considered these brain regions found by feature selection methods commonly as the potential biomarkers related to PD. On the other hand, we compared these brain changes with previous findings reported by conventional statistical analysis to evaluate their stability. Our experiments have demonstrated that the proposed machine learning techniques achieve satisfactory and robust classification performance. The highest classification performance was 92.24% (specificity), 92.42% (sensitivity), 89.58% (accuracy), and 89.77% (AUC) for GM and 71.93% (specificity), 74.87% (sensitivity), 71.18% (accuracy), and 71.82% (AUC) for WM. Moreover, most brain regions identified by machine learning were consistent with previous findings, which means that these brain regions are related to the pathological brain changes characteristic of PD and can be regarded as potential biomarkers of PD. Besides, we also found the brain abnormality of superior frontal gyrus (dorsolateral, SFGdor) and lingual gyrus (LING), which have been confirmed in other studies of PD. This further demonstrates that machine learning models are beneficial for clinicians as a decision support system in diagnosing PD.

Subcortical grey matter changes associated with motor symptoms evaluated by the Unified Parkinson's disease Rating Scale (part III): A longitudinal study in Parkinson's disease

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Decreased grey matter volume over time suggests a subcortical alteration in PD.

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Decreased volume in the thalamus may be related to the decline in motor skills.

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Increased volume in the pallidum may contribute to motor impairment.

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Structural changes in line with the model of basal ganglia-thalamocortical circuits.

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VBM and volumetry might capture complementary aspects of structural changes in PD.

Parkinson disease (PD) is characterized by motor deficits related to structural changes in the basal ganglia-thalamocortical circuits. However, it is still unclear the exact nature of the association between grey matter alterations and motor symptoms.

Therefore, the aim of our investigation was to identify the subcortical modifications associated with motor symptoms of PD over time - adopting voxel-based morphometry (VBM) and automated volumetry methods.

We selected fifty subjects with PD from the Parkinson’s Progression Markers Initiative (PPMI) database, who performed an MRI session at two time points: at baseline (i.e. at maximum 2 years after clinical diagnosis of PD) and after 48 months. Motor symptoms were assessed using the part III of the Unified Parkinson’s Disease Rating Scale at the two time points.

Our VBM and volumetric analyses showed a general atrophy in all subcortical regions when comparing baseline with 48 months. These findings confirmed previous observations indicating a subcortical alteration over time in PD. Furthermore, our findings supported the idea that a reduced volume in the thalamus and an increased volume in pallidum may be related to the decline in motor skills. These structural modifications are in accordance with the functional model of the basal ganglia-thalamocortical circuits controlling movements. Moreover, VBM and volumetry provided partially overlapping results, suggesting that these methods might capture complementary aspects of brain degeneration in PD.

Differential functional dysconnectivity of caudate nucleus subdivisions in Parkinson's disease

Caudate dopaminergic dysfunction is implied in the pathophysiology of patients with Parkinson's disease (PD). Still, connectivity specificities of the caudate nucleus (CN) subdivisions and the effect of dopamine are poorly understood. We collected MRI and neuropsychological data from 34 PD patients and 26 age- and sex-matched healthy elderly individuals (HEs) in this study. Resting-state functional connectivity analysis revealed that compared to the other CN subdivisions, the CN head was more strongly connected to the default mode network (DMN), the CN body to the frontoparietal network (FPN), and the CN tail to the visual network in HEs. PD patients off medication showed reduced connectivity within all these subdivision networks. In PD patients on medication, functional connectivity in the CN head network was significantly improved in the medial prefrontal cortex and in the body network it was improved in the dorsolateral prefrontal cortex. These improvements contributed to ameliorated motivation and cognitive function in PD patients. Our results highlighted the specific alterations and dopamine modulation in these CN subdivision networks in PD, which may provide insight into the pathophysiology and therapeutics of this disease.

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.

Brain Morphological Alterations Are Detected in Early-Stage Parkinson's Disease with MRI Morphometry

BACKGROUND AND PURPOSE

To detect brain morphological alterations in patients with early Parkinson's disease (PD) by using magnetic resonance imaging (MRI) morphometry under radiological diagnostic conditions.

METHODS

T1-weighted brain images of 18 early PD patients and 18 age-sex-matched healthy controls (HCs) were analyzed with free software Computational Anatomy Toolbox (CAT12). Regional cortical thickness (rCTh) in 68 atlas-defined regions-of-interest (ROIs) and subcortical gray matter volume (SGMV) in 14 atlas-defined ROIs were determined and compared between patients and HCs by paired comparison using both ROI-wise and voxel-wise analyses. False-discovery rate (FDR) was used multiple comparison correction. Possible correlations between brain morphological changes in patients and clinical observations were also analyzed.

RESULTS

Comparing to the HCs, the ROI-wise analysis revealed rCTh thinning significantly in left medial orbitofrontal (P = .001), by trend (P < .05 but not significant after FDR correction) in four other ROIs located in frontal and temporal lobes, and a volume decreasing trend in left pallidum of the PD patients, while the voxel-wise analysis revealed one cluster with rCTh thinning trend located between left insula and superior temporal region of the patients. In addition, the patients showed more distinct rCTh thinning in ipsilateral hemisphere and SGMV deceasing trends in contralateral hemisphere in respect of the symptom-onset body side.

CONCLUSION

Brain morphological alterations in early PD patients are evident despite of their inconspicuous findings in standard MRI. Quantitative morphological measurements with CAT12 may be an applicable add-on tool for clinical diagnosis of early PD. These results have to be verified in future studies with larger patient samples.

Joint Multi-modal Parcellation of the Human Striatum: Functions and Clinical Relevance

The human striatum is essential for both low- and high-level functions and has been implicated in the pathophysiology of various prevalent disorders, including Parkinson's disease (PD) and schizophrenia (SCZ). It is known to consist of structurally and functionally divergent subdivisions. However, previous parcellations are based on a single neuroimaging modality, leaving the extent of the multi-modal organization of the striatum unknown. Here, we investigated the organization of the striatum across three modalities-resting-state functional connectivity, probabilistic diffusion tractography, and structural covariance-to provide a holistic convergent view of its structure and function. We found convergent clusters in the dorsal, dorsolateral, rostral, ventral, and caudal striatum. Functional characterization revealed the anterior striatum to be mainly associated with cognitive and emotional functions, while the caudal striatum was related to action execution. Interestingly, significant structural atrophy in the rostral and ventral striatum was common to both PD and SCZ, but atrophy in the dorsolateral striatum was specifically attributable to PD. Our study revealed a cross-modal convergent organization of the striatum, representing a fundamental topographical model that can be useful for investigating structural and functional variability in aging and in clinical conditions.

Regional transcriptional architecture of Parkinson's disease pathogenesis and network spread

Although a significant genetic contribution to the risk of developing sporadic Parkinson's disease has been well described, the relationship between local genetic factors, pathogenesis, and subsequent spread of pathology throughout the brain has been largely unexplained in humans. To address this question, we use network diffusion modelling to infer probable pathology seed regions and patterns of disease spread from MRI atrophy maps derived from 232 de novo subjects in the Parkinson's Progression Markers Initiative study. Allen Brain Atlas regional transcriptional profiles of 67 Parkinson's disease risk factor genes were mapped to the inferred seed regions to determine the local influence of genetic risk factors. We used hierarchical clustering and L1 regularized regression analysis to show that transcriptional profiles of immune-related and lysosomal risk factor genes predict seed region location and the pattern of disease propagation from the most likely seed region, substantia nigra. By leveraging recent advances in transcriptomics, we show that regional microglial abundance quantified by high fidelity gene expression also predicts seed region location. These findings suggest that early disease sites are genetically susceptible to dysfunctional lysosomal α-synuclein processing and microglia-mediated neuroinflammation, which may initiate the disease process and contribute to spread of pathology along neural connectivity pathways.

Progression of grey and white matter brain damage in Parkinson's disease: a critical review of structural MRI literature

The current review summarizes the current knowledge on longitudinal cortical and subcortical grey and white matter MRI findings assessed using T1-weighted and one-tensor diffusion-weighted MRI in Parkinson's disease (PD) patients. Results were reviewed according to disease duration, disease severity and cognitive impairment. The most consistent findings are those showing a progressive cortical atrophy accumulation in caudate, putamen, temporal/hippocampal, frontal and parietal areas in de novo PD cases and in the early/middle phase of the disease, with the achievement of a plateau in the later stage. Analyzing results according to the patient cognitive status, only a few studies used longitudinal MRI metrics to predict mild cognitive impairment or dementia conversion in PD patients, suggesting that atrophy of the hippocampus, fronto-temporal areas, caudate, thalamus and accumbens might play a role in this process. Stratifying patients according to disease severity, findings appear partially controversial, although showing a progressive atrophy of basal ganglia over 1 year of follow up and a widespread cortical thinning over 3-6 years in mild to moderate PD patients. Finally, microstructural damage of the main motor and associative WM tracts seems to be present, and rapidly progress, even in the early phase of PD. The utility of structural MRI metrics as biomarkers of PD progression and their role in improving the accuracy of disease progression prediction is still debated.

Clinical and Neuropathological Features Associated With Loss of RAB39B

BACKGROUND

Pathogenic variants in the small GTPase Ras Analogue in Brain 39b (RAB39B) have been linked to the development of early-onset parkinsonism. The study was aimed at delineating the clinical and neuropathological features associated with a previously reported pathogenic variant in RAB39B (c.503C>A p.T168K) and testing for dysregulation of RAB39B in idiopathic PD.

METHODS

Clinical details of a male individual hemizygous for the T168K variant were collected by systematic review of medical records. Neuropathological studies of fixed brain tissue were performed and steady-state RAB39B levels were determined by western blot analysis.

RESULTS

Neuropathological examination showed extensive dopaminergic neuron loss, widespread Lewy pathology, and iron accumulation in the substantia nigra. Additional pathology was observed in the hippocampus and thalamus. Western blot analysis demonstrated that the T168K variant results in loss of RAB39B. In individuals with idiopathic PD (n = 10, 6 male/4 female), steady-state RAB39B was significantly reduced in the prefrontal cortex and substantia nigra.

CONCLUSIONS

T168K RAB39B is unstable in vivo and associated with dopaminergic neuron loss and Lewy pathology. Dysregulation of RAB39B in the prefrontal cortex and substantia nigra of individuals with idiopathic PD potentially implicates the protein more broadly in the pathological mechanisms underlying PD and related Lewy body disorders. © 2020 International Parkinson and Movement Disorder Society.

Differentiating Parkinson's disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load

Background

Periventricular leukoaraiosis may be an important pathological change in postural instability gait disorder (PIGD), a motor subtype of Parkinson's disease (PD). Clinical diagnosis of PIGD may be challenging for the general neurologist.

Purpose

To evaluate 1) the utility of a fully automated volume‐based morphometry (Vol‐BM) in characterizing imaging diagnostic markers in PD and PIGD, including, 2) novel deep gray nuclear lesion load (GMab), and 3) discriminatory performance of a Vol‐BM model construct in classifying the PIGD subtype.

Study Type

Prospective.

Subjects

In all, 23 PIGD, 21 PD, and 20 age‐matched healthy controls (HC) underwent MRI brain scans and clinical assessments.

Field Strength/Sequence

3.0T, sagittal 3D‐magnetization‐prepared rapid gradient echo (MPRAGE), and fluid‐attenuated inversion recovery imaging (FLAIR) sequences.

Assessment

Clinical assessment was conducted by a movement disorder neurologist. The MR brain images were then segmented using an automated multimodal Vol‐BM algorithm (MorphoBox) and reviewed by two authors independently.

Statistical Testing

Brain segmentation and clinical parameter differences and dependence were assessed using analysis of variance (ANOVA) and regression analysis, respectively. Logistic regression was performed to differentiate PIGD from PD, and discriminative reliability was evaluated using receiver operating characteristic (ROC) analysis.

Results

Significantly higher white matter lesion load (WMab) (P < 0.01), caudate GMab (P < 0.05), and lateral and third ventricular (P < 0.05) volumetry were found in PIGD, compared with PD and HC. WMab, caudate and putamen GMab, and caudate, lateral, and third ventricular volumetry showed significant coefficients (P < 0.005) in linear regressions with balance and gait assessments in both patient groups. A model incorporating WMab, caudate GMab, and caudate GM discriminated PIGD from PD and HC with a sensitivity = 0.83 and specificity = 0.76 (AUC = 0.84).

Data Conclusion

Fast, unbiased quantification of microstructural brain changes in PD and PIGD is feasible using automated Vol‐BM. Composite lesion load in the white matter and caudate, and caudate volumetry discriminated PIGD from PD and HC, and showed potential in classification of these disorders using supervised machine learning.

Level of Evidence: 1

Technical Efficacy: Stage 1

J. Magn. Reson. Imaging 2020;51:748–756.

Precuneus Dysfunction in Parkinson's Disease With Mild Cognitive Impairment

Background: Mild cognitive impairment (MCI) frequently occurs in Parkinson’s disease (PD). Neurovascular changes interact with neurodegenerative processes in PD. However, the deficits of cerebral blood flow (CBF) perfusion and the associated functional connectivity (FC) in PD patients with MCI (PD-MCI) remain unclear.

Purpose: This study aimed to explore the specific neurovascular perfusion alterations in PD-MCI compared to PD with normal cognition (PD-NC) and healthy controls (HCs), and to further examine the resultant whole brain FC changes in the abnormal perfusion regions.

Methods: Relative CBF (rCBF) was calculated using arterial spin labeling (ASL) in 54 patients with PD (27 patients with PD-NC and 27 patients with PD-MCI) and 25 HCs matched for age and gender ratio, who also underwent the structural MRI, resting-state functional MRI (rs-fMRI) and neuropsychological examinations. The gray matter (GM) changes in PD patients were analyzed using voxel-based morphometry (VBM). The alterations in rCBF perfusion and FC among groups were then analyzed respectively. Additionally, correlations between these alterations and neuropsychological performances were further examined.

Results: Compared to HC, left caudate atrophy was detected in patients with PD. In comparison to both PD-NC and HC, patients with PD-MCI specifically exhibited hypoperfusion in the parietal memory network (PMN) in the precuneus (PCu) and decreased PCu-FC in the right striatum. Moreover, PCu perfusion and PCu-FC strengths in the right striatum were positively associated with memory performance in PD-MCI.

Conclusions: These findings suggest that the posterior PMN dysfunction underlies memory deficits in PD-MCI.

Changes in Cortical Thickness in Patients With Early Parkinson's Disease at Different Hoehn and Yahr Stages

Objectives: This study was designed to explore changes in cortical thickness in patients with early Parkinson’s disease (PD) at different Hoehn and Yahr (H-Y) stages and to demonstrate the association of abnormally altered brain regions with part III of the Unified Parkinson’s Disease Rating Scale (UPDRS-III).

Materials and Methods: Sixty early PD patients and 29 age- and gender-matched healthy controls (HCs) were enrolled in this study. All PD patients underwent comprehensive clinical and neuropsychological evaluations and 3.0 T magnetic resonance scanning. Patients with H-Y stage ≤1.5 were included in the mild group, and all other patients were included in the moderate group. FreeSurfer software was used to calculate cortical thickness. We assessed the relationship between UPDRS-III and regional changes in cortical thinning, including the bilateral fusiform and the temporal lobe.

Results: The average cortical thickness of the temporal pole, fusiform gyrus, insula of the left hemisphere and fusiform gyrus, isthmus cingulate cortex, inferior temporal gyrus, middle temporal cortex and posterior cingulate cortex of the right hemisphere exhibited significant decreasing trends in HCs group and PD groups (i.e., the mild group and moderate group). After controlling for the effects of age, gender, and disease duration, the UPDRS-III scores in patients with early PD were correlated with the cortical thickness of the left and right fusiform gyrus and the left temporal pole (p < 0.05).

Conclusion: The average cortical thickness of specific brain regions reduced with increasing disease severity in early PD patients at different H-Y stages, and the UPDRS-III scores of early PD patients were correlated with cortical thickness of the bilateral fusiform gyrus and the left temporal pole.

Performance analysis of different classification algorithms using different feature selection methods on Parkinson's disease detection

BACKGROUND

In diagnosis of neurodegenerative diseases, the three-dimensional magnetic resonance imaging (3D-MRI) has been heavily researched. Parkinson's disease (PD) is one of the most common neurodegenerative disorders.

NEW METHOD

The performances of five different classification approaches using five different attribute rankings each followed with an adaptive Fisher stopping criteria feature selection (FS) method are evaluated. To improve the performance of PD detection, a source fusion technique which combines the gray matter (GM) and white (WM) tissue maps and a decision fusion technique which combines the outputs of all classifiers using the correlation-based feature selection (CFS) method by majority voting are used.

RESULTS

Among the five FS methods, the CFS provides the highest results for all five classification algorithms and the SVM provides the best classification performances for all five different FS methods. The classification accuracy of 77.50% and 81.25% are obtained for the GM and WM tissues, respectively. However, the fusion of GM and WM datasets improves the classification accuracy of the proposed methodology up to 95.00%.

COMPARISON WITH EXISTING METHODS

An f-contrast is used to generate 3D masks for GM and WM datasets and a fusion technique, combining the GM and WM datasets is used. Several classification algorithms using several FS methods are performed and a decision fusion technique is used.

CONCLUSIONS

Using the combination of the 3D masked GM and WM tissue maps and the fusion of the outputs of multiple classifiers with CFS method gives the classification accuracy of 95.00%.

Longitudinal Progression Markers of Parkinson's Disease: Current View on Structural Imaging

PURPOSE OF REVIEW

Advances in neuroimaging techniques pave a rich avenue for in vivo progression biomarkers, which can objectively and noninvasively assess the long-term dynamic alterations in the brain of Parkinson's disease (PD) patients. This article reviews recent progress in structural magnetic resonance imaging (MRI) tools to track disease progression in PD, and discusses specific criteria a neuroimaging tool needs to meet to be a progression biomarker of PD and the potential applications of these techniques in PD based on current evidence.

RECENT FINDINGS

Recent longitudinal studies showed that quantitative structural MRI markers derived from T1-weighted, diffusion-weighted, neuromelanin-sensitive, and iron-sensitive imaging have the potential to track disease progression in PD. However, validation of these progression biomarkers is only beginning, and more work is required for multisite validation, the sample size for use in a clinical trial, and drug-responsiveness of most of these biomarkers. At present, the most clinical trial-ready biomarker is free-water diffusion imaging of the substantia nigra and seems well established to be used in disease-modifying studies in PD. A variety of structural imaging biomarkers are promising candidates to be progression biomarkers in PD. Further studies are needed to elucidate the sensitivity, reliability, sample size, and effect of confounding factors of these progression biomarkers.

Effects of different covariates and contrasts on classification of Parkinson's disease using structural MRI

Three-dimensional magnetic resonance imaging (3D-MRI) has been effectively used in the diagnosis of progressive neurodegenerative diseases including Parkinson's disease (PD). Pre-processing of 3D-MRI scans plays an important role for post-processing. In this paper, voxel-based morphometry (VBM) technique is used to compare morphological dierences of PDs versus healthy controls (HCs) in gray matter (GM) and white matter (WM). The effects of using different covariates (i.e. total intracranial volume (TIV), age, sex and combination of them) as well as two different hypotheses, t-contrast and f-contrast, on classification of PD from HCs have been studied. 3D masks for GM as well as WM tissues are obtained separately by utilizing local differences between PD and HC and using the two sample t-test method. PCA is used to perform dimensionality reduction and SVM is used for classification. The proposed method is evaluated on 40 PDs and 40 HCs obtained from the ppmi dataset. The classification results using f-contrast show a superior performance for GM, WM, and the combination of GM as well as WM compared to t-contrast. Furthermore, the experimental results indicate that using TIV as a covariate provides more robust results for PD classification compared to other covariate settings. The highest accuracies of distinguishing between PDs and HCs are obtained when TIV is used as a covariate and f-contrast is used for model building: 73.75%, 72.50%, and 93.7% for GM, WM, and the combination of them, respectively.

Brain gray matter volume differences in obese youth with type 2 diabetes: a pilot study

BACKGROUND

Adults with type 2 diabetes (T2D) have significantly lower gray matter volume (GMV) compared to healthy peers. Whether GMV differences exist in youth with T2D remains unclear. Thus, we compared global and regional GMV between obese youth with T2D with age, race and sex similar healthy controls.

METHODS

In a cross-sectional study, 20 obese youth with T2D underwent T1-weighted brain magnetic resonance imaging (MRI). Comparisons were made to 20 age, race and sex similar controls. Differences in global and regional GMV between groups were identified using voxel-based morphometry (VBM).

RESULTS

Youth with T2D had a significantly lower global GMV-to-intracranial volume ratio (0.51±0.02 in T2D vs. 0.53±0.02 in controls, p=0.02, Cohen's d=0.85). There were 14 regions where GMV was significantly lower in the T2D group, and nine of these were found in either the temporal or occipital lobes. There were six regions with increased GMV in T2D. All regional differences were significant at p<0.05 after adjusting for multiple comparisons.

CONCLUSIONS

Results from this pilot study show obese youth with T2D have significantly lower global GMV and regional GMV differences, when compared to their age, race and sex similar peers. Future work is needed to determine whether these brain findings are a direct result of adolescent-onset T2D.

Method for retrospective estimation of natural head movement during structural MRI

BACKGROUND

Head motion during brain structural MRI scans biases brain morphometry measurements but quantitative retrospective methods estimating head motion from structural MRI have not been evaluated.

PURPOSE

To verify the hypothesis that two metrics retrospectively computed from MR images: 1) average edge strength (AES, reduced with image blurring) and 2) entropy (ENT, increased with blurring and ringing artifacts) could be sensitive to in-scanner head motion during acquisition of T₁ -weighted MR images.

STUDY TYPE

Retrospective.

POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODEL

In all, 83 healthy control (HC) and 120 Parkinson's disease (PD) patients.

FIELD STRENGTH/SEQUENCE

3D magnetization-prepared rapid gradient-echo (MPRAGE) images at 3T.

ASSESSMENT

We 1) compared AES and ENT distribution between HC and PD; 2) evaluated the correlation between tremor score (TS) and AES (or ENT) in PD; and 3) investigated cortical regions showing an association between AES (or ENT) and local and network-level covariance measures of cortical thickness (CT), gray to white matter contrast (GWC) and gray matter density maps (GMx).

STATISTICAL TESTS

1) Student's t-test. 2) Spearman's rank correlation. 3) General linear model and partial least square analysis.

RESULTS

AES, but not ENT, differentiated HC and PD (P = 0.02, HC median AES = 39.8, interquartile range = 9.8, PD median AES = 37.6, interquartile range = 8.1). In PD, AES correlated negatively with TS (ρ = -0.21, P = 0.02) and showed a significant relationship (|Z| >3, P < 0.001) with structural covariance of CT and GWC in 54 out of 68 cortical regions.

DATA CONCLUSION

In clinical populations prone to head motion, AES can provide a reliable retrospective index of motion during structural scans, identifying brain areas whose morphometric measures covary with motion.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:927-937.

Movement disorders in genetically confirmed mitochondrial disease and the putative role of the cerebellum

BACKGROUND

Mitochondrial disease can present as a movement disorder. Data on this entity's epidemiology, genetics, and underlying pathophysiology, however, is scarce.

OBJECTIVE

The objective of this study was to describe the clinical, genetic, and volumetric imaging data from patients with mitochondrial disease who presented with movement disorders.

METHODS

In this retrospective analysis of all genetically confirmed mitochondrial disease cases from three centers (n = 50), the prevalence and clinical presentation of video-documented movement disorders was assessed. Voxel-based morphometry from high-resolution MRI was employed to compare cerebral and cerebellar gray matter volume between mitochondrial disease patients with and without movement disorders and healthy controls.

RESULTS

Of the 50 (30%) patients with genetically confirmed mitochondrial disease, 15 presented with hypokinesia (parkinsonism 3/15), hyperkinesia (dystonia 5/15, myoclonus 3/15, chorea 2/15), and ataxia (3/15). In 3 patients, mitochondrial disease presented as adult-onset isolated dystonia. In comparison to healthy controls and mitochondrial disease patients without movement disorders, patients with hypo- and hyperkinetic movement disorders had significantly more cerebellar atrophy and an atrophy pattern predominantly involving cerebellar lobules VI and VII.

CONCLUSION

This series provides clinical, genetic, volumetric imaging, and histologic data that indicate major involvement of the cerebellum in mitochondrial disease when it presents with hyper- and hypokinetic movement disorders. As a working hypothesis addressing the particular vulnerability of the cerebellum to energy deficiency, this adds substantially to the pathophysiological understanding of movement disorders in mitochondrial disease. Furthermore, it provides evidence that mitochondrial disease can present as adult-onset isolated dystonia. © 2017 International Parkinson and Movement Disorder Society.

Correlation between Dopamine Transporter Degradation and Striatocortical Network Alteration in Parkinson's Disease

The association between dopamine neuron loss and functional change in the striatocortical network was analyzed in 31 patients with Parkinson's disease (PD) [mean disease duration 4.03 ± 4.20 years; Hoehn and Yahr (HY) stage 2.2 ± 1.2] and 37 age-matched normal control subjects. We performed ^99mTc-TRODAT-1 SPECT/CT imaging to detect neuron losses and resting-state functional magnetic resonance imaging to detect functional changes. Mean striatal dopamine transporter binding ratios were determined by region of interest analysis. The functional connectivity correlation coefficient (fc-cc) was determined in six striatal subregions, and interactions between these binding ratios and the striatocortical fc-cc values were analyzed. The PD patients had significant functional network alterations in all striatal subregions. Lower striatal dopamine transporter binding correlated significantly with lower fc-cc values in the superior medial frontal (SMF) lobe and superior frontal lobe and higher fc-cc values in the cerebellum and parahippocampus. The difference in fc-cc between the ventral inferior striatum and SMF lobe was significantly correlated with increased disease duration (r = -0.533, P = 0.004), higher HY stage (r = -0.431, P = 0.020), and lower activities of daily living score (r = 0.369, P = 0.049). The correlation of frontostriatal network changes with clinical manifestations suggests that fc-cc may serve as a surrogate marker of disease progression.

Longitudinal changes of cortical microstructure in Parkinson's disease assessed with T1 relaxometry

BACKGROUND

Histological evidence suggests that pathology in Parkinson's disease (PD) goes beyond nigrostriatal degeneration and also affects the cerebral cortex. Quantitative MRI (qMRI) techniques allow the assessment of changes in brain tissue composition. However, the development and pattern of disease-related cortical changes have not yet been demonstrated in PD with qMRI methods. The aim of this study was to investigate longitudinal cortical microstructural changes in PD with quantitative T1 relaxometry.

METHODS

13 patients with mild to moderate PD and 20 matched healthy subjects underwent high resolution T1 mapping at two time points with an interval of 6.4 years (healthy subjects: 6.5 years). Data from two healthy subjects had to be excluded due to MRI artifacts. Surface-based analysis of cortical T1 values was performed with the FreeSurfer toolbox.

RESULTS

In PD patients, a widespread decrease of cortical T1 was detected during follow-up which affected large parts of the temporo-parietal and occipital cortices and also frontal areas. In contrast, age-related T1 decrease in the healthy control group was much less pronounced and only found in lateral frontal, parietal and temporal areas. Average cortical T1 values did not differ between the groups at baseline (p = 0.17), but were reduced in patients at follow-up (p = 0.0004). Annualized relative changes of cortical T1 were higher in patients vs. healthy subjects (patients: - 0.72 ± 0.64%/year; healthy subjects: - 0.17 ± 0.41%/year, p = 0.007).

CONCLUSIONS

In patients with PD, the development of widespread changes in cortical microstructure was observed as reflected by a reduction of cortical T1. The pattern of T1 decrease in PD patients exceeded the normal T1 decrease as found in physiological aging and showed considerable overlap with the pattern of cortical thinning demonstrated in previous PD studies. Therefore, cortical T1 might be a promising additional imaging marker for future longitudinal PD studies. The biological mechanisms underlying cortical T1 reductions remain to be further elucidated.