Disrupted midbrain energy homeostasis, derived from phosphorus 31 ( 31 P) MR spectroscopy, effectively differentiated early-stage Parkinson disease from its mimics, outperforming conventional hydrogen 1 MRI markers for iron and neuromelanin, establishing 31 P MR spectroscopy as a promising diagnostic tool.

Background Whether phosphorus 31 (31P) MR spectroscopy outperforms or complements conventional hydrogen 1 (1H) MRI biomarkers in the differential diagnosis of early-stage Parkinson disease (PD) remains unclear. Purpose To evaluate 31P MR spectroscopy and its integration with conventional 1H MRI for discriminating early-stage PD from mimics. Materials and Methods This prospective study consecutively enrolled participants with early-stage PD, participants with PD mimics, and controls (November 2023 to October 2024). Participants underwent 31P MR spectroscopy for inorganic phosphate (Pi), phosphocreatine (PCr), and adenosine triphosphate (ATP); quantitative susceptibility mapping for iron; and neuromelanin-sensitive MRI. Intergroup imaging differences were assessed using multivariable general linear models, and partial correlations with clinical scores were analyzed. A penalized logistic regression classifier evaluated discrimination performance of energy metabolites, alone and combined with iron and/or neuromelanin. Results Seventy-two participants with early-stage PD (mean age, 60.1 years ± 6.8 [SD]; 44 male participants), 34 with PD mimics (mean age, 62.1 years ± 7.9; 18 male participants), and 46 controls (mean age, 56.3 years ± 9.6; 33 female controls) were included. Early-stage PD showed a decreased Pi/PCr ratio (mean, 0.47 [95% CI: 0.44, 0.50] vs 0.57 [95% CI: 0.52, 0.63]) and an increased total ATP/Pi ratio (mean, 4.89 [95% CI: 4.57, 5.24] vs 4.06 [95% CI: 3.50, 4.68]) in the left midbrain compared with participants with PD mimics (both Bonferroni-corrected P < .05). The Pi/PCr ratio correlated with nonmotor symptom scores (r = 0.42; 95% CI: 0.12, 0.69; P = .008) and autonomic symptom scores (r = 0.60; 95% CI: 0.34, 0.80; P < .001) in PD. Energy metabolites outperformed both iron (area under the receiver operating characteristic curve [AUC], 0.90 vs 0.50; P < .001) and neuromelanin (AUC, 0.90 vs 0.64; P = .04) in differentiating PD from mimics. There was no evidence that combining iron and/or neuromelanin improved AUC over energy metabolites alone (0.90 vs 0.90 with the addition of iron [P > .99] vs 0.91 with the addition of neuromelanin [P = .70] vs 0.93 with the addition of both [P = .42]). Conclusion 31P MR spectroscopy revealed disrupted midbrain energy homeostasis in early-stage PD and effectively helped differentiate it from mimics. © RSNA, 2026 Supplemental material is available for this article.

REM sleep behavior disorder (RBD) in Parkinson's disease (PD) marks a more aggressive subtype. While mitochondrial dysfunction, iron deposition, and neuromelanin loss are central to PD pathobiology, their contributions to RBD in PD remain unclear. This study noninvasively compared brain bioenergetics, phospholipid metabolism, iron accumulation, and neuromelanin integrity across PD patients with RBD (PD-RBD), without RBD (PD-noRBD), and healthy controls (HCs) to identify RBD-related metabolic alterations. Twenty-six PD-RBD, 46 PD-noRBD, and 48 HCs were recruited consecutively. All participants underwent 3T MRI, including phosphorus-31 MR spectroscopic imaging (31P-MRSI), quantitative susceptibility mapping (QSM), and neuromelanin-sensitive MRI (NM-MRI), all acquired in a standardized OFF-medication state (≥12 h withdrawal). Phosphorus metabolites included phosphoethanolamine (PE), total adenosine triphosphate (tATP), phosphodiester (PDE), and others. Group differences and associations with clinical scales were analyzed, and PD subtype discrimination performance was assessed using ROC analysis. PD-RBD patients demonstrated right basal ganglia metabolic alterations characterized by elevated α-ATP/Pi (p = 0.003 vs HCs) and PCr/Pi ratios (p = 0.001 vs HCs), together with reduced PE-related phospholipid turnover indices, including lower PE/tATP relative to PD-noRBD (p = 0.003). In contrast, PD-noRBD showed milder alterations primarily involving high-energy phosphate metabolism compared with HCs, whereas phospholipid turnover remained relatively preserved. No significant differences in iron or neuromelanin measures were observed between PD-RBD and PD-noRBD groups. However, both PD subgroups showed significantly reduced neuromelanin-related contrast in the substantia nigra compared to HCs. PE/tATP (R = -0.350, p < 0.01) and PE/PDE ratios (R = -0.441, p < 0.001) were negatively correlated with RBD symptom severity across the PD cohort. 31P-MRSI demonstrated superior discrimination performance (AUC = 0.80) compared with QSM (AUC = 0.72) or NM-MRI (AUC = 0.69), while multimodal integration achieved the highest diagnostic accuracy (AUC = 0.86). These findings support a role for altered bioenergetic and membrane phospholipid metabolism in PD-RBD and highlight 31P-MRSI as a promising imaging approach for characterizing PD heterogeneity.

BACKGROUND:Differentiating Parkinson's disease (PD) from multiple system atrophy (MSA), especially the parkinsonian variant (MSA-P), remains challenging. Diagnostic inaccuracy contributes to suboptimal clinical outcomes. Therefore, clinically accessible biomarkers are warranted to support differential diagnosis in routine practice. OBJECTIVE:The aim was to develop a multimodal imaging model combining T1-weighted (T1W) volumetry, quantitative susceptibility mapping, and neuromelanin (NM) magnetic resonance imaging (MRI) for distinguishing PD from MSA subtypes. METHODS:A total of 387 participants were analyzed, comprising 141 with PD, 86 with MSA (51 cerebellar variant [MSA-C], 35 MSA-P), and 160 age- and sex-matched healthy controls. Group comparisons were performed for brain volumetry, susceptibility in deep gray matter, spatial heterogeneity of putaminal iron, NM content in the substantia nigra pars compacta (SNpc), and locus coeruleus (LC). Classification was conducted using a Gaussian Naïve Bayes classifier with fivefold cross-validation. RESULTS:Compared with PD and controls, (1) corrected volumes of the brainstem, bilateral cerebellar white matter, and gray matter were significantly reduced in MSA-C and MSA-P, (2) susceptibility was increased in the bilateral putamen in MSA-P and in the bilateral dentate nucleus in MSA-C, and (3) NM contrast was reduced in the bilateral SNpc in MSA-P and in the bilateral LC in MSA-C. The multimodal models yielded area under the curve values of 0.967 (PD vs. MSA-C), 0.884 (PD vs. MSA-P), and 0.879 (PD vs. MSA-P vs. MSA-C). CONCLUSION:Integration of volumetric, susceptibility, and NM measures with machine learning enables accurate differentiation of PD from MSA subtypes, which provides a potential means to differentiate parkinsonism in clinical practice. © 2025 International Parkinson and Movement Disorder Society.

This study aimed to examine variations in iron deposition, neuromelanin (NM) content, gamma-aminobutyric acid (GABA) and glutamate-glutamine complex (Glx) levels within the nigrostriatal pathway in early-stage Parkinson’s disease (PD) patients with and without Rapid Eye Movement Sleep Behavior Disorder (RBD). Twenty-two early-stage PD patients with RBD (PD-RBD), 33 without RBD (PD-nRBD), and 36 healthy controls (HC) were prospectively recruited and underwent 3 T MRI and 1H-MRS scans. GABA levels in the left basal ganglia were elevated in PD-nRBD versus HC (P = 0.014), whereas they were decreased in the PD-RBD relative to the PD-nRBD (P = 0.018). Iron deposition and NM content in the bilateral substantia nigra (SN) showed no differences between PD subgroups. Despite similar SN iron and NM content, the GABAergic system alteration provides novel insights into the pathophysiology of RBD in PD.

BACKGROUND AND PURPOSE:The pathological relationship between white matter hyperintensities (WMH) and cognitive impairment in Parkinson's disease (PD) remains unclear due to their variable locations, heterogeneity, and limited assessment of underlying tissue properties. This study integrates T2-FLAIR and quantitative MRI (qMRI) to investigate burden, spatial distribution, and extent of tissue alterations in WMH, aiming to elucidate their role in cognitive decline among PD patients. METHODS:A total of 122 age- and sex-matched PD patients and 65 healthy controls (HC) were recruited, with PD patients grouped by Montreal Cognitive Assessment (MoCA) score including normal, mild cognitive impairment (MCI) or PD with dementia (PDD). WMH burden was compared across groups and cognitive status. Water content, T1, and T2* measures were derived from qMRI data and tissue property heatmaps and periventricular distance profiles were constructed for all groups to visualize location-dependent tissue alterations of WMH relative to the lateral ventricles. In addition, voxel-wise analysis was performed to examine the correlation between WMH lesion tissue properties and MoCA scores. RESULTS:WMH volume was significantly higher in PDD compared to other groups (p < 0.05) and negatively correlated with MoCA scores (r = -0.352, p < 0.001). WMH appeared predominantly around the lateral ventricles, with anterior horn involvement common to all groups and posterior horn involvement specific to PDD. qMRI measures were significantly elevated in WMH compared to normal appearing white matter (NAWM) (p < 0.001), with heatmaps showing a negative gradient of tissue property changes from the lateral ventricles to the NAWM. Voxel-wise analysis revealed a significant negative correlation between the qMRI tissue properties of periventricular WMH and MoCA scores, with the strongest association observed in the periventricular WM situated just beyond the boundary of the lateral ventricles. CONCLUSION:Over and above volume differences, the spatial distribution and tissue property variations of WMH were closely linked to cognitive impairment in PD patients, with distinct patterns across different cognitive stages.

Background: Parkinson's disease (PD) is associated with the loss of neuromelanin (NM) and increased iron in the substantia nigra (SN). Magnetization transfer contrast (MTC) is widely used for NM visualization but has limitations in brain coverage and scan time. This study aimed to develop a new approach called Proton-density Enhanced Neuromelanin Contrast in Low flip angle gradient echo (PENCIL) imaging to visualize NM in the SN.Methods: This study included 30 PD subjects and 50 healthy controls (HCs) scanned at 3T. PENCIL and MTC images were acquired. NM volume in the SN pars compacta (SNpc), normalized image contrast (Cnorm), and contrast-to-noise ratio (CNR) were calculated. The change of NM volume in the SNpc with age was analyzed using the HC data. A group analysis compared differences between PD subjects and HCs. Receiver operating characteristic (ROC) analysis and area under the curve (AUC) calculations were used to evaluate the diagnostic performance of NM volume and CNR in the SNpc.Results: PENCIL provided similar visualization and structural information of NM compared to MTC. In HCs, PENCIL showed higher NM volume in the SNpc than MTC, but this difference was not observed in PD subjects. PENCIL had higher CNR, while MTC had higher Cnorm. Both methods revealed a similar pattern of NM volume in SNpc changes with age. There were no significant differences in AUCs between NM volume in SNpc measured by PENCIL and MTC. Both methods exhibited comparable diagnostic performance in this regard.Conclusions: PENCIL imaging provided improved CNR compared to MTC and showed similar diagnostic performance for differentiating PD subjects from HCs. The major advantage is PENCIL has rapid whole-brain coverage and, when using STAGE imaging, offers a one-stop quantitative assessment of tissue properties.

ObjectiveThis study aimed to examine the structural alterations of the deep gray matter (DGM) in the basal ganglia circuitry of Parkinson's disease (PD) patients with freezing of gait (FOG) using quantitative susceptibility mapping (QSM) and neuromelanin-sensitive magnetic resonance imaging (NM-MRI).MethodsTwenty-five (25) PD patients with FOG (PD-FOG), 22 PD patients without FOG (PD-nFOG), and 30 age- and sex-matched healthy controls (HCs) underwent 3-dimensional multi-echo gradient recalled echo and NM-MRI scanning. The mean volume and susceptibility of the DGM on QSM data and the relative contrast (NMRC-SNpc) and volume (NMvolume-SNpc) of the substantia nigra pars compacta on NM-MRI were analyzed among groups. A multiple linear regression analysis was performed to explore the associations of FOG severity with MRI measurements and disease stage.ResultsThe PD-FOG group showed higher susceptibility in the bilateral caudal substantia nigra (SN) compared to the HC group. Both the PD-FOG and PD-nFOG groups showed lower volumes than the HC group in the bilateral caudate and putamen as determined from the QSM data. The NMvolume-SNpc on NM-MRI in the PD-FOG group was significantly lower than in the HC and PD-nFOG groups. Both the PD-FOG and PD-nFOG groups showed significantly decreased NMRC-SNpc.ConclusionsThe PD-FOG patients showed abnormal neostriatum atrophy, increases in iron deposition in the SN, and lower NMvolume-SNpc. The structural alterations of the DGM in the basal ganglia circuits could lead to the abnormal output of the basal ganglia circuit to trigger the FOG in PD patients.

Introduction: Although locus coeruleus (LC) has been demonstrated to play a critical role in the cognitive function of Parkinson's disease (PD), the underlying mechanism has not been elucidated. The objective was to investigate the relationship among LC degeneration, cognitive performance, and the glymphatic function in PD. Methods: In this retrospective study, 71 PD subjects (21 with normal cognition; 29 with cognitive impairment (PD-MCI); 21 with dementia (PDD)) and 26 healthy controls were included. All participants underwent neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and diffusion tensor image scanning on a 3.0 T scanner. The brain glymphatic function was measured using diffusion along the perivascular space (ALPS) index, while LC degeneration was estimated using the NM contrast-to-noise ratio of LC (CNRLC). Results: The ALPS index was significantly lower in both the whole PD group (P = 0.04) and the PDD subgroup (P = 0.02) when compared to the controls. Similarly, the CNRLC was lower in the whole PD group (P < 0.001) compared to the controls. In the PD group, a positive correlation was found between the ALPS index and both the Montreal Cognitive Assessment (MoCA) score (r = 0.36; P = 0.002) and CNRLC (r = 0.26; P = 0.03). Mediation analysis demonstrated that the ALPS index acted as a significant mediator between CNRLC and the MoCA score in PD subjects. Conclusion: The ALPS index, a neuroimaging marker of glymphatic function, serves as a mediator between LC degeneration and cognitive function in PD.

Compared with MR plain scanning, gadolinium (Gd)-enhanced MR scanning can provide more diagnostic in-formation. Gadopentetate dimeglumine is generally used as an MR enhancement contrast agent in some coun-tries. It is a member of linear Gd-based contrast agents (GBCAs) which are considered more likely to release free Gd ions (Gd3+) than macrocyclic GBCAs. Gd3+ is one of the most effective known calcium antagonists, and can compete with calcium ions (Ca2+) in Ca2+-related biological reactions. In this study, animal models of tissue regeneration were established by cutting the caudal fins of zebrafish, and the models were exposed with gadopentetate dimeglumine solution for different immersion times of 1, 3, and 5 min. Three GBCA exposures per week were performed in the first 3 weeks of the follow-up time. Morphological parameters such as regenerative area (RA), bone density, bone thickness and regenerative bone volume (RBV) were quantified using a camera and synchrotron radiation micro CT. RA decreased as total Gd intake increased in both the female group (rho =-0.784, P < 0.0001) and the male group (rho =-0.471, P = 0.011). The bone density of the regenerated bone increased after Gd exposure in the treated groups. The morphology of the regenerated bone from the treated groups became shorter and thicker. Our results showed that gadopentetate dimeglumine had osteogenic toxicity in zebrafish.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.

Although the NeRF approach can achieve outstanding view synthesis, it is limited in practical use because it requires many views (hundreds) for training. With only a few input views, the Depth-DYN NeRF that we propose can accurately match the shape. First, we adopted the ip_basic depth-completion method, which can recover the complete depth map from sparse radar depth data. Then, we further designed the Depth-DYN MLP network architecture, which uses a dense depth prior to constraining the NeRF optimization and combines the depthloss to supervise the Depth-DYN MLP network. When compared to the color-only supervised-based NeRF, the Depth-DYN MLP network can better recover the geometric structure of the model and reduce the appearance of shadows. To further ensure that the depth depicted along the rays intersecting these 3D points is close to the measured depth, we dynamically modified the sample space based on the depth of each pixel point. Depth-DYN NeRF considerably outperforms depth NeRF and other sparse view versions when there are a few input views. Using only 10–20 photos to render high-quality images on the new view, our strategy was tested and confirmed on a variety of benchmark datasets. Compared with NeRF, we obtained better image quality (NeRF average at 22.47 dB vs. our 27.296 dB).

Abstract Objectives To predict CTLA4 expression levels and prognosis of clear cell renal cell carcinoma (ccRCC) by constructing a computed tomography‐based radiomics model and establishing a nomogram using clinicopathologic factors. Methods The clinicopathologic parameters and genomic data were extracted from 493 ccRCC cases of the Cancer Genome Atlas (TCGA)‐KIRC database. Univariate and multivariate Cox regression and Kaplan–Meier analysis were performed for prognosis analysis. Cibersortx was applied to evaluate the immune cell composition. Radiomic features were extracted from the TCGA/the Cancer Imaging Archive (TCIA) (n = 102) datasets. The support vector machine (SVM) was employed to establish the radiomics signature for predicting CTLA4 expression. Receiver operating characteristic curve (ROC), decision curve analysis (DCA), and precision‐recall curve were utilized to assess the predictive performance of the radiomics signature. Correlations between radiomics score (RS) and selected features were also evaluated. An RS‐based nomogram was constructed to predict prognosis. Results CTLA4 was significantly overexpressed in ccRCC tissues and was related to lower overall survival. A higher CTLA4 expression was independently linked to the poor prognosis (HR = 1.458, 95% CI 1.13–1.881, p = 0.004). The radiomics model for the prediction of CTLA4 expression levels (AUC = 0.769 in the training set, AUC = 0.724 in the validation set) was established using seven radiomic features. A significant elevation in infiltrating M2 macrophages was observed in the RS high group (p < 0.001). The predictive efficiencies of the RS‐based nomogram measured by AUC were 0.826 at 12 months, 0.805 at 36 months, and 0.76 at 60 months. Conclusions CTLA4 mRNA expression status in ccRCC could be predicted noninvasively using a radiomics model based on nephrographic phase contrast‐enhanced CT images. The nomogram established by combining RS and clinicopathologic factors could predict overall survival for ccRCC patients. Our findings may help stratify prognosis of ccRCC patients and identify those who may respond best to ICI‐based treatments.

We proposed an automatic cascaded framework based on deep learning to segment deep brain nuclei and distinguish Parkinson’s disease from normal controls using quantitative susceptibility mapping (QSM) images. A 3D CA-Net model integrating channel attention, spatial attention and scale attention module was utilized to segment 5 brain nuclei from QSM and T1W data. Then, the QSM images and the segmented brain nuclei ROIs were fed into the SE-ResNeXt50 with anatomical attention mechanism to get the predicted PD probability. The proposed method provided good interpretability and achieved AUC values of 0.97 and 0.90 on training and testing cohort, respectively.

Background: Differential diagnosis of essential tremor (ET) and Parkinson's disease (PD) can still be a challenge in clinical practice. These two tremor disorders may have different pathogenesis related to the substantia nigra (SN) and locus coeruleus (LC). Characterizing neuromelanin (NM) in these structures may help improve the differential diagnosis.Methods: Forty-three subjects with tremor-dominant PD (PDTD), 31 subjects with ET, and 30 age- and sex-matched healthy controls were included. All subjects were scanned with NM magnetic resonance imaging (NM-MRI). NM volume and contrast measures for the SN and contrast for the LC were evaluated. Logistic regression was used to calculate predicted probabilities by using the combination of SN and LC NM measures. The discriminative power of the NM measures in detecting subjects with PDTD from ET was assessed with a receiver operative characteristic curve, and the area under the curve (AUC) was calculated.Results: The NM contrast-to-noise ratio (CNR) of the LC, the NM volume, and CNR of the SN on the right and left sides were significantly lower in PD(TD )subjects than in ET subjects or healthy controls (all P < 0.05). Furthermore, when combining the best model constructed from the NM measures, the AUC reached 0.92 in differentiating PDTD from ET.Conclusion: The NM volume and contrast measures of the SN and contrast for the LC provided a new perspective on the differential diagnosis of PDTD and ET, and the investigation of the underlying pathophysiology.

The emergence of large language models exerts significant impact in the field of natural language processing. These models, which are based on attention networks, have shown remarkable capabilities in understanding and generating human conversation context, surpassing most of the state-of-the-art models in Natural Language Processing (NLP). Though Generative Pre-trained GPT-series open their public Application Programming Interfaces (APIs) for direct inference, such services require users to upload (relinquish) their data to servers, thus not suitable for domains operating sensitive data, such as the medical field. Due to comparable smaller model size, existing local pre-trained model deployments are usually not that helpful on domain-specific data without further fine-tuning. In this paper, we leverage Microsoft’s recent open source DeepSpeed-Chat platform and one of our selected pre-trained models, to locally conduct fine-tuning on our medical imaging reports (in Chinese), to infer diagnosis recommendations based on the imaging description. Our output models show very promising results according to both NLP metrics and experts’ evaluation criteria. This paper provides an initial report on our latest progress on locally fine-tuning large language model for medical data.

Smiling has often been incorrectly interpreted as “happy” in the popular facial expression datasets (AffectNet, RAF-DB, FERPlus). Smiling is the most complex human expression, with positive, neutral, and negative smiles. We focused on fine-grained facial expression recognition (FER) and built a new smiling face dataset, named Facial Expression Emotions. This dataset categorizes smiles into six classes of smiles, containing a total of 11,000 images labeled with corresponding fine-grained facial expression classes. We propose Smile Transformer, a network architecture for FER based on the Swin Transformer, to enhance the local perception capability of the model and improve the accuracy of fine-grained face recognition. Moreover, a convolutional block attention module (CBAM) was designed, to focus on important features of the face image and suppress unnecessary regional responses. For better classification results, an image quality evaluation module was used to assign different labels to images with different qualities. Additionally, a dynamic weight loss function was designed, to assign different learning strategies according to the labels during training, focusing on hard yet recognizable samples and discarding unidentifiable samples, to achieve better recognition. Overall, we focused on (a) creating a novel dataset of smiling facial images from online annotated images, and (b) developing a method for improved FER in smiling images. Facial Expression Emotions achieved an accuracy of 88.56% and could serve as a new benchmark dataset for future research on fine-grained FER.

INTRODUCTION:Cognitive training and physical exercise have shown positive effects on delaying progression of mild cognitive impairment (MCI) to dementia.METHODS:We explored the enhancing effect from Tai Chi when it was provided with cognitive training for MCI. In the first 12 months, the cognitive training group (CT) had cognitive training, and the mixed group (MixT) had additional Tai Chi training. In the second 12 months, training was only provided for a subgroup of MixT.RESULTS:In the first 12 months, MixT and CT groups were benefited from training. Compared to the CT group, MixT had additional positive effects with reference to baseline. In addition, Compared to short-time training, prolonged mixed training further delayed decline in global cognition and memory. Functional magnetic resonance imaging showed more increased regional activity in both CT and MixT.DISCUSSION:Tai Chi enhanced cognitive training effects in MCI. Moreover, Tai Chi and cognitive mixed training showed effects on delaying cognitive decline.

The understanding of brain structural abnormalities across different clinical forms of dystonia and their contribution to clinical characteristics remains unclear. The objective of this study is to investigate shared and specific gray matter volume (GMV) abnormalities in various forms of isolated idiopathic dystonia. We collected imaging data from 73 isolated idiopathic dystonia patients and matched them with healthy controls to explore the GMV alterations in patients and their correlations with clinical characteristics using the voxel-based morphometry (VBM) technique. In addition, we conducted an activation likelihood estimation (ALE) meta-analysis of previous VBM studies. Our study demonstrated widespread morphometry alterations in patients with idiopathic dystonia. Multiple systems were affected, which mainly included basal ganglia, sensorimotor, executive control, and visual networks. As the result of the ALE meta-analysis, a convergent cluster with increased GMV was found in the left globus pallidus. In subgroup VBM analyses, decreased putamen GMV was observed in all clinic forms, while the increased GMV was observed in parahippocampal, lingual, and temporal gyrus. GD demonstrated the most extensive GMV abnormalities in cortical regions, and the aberrant GMV of the posterior cerebellar lobe was prominent in CD. Moreover, trends of increased GMV regions of the left precuneus and right superior frontal gyrus were demonstrated in the moderate-outcome group compared with the superior-outcome group. Results of our study indicated shared pathophysiology of the disease-centered on the dysfunction of the basal ganglia-thalamo-cortical circuit, impairing sensorimotor integration, high-level motor execution, and cognition of patients. Dysfunction of the cerebello-thalamo-cortical circuit could also be involved in CD especially. Finally, the frontal-parietal pathway may act as a potential marker for predicting treatment outcomes such as deep brain stimulation.

The understanding of brain structural abnormalities across different clinical forms of dystonia and their contribution to clinical characteristics remains unclear. The objective of this study is to investigate shared and specific gray matter volume (GMV) abnormalities in various forms of isolated idiopathic dystonia. We collected imaging data from 73 isolated idiopathic dystonia patients and matched them with healthy controls to explore the GMV alterations in patients and their correlations with clinical characteristics using the voxel-based morphometry (VBM) technique. In addition, we conducted an activation likelihood estimation (ALE) meta-analysis of previous VBM studies. Our study demonstrated widespread morphometry alterations in patients with idiopathic dystonia. Multiple systems were affected, which mainly included basal ganglia, sensorimotor, executive control, and visual networks. As the result of the ALE meta-analysis, a convergent cluster with increased GMV was found in the left globus pallidus. In subgroup VBM analyses, decreased putamen GMV was observed in all clinic forms, while the increased GMV was observed in parahippocampal, lingual, and temporal gyrus. GD demonstrated the most extensive GMV abnormalities in cortical regions, and the aberrant GMV of the posterior cerebellar lobe was prominent in CD. Moreover, trends of increased GMV regions of the left precuneus and right superior frontal gyrus were demonstrated in the moderate-outcome group compared with the superior-outcome group. Results of our study indicated shared pathophysiology of the disease-centered on the dysfunction of the basal ganglia-thalamo-cortical circuit, impairing sensorimotor integration, high-level motor execution, and cognition of patients. Dysfunction of the cerebello-thalamo-cortical circuit could also be involved in CD especially. Finally, the frontal-parietal pathway may act as a potential marker for predicting treatment outcomes such as deep brain stimulation.