The emerging toxicant N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine quinone (6PPD-Q) is of wide concern due to its ubiquitous occurrence and high toxicity. Despite regular human exposure, limited evidence exists about its presence in the body and potential health risks. Herein, we analyzed cerebrospinal fluid (CSF) samples from Parkinson's disease (PD) patients and controls. The CSF levels of 6PPD-Q were twice as high in PD patients compared to controls. Immunostaining assays performed with primary dopaminergic neurons confirm that 6PPD-Q at environmentally relevant concentrations can exacerbate the formation of Lewy neurites induced by α-synuclein preformed fibrils (α-syn PFF). Assessment of cellular respiration reveals a considerable decrease in neuronal spare respiratory and ATP-linked respiration, potentially due to changes in mitochondrial membrane potential. Moreover, 6PPD-Q-induced mitochondrial impairment correlates with an upsurge in mitochondrial reactive oxygen species (mROS), and Mito-TEMPO-driven scavenging of mROS can lessen the amount of pathologic phospho-serine 129 α-synuclein. Untargeted metabolomics provides supporting evidence for the connection between 6PPD-Q exposure and changes in neuronal metabolite profiles. In-depth targeted metabolomics further unveils an overall reduction in glycolysis metabolite pool and fluctuations in the quantity of TCA cycle intermediates. Given its potentially harmful attributes, the presence of 6PPD-Q in human brain could potentially be a risk factor for PD.

Parkinson's disease (PD) is an aging-associated neurodegenerative movement disorder with increasing morbidity and mortality rates. The current gold standard for diagnosing PD is clinical evaluation, which is often challenging and inaccurate. Metabolomics and lipidomics approaches have been extensively applied because of their potential in discovering valuable biomarkers for medical diagnostics. Here, we used comprehensive untargeted metabolomics and lipidomics methodology based on liquid chromatography-mass spectrometry to evaluate metabolic abnormalities linked with PD. Two well-characterized cohorts of 288 plasma samples (143 PD and 145 control subjects in total) were used to examine metabolic alterations and identify diagnostic biomarkers. Unbiased multivariate and univariate studies were combined to identify the promising metabolic signatures, based on which the discriminant models for PD were established by integrating multiple machine learning algorithms. A 6-biomarker predictive model was constructed based on the omics profile in the discovery cohort, and the discriminant performance of the biomarker panel was evaluated with an accuracy over 81.6% both in the discovery cohort and validation cohort. The results indicated that PC (40:7), eicosatrienoic acid were negatively correlated with severity of PD, and pentalenic acid, PC (40:6p) and aspartic acid were positively correlated with severity of PD. In summary, we developed a multi-metabolite predictive model which can diagnose PD with over 81.6% accuracy based on this unique metabolic signature. Future clinical diagnosis of PD may benefit from the biomarker panel reported in this study.

PURPOSE:Microvascular decompression (MVD) is a widely used neurosurgical intervention for the treatment of cranial nerves compression. Segmentation of MVD-related structures, including the brainstem, nerves, arteries, and veins, is critical for preoperative planning and intraoperative decision-making. Automatically segmenting structures related to MVD is still challenging for current methods due to the limited information from a single modality and the complex topology of vessels and nerves. METHODS:Considering that it is hard to distinguish MVD-related structures, especially for nerve and vessels with similar topology, we design a multimodal segmentation network with a shared encoder-dual decoder structure and propose a clinical knowledge-driven distillation scheme, allowing reliable knowledge transferred from each decoder to the other. Besides, we introduce a class-wise contrastive module to learn the discriminative representations by maximizing the distance among classes across modalities. Then, a projected topological loss based on persistent homology is proposed to constrain topological continuity. RESULTS:We evaluate the performance of our method on in-house dataset consisting of 100 paired HR-T2WI and 3D TOF-MRA volumes. Experiments indicate that our model outperforms the SOTA in DSC by 1.9% for artery, 3.3% for vein and 0.5% for nerve. Visualization results show our method attains improved continuity and less breakage, which is also consistent with intraoperative images. CONCLUSION:Our method can comprehensively extract the distinct features from multimodal data to segment the MVD-related key structures and preserve the topological continuity, allowing surgeons precisely perceiving the patient-specific target anatomy and substantially reducing the workload of surgeons in the preoperative planning stage. Our resources will be publicly available at https://github.com/JaronTu/Multimodal_MVD_Seg .

BACKGROUND:Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective therapy in ameliorating the motor symptoms of Parkinson disease. However, postoperative optimal contact selection is crucial for achieving the best outcome of deep brain stimulation of the subthalamic nucleus surgery, but the process is currently a trial-and-error and time-consuming procedure that relies heavily on surgeons' clinical experience. METHODS:In this study, we propose a structural brain connectivity guided optimal contact selection method for deep brain stimulation of the subthalamic nucleus. Firstly, we reconstruct the DBS electrode location and estimate the stimulation range using volume of tissue activated from each DBS contact. Then, we extract the structural connectivity features by concatenating fractional anisotropy and the number of streamlines features of activated regions and the whole brain regions. Finally, we use a convolutional neural network with convolutional block attention module to identify the structural connectivity features for the optimal contact selection. RESULTS:We review the data of 800 contacts from 100 patients with Parkinson disease for the experiment. The proposed method achieves promising results, with the average accuracy of 97.63%, average precision of 94.50%, average recall of 94.46%, and average specificity of 98.18%, respectively. Our method can provide the suggestion for optimal contact selection. CONCLUSIONS:Our proposed method can improve the efficiency and accuracy of DBS optimal contact selection, reduce the dependence on surgeons' experience, and has the potential to facilitate the development of advanced DBS technology.

Objective:Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume.Methods:This paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively.Result:Experimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a p-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively.Conclusion:The paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research.

Parkinson's disease (PD) is a complex neurological disorder that typically worsens with age. A wide range of pathologies makes PD a very heterogeneous condition, and there are currently no reliable diagnostic tests for this disease. The application of metabolomics to the study of PD has the potential to identify disease biomarkers through the systematic evaluation of metabolites. In this study, urine metabolic profiles of 215 urine samples from 104 PD patients and 111 healthy individuals were assessed based on liquid chromatography-mass spectrometry. The urine metabolic profile was first evaluated with partial least-squares discriminant analysis, and then we integrated the metabolomic data with ensemble machine learning techniques using the voting strategy to achieve better predictive performance. A combination of 8-metabolite predictive panel performed well with an accuracy of over 90.7%. Compared to control subjects, PD patients had higher levels of 3-methoxytyramine, N-acetyl-l-tyrosine, orotic acid, uric acid, vanillic acid, and xanthine, and lower levels of 3,3-dimethylglutaric acid and imidazolelactic acid in their urine. The multi-metabolite prediction model developed in this study can serve as an initial point for future clinical studies.

Parkinson's disease(PD) is treated effectively by deep brain stimulation(DBS) of the subthalamic nucleus(STN), using an electrode inserted into the head of a PD patient. The electrode has multiple electrical contacts along its length, so the best may be chosen for selectively stimulating the STN. Neurosurgeons usually determine the optimal stimulated contact via the clinical experience of the neurosurgeon and the motor improvement of PD patients. This is a time-consuming and labor-intensive trial-and-error process. The selection of optimal stimulated contact highly depends on the locations of sweet spots, which are manually identified by the characteristic features of microelectrode recordings(MERs). This paper presents an amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS electrodes.The method first obtains the amplitude-frequency fusion features by combining the MERs time sequence features and the amplitude sequence features, and then uses the convolutional neural network(CNN) with convolutional block attention module(CBAM) to identify both the border of the STN and the sweet spots to implant the electrode. The optimal stimulated contact can be selected according to the distribution of the sweet spots. Experimental results indicate that, for successful surgeries, neurosurgeons and the proposed AI solution selected the same optimal contacts. Furthermore, the proposed method outperforms the state-of-the-art methods for STN and sweet spot identification. The proposed method shows great potential for optimal contact selection to improve the efficiency of STN-DBS surgery and reduce the dependence on clinicians' experience.

BACKGROUND:Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an effective treatment for patients with advanced Parkinson's disease, the outcome of this surgery is highly dependent on the accurate placement of the electrode in the optimal target of STN. PURPOSE:In this study, we aim to develop a target localization pipeline for DBS surgery, considering that the heart of this matter is to achieve the STN and red nucleus segmentation, a deep learning-based automatic segmentation approach is proposed to tackle this issue. METHODS:To address the problems of ambiguous boundaries and variable shape of the segmentation targets, the hierarchical attention mechanism with two different attention strategies is integrated into an encoder-decoder network for mining both semantics and fine-grained details for segmentation. The hierarchical attention mechanism is utilized to suppress irrelevant regions in magnetic resonance (MR) images while build long-range dependency among segmentation targets. Specifically, the attention gate (AG) is integrated into low-level features to suppress irrelevant regions in an input image while highlighting the salient features useful for segmentation. Besides, the self-attention involved in the transformer block is integrated into high-level features to model the global context. Ninety-nine brain magnetic resonance imaging (MRI) studies were collected from 99 patients with Parkinson's disease undergoing STN-DBS surgery, among which 80 samples were randomly selected as the training datasets for deep learning training, and ground truths (segmentation masks) were manually generated by radiologists. RESULTS:We applied five-fold cross-validation on these data to train our model, the mean results on 19 test samples are used to conduct the comparison experiments, the Dice similarity coefficient (DSC), Jaccard (JA), sensitivity (SEN), and HD95 of the segmentation for STN are 88.20%, 80.32%, 90.13%, and 1.14 mm, respectively, outperforming the state-of-the-art STN segmentation method with 2.82%, 4.52%, 2.56%, and 0.02 mm respectively. The source code and trained models of this work have been released in the URL below: https://github.com/liuruiqiang/HAUNet/tree/master. CONCLUSIONS:In this study, we demonstrate the effectiveness of the hierarchical attention mechanism for building global dependency on high-level semantic features and enhancing the fine-grained details on low-level features, the experimental results show that our method has considerable superiority for STN and red nucleus segmentation, which can provide accurate target localization for STN-DBS.

In order to accurately estimate disparities in textureless and slim regions, spatial pyramid pooling and stacked 3D CNN, which can capture global context information, are widely used in state-of-the-art stereo matching algorithms. Unfortunately, the computational complexity and high memory consumption make these methods not friendly to real-time applications such as autonomous driving and augmented realities. In order to balance the real-time performance and accuracy, we design lightweight multi-scale convolutional neural network for real-time stereo matching. First, Lightweight multi-scale 2D and 3D CNN modules are proposed for feature extraction and initial disparity computation respectively. Both of above modules only run on a low resolution to further reduce the amount of calculation. Second, multi-scale RGB images guided network is utilized to refine the final disparity estimation. Experiments on several datasets show that the proposed algorithm can achieve competitive results with speed of 64fps on a NIVDIA 1080 GPU.

Despite great progress, previous stereo matching algorithms still lack the ability to match textureless regions and slender structure areas. To tackle this problem, we propose ASM-Net, an attention stereo matching network. Attention module and disparity refinement module are constructed in the ASMNet. The attention module can improve correlation information between two images by channels and spatial attention. The feature-guided disparity refinement module learns more geometry information in different feature levels to refine the coarse prediction resolution constantly. The proposed approach was evaluated on several benchmark datasets. Experiments show that the proposed method achieves competitive results on KITTI and Scene-Flow datasets while running in real-time at 14ms.

Treatment-resistant depression (TRD) is a chronic and severe psychiatric illness associated with limited therapeutic options. Deep brain stimulation (DBS) is a promising therapy for TRD patients. However, its safety and efficacy are still unclear. Here we reported the safety and efficacy of lateral habenula (LHb) DBS for a TRD patient who had failed medical, psychological, electroconvulsive, and ketamine therapy. The DBS system is compatible with 3T magnetic resonance imaging along with local field potential (LFP) streaming. Two DBS electrodes were implanted at the bilateral LHb without any complication. The patient showed acute stimulation effects and achieved long-term improvements in his depression, anxiety, and sleep with left LHb 160 Hz frequency stimulation, accompanying the change of LFPs. These results provided clinical evidence toward the safety and efficacy and electrophysiological basis of LHb DBS for TRD.

Gestational exposure to PM2.5 is associated with adverse postnatal outcomes.PM2.5 can enter alveoli by using intratracheal instillation,even penetrate through lung cells into the blood circulation.Subsequently,they are transferred across the placenta and fetal blood brain barrier,causing the adverse birth outcomes of offspring.This study demonstrated that the gestational exposure resulted in cognitive and emotional disorders in female offspring although the offspring were not exposed to PM2,5.Placental metabolic pathways modulated fetal brain development and played a pivotal role for maternal-placental-fetal interactions in the fetal programming of adult behavioral and mental disorders.Samples of fetus,offspring hippocampus and placenta from the mice exposed to PM2.5 were investigated using a compre-hensive approach including mass spectrometry-based lipidomics and three-dimensional imaging.The exposure induced the neuro-degeneration in hippocampus,impairment of placental cytoarchitecture,and reprogramming of lipidome,which might affect the modulation of maternal-fetal cross-talk and result in the behavior disorders of offspring.The variation of spatial distribution of lipids was profoundly affected in dorsal pallium and hippocampal formation regions of fetal brain,offspring hippocampus,as well as labyrinth and junctional zones of placenta.The abundance alteration of lipid markers associated with neurodegenerative diseases was validated in transgenic mouse model with Alzheimer's disease and human cerebrospinal fluid from patients with Parkinson's disease.The finding could help with the selec-tion of more suitable heterogeneous-related substructures targeting PM2.5 exposure and the exploration of PM2.5-induced toxicological effects on neurodegenerative diseases.

Abstract Objective This study aimed to explore a shorter and safer contralateral C7 transposition pathway for the treatment of central upper limb paralysis. Methods From July 2018 to March 2019, 10 patients with central upper limb paralysis underwent posterior cervical 7 nerve transposition. The age of these patients ranged within 31–58 years old (average: 44 years old). These patients comprised of eight male patients and two female patients. Nine patients had cerebral hemorrhage, and one patient had a cerebral infarction. Furthermore, nine patients presented with spastic paralysis of the upper limbs and one patient presented with nonspastic paralysis. The duration of plegia before the operation ranged from 6 to 60 months (average: 26 months). The surgical procedure included transposition of the contralateral cervical 7 nerve root via a posterior vertebral approach under general anesthesia, and the distal part of the contralateral cervical 7 nerve was anastomosed with the proximal part of the ipsilateral cervical 7 nerve. Results The length of the contralateral cervical 7 nerve was 5.16 ± 0.21 cm, which was directly anastomosed with the ipsilateral cervical 7 nerve. Neither case needed nerve transplantation. Most patients had temporary numbness in their healthy fingers, which all disappeared within three months. Up to now, the follow‐up results are as follows: The spasticity of the affected upper limbs in five patients is lower than that before the operation, the pain and temperature sensation of the affected upper limbs in six patients are better than before the operation. Conclusion The distance of nerve transposition can be shortened by a posterior vertebral approach operation, where the contralateral C7 nerve can be anastomosed directly with the ipsilateral C7 nerve which may be effective for nerve regeneration and functional recovery. However, this conclusion still needs further research and verification.

The clinical benefit of deep brain stimulation (DBS) for Parkinson's disease (PD) is relevant to the tracts adjacent to the stimulation site, but it remains unclear what connectivity pattern is associated with effective DBS. The aim of this study was to identify clinically effective electrode contacts on the basis of brain connectivity markers derived from diffusion tensor tractography. We reviewed 77 PD patients who underwent bilateral subthalamic nucleus DBS surgery. The patients were assigned into the training (n = 58) and validation (n = 19) groups. According to the therapeutic window size, all contacts were classified into effective and ineffective groups. The whole-brain connectivity of each contact's volume of tissue activated was estimated using tractography with preoperative diffusion tensor data. Extracted connectivity features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for contact classification. A total of 616 contacts on 154 DBS leads were discriminated, with 388 and 228 contacts being classified as effective and ineffective ones, respectively. After the feature selection, the connectivity of contacts with the thalamus, pallidum, hippocampus, primary motor area, supplementary motor area and superior frontal gyrus was identified to significantly contribute to contact classification. Based on these relevant features, the random forest model constructed from the training group achieved an accuracy of 84.9% in the validation group, to discriminate effective contacts from the ineffective. Our findings advanced the understanding of the specific brain connectivity patterns associated with clinical effective electrode contacts, which potentially guided postoperative DBS programming.

BACKGROUND:Depression is a common comorbid condition in Parkinson's disease and a major contributor to poor quality of life. Despite this, depression in PD is under-diagnosed due to overlapping symptoms and difficulties in the assessment of depression in cognitively impaired old patients.OBJECTIVES:This study is to explore functional connectivity markers of depression in PD patients using resting-state fMRI and help diagnose whether patients have depression or not.METHODS:We reviewed 156 advanced PD patients (duration > 5 years; 59 depressed ones) and 45 healthy control subjects who underwent a resting-state fMRI scanning. Functional connectivity analysis was employed to characterize intrinsic connectivity networks using group independent component analysis and extract connectivity features. Features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for classification. Random forest classifiers were built for depression diagnosis, on the basis of identified features.RESULTS:42 intrinsic connectivity networks were identified and arranged into subcortical, auditory, somatomotor, visual, cognitive control, default-mode and cerebellar networks. Six features were significantly relevant to classification. They were connectivity within posterior cingulate cortex, within insula, between posterior cingulate cortex and insula/hippocampus+amygdala, between insula and precuneus, and between superior parietal lobule and medial prefrontal cortex. The mean accuracy achieved with classifiers to discriminate depressed patients from the non-depressed was 82.4%.CONCLUSIONS:Our findings provide preliminary evidence that resting-state functional connectivity can characterize depressed PD patients and help distinguish them from non-depressed ones.

BACKGROUND:Postoperative contact selection of deep brain stimulation (DBS), testing one contact at a time, is a clinically time-consuming procedure being challenged by incoming applications of more complex DBS leads. The objective of this study is to guide clinicians to select the optimal contact by neuroanatomic information derived from electrode reconstruction.METHODS:We reviewed 33 patients with Parkinson disease who underwent bilateral subthalamic nucleus (STN) DBS with preoperative magnetic resonance imaging and postoperative computed tomography scan. All electrodes were reconstructed in standardized Montreal Neurological Institute and Hospital space, and the volume of tissue activated (VTA) was estimated for each contact. According to the DISTAL atlas, we calculated 6 neuroanatomic parameters: the distances of each contact to the STN and the motor part of the STN (M-STN), the volumes of the overlapping areas of the VTA and STN/M-STN, and the number and ratio of fiber tracts through both the VTA and motor areas (primary motor cortex and supplementary motor area).RESULTS:All 6 parameters showed significant group differences between clinical optimal and nonoptimal contacts by permutation test. Moreover, the possibility of being the optimal contact was estimated for all contacts under the combination of these 6 parameters using a generalized linear model, which resulted in accuracy of 72.7% for prediction of contact selection.CONCLUSIONS:Electrode reconstruction could assist in selecting the clinical optimal contact and improving its efficiency.

BACKGROUND: Primary dystonia is a neurologic disease with characteristics of abnormal, involuntary twisting and turning movements, which greatly affect quality of life of patients. Treatments for dystonia consist of oral medications, botulinum neurotoxin injections, physical therapy, and surgery. For medication-refractory dystonia, surgery, especially deep brain stimulation (DBS), is the optimal option. CASE DESCRIPTION: The patient was a 13-year-old boy suffering from extremely severe primary dystonia, with a Burke-Fahn-Marsden Dystonia Rating Scale-motor score of 118 and a Toronto Western Spasmodic Torticollis Rating Scale-severity score of 29. The examination of 173 genes, including DYT, failed to identify any abnormality. He responded ineffectively to medications. After both bilateral subthalamic nucleus DBS and unilateral thalamic lesion in ventralis intermedius nucleus and ventralis oralis nucleus (Vim-Vo thalamotomy), his movement disorder improved dramatically. Four and 7 months after the operation, the scores of 2 rating scales sharply decreased. Potential brain structural changes were reflected in sensorimotor-related cortical thickness, surface area, and gray matter volume from magnetic resonance imaging, which may reveal a valid method to evaluate surgical effect on the brain with enough patients. CONCLUSIONS: DBS and thalamotomy is potentially an effective combination of treatments for severe medication-refractory dystonia.

Background Investigating cell fate decision and subpopulation specification in the context of the neural lineage is fundamental to understanding neurogenesis and neurodegenerative diseases. The differentiation process of neural-tube-like rosettes in vitro is representative of neural tube structures, which are composed of radially organized, columnar epithelial cells and give rise to functional neural cells. However, the underlying regulatory network of cell fate commitment during early neural differentiation remains elusive. Results In this study, we investigated the genome-wide transcriptome profile of single cells from six consecutive reprogramming and neural differentiation time points and identified cellular subpopulations present at each differentiation stage. Based on the inferred reconstructed trajectory and the characteristics of subpopulations contributing the most towards commitment to the central nervous system (CNS) lineage at each stage during differentiation, we identified putative novel transcription factors in regulating neural differentiation. In addition, we dissected the dynamics of chromatin accessibility at the neural differentiation stages and revealed active c/s-regulatory elements for transcription factors known to have a key role in neural differentiation as well as for those that we suggest are also involved. Further, communication network analysis demonstrated that cellular interactions most frequently occurred among embryoid body (EB) stage and each cell subpopulation possessed a distinctive spectrum of ligands and receptors associated with neural differentiation which could reflect the identity of each subpopulation. Conclusions Our study provides a comprehensive and integrative study of the transcriptomics and epigenetics of human early neural differentiation, which paves the way for a deeper understanding of the regulatory mechanisms driving the differentiation of the neural lineage.

目的采用CT和MRI图像融合技术评价帕金森病（PD）患者深部脑电刺激（DBS）术后电极位置准确性的应用价值。方法收集接受双侧DBS疗法的32例PD患者,采用术后颅脑薄层CT和术前MRI图像融合（融合组）技术并与常规术后复查MRI图像（常规组）比较,分析电极位置精确度及检查耗费时间。结果融合组与常规组测量的电极位置相关性较好（P均＜0.008）;融合组与常规组电极尖端位置比较,除左侧电极y轴、z轴方向差异有统计学意义外（t=-2.34、-3.08,P均＜0.05）,余差异均无统计学意义（P均＞0.05）。术后复查MR检查时间为（7.65±0.33）min,术后复查CT时间为（2.85±0.29）min,差异有统计学意义（P＜0.05）。结论 DBS术后应用头部薄层CT与MRI图像融合能精确定位电极位置,并可避免PD术后复查MRI的潜在风险,缩短术后复查的时间。

Objective To investigate and comparative the best MRI sequences to visualize the subthalamic nucle-us ( STN) as the operation target .Methods Ten patients with PD were included .Ten healthy subjects were taken as control group .Sequences that we chose were compared with the conventional sequences .Different sequences were ana-lyzed semi-quantitatively when applied to visualize the STN before DBS procedures .The intra-operative microelectrode recording ( MER) electrode probe positioning was reviewed and confirmed after operation .Results The T2 flash 2D, T2-SPACE and SWI sequences were higher than conventional T 1-mprage, T2-TSE and FLAIR in semi-quantitative scores, with higher consistency with the intra -operative MER confirmed electrode probe positioning .Conclusion T2 flash 2D, T2-SPACE and SWI sequences can provide accurate targeting for STN deep brain stimulation , and can improve the positioning accuracy , avoid complications and improve the operation efficiency .