Objective To evaluate metabolic abnormalities in patients with mesial temporal lobe epilepsy （MTLE） with proton magnetic resonance spectroscopy （MRS） using a 3.0T MR scanner. Methods Sixty-three patients （32 wom...Objective To evaluate metabolic abnormalities in patients with mesial temporal lobe epilepsy （MTLE） with proton magnetic resonance spectroscopy （MRS） using a 3.0T MR scanner. Methods Sixty-three patients （32 women and 31 men） with diagnosed MTLE underwent diagnostic MR imaging （MRI） and proton MRS using a 3.0T MR scanner. The clinical history and interictal epileptiform discharges （IEDs） were recorded. Sixteen healthy volunteers served as control. The results of proton MRS were compared with the findings of electroencephalogram and structural MRI findings. Results Twenty-seven of the 63 patients with MTLE showed unilateral hippocampal sclerosis, and 9 showed bilateral hippocampal sclerosis. The concentration ratio of N-acytelaspartate （NAA） / [ creatine （ Cr ） ＋ choline （Cho） ] in the hippocampal region of MTLE patients （0. 64±0. 07） was significantly lower than control （0. 80±0. 05, P = 0.023）. In the patients with unilateral hippocampal sclerosis, NAA/（Cr ＋ Cho） in the hippocampal region ipsilateral to the sclerotic hippocampus （0.56±0.06） was significantly lower than the ratio in the contralateral hippocampal region （0.69±0.07, P 〈 0. 001 ）. There was significant difference in hippocampal NAA/（ Cr ＋ Cho） between the refractory patients and the non-refractory patients （0. 64±0. 05 vs.0.71±0. 07, P =0. 04）. Forty-five patients were lateralized by IEDs, while 49 patients were lateralized by metabolite ratio. And lateralization determined by proton MRS and IEDs was concordant in 33 patients. Conclusions MRS as a noninvasive tool adds helpful metabolite information to routine MRI in evaluation of MTLE. The method is well established and should be a routine clinical application in the investigation of epilepsy.展开更多
We have developed a new three dimensional (3-D) conductivity imaging approach and have used it to detect human brain conductivity changes corresponding to acute cerebral stroke. The proposed Magnetic Resonance Electri...We have developed a new three dimensional (3-D) conductivity imaging approach and have used it to detect human brain conductivity changes corresponding to acute cerebral stroke. The proposed Magnetic Resonance Electrical Impedance Tomography (MREIT) approach is based on the J-Substitution algorithm and is expanded to imaging 3-D subject conductivity distribution changes. Computer simulation studies have been conducted to evaluate the present MREIT imaging approach. Simulations of both types of cerebral stroke, hemorrhagic stroke and ischemic stroke, were performed on a four-sphere head model. Simulation results showed that the correlation coefficient (CC) and relative error (RE) between target and estimated conductivity distributions were 0.9245±0.0068 and 8.9997%±0.0084%, for hemorrhagic stroke, and 0.6748±0.0197 and 8.8986%±0.0089%, for ischemic stroke, when the SNR (signal-to-noise radio) of added GWN (Gaussian White Noise) was 40. The convergence characteristic was also evaluated according to the changes of CC and RE with different iteration numbers. The CC increases and RE decreases monotonously with the increasing number of iterations. The present simulation results show the feasibility of the proposed 3-D MREIT approach in hemorrhagic and ischemic stroke detection and suggest that the method may become a useful alternative in clinical diagnosis of acute cerebral stroke in humans.展开更多
Much like genomics, brain connectomics has rapidly become a core component of most national brain projects around the world. Beyond the ambitious aims of these projects, a fundamental challenge is the need for an effi...Much like genomics, brain connectomics has rapidly become a core component of most national brain projects around the world. Beyond the ambitious aims of these projects, a fundamental challenge is the need for an efficient, robust, reliable and easy-to-use pipeline to mine such large neuroscience datasets. Here, we introduce a computational pipeline--namely the Connectome Compu- tation System （CCS）-for discovery science of human brain connectomes at the macroscale with multimodal magnetic resonance imaging technologies. The CCS is designed with a three-level hierarchical structure that includes data cleaning and preprocessing, individual connectome mapping andconnectome mining, and knowledge discovery. Several functional modules are embedded into this hierarchy to implement quality control procedures, reliability analysis and connectome visualization. We demonstrate the utility of the CCS based upon a publicly available dataset, the NKI- Rockland Sample, to delineate the normative trajectories of well-known large-scale neural networks across the natural life span （6-85 years of age）. The CCS has been made freely available to the public via GitHub （https：//github.com/ zuoxinian/CCS） and our laboratory＇s Web site （http：//lfcd. psych.ac.cn/ccs.html） to facilitate progress in discovery science in the field of human brain connectomics.展开更多
文摘Objective To evaluate metabolic abnormalities in patients with mesial temporal lobe epilepsy （MTLE） with proton magnetic resonance spectroscopy （MRS） using a 3.0T MR scanner. Methods Sixty-three patients （32 women and 31 men） with diagnosed MTLE underwent diagnostic MR imaging （MRI） and proton MRS using a 3.0T MR scanner. The clinical history and interictal epileptiform discharges （IEDs） were recorded. Sixteen healthy volunteers served as control. The results of proton MRS were compared with the findings of electroencephalogram and structural MRI findings. Results Twenty-seven of the 63 patients with MTLE showed unilateral hippocampal sclerosis, and 9 showed bilateral hippocampal sclerosis. The concentration ratio of N-acytelaspartate （NAA） / [ creatine （ Cr ） ＋ choline （Cho） ] in the hippocampal region of MTLE patients （0. 64±0. 07） was significantly lower than control （0. 80±0. 05, P = 0.023）. In the patients with unilateral hippocampal sclerosis, NAA/（Cr ＋ Cho） in the hippocampal region ipsilateral to the sclerotic hippocampus （0.56±0.06） was significantly lower than the ratio in the contralateral hippocampal region （0.69±0.07, P 〈 0. 001 ）. There was significant difference in hippocampal NAA/（ Cr ＋ Cho） between the refractory patients and the non-refractory patients （0. 64±0. 05 vs.0.71±0. 07, P =0. 04）. Forty-five patients were lateralized by IEDs, while 49 patients were lateralized by metabolite ratio. And lateralization determined by proton MRS and IEDs was concordant in 33 patients. Conclusions MRS as a noninvasive tool adds helpful metabolite information to routine MRI in evaluation of MTLE. The method is well established and should be a routine clinical application in the investigation of epilepsy.
基金Project supported partly by the National Science Foundation (No.BES-0411898) and the National Institues of Health (No. R01EB00178) USA
文摘We have developed a new three dimensional (3-D) conductivity imaging approach and have used it to detect human brain conductivity changes corresponding to acute cerebral stroke. The proposed Magnetic Resonance Electrical Impedance Tomography (MREIT) approach is based on the J-Substitution algorithm and is expanded to imaging 3-D subject conductivity distribution changes. Computer simulation studies have been conducted to evaluate the present MREIT imaging approach. Simulations of both types of cerebral stroke, hemorrhagic stroke and ischemic stroke, were performed on a four-sphere head model. Simulation results showed that the correlation coefficient (CC) and relative error (RE) between target and estimated conductivity distributions were 0.9245±0.0068 and 8.9997%±0.0084%, for hemorrhagic stroke, and 0.6748±0.0197 and 8.8986%±0.0089%, for ischemic stroke, when the SNR (signal-to-noise radio) of added GWN (Gaussian White Noise) was 40. The convergence characteristic was also evaluated according to the changes of CC and RE with different iteration numbers. The CC increases and RE decreases monotonously with the increasing number of iterations. The present simulation results show the feasibility of the proposed 3-D MREIT approach in hemorrhagic and ischemic stroke detection and suggest that the method may become a useful alternative in clinical diagnosis of acute cerebral stroke in humans.
基金partially supported by the National Basic Research Program (973) of China (2015CB351702)the National Natural Science Foundation of China (81220108014, 81471740, 81201153, 81171409, and 81270023)+4 种基金the Key Research Program (KSZD-EW-TZ-002)the Hundred Talents Program of the Chinese Academy of SciencesDr. Xiu-Xia Xing acknowledges the Beijing Higher Education Young Elite Teacher Project (No. YETP1593)Dr. Zhi Yang acknowledges the Foundation of Beijing Key Laboratory of Mental Disorders (2014JSJB03)the Outstanding Young Researcher Award from Institute of Psychology, Chinese Academy of Sciences (Y4CX062008)
文摘Much like genomics, brain connectomics has rapidly become a core component of most national brain projects around the world. Beyond the ambitious aims of these projects, a fundamental challenge is the need for an efficient, robust, reliable and easy-to-use pipeline to mine such large neuroscience datasets. Here, we introduce a computational pipeline--namely the Connectome Compu- tation System （CCS）-for discovery science of human brain connectomes at the macroscale with multimodal magnetic resonance imaging technologies. The CCS is designed with a three-level hierarchical structure that includes data cleaning and preprocessing, individual connectome mapping andconnectome mining, and knowledge discovery. Several functional modules are embedded into this hierarchy to implement quality control procedures, reliability analysis and connectome visualization. We demonstrate the utility of the CCS based upon a publicly available dataset, the NKI- Rockland Sample, to delineate the normative trajectories of well-known large-scale neural networks across the natural life span （6-85 years of age）. The CCS has been made freely available to the public via GitHub （https：//github.com/ zuoxinian/CCS） and our laboratory＇s Web site （http：//lfcd. psych.ac.cn/ccs.html） to facilitate progress in discovery science in the field of human brain connectomics.
