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Rajagopalan V, Pioro EP. Graph network measures reveal distinct white matter abnormalities in motor and extra-motor brain regions of two UMN-predominant ALS subtypes. J Neurol Sci 2023; 452:120765. [PMID: 37672915 DOI: 10.1016/j.jns.2023.120765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 09/08/2023]
Abstract
BACKGROUND Routine clinical magnetic resonance imaging (MRI) shows bilateral corticospinal tract (CST) hyperintensity in some patients with upper motor neuron (UMN)-predominant ALS (ALS-CST+) but not in others (ALS-CST-). Although, similar in their UMN features, the ALS-CST+ patient group is significantly younger in age, has faster disease progression and shorter survival than the ALS-CST- patient group. Reasons for the differences are unclear. METHOD In order to evaluate more objective MRI measures of these ALS subgroups, we used diffusion tensor images (DTI) obtained using single shot echo planar imaging sequence from 1.5 T Siemens MRI Scanner. We performed an exploratory whole brain white matter (WM) network analysis using graph theory approach on 45 ALS patients (ALS-CST+) (n = 21), and (ALS-CST-) (n = 24) and neurological controls (n = 14). RESULTS Significant (p < 0.05) differences in nodal degree measure between ALS patients and controls were observed in motor and extra motor regions, supplementary motor area, subcortical WM regions, cerebellum and vermis. Importantly, WM network abnormalities were significantly (p < 0.05) different between ALS-CST+ and ALS-CST- subgroups. Compared to neurologic controls, both ALS subgroups showed hubs in the right superior occipital gyrus and cuneus as well as significantly (p < 0.05) reduced small worldness supportive of WM network damage. CONCLUSIONS Significant differences between ALS-CST+ and ALS-CST- subgroups of WM network abnormalities, age of onset, symptom duration prior to MRI, and progression rate suggest these patients represent distinct clinical phenotypes and possibly pathophysiologic mechanisms of ALS.
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Affiliation(s)
- Venkateswaran Rajagopalan
- Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad 500078, India
| | - Erik P Pioro
- Neuromuscular Center, Department of Neurology, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Neurosciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
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Li X, Wu Y, Wei M, Guo Y, Yu Z, Wang H, Li Z, Fan H. A novel index of functional connectivity: phase lag based on Wilcoxon signed rank test. Cogn Neurodyn 2021; 15:621-36. [PMID: 34367364 DOI: 10.1007/s11571-020-09646-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 09/07/2020] [Accepted: 10/21/2020] [Indexed: 10/23/2022] Open
Abstract
Phase synchronization has been an effective measurement of functional connectivity, detecting similar dynamics over time among distinct brain regions. However, traditional phase synchronization-based functional connectivity indices have been proved to have some drawbacks. For example, the phase locking value (PLV) index is sensitive to volume conduction, while the phase lag index (PLI) and the weighted phase lag index (wPLI) are easily affected by noise perturbations. In addition, thresholds need to be applied to these indices to obtain the binary adjacency matrix that determines the connections. However, the selection of the thresholds is generally arbitrary. To address these issues, in this paper we propose a novel index of functional connectivity, named the phase lag based on the Wilcoxon signed-rank test (PLWT). Specifically, it characterizes the functional connectivity based on the phase lag with a weighting procedure to reduce the influence of volume conduction and noise. Besides, it automatically identifies the important connections without relying on thresholds, by taking advantage of the framework of the Wilcoxon signed-rank test. The performance of the proposed PLWT index is evaluated on simulated electroencephalograph (EEG) datasets, as well as on two resting-state EEG datasets. The experimental results on the simulated EEG data show that the PLWT index is robust to volume conduction and noise. Furthermore, the brain functional networks derived by PLWT on the real EEG data exhibit a reasonable scale-free characteristic and high test-retest (TRT) reliability of graph measures. We believe that the proposed PLWT index provides a useful and reliable tool to identify the underlying neural interactions, while effectively diminishing the influence of volume conduction and noise.
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Zhao Z, Wang C, Yuan Q, Zhao J, Ren Q, Xu Y, Li J, Yu Y. Dynamic changes of brain networks during feedback-related processing of reinforcement learning in schizophrenia. Brain Res 2020; 1746:146979. [PMID: 32544500 DOI: 10.1016/j.brainres.2020.146979] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 06/01/2020] [Accepted: 06/09/2020] [Indexed: 12/28/2022]
Abstract
Previous studies have reported that schizophrenia (SZ) patients showed selective reinforcement learning deficits and abnormal feedback-related event-related potential (ERP) components. However, how the brain networks and their topological properties evolve over time during transient feedback-related cognition processing in SZ patients has not been investigated so far. In this paper, using publicly available feedback-related ERP data which were recorded from SZ patients and healthy controls (HC) when they performed a reinforcement learning task, we carried out an event-related network analysis where topology of brain functional networks was characterized with some graph measures including clustering coefficient (C), global efficiency (Eglobal) and local efficiency (Elocal) on a millisecond timescale. Our results showed that the brain functional networks displayed rapid rearrangements of topological properties during transient feedback-related cognition process for both two groups. More importantly, we found that SZ patients exhibited significantly reduced theta-band (time window of 170-350 ms after stimuli onset) brain functional connectivity strength, Eglobal, Elocal and C in response to negative feedback stimuli compared to HC group. The network based statistic (NBS) analysis detected one significantly decreased theta-band subnetwork in SZ patients mainly involving in frontal-occipital and temporal-occipital connections compared to HC group. In addition, clozapine treatment seemed to greatly reduce theta-band power and topological measures of brain networks in SZ patients. Finally, the theta-band power, graph measures and functional connectivity were extracted to train a support vector machine classifier for classification of HC from SZ, or Cloz + SZ or Cloz- SZ, and a relatively good classification accuracy of 84.48%, 89.47% and 78.26% was obtained, respectively. The above results suggested a less optimal organization of theta-band brain network in SZ patients, and studying the topological parameters of brain networks evolve over time during transient feedback-related processing could be useful for understanding the pathophysiologic mechanisms underlying reinforcement learning deficits in SZ patients.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China.
| | - Chang Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China
| | - Qingli Yuan
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China
| | - Junqiang Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China
| | - Qiongqiong Ren
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China
| | - Yongtao Xu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China
| | - Jie Li
- Department of Neurology, The First Affiliated Hospital of Xinxiang Medical University, Weihui 453100, Henan Province, China
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang 453003, PR China; Engineering Technology Research Center of Neurosense and Control of Xinxiang City, Xinxiang 453003, PR China; Xinxiang Key Laboratory of Biomedical Information Research, Henan Engineering Laboratory of Combinatorial Technique for Clinical and Biomedical Big Data, Xinxiang 453003, PR China.
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Babajani-Feremi A, Noorizadeh N, Mudigoudar B, Wheless JW. Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology. Neuroimage Clin 2018; 19:990-999. [PMID: 30003036 PMCID: PMC6039837 DOI: 10.1016/j.nicl.2018.06.017] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 06/10/2018] [Accepted: 06/15/2018] [Indexed: 12/19/2022]
Abstract
Vagus nerve stimulation (VNS) is a low-risk surgical option for patients with drug resistant epilepsy, although it is impossible to predict which patients may respond to VNS treatment. Resting-state magnetoencephalography (rs-MEG) connectivity analysis has been increasingly utilized to investigate the impact of epilepsy on brain networks and identify alteration of these networks after different treatments; however, there is no study to date utilizing this modality to predict the efficacy of VNS treatment. We investigated whether the rs-MEG network topology before VNS implantation can be used to predict efficacy of VNS treatment. Twenty-three patients with epilepsy who had MEG before VNS implantation were included in this study. We also included 89 healthy control subjects from the Human Connectome Project. Using the phase-locking value in the theta, alpha, and beta frequency bands as a measure of rs-MEG functional connectivity, we calculated three global graph measures: modularity, transitivity, and characteristic path length (CPL). Our results revealed that the rs-MEG graph measures were significantly heritable and had an overall good test-retest reliability, and thus these measures may be used as potential biomarkers of the network topology. We found that the modularity and transitivity in VNS responders were significantly larger and smaller, respectively, than those observed in VNS non-responders. We also observed that the modularity and transitivity in three frequency bands and CPL in delta and beta bands were significantly different in controls than those found in responders or non-responders, although the values of the graph measures in controls were closer to those of responders than non-responders. We used the modularity and transitivity as input features of a naïve Bayes classifier, and achieved an accuracy of 87% in classification of non-responders, responders, and controls. The results of this study revealed that MEG-based graph measures are reliable biomarkers, and that these measures may be used to predict seizure outcome of VNS treatment.
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Affiliation(s)
- Abbas Babajani-Feremi
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA; Le Bonheur Children's Hospital, Neuroscience Institute, Memphis, TN, USA; Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, USA.
| | - Negar Noorizadeh
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA; Le Bonheur Children's Hospital, Neuroscience Institute, Memphis, TN, USA
| | - Basanagoud Mudigoudar
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA; Le Bonheur Children's Hospital, Neuroscience Institute, Memphis, TN, USA
| | - James W Wheless
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN, USA; Le Bonheur Children's Hospital, Neuroscience Institute, Memphis, TN, USA
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