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Kumari R, Jarjees M, Susnoschi-Luca I, Purcell M, Vučković A. Effective Connectivity in Spinal Cord Injury-Induced Neuropathic Pain. SENSORS (BASEL, SWITZERLAND) 2022; 22:6337. [PMID: 36080805 PMCID: PMC9460641 DOI: 10.3390/s22176337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/05/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
AIM The aim of this study was to differentiate the effects of spinal cord injury (SCI) and central neuropathic pain (CNP) on effective connectivity during motor imagery of legs, where CNP is typically experienced. METHODS Multichannel EEG was recorded during motor imagery of the legs in 3 groups of people: able-bodied (N = 10), SCI with existing CNP (N = 10), and SCI with no CNP (N = 20). The last group was followed up for 6 months to check for the onset of CNP. Source reconstruction was performed to obtain cortical activity in 17 areas spanning sensorimotor regions and pain matrix. Effective connectivity was calculated using the directed transfer function in 4 frequency bands and compared between groups. RESULTS A total of 50% of the SCI group with no CNP developed CNP later. Statistically significant differences in effective connectivity were found between all groups. The differences between groups were not dependent on the frequency band. Outflows from the supplementary motor area were greater for the able-bodied group while the outflows from the secondary somatosensory cortex were greater for the SCI groups. The group with existing CNP showed the least differences from the able-bodied group, appearing to reverse the effects of SCI. The connectivities involving the pain matrix were different between able-bodied and SCI groups irrespective of CNP status, indicating their involvement in motor networks generally. SIGNIFICANCE The study findings might help guide therapeutic interventions targeted at the brain for CNP alleviation as well as motor recovery post SCI.
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Affiliation(s)
- Radha Kumari
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mohammed Jarjees
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
- Medical Instrumentation Techniques Engineering Department, Northern Technical University, Mosul 41002, Iraq
| | - Ioana Susnoschi-Luca
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
| | - Mariel Purcell
- Queen Elizabeth National Spinal Injuries Unit, Queen Elizabeth University Hospital, Glasgow G51 4TF, UK
| | - Aleksandra Vučković
- Biomedical Engineering Research Division, University of Glasgow, Glasgow G12 8QQ, UK
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Topographical assessment of neurocortical connectivity by using directed transfer function and partial directed coherence during meditation. Cogn Process 2018; 19:527-536. [PMID: 29774480 DOI: 10.1007/s10339-018-0869-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 05/11/2018] [Indexed: 12/15/2022]
Abstract
Due to the presence of nonlinearity and volume conduction in electroencephalography (EEG), sometimes it's challenging to find out the actual brain network from neurodynamical alteration. In this paper, two well-known time-frequency brain connectivity measures, namely partial directed coherence (PDC) and directed transfer function (DTF), have been applied to evaluate the performance analysis of EEG signals obtained during meditation. These measures are implemented to the multichannel meditation EEG data to get the directed neural information flow. Mostly the assessment of PDC and DTF is entirely subjective and there are probabilities to have erroneous connectivity estimation. To avoid the subjective evaluation, the performance results are compared in terms of absolute energy, signal-to-noise ratio (SNR) and relative SNR (R-SNR) scale. In most of the cases, the PDC result is found to be more efficient than DTF. The limitation of DTF and PDC in terms of the time-varying multivariate autoregressive (MVAR) model is highlighted. The time-varying MVAR model can track the neurodynamical changes better than any other method. In the present study, we would like to show that the PDC-based connectivity gives a better understanding of the non-symmetric relation in EEG obtained during Kriya Yoga meditation in comparison to DTF. However, it needs to be investigated further to warrant this claim.
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Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury. Neural Plast 2018; 2018:9354207. [PMID: 29853852 PMCID: PMC5954936 DOI: 10.1155/2018/9354207] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 03/06/2018] [Accepted: 03/21/2018] [Indexed: 12/18/2022] Open
Abstract
Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.
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Cekic S, Grandjean D, Renaud O. Time, frequency, and time-varying Granger-causality measures in neuroscience. Stat Med 2018. [PMID: 29542141 DOI: 10.1002/sim.7621] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned.
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Affiliation(s)
- Sezen Cekic
- Methodology and Data Analysis, Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Didier Grandjean
- Neuroscience of Emotion and Affective Dynamics Lab, Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Olivier Renaud
- Methodology and Data Analysis, Department of Psychology, University of Geneva, Geneva, Switzerland
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Athanasiou A, Klados MA, Pandria N, Foroglou N, Kavazidi KR, Polyzoidis K, Bamidis PD. A Systematic Review of Investigations into Functional Brain Connectivity Following Spinal Cord Injury. Front Hum Neurosci 2017; 11:517. [PMID: 29163098 PMCID: PMC5669283 DOI: 10.3389/fnhum.2017.00517] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 10/11/2017] [Indexed: 12/21/2022] Open
Abstract
Background: Complete or incomplete spinal cord injury (SCI) results in varying degree of motor, sensory and autonomic impairment. Long-lasting, often irreversible disability results from disconnection of efferent and afferent pathways. How does this disconnection affect brain function is not so clear. Changes in brain organization and structure have been associated with SCI and have been extensively studied and reviewed. Yet, our knowledge regarding brain connectivity changes following SCI is overall lacking. Methods: In this study we conduct a systematic review of articles regarding investigations of functional brain networks following SCI, searching on PubMed, Scopus and ScienceDirect according to PRISMA-P 2015 statement standards. Results: Changes in brain connectivity have been shown even during the early stages of the chronic condition and correlate with the degree of neurological impairment. Connectivity changes appear as dynamic post-injury procedures. Sensorimotor networks of patients and healthy individuals share similar patterns but new functional interactions have been identified as unique to SCI networks. Conclusions: Large-scale, multi-modal, longitudinal studies on SCI patients are needed to understand how brain network reorganization is established and progresses through the course of the condition. The expected insight holds clinical relevance in preventing maladaptive plasticity after SCI through individualized neurorehabilitation, as well as the design of connectivity-based brain-computer interfaces and assistive technologies for SCI patients.
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Affiliation(s)
- Alkinoos Athanasiou
- Laboratory of Medical Physics, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.,First Department of Neurosurgery, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Manousos A Klados
- Department of Biomedical Engineering, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Niki Pandria
- Laboratory of Medical Physics, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nicolas Foroglou
- First Department of Neurosurgery, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Kyriaki R Kavazidi
- Laboratory of Medical Physics, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Konstantinos Polyzoidis
- First Department of Neurosurgery, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Laboratory of Medical Physics, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Mierau A, Pester B, Hülsdünker T, Schiecke K, Strüder HK, Witte H. Cortical Correlates of Human Balance Control. Brain Topogr 2017; 30:434-446. [PMID: 28466295 PMCID: PMC5495870 DOI: 10.1007/s10548-017-0567-x] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Accepted: 04/25/2017] [Indexed: 02/07/2023]
Abstract
Balance control is a fundamental component of human every day motor activities such as standing or walking, and its impairment is associated with an increased risk of falling. However, in humans the exact neurobiological mechanisms underlying balance control are still unclear. Specifically, although previous studies have identified a number of cortical regions that become significantly activated during real or imagined balancing, the interactions within and between the relevant cortical regions remain to be investigated. The working hypothesis of this study is that cortical networks contribute to an optimization of balance control, and that this contribution can be revealed by partial directed coherence—a time-variant, frequency-selective and directed functional connectivity analysis tool. Electroencephalographic activity was recorded in 37 subjects during single-leg balancing on a stable as well as an unstable surface. Results of this study show that in the transition from balancing on a stable surface to an unstable surface, two topographically delimitable connectivity networks (weighted directed networks) are established; one associated with the alpha and one with the theta frequency band. The theta network sequence can be described as a set of subnetworks (modules) comprising the frontal, central and parietal cortex with individual temporal and spatial developments within and between those modules. In the alpha network, the occipital electrodes O1 and O2 act as a source, and the interactions propagate predominantly in the directions from occipital to parietal and to centro-parietal areas. These important findings indicate that balance control is supported by at least two functional cortical networks.
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Affiliation(s)
- Andreas Mierau
- Institute of Movement and Neurosciences, German Sport University Cologne, Am Sportpark Muengersdorf 6, 50933, Cologne, Germany.
| | - Britta Pester
- Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Bachstraße 18, 07743, Jena, Germany
| | - Thorben Hülsdünker
- Institute of Movement and Neurosciences, German Sport University Cologne, Am Sportpark Muengersdorf 6, 50933, Cologne, Germany
| | - Karin Schiecke
- Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Bachstraße 18, 07743, Jena, Germany
| | - Heiko K Strüder
- Institute of Movement and Neurosciences, German Sport University Cologne, Am Sportpark Muengersdorf 6, 50933, Cologne, Germany
| | - Herbert Witte
- Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Bachstraße 18, 07743, Jena, Germany
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Zhao Y, Billings SA, Wei H, Sarrigiannis PG. Tracking time-varying causality and directionality of information flow using an error reduction ratio test with applications to electroencephalography data. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2012; 86:051919. [PMID: 23214826 DOI: 10.1103/physreve.86.051919] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2012] [Indexed: 06/01/2023]
Abstract
This paper introduces an error reduction ratio-causality (ERR-causality) test that can be used to detect and track causal relationships between two signals. In comparison to the traditional Granger method, one significant advantage of the new ERR-causality test is that it can effectively detect the time-varying direction of linear or nonlinear causality between two signals without fitting a complete model. Another important advantage is that the ERR-causality test can detect both the direction of interactions and estimate the relative time shift between the two signals. Numerical examples are provided to illustrate the effectiveness of the new method together with the determination of the causality between electroencephalograph signals from different cortical sites for patients during an epileptic seizure.
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Affiliation(s)
- Yifan Zhao
- Department of Automatic Control and System Engineering, University of Sheffield, Sheffield, United Kingdom
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An analytical approach to network motif detection in samples of networks with pairwise different vertex labels. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2012; 2012:910380. [PMID: 22666306 PMCID: PMC3359789 DOI: 10.1155/2012/910380] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2011] [Revised: 01/17/2012] [Accepted: 02/06/2012] [Indexed: 11/18/2022]
Abstract
Network motifs, overrepresented small local connection patterns, are assumed to act as functional meaningful building blocks of a network and, therefore, received considerable attention for being useful for understanding design principles and functioning of networks. We present an extension of the original approach to network motif detection in single, directed networks without vertex labeling to the case of a sample of directed networks with pairwise different vertex labels. A characteristic feature of this approach to network motif detection is that subnetwork counts are derived from the whole sample and the statistical tests are adjusted accordingly to assign significance to the counts. The associated computations are efficient since no simulations of random networks are involved. The motifs obtained by this approach also comprise the vertex labeling and its associated information and are characteristic of the sample. Finally, we apply this approach to describe the intricate topology of a sample of vertex-labeled networks which originate from a previous EEG study, where the processing of painful intracutaneous electrical stimuli and directed interactions within the neuromatrix of pain in patients with major depression and healthy controls was investigated. We demonstrate that the presented approach yields characteristic patterns of directed interactions while preserving their important topological information and omitting less relevant interactions.
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