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Guo X, Zhang X, Liu J, Zhai G, Zhang T, Zhou R, Lu H, Gao L. Resolving heterogeneity in dynamics of synchronization stability within the salience network in autism spectrum disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 131:110956. [PMID: 38296155 DOI: 10.1016/j.pnpbp.2024.110956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 01/16/2024] [Accepted: 01/28/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND Heterogeneity in resting-state functional connectivity (FC) are one of the characteristics of autism spectrum disorder (ASD). Traditional resting-state FC primarily focuses on linear correlations, ignoring the nonlinear properties involved in synchronization between networks or brain regions. METHODS In the present study, the cross-recurrence quantification analysis, a nonlinear method based on dynamical systems, was utilized to quantify the synchronization stability between brain regions within the salience network (SN) of ASD. Using the resting-state functional magnetic resonance imaging data of 207 children (ASD/typically-developing controls (TC): 105/102) in Autism Brain Imaging Data Exchange database, we analyzed the laminarity and trapping time differences of the synchronization stability between the ASD subtype derived by a K-means clustering analysis and the TC group, and examined the relationship between synchronization stability and the severity of clinical symptoms of the ASD subtypes. RESULTS Based on the synchronization stability within the SN of ASD, we identified two subtypes that showed opposite changes in synchronization stability relative to the TC group. In addition, the synchronization stability of ASD subtypes 1 and 2 can predict the social interaction and communication impairments, respectively. CONCLUSIONS These findings reveal that ASD subgroups with different patterns of synchronization stability within the SN appear distinct clinical symptoms, and highlight the importance of exploring the potential neural mechanism of ASD from a nonlinear perspective.
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Affiliation(s)
- Xiaonan Guo
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
| | - Xia Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Junfeng Liu
- Department of Neurology, West China Hospital, Sichuan University, China, Chengdu, 610041, China
| | - Guangjin Zhai
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Tao Zhang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Rongjuan Zhou
- Maternity and Child Health Hospital of Qinhuangdao, Qinhuangdao 066000, China
| | - Huibin Lu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China
| | - Le Gao
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao 066004, China.
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Riehm CD, Bonnette S, Rush JL, Diekfuss JA, Koohestani M, Myer GD, Norte GE, Sherman DA. Corticomuscular cross-recurrence analysis reveals between-limb differences in motor control among individuals with ACL reconstruction. Exp Brain Res 2024; 242:355-365. [PMID: 38092900 PMCID: PMC10872341 DOI: 10.1007/s00221-023-06751-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/16/2023] [Indexed: 01/04/2024]
Abstract
Surgical reconstruction of the anterior cruciate ligament (ACL) and subsequent physical therapy can help athletes return to competition; however, re-injury rates remain disproportionately high due, in part, to lingering biomechanical and neurological factors that are not fully addressed during rehabilitation. Prior reports indicate that individuals exhibit altered electrical activity in both brain and muscle after ACL reconstruction (ACLR). In this investigation, we aimed to extend existing approaches by introducing a novel non-linear analysis of corticomuscular dynamics, which does not assume oscillatory coupling between brain and muscle: Corticomuscular cross-recurrence analysis (CM-cRQA). Our findings indicate that corticomuscular dynamics vary significantly between involved (injured) and uninvolved legs of participants with ACLR during voluntary isometric contractions between the brain and both the vastus medialis and lateralis. This finding points to a potential lingering neural deficit underlying re-injury for athletes after surgical reconstruction, namely the dynamical structure of neuromuscular (brain to quad muscle) coordination, which is significantly asymmetric, between limbs, in those who have ACLR.
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Affiliation(s)
- Christopher D Riehm
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, GA, USA.
- Emory Sports Medicine Center, Atlanta, GA, USA.
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA.
| | - Scott Bonnette
- Division of Sports Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Justin L Rush
- Division of Physical Therapy, School of Rehabilitation Sciences, Ohio University, Athens, OH, USA
| | - Jed A Diekfuss
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, GA, USA
- Emory Sports Medicine Center, Atlanta, GA, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
| | - Moein Koohestani
- Neuroplasticity, & Sarcopenia (CNS) Lab, Institute of Exercise Physiology and Rehabilitation Science, University of Central Florida, Orlando, FL, USA
| | - Gregory D Myer
- Emory Sports Performance And Research Center (SPARC), Flowery Branch, GA, USA
- Emory Sports Medicine Center, Atlanta, GA, USA
- Department of Orthopaedics, Emory University School of Medicine, Atlanta, GA, USA
- The Micheli Center for Sports Injury Prevention, Waltham, MA, USA
- Youth Physical Development Centre, Cardiff Metropolitan University, Wales, UK
| | - Grant E Norte
- Neuroplasticity, & Sarcopenia (CNS) Lab, Institute of Exercise Physiology and Rehabilitation Science, University of Central Florida, Orlando, FL, USA
| | - David A Sherman
- Live4 Physical Therapy and Wellness, Acton, MA, USA
- Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, USA
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Pentari A, Simos N, Tzagarakis G, Kagialis A, Bertsias G, Kavroulakis E, Gratsia E, Sidiropoulos P, Boumpas DT, Papadaki E. Altered hippocampal connectivity dynamics predicts memory performance in neuropsychiatric lupus: a resting-state fMRI study using cross-recurrence quantification analysis. Lupus Sci Med 2023; 10:e000920. [PMID: 37400223 DOI: 10.1136/lupus-2023-000920] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023]
Abstract
OBJECTIVE Τo determine whole-brain and regional functional connectivity (FC) characteristics of patients with neuropsychiatric SLE (NPSLE) or without neuropsychiatric manifestations (non-NPSLE) and examine their association with cognitive performance. METHODS Cross-recurrence quantification analysis (CRQA) of resting-state functional MRI (rs-fMRI) data was performed in 44 patients with NPSLE, 20 patients without NPSLE and 35 healthy controls (HCs). Volumetric analysis of total brain and specific cortical and subcortical regions, where significant connectivity changes were identified, was performed. Cognitive status of patients with NPSLE was assessed by neuropsychological tests. Group comparisons on nodal FC, global network metrics and regional volumetrics were conducted, and associations with cognitive performance were estimated (at p<0.05 false discovery rate corrected). RESULTS FC in patients with NPSLE was characterised by increased modularity (mean (SD)=0.31 (0.06)) as compared with HCs (mean (SD)=0.27 (0.06); p=0.05), hypoconnectivity of the left (mean (SD)=0.06 (0.018)) and right hippocampi (mean (SD)=0.051 (0.0.16)), and of the right amygdala (mean (SD)=0.091 (0.039)), as compared with HCs (mean (SD)=0.075 (0.022), p=0.02; 0.065 (0.019), p=0.01; 0.14 (0.096), p=0.05, respectively). Hyperconnectivity of the left angular gyrus (NPSLE/HCs: mean (SD)=0.29 (0.26) and 0.10 (0.09); p=0.01), left (NPSLE/HCs: mean (SD)=0.16 (0.09) and 0.09 (0.05); p=0.01) and right superior parietal lobule (SPL) (NPSLE/HCs: mean (SD)=0.25 (0.19) and 0.13 (0.13), p=0.01) was noted in NPSLE versus HC groups. Among patients with NPSLE, verbal episodic memory scores were positively associated with connectivity (local efficiency) of the left hippocampus (r2=0.22, p=0.005) and negatively with local efficiency of the left angular gyrus (r2=0.24, p=0.003). Patients without NPSLE displayed hypoconnectivity of the right hippocampus (mean (SD)=0.056 (0.014)) and hyperconnectivity of the left angular gyrus (mean (SD)=0.25 (0.13)) and SPL (mean (SD)=0.17 (0.12)). CONCLUSION By using dynamic CRQA of the rs-fMRI data, distorted FC was found globally, as well as in medial temporal and parietal brain regions in patients with SLE, that correlated significantly and adversely with memory capacity in NPSLE. These results highlight the value of dynamic approaches to assessing impaired brain network function in patients with lupus with and without neuropsychiatric symptoms.
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Affiliation(s)
- Anastasia Pentari
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece
| | - Nicholas Simos
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece
| | - George Tzagarakis
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece
| | - Antonios Kagialis
- Department of Psychiatry, University of Crete School of Medicine, Heraklion, Greece
- Department of Radiology, University of Crete School of Medicine, Heraklion, Greece
| | - George Bertsias
- Laboratory of Autoimmunity and Inflammation, Institute of Molecular Biology and Biotechnology, Heraklion, Greece
- Department of Rheumatology, Clinical Immunology and Allergy, School of Medicine, University of Crete, University Hospital of Heraklion, Heraklion, Greece
| | | | - Eirini Gratsia
- Department of Radiology, University of Crete School of Medicine, Heraklion, Greece
| | - Prodromos Sidiropoulos
- Department of Rheumatology, Clinical Immunology and Allergy, School of Medicine, University of Crete, University Hospital of Heraklion, Heraklion, Greece
| | - Dimitrios T Boumpas
- Department of Rheumatology, Clinical Immunology and Allergy, School of Medicine, University of Crete, University Hospital of Heraklion, Heraklion, Greece
- Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Efrosini Papadaki
- Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Greece
- Department of Radiology, University of Crete School of Medicine, Heraklion, Greece
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Hasselman F. Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks. Front Physiol 2022; 13:859127. [PMID: 35600293 PMCID: PMC9114511 DOI: 10.3389/fphys.2022.859127] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
The detection of Early Warning Signals (EWS) of imminent phase transitions, such as sudden changes in symptom severity could be an important innovation in the treatment or prevention of disease or psychopathology. Recurrence-based analyses are known for their ability to detect differences in behavioral modes and order transitions in extremely noisy data. As a proof of principle, the present paper provides an example of a recurrence network based analysis strategy which can be implemented in a clinical setting in which data from an individual is continuously monitored for the purpose of making decisions about diagnosis and intervention. Specifically, it is demonstrated that measures based on the geometry of the phase space can serve as Early Warning Signals of imminent phase transitions. A publicly available multivariate time series is analyzed using so-called cumulative Recurrence Networks (cRN), which are recurrence networks with edges weighted by recurrence time and directed towards previously observed data points. The results are compared to previous analyses of the same data set, benefits, limitations and future directions of the analysis approach are discussed.
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Affiliation(s)
- Fred Hasselman
- Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
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5
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Pentari A, Tzagkarakis G, Tsakalides P, Simos P, Bertsias G, Kavroulakis E, Marias K, Simos NJ, Papadaki E. Changes in resting-state functional connectivity in neuropsychiatric lupus: A dynamic approach based on recurrence quantification analysis. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Studying the functional connectivity of the primary motor cortex with the binarized cross recurrence plot: The influence of Parkinson's disease. PLoS One 2021; 16:e0252565. [PMID: 34097691 PMCID: PMC8183987 DOI: 10.1371/journal.pone.0252565] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 05/18/2021] [Indexed: 11/20/2022] Open
Abstract
Two new recurrence plot methods (the binary recurrence plot and binary cross recurrence plot) were introduced here to study the long-term dynamic of the primary motor cortex and its interaction with the primary somatosensory cortex, the anterior motor thalamus of the basal ganglia motor loop and the precuneous nucleus of the default mode network. These recurrence plot methods: 1. identify short-term transient interactions; 2. identify long-lasting delayed interactions that are common in complex systems; 3. work with non-stationary blood oxygen level dependent (BOLD) data; 4. may study the relationship of centers with non-linear functional interactions; 5 may compare different experimental groups performing different tasks. These methods were applied to BOLD time-series obtained in 20 control subjects and 20 Parkinson´s patients during the execution of motor activity and body posture tasks (task-block design). The binary recurrence plot showed the task-block BOLD response normally observed in the primary motor cortex with functional magnetic resonance imaging methods, but also shorter and longer BOLD-fluctuations than the task-block and which provided information about the long-term dynamic of this center. The binary cross recurrence plot showed short-lasting and long-lasting functional interactions between the primary motor cortex and the primary somatosensory cortex, anterior motor thalamus and precuneous nucleus, interactions which changed with the resting and motor tasks. Most of the interactions found in healthy controls were disrupted in Parkinson's patients, and may be at the basis of some of the motor disorders and side-effects of dopaminergic drugs commonly observed in these patients.
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Bielski K, Adamus S, Kolada E, Rączaszek-Leonardi J, Szatkowska I. Parcellation of the human amygdala using recurrence quantification analysis. Neuroimage 2020; 227:117644. [PMID: 33338610 DOI: 10.1016/j.neuroimage.2020.117644] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 11/26/2020] [Accepted: 12/03/2020] [Indexed: 01/05/2023] Open
Abstract
Several previous attempts have been made to divide the human amygdala into smaller subregions based on the unique functional properties of the subregions. Although these attempts have provided valuable insight into the functional heterogeneity in this structure, the possibility that spatial patterns of functional characteristics can quickly change over time has rarely been considered in previous studies. In the present study, we explicitly account for the dynamic nature of amygdala activity. Our goal was not only to develop another parcellation method but also to augment existing methods with novel information about amygdala subdivisions. We performed state-specific amygdala parcellation using resting-state fMRI (rsfMRI) data and recurrence quantification analysis (RQA). RsfMRI data from 102 subjects were acquired with a 3T Trio Siemens scanner. We analyzed values of several RQA measures across all voxels in the amygdala and found two amygdala subdivisions, the ventrolateral (VL) and dorsomedial (DM) subdivisions, that differ with respect to one of the RQA measures, Shannon's entropy of diagonal lines. Compared to the DM subdivision, the VL subdivision can be characterized by a higher value of entropy. The results suggest that VL activity is determined and influenced by more brain structures than is DM activity. To assess the biological validity of the obtained subdivisions, we compared them with histological atlases and currently available parcellations based on structural connectivity patterns (Anatomy Probability Maps) and cytoarchitectonic features (SPM Anatomy toolbox). Moreover, we examined their cortical and subcortical functional connectivity. The obtained results are similar to those previously reported on parcellation performed on the basis of structural connectivity patterns. Functional connectivity analysis revealed that the VL subdivision has strong connections to several cortical areas, whereas the DM subdivision is mainly connected to subcortical regions. This finding suggests that the VL subdivision corresponds to the basolateral subdivision of the amygdala (BLA), while the DM subdivision has some characteristics typical of the centromedial amygdala (CMA). The similarity in functional connectivity patterns between the VL subdivision and BLA, as well as between the DM subdivision and CMA, confirm the utility of our parcellation method. Overall, the study shows that parcellation based on BOLD signal dynamics is a powerful tool for identifying distinct functional systems within the amygdala. This tool might be useful for future research on functional brain organization.
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Affiliation(s)
- Krzysztof Bielski
- Laboratory of Emotions Neurobiology, BRAINCITY - Centre of Excellence for Neural Plasticity and Brain Disorders, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Sylwia Adamus
- Laboratory of Emotions Neurobiology, BRAINCITY - Centre of Excellence for Neural Plasticity and Brain Disorders, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Emilia Kolada
- Laboratory of Emotions Neurobiology, BRAINCITY - Centre of Excellence for Neural Plasticity and Brain Disorders, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | | | - Iwona Szatkowska
- Laboratory of Emotions Neurobiology, BRAINCITY - Centre of Excellence for Neural Plasticity and Brain Disorders, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland.
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Lombardi A, Guaragnella C, Amoroso N, Monaco A, Fazio L, Taurisano P, Pergola G, Blasi G, Bertolino A, Bellotti R, Tangaro S. Modelling cognitive loads in schizophrenia by means of new functional dynamic indexes. Neuroimage 2019; 195:150-164. [DOI: 10.1016/j.neuroimage.2019.03.055] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 03/20/2019] [Accepted: 03/25/2019] [Indexed: 01/21/2023] Open
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Rodrigues PG, Filho CAS, Attux R, Castellano G, Soriano DC. Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces. Med Biol Eng Comput 2019; 57:1709-1725. [PMID: 31127535 DOI: 10.1007/s11517-019-01989-w] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Accepted: 05/03/2019] [Indexed: 12/18/2022]
Abstract
This work presents a classification performance comparison between different frameworks for functional connectivity evaluation and complex network feature extraction aiming to distinguish motor imagery classes in electroencephalography (EEG)-based brain-computer interfaces (BCIs). The analysis was performed in two online datasets: (1) a classical benchmark-the BCI competition IV dataset 2a-allowing a comparison with a representative set of strategies previously employed in this BCI paradigm and (2) a statistically representative dataset for signal processing technique comparisons over 52 subjects. Besides exploring three classical similarity measures-Pearson correlation, Spearman correlation, and mean phase coherence-this work also proposes a recurrence-based alternative for estimating EEG brain functional connectivity, which takes into account the recurrence density between pairwise electrodes over a time window. These strategies were followed by graph feature evaluation considering clustering coefficient, degree, betweenness centrality, and eigenvector centrality. The features were selected by Fisher's discriminating ratio and classification was performed by a least squares classifier in agreement with classical and online BCI processing strategies. The results revealed that the recurrence-based approach for functional connectivity evaluation was significantly better than the other frameworks, which is probably associated with the use of higher order statistics underlying the electrode joint probability estimation and a higher capability of capturing nonlinear inter-relations. There were no significant differences in performance among the evaluated graph features, but the eigenvector centrality was the best feature regarding processing time. Finally, the best ranked graph-based attributes were found in classical EEG motor cortex positions for the subjects with best performances, relating functional organization and motor activity. Graphical Abstract Evaluating functional connectivity based on Space-Time Recurrence Counting for motor imagery classification in brain-computer interfaces. Recurrences are evaluated between electrodes over a time window, and, after a density threshold, the electrodes adjacency matrix is stablish, leading to a graph. Graph-based topological measures are used for motor imagery classification.
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Affiliation(s)
- Paula G Rodrigues
- Engineering, Modeling and Applied Social Sciences Center (CECS), Federal University of ABC (UFABC), São Bernardo do Campo, SP, Brazil.
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil.
| | - Carlos A Stefano Filho
- Neurophysics Group, Institute of Physics Gleb Wataghin (IFGW), University of Campinas (UNICAMP), Campinas, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil
| | - Romis Attux
- School of Electrical and Computer Engineering (FEEC), UNICAMP, Campinas, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil
| | - Gabriela Castellano
- Neurophysics Group, Institute of Physics Gleb Wataghin (IFGW), University of Campinas (UNICAMP), Campinas, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil
| | - Diogo C Soriano
- Engineering, Modeling and Applied Social Sciences Center (CECS), Federal University of ABC (UFABC), São Bernardo do Campo, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil
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Yargholi E, Nasrabadi AM. Chaos-chaos transition of left hemisphere EEGs during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility. J Med Eng Technol 2015; 39:281-5. [PMID: 26000729 DOI: 10.3109/03091902.2015.1048317] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
A recent study, recurrence quantification analysis of EEG signals during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility investigated recurrence quantifiers (RQs) of hypnotic electroencephalograph (EEG) signals recorded after hypnotic induction while subjects were doing standard tasks of Waterloo-Stanford Group Scale (WSGS) of hypnotic susceptibility to distinguish subjects of different hypnotizability levels. Following the same analysis, the current study determines the capability of different RQs to distinguish subjects of low, medium and high hypnotizability level and studies the influence of hypnotizability level on underlying dynamic of tasks. Besides, EEG channels were sorted according to the number of their RQs, which differed significantly among subjects of different hypnotizability levels. Another valuable result was determination of major brain regions in observing significant differences in various task types (ideomotors, hallucination, challenge and memory).
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Affiliation(s)
- Elahe' Yargholi
- School of Biomedical Engineering, Islamic Azad University, Science and Research Branch , Tehran , Iran and
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11
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Hasselman F. Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal. PeerJ 2015; 3:e837. [PMID: 25834769 PMCID: PMC4380160 DOI: 10.7717/peerj.837] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Accepted: 02/24/2015] [Indexed: 11/25/2022] Open
Abstract
Several competing aetiologies of developmental dyslexia suggest that the problems with acquiring literacy skills are causally entailed by low-level auditory and/or speech perception processes. The purpose of this study is to evaluate the diverging claims about the specific deficient peceptual processes under conditions of strong inference. Theoretically relevant acoustic features were extracted from a set of artificial speech stimuli that lie on a /bAk/-/dAk/ continuum. The features were tested on their ability to enable a simple classifier (Quadratic Discriminant Analysis) to reproduce the observed classification performance of average and dyslexic readers in a speech perception experiment. The ‘classical’ features examined were based on component process accounts of developmental dyslexia such as the supposed deficit in Envelope Rise Time detection and the deficit in the detection of rapid changes in the distribution of energy in the frequency spectrum (formant transitions). Studies examining these temporal processing deficit hypotheses do not employ measures that quantify the temporal dynamics of stimuli. It is shown that measures based on quantification of the dynamics of complex, interaction-dominant systems (Recurrence Quantification Analysis and the multifractal spectrum) enable QDA to classify the stimuli almost identically as observed in dyslexic and average reading participants. It seems unlikely that participants used any of the features that are traditionally associated with accounts of (impaired) speech perception. The nature of the variables quantifying the temporal dynamics of the speech stimuli imply that the classification of speech stimuli cannot be regarded as a linear aggregate of component processes that each parse the acoustic signal independent of one another, as is assumed by the ‘classical’ aetiologies of developmental dyslexia. It is suggested that the results imply that the differences in speech perception performance between average and dyslexic readers represent a scaled continuum rather than being caused by a specific deficient component.
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Affiliation(s)
- Fred Hasselman
- School of Pedagogical and Educational Science, Radboud University Nijmegen , The Netherlands
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Yargholi E, Nasrabadi AM. Recurrence quantification analysis of electroencephalograph signals during standard tasks of Waterloo-Stanford group scale of hypnotic susceptibility. J Med Eng Technol 2014; 39:26-34. [PMID: 25367766 DOI: 10.3109/03091902.2014.973616] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
The purpose of this study was to apply RQA (recurrence quantification analysis) on hypnotic electroencephalograph (EEG) signals recorded after hypnotic induction while subjects were doing standard tasks of the Waterloo-Stanford Group Scale (WSGS) of hypnotic susceptibility. Then recurrence quantifiers were used to analyse the influence of hypnotic depth on EEGs. By the application of this method, the capability of tasks to distinguish subjects of different hypnotizability levels was determined. Besides, medium hypnotizable subjects showed the highest disposition to be inducted by hypnotizer. Similarities between brain governing dynamics during tasks of the same type were also observed. The present study demonstrated two remarkable innovations; investigating the EEGs of the hypnotized as doing mental tasks of Waterloo-Stanford Group Scale (WSGS) and applying RQA on hypnotic EEGs.
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Affiliation(s)
- Elahe' Yargholi
- School of Biomedical Engineering, Islamic Azad University, Science and Research Branch , Tehran , Iran and
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Ouyang G, Zhu X, Ju Z, Liu H. Dynamical Characteristics of Surface EMG Signals of Hand Grasps via Recurrence Plot. IEEE J Biomed Health Inform 2014; 18:257-65. [DOI: 10.1109/jbhi.2013.2261311] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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14
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Carrubba S, Minagar A, Chesson AL, Frilot C, Marino AA. Increased determinism in brain electrical activity occurs in association with multiple sclerosis. Neurol Res 2013; 34:286-90. [DOI: 10.1179/1743132812y.0000000010] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
Affiliation(s)
- Simona Carrubba
- Natural Sciences DepartmentDaemen College, Amherst, New York, USA
| | | | | | - Clifton Frilot
- School of Allied Health Professions, Louisiana State University Health Sciences Center, Shreveport, USA
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Kinsella-Shaw JM, Harrison SJ, Turvey MT. Interleg Coordination in Quiet Standing: Influence of Age and Visual Environment on Noise and Stability. J Mot Behav 2011; 43:285-94. [DOI: 10.1080/00222895.2011.580389] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Spatiotemporal nonlinearity in resting-state fMRI of the human brain. Neuroimage 2008; 40:1672-85. [DOI: 10.1016/j.neuroimage.2008.01.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2007] [Revised: 01/04/2008] [Accepted: 01/11/2008] [Indexed: 10/22/2022] Open
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