1
|
N P GS, Singh BK. Analysis of reading-task-based brain connectivity in dyslexic children using EEG signals. Med Biol Eng Comput 2024:10.1007/s11517-024-03085-0. [PMID: 38584207 DOI: 10.1007/s11517-024-03085-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 03/21/2024] [Indexed: 04/09/2024]
Abstract
Developmental dyslexia, a neurodevelopment reading disorder, can impact even children with average intelligence. The present study examined the brain connectivity in dyslexic and control children during the reading task using graph theory. 19-channel electroencephalogram (EEG) signals were recorded from 15 dyslexic children and 15 control children. Functional connectivity was estimated by measuring the EEG coherence at 19 electrode locations, and graph measures were calculated using the graph theory method. Reading task results identified deprived task performance in dyslexic children against controls. Graph measures revealed longer path length, reduced clustering coefficient and reduced network efficiencies (in theta and alpha bands) of dyslexic group. At the nodal level, we found a significant increase in delta strength (T4 and T5 electrode locations) and reduced strength in theta (T6, P4, Fp1, F8 and F3) and alpha bands (T4, T3, P4 and F3) during the reading task in dyslexic group. In conclusion, the present study identified distinct graph measures between groups when performing a reading task and showed possible evidence for compromised brain networks in dyslexic group.
Collapse
Affiliation(s)
- Guhan Seshadri N P
- Department of Biomedical Engineering, National Institute of Technology Raipur, G.E Road, Raipur, 492010, India
| | - Bikesh Kumar Singh
- Department of Biomedical Engineering, National Institute of Technology Raipur, G.E Road, Raipur, 492010, India.
| |
Collapse
|
2
|
Research on the Correlation between Multisource Big Data Virtual Assisted Preschool Education and the Development of Children’s Innovative Ability. Occup Ther Int 2022; 2022:3880201. [PMID: 35572165 PMCID: PMC9068341 DOI: 10.1155/2022/3880201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/02/2022] [Accepted: 04/06/2022] [Indexed: 11/17/2022] Open
Abstract
Objective. Innovation ability is an important part of children’s core literacy and the core goal of science curriculum. As one of the important contents of scientific literacy, innovation ability is the key ability of young children to make informed decisions when facing scientific problems in social and personal life. In order to adapt to the future life, it is very important for children to have the ability to innovate. This research will provide a reference for the cultivation of children’s innovative ability. Method. In the process of database design, certain design principles need to be followed. In this paper, the system and user experience are greatly optimized by reasonably constructing table structure, allocating storage space, and establishing indexes. In this system, the MySQL database is used to store system data, such as user registration information, subscription information, and system-provided services, and the data uploaded by users that needs to be processed is stored in Hive. Although the GFP algorithm can solve the problem of load balancing, when the largest conditional pattern base of a frequent item is projected to other nodes, a large amount of data transmission will occur, resulting in increased communication between nodes. In order to solve this problem, the FP-growth parallel algorithm based on traffic optimization gives priority to assigning each frequent item to the node that needs the least traffic when grouping it. Results/Discussion. Experiments show that the TFP algorithm not only satisfies the load balance of nodes but also ensures a small amount of communication between nodes, which is more efficient than the traditional FP-growth parallel algorithm. The survey results of the influencing factors of children’s innovation ability match the theoretical hypothesis, and different influencing factors have different effects on each dimension of children’s innovation ability. Through the basic fit index of the model, the evaluation of the external quality of the model and the test of the internal quality of the model, it is shown that the survey results of the influencing factors of children’s innovation ability match the theoretical hypothesis. The three influencing factors of family participation and investment, teacher teaching, and peer collaboration and communication have a positive role in promoting children’s innovation ability.
Collapse
|
3
|
Formoso MA, Ortiz A, Martinez-Murcia FJ, Gallego N, Luque JL. Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis. SENSORS 2021; 21:s21217061. [PMID: 34770378 PMCID: PMC8588444 DOI: 10.3390/s21217061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 01/07/2023]
Abstract
Objective Dyslexia diagnosis is a challenging task, since traditional diagnosis methods are not based on biological markers but on behavioural tests. Although dyslexia diagnosis has been addressed by these tests in clinical practice, it is difficult to extract information about the brain processes involved in the different tasks and, then, to go deeper into its biological basis. Thus, the use of biomarkers can contribute not only to the diagnosis but also to a better understanding of specific learning disorders such as dyslexia. In this work, we use Electroencephalography (EEG) signals to discover differences among controls and dyslexic subjects using signal processing and artificial intelligence techniques. Specifically, we measure phase synchronization among channels, to reveal the functional brain network activated during auditory processing. On the other hand, to explore synchronicity patterns risen by low-level auditory processing, we used specific stimuli consisting in band-limited white noise, modulated in amplitude at different frequencies. The differential information contained in the functional (i.e., synchronization) network has been processed by an anomaly detection system that addresses the problem of subjects variability by an outlier-detection method based on vector quantization. The results, obtained for 7 years-old children, show that the proposed method constitutes an useful tool for clinical use, with the area under ROC curve (AUC) values up to 0.95 in differential diagnosis tasks.
Collapse
Affiliation(s)
- Marco A. Formoso
- Communications Engineering Department, University of Málaga, 29071 Málaga, Spain; (M.A.F.); (N.G.)
| | - Andrés Ortiz
- Communications Engineering Department, University of Málaga, 29071 Málaga, Spain; (M.A.F.); (N.G.)
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), 18014 Granada, Spain;
- Correspondence: ; Tel.: +34-952133353
| | - Francisco J. Martinez-Murcia
- Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), 18014 Granada, Spain;
- Department of Signal Theory, Networking and Communications, University of Granada, 18014 Granada, Spain
| | - Nicolás Gallego
- Communications Engineering Department, University of Málaga, 29071 Málaga, Spain; (M.A.F.); (N.G.)
| | - Juan L. Luque
- Department of Basic Psychology, University of Malaga, 29019 Málaga, Spain;
| |
Collapse
|
4
|
Dimitriadis SI, Lyssoudis C, Tsolaki AC, Lazarou E, Kozori M, Tsolaki M. Greek High Phenolic Early Harvest Extra Virgin Olive Oil Reduces the Over-Excitation of Information-Flow Based on Dominant Coupling Mode (DoCM) Model in Patients with Mild Cognitive Impairment: An EEG Resting-State Validation Approach. J Alzheimers Dis 2021; 83:191-207. [PMID: 34308906 DOI: 10.3233/jad-210454] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Extra virgin olive oil (EVOO) constitutes a natural compound with high protection over cognitive function that could positively alter brain dynamics and the mixture of within and between-frequency connectivity. OBJECTIVE The balance of cross-frequency coupling over within-frequency coupling can build a nonlinearity index (NI) that encapsulates the over-excitation of information flow between brain areas and across experimental time. The present study investigated for the very first time how the Greek High Phenolic Early Harvest Extra Virgin Olive Oil (HP-EH-EVOO) versus Moderate Phenolic (MP-EVOO) and Mediterranean Diet (MeDi) intervention in people with mild cognitive impairment (MCI) could affect their spontaneous EEG dynamic connectivity. METHODS Forty-three subjects (14 in MeDi, 16 in MP-EVOO, and 13 in HP-EH-EVOO) followed an EEG resting-state recording session (eyes-open and closed) before and after the treatment. Following our dominant coupling mode model, we built a dynamic integrated dynamic functional connectivity graph that tabulates the functional strength and the dominant coupling mode model of every pair of brain areas. RESULTS Signal spectrum within 1-13 Hz and theta/beta ratio have decreased in the HP-EH-EVOO group in the eyes-open condition. The intervention improved the FIDoCM across groups and conditions but was more prominent in the HP-EH-EVOO group (p < 0.001). Finally, we revealed a significant higher post-intervention reduction of NI (ΔNITotal and α) for the HP-EH-EVOO compared to the MP-EVOO and MeDi groups (p < 0.0001). CONCLUSION Long-term intervention with HP-EH-EVOO reduced the over-excitation of information flow in spontaneous brain activity and altered the signal spectrum of EEG rhythms.
Collapse
Affiliation(s)
- Stavros I Dimitriadis
- 1st Department of Neurology, G.H. "AHEPA, " School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece.,Integrative Neuroimaging Lab, Thessaloniki, Greece.,Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, United Kingdom.,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom.,MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, United Kingdom
| | - Christos Lyssoudis
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| | - Anthoula C Tsolaki
- 1st Department of Neurology, G.H. "AHEPA, " School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece
| | - Eftychia Lazarou
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| | - Mahi Kozori
- Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| | - Magda Tsolaki
- 1st Department of Neurology, G.H. "AHEPA, " School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.,Greek Association of Alzheimer's Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| |
Collapse
|
5
|
Dimitriadis SI. Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders. Prog Neuropsychopharmacol Biol Psychiatry 2021; 108:110073. [PMID: 32805332 DOI: 10.1016/j.pnpbp.2020.110073] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 08/10/2020] [Accepted: 08/10/2020] [Indexed: 12/16/2022]
Abstract
Electroencephalography (EEG) based biomarkers have been shown to correlate with the presence of psychotic disorders. Increased delta and decreased alpha power in psychosis indicate an abnormal arousal state. We investigated brain activity across the basic EEG frequencies and also dynamic functional connectivity of both intra and cross-frequency coupling that could reveal a neurophysiological biomarker linked to an aberrant modulating role of alpha frequency in adolescents with schizophrenia spectrum disorders (SSDs). A dynamic functional connectivity graph (DFCG) has been estimated using the imaginary part of phase lag value (iPLV) and correlation of the envelope (corrEnv). We analyzed DFCG profiles of electroencephalographic resting state (eyes closed) recordings of healthy controls (HC) (n = 39) and SSDs subjects (n = 45) in basic frequency bands {δ,θ,α1,α2,β1,β2,γ}. In our analysis, we incorporated both intra and cross-frequency coupling modes. Adopting our recent Dominant Coupling Mode (DοCM) model leads to the construction of an integrated DFCG (iDFCG) that encapsulates the functional strength and the DοCM of every pair of brain areas. We revealed significantly higher ratios of delta/alpha1,2 power spectrum in SSDs subjects versus HC. The probability distribution (PD) of amplitude driven DoCM mediated by alpha frequency differentiated SSDs from HC with absolute accuracy (100%). The network Flexibility Index (FI) was significantly lower for subjects with SSDs compared to the HC group. Our analysis supports the central role of alpha frequency alterations in the neurophysiological mechanisms of SSDs. Currents findings open up new diagnostic pathways to clinical detection of SSDs and support the design of rational neurofeedback training.
Collapse
Affiliation(s)
- Stavros I Dimitriadis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom; Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom; School of Psychology, College of Biomedical and Life Sciences,Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences,Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.
| |
Collapse
|
6
|
Ortiz A, Martinez-Murcia FJ, Luque JL, Giménez A, Morales-Ortega R, Ortega J. Dyslexia Diagnosis by EEG Temporal and Spectral Descriptors: An Anomaly Detection Approach. Int J Neural Syst 2020; 30:2050029. [PMID: 32496139 DOI: 10.1142/s012906572050029x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Diagnosis of learning difficulties is a challenging goal. There are huge number of factors involved in the evaluation procedure that present high variance among the population with the same difficulty. Diagnosis is usually performed by scoring subjects according to results obtained in different neuropsychological (performance-based) tests specifically designed to this end. One of the most frequent disorders is developmental dyslexia (DD), a specific difficulty in the acquisition of reading skills not related to mental age or inadequate schooling. Its prevalence is estimated between 5% and 12% of the population. Traditional tests for DD diagnosis aim to measure different behavioral variables involved in the reading process. In this paper, we propose a diagnostic method not based on behavioral variables but on involuntary neurophysiological responses to different auditory stimuli. The experiments performed use electroencephalography (EEG) signals to analyze the temporal behavior and the spectral content of the signal acquired from each electrode to extract relevant (temporal and spectral) features. Moreover, the relationship of the features extracted among electrodes allows to infer a connectivity-like model showing brain areas that process auditory stimuli in a synchronized way. Then an anomaly detection system based on the reconstruction residuals of an autoencoder using these features has been proposed. Hence, classification is performed by the proposed system based on the differences in the resulting connectivity models that have demonstrated to be a useful tool for differential diagnosis of DD as well as a method to step towards gaining a better knowledge of the brain processes involved in DD. The results corroborate that nonspeech stimulus modulated at specific frequencies related to the sampling processes developed in the brain to capture rhymes, syllables and phonemes produces effects in specific frequency bands that differentiate between controls and DD subjects. The proposed method showed relatively high sensitivity above 0.6, and up to 0.9 in some of the experiments.
Collapse
Affiliation(s)
- Andrés Ortiz
- Department of Communications Engineering, University of Malaga, Campus de Teatinos s/n, 29071 Malaga, Spain.,Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, C/Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain
| | - Francisco J Martinez-Murcia
- Department of Communications Engineering, University of Malaga, Campus de Teatinos s/n, 29071 Malaga, Spain.,Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, C/Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain
| | - Juan L Luque
- Department of Developmental and Educational Psychology, University of Malaga, Campus de Teatinos s/n, 29071 Malaga, Spain
| | - Almudena Giménez
- Department of Basic Psychology, Faculty of Psychology, University of Malaga, Campus de Teatinos s/n, 29071 Malaga, Spain
| | - Roberto Morales-Ortega
- Department of Computer Architecture and Technology, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| | - Julio Ortega
- Department of Computer Architecture and Technology, University of Granada, Periodista Daniel Saucedo Aranda, 18071 Granada, Spain
| |
Collapse
|
7
|
Resting-state EEG reveals global network deficiency in dyslexic children. Neuropsychologia 2020; 138:107343. [DOI: 10.1016/j.neuropsychologia.2020.107343] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 01/01/2020] [Accepted: 01/13/2020] [Indexed: 12/22/2022]
|
8
|
Dimitriadis SI, Simos PG, Fletcher JΜ, Papanicolaou AC. Typical and Aberrant Functional Brain Flexibility: Lifespan Development and Aberrant Organization in Traumatic Brain Injury and Dyslexia. Brain Sci 2019; 9:E380. [PMID: 31888230 PMCID: PMC6956162 DOI: 10.3390/brainsci9120380] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 11/22/2019] [Accepted: 12/12/2019] [Indexed: 12/03/2022] Open
Abstract
Intrinsic functional connectivity networks derived from different neuroimaging methods and connectivity estimators have revealed robust developmental trends linked to behavioural and cognitive maturation. The present study employed a dynamic functional connectivity approach to determine dominant intrinsic coupling modes in resting-state neuromagnetic data from 178 healthy participants aged 8-60 years. Results revealed significant developmental trends in three types of dominant intra- and inter-hemispheric neuronal population interactions (amplitude envelope, phase coupling, and phase-amplitude synchronization) involving frontal, temporal, and parieto-occipital regions. Multi-class support vector machines achieved 89% correct classification of participants according to their chronological age using dynamic functional connectivity indices. Moreover, systematic temporal variability in functional connectivity profiles, which was used to empirically derive a composite flexibility index, displayed an inverse U-shaped curve among healthy participants. Lower flexibility values were found among age-matched children with reading disability and adults who had suffered mild traumatic brain injury. The importance of these results for normal and abnormal brain development are discussed in light of the recently proposed role of cross-frequency interactions in the fine-grained coordination of neuronal population activity.
Collapse
Affiliation(s)
- Stavros I. Dimitriadis
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
- School of Psychology, Cardiff University, Cardiff CF10 3AT, UK
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff CF24 4HQ, UK
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff CF24 4HQ, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff CF24 4HQ, UK
| | - Panagiotis G. Simos
- School of Medicine, University of Crete, Herakleion 70013, Greece;
- Institute of Computer Science, Foundation for Research and Technology, Herakleion 70013, Greece
| | - Jack Μ. Fletcher
- Department of Psychology, University of Houston, Houston, Texas, TX 77204-5022, USA;
| | - Andrew C. Papanicolaou
- Division of Clinical Neurosciences, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN 38103, USA;
- Le Bonheur Neuroscience Institute, Le Bonheur Children’s Hospital, Memphis, TN 38103, USA
| |
Collapse
|