1
|
Kaushik P. QEEG Characterizations During Hyperventilation, Writing and Reading Conditions: A Pre-Post Cognitive-Behavioral Intervention Study on Students with Learning Difficulty. Clin EEG Neurosci 2024; 55:52-63. [PMID: 36591866 DOI: 10.1177/15500594221147158] [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] [Indexed: 01/03/2023]
Abstract
Background: Electroencephalography (EEG) has been used to measure neural correlates of cognitive and social development in children for decades. It is essential to evaluate the relationship between EEG parameters and cognitive measures to understand the mechanisms of learning problems better. Methods and procedure: Fifty school-going children with complaints of learning problems were studied. EEG and other cognitive measures were used to assess children before and after PEABLS; a cognitive-behavioral intervention was imparted. EEG was recorded while hyperventilation, writing, and reading conditions, and the values for absolute and relative powers were calculated. Results: The results suggested that the post-intervention absolute (in the theta and alpha bands) and relative (delta, theta, and alpha) power values were higher, and the relative power beta value was significantly lower at most of the electrodes in comparison to pre-intervention EEG measures. A significant high positive correlation in the children with learning problems between the relative power of alpha, beta O1O2, the relative power of theta, delta T3T4, and the academic scores, IQ, working memory, DTLD, and BGT values. Conclusion: These quantitative electroencephalogram findings in children with learning problems are related to cognitive measures. The findings could be due to brain immaturity and lack of learning opportunities.
Collapse
Affiliation(s)
- Pratima Kaushik
- Department of Psychology, Jesus and Mary College, University of Delhi, New Delhi, India
| |
Collapse
|
2
|
Booth SJ, Garg S, Brown LJE, Green J, Pobric G, Taylor JR. Aberrant oscillatory activity in neurofibromatosis type 1: an EEG study of resting state and working memory. J Neurodev Disord 2023; 15:27. [PMID: 37608248 PMCID: PMC10463416 DOI: 10.1186/s11689-023-09492-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 06/30/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND Neurofibromatosis type 1 (NF1) is a genetic neurodevelopmental disorder commonly associated with impaired cognitive function. Despite the well-explored functional roles of neural oscillations in neurotypical populations, only a limited number of studies have investigated oscillatory activity in the NF1 population. METHODS We compared oscillatory spectral power and theta phase coherence in a paediatric sample with NF1 (N = 16; mean age: 13.03 years; female: n = 7) to an age/sex-matched typically developing control group (N = 16; mean age: 13.34 years; female: n = 7) using electroencephalography measured during rest and during working memory task performance. RESULTS Relative to typically developing children, the NF1 group displayed higher resting state slow wave power and a lower peak alpha frequency. Moreover, higher theta power and frontoparietal theta phase coherence were observed in the NF1 group during working memory task performance, but these differences disappeared when controlling for baseline (resting state) activity. CONCLUSIONS Overall, results suggest that NF1 is characterised by aberrant resting state oscillatory activity that may contribute towards the cognitive impairments experienced in this population. TRIAL REGISTRATION ClinicalTrials.gov, NCT03310996 (first posted: October 16, 2017).
Collapse
Affiliation(s)
- Samantha J Booth
- Division of Psychology, Communication and Human Neuroscience, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Shruti Garg
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Child & Adolescent Mental Health Services, Royal Manchester Children's Hospital, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Laura J E Brown
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Jonathan Green
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Child & Adolescent Mental Health Services, Royal Manchester Children's Hospital, Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Gorana Pobric
- Division of Psychology, Communication and Human Neuroscience, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Jason R Taylor
- Division of Psychology, Communication and Human Neuroscience, School of Health Sciences, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
| |
Collapse
|
3
|
Martínez-Briones BJ, Fernández T, Silva-Pereyra J. Semantic Priming and Its Link to Verbal Comprehension and Working Memory in Children with Learning Disorders. Brain Sci 2023; 13:1022. [PMID: 37508954 PMCID: PMC10377304 DOI: 10.3390/brainsci13071022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/23/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023] Open
Abstract
Children with learning disorders (LD children) often have heterogeneous cognitive impairments that affect their ability to learn and use basic academic skills. A proposed cause for this variability has been working memory (WM) capacity. Altered patterns of event-related potentials (ERPs) in these children have also been found in the N400 component associated with semantic priming. However, regarding the semantic priming effect in LD children, no distinction has been made for children with varying WM abilities. This study aims to explore the relationship of WM with the brain's electrophysiological response that underlies semantic priming in LD children that performed a lexical decision task. A total of 40 children (8-10 years old) participated: 28 children with LD and 12 age-matched controls. The ERPs were recorded for each group and analyzed with permutation-based t-tests. The N400 effect was observed only in the control group, and both groups showed a late positive complex (LPC). Permutation-based regression analyses were performed for the results from the LD group using the WISC-IV indices (e.g., Verbal Comprehension and WM) as independent predictors of the ERPs. The Verbal Comprehension Index, but not the WM index, was a significant predictor of the N400 and LPC effects in LD children.
Collapse
Affiliation(s)
| | - Thalía Fernández
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro 76230, Mexico
| | - Juan Silva-Pereyra
- Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlanepantla 54090, Mexico
| |
Collapse
|
4
|
Oliaee A, Mohebbi M, Shirani S, Rostami R. Extraction of discriminative features from EEG signals of dyslexic children; before and after the treatment. Cogn Neurodyn 2022; 16:1249-1259. [PMID: 36408072 PMCID: PMC9666605 DOI: 10.1007/s11571-022-09794-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 02/16/2022] [Accepted: 02/20/2022] [Indexed: 11/25/2022] Open
Abstract
Dyslexia is a neurological disorder manifested as difficulty reading and writing. It can occur despite adequate instruction, intelligence, and intact sensory abilities. Different electroencephalogram (EEG) patterns have been demonstrated between dyslexic and healthy subjects in previous studies. This study focuses on the difference between patients before and after treatment. The main goal is to identify the subset of features that adequately discriminate subjects before and after a specific treatment plan. The treatment consists of Transcranial Direct Current Stimulation (tDCS) and occupational therapy using the BrainWare SAFARI software. The EEG signals of sixteen dyslexic children were recorded during the eyes-closed resting state before and after treatment. The preprocessing step was followed by the extraction of a wide range of features to investigate the differences related to the treatment. An optimal subset of features extracted from recorded EEG signals was determined using Principal Component Analysis (PCA) in conjunction with the Sequential Floating Forward Selection (SFFS) algorithm. The results showed that treatment leads to significant changes in EEG features like spectral and phase-related EEG features, in various regions. It has been demonstrated that the extracted subset of discriminative features can be useful for classification applications in treatment assessment. The most discriminative subset of features could classify the data with an accuracy of 92% with SVM classifier. The above result confirms the efficacy of the treatment plans in improving dyslexic children's cognitive skills.
Collapse
Affiliation(s)
- Anahita Oliaee
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
| | - Maryam Mohebbi
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
| | - Sepehr Shirani
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
| | - Reza Rostami
- Department of Psychology, Faculty of Psychology, University of Tehran, Tehran, Iran
| |
Collapse
|
5
|
Wang J, Huo S, Wu KC, Mo J, Wong WL, Maurer U. Behavioral and neurophysiological aspects of working memory impairment in children with dyslexia. Sci Rep 2022; 12:12571. [PMID: 35869126 PMCID: PMC9307804 DOI: 10.1038/s41598-022-16729-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractThe present study aimed to identify behavioral and neurophysiological correlates of dyslexia which could potentially predict reading difficulty. One hundred and three Chinese children with and without dyslexia (Grade 2 or 3, 7- to 11-year-old) completed both verbal and visual working memory (n-back) tasks with concurrent EEG recording. Data of 74 children with sufficient usable EEG data are reported here. Overall, the typically developing control group (N = 28) responded significantly faster and more accurately than the group with dyslexia (N = 46), in both types of tasks. Group differences were also found in EEG band power in the retention phase of the tasks. Moreover, forward stepwise logistic regression demonstrated that both behavioral and neurophysiological measures predicted reading difficulty uniquely. Dyslexia was associated with higher frontal midline theta activity and reduced upper-alpha power in the posterior region. This finding is discussed in relation to the neural efficiency hypothesis. Whether these behavioral and neurophysiological patterns can longitudinally predict later reading development among preliterate children requires further investigation.
Collapse
|
6
|
Assessing Distinct Cognitive Workload Levels Associated with Unambiguous and Ambiguous Pronoun Resolutions in Human–Machine Interactions. Brain Sci 2022; 12:brainsci12030369. [PMID: 35326325 PMCID: PMC8946822 DOI: 10.3390/brainsci12030369] [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: 02/18/2022] [Revised: 03/07/2022] [Accepted: 03/08/2022] [Indexed: 02/05/2023] Open
Abstract
Pronoun resolution plays an important role in language comprehension. However, little is known about its recruited cognitive mechanisms. Our investigation aims to explore the cognitive mechanisms underlying various types of pronoun resolution in Chinese using an electroencephalograph (EEG). We used three convolutional neural networks (CNNs)—LeNeT-5, GoogleNet, and EffifcientNet—to discover high-level feature abstractions of the EEG spatial topologies. The output of the three models was then fused using different scales by principal component analysis (PCA) to achieve cognitive workload classification. Overall, the workload classification rate by fusing three deep networks can be achieved at 55–63% in a participant-specific manner. We provide evidence that both the behavioral indicator of reaction time and the neural indicator of cognitive workload collected during pronoun resolution vary depending on the type of the pronoun. We observed an increase in reaction time accompanied by a decrease of the theta power while participants were processing ambiguous pronoun resolution compared to unambiguous controls. We propose that ambiguous pronoun resolution involves a more time-consuming yet more flexible cognitive mechanism, consistent with the predictions of the decision-making framework from an influential pragmatic tradition. Our results extend previous research that the cognitive states of resolving ambiguous and unambiguous pronouns are differentiated, indicating that cognitive workload evaluated using the method of machine learning for analysis of EEG signals acts as a complementary indicator for studying pronoun resolution and serves as an important inspiration for human–machine interaction.
Collapse
|
7
|
Alotaibi N, Bakheet D, Konn D, Vollmer B, Maharatna K. Cognitive Outcome Prediction in Infants With Neonatal Hypoxic-Ischemic Encephalopathy Based on Functional Connectivity and Complexity of the Electroencephalography Signal. Front Hum Neurosci 2022; 15:795006. [PMID: 35153702 PMCID: PMC8830486 DOI: 10.3389/fnhum.2021.795006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 12/10/2021] [Indexed: 12/03/2022] Open
Abstract
Impaired neurodevelopmental outcome, in particular cognitive impairment, after neonatal hypoxic-ischemic encephalopathy is a major concern for parents, clinicians, and society. This study aims to investigate the potential benefits of using advanced quantitative electroencephalography analysis (qEEG) for early prediction of cognitive outcomes, assessed here at 2 years of age. EEG data were recorded within the first week after birth from a cohort of twenty infants with neonatal hypoxic-ischemic encephalopathy (HIE). A proposed regression framework was based on two different sets of features, namely graph-theoretical features derived from the weighted phase-lag index (WPLI) and entropies metrics represented by sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn). Both sets of features were calculated within the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain. Correlation analysis showed a significant association in the delta band between the proposed features, graph attributes (radius, transitivity, global efficiency, and characteristic path length) and entropy features (Pen and SpEn) from the neonatal EEG data and the cognitive development at age two years. These features were used to train and test the tree ensemble (boosted and bagged) regression models. The highest prediction performance was reached to 14.27 root mean square error (RMSE), 12.07 mean absolute error (MAE), and 0.45 R-squared using the entropy features with a boosted tree regression model. Thus, the results demonstrate that the proposed qEEG features show the state of brain function at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate identifying those who might benefit from early targeted intervention.
Collapse
Affiliation(s)
- Noura Alotaibi
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
- Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia
| | - Dalal Bakheet
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
- Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah, Saudi Arabia
| | - Daniel Konn
- Clinical Neurophysiology, University Hospital Southampton, Southampton, United Kingdom
| | - Brigitte Vollmer
- Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Paediatric Neurology, Southampton Children’s Hospital, Southampton, United Kingdom
| | - Koushik Maharatna
- School of Electronics and Computer Science, University of Southampton, Southampton, United Kingdom
| |
Collapse
|
8
|
Martínez-Briones BJ, Bosch-Bayard J, Biscay-Lirio RJ, Silva-Pereyra J, Albarrán-Cárdenas L, Fernández T. Effects of Neurofeedback on the Working Memory of Children with Learning Disorders-An EEG Power-Spectrum Analysis. Brain Sci 2021; 11:brainsci11070957. [PMID: 34356191 PMCID: PMC8303215 DOI: 10.3390/brainsci11070957] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 07/07/2021] [Accepted: 07/15/2021] [Indexed: 01/10/2023] Open
Abstract
Learning disorders (LDs) are diagnosed in children impaired in the academic skills of reading, writing and/or mathematics. Children with LDs usually exhibit a slower resting-state electroencephalogram (EEG), corresponding to a neurodevelopmental lag. Frequently, children with LDs show working memory (WM) impairment, associated with an abnormal task-related EEG with overall slower EEG activity (more delta and theta power, and less gamma activity in posterior sites). These EEG patterns indicate inefficient neural resource management. Neurofeedback (NFB) treatments aimed at normalizing the resting-state EEG of LD children have shown improvements in cognitive-behavioral indices and diminished EEG abnormalities. Given the typical findings of WM impairment in children with LDs, we aimed to explore the effects of an NFB treatment on the WM of children with LDs by analyzing the WM-related EEG power spectrum. EEGs of 18 children (8–11 y.o.) with LDs were recorded, pre- and post-treatment, during performance of a Sternberg-type WM task. Thirty sessions of an NFB treatment (NFB-group, n = 10) or 30 sessions of a placebo-sham treatment (sham-group, n = 8) were administered. We analyzed the before and after treatment group differences for the behavioral performance and the WM-related EEG power spectrum. The NFB group showed faster response times in the WM task post-treatment. They also exhibited a decreased theta power and increased beta and gamma power at the frontal and posterior sites post-treatment. We explain these findings in terms of NFB improving the efficiency of neural resource management, maintenance of memory representations, and improved subvocal memory rehearsal.
Collapse
Affiliation(s)
- Benito J. Martínez-Briones
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro QE 76230, Mexico; (B.J.M.-B.); (J.B.-B.); (L.A.-C.)
| | - Jorge Bosch-Bayard
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro QE 76230, Mexico; (B.J.M.-B.); (J.B.-B.); (L.A.-C.)
- McGill Centre for Integrative Neuroscience (MCIN), Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal, QC H3A 2B4, Canada
| | | | - Juan Silva-Pereyra
- Facultad de Estudios Superiores Iztacala, Universidad Nacional Autónoma de México, Tlanepantla, Estado de México MX 54090, Mexico;
| | - Lucero Albarrán-Cárdenas
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro QE 76230, Mexico; (B.J.M.-B.); (J.B.-B.); (L.A.-C.)
| | - Thalía Fernández
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro QE 76230, Mexico; (B.J.M.-B.); (J.B.-B.); (L.A.-C.)
- Correspondence:
| |
Collapse
|
9
|
Individual Alpha Peak Frequency, an Important Biomarker for Live Z-Score Training Neurofeedback in Adolescents with Learning Disabilities. Brain Sci 2021; 11:brainsci11020167. [PMID: 33525458 PMCID: PMC7911657 DOI: 10.3390/brainsci11020167] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/22/2021] [Accepted: 01/24/2021] [Indexed: 01/19/2023] Open
Abstract
Learning disabilities (LDs) have an estimated prevalence between 5% and 9% in the pediatric population and are associated with difficulties in reading, arithmetic, and writing. Previous electroencephalography (EEG) research has reported a lag in alpha-band development in specific LD phenotypes, which seems to offer a possible explanation for differences in EEG maturation. In this study, 40 adolescents aged 10–15 years with LDs underwent 10 sessions of Live Z-Score Training Neurofeedback (LZT-NF) Training to improve their cognition and behavior. Based on the individual alpha peak frequency (i-APF) values from the spectrogram, a group with normal i-APF (ni-APF) and a group with low i-APF (li-APF) were compared in a pre-and-post-LZT-NF intervention. There were no statistical differences in age, gender, or the distribution of LDs between the groups. The li-APF group showed a higher theta absolute power in P4 (p = 0.016) at baseline and higher Hi-Beta absolute power in F3 (p = 0.007) post-treatment compared with the ni-APF group. In both groups, extreme waves (absolute Z-score of ≥1.5) were more likely to move toward the normative values, with better results in the ni-APF group. Conversely, the waves within the normal range at baseline were more likely to move out of the range after treatment in the li-APF group. Our results provide evidence of a viable biomarker for identifying optimal responders for the LZT-NF technique based on the i-APF metric reflecting the patient’s neurophysiological individuality.
Collapse
|