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Yang H, Wei X, Huang K, Wu Z, Zhang Q, Wen S, Wang Q, Feng L. Features of attention network impairment in patients with temporal lobe epilepsy: Evidence from eye-tracking and electroencephalogram. Epilepsy Behav 2024; 157:109887. [PMID: 38905916 DOI: 10.1016/j.yebeh.2024.109887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 06/03/2024] [Accepted: 06/08/2024] [Indexed: 06/23/2024]
Abstract
AIM To explore multiple features of attention impairments in patients with temporal lobe epilepsy (TLE). METHODS A total of 93 patients diagnosed with TLE at Xiangya Hospital during May 2022 and December 2022 and 85 healthy controls were included in this study. Participants were asked to complete neuropsychological scales and attention network test (ANT) with recording of eye-tracking and electroencephalogram. RESULTS All means of evaluation showed impaired attention functions in TLE patients. ANT results showed impaired orienting (p < 0.001) and executive control (p = 0.041) networks. Longer mean first saccade time (p = 0.046) and more total saccadic counts (p = 0.035) were found in eye-tracking results, indicating abnormal alerting and orienting networks. Both alerting, orienting and executive control networks were abnormal, manifesting as decreased amplitudes (N1 & P3, p < 0.001) and extended latency (P3, p = 0.002). The energy of theta, alpha and beta were all sensitive to the changes of alerting and executive control network with time, but only beta power was sensitive to the changes of orienting network. CONCLUSION Our findings are helpful for early identification of patients with TLE combined with attention impairments, which have strong clinical guiding significance for long-term monitoring and intervention.
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Affiliation(s)
- Haojun Yang
- Department of Anesthesiology, Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaojie Wei
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China; Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China; University of Chinese Academy of Sciences, Beijing 101400, China
| | - Kailing Huang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Zhongling Wu
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China; Department of Clinical Nursing Teaching and Research Section, Xiangya Hospital, Central South University, Changsha, China
| | - Qiong Zhang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Shirui Wen
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Quan Wang
- Key Laboratory of Biomedical Spectroscopy of Xi'an, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China; Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China.
| | - Li Feng
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
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Barone V, van Dijk JP, Debeij-van Hall MH, van Putten MJ. A Potential Multimodal Test for Clinical Assessment of Visual Attention in Neurological Disorders. Clin EEG Neurosci 2023; 54:512-521. [PMID: 36189613 PMCID: PMC10411032 DOI: 10.1177/15500594221129962] [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: 04/25/2022] [Revised: 08/05/2022] [Accepted: 09/14/2022] [Indexed: 11/17/2022]
Abstract
Attention is an important aspect of human brain function and often affected in neurological disorders. Objective assessment of attention may assist in patient care, both for diagnostics and prognostication. We present a compact test using a combination of a choice reaction time task, eye-tracking and EEG for assessment of visual attention in the clinic. The system quantifies reaction time, parameters of eye movements (i.e. saccade metrics and fixations) and event related potentials (ERPs) in a single and fast (15 min) experimental design. We present pilot data from controls, patients with mild traumatic brain injury and epilepsy, to illustrate its potential use in assessing attention in neurological patients. Reaction times and eye metrics such as fixation duration, saccade duration and latency show significant differences (p < .05) between neurological patients and controls. Late ERP components (200-800 ms) can be detected in the central line channels for all subjects, but no significant group differences could be found in the peak latencies and mean amplitudes. Our system has potential to assess key features of visual attention in the clinic. Pilot data show significant differences in reaction times and eye metrics between controls and patients, illustrating its promising use for diagnostics and prognostication.
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Affiliation(s)
- Valentina Barone
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, Enschede, Netherlands
- Twente Medical System International B.V. (TMSi), Oldenzaal, Netherlands
| | - Johannes P. van Dijk
- Academic Center for Epileptology Kempenhaeghe, Heeze, Netherlands
- Eindhoven University of Technology, Eindhoven, Netherlands
| | | | - Michel J.A.M. van Putten
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, Enschede, Netherlands
- Department of Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, Netherlands
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Kwasa JA, Noyce AL, Torres LM, Richardson BN, Shinn-Cunningham BG. Top-down auditory attention modulates neural responses more strongly in neurotypical than ADHD young adults. Brain Res 2023; 1798:148144. [PMID: 36328068 PMCID: PMC9749882 DOI: 10.1016/j.brainres.2022.148144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 10/24/2022] [Accepted: 10/26/2022] [Indexed: 11/06/2022]
Abstract
Human cognitive abilities naturally vary along a spectrum, even among those we call "neurotypical". Individuals differ in their ability to selectively attend to goal-relevant auditory stimuli. We sought to characterize this variability in a cohort of people with diverse attentional functioning. We recruited both neurotypical (N = 20) and ADHD (N = 25) young adults, all with normal hearing. Participants listened to one of three concurrent, spatially separated speech streams and reported the order of the syllables in that stream while we recorded electroencephalography (EEG). We tested both the ability to sustain attentional focus on a single "Target" stream and the ability to monitor the Target but flexibly either ignore or switch attention to an unpredictable "Interrupter" stream from another direction that sometimes appeared. Although differences in both stimulus structure and task demands affected behavioral performance, ADHD status did not. In both groups, the Interrupter evoked larger neural responses when it was to be attended compared to when it was irrelevant, including for the P3a "reorienting" response previously described as involuntary. This attentional modulation was weaker in ADHD listeners, even though their behavioral performance was the same. Across the entire cohort, individual performance correlated with the degree of top-down modulation of neural responses. These results demonstrate that listeners differ in their ability to modulate neural representations of sound based on task goals, while suggesting that adults with ADHD may have weaker volitional control of attentional processes than their neurotypical counterparts.
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Affiliation(s)
- Jasmine A. Kwasa
- Neuroscience Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, United States, Department of Biomedical Engineering, Boston University, 1 Silber Way, Boston, MA, 02215, United States, Corresponding author at: 4825 Frew St, A52A Baker Hall, Pittsburgh, PA 15213, United States. (J.A. Kwasa)
| | - Abigail L. Noyce
- Neuroscience Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, United States
| | - Laura M. Torres
- Department of Biomedical Engineering, Boston University, 1 Silber Way, Boston, MA, 02215, United States
| | - Benjamin N. Richardson
- Neuroscience Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, United States
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Can electroencephalography (EEG) identify ADHD subtypes? A systematic review. Neurosci Biobehav Rev 2022; 139:104752. [PMID: 35760387 DOI: 10.1016/j.neubiorev.2022.104752] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 06/12/2022] [Accepted: 06/22/2022] [Indexed: 11/21/2022]
Abstract
Attention Deficit/Hyperactivity Disorder (ADHD) has been associated with atypical patterns of neural activity measured by electroencephalography (EEG). However, the identification of EEG diagnostic biomarkers has been complicated by the disorder's heterogeneity. The objective of this review was to synthesize the literature investigating EEG variation in patients diagnosed with ADHD, addressing the following questions: 1) Are the diagnostic ADHD subtypes associated with different EEG characteristics? 2) Are EEG measures correlated with ADHD traits and/or symptom severity? and 3) Do classification techniques using EEG measures reveal different clinical presentations of ADHD? Outcomes highlight the potential for electrophysiological measures to provide meaningful insights into the heterogeneity of ADHD, although direct translation of EEG biomarkers for diagnostic purposes is not yet supported. Key measures that show promise for the discrimination of existing ADHD subtypes and symptomatology include: resting state and task-related modulation of alpha, beta and theta power, and the event-related N2 and P3 components. Prescriptions are discussed for future studies that may help to bridge the gap between research and clinical application.
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Münger M, Candrian G, Kasper J, Abdel-Rehim H, Eich D, Müller A, Jäncke L. Behavioral and Neurophysiological Markers of ADHD in Children, Adolescents, and Adults: A Large-Scale Clinical Study. Clin EEG Neurosci 2021; 52:311-320. [PMID: 33764193 PMCID: PMC8315002 DOI: 10.1177/1550059421993340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This study aimed to re-evaluate the possible differences between attention-deficit/hyperactivity disorder (ADHD) subjects and healthy controls in the context of a standard Go/NoGo task (visual continuous performance test [VCPT]), frequently used to measure executive functions. In contrast to many previous studies, our sample comprises children, adolescents, and adults. We analyzed data from 447 ADHD patients and 227 healthy controls. By applying multivariate linear regression analyses, we controlled the group differences between ADHD patients and controls for age and sex. As dependent variables we used behavioral (number of omission and commission errors, reaction time, and reaction time variability) and neurophysiological measures (event-related potentials [ERPs]). In summary, we successfully replicated the deviations of ADHD subjects from healthy controls. The differences are small to moderate when expressed as effect size measures (number of omission errors: d = 0.60, reaction time variability: d = 0.56, contingent negative variation (CNV) and P3 amplitudes: -0.35 < d < -0.47, ERP latencies: 0.21 < d < 0.29). Further analyses revealed no substantial differences between ADHD subtypes (combined, inattentive, and hyperactive/impulsive presentation), subgroups according to high- and low-symptomatic burden or methylphenidate intake for their daily routine. We successfully replicated known differences between ADHD subjects and controls for the behavioral and neurophysiological variables. However, the small-to-moderate effect sizes limit their utility as biomarkers in the diagnostic procedure. The incongruence of self-reported symptomatic burden and clinical diagnosis emphasizes the challenges of the present clinical diagnosis with low reliability, which partially accounts for the low degree of discrimination between ADHD subjects and controls.
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Affiliation(s)
| | - Gian Candrian
- 399140Brain and Trauma Foundation Grisons, Chur, Switzerland
| | - Johannes Kasper
- Praxisgemeinschaft für Psychiatrie und Psychotherapie, Lucerne, Switzerland
| | | | | | - Andreas Müller
- 399140Brain and Trauma Foundation Grisons, Chur, Switzerland
| | - Lutz Jäncke
- University of Zürich, Zürich, Switzerland.,University Research Priority Program (URPP) "Dynamics of Healthy Aging", Zurich, Switzerland
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Moghaddari M, Lighvan MZ, Danishvar S. Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105738. [PMID: 32927404 DOI: 10.1016/j.cmpb.2020.105738] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 08/31/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Attention-Deficit/Hyperactivity Disorder (ADHD) is a chronic behavioral disorder in children. Children with ADHD face many difficulties in maintaining their concentration and controlling their behaviors. Early diagnosis of this disorder is one of the most important challenges in its control and treatment. No definitive expert method has been found to detect this disorder early. Our goal in this study is to develop an assistive tool for physicians to recognize ADHD children from healthy children using electroencephalography (EEG) based on a continuous mental task. METHODS We used EEG signals recorded from 31 ADHD children and 30 healthy children. In this study, we developed a deep learning model using a convolutional neural network that have had significant performance in image processing fields. For this purpose, we first preprocessed EEG signals to eliminate noise and artifacts. Then we segmented preprocessed samples into more samples. We extracted the theta, alpha, beta, and gamma frequency bands from each segmented sample and formed a color RGB image with three channels. Eventually, we imported the resulting images into a 13-layer convolutional neural network for feature extraction and classification. RESULTS The proposed model was evaluated by 5-fold cross validation for train, evaluation, and test data and achieved an average accuracy of 99.06%, 97.81%, 97.47% for segmented samples. The average accuracy for subject-based test samples was 98.48%. Also, the performance of the model was evaluated using the confusion matrix with precision, recall, and f1-score metrics. The results of these metrics also confirmed the outstanding performance of the model. CONCLUSIONS The accuracy, precision, recall, and f1-score of our model were better than all previous works for diagnosing ADHD in children. Based on these prominent and reliable results, this technique can be used as an assistive tool for the physicians in the early diagnosis of ADHD in children.
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Affiliation(s)
- Majid Moghaddari
- Department of Electronic and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Mina Zolfy Lighvan
- Department of Electronic and Computer Engineering, University of Tabriz, Tabriz, Iran.
| | - Sebelan Danishvar
- Department of Electronic and Computer Engineering, University of Tabriz, Tabriz, Iran; Department of Electronic and Computer Engineering, College of Engineering, Design and Physical Sciences, Brunel University, UK
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Li M, Lin F, Xu G. A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold. Int J Neural Syst 2020; 30:2050009. [PMID: 32116091 DOI: 10.1142/s0129065720500094] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-validation and an adaptive threshold (CV-T-TAB), to reduce the amount of data required for training by selecting and combining multiple subjects' classifiers that perform well on a new subject to train a classifier. This method adopts cross-validation to extend the amount of the new subject's training data and sets an adaptive threshold to select the optimal combination of the classifiers. Twenty-five subjects participated in the N200- and P300-based brain-computer interface. The study compares CV-T-TAB to five traditional training methods by testing them on the training of a support vector machine. The accuracy, information transfer rate, area under the curve, recall and precision are used to evaluate the performances under nine conditions with different amounts of data. CV-T-TAB outperforms the other methods and retains a high accuracy even when the amount of data is reduced to one-third of the original amount. The results imply that CV-T-TAB is effective in improving the performance of a subject-specific classifier with a small amount of data by adopting multiple subjects' classifiers, which reduces the training cost.
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Affiliation(s)
- Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China
| | - Fang Lin
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300401, P. R. China
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