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Yang D, Ghafoor U, Eggebrecht AT, Hong KS. Effectiveness assessment of repetitive transcranial alternating current stimulation with concurrent EEG and fNIRS measurement. Health Inf Sci Syst 2023; 11:35. [PMID: 37545487 PMCID: PMC10397167 DOI: 10.1007/s13755-023-00233-y] [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: 07/26/2022] [Accepted: 07/11/2023] [Indexed: 08/08/2023] Open
Abstract
Transcranial alternating current stimulation (tACS) exhibits the capability to interact with endogenous brain oscillations using an external low-intensity sinusoidal current and influences cerebral function. Despite its potential benefits, the physiological mechanisms and effectiveness of tACS are currently a subject of debate and disagreement. The aims of our study are to (i) evaluate the neurological and behavioral impact of tACS by conducting repetitive sham-controlled experiments and (ii) propose criteria to evaluate effectiveness, which can serve as a benchmark to determine optimal individual-based tACS protocols. In this study, 15 healthy adults participated in the experiment over two visiting: sham and tACS (i.e., 5 Hz, 1 mA). During each visit, we used multimodal recordings of the participants' brain, including simultaneous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), along with a working memory (WM) score to quantify neurological effects and cognitive changes immediately after each repetitive sham/tACS session. Our results indicate increased WM scores, hemodynamic response strength, and EEG power in theta and delta bands both during and after the tACS period. Additionally, the observed effects do not increase with prolonged stimulation time, as the effects plateau towards the end of the experiment. In conclusion, our proposed closed-loop scheme offers a promising advance for evaluating the effectiveness of tACS during the stimulation session. Specifically, the assessment criteria use participant-specific brain-based signals along with a behavioral output. Moreover, we propose a feedback efficacy score that can aid in determining the optimal stimulation duration based on a participant-specific brain state, thereby preventing the risk of overstimulation.
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Affiliation(s)
- Dalin Yang
- School of Mechanical Engineering, Pusan National University, Busan, 46241 Republic of Korea
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63100 USA
| | - Usman Ghafoor
- School of Mechanical Engineering, Pusan National University, Busan, 46241 Republic of Korea
| | - Adam Thomas Eggebrecht
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63100 USA
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63130 USA
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, 46241 Republic of Korea
- Institute for Future, School of Automation, Qingdao University, Qingdao, 266071 Shandong China
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Müller D, Habel U, Brodkin ES, Clemens B, Weidler C. HD-tDCS induced changes in resting-state functional connectivity: Insights from EF modeling. Brain Stimul 2023; 16:1722-1732. [PMID: 38008154 DOI: 10.1016/j.brs.2023.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND High-definition transcranial direct current stimulation (HD-tDCS) holds promise for therapeutic use in psychiatric disorders. One obstacle for the implementation into clinical practice is response variability. One way to tackle this obstacle is the use of Individualized head models. OBJECTIVE This study investigated the variability of HD-tDCS induced electric fields (EFs) and its impact on resting-state functional connectivity (rsFC) during different time windows. METHODS In this randomized, double-blind, and sham controlled study, seventy healthy males underwent 20 min of 1.5 mA HD-tDCS on the right inferior frontal gyrus (rIFG) while undergoing resting-state functional magnetic resonance imaging (rs-fMRI). Individual head models and EF simulations were created from anatomical images. The effects of HD-tDCS on rsFC were assessed using a seed-to-voxel analysis. A subgroup analysis explored the relationship between EF magnitude and rsFC during different stimulation time windows. RESULTS Results highlighted significant variability in HD-tDCS-induced EFs. Compared to the sham group, the active group showed increased rsFC between the rIFG and the left prefrontal cortex, during and after stimulation. During active stimulation, EF magnitude correlated positively with rsFC between the rIFG and the left hippocampus initially, and negatively during the subsequent period. CONCLUSION This study indicated an HD-tDCS induced increase of rsFC between left and right prefrontal areas. Furthermore, an interaction between the magnitude and the duration of HD-tDCS on rsFC was observed. Due to the high EF variability that was apparent, these findings highlight the need for individualized HD-tDCS protocols and the creation of head models to optimize effects and reduce response heterogeneity.
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Affiliation(s)
- Dario Müller
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany; JARA-BRAIN Institute Brain Structure-Function Relationships, Research Center Jülich and RWTH Aachen, Germany; Institute of Neuroscience and Medicine 10, Research Center Jülich, 52438, Jülich, Germany
| | - Edward S Brodkin
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, 3535 Market Street, Suite 3080, Philadelphia, PA, 19104-3309, USA
| | - Benjamin Clemens
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Carmen Weidler
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
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Du J, Morales A. Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models. Int J Neural Syst 2023; 33:2350022. [PMID: 36916993 PMCID: PMC10561898 DOI: 10.1142/s0129065723500223] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Electrical stimulation of the peripheral nervous system is a promising therapeutic option for several conditions; however, its effects on tissue and the safety of the stimulation remain poorly understood. In order to devise stimulation protocols that enhance therapeutic efficacy without the risk of causing tissue damage, we constructed computational models of peripheral nerve and stimulation cuffs based on extremely high-resolution cross-sectional images of the nerves using the most recent advances in computing power and machine learning techniques. We developed nerve models using nonstimulated (healthy) and over-stimulated (damaged) rat sciatic nerves to explore how nerve damage affects the induced current density distribution. Using our in-house computational, quasi-static, platform, and the Admittance Method (AM), we estimated the induced current distribution within the nerves and compared it for healthy and damaged nerves. We also estimated the extent of localized cell damage in both healthy and damaged nerve samples. When the nerve is damaged, as demonstrated principally by the decreased nerve fiber packing, the current penetrates deeper into the over-stimulated nerve than in the healthy sample. As safety limits for electrical stimulation of peripheral nerves still refer to the Shannon criterion to distinguish between safe and unsafe stimulation, the capability this work demonstrated is an important step toward the development of safety criteria that are specific to peripheral nerve and make use of the latest advances in computational bioelectromagnetics and machine learning, such as Python-based AM and CNN-based nerve image segmentation.
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Zafar A, Dad Kallu K, Atif Yaqub M, Ali MU, Hyuk Byun J, Yoon M, Su Kim K. A Hybrid GCN and Filter-Based Framework for Channel and Feature Selection: An fNIRS-BCI Study. INT J INTELL SYST 2023. [DOI: 10.1155/2023/8812844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
In this study, a channel and feature selection methodology is devised for brain-computer interface (BCI) applications using functional near-infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter-based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left- and right-hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two-class BCI applications (i.e., left- vs. right-hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four-class BCI applications (i.e., left- vs. right-hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two-class and four-class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four-class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four-class) yielded high average classification accuracy
. However, the results of the ReliefF algorithm are more stable and significant.
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Mughal NE, Khan MJ, Khalil K, Javed K, Sajid H, Naseer N, Ghafoor U, Hong KS. EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM. Front Neurorobot 2022; 16:873239. [PMID: 36119719 PMCID: PMC9472125 DOI: 10.3389/fnbot.2022.873239] [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: 02/10/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
The constantly evolving human–machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain–computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.
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Affiliation(s)
- Nabeeha Ehsan Mughal
- School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Jawad Khan
- School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
- National Center of Artificial Intelligence (NCAI) – NUST, Islamabad, Pakistan
| | - Khurram Khalil
- School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Kashif Javed
- School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Hasan Sajid
- School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
- National Center of Artificial Intelligence (NCAI) – NUST, Islamabad, Pakistan
| | - Noman Naseer
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad, Pakistan
| | - Usman Ghafoor
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
- *Correspondence: Keum-Shik Hong
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Hong KS, Khan MNA, Ghafoor U. Non-invasive transcranial electrical brain stimulation guided by functional near-infrared spectroscopy for targeted neuromodulation: A review. J Neural Eng 2022; 19. [PMID: 35905708 DOI: 10.1088/1741-2552/ac857d] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 07/29/2022] [Indexed: 11/12/2022]
Abstract
One of the primary goals in cognitive neuroscience is to understand the neural mechanisms on which cognition is based. Researchers are trying to find how cognitive mechanisms are related to oscillations generated due to brain activity. The research focused on this topic has been considerably aided by developing non-invasive brain stimulation techniques. The dynamics of brain networks and the resultant behavior can be affected by non-invasive brain stimulation techniques, which make their use a focus of interest in many experiments and clinical fields. One essential non-invasive brain stimulation technique is transcranial electrical stimulation (tES), subdivided into transcranial direct and alternating current stimulation. tES has recently become more well-known because of the effective results achieved in treating chronic conditions. In addition, there has been exceptional progress in the interpretation and feasibility of tES techniques. Summarizing the beneficial effects of tES, this article provides an updated depiction of what has been accomplished to date, brief history, and the open questions that need to be addressed in the future. An essential issue in the field of tES is stimulation duration. This review briefly covers the stimulation durations that have been utilized in the field while monitoring the brain using functional-near infrared spectroscopy-based brain imaging.
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Affiliation(s)
- Keum-Shik Hong
- Department of Cogno-mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumgeong-gu, Busan, Busan, 609735, Korea (the Republic of)
| | - M N Afzal Khan
- Pusan National University, Department of Mechanical Engineering, Busan, 46241, Korea (the Republic of)
| | - Usman Ghafoor
- School of Mechanical Engineering, Pusan National University College of Engineering, room 204, Busan, 46241, Korea (the Republic of)
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