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Parsaei M, Barahman G, Roumiani PH, Ranjbar E, Ansari S, Najafi A, Karimi H, Aarabi MH, Moghaddam HS. White matter correlates of cognition: A diffusion magnetic resonance imaging study. Behav Brain Res 2024; 476:115222. [PMID: 39216828 DOI: 10.1016/j.bbr.2024.115222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 08/23/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Our comprehension of the interplay of cognition and the brain remains constrained. While functional imaging studies have identified cognitive brain regions, structural correlates of cognitive functions remain underexplored. Advanced methods like Diffusion Magnetic Resonance Imaging (DMRI) facilitate the exploration of brain connectivity and White Matter (WM) tract microstructure. Therefore, we conducted connectometry method on DMRI data, to reveal WM tracts associated with cognition. METHODS 125 healthy participants from the National Institute of Mental Health Intramural Healthy Volunteer Dataset were recruited. Multiple regression analyses were conducted between DMRI-derived Quantitative Anisotropy (QA) values within WM tracts and scores of participants in Flanker Inhibitory Control and Attention Test (attention), Dimensional Change Card Sort (executive function), Picture Sequence Memory Test (episodic memory), and List Sorting Working Memory Test (working memory) tasks from National Institute of Health toolbox. The significance level was set at False Discovery Rate (FDR)<0.05. RESULTS We identified significant positive correlations between the QA of WM tracts within the left cerebellum and bilateral fornix with attention, executive functioning, and episodic memory (FDR=0.018, 0.0002, and 0.0002, respectively), and a negative correlation between QA of WM tracts within bilateral cerebellum with attention (FDR=0.028). Working memory demonstrated positive correlations with QA of left inferior longitudinal and left inferior fronto-occipital fasciculi (FDR=0.0009), while it showed a negative correlation with QA of right cerebellar tracts (FDR=0.0005). CONCLUSION Our results underscore the intricate link between cognitive performance and WM integrity in frontal, temporal, and cerebellar regions, offering insights into early detection and targeted interventions for cognitive disorders.
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Affiliation(s)
- Mohammadamin Parsaei
- Maternal, Fetal & Neonatal Research Center, Family Health Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Gelayol Barahman
- School of Medicine, Islamic Azad University, Tehran Medical Sciences Branch, Tehran, Iran
| | | | - Ehsan Ranjbar
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Sahar Ansari
- Psychosomatic Medicine Research Center, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Anahita Najafi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hanie Karimi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Hadi Aarabi
- Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Hossein Sanjari Moghaddam
- Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran.
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Park HJ, Lee B. Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network. Front Hum Neurosci 2023; 17:1186594. [PMID: 37645689 PMCID: PMC10461632 DOI: 10.3389/fnhum.2023.1186594] [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: 03/15/2023] [Accepted: 07/21/2023] [Indexed: 08/31/2023] Open
Abstract
Introduction In this study, we classified electroencephalography (EEG) data of imagined speech using signal decomposition and multireceptive convolutional neural network. The imagined speech EEG with five vowels /a/, /e/, /i/, /o/, and /u/, and mute (rest) sounds were obtained from ten study participants. Materials and methods First, two different signal decomposition methods were applied for comparison: noise-assisted multivariate empirical mode decomposition and wavelet packet decomposition. Six statistical features were calculated from the decomposed eight sub-frequency bands EEG. Next, all features obtained from each channel of the trial were vectorized and used as the input vector of classifiers. Lastly, EEG was classified using multireceptive field convolutional neural network and several other classifiers for comparison. Results We achieved an average classification rate of 73.09 and up to 80.41% in a multiclass (six classes) setup (Chance: 16.67%). In comparison with various other classifiers, significant improvements for other classifiers were achieved (p-value < 0.05). From the frequency sub-band analysis, high-frequency band regions and the lowest-frequency band region contain more information about imagined vowel EEG data. The misclassification and classification rate of each vowel imaginary EEG was analyzed through a confusion matrix. Discussion Imagined speech EEG can be classified successfully using the proposed signal decomposition method and a convolutional neural network. The proposed classification method for imagined speech EEG can contribute to developing a practical imagined speech-based brain-computer interfaces system.
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Affiliation(s)
- Hyeong-jun Park
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Boreom Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
- AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
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Gramacki A, Gramacki J. A deep learning framework for epileptic seizure detection based on neonatal EEG signals. Sci Rep 2022; 12:13010. [PMID: 35906248 PMCID: PMC9338048 DOI: 10.1038/s41598-022-15830-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 06/29/2022] [Indexed: 01/19/2023] Open
Abstract
Electroencephalogram (EEG) is one of the main diagnostic tests for epilepsy. The detection of epileptic activity is usually performed by a human expert and is based on finding specific patterns in the multi-channel electroencephalogram. This is a difficult and time-consuming task, therefore various attempts are made to automate it using both conventional and Deep Learning (DL) techniques. Unfortunately, authors do not often provide sufficiently detailed and complete information to be able to reproduce their results. Our work is intended to fill this gap. Using a carefully selected 79 neonatal EEG recordings we developed a complete framework for seizure detection using DL approch. We share a ready to use R and Python codes which allow: (a) read raw European Data Format files, (b) read data files containing the seizure annotations made by human experts, (c) extract train, validation and test data, (d) create an appropriate Convolutional Neural Network (CNN) model, (e) train the model, (f) check the quality of the neural classifier, (g) save all learning results.
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Affiliation(s)
- Artur Gramacki
- Institute of Control and Computation Engineering, University of Zielona Góra, Zielona Góra, Poland.
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Super-resolution generative adversarial networks with static T2*WI-based subject-specific learning to improve spatial difference sensitivity in fMRI activation. Sci Rep 2022; 12:10319. [PMID: 35725788 PMCID: PMC9209532 DOI: 10.1038/s41598-022-14421-5] [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: 03/25/2022] [Accepted: 06/07/2022] [Indexed: 11/18/2022] Open
Abstract
The spatial resolution of fMRI is relatively poor and improvements are needed to indicate more specific locations for functional activities. Here, we propose a novel scheme, called Static T2*WI-based Subject-Specific Super Resolution fMRI (STSS-SRfMRI), to enhance the functional resolution, or ability to discriminate spatially adjacent but functionally different responses, of fMRI. The scheme is based on super-resolution generative adversarial networks (SRGAN) that utilize a T2*-weighted image (T2*WI) dataset as a training reference. The efficacy of the scheme was evaluated through comparison with the activation maps obtained from the raw unpreprocessed functional data (raw fMRI). MRI images were acquired from 30 healthy volunteers using a 3 Tesla scanner. The modified SRGAN reconstructs a high-resolution image series from the original low-resolution fMRI data. For quantitative comparison, several metrics were calculated for both the STSS-SRfMRI and the raw fMRI activation maps. The ability to distinguish between two different finger-tapping tasks was significantly higher [p = 0.00466] for the reconstructed STSS-SRfMRI images than for the raw fMRI images. The results indicate that the functional resolution of the STSS-SRfMRI scheme is superior, which suggests that the scheme is a potential solution to realizing higher functional resolution in fMRI images obtained using 3T MRI.
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Llana T, Fernandez-Baizan C, Mendez-Lopez M, Fidalgo C, Mendez M. Functional near-infrared spectroscopy in the neuropsychological assessment of spatial memory: A systematic review. Acta Psychol (Amst) 2022; 224:103525. [PMID: 35123299 DOI: 10.1016/j.actpsy.2022.103525] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 01/19/2022] [Accepted: 01/26/2022] [Indexed: 11/17/2022] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical imaging technique that employs near-infrared light to measure cortical brain oxygenation. The use of fNIRS has increased exponentially in recent years. Spatial memory is defined as the ability to learn and use spatial information. This neuropsychological process is constantly used in our daily lives and can be measured by fNIRS but no research has reviewed whether this technique can be useful in the neuropsychological assessment of spatial memory. This study aimed to review empirical work on the use of fNIRS in the neuropsychological assessment of human spatial memory. We used four databases: PubMed, PsycINFO, Scopus and Web of Science, and a total of 18 articles were found to be eligible. Most of the articles assessed spatial or visuospatial working memory with a predominance in computer-based tasks, used fNIRS equipment of 16 channels and mainly measured the prefrontal cortex (PFC). The studies analysed found linear or quadratic relationships between working memory load and PFC activity, greater activation of PFC activity and worse behavioural results in healthy older people in comparison with healthy adults, and hyperactivation of PFC as a form of compensation in clinical samples. We conclude that fNIRS is compatible with the standard neuropsychological assessment of spatial memory, making it possible to complement behavioural results with data of cortical functional activity.
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Affiliation(s)
- Tania Llana
- Department of Psychology, University of Oviedo, Faculty of Psychology, Plaza Feijoo s/n, 33003 Oviedo, Asturias, Spain
| | - Cristina Fernandez-Baizan
- Department of Psychology, University of Oviedo, Faculty of Psychology, Plaza Feijoo s/n, 33003 Oviedo, Asturias, Spain; Neuroscience Institute of Principado de Asturias (INEUROPA), Faculty of Psychology, Plaza Feijoo s/n, 33003 Oviedo, Asturias, Spain.
| | - Magdalena Mendez-Lopez
- Department of Psychology and Sociology, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Aragón, Spain; IIS Aragón, San Juan Bosco, 13, 50009 Zaragoza, Aragón, Spain
| | - Camino Fidalgo
- Department of Psychology and Sociology, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Aragón, Spain; IIS Aragón, San Juan Bosco, 13, 50009 Zaragoza, Aragón, Spain
| | - Marta Mendez
- Department of Psychology, University of Oviedo, Faculty of Psychology, Plaza Feijoo s/n, 33003 Oviedo, Asturias, Spain; Neuroscience Institute of Principado de Asturias (INEUROPA), Faculty of Psychology, Plaza Feijoo s/n, 33003 Oviedo, Asturias, Spain; Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Av. del Hospital Universitario, s/n, 33011 Oviedo, Asturias, Spain
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The infamous “Like” feature - A neuro perspective. INTERNATIONAL JOURNAL OF TECHNOLOGY AND HUMAN INTERACTION 2022. [DOI: 10.4018/ijthi.299073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
With the recent rise of excessive use of social media and its damaging effects, there is an urgent need to systematically recognize how users behave towards the “Like” button, which has been considered the most toxic feature on social media. To date, scholars know little about the neurophysiological responses of users towards the ‘Like’ feature despite its pervasiveness. Thus, through the lens of cybernetic theory, this research measured user behavior towards the “Like” feature by experimenting with two neuro tools (i.e., electrocardiogram (EKG/ECG) and electroencephalography (EEG)). Sixteen participants, allocated within three separate groups, completed a simple experimental task of ‘’liking’’ content. Unexpectedly, the findings revealed that participants who frequently and infrequently received “Likes” shared similar biometrics (i.e., high neurophysiological activities). Furthermore, this research raised concerns over the underlying AI algorithms related to recommendation engines/systems.
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New chemical biopsy tool for spatially resolved profiling of human brain tissue in vivo. Sci Rep 2021; 11:19522. [PMID: 34593948 PMCID: PMC8484280 DOI: 10.1038/s41598-021-98973-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/06/2021] [Indexed: 11/08/2022] Open
Abstract
It is extremely challenging to perform chemical analyses of the brain, particularly in humans, due to the restricted access to this organ. Imaging techniques are the primary approach used in clinical practice, but they only provide limited information about brain chemistry. Solid-phase microextraction (SPME) has been presented recently as a chemical biopsy tool for the study of animal brains. The current work demonstrates for the first time the use of SPME for the spatially resolved sampling of the human brain in vivo. Specially designed multi-probe sampling device was used to simultaneously extract metabolites from the white and grey matter of patients undergoing brain tumor biopsies. Samples were collected by inserting the probes along the planned trajectory of the biopsy needle prior to the procedure, which was followed by metabolomic and lipidomic analyses. The results revealed that studied brain structures were predominantly composed of lipids, while the concentration and diversity of detected metabolites was higher in white than in grey matter. Although the small number of participants in this research precluded conclusions of a biological nature, the results highlight the advantages of the proposed SPME approach, as well as disadvantages that should be addressed in future studies.
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Mishra S, Kumar A, Padmanabhan P, Gulyás B. Neurophysiological Correlates of Cognition as Revealed by Virtual Reality: Delving the Brain with a Synergistic Approach. Brain Sci 2021; 11:brainsci11010051. [PMID: 33466371 PMCID: PMC7824819 DOI: 10.3390/brainsci11010051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 12/16/2020] [Accepted: 12/25/2020] [Indexed: 12/11/2022] Open
Abstract
The synergy of perceptual psychology, technology, and neuroscience can be used to comprehend how virtual reality affects cognition of human brain. Numerous studies have used neuroimaging modalities to assess the cognitive state and response of the brain with various external stimulations. The virtual reality-based devices are well known to incur visual, auditory, and haptic induced perceptions. Neurophysiological recordings together with virtual stimulations can assist in correlating humans’ physiological perception with response in the environment designed virtually. The effective combination of these two has been utilized to study human behavior, spatial navigation performance, and spatial presence, to name a few. Moreover, virtual reality-based devices can be evaluated for the neurophysiological correlates of cognition through neurophysiological recordings. Challenges exist in the integration of real-time neuronal signals with virtual reality-based devices, and enhancing the experience together with real-time feedback and control through neuronal signals. This article provides an overview of neurophysiological correlates of cognition as revealed by virtual reality experience, together with a description of perception and virtual reality-based neuromodulation, various applications, and existing challenges in this field of research.
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Affiliation(s)
- Sachin Mishra
- Cognitive Neuroimaging Centre, 59 Nanyang Drive, Nanyang Technological University, Singapore 636921, Singapore; (S.M.); (A.K.)
| | - Ajay Kumar
- Cognitive Neuroimaging Centre, 59 Nanyang Drive, Nanyang Technological University, Singapore 636921, Singapore; (S.M.); (A.K.)
- Institute of Biomedical Sciences, National Sun Yat-sen University, Gushan District, Kaohsiung 804, Taiwan
- Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Gushan District, Kaohsiung 804, Taiwan
| | - Parasuraman Padmanabhan
- Cognitive Neuroimaging Centre, 59 Nanyang Drive, Nanyang Technological University, Singapore 636921, Singapore; (S.M.); (A.K.)
- Correspondence: (P.P.); (B.G.)
| | - Balázs Gulyás
- Cognitive Neuroimaging Centre, 59 Nanyang Drive, Nanyang Technological University, Singapore 636921, Singapore; (S.M.); (A.K.)
- Department of Clinical Neuroscience, Karolinska Institute, 17176 Stockholm, Sweden
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 608232, Singapore
- Correspondence: (P.P.); (B.G.)
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