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Clinical Neurophysiology, Neuroimaging, and Neuromodulation of Neuropsychiatric Disorders Series II

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A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 15212

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Special Issue Editor

Dr. Yoshihiro Noda

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Guest Editor

Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan
Interests: neuroplasticity; neuromodulation; neurophysiology; TMS; EEG; TMS-EEG; TMS-EMG; MRI; MRS; omics; neuroinformatics; database
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

More than 90 years have passed since Dr. Hans Berger reported the world’s first electrical activity of the human brain in 1929. In recent years, Dr. György Buzsáki has advocated such ideas as, “Without the rhythm of the brain, the mind is not born; the brain is a predictive device, and it is the rhythm of the brain that produces predictive ability.” Furthermore, Dr. Jakob Hohwy has stated that “the brain is a sophisticated hypothesis tester, supported by a predictive mechanism that constantly seeks to improve the prediction of sensory input, which consists of the brain updating its perception and selectively sampling sensory input that conforms to its predictions.”

Recent advances in neuroimaging technology, including EEG, have made it possible to visualize various brain activities. In addition, research on neuromodulation, as an intervention for brain dynamics, the underlying neurophysiological basis of the brain, was rapidly accelerated by the development of TMS in 1985 by Dr. Anthony Barker. On the other hand, as a trend in the field of psychiatry in recent years, the concept of precision medicine has become important to elucidating mental disorders as well as to developing new therapeutic strategies. Research in these areas is progressing rapidly, and new knowledge is being accumulated every day. In addition, to implement precision medicine, it will be important to conduct not only sophisticated translational research but also large-scale clinical research using multimodal measurements, as well as the construction of a database that integrates these studies. Moreover, sophisticated analysis techniques based on data science will be increasingly required for the database.

This Special Issue, “Clinical Neurophysiology, Neuroimaging, and Neuromodulation of Neuropsychiatric Disorders Series II”, aims not only to introduce state-of-the-art research in clinical neurophysiology, neuroimaging and neuromodulation, but also to deal with the topics of database construction and neuroinfomatics in these fields. We welcome original research papers, methodology papers, and review papers describing the use of the latest brain science approaches in the above fields, especially those that incorporate the concept of precision medicine.

Dr. Yoshihiro Noda
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

EEG
rTMS
TMS–EEG
Neuromodulation
Database
Precision medicine
Neuroinfomatics

Published Papers (4 papers)

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Research

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7 pages, 1385 KiB

Open AccessCommunication

Explainable Artificial Intelligence for Human-Machine Interaction in Brain Tumor Localization

by Morteza Esmaeili, Riyas Vettukattil, Hasan Banitalebi, Nina R. Krogh and Jonn Terje Geitung

J. Pers. Med. 2021, 11(11), 1213; https://0-doi-org.brum.beds.ac.uk/10.3390/jpm11111213 - 16 Nov 2021

Cited by 22 | Viewed by 3314

Abstract

Primary malignancies in adult brains are globally fatal. Computer vision, especially recent developments in artificial intelligence (AI), have created opportunities to automatically characterize and diagnose tumor lesions in the brain. AI approaches have provided scores of unprecedented accuracy in different image analysis tasks, including differentiating tumor-containing brains from healthy brains. AI models, however, perform as a black box, concealing the rational interpretations that are an essential step towards translating AI imaging tools into clinical routine. An explainable AI approach aims to visualize the high-level features of trained models or integrate into the training process. This study aims to evaluate the performance of selected deep-learning algorithms on localizing tumor lesions and distinguishing the lesion from healthy regions in magnetic resonance imaging contrasts. Despite a significant correlation between classification and lesion localization accuracy (R = 0.46, p = 0.005), the known AI algorithms, examined in this study, classify some tumor brains based on other non-relevant features. The results suggest that explainable AI approaches can develop an intuition for model interpretability and may play an important role in the performance evaluation of deep learning models. Developing explainable AI approaches will be an essential tool to improve human–machine interactions and assist in the selection of optimal training methods. Full article

(This article belongs to the Special Issue Clinical Neurophysiology, Neuroimaging, and Neuromodulation of Neuropsychiatric Disorders Series II)

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12 pages, 2550 KiB

Open AccessArticle

Development of an Advanced Sham Coil for Transcranial Magnetic Stimulation and Examination of Its Specifications

by Mayuko Takano, Jiri Havlicek, Dan Phillips, Shinichiro Nakajima, Masaru Mimura and Yoshihiro Noda

J. Pers. Med. 2021, 11(11), 1058; https://0-doi-org.brum.beds.ac.uk/10.3390/jpm11111058 - 21 Oct 2021

Cited by 5 | Viewed by 3097

Abstract

Transcranial magnetic stimulation (TMS) neurophysiology has been widely applied worldwide, but it is often contaminated by confounders other than cortical stimulus-evoked activities. Although advanced sham coils that elaborately mimic active stimulation have recently been developed, their performance is not examined in detail. Developing such sham coils is crucial to improve the accuracy of TMS neurophysiology. Herein, we examined the specifications of the sham coil by comparison with the active coil. The magnetic flux and click sound pressure changes were measured when the stimulus intensity was varied from 10% to 100% maximum stimulator output (MSO), and the changes in coil surface temperature over time with continuous stimulation at 50% MSO for each coil. The magnetic flux change at the center of the coil showed a peak of 12.51 (kT/s) for the active coil, whereas it was 0.41 (kT/s) for the sham coil. Although both coils showed a linear change in magnetic flux as the stimulus intensity increased, due to the difference in coil winding structure, the sham coil took less than half the time to overheat and had 5 dB louder coil click sounds than the active coil. The sham coil showed a sufficiently small flux change at the center of the coil, but the flux change from the periphery of the coil was comparable to that of the active coil. Future use of high-quality sham coil will extend our understanding of the TMS neurophysiology of the cortex at the stimulation site. Full article

(This article belongs to the Special Issue Clinical Neurophysiology, Neuroimaging, and Neuromodulation of Neuropsychiatric Disorders Series II)

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10 pages, 964 KiB

Open AccessCommunication

20 Hz Transcranial Alternating Current Stimulation Inhibits Observation-Execution-Related Motor Cortex Excitability

by Lijuan Wang, Michael A. Nitsche, Volker R. Zschorlich, Hui Liu, Zhaowei Kong and Fengxue Qi

J. Pers. Med. 2021, 11(10), 979; https://0-doi-org.brum.beds.ac.uk/10.3390/jpm11100979 - 29 Sep 2021

Cited by 5 | Viewed by 1757

Abstract

The present study aimed to investigate the effect of transcranial alternating current stimulation (tACS) on the primary motor cortex (M1) during action observation, and subsequent action execution, on motor cortex excitability. The participants received tACS at 10 Hz or 20 Hz, or a sham stimulation over the left M1 for 10 min while they observed a video displaying a repeated button-tapping task using the right hand, and then performed an identical task with their right hand. Motor-evoked potential (MEP) amplitudes were measured before (T0) and after the action observation paired with tACS or a sham stimulation (T1), and after the performance of the action (T2). The results showed that MEPs were significantly reduced at time point T1 (p = 0.042, Cohen’s d = 0.611) and T2 (p = 0.0003, Cohen’s d = 0.852) in the 20 Hz tACS condition, in contrast with the sham stimulation. There was a significantly smaller MEP amplitude at time point T2 in the 20 Hz tACS condition, as compared to the 10 Hz tACS condition (p = 0.01, Cohen’s d = 0.622), but the MEP amplitude did not significantly change at time point T1 between the 20 Hz and 10 Hz tACS conditions (p = 0.136, Cohen’s d = 0.536). There were no significant differences at time point T1 and T2 between the 10 Hz tACS condition and the sham stimulation. We conclude that 20 Hz tACS during action observation inhibited motor cortex excitability and subsequently inhibited execution-related motor cortex excitability. The effects of tACS on task-related motor cortex excitability are frequency-dependent. Full article

(This article belongs to the Special Issue Clinical Neurophysiology, Neuroimaging, and Neuromodulation of Neuropsychiatric Disorders Series II)

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Review

Jump to: Research

24 pages, 1927 KiB

Open AccessFeature PaperReview

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

by J. Prasanna, M. S. P. Subathra, Mazin Abed Mohammed, Robertas Damaševičius, Nanjappan Jothiraj Sairamya and S. Thomas George

J. Pers. Med. 2021, 11(10), 1028; https://0-doi-org.brum.beds.ac.uk/10.3390/jpm11101028 - 15 Oct 2021

Cited by 42 | Viewed by 6068

Abstract

Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed. Full article

(This article belongs to the Special Issue Clinical Neurophysiology, Neuroimaging, and Neuromodulation of Neuropsychiatric Disorders Series II)

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