New Advances in Neuroimaging and Brain Functions: 2nd Edition

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".

Deadline for manuscript submissions: 30 May 2025 | Viewed by 4633

Special Issue Editors


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Guest Editor
Department of Life Science, College of Science and Engineering, Fu-Jen Catholic University, New Taipei City 242062, Taiwan
Interests: neuroscience; translation brain science
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Guest Editor
Department of International Business, Ming Chuan University, Taipei, Taiwan
Interests: social network; neuromarketing
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Special Issue Information

Dear Colleagues,

The editors are grateful to the many researchers who contributed to the success of the first volume of this issue (https://www.mdpi.com/journal/life/special_issues/Neuroimaging_Brain). We are very pleased to announce the second volume of our Special Issue “New Advances in Neuroimaging and Brain Functions".

Over the past few decades, neuroimaging has become a powerful tool for studying brain function in basic and clinical research. The ever-changing technologies for brain activity detection and simulation make it possible to study the brain mechanisms of human mental activities. Developing and applying various detection methods can enable researchers to explore brain structure and function, to build understanding of normal mental functioning and neurological diseases. Neurological disease research covers the nervous system's development, functioning, and pathology. Neurological disease encompasses some of the most concerning conditions brought about by aging, dementia, neurodegenerative diseases, stroke, depression, schizophrenia, alcohol abuse, and sleep medicine. Brain imaging research enables social neuroscience and neuromarketing related to humanities and social sciences to combine with brain science to explore human behavior. In addition, it enables the development of artificial intelligence for automatic analysis and interpretation of medical images for brain imaging.

This Special Issue welcomes the submission of original research articles or review papers on new advances in neuroimaging and brain function in neurological disease research, humanities, and social sciences. We welcome research on the following three aspects:

(1) EEG, MRI, and MEG to observe brain activity changes, supplemented by other noninvasive methods including eye-tracking, NIRS, and face readers to analyze human responses;

(2) various tools for brain dysfunction and rehabilitation, including new treatments for noninvasive methods, such as transcranial magnetic stimulation, transcranial electrical stimulation, and focused ultrasound;

(3) development of AI deep learning in image recognition and neural signals to evaluate brain structure, connectivity, and neural network function.

Dr. Ming-Chang Chiang
Dr. Chiahui Yen
Guest Editors

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Keywords

  • neuroimaging
  • brain functions
  • neurological diseases
  • humanities and social sciences
  • noninvasive methods
  • AI deep learning

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Published Papers (2 papers)

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Research

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19 pages, 1485 KiB  
Article
Decoding Imagined Speech from EEG Data: A Hybrid Deep Learning Approach to Capturing Spatial and Temporal Features
by Yasser F. Alharbi and Yousef A. Alotaibi
Life 2024, 14(11), 1501; https://doi.org/10.3390/life14111501 - 18 Nov 2024
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Abstract
Neuroimaging is revolutionizing our ability to investigate the brain’s structural and functional properties, enabling us to visualize brain activity during diverse mental processes and actions. One of the most widely used neuroimaging techniques is electroencephalography (EEG), which records electrical activity from the brain [...] Read more.
Neuroimaging is revolutionizing our ability to investigate the brain’s structural and functional properties, enabling us to visualize brain activity during diverse mental processes and actions. One of the most widely used neuroimaging techniques is electroencephalography (EEG), which records electrical activity from the brain using electrodes positioned on the scalp. EEG signals capture both spatial (brain region) and temporal (time-based) data. While a high temporal resolution is achievable with EEG, spatial resolution is comparatively limited. Consequently, capturing both spatial and temporal information from EEG data to recognize mental activities remains challenging. In this paper, we represent spatial and temporal information obtained from EEG signals by transforming EEG data into sequential topographic brain maps. We then apply hybrid deep learning models to capture the spatiotemporal features of the EEG topographic images and classify imagined English words. The hybrid framework utilizes a sequential combination of three-dimensional convolutional neural networks (3DCNNs) and recurrent neural networks (RNNs). The experimental results reveal the effectiveness of the proposed approach, achieving an average accuracy of 77.8% in identifying imagined English speech. Full article
(This article belongs to the Special Issue New Advances in Neuroimaging and Brain Functions: 2nd Edition)
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Review

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25 pages, 1479 KiB  
Review
The Use of Transcranial Magnetic Stimulation in Attention Optimization Research: A Review from Basic Theory to Findings in Attention-Deficit/Hyperactivity Disorder and Depression
by Chiahui Yen, Ethan P. Valentine and Ming-Chang Chiang
Life 2024, 14(3), 329; https://doi.org/10.3390/life14030329 - 29 Feb 2024
Viewed by 3387
Abstract
This review explores the pivotal role of attention in everyday life, emphasizing the significance of studying attention-related brain functions. We delve into the development of methodologies for investigating attention and highlight the crucial role of brain neuroimaging and transcranial magnetic stimulation (TMS) in [...] Read more.
This review explores the pivotal role of attention in everyday life, emphasizing the significance of studying attention-related brain functions. We delve into the development of methodologies for investigating attention and highlight the crucial role of brain neuroimaging and transcranial magnetic stimulation (TMS) in advancing attention research. Attention optimization theory is introduced to elucidate the neural basis of attention, identifying key brain regions and neural circuits involved in attention processes. The theory further explores neuroplasticity, shedding light on how the brain dynamically adapts and changes to optimize attention. A comprehensive overview of TMS is provided, elucidating the principles and applications of this technique in affecting brain activity through magnetic field stimulation. The application of TMS in attention research is discussed, outlining how it can be employed to regulate attention networks. The clinical applications of TMS are explored in attention-deficit/hyperactivity disorder (ADHD) and depression. TMS emerges as an effective clinical treatment for ADHD, showcasing its potential in addressing attention-related disorders. Additionally, the paper emphasizes the efficacy of TMS technology as a method for regulating depression, further underlining the versatility and therapeutic potential of TMS in clinical settings. In conclusion, this review underscores the interdisciplinary approach to attention research, integrating neuroimaging, neuroplasticity, and TMS. The presented findings contribute to our understanding of attention mechanisms and highlight the promising clinical applications of TMS in addressing attention-related disorders. This synthesis of theoretical and practical insights aims to propel further advancements in attention research and its therapeutic applications. Full article
(This article belongs to the Special Issue New Advances in Neuroimaging and Brain Functions: 2nd Edition)
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