• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.4
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• pp.436-442
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• 2016

An electroencephalogram (EEG)-connectome processor to monitor and diagnose mental health is proposed. From 19-channel EEG signals, the proposed processor determines whether the mental state is healthy or unhealthy by extracting significant features from EEG signals and classifying them. Connectome approach is adopted for the best diagnosis accuracy, and synchronization likelihood (SL) is chosen as the connectome feature. Before computing SL, reconstruction optimizer (ReOpt) block compensates some parameters, resulting in improved accuracy. During SL calculation, a sparse matrix inscription (SMI) scheme is proposed to reduce the memory size to 1/24. From the calculated SL information, a small world feature extractor (SWFE) reduces the memory size to 1/29. Finally, using SLs or small word features, radial basis function (RBF) kernel-based support vector machine (SVM) diagnoses user's mental health condition. For RBF kernels, look-up-tables (LUTs) are used to replace the floating-point operations, decreasing the required operation by 54%. Consequently, The EEG-connectome processor improves the diagnosis accuracy from 89% to 95% in Alzheimer's disease case. The proposed processor occupies $3.8mm^2$ and consumes 1.71 mW with $0.18{\mu}m$ CMOS technology.

• Annals of Clinical Neurophysiology
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• v.23 no.2
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• pp.69-81
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• 2021

Cortico-cortical evoked potential (CCEP) mapping is a rapidly developing method for visualizing the brain network and estimating cortical excitability. The CCEP comprises the early N1 component the occurs at 10-30 ms poststimulation, indicating anatomic connectivity, and the late N2 component that appears at < 200 ms poststimulation, suggesting long-lasting effective connectivity. A later component at 200-1,000 ms poststimulation can also appear as a delayed response in some studied areas. Such delayed responses occur in areas with changed excitability, such as an epileptogenic zone. CCEP mapping has been used to examine the brain connections causally in functional systems such as the language, auditory, and visual systems as well as in anatomic regions including the frontoparietal neocortices and hippocampal limbic areas. Task-based CCEPs can be used to measure behavior. In addition to evaluations of the brain connectome, single-pulse electrical stimulation (SPES) can reflect cortical excitability, and so it could be used to predict a seizure onset zone. CCEP brain mapping and SPES investigations could be applied both extraoperatively and intraoperatively. These underused electrophysiologic tools in basic and clinical neuroscience might be powerful methods for providing insight into measures of brain connectivity and dynamics. Analyses of CCEPs might enable us to identify causal relationships between brain areas during cortical processing, and to develop a new paradigm of effective therapeutic neuromodulation in the future.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.507-518
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• 2021

Magnetic resonance imaging (MRI) is a key technology that has been seeing increasing use in studying the structural and functional innerworkings of the brain. Analyzing the variability of brain connectome through tractography analysis has been used to increase our understanding of disease pathology in humans. However, there lacks standardization of analysis methods for small animals such as mice, and lacks scientific consensus in regard to accurate preprocessing strategies and atlas-based neuroinformatics for images. In addition, it is difficult to acquire high resolution images for mice due to how significantly smaller a mouse brain is compared to that of humans. In this study, we present an Allen Mouse Brain Atlas-based image data analysis pipeline for structural connectivity analysis involving structural region segmentation using mouse brain structural images and diffusion tensor images. Each analysis method enabled the analysis of mouse brain image data using reliable software that has already been verified with human and mouse image data. In addition, the pipeline presented in this study is optimized for users to efficiently process data by organizing functions necessary for mouse tractography among complex analysis processes and various functions.

• Applied Microscopy
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• v.46 no.4
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• pp.179-183
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• 2016

Mapping brain structural and functional connections through the whole brain is essential for understanding brain mechanisms and the physiological bases of brain diseases. Although region specific structural or functional deficits cause brain diseases, the changes of interregional connections could also be important factors of brain diseases. This review will introduce common neuroimaging modalities, including structural magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging, and other recent neuroimaging analyses methods, such as voxel-based morphometry, cortical thickness analysis, local gyrification index, and shape analysis for structural imaging. Tract-Based Spatial Statistics, TRActs Constrained by UnderLying Anatomy for diffusion MRI, and independent component analysis for fMRI also will also be introduced.

• Electronics and Telecommunications Trends
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• v.36 no.3
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• pp.106-118
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• 2021

The field of brain science (or neuroscience in a broader sense) has inspired researchers in artificial intelligence (AI) for a long time. The outcomes of neuroscience such as Hebb's rule had profound effects on the early AI models, and the models have developed to become the current state-of-the-art artificial neural networks. However, the recent progress in AI led by deep learning architectures is mainly due to elaborate mathematical methods and the rapid growth of computing power rather than neuroscientific inspiration. Meanwhile, major limitations such as opacity, lack of common sense, narrowness, and brittleness have not been thoroughly resolved. To address those problems, many AI researchers turn their attention to neuroscience to get insights and inspirations again. Biologically plausible neural networks, spiking neural networks, and connectome-based networks exemplify such neuroscience-inspired approaches. In addition, the more recent field of brain network analysis is unveiling complex brain mechanisms by handling the brain as dynamic graph models. We argue that the progress toward the human-level AI, which is the goal of AI, can be accelerated by leveraging the novel findings of the human brain network.

• Molecules and Cells
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• v.45 no.2
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• pp.84-92
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• 2022

To understand the microcircuitry of the brain, the anatomical and functional connectivity among neurons must be resolved. One of the technical hurdles to achieving this goal is that the anatomical connections, or synapses, are often smaller than the diffraction limit of light and thus are difficult to resolve by conventional microscopy, while the microcircuitry of the brain is on the scale of 1 mm or larger. To date, the gold standard method for microcircuit reconstruction has been electron microscopy (EM). However, despite its rapid development, EM has clear shortcomings as a method for microcircuit reconstruction. The greatest weakness of this method is arguably its incompatibility with functional and molecular analysis. Fluorescence microscopy, on the other hand, is readily compatible with numerous physiological and molecular analyses. We believe that recent advances in various fluorescence microscopy techniques offer a new possibility for reliable synapse detection in large volumes of neural circuits. In this minireview, we summarize recent advances in fluorescence-based microcircuit reconstruction. In the same vein as these studies, we introduce our recent efforts to analyze the long-range connectivity among brain areas and the subcellular distribution of synapses of interest in relatively large volumes of cortical tissue with array tomography and superresolution microscopy.

• Journal of Biomedical Engineering Research
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• v.44 no.1
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• pp.53-63
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• 2023

There have been many studies from the genetic system to physical activity and emotional expression such that there are gender differences. The purpose of this study was to determine how the structural characteristics of cortical thickness differ between males and females. This study used data from the Human Connectome Project (HCP). To analyze age-specific sexual dimorphisms of cortical thickness, selected 8-80 year old subjects were divided into five detailed age range groups according to each criterion. A total of 1,700 individual brain MRI T1 data were registered in stereotaxic space for analysis and classified into white matter (WM), gray matter (GM), and cerebro-spinal fluid (CSF). For surface-based analysis, the WM/GM surface was reconstructed from a spherical polygon model with 40962 vertices per hemisphere, and each vertex was extended to the GM/CSF boundary. Cortical thickness was then measured between each vertex using the t-link method. In the statistical analysis, intracranial volume was used as a covariate to exclude the effect of the difference in brain size of each individual, and the result of using age as a covariate was added to confirm the age effect within each group. Gender differences in cortical thickness had significant results by group. This may be an index to explain diseases with sexual dimorphism in prevalence or become a basis for explaining the characteristics of each sex that appear in behavior, personality, and aging. Therefore, the results of our study could be a criterion for age classification in future studies and for understanding 'normal' sexual dimorphism.

• Korean Journal of Biological Psychiatry
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• v.22 no.3
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• pp.101-108
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• 2015

Recent breakthroughs in functional neuroimaging techniques have launched the quest of mapping the connections of the human brain, otherwise known as the human connectome. Imaging connectomics is an umbrella term that refers to the neuroimaging techniques used to generate these maps, which recently has enabled comprehensive brain mapping of network connectivity combined with graph theoretic methods. In this review, we present an overview of the key concepts in functional connectomics. Furthermore, we discuss articles that applied task-based and/or resting-state functional magnetic resonance imaging to examine network deficits in post-traumatic stress disorder (PTSD). These studies have provided important insights regarding the etiology of PTSD, as well as the overall organization of the brain network. Advances in functional connectomics are expected to provide insight into the pathophysiology and the development of biomarkers for diagnosis and treatment of PTSD.

• Applied Microscopy
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• v.46 no.4
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• pp.184-187
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• 2016

Neuronal connectivity determines brain function. Therefore, understanding the full map of brain connectivity with functional annotations is one of the most desirable but challenging tasks in science. Current methods to achieve this goal are limited by the resolution of imaging tools and the field of view. Macroscale imaging tools (e.g., magnetic resonance imaging, diffusion tensor images, and positron emission tomography) are suitable for large-volume analysis, and the resolution of these methodologies is being improved by developing hardware and software systems. Microscale tools (e.g., serial electron microscopy and array tomography), on the other hand, are evolving to efficiently stack small volumes to expand the dimension of analysis. The advent of mesoscale tools (e.g., tissue clearing and single plane ilumination microscopy super-resolution imaging) has greatly contributed to filling in the gaps between macroscale and microscale data. To achieve anatomical maps with gene expression and neural connection tags as multimodal information hubs, much work on information analysis and processing is yet required. Once images are obtained, digitized, and cumulated, these large amounts of information should be analyzed with information processing tools. With this in mind, post-imaging processing with the aid of many advanced information processing tools (e.g., artificial intelligence-based image processing) is set to explode in the near future, and with that, anatomic problems will be transformed into informatics problems.

• Investigative Magnetic Resonance Imaging
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• v.15 no.2
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• pp.110-122
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• 2011

Purpose : The purpose of this study is to establish the method generating human brain anatomical connectivity from Korean children and evaluating the network topological properties using small-world network analysis. Materials and Methods : Using diffusion tensor images (DTI) and parcellation maps of structural MRIs acquired from twelve healthy Korean children, we generated a brain structural connectivity matrix for individual. We applied one sample t-test to the connectivity maps to derive a representative anatomical connectivity for the group. By spatially normalizing the white matter bundles of participants into a template standard space, we obtained the anatomical brain network model. Network properties including clustering coefficient, characteristic path length, and global/local efficiency were also calculated. Results : We found that the structural connectivity of Korean children group preserves the small-world properties. The anatomical connectivity map obtained in this study showed that children group had higher intra-hemispheric connectivity than inter-hemispheric connectivity. We also observed that the neural connectivity of the group is high between brain stem and motorsensory areas. Conclusion : We suggested a method to examine the anatomical brain network of Korean children group. The proposed method can be used to evaluate the efficiency of anatomical brain networks in people with disease.