• 대한의용생체공학회:의공학회지
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• 제32권1호
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• pp.74-78
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• 2011

Although recent studies have shown the usibility of [F-18]FDG small animal Positron Emission Tommography (PET) as a functional neuroimaging technique in behavioural small animal study, researches showing the detection power of functional changes in the brain are still limited. Thus, in the study, we performed [F-18]FDG small animal PET neuroimaging in the well-established fear behavioural experiment. Twelve rats were exposed on cat for 30 minutes after the [F-18]FDG injection. As a result, the brain activity in bilateral amygdala areas significantly increased in the fear condition. In addition, the fear condition evoked the functional activities of hypothalamus, which seemed to be related to the response to stress. These clear localization of fear related brain regions may reflect that a functional neuroimaging technique using [F-18]FDG small animal PET has functional detectibility enough to be applied in small animal behavioral research.

• Nuclear Medicine and Molecular Imaging
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• 제41권2호
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• pp.71-77
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• 2007

Advance of neuroimaging technique has greatly influenced recent brain research field. Among various neuroimaging modalities, positron emission tomography has played a key role in molecular neuroimaging though functional MRI has taken over its role in the cognitive neuroscience. As the analysis technique for PET data is more sophisticated, the complexity of the method is more increasing. Despite the wide usage of the neuroimaging techniques, the assumption and limitation of procedures have not often been dealt with for the clinician and researchers, which might be critical for reliability and interpretation of the results. In the current paper, steps of voxel-based statistical analysis of PET including preprocessing, intensity normalization, spatial normalization, and partial volume correction will be revisited in terms of the principles and limitations. Additionally, new image analysis techniques such as surface-based PET analysis, correlational analysis and multimodal imaging by combining PET and DTI, PET and TMS or EEG will also be discussed.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제31권3호
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• pp.97-104
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• 2020

Deep learning (DL) is a kind of machine learning technique that uses artificial intelligence to identify the characteristics of given data and efficiently analyze large amounts of information to perform tasks such as classification and prediction. In the field of neuroimaging of neurodevelopmental disorders, various biomarkers for diagnosis, classification, prognosis prediction, and treatment response prediction have been examined; however, they have not been efficiently combined to produce meaningful results. DL can be applied to overcome these limitations and produce clinically helpful results. Here, we review studies that combine neurodevelopmental disorder neuroimaging and DL techniques to explore the strengths, limitations, and future directions of this research area.

• The Korean Journal of Pain
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• 제23권3호
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• pp.159-165
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• 2010

The evolution of brain imaging techniques over the last decade has been remarkable. Along with such technical developments, research into chronic pain has made many advances. Given that brain imaging is a non-invasive technique with great spatial resolution, it has played an important role in finding the areas of the brain related to pain perception as well as those related to many chronic pain disorders. Therefore, in the near future, brain imaging techniques are expected to be the key to the discovery of many unknown etiologies of chronic pain disorders and to the subjective diagnoses of such disorders.

• Journal of Yeungnam Medical Science
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• 제35권1호
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• pp.1-6
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• 2018

Alzheimer's disease (AD) is a neurodegenerative disorder associated with extracellular plaques, composed of amyloid-beta ($A{\beta}$), in the brain. Although the precise mechanism underlying the neurotoxicity of $A{\beta}$ has not been established, $A{\beta}$ accumulation is the primary event in a cascade of events that lead to neurofibrillary degeneration and dementia. In particular, the $A{\beta}$ burden, as assessed by neuroimaging, has proved to be an excellent predictive biomarker. Positron emission tomography, using ligands such as $^{11}C$-labeled Pittsburgh Compound B or $^{18}F$-labeled tracers, such as $^{18}F$-florbetaben, $^{18}F$-florbetapir, and $^{18}F$-flutemetamol, which bind to $A{\beta}$ deposits in the brain, has been a valuable technique for visualizing and quantifying the deposition of $A{\beta}$ throughout the brain in living subjects. $A{\beta}$ imaging has very high sensitivity for detecting AD pathology. In addition, it can predict the progression from mild cognitive impairment to AD, and contribute to the development of disease-specific therapies.

• Molecules and Cells
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• 제40권8호
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• pp.523-532
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• 2017

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique that indirectly assesses neuronal activity by measuring changes in oxygenated and deoxygenated hemoglobin in tissues using near-infrared light. fNIRS has been used not only to investigate cortical activity in healthy human subjects and animals but also to reveal abnormalities in brain function in patients suffering from neurological and psychiatric disorders and in animals that exhibit disease conditions. Because of its safety, quietness, resistance to motion artifacts, and portability, fNIRS has become a tool to complement conventional imaging techniques in measuring hemodynamic responses while a subject performs diverse cognitive and behavioral tasks in test settings that are more ecologically relevant and involve social interaction. In this review, we introduce the basic principles of fNIRS and discuss the application of this technique in human and animal studies.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.209-216
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• 2019

Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer's Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.

• Journal of International Society for Simulation Surgery
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• 제2권2호
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• pp.58-63
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• 2015

Image-guided neurosurgery (IGN) is a technique for localizing objects of surgical interest within the brain. In the past, its main use was placement of electrodes; however, the advent of computed tomography has led to a rebirth of IGN. Advances in computing techniques and neuroimaging tools allow improved surgical planning and intraoperative information. IGN influences many neurosurgical fields including neuro-oncology, functional disease, and radiosurgery. As development continues, several problems remain to be solved. This article provides a general overview of IGN with a brief discussion of future directions.

• 대한불안의학회지
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• 제1권1호
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• pp.25-30
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• 2005

In the 40 years since the first benzodiazepine was brought into clinical use there has been a substantial growth in understanding the molecular basis of action of these drugs and the role of their receptors in anxiety disorders. Benzodiazepine receptors are present throughout the brain with the highest concentration in cortex, and it potentiate and prolong the synaptic action of the inhibitory neurotransmitter GABA. Central benzodiazepine receptors and $GABA_A$ receptors are part of the same macromolecular complex. Abnormalities of these $GABA_A$-benzodiazepine receptors as a result of drug challenge tests and neuroimaging studies may underlie some anxiety disorders. The role of $GABA_A$-benzodiazepine receptors in the action of benzodiazepine and as a factor in anxiety disorder, in both animal and humans including knock-out and knock in technique, may lead to new anxiolytics that have potentially significant therapeutic gains without unwanted side effects.

• 응용통계연구
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• 제35권1호
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• pp.161-175
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• 2022

뇌활동으로 발생하는 전기신호는 다시 자기신호로 유도되는데 센서로 측정한 것을 뇌자도(magnetoencephalography, MEG)라고 한다. MEG 기술은 비접촉, 비침습적인 측정방법이고 시간분해능과 공간분해능력이이 우수하기 때문에 뇌의 기능적인 정보를 얻는데 유용하게 사용될 수 있다. 또한 MEG 신호를 측정하고 분석하여 뇌신경전류의 활동을 이해할 수 있고 나아가 정밀한 뇌기능 연구가 가능하다. 본 연구에서는 뇌 활동(brain activity) 현상에 관한 궁극적 정보를 얻기위해 MEG 데이터의 특성을 설명하고 통계적 문제를 다루어 앞으로 뇌연구에 통계학의 필요성과 뇌정보학의 중요성을 강조하고자 한다.