• Proceedings of the Korean Information Science Society Conference
• /
• 2011.06c
• /
• pp.393-396
• /
• 2011

In this paper, we propose a constrained alternating least squares nonnegative matrix factorization algorithm (cALSNMF) to detect active brain regions from single subject's task-related fMRI data. In cALSNMF, we define a new cost function which considers the uncorrelation and noisy problems of fMRI data by adding decorrelation and smoothing constraints in original Euclidean distance cost function. We also generate a novel training procedure by modifying the update rules and combining with optimal brain surgeon (OBS) algorithm. The experimental results on visuomotor task fMRI data show that our cALSNMF fits fMRI data better than original ALSNMF in detecting task-related brain activation from single subject's fMRI data.

• Smart Media Journal
• /
• v.13 no.4
• /
• pp.86-93
• /
• 2024

The development of functional magnetic resonance imaging (fMRI) has significantly contributed to mapping brain functions and understanding brain networks during rest. This paper proposes a CNN-LSTM-based classification model to classify the progression stages of Alzheimer's disease. Firstly, four preprocessing steps are performed to remove noise from the fMRI data before feature extraction. Secondly, the U-Net architecture is utilized to extract spatial features once preprocessing is completed. Thirdly, the extracted spatial features undergo LSTM processing to extract temporal features, ultimately leading to classification. Experiments were conducted by adjusting the temporal dimension of the data. Using 5-fold cross-validation, an average accuracy of 96.4% was achieved, indicating that the proposed method has high potential for identifying the progression of Alzheimer's disease by analyzing fMRI data.

• Sleep Medicine and Psychophysiology
• /
• v.25 no.2
• /
• pp.82-91
• /
• 2018

Objectives: Synchronous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been used to explore sleep stage dependent functional brain networks. Despite a growing number of sleep studies using EEG-fMRI, few studies have conducted network analysis on whole night sleep due to difficulty in data acquisition, artifacts, and sleep management within the MRI scanner. Methods: In order to perform network analysis for whole night sleep, we proposed experimental procedures and data processing techniques for EEG-fMRI. We acquired 6-7 hours of EEG-fMRI data per participant and conducted signal processing to reduce artifacts in both EEG and fMRI. We then generated a functional brain atlas with 68 brain regions using independent component analysis of sleep fMRI data. Using this functional atlas, we constructed sleep level dependent functional brain networks. Results: When we evaluated functional connectivity distribution, sleep showed significantly reduced functional connectivity for the whole brain compared to that during wakefulness. REM sleep showed statistically different connectivity patterns compared to non-REM sleep in sleep-related subcortical brain circuits. Conclusion: This study suggests the feasibility of exploring functional brain networks using sleep EEG-fMRI for whole night sleep via appropriate experimental procedures and signal processing techniques for fMRI and EEG.

• Investigative Magnetic Resonance Imaging
• /
• v.3 no.2
• /
• pp.188-195
• /
• 1999

The fMRI signals are composed of many various signals. It is very difficult to find the accurate parameter for the model of fMRI signal containing only neural activity, though we may estimating the signal patterns by the modeling of several signal components. Besides the nose by the physiologic motion, the motion of object and noise of MR instruments make it more difficult to analyze signals of fMRI. Therefore, it is not easy to select an accurate reference data that can accurately reflect neural activity, and the method of an analysis of various signal patterns containing the information of neural activity is an issue of the post-processing methods for fMRI. In the present study, fMRI data was analyzed with the Independent Component Analysis(ICA) method that doesn't need a priori-knowledge or reference data. ICA can be more effective over the analytic method using cross-correlation analysis and can separate the signal patterns of the signals with delayed response or motion related components. The Principal component Analysis (PCA) threshold, wavelet spatial filtering and analysis of a part of whole images can be used for the reduction of the freedom of data before ICA analysis, and these preceding analyses may be useful for a more effective analysis. As a result, ICA method will be effective for the degree of freedom of the data.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.251-254
• /
• 2001

본 논문에서는 Self Organizing Map을 이용하여 fMRI data를 분석해 보았다. fMRl (functional Magnetic Resonance Imaging)는 인간의 뇌에 대한 비 침투적 연구 방법 중 최근에 각광받고 있는 것이다. Motor task를 수행하고 있는 피험자로부터 image data를 얻어내어 SOM을 적용하여 clustering한 결과 motor cortex 영역이 뚜렷하게 clustering 되었음을 알 수 있었다.

• Science of Emotion and Sensibility
• /
• v.20 no.4
• /
• pp.15-30
• /
• 2017

The purpose of this study was to verify the basic emotion theory based on the emotion-related research using functional brain imaging technology. For this purpose, a meta-analysis on the functional magnetic resonance imaging (fMRI) studies was performed. Six individual emotions-joy, happiness, fear, anger, disgust, sadness-were selected. In order to collect the fMRI data of individual emotions, we searched the electronic journals such as Medline, PsychInfo, PubMed for the past 10 years. fMRI experiment data aimed at healthy subjects for 6 emotions were collected, and only studies reported in Talairach or MNI standard coordinate system were included. In order to eliminate the difference between Talairach and MNI coordinate systems, we analyzed fMRI data based on the Talairach coordinate system. A meta-analysis using GingerALE 2.3 program adopting the activation likelihood estimates (ALE) techniques was performed. In this study, we confirmed that the individual emotions are associated with consistent and distinguishable regional brain responses within the framework of the basic emotion theory. The conclusion of this study of the brain areas associated with each individual emotional reaction was substantially consistent with the results of existing review articles. Finally, the limitations of this study and some suggestions for the future research were presented.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.6
• /
• pp.832-837
• /
• 2007

Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.

• International Journal of Contents
• /
• v.8 no.4
• /
• pp.56-63
• /
• 2012

In recent decades, analyzing the activities of human brain achieved some accomplishments by using the functional Magnetic Resonance Imaging (fMRI) technique. fMRI data provide a sequence of three-dimensional images related to human brain's activity which can be used to detect instantaneous cognitive states by applying machine learning methods. In this paper, we propose a new approach for distinguishing human's cognitive states such as "observing a picture" versus "reading a sentence" and "reading an affirmative sentence" versus "reading a negative sentence". Since fMRI data are high dimensional (about 100,000 features in each sample), extremely sparse and noisy, feature selection is a very important step for increasing classification accuracy and reducing processing time. We used the Fisher Discriminant Ratio to select the most powerful discriminative features from some Regions of Interest (ROIs). The experimental results showed that our approach achieved the best performance compared to other feature extraction methods with the average accuracy approximately 95.83% for the first study and 99.5% for the second study.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.12
• /
• pp.11-18
• /
• 2016

Magnetic resonance image (MRI) is widely used in brain research field and medical image. Especially, non-invasive brain activation acquired image technique, which is functional magnetic resonance image (fMRI) is used in brain study. In this study, we investigate brain activation occurred by LED light stimulation. For investigate of brain activation in experimental small animal, we used high magnetic field 9.4T MRI. Experimental small animal is Balb/c mouse, method of fMRI is using echo planar image (EPI). EPI method spend more less time than any other MRI method. For this reason, however, EPI data has low contrast. Due to the low contrast, image pre-processing is very hard and inaccuracy. In this study, we planned the study protocol, which is called block design in fMRI research field. The block designed has 8 LED light stimulation session and 8 rest session. All block is consist of 6 EPI images and acquired 1 slice of EPI image is 16 second. During the light session, we occurred LED light stimulation for 1 minutes 36 seconds. During the rest session, we do not occurred light stimulation and remain the light off state for 1 minutes 36 seconds. This session repeat the all over the EPI scan time, so the total spend time of EPI scan has almost 26 minutes. After acquired EPI data, we performed the analysis of this image data. In this study, we analysis of EPI data using statistical parametric map (SPM) software and performed image pre-processing such as realignment, co-registration, normalization, smoothing of EPI data. The pre-processing of fMRI data have to segmented using this software. However this method has 3 different method which is Gaussian nonparametric, warped modulate, and tissue probability map. In this study we performed the this 3 different method and compared how they can change the result of fMRI analysis results. The result of this study show that LED light stimulation was activate superior colliculus region in mouse brain. And the most higher activated value of segmentation method was using tissue probability map. this study may help to improve brain activation study using EPI and SPM analysis.

• Journal of Korean Neurosurgical Society
• /
• v.58 no.3
• /
• pp.242-247
• /
• 2015

Objective : The purpose of this study is to determine whether the changes of contralateral sensorimotor cortical activation on functional magnetic resonance imaging (fMRI) can predict the neurological outcome among spinal cord injury (SCI) patients when the great toes are stimulated without notice. Methods : This study enrolled a total of 49 patients with SCI and investigated each patient's preoperative fMRI, postoperative fMRI, American Spinal Injury Association (ASIA) score, and neuropathic pain occurrence. Patients were classified into 3 groups according to the change of blood oxygenation level dependent (BOLD) response on perioperative fMRI during proprioceptive stimulation with repetitive passive toe movements : 1) patients with a response of contralateral sensorimotor cortical activation in fMRI were categorized; 2) patients with a response in other regions; and 3) patients with no response. Correlation between the result of fMRI and each parameter was analyzed. Results : In fMRI data, ASIA score was likely to show greater improvement in patients in group A compared to those belonging to group B or C (p<0.001). No statistical significance was observed between the result of fMRI and neuropathic pain (p=0.709). However, increase in neuropathic pain in response to the signal change of the ipsilateral frontal lobe on fMRI was statistically significant (p=0.030). Conclusion : When there was change of BOLD response at the contralateral sensorimotor cortex on perioperative fMRI after surgery, relief of neurological symptoms was highly likely for traumatic SCI patients. In addition, development of neuropathic pain was likely to occur when there was change of BOLD response at ipsilateral frontal lobe.