• Clinical and Experimental Pediatrics
• /
• v.51 no.12
• /
• pp.1342-1345
• /
• 2008

Megalencephalic leukoencephalopathy with subcortical cysts (MLC) is a rare white matter disorder, first described in the early 1990s. The brain in patients with MLC appears swollen on MRI, with diffuse white matter abnormalities; in addition, there is an invariable presence of subcortical cysts, primarily in the anterior temporal region sparing the deep white matter, basal ganglia, thalami, and cerebellum. Patients with MLC present with macrocephaly and neurological abnormalities such as motor deterioration, ataxia, spasticity, and cognitive deficits. We report a twenty-month-old boy who presented with seizures and macrocephaly, delay in development, and abnormal brain MRI findings compatible with the diagnosis of MLC. The brain MRI revealed bilateral hypersignal intense subcortical white matter regions in the frontal, temporal, and parietal lobes on T2-weighted images, which were not yet associated with cystic changes. During follow-up, the frequency of seizures decreased after anticonvulsant medication was started, but the head circumference remained above the 97th percentile, and the patient continued to have developmental delay.

• Investigative Magnetic Resonance Imaging
• /
• v.21 no.1
• /
• pp.61-64
• /
• 2017

A characteristic imaging finding in cases of methanol intoxication is putaminal necrosis, but its presence is usually not suspected due to its rarity. Methanol intoxication generally produces serious neurological symptoms that include visual disturbances and diminished consciousness, characteristically with metabolic acidosis. We reported the case of a 59-year-old man who was admitted to the hospital with diminished consciousness. Acute methanol intoxication was determined as the cause. Laboratory tests revealed high anion gap metabolic acidosis. Diffusion-weighted MRI indicated diffuse symmetric diffusion restriction lesions in the subcortical white matter of both cerebral hemispheres.

• Journal of Korea Multimedia Society
• /
• v.18 no.7
• /
• pp.827-833
• /
• 2015

In this paper, we have compared three level set-based active contour (LSAC) methods on inhomogeneous MR image segmentation which is known as an important role of brain diseases to diagnosis and treatment in early. MR image is often occurred a problem with similar intensities and weak boundaries which have been causing many segmentation methods. However, LSAC method could be able to segment the targets such as the level set based on the local image fitting energy, the local binary fitting energy, and local Gaussian distribution fitting energy. Our implemented and tested the subcortical image segmentations were the corpus callosum and hippocampus and finally demonstrated their effectiveness. Consequently, the level set based on local Gaussian distribution fitting energy has obtained the best model to accurate and robust for the subcortical image segmentation.

• Journal of the Korean Society of Radiology
• /
• v.84 no.4
• /
• pp.855-865
• /
• 2023

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is a hereditary small artery vasculopathy caused by mutations in the NOTCH3 gene on chromosome 19. Jeju Island has the highest reported prevalence of CADASIL patients in the world. Even though most studies on the neuroimaging characteristics of CADASIL have focused on Western populations, there are notable differences in Korean CADASIL patients compared to those in Western countries, which may impact their clinical manifestations and prognosis. Herein, this pictorial essay presents the neuroimaging patterns of CADASIL in patients in Korea, with an emphasis on the differences observed from previous reports based on a Western patient population.

• Anatomy and Cell Biology
• /
• v.57 no.1
• /
• pp.45-60
• /
• 2024

Morphologic changes in the brain through aging, as a physiologic process, may involve a wide range of variables including ventricular dilation, and sulcus widening. This study reports normal ranges of these changes as standard criteria. Normal brain computed tomography scans of 400 patients (200 males, 200 females) in every decade of life (20 groups each containing 20 participants) were investigated for subcortical/cortical atrophy (bicaudate width [BCW], third ventricle width [ThVW], maximum length of lateral ventricle at cella media [MLCM], bicaudate index [BCI], third ventricle index [ThVI], and cella media index 3 [CMI3], interhemispheric sulcus width [IHSW], right hemisphere sulci diameter [RHSD], and left hemisphere sulci diameter [LHSD]), ventricular symmetry. Distribution and correlation of all the variables were demonstrated with age and a multiple linear regression model was reported for age prediction. Among the various parameters of subcortical atrophy, BCW, ThVW, MLCM, and the corresponding indices of BCI, ThVI, and CMI3 demonstrated a significant correlation with age (R²≥0.62). All the cortical atrophy parameters including IHSW, RHSD, and LHSD demonstrated a significant correlation with age (R²≥0.63). This study is a thorough investigation of variables in a normal brain which can be affected by aging disclosing normal ranges of variables including major ventricular variables, derived ventricular indices, lateral ventricles asymmetry, cortical atrophy, in every decade of life introducing BW, ThVW, MLCM, BCI, ThVI, CMI3 as most significant ventricular parameters, and IHSW, RHSD, LHSD as significant cortical parameters associated with age.

• Korean Journal of Psychosomatic Medicine
• /
• v.6 no.1
• /
• pp.79-84
• /
• 1998

Authors report a case of poststroke bipolar disorder that occurs much less frequently than poststroke depression(PSD). A MRI study performed to identify the etiology of a secondary manic episode in a patient with preceded PSD after left basal ganglia infarction revealed newly developed right basal ganglia infarction associated with poststroke bipolar disorder. It is interesting to note that (1) the temporal relationship was found between the occurrence of PSD after left hemisphere stroke and the occurrence of poststroke bipolar disorder following right hemisphere stroke, and that (2) the occurrence of PSD and the occurrence of poststroke bipolar disorder are associated with lesion location respectively. It has been reported that bipolar disorders were associated with subcortical lesions of the right hemisphere, whereas right-cortical lesions led to unipolar mania and that risk factors for mania included a family history of psychiatric disorders and mild subcortical atrophy. In this case, MR image shows subcortical lesions of the right hemisphere and mild subcortical atrophy. The investigation of the relationships among stroke lesion locations and potstroke mood disorders and risk factors for poststroke bipolar disorder may contribute to understanding the neurobiology of primary mood disorder. A clinical implication is that the risk of secondary bipolar disorder after cerebral infarction should be highlighted.

• Sleep Medicine and Psychophysiology
• /
• v.4 no.1
• /
• pp.29-38
• /
• 1997

Dementia is defined as a syndrome which is characterized by various impairments in cognitive functions, especially memory function. Most of the diagnostic criteria for dementia include memory impairment as on essential feature. Memory decline can be present as a consequence of the aging process. But it does not cause significant distress or impairment in social and occupational functionings while dementiadoes. Depression may also be associated with memory impairment. However, unlike dementia, depression dose not cause decrease in delayed verbal learning and recognition memory. In dementia, different features of memory impairment may be present depending on the involved area. Memory impairment in cortical dementia is affected by the disturbance of encoding of information and memory consolidation, while memory imparnene in subcortical denentiy is affected by the disturbance of retrieval in subcortical dementia.

• Journal of Korea Multimedia Society
• /
• v.24 no.5
• /
• pp.667-675
• /
• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• The Journal of Korean Physical Therapy
• /
• v.33 no.6
• /
• pp.304-306
• /
• 2021

Objectives: We report on a patient who showed mild bradykinesia due to injury of the corticofugal tract (CFT) from the secondary motor area following direct head trauma, which was demonstrated on diffusion tensor tractography (DTT). Case summary: A 58-year-old male patient underwent conservative management for subarachnoid hemorrhages caused by direct head trauma resulting from a fall from six-meter height at the department of neurosurgery of a local hospital. His Glasgow Coma Scale score was 3. He developed mildly slow movements following the head trauma and visited the rehabilitation department of a university hospital at ten weeks after the fall. The patient exhibited mild bradykinesia during walking and arm movements with mild weakness in all four extremities (G/G^-). Results: On ten-week DTT, narrowing of the right CFT from the supplementary motor area (SMA-CFT), and partial tearing of the left SMA-CFT, left CFTs from the dorsal premotor cortex (dPMC-CFT) and both corticospinal tracts (CSTs) at the subcortical white matter were observed. Conclusion: This case demonstrated abnormalities in both CSTs (partial tearing at the subcortical white matter and narrowing), both SMA-CFTs (narrowing and partial tearing) and left dPMC-CFT. We believe our findings suggest the necessity of assessment of the CFTs from the secondary motor area for patients with unexplained bradykinesia following direct head trauma.

• Journal of Multimedia Information System
• /
• v.8 no.4
• /
• pp.233-242
• /
• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.