• Investigative Magnetic Resonance Imaging
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• 제13권1호
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• pp.63-73
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• 2009

배경: 두개강내 용적에 대한 수동과 자동 측정법이 여성 주요 우울증 환자의 해마의 용적측정술과 modulated voxel-based morphometry (mVBM)의 결과에 미치는 영향을 알아보고자 한다. 방법: 21명의 여성 주요 우울증 환자와 성별, 나이의 분포가 비슷한 20명의 여성 정상인을 연구대상에 포함시켰다. 해마와 두개강내 용적은 수동으로 측정하였고, FreeSurfer 프로그램을 이용하여 두개강내 용적을 자동으로 측정하였다. 또한 회색질과 백색질의 부피도 SPM을 이용하여 자동으로 측정하였다. 결과: 수동으로 측정한 두개강의 용적을 통제변인으로 하여 분석한 통계분석의 결과가 FreeSurfer에 의해 측정된 두개강내 용적이나 뇌실질의 용적을 통제변인으로 한 통계분석의 결과보다 우울증 환자의 해마부피 감소와 mVBM 분석의 국조적 부피감소를 보다 민감하게 보여주었다. 수동적인 방법과 FreeSurfer에 의해 측정된 두개강내 용적은 정상인에서는 차이가 없었지만 (p = 0.696), 우울증 환자의 두개강 부피는 FreeSurfer를 이용해 측정한 두 개강의 부피가 더 작았다 (p = 0.000002). 우울증 환자의 전체 회색질의 부피는 수동으로 측정한 두개강의 용적을 통제변인으로 적용하였을 때 정상인의 회색질의 부피보다 작았고 (p = 0.000002), 해마의 부피도 수동으로 측정한 두 개 강의 부피를 통제변인으로 통계처리를 했을 때는 우울증환자의 해마가 뚜렷한 위축을 보였지만 (오른쪽, p = 0.014; 왼쪽, p = 0.004), 다른 측정법을 통제변인으로 했을 때는 유의하지 않았다 (p > 0.05). mVBM 분석에서는 수동으로 측정한 두개강의 부피를 통제변인으로 사용했을 때만 다중비교교정 후에 유의한 결과를 보였다 (FDR p < 0.05). 결론: 수동적인 방법으로 측정한 두개강의 용적이 FreeSurfer에 의해 자동으로 측정된 두개강의 용적이나 뇌실질의 부피보다 해마용적측정술과 mVBM 의 결과에 있어서 더 효율적으로 우울증이 있는 그룹과 없는 그룹의 차이를 보여주는 것에 민감한 결과를 보였다.

• Investigative Magnetic Resonance Imaging
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• 제19권2호
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• pp.67-75
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• 2015

Purpose: To investigate the value of image post-processing software (FreeSurfer, IBASPM [individual brain atlases using statistical parametric mapping software]) and inversion time (TI) in volumetric analyses of the hippocampus and to identify differences in comparison with manual tracing. Materials and Methods: Brain images from 12 normal adults were acquired using magnetization prepared rapid acquisition gradient echo (MPRAGE) with a slice thickness of 1.3 mm and TI of 800, 900, 1000, and 1100 ms. Hippocampal volumes were measured using FreeSurfer, IBASPM and manual tracing. Statistical differences were examined using correlation analyses accounting for spatial interpretations percent volume overlap and percent volume difference. Results: FreeSurfer revealed a maximum percent volume overlap and maximum percent volume difference at TI = 800 ms ($77.1{\pm}2.9%$) and TI = 1100 ms ($13.1{\pm}2.1%$), respectively. The respective values for IBASPM were TI = 1100 ms ($55.3{\pm}9.1%$) and TI = 800 ms ($43.1{\pm}10.7%$). FreeSurfer presented a higher correlation than IBASPM but it was not statistically significant. Conclusion: FreeSurfer performed better in volumetric determination than IBASPM. Given the subjective nature of manual tracing, automated image acquisition and analysis image is accurate and preferable.

• 스마트미디어저널
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• 제12권7호
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• pp.52-58
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• 2023

Alzheimer's disease is one type of dementia, the symptoms can be treated by detecting the disease at its early stages. Recently, many computer-aided diagnosis using magnetic resonance image(MRI) have shown a good results in the classification of AD. Taken these MRI images and feed to Free surfer software to extra the features. In consideration, using T1-weighted images and classifying using the convolution neural network (CNN) model are proposed. In this paper, taking the subjects from ADNI of subcortical and cortical features of 190 subjects. Consider the study to reduce the complexity of the model by using the single layer in the Res-Net, VGG, and Alex Net. Multi-class classification is used to classify four different stages, CN, EMCI, LMCI, AD. The following experiment shows for respective classification Res-Net, VGG, and Alex Net with the best accuracy with VGG at 96%, Res-Net, GoogLeNet and Alex Net at 91%, 93% and 89% respectively.

• 생물정신의학
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• 제21권3호
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• pp.81-86
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• 2014

Objectives Total intracranial volume (TIV) is a major nuisance of neuroimaging research for interindividual differences of brain structure and function. Authors intended to prove the reliability of the atlas scaling factor (ASF) method for TIV estimation in FreeSurfer by comparing it with the results of manual tracing as reference method. Methods The TIVs of 26 normal children and 26 children with attention-deficit hyperactivity disorder (ADHD) were obtained by using FreeSurfer reconstruction and manual tracing with T1-weighted images. Manual tracing performed in every 10th slice of MRI dataset from midline of sagittal plane by one researcher who was blinded from clinical data. Another reseacher performed manual tracing independently for randomly selected 20 dataset to verify interrater reliability. Results The interrater reliability was excellent (intraclass coefficient = 0.91, p < 7.1e-07). There were no significant differences of age and gender distribution between normal and ADHD groups. No significant differences were found between TIVs from ASF method and manual tracing. Strong correlation between TIVs from 2 different methods were shown (r = 0.90, p < 2.2e-16). Conclusions The ASF method for TIV estimation by using FreeSurfer showed good agreement with the reference method. We can use the TIV from ASF method for correction in analysis of structural and functional neuroimaging studies with not only elderly subjects but also children, even with ADHD.

• 생물정신의학
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• 제22권3호
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• pp.118-127
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• 2015

Objectives Standardization of head size is essential for the volume study. Cortical thickness analyses are increasingly being used in many fields of neuroscience. However, it is not established whether head size correction should be done for thickness study. Methods Using the Open Access Series of Imaging Studies data, we determined cortical thickness of 316 cognitively normal participants aged 18-94 with FreeSurfer. The association between head size and cortical thickness of whole cortical mantle and in each lobe among age tertile groups was assessed. Estimated total intracranial volume (eTIV) was calculated for determining head size. Results Across all participants, cortical thickness in whole brain except some areas in cingulate and insula decreased with aging. eTIV had positive correlation with the thickness of frontal, parietal, occipital and whole brain areas. However, the age effect was not shown in whole brain of the first tertile group and in cingulate areas of the third tertile group. eTIV had negative correlation with the thickness of cingulate in the third tertile group. Gender effects were shown in some areas in third tertile group, but it would be due to difference of head size. Conclusions These findings suggest that head size standardization might be done especially in older population and in studies of paralimbic areas.

• Journal of Multimedia Information System
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• 제9권1호
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• pp.21-32
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• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• 생물정신의학
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• 제24권2호
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• pp.68-74
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• 2017

Objectives It is well known that problem drinking is associated with alterations of brain structures and functions. Brain functions related to alcohol consumption can be determined by the resting state functional connectivity in various resting state networks (RSNs). This study aims to ascertain the alcohol effect on the structures forming predetermined RSNs by assessing their cortical thickness. Methods Twenty-six abstinent male patients with alcohol dependence and the same number of age-matched healthy control were recruited from an inpatient mental hospital and community. All participants underwent a 3T MRI scan. Averaged cortical thickness of areas constituting 7 RSNs were determined by using FreeSurfer with Yeo atlas derived from cortical parcellation estimated by intrinsic functional connectivity. Results There were significant group differences of mean cortical thicknesses (Cohen's d, corrected p) in ventral attention (1.01, < 0.01), dorsal attention (0.93, 0.01), somatomotor (0.90, 0.01), and visual (0.88, 0.02) networks. We could not find significant group differences in the default mode network. There were also significant group differences of gray matter volumes corrected by head size across the all networks. However, there were no group differences of surface area in each network. Conclusions There are differences in degree and pattern of structural recovery after abstinence across areas forming RSNs. Considering the previous observation that group differences of functional connectivity were significant only in networks related to task-positive networks such as dorsal attention and cognitive control networks, we can explain recovery pattern of cognition and emotion related to the default mode network and the mechanisms for craving and relapse associated with task-positive networks.

• 대한치매학회지
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• 제21권4호
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• pp.138-146
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• 2022

Background and Purpose: Magnetic resonance imaging (MRI) helps with brain development analysis and disease diagnosis. Brain volumes measured from different ages using MRI provides useful information in clinical evaluation and research. Therefore, we trained machine learning models that predict the brain age gap of healthy subjects in the East Asian population using T1 brain MRI volume images. Methods: In total, 154 T1-weighted MRIs of healthy subjects (55-83 years of age) were collected from an East Asian community. The information of age, gender, and education level was collected for each participant. The MRIs of the participants were preprocessed using FreeSurfer(https://surfer.nmr.mgh.harvard.edu/) to collect the brain volume data. We trained the models using different supervised machine learning regression algorithms from the scikit-learn (https://scikit-learn.org/) library. Results: The trained models comprised 19 features that had been reduced from 55 brain volume labels. The algorithm BayesianRidge (BR) achieved a mean absolute error (MAE) and r squared (R²) of 3 and 0.3 years, respectively, in predicting the age of the new subjects compared to other regression methods. The results of feature importance analysis showed that the right pallidum, white matter hypointensities on T1-MRI scans, and left hippocampus comprise some of the essential features in predicting brain age. Conclusions: The MAE and R² accuracies of the BR model predicting brain age gap in the East Asian population showed that the model could reduce the dimensionality of neuroimaging data to provide a meaningful biomarker for individual brain aging.

• 한국방사선학회논문지
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• 제18권2호
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• pp.73-81
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• 2024

본 연구는 기분 장애(mood disorder) 환자들과 정상 대조군간의 대뇌 피질 두께를 측정 하여 구조적 이상을 비교하였다. 2020년 9월부터 2022년 8월까지 경상남도 양산 P 병원 정신건강의학과에서 기분 장애 진단을 받은 44명과 이상 병변이 없는 정상인 59명을 대상으로 후향적 연구를 시행하였다. 자기공명영상(MRI) 검사 후 획득한 3D-T1 MPRAGE 영상을 이용하였고, FreeSurfer 소프트웨어를 사용하여 대뇌 피질 두께를 측정하였다. 통계분석은 독립표본 t-검정을 이용하여 두 그룹간 평균의 차이를 측정하고, cohen's d 검정을 통해 두 그룹간 평균 차이의 크기를 평가하였다. 또한, 측정된 평균 피질 두께와 환자의 양성·음성증상(Positive and Negative Syndrome Scale, PANSS)간의 상관관계를 분석하였다. 기분장애 환자는 정상대조군에 비해 양측 상전두이랑(both superior frontal), 주둥이 중전두이랑(both rostral middle frontal), 꼬리 중전두이랑(both caudal middle frontal), 하전두이랑 주름 세곳(both pars opercularis, pars orbitals, pars triangularis), 상측두이랑(both superior temporal), 하측두이랑(both inferior temporal), 외측안와전두피질(both lateral orbito frontal), 내측안와전두피질(both medial orbito frontal), 방추형이랑(both fusiform), 후대상피질(both posterior cingulate), 대상이랑의 협부(both isthmus cingulate), 상두정수리소엽(both superior parietal), 하두정엽(both inferior parietal), 변연상이랑(both supramarginal), 좌측 후중심이랑(left post central), 우측 상부측두고랑(right bank of the superior temporal sulcus), 중측두이랑(right middle temporal), 전대상피질(right rostral anterior cingulate), 뇌섬엽(right insula)의 두께가 유의미하게 감소하였다(p<0.05). 그 중 평균 차이의 크기(cohen's d)가 큰 영역은 좌측 fusiform (d=0.82), pars opercularis (d=0.94), superior frontal (d=0.88), 우측 lateral orbito frontal (d=0.85), pars orbitalis (d=0.89) 로 나타났다. 또한, PANSS와 양측 대뇌 피질의 평균 두께는 약한 음의 상관관계(left hemisphere r=-0.234, right hemisphere r=-0.230)를 나타내었다. 이러한 연구의 결과는 정상인과 비교하여 기분장애 환자의 피질 두께 감소영역을 확인하였고 질환의 증상 정도와 피질 두께 변화의 관련성을 확인하는 데 도움이 될 것으로 기대된다.

• Korean Journal of Radiology
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• 제23권10호
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• pp.959-975
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• 2022

Objective: To investigate the agreement and reliability of estimating the volumes and normative percentiles (N%) of segmented brain regions among NeuroQuant (NQ), DeepBrain (DB), and FreeSurfer (FS) software programs, focusing on the comparison between NQ and DB. Materials and Methods: Three-dimensional T1-weighted images of 145 participants (48 healthy participants, 50 patients with mild cognitive impairment, and 47 patients with Alzheimer's disease) from a single medical center (SMC) dataset and 130 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were included in this retrospective study. All images were analyzed with DB, NQ, and FS software to obtain volume estimates and N% of various segmented brain regions. We used Bland-Altman analysis, repeated measures ANOVA, reproducibility coefficient, effect size, and intraclass correlation coefficient (ICC) to evaluate inter-method agreement and reliability. Results: Among the three software programs, the Bland-Altman plot showed a substantial bias, the ICC showed a broad range of reliability (0.004-0.97), and repeated-measures ANOVA revealed significant mean volume differences in all brain regions. Similarly, the volume differences of the three software programs had large effect sizes in most regions (0.73-5.51). The effect size was largest in the pallidum in both datasets and smallest in the thalamus and cerebral white matter in the SMC and ADNI datasets, respectively. N% of NQ and DB showed an unacceptably broad Bland-Altman limit of agreement in all brain regions and a very wide range of ICC values (-0.142-0.844) in most brain regions. Conclusion: NQ and DB showed significant differences in the measured volume and N%, with limited agreement and reliability for most brain regions. Therefore, users should be aware of the lack of interchangeability between these software programs when they are applied in clinical practice.