• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.63-73
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• 2009

Purpose : To investigate the effects of various intracranial volume (ICV) measurement methods on the sensitivity of hippocampal volumetry and modulated voxel-based morphometry (mVBM) in female patients with major depressive disorder (MDD). Materials and Methods : T1 magnetic resonance imaging (MRI) data for 41 female subjects (21 MDD patients, 20 normal subjects) were analyzed. Hippocampal volumes were measured manually, and ICV was measured manually and automatically using the FreeSurfer package. Gray and white matter volumes were measured separately. Results : Manual ICV normalization provided the greatest sensitivity in hippocampal volumetry and mVBM, followed by FreeSurfer ICV, GWMV, and GMV. Manual and FreeSurfer ICVs were similar in normal subjects (p = 0.696), but distinct in MDD patients (p = 0.000002). Manual ICV-corrected total gray matter volume (p = 0.0015) and Manual ICV-corrected bilateral hippocampal volumes (right, p = 0.014; left, p = 0.004) were decreased significantly in MDD patients, but the differences of hippocampal volumes corrected by FreeSurfer ICV, GWMV, or GMV were not significant between two groups (p > 0.05). Only manual ICV-corrected mVBM analysis was significant after correction for multiple comparisons. Conclusion : The method of ICV measurement greatly affects the sensitivity of hippocampal volumetry and mVBM. Manual ICV normalization showed the ability to detect differences between women with and without MDD for both methods.

• Investigative Magnetic Resonance Imaging
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• v.19 no.2
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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.

• Smart Media Journal
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• v.12 no.7
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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.

• Korean Journal of Biological Psychiatry
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• v.21 no.3
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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.

• Korean Journal of Biological Psychiatry
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• v.22 no.3
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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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• v.9 no.1
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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.

• Korean Journal of Biological Psychiatry
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• v.24 no.2
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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.

• Dementia and Neurocognitive Disorders
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• v.21 no.4
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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.

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.73-81
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• 2024

This study compared the cortical thickness of patients with mood disorders and a control group to assess structural abnormalities. A retrospective study was conducted from September 2020 to August 2022 at the Department of Psychiatry, P Hospital in Yangsan, Gyeongsangnam-do. The study included 44 individuals diagnosed with mood disorders and 59 healthy individuals without any pathological lesions. The 3D-T1 MPRAGE images obtained from magnetic resonance imaging examinations were utilized, and FreeSurfer software was employed to measure cortical thickness. Statistical analysis involved independent samples t-tests to measure the differences in means between the two groups, and Cohen's d test was used to compare the effect sizes of the differences. Furthermore, the correlation between the measured average cortical thickness and Positive and Negative Syndrome Scale scores was analyzed. The research results revealed that patients with mood disorders exhibited decreased cortical thickness compared to the normal control group in both superior frontal regions, both rostral middle frontal regions, both caudal middle frontal regions, both pars opercularis, pars orbitals, pars triangularis regions, both superior temporal regions, both inferior temporal regions, both lateral orbitofrontal regions, both medial orbitofrontal regions, both fusiform regions, both posterior cingulate regions, both isthmus cingulate regions, both superior parietal regions, both inferior parietal regions, both supramarginal regions, left postcentral region, right bank of the superior temporal sulcus region, right middle temporal region, right rostral anterior cingulate region, and right insula region. Among them, regions that showed differences with effect sizes of 0.8 or higher were left fusiform (d=0.82), pars opercularis (d=0.94), superior frontal (d=0.88), right lateral orbitofrontal (d=0.85), and pars orbitalis (d=0.89). Additionally, there was a weak negative correlation between PANSS scores and average cortical thickness in both the left hemisphere (r=-0.234) and right hemisphere (r=-0.230). These findings are expected to be helpful in identifying areas of cortical thickness reduction in patients with mood disorders compared to healthy individuals and understanding the relationship between symptom severity and cortical thickness changes.

• Korean Journal of Radiology
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• v.23 no.10
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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.