1
|
Kim SJ, Jang H, Yoo H, Na DL, Ham H, Kim HJ, Kim JP, Farrar G, Moon SH, Seo SW. Clinical and Pathological Validation of CT-Based Regional Harmonization Methods of Amyloid PET. Clin Nucl Med 2024; 49:1-8. [PMID: 38048354 DOI: 10.1097/rlu.0000000000004937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
PURPOSE The CT-based regional direct comparison Centiloid (dcCL) method was developed to harmonize and quantify regional β-amyloid (Aβ) burden. In the present study, we aimed to investigate correlations between the CT-based regional dcCL scales and Aβ pathological burdens and to validate the clinical utility using thresholds derived from pathological assessment. PATIENTS AND METHODS We included a pathological cohort of 63 cases and a clinical cohort of 4062 participants, and obtained modified Consortium to Establish a Registry for Alzheimer's Disease criteria (mCERAD) scores by assessment of neuritic plaque burdens in multiple areas of each cortical region. PET and CT images were processed using the CT-based regional dcCL method to calculate scales in 6 distinct regions. RESULTS The CT-based regional dcCL scales were correlated with neuritic plaque burdens represented by mCERAD scores, globally and regionally ( r = 0.56~0.76). In addition, striatum dcCL scales reflected Aβ involvement in the striatum ( P < 0.001). The regional dcCL scales could predict significant Aβ deposition in specific brain regions with high accuracy: area under the receiver operating characteristic curve of 0.81-0.97 with an mCERAD cutoff of 1.5 and area under the receiver operating characteristic curve of 0.88-0.93 with an mCERAD cutoff of 0.5. When applying the dcCL thresholds of 1.5 mCERAD scores, the G(-)R(+) group showed lower performances in memory and global cognitive functions and had less hippocampal volume compared with the G(-)R(-) group ( P < 0.001). However, when applying the dcCL thresholds of 0.5 mCERAD scores, there were no differences in the global cognitive functions between the 2 groups. CONCLUSIONS The thresholds of regional dcCL scales derived from pathological assessments might provide clinicians with a better understanding of biomarker-guided diagnosis and distinguishable clinical phenotypes, which are particularly useful when harmonizing different PET ligands with only PET/CT.
Collapse
Affiliation(s)
| | | | - Heejin Yoo
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center
| | | | | | | | | | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St Giles, United Kingdom
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | | |
Collapse
|
2
|
Kang SH, Yoo H, Cheon BK, Park YH, Kim SJ, Ham H, Jang H, Kim HJ, Oh K, Koh SB, Na DL, Kim JP, Seo SW. Distinct effects of cholesterol profile components on amyloid and vascular burdens. Alzheimers Res Ther 2023; 15:197. [PMID: 37950256 PMCID: PMC10636929 DOI: 10.1186/s13195-023-01342-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 10/25/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Cholesterol plays important roles in β-amyloid (Aβ) metabolism and atherosclerosis. However, the relationships of plasma cholesterol levels with Aβ and cerebral small vessel disease (CSVD) burdens are not fully understood in Asians. Herein, we investigated the relationships between plasma cholesterol profile components and Aβ and CSVD burdens in a large, non-demented Korean cohort. METHODS We enrolled 1,175 non-demented participants (456 with unimpaired cognition [CU] and 719 with mild cognitive impairment [MCI]) aged ≥ 45 years who underwent Aβ PET at the Samsung Medical Center in Korea. We performed linear regression analyses with each cholesterol (low-density lipoprotein cholesterol [LDL-c], high-density lipoprotein cholesterol [HDL-c], and triglyceride) level as a predictor and each image marker (Aβ uptake on PET, white matter hyperintensity [WMH] volume, and hippocampal volume) as an outcome after controlling for potential confounders. RESULTS Increased LDL-c levels (β = 0.014 to 0.115, p = 0.013) were associated with greater Aβ uptake, independent of the APOE e4 allele genotype and lipid-lowering medication. Decreased HDL-c levels (β = - 0.133 to - 0.006, p = 0.032) were predictive of higher WMH volumes. Increased LDL-c levels were also associated with decreased hippocampal volume (direct effect β = - 0.053, p = 0.040), which was partially mediated by Aβ uptake (indirect effect β = - 0.018, p = 0.006). CONCLUSIONS Our findings highlight that increased LDL-c and decreased HDL-c levels are important risk factors for Aβ and CSVD burdens, respectively. Furthermore, considering that plasma cholesterol profile components are potentially modified by diet, exercise, and pharmacological agents, our results provide evidence that regulating LDL-c and HDL-c levels is a potential strategy to prevent dementia.
Collapse
Grants
- 2022R1I1A1A01056956 Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education
- HI19C1132 a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea
- grant number: HU20C0111, HU22C0170 a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare and Ministry of science and ICT, Republic of Korea
- NRF-2019R1A5A2027340, NRF-2022R1F1A1063966 the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)
- 2021-ER1006-01 the "National Institute of Health" research project
- a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea
- a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare and Ministry of science and ICT, Republic of Korea
Collapse
Affiliation(s)
- Sung Hoon Kang
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Heejin Yoo
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea
| | - Bo Kyoung Cheon
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
| | - Yu Hyun Park
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Soo-Jong Kim
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Hongki Ham
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea
| | - Hyemin Jang
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
| | - Hee Jin Kim
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
| | - Kyungmi Oh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Duk L Na
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea
| | - Jun Pyo Kim
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea.
| | - Sang Won Seo
- Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Korea.
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea.
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.
| |
Collapse
|
3
|
Kim BH, Lee H, Ham H, Kim HJ, Jang H, Kim JP, Park YH, Kim M, Seo SW. Clinical effects of novel susceptibility genes for beta-amyloid: a gene-based association study in the Korean population. Front Aging Neurosci 2023; 15:1278998. [PMID: 37901794 PMCID: PMC10602697 DOI: 10.3389/fnagi.2023.1278998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
Amyloid-beta (Aβ) is a pathological hallmark of Alzheimer's disease (AD). We aimed to identify genes related to Aβ uptake in the Korean population and investigate the effects of these novel genes on clinical outcomes, including neurodegeneration and cognitive impairments. We recruited a total of 759 Korean participants who underwent neuropsychological tests, brain magnetic resonance imaging, 18F-flutemetamol positron emission tomography, and microarray genotyping data. We performed gene-based association analysis, and also performed expression quantitative trait loci and network analysis. In genome-wide association studies, no single nucleotide polymorphism (SNP) passed the genome-wide significance threshold. In gene-based association analysis, six genes (LCMT1, SCRN2, LRRC46, MRPL10, SP6, and OSBPL7) were significantly associated with Aβ standardised uptake value ratio in the brain. The three most significant SNPs (rs4787307, rs9903904, and rs11079797) on these genes are associated with the regulation of the LCMT1, OSBPL7, and SCRN2 genes, respectively. These SNPs are involved in decreasing hippocampal volume and cognitive scores by mediating Aβ uptake. The 19 enriched gene sets identified by pathway analysis included axon and chemokine activity. Our findings suggest novel susceptibility genes associated with the uptake of Aβ, which in turn leads to worse clinical outcomes. Our findings might lead to the discovery of new AD treatment targets.
Collapse
Affiliation(s)
- Bo-Hyun Kim
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - HyunWoo Lee
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hongki Ham
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Kim
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyemin Jang
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yu Hyun Park
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Mansu Kim
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Sang Won Seo
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| |
Collapse
|
4
|
Kim YJ, Kim SE, Hahn A, Jang H, Kim JP, Kim HJ, Na DL, Chin J, Seo SW. Classification and prediction of cognitive trajectories of cognitively unimpaired individuals. Front Aging Neurosci 2023; 15:1122927. [PMID: 36993907 PMCID: PMC10040799 DOI: 10.3389/fnagi.2023.1122927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/27/2023] [Indexed: 03/14/2023] Open
Abstract
Objectives Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts. Methods A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model. Results Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model). Conclusion Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.
Collapse
Affiliation(s)
- Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Si Eun Kim
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Alice Hahn
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Institute of Stem Cell and Regenerative Medicine, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Juhee Chin
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Institute of Stem Cell and Regenerative Medicine, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, Seoul, Republic of Korea
- Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| |
Collapse
|
5
|
Kim J, Choe YS, Park Y, Kim Y, Kim JP, Jang H, Kim HJ, Na DL, Cho SJ, Moon SH, Seo SW. Clinical outcomes of increased focal amyloid uptake in individuals with subthreshold global amyloid levels. Front Aging Neurosci 2023; 15:1124445. [PMID: 36936497 PMCID: PMC10017468 DOI: 10.3389/fnagi.2023.1124445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Background Although the standardized uptake value ratio (SUVR) method is objective and simple, cut-off optimization using global SUVR values may not reflect focal increased uptake in the cerebrum. The present study investigated clinical and neuroimaging characteristics according to focally increased β-amyloid (Aβ) uptake and global Aβ status. Methods We recruited 968 participants with cognitive continuum. All participants underwent neuropsychological tests and 498 18F-florbetaben (FBB) amyloid positron emission tomography (PET) and 470 18F-flutemetamol (FMM) PET. Each PET scan was assessed in 10 regions (left and right frontal, lateral temporal, parietal, cingulate, and striatum) with focal-quantitative SUVR-based cutoff values for each region by using an iterative outlier approach. Results A total of 62 (6.4%) subjects showed increased focal Aβ uptake with subthreshold global Aβ status [global (-) and focal (+) Aβ group, G(-)F(+) group]. The G(-)F(+) group showed worse performance in memory impairment (p < 0.001), global cognition (p = 0.009), greater hippocampal atrophy (p = 0.045), compared to those in the G(-)F(-). Participants with widespread Aβ involvement in the whole region [G(+)] showed worse neuropsychological (p < 0.001) and neuroimaging features (p < 0.001) than those with focal Aβ involvement G(-)F(+). Conclusion Our findings suggest that individuals show distinctive clinical outcomes according to focally increased Aβ uptake and global Aβ status. Thus, researchers and clinicians should pay more attention to focal increased Aβ uptake in addition to global Aβ status.
Collapse
Affiliation(s)
- Jaeho Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yuhyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University College of Medicine, Chuncheon-si, Gangwon-do, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Samsung Medical Center, Stem Cell and Regenerative Medicine Institute, Seoul, Republic of Korea
| | - Soo-Jin Cho
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- *Correspondence: Seung Hwan Moon,
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea
- Sang Won Seo,
| |
Collapse
|
6
|
Han J, Kim MN, Lee HW, Jeong SY, Lee SW, Yoon U, Kang K. Distinct volumetric features of cerebrospinal fluid distribution in idiopathic normal-pressure hydrocephalus and Alzheimer's disease. Fluids Barriers CNS 2022; 19:66. [PMID: 36045420 PMCID: PMC9434899 DOI: 10.1186/s12987-022-00362-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 07/13/2022] [Indexed: 12/04/2022] Open
Abstract
Objective The aims of the study were to measure the cerebrospinal fluid (CSF) volumes in the lateral ventricle, high-convexity subarachnoid space, and Sylvian fissure region in patients with idiopathic normal-pressure hydrocephalus (INPH) and Alzheimer’s disease (AD), and to evaluate differences in these volumes between INPH and AD groups and healthy controls. Methods Forty-nine INPH patients, 59 AD patients, and 26 healthy controls were imaged with automated three-dimensional volumetric MRI. Results INPH patients had larger lateral ventricles and CSF spaces of the Sylvian fissure region and smaller high-convexity subarachnoid spaces than other groups, and AD patients had larger lateral ventricles and CSF spaces of the Sylvian fissure region than the control group. The INPH group showed a negative correlation between lateral ventricle and high-convexity subarachnoid space volumes, while the AD group showed a positive correlation between lateral ventricle volume and volume for CSF spaces of the Sylvian fissure region. The ratio of lateral ventricle to high-convexity subarachnoid space volumes yielded an area under the curve of 0.990, differentiating INPH from AD. Conclusions Associations between CSF volumes suggest that there might be different mechanisms between INPH and AD to explain their respective lateral ventricular dilations. The ratio of lateral ventricle to high-convexity subarachnoid space volumes distinguishes INPH from AD with good diagnostic sensitivity and specificity. We propose to refer to this ratio as the VOSS (ventricle over subarachnoid space) index.
Collapse
Affiliation(s)
- Jaehwan Han
- Department of Biomedical Engineering, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Myoung Nam Kim
- Department of Biomedical Engineering, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Ho-Won Lee
- Department of Neurology, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, South Korea.,Brain Science and Engineering Institute, Kyungpook National University, Daegu, South Korea
| | - Shin Young Jeong
- Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Sang-Woo Lee
- Department of Nuclear Medicine, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Uicheul Yoon
- Department of Biomedical Engineering, Daegu Catholic University, 13-13 Hayang- ro, Hayang-eup, Gyeongsan, Gyeongbuk, 38430, South Korea.
| | - Kyunghun Kang
- Department of Neurology, School of Medicine, Kyungpook National University, 680 Gukchaebosang-ro, Jung-gu, Daegu, 41944, South Korea.
| |
Collapse
|
7
|
Kim J, Jung SH, Choe YS, Kim S, Kim B, Kim HR, Son SJ, Hong CH, Na DL, Kim HJ, Cho SJ, Won HH, Seo SW. Ethnic differences in the frequency of β-amyloid deposition in cognitively normal individuals. Neurobiol Aging 2022; 114:27-37. [DOI: 10.1016/j.neurobiolaging.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 10/18/2022]
|
8
|
Kim SJ, Woo SY, Kim YJ, Jang H, Kim HJ, Na DL, Kim S, Seo SW, the Alzheimer's Disease Neuroimaging Initiative. Development of prediction models for distinguishable cognitive trajectories in patients with amyloid positive mild cognitive impairment. Neurobiol Aging 2022; 114:84-93. [DOI: 10.1016/j.neurobiolaging.2022.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 02/21/2022] [Accepted: 02/23/2022] [Indexed: 11/29/2022]
|
9
|
Kim YJ, Hahn A, Park YH, Na DL, Chin J, Seo SW. Longitudinal Amyloid Cognitive Composite in Preclinical Alzheimer's Disease. Eur J Neurol 2021; 29:980-989. [PMID: 34972256 DOI: 10.1111/ene.15241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/16/2021] [Accepted: 12/23/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Previous studies have developed several cognitive composites in preclinical AD. However, more sensitive measures to track cognitive changes and therapeutic efficacy in preclinical Alzheimer's disease (AD) are needed considering diverse sociocultural and linguistic backgrounds. This study developed a composite score that can sensitively detect the Aβ-related cognitive trajectory of preclinical AD using Korean data. METHODS A total of 196 cognitively normal (CN) participants who underwent amyloid positron emission tomography were followed-up with neuropsychological assessments. We developed the Longitudinal Amyloid cognitive Composite in Preclinical AD (LACPA) using the linear mixed-effects model (LMM) and z-scores. The LMM was also used to investigate the longitudinal sensitivity of LACPA and the association between time-varying brain atrophy and LACPA. RESULTS Considering the group-time interaction effects of each subtest, the Seoul Verbal Learning Test-Elderly's version (SVLT-E) immediate recall/delayed recall/recognition, the Korean Trail Making Test B time, and the Korean Mini-Mental State Examination were selected as components of LACPA. LACPA exhibited a significant group-time interaction effect between the Aβ+ and Aβ- groups (t = -3.288, p = 0.001). Associations between time-varying LACPA and brain atrophy were found in the bilateral medial temporal, right lateral parietal, and right lateral frontal regions, and hippocampal volume. CONCLUSION LACPA may contribute to reduction in time and financial burden when monitoring Aβ-related cognitive decline and therapeutic efficacy of the disease-modifying agents specifically targeting Aβ in secondary prevention trials.
Collapse
Affiliation(s)
- Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Alice Hahn
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Yu Hyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Stem Cell & Regenerative Medicine Institute
| | - Juhee Chin
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Samsung Alzheimer Research Center.,Center for Clinical Epidemiology, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Health Sciences and Technology.,Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| |
Collapse
|
10
|
Ren X, Wu Y, Cao Z. Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:3937222. [PMID: 34608408 PMCID: PMC8487389 DOI: 10.1155/2021/3937222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/10/2021] [Accepted: 09/16/2021] [Indexed: 11/17/2022]
Abstract
Since the hippocampus is of small size, low contrast, and irregular shape, a novel hippocampus segmentation method based on subspace patch-sparsity clustering in brain MRI is proposed to improve the segmentation accuracy, which requires that the representation coefficients in different subspaces should be as sparse as possible, while the representation coefficients in the same subspace should be as average as possible. By restraining the coefficient matrix with the patch-sparse constraint, the coefficient matrix contains a patch-sparse structure, which is helpful to the hippocampus segmentation. The experimental results show that our proposed method is effective in the noisy brain MRI data, which can well deal with hippocampus segmentation problem.
Collapse
Affiliation(s)
- Xiaogang Ren
- Changshu Hospital of Chinese Medicine, Changshu 215516, Jiangsu, China
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
| | - Yue Wu
- The Affiliated Changshu Hospital of Soochow University (Changshu No. 1 People's Hospital), Suzhou, Jiangsu 215500, China
| | - Zhiying Cao
- The Affiliated Changshu Hospital of Soochow University (Changshu No. 1 People's Hospital), Suzhou, Jiangsu 215500, China
| |
Collapse
|
11
|
Kim J, Park Y, Park S, Jang H, Kim HJ, Na DL, Lee H, Seo SW. Prediction of tau accumulation in prodromal Alzheimer's disease using an ensemble machine learning approach. Sci Rep 2021; 11:5706. [PMID: 33707488 PMCID: PMC7970986 DOI: 10.1038/s41598-021-85165-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 02/17/2021] [Indexed: 01/07/2023] Open
Abstract
We developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal AD. We recruited 64 patients with prodromal AD using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Supervised ML approaches based on the random forest (RF) and a gradient boosting machine (GBM) were used. The GBM resulted in an AUC of 0.61 (95% confidence interval [CI] 0.579–0.647) with clinical data (age, sex, years of education) and a higher AUC of 0.817 (95% CI 0.804–0.830) with clinical and neuropsychological data. The highest AUC was 0.86 (95% CI 0.839–0.885) achieved with additional information such as cortical thickness in clinical data and neuropsychological results. Through the analysis of the impact order of the variables in each ML classifier, cortical thickness of the parietal lobe and occipital lobe and neuropsychological tests of memory domain were found to be more important features for each classifier. Our ML algorithms predicting tau burden may provide important information for the recruitment of participants in potential clinical trials of tau targeting therapies.
Collapse
Affiliation(s)
- Jaeho Kim
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea.,Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Yuhyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - Seongbeom Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea.,Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hyejoo Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea. .,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea. .,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea. .,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
| |
Collapse
|
12
|
Kim JP, Kim J, Kim Y, Moon SH, Park YH, Yoo S, Jang H, Kim HJ, Na DL, Seo SW, Seong JK. Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes. Eur J Nucl Med Mol Imaging 2019; 47:1971-1983. [PMID: 31884562 PMCID: PMC7299909 DOI: 10.1007/s00259-019-04663-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 12/16/2019] [Indexed: 01/18/2023]
Abstract
Purpose We developed a machine learning–based classifier for in vivo amyloid positron emission tomography (PET) staging, quantified cortical uptake of the PET tracer by using a machine learning method, and investigated the impact of these amyloid PET parameters on clinical and structural outcomes. Methods A total of 337 18F-florbetaben PET scans obtained at Samsung Medical Center were assessed. We defined a feature vector representing the change in PET tracer uptake from grey to white matter. Using support vector machine (SVM) regression and SVM classification, we quantified the cortical uptake as predicted regional cortical tracer uptake (pRCTU) and categorised the scans as positive and negative. Positive scans were further classified into two stages according to the striatal uptake. We compared outcome parameters among stages and further assessed the association between the pRCTU and outcome variables. Finally, we performed path analysis to determine mediation effects between PET variables. Results The classification accuracy was 97.3% for cortical amyloid positivity and 91.1% for striatal positivity. The left frontal and precuneus/posterior cingulate regions, as well as the anterior portion of the striatum, were important in determination of stages. The clinical scores and magnetic resonance imaging parameters showed negative associations with PET stage. However, except for the hippocampal volume, most outcomes were associated with the stage through the complete mediation effect of pRCTU. Conclusion Using a machine learning algorithm, we achieved high accuracy for in vivo amyloid PET staging. The in vivo amyloid stage was associated with cognitive function and cerebral atrophy mostly through the mediation effect of cortical amyloid. Electronic supplementary material The online version of this article (10.1007/s00259-019-04663-3) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Jeonghun Kim
- Department of Bio-convergence Engineering, Korea University, Seoul, South Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Chuncheon, South Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Seoul, South Korea
| | - Yu Hyun Park
- Department of Neurology, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea
| | - Sole Yoo
- Department of Cognitive Science, Yonsei University, Seoul, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Seoul, South Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Seoul, South Korea. .,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, South Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, South Korea. .,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea. .,Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea.
| | - Joon-Kyung Seong
- Department of Bio-convergence Engineering, Korea University, Seoul, South Korea. .,School of Biomedical Engineering, Korea University, Seoul, South Korea. .,Department of Artificial Intelligence, Korea University, Seoul, South Korea.
| |
Collapse
|
13
|
Safavian N, Batouli SAH, Oghabian MA. An automatic level set method for hippocampus segmentation in MR images. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2019. [DOI: 10.1080/21681163.2019.1706054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Nazanin Safavian
- Neuroimaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Seyed Amir Hossein Batouli
- Neuroimaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Neuroimaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
14
|
Application of an amyloid and tau classification system in subcortical vascular cognitive impairment patients. Eur J Nucl Med Mol Imaging 2019; 47:292-303. [DOI: 10.1007/s00259-019-04498-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 08/21/2019] [Indexed: 10/26/2022]
|
15
|
Kwak K, Yun HJ, Park G, Lee JM. Multi-Modality Sparse Representation for Alzheimer's Disease Classification. J Alzheimers Dis 2019; 65:807-817. [PMID: 29562503 DOI: 10.3233/jad-170338] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) and mild cognitive impairment (MCI) are age-related neurodegenerative diseases characterized by progressive loss of memory and irreversible cognitive functions. The hippocampus, a brain area critical for learning and memory processes, is especially susceptible to damage at early stages of AD. OBJECTIVE We aimed to develop prediction model using a multi-modality sparse representation approach. METHODS We proposed a sparse representation approach to the hippocampus using structural T1-weighted magnetic resonance imaging (MRI) and 18-fluorodeoxyglucose-positron emission tomography (FDG-PET) to distinguish AD/MCI from healthy control subjects (HCs). We considered structural and function information for the hippocampus and applied a sparse patch-based approach to effectively reduce the dimensions of neuroimaging biomarkers. RESULTS In experiments using Alzheimer's Disease Neuroimaging Initiative data, our proposed method demonstrated more reliable than previous classification studies. The effects of different parameters on segmentation accuracy were also evaluated. The mean classification accuracy obtained with our proposed method was 0.94 for AD/HCs, 0.82 for MCI/HCs, and 0.86 for AD/MCI. CONCLUSION We extracted multi-modal features from automatically defined hippocampal regions of training subjects and found this method to be discriminative and robust for AD and MCI classification. The extraction of features in T1 and FDG-PET images is expected to improve classification performance due to the relationship between brain structure and function.
Collapse
Affiliation(s)
- Kichang Kwak
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Hyuk Jin Yun
- Fetal Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gilsoon Park
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | | |
Collapse
|
16
|
Hou B, Kang G, Zhang N, Liu K. Multi-target Interactive Neural Network for Automated Segmentation of the Hippocampus in Magnetic Resonance Imaging. Cognit Comput 2019. [DOI: 10.1007/s12559-019-09645-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
17
|
Nogovitsyn N, Souza R, Muller M, Srajer A, Hassel S, Arnott SR, Davis AD, Hall GB, Harris JK, Zamyadi M, Metzak PD, Ismail Z, Bray SL, Lebel C, Addington JM, Milev R, Harkness KL, Frey BN, Lam RW, Strother SC, Goldstein BI, Rotzinger S, Kennedy SH, MacQueen GM. Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants. Neuroimage 2019; 197:589-597. [PMID: 31075395 DOI: 10.1016/j.neuroimage.2019.05.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/15/2019] [Accepted: 05/06/2019] [Indexed: 12/31/2022] Open
Abstract
Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation methods that are both fast and reliable over time. Segmentation algorithms that employ deep convolutional neural networks (CNN) have emerged as a promising solution for large longitudinal neuroimaging studies. However, for these novel algorithms to be useful in clinical studies, the accuracy and reproducibility should be established on independent datasets. Here, we evaluate the performance of a CNN-based hippocampal segmentation algorithm that was developed by Thyreau and colleagues - Hippodeep. We compared its segmentation outputs to manual segmentation and FreeSurfer 6.0 in a sample of 200 healthy participants scanned repeatedly at seven sites across Canada, as part of the Canadian Biomarker Integration Network in Depression consortium. The algorithm demonstrated high levels of stability and reproducibility of volumetric measures across all time points compared to the other two techniques. Although more rigorous testing in clinical populations is necessary, this approach holds promise as a viable option for tracking volumetric changes in longitudinal neuroimaging studies.
Collapse
Affiliation(s)
- Nikita Nogovitsyn
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
| | - Roberto Souza
- Department of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Meghan Muller
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Amelia Srajer
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Stefanie Hassel
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada
| | | | - Andrew D Davis
- Department of Psychology, Neuroscience & Behaviour, McMaster University, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience & Behaviour, McMaster University, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | | | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | - Paul D Metzak
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Zahinoor Ismail
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Signe L Bray
- Department of Radiology, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Calgary, AB, Canada; Child & Adolescent Imaging Research (CAIR) Program, Calgary, AB, Canada
| | - Catherine Lebel
- Department of Radiology, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Calgary, AB, Canada; Child & Adolescent Imaging Research (CAIR) Program, Calgary, AB, Canada
| | - Jean M Addington
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Roumen Milev
- Department of Psychiatry, Queen's University and Providence Care Hospital, Kingston, ON, Canada; Department of Psychology, Queen's University, Kingston, ON, Canada
| | - Kate L Harkness
- Department of Psychology, Queen's University, Kingston, ON, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, ON, Canada
| | - Raymond W Lam
- University of British Columbia and Vancouver Coastal Health Authority, Vancouver, BC, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest and Department of Medical Biophysics, University of Toronto, ON, Canada
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Departments of Psychiatry and Pharmacology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Susan Rotzinger
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, Krembil Research Centre, University Health Network, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, University of Toronto, Toronto, ON, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Glenda M MacQueen
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| |
Collapse
|
18
|
Kang K, Yoon U, Hong J, Jeong S, Ko PW, Lee SW, Lee HW. Amyloid Deposits and Idiopathic Normal-Pressure Hydrocephalus: An 18F-Florbetaben Study. Eur Neurol 2018; 79:192-199. [DOI: 10.1159/000487133] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Accepted: 01/23/2018] [Indexed: 01/21/2023]
Abstract
Background: The first aim of our study was to determine whether cortical 18F-florbetaben retention was different between healthy controls and idiopathic normal-pressure hydrocephalus (INPH) patients. Our second aim was to investigate whether there were any relationships between 18F-florbetaben retention and either hippocampal volume or clinical symptoms in INPH patients. Methods: Seventeen patients diagnosed with INPH and 8 healthy controls underwent studies with magnetic resonance imaging and 18F-florbetaben positron emission tomography imaging. Results: Automated region-of-interest analysis showed significant increases in 18F-florbetaben uptake in several brain regions in INPH patients compared to control subjects, with especially remarkable increases in the frontal (bilateral), parietal (bilateral), and occipital (bilateral) cortices. In the INPH group, right hippocampal volume was found to be negatively correlated with right frontal 18F-florbetaben retention. Korean-Mini Mental State Examination scores negatively correlated with right occipital 18F-florbetaben retention. Higher 18F-florbetaben retention correlated significantly with a higher Clinical Dementia Rating Scale score in the right occipital cortex. Conclusions: Our results indicate that INPH might be a disease exhibiting a characteristic pattern of cortical 18F-florbetaben retention. 18F-florbetaben retention in the frontal cortex may be related to hippocampal neuronal degeneration. Our findings may also help us understand the potential pathophysiology of cognitive impairments associated with INPH.
Collapse
|
19
|
Alizadeh M, Conklin CJ, Middleton DM, Shah P, Saksena S, Krisa L, Finsterbusch J, Faro SH, Mulcahey MJ, Mohamed FB. Identification of ghost artifact using texture analysis in pediatric spinal cord diffusion tensor images. Magn Reson Imaging 2017; 47:7-15. [PMID: 29154897 DOI: 10.1016/j.mri.2017.11.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2017] [Revised: 11/11/2017] [Accepted: 11/13/2017] [Indexed: 01/18/2023]
Abstract
PURPOSE Ghost artifacts are a major contributor to degradation of spinal cord diffusion tensor images. A multi-stage post-processing pipeline was designed, implemented and validated to automatically remove ghost artifacts arising from reduced field of view diffusion tensor imaging (DTI) of the pediatric spinal cord. METHOD A total of 12 pediatric subjects including 7 healthy subjects (mean age=11.34years) with no evidence of spinal cord injury or pathology and 5 patients (mean age=10.96years) with cervical spinal cord injury were studied. Ghost/true cords, labeled as region of interests (ROIs), in non-diffusion weighted b0 images were segmented automatically using mathematical morphological processing. Initially, 21 texture features were extracted from each segmented ROI including 5 first-order features based on the histogram of the image (mean, variance, skewness, kurtosis and entropy) and 16s-order feature vector elements, incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence matrices in directions of 0°, 45°, 90° and 135°. Next, ten features with a high value of mutual information (MI) relative to the pre-defined target class and within the features were selected as final features which were input to a trained classifier (adaptive neuro-fuzzy interface system) to separate the true cord from the ghost cord. RESULTS The implemented pipeline was successfully able to separate the ghost artifacts from true cord structures. The results obtained from the classifier showed a sensitivity of 91%, specificity of 79%, and accuracy of 84% in separating the true cord from ghost artifacts. CONCLUSION The results show that the proposed method is promising for the automatic detection of ghost cords present in DTI images of the spinal cord. This step is crucial towards development of accurate, automatic DTI spinal cord post processing pipelines.
Collapse
Affiliation(s)
- Mahdi Alizadeh
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States; Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, United States.
| | - Chris J Conklin
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Devon M Middleton
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Pallav Shah
- Department of Radiology, Temple University, Philadelphia, PA, United States
| | - Sona Saksena
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Laura Krisa
- Department of Occupational Therapy, Thomas Jefferson University, Philadelphia, PA, United States
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Scott H Faro
- Department of Radiology, Johns Hopkins University, Baltimore, MD, United States
| | - M J Mulcahey
- Department of Occupational Therapy, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B Mohamed
- Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, United States
| |
Collapse
|
20
|
Chang C, Huang C, Zhou N, Li SX, Ver Hoef L, Gao Y. The bumps under the hippocampus. Hum Brain Mapp 2017; 39:472-490. [PMID: 29058349 DOI: 10.1002/hbm.23856] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 10/09/2017] [Accepted: 10/11/2017] [Indexed: 12/27/2022] Open
Abstract
Shown in every neuroanatomy textbook, a key morphological feature is the bumpy ridges, which we refer to as hippocampal dentation, on the inferior aspect of the hippocampus. Like the folding of the cerebral cortex, hippocampal dentation allows for greater surface area in a confined space. However, examining numerous approaches to hippocampal segmentation and morphology analysis, virtually all published 3D renderings of the hippocampus show the inferior surface to be quite smooth or mildly irregular; we have rarely seen the characteristic bumpy structure on reconstructed 3D surfaces. The only exception is a 9.4T postmortem study (Yushkevich et al. [2009]: NeuroImage 44:385-398). An apparent question is, does this indicate that this specific morphological signature can only be captured using ultra high-resolution techniques? Or, is such information buried in the data we commonly acquire, awaiting a computation technique that can extract and render it clearly? In this study, we propose an automatic and robust super-resolution technique that captures the fine scale morphometric features of the hippocampus based on common 3T MR images. The method is validated on 9.4T ultra-high field images and then applied on 3T data sets. This method opens possibilities of future research on the hippocampus and other sub-cortical structural morphometry correlating the degree of dentation with a range of diseases including epilepsy, Alzheimer's disease, and schizophrenia. Hum Brain Mapp 39:472-490, 2018. © 2017 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Cheng Chang
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, New York, 11794
| | - Chuan Huang
- Department of Radiology, Stony Brook University, Stony Brook, New York, 11794.,Department of Psychiatry, Stony Brook University, Stony Brook, New York, 11794
| | - Naiyun Zhou
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, 11794
| | - Shawn Xiang Li
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China
| | - Lawrence Ver Hoef
- Department of Neurology, The University of Alabama at Birmingham, CIRC 312, Birmingham, Alabama, 35294.,Epilepsy center, The University of Alabama at Birmingham, CIRC 312, Birmingham, Alabama, 35294
| | - Yi Gao
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, 518060, China.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, 11794
| |
Collapse
|
21
|
Hippocampus Segmentation Based on Local Linear Mapping. Sci Rep 2017; 7:45501. [PMID: 28368016 PMCID: PMC5377362 DOI: 10.1038/srep45501] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 03/01/2017] [Indexed: 01/18/2023] Open
Abstract
We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.
Collapse
|
22
|
Level Set Based Hippocampus Segmentation in MR Images with Improved Initialization Using Region Growing. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:5256346. [PMID: 28191031 PMCID: PMC5274694 DOI: 10.1155/2017/5256346] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Revised: 12/10/2016] [Accepted: 12/22/2016] [Indexed: 02/05/2023]
Abstract
The hippocampus has been known as one of the most important structures referred to as Alzheimer's disease and other neurological disorders. However, segmentation of the hippocampus from MR images is still a challenging task due to its small size, complex shape, low contrast, and discontinuous boundaries. For the accurate and efficient detection of the hippocampus, a new image segmentation method based on adaptive region growing and level set algorithm is proposed. Firstly, adaptive region growing and morphological operations are performed in the target regions and its output is used for the initial contour of level set evolution method. Then, an improved edge-based level set method utilizing global Gaussian distributions with different means and variances is developed to implement the accurate segmentation. Finally, gradient descent method is adopted to get the minimization of the energy equation. As proved by experiment results, the proposed method can ideally extract the contours of the hippocampus that are very close to manual segmentation drawn by specialists.
Collapse
|
23
|
Hosseini MP, Nazem-Zadeh MR, Pompili D, Jafari-Khouzani K, Elisevich K, Soltanian-Zadeh H. Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients. Med Phys 2016; 43:538. [PMID: 26745947 DOI: 10.1118/1.4938411] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus. METHODS A template database of 195 (81 males, 114 females; age range 32-67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark. RESULTS Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, -4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than LocalInfo, FreeSurfer, and hammer, respectively. The Bland-Altman similarity analysis revealed a low bias for the ABSS and LocalInfo techniques compared to the others. CONCLUSIONS The ABSS method for automated hippocampal segmentation outperformed other methods, best approximating what could be achieved by manual tracing. This study also shows that four categories of input data can cause automated segmentation methods to fail. They include incomplete studies, artifact, low signal-to-noise ratio, and inhomogeneity. Different scanner platforms and pulse sequences were considered as means by which to improve reliability of the automated methods. Other modifications were specially devised to enhance a particular method assessed in this study.
Collapse
Affiliation(s)
- Mohammad-Parsa Hosseini
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854 and Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202
| | - Mohammad-Reza Nazem-Zadeh
- Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202
| | - Dario Pompili
- Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, New Jersey 08854
| | - Kourosh Jafari-Khouzani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02129
| | - Kost Elisevich
- Department of Clinical Neuroscience, Spectrum Health System, Grand Rapids, Michigan 49503 and Division of Neurosurgery, College of Human Medicine, Michigan State University, Grand Rapids, Michigan 49503
| | - Hamid Soltanian-Zadeh
- Medical Image Analysis Laboratory, Departments of Radiology and Research Administration, Henry Ford Health System, Detroit, Michigan 48202; Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran 1439957131, Iran; and School of Cognitive Sciences, Institute for Studies in Theoretical Physics and Mathematics (IPM), Tehran 1954856316, Iran
| |
Collapse
|
24
|
Amoroso N, Errico R, Bruno S, Chincarini A, Garuccio E, Sensi F, Tangaro S, Tateo A, Bellotti R. Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool. Phys Med Biol 2015; 60:8851-67. [PMID: 26531765 DOI: 10.1088/0031-9155/60/22/8851] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
25
|
Dolz J, Massoptier L, Vermandel M. Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey. Ing Rech Biomed 2015. [DOI: 10.1016/j.irbm.2015.06.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
|
26
|
Yun HJ, Kwak K, Lee JM. Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism. PLoS One 2015; 10:e0129250. [PMID: 26061669 PMCID: PMC4463854 DOI: 10.1371/journal.pone.0129250] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 05/06/2015] [Indexed: 01/18/2023] Open
Abstract
Structural MR image (MRI) and 18F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) have been widely employed in diagnosis of both Alzheimer’s disease (AD) and mild cognitive impairment (MCI) pathology, which has led to the development of methods to distinguish AD and MCI from normal controls (NC). Synaptic dysfunction leads to a reduction in the rate of metabolism of glucose in the brain and is thought to represent AD progression. FDG-PET has the unique ability to estimate glucose metabolism, providing information on the distribution of hypometabolism. In addition, patients with AD exhibit significant neuronal loss in cerebral regions, and previous AD research has shown that structural MRI can be used to sensitively measure cortical atrophy. In this paper, we introduced a new method to discriminate AD from NC based on complementary information obtained by FDG and MRI. For accurate classification, surface-based features were employed and 12 predefined regions were selected from previous studies based on both MRI and FDG-PET. Partial least square linear discriminant analysis was employed for making diagnoses. We obtained 93.6% classification accuracy, 90.1% sensitivity, and 96.5% specificity in discriminating AD from NC. The classification scheme had an accuracy of 76.5% and sensitivity and specificity of 46.5% and 89.6%, respectively, for discriminating MCI from AD. Our method exhibited a superior classification performance compared with single modal approaches and yielded parallel accuracy to previous multimodal classification studies using MRI and FDG-PET.
Collapse
Affiliation(s)
- Hyuk Jin Yun
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Kichang Kwak
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
- * E-mail:
| | | |
Collapse
|
27
|
Dill V, Franco AR, Pinho MS. Automated Methods for Hippocampus Segmentation: the Evolution and a Review of the State of the Art. Neuroinformatics 2014; 13:133-50. [DOI: 10.1007/s12021-014-9243-4] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|