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477 results on '"Jiang, Jiehui"'

1. Dynamic proportional loss of functional connectivity revealed change of left superior frontal gyrus in subjective cognitive decline: an explanatory study based on Chinese and Western cohorts

Author

Wang, Luyao, Hu, Wenjing, Dong, Fan, Sheng, Can, Wu, Jinglong, Han, Ying, Jiang, Jiehui, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Jagust, William, Trojanowski, John Q., Toga, Arthur W., Beckett, Laurel, Green, Robert C., Saykin, Andrew J., Morris, John, Shaw, Leslie M., Khachaturian, Zaven, Sorensen, Greg, Kuller, Lew, Raichle, Marcus, Paul, Steven, Davies, Peter, Fillit, Howard, Hefti, Franz, Holtzman, David, Mesulam, Marek M., Potter, William, Snyder, Peter, Schwartz, Adam, Montine, Tom, Thomas, Ronald G., Donohue, Michael, Walter, Sarah, Gessert, Devon, Sather, Tamie, Jiminez, Gus, Harvey, Danielle, Bernstein, Matthew, Thompson, Paul, Schuff, Norbert, Borowski, Bret, Gunter, Jeff, Senjem, Matt, Vemuri, Prashanthi, Jones, David, Kantarci, Kejal, Ward, Chad, Koeppe, Robert A., Foster, Norm, Reiman, Eric M., Chen, Kewei, Mathis, Chet, Landau, Susan, Cairns, Nigel J., Householder, Erin, Taylor-Reinwald, Lisa, Lee, Virginia, Korecka, Magdalena, Figurski, Michal, Crawford, Karen, Neu, Scott, Foroud, Tatiana M., Potkin, Steven G., Shen, Li, Faber, Kelley, Kim, Sungeun, Nho, Kwangsik, Thal, Leon, Buckholtz, Neil, Albert, Marylyn, Frank, Richard, Hsiao, John, Kaye, Jeffrey, Quinn, Joseph, Lind, Betty, Carter, Raina, Dolen, Sara, Schneider, Lon S., Pawluczyk, Sonia, Beccera, Mauricio, Teodoro, Liberty, Spann, Bryan M., Brewer, James, Vanderswag, Helen, Fleisher, Adam, Heidebrink, Judith L., Lord, Joanne L., Mason, Sara S., Albers, Colleen S., Knopman, David, Johnson, Kris, Doody, Rachelle S., Villanueva-Meyer, Javier, Chowdhury, Munir, Rountree, Susan, Dang, Mimi, Stern, Yaakov, Honig, Lawrence S., Bell, Karen L., Ances, Beau, Carroll, Maria, Leon, Sue, Mintun, Mark A., Schneider, Stacy, Oliver, Angela, Marson, Daniel, Griffith, Randall, Clark, David, Geldmacher, David, Brockington, John, Roberson, Erik, Grossman, Hillel, Mitsis, Effie, de Toledo-Morrell, Leyla, Shah, Raj C., Duara, Ranjan, Varon, Daniel, Greig, Maria T., Roberts, Peggy, Onyike, Chiadi, D’Agostino, Daniel, Kielb, Stephanie, Galvin, James E., Cerbone, Brittany, Michel, Christina A., Rusinek, Henry, de Leon, Mony J., Glodzik, Lidia, De Santi, Susan, Doraiswamy, PMurali, Petrella, Jeffrey R., Wong, Terence Z., Arnold, Steven E., Karlawish, Jason H., Wolk, David, Smith, Charles D., Jicha, Greg, Hardy, Peter, Sinha, Partha, Oates, Elizabeth, Conrad, Gary, Lopez, Oscar L., Oakley, MaryAnn, Simpson, Donna M., Porsteinsson, Anton P., Goldstein, Bonnie S., Martin, Kim, Makino, Kelly M., Ismail, MSaleem, Brand, Connie, Mulnard, Ruth A., Thai, Gaby, McAdams-Ortiz, Catherine, Womack, Kyle, Mathews, Dana, Quiceno, Mary, Diaz-Arrastia, Ramon, King, Richard, Weiner, Myron, Martin-Cook, Kristen, DeVous, Michael, Levey, Allan I., Lah, James J., Cellar, Janet S., Burns, Jeffrey M., Anderson, Heather S., Swerdlow, Russell H., Apostolova, Liana, Tingus, Kathleen, Woo, Ellen, Silverman, Daniel H. S., Lu, Po H., Bartzokis, George, Graff-Radford, Neill R., Parfitt, Francine, Kendall, Tracy, Johnson, Heather, Farlow, Martin R., Hake, Ann Marie, Matthews, Brandy R., Herring, Scott, Hunt, Cynthia, van Dyck, Christopher H., Carson, Richard E., MacAvoy, Martha G., Chertkow, Howard, Bergman, Howard, Hosein, Chris, Hsiung, Ging-Yuek Robin, Feldman, Howard, Mudge, Benita, Assaly, Michele, Bernick, Charles, Munic, Donna, Kertesz, Andrew, Rogers, John, Trost, Dick, Kerwin, Diana, Lipowski, Kristine, Wu, Chuang-Kuo, Johnson, Nancy, Sadowsky, Carl, Martinez, Walter, Villena, Teresa, Turner, Raymond Scott, Johnson, Kathleen, Reynolds, Brigid, Sperling, Reisa A., Johnson, Keith A., Marshall, Gad, Frey, Meghan, Lane, Barton, Rosen, Allyson, Tinklenberg, Jared, Sabbagh, Marwan N., Belden, Christine M., Jacobson, Sandra A., Sirrel, Sherye A., Kowall, Neil, Killiany, Ronald, Budson, Andrew E., Norbash, Alexander, Johnson, Patricia Lynn, Allard, Joanne, Lerner, Alan, Ogrocki, Paula, Hudson, Leon, Fletcher, Evan, Carmichae, Owen, Olichney, John, DeCarli, Charles, Kittur, Smita, Borrie, Michael, Lee, T.-Y., Bartha, Rob, Johnson, Sterling, Asthana, Sanjay, Carlsson, Cynthia M., Preda, Adrian, Nguyen, Dana, Tariot, Pierre, Reeder, Stephanie, Bates, Vernice, Capote, Horacio, Rainka, Michelle, Scharre, Douglas W., Kataki, Maria, Adeli, Anahita, Zimmerman, Earl A., Celmins, Dzintra, Brown, Alice D., Pearlson, Godfrey D., Blank, Karen, Anderson, Karen, Santulli, Robert B., Kitzmiller, Tamar J., Schwartz, Eben S., Sink, Kaycee M., Williamson, Jeff D., Garg, Pradeep, Watkins, Franklin, Ott, Brian R., Querfurth, Henry, Tremont, Geoffrey, Salloway, Stephen, Malloy, Paul, Correia, Stephen, Rosen, Howard J., Miller, Bruce L., Mintzer, Jacobo, Spicer, Kenneth, Bachman, David, Pasternak, Stephen, Rachinsky, Irina, Drost, Dick, Pomara, Nunzio, Hernando, Raymundo, Sarrael, Antero, Schultz, Susan K., Ponto, Laura L. Boles, Shim, Hyungsub, Smith, Karen Elizabeth, Relkin, Norman, Chaing, Gloria, Raudin, Lisa, Smith, Amanda, Fargher, Kristin, Raj, Balebail Ashok, Neylan, Thomas, Grafman, Jordan, Davis, Melissa, Morrison, Rosemary, Hayes, Jacqueline, Finley, Shannon, Friedl, Karl, Fleischman, Debra, Arfanakis, Konstantinos, James, Olga, Massoglia, Dino, Fruehling, JJay, Harding, Sandra, Peskind, Elaine R., Petrie, Eric C., Li, Gail, Yesavage, Jerome A., Taylor, Joy L., and Furst, Ansgar J.

Published
2025
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10. Glucose metabolism patterns: A potential index to characterize brain ageing and predict high conversion risk into cognitive impairment

Author

Jiang, Jiehui, Sheng, Can, Chen, Guanqun, Liu, Chunhua, Jin, Shichen, Li, Lanlan, Jiang, Xueyan, and Han, Ying

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Genetics, Biomedical Imaging, Neurodegenerative, Prevention, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Alzheimer's Disease, Neurosciences, Dementia, Brain Disorders, Aging, Neurological, Mental health, Humans, Aged, Alzheimer Disease, Positron-Emission Tomography, Cognitive Dysfunction, Fluorodeoxyglucose F18, Brain, Glucose, Pattern, Brain ageing, Positron emission tomography, Glucose metabolism, Alzheimer’s Disease Neuroimaging Initiative, Clinical sciences
Abstract

Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p

Published
2022

22. Traditional Chinese Medicine Information Analysis Based on Multi-task Joint Learning Model

Author

Hu, Chenyuan, Yan, Zhuangzhi, Jiang, Jiehui, Zhang, Shuoyan, Gu, Tianyu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yao, Jian, editor, Xiao, Yang, editor, You, Peng, editor, and Sun, Guang, editor

Published
2022
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23. TongueCaps: A Model for the Multiclassification of Tongue Color

Author

Ni, Jinghong, Yan, Zhuangzhi, Jiang, Jiehui, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yao, Jian, editor, Xiao, Yang, editor, You, Peng, editor, and Sun, Guang, editor

Published
2022
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24. Prediction of Apoplexy Syndrome Based on Graph Neural Network

Author

Zhang, Shuoyan, Yan, Zhuangzhi, Jiang, Jiehui, Gu, Tianyu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Yao, Jian, editor, Xiao, Yang, editor, You, Peng, editor, and Sun, Guang, editor

Published
2022
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31. Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer’s Disease

Author

Zhou, Hucheng, Jiang, Jiehui, Lu, Jiaying, Wang, Min, Zhang, Huiwei, Zuo, Chuantao, and Initiative, Alzheimer’s Disease Neuroimaging

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Aging, Acquired Cognitive Impairment, Clinical Research, Alzheimer's Disease, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Neurodegenerative, Bioengineering, Dementia, Biomedical Imaging, Brain Disorders, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Good Health and Well Being, Alzheimer's disease, mild cognitive impairment, radiomics, image fusion, Cox model, Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s disease, Psychology, Cognitive Sciences, Biological psychology
Abstract

Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

Published
2019

34. Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease.

Author

Zhou, Hucheng, Jiang, Jiehui, Lu, Jiaying, Wang, Min, Zhang, Huiwei, Zuo, Chuantao, and Alzheimer’s Disease Neuroimaging Initiative

Subjects
Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s disease, Cox model, image fusion, mild cognitive impairment, radiomics, Alzheimer's disease, Acquired Cognitive Impairment, Aging, Neurosciences, Biomedical Imaging, Bioengineering, Prevention, Alzheimer's Disease, Neurodegenerative, Brain Disorders, Clinical Research, Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Neurological, Psychology, Cognitive Sciences
Abstract

Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

Published
2018

38. A low-carbon supply chain pricing mechanism considering CSR under carbon cap-and-trade policy.

Author

Zou, Hao, Xiao, Jin, Lou, Yuanyuan, Liao, Dan, Deng, Huwei, and Jiang, Jiehui

Subjects
ENVIRONMENTAL degradation, SOCIAL responsibility of business, CONSUMER preferences, GREENHOUSE gas mitigation, EMISSIONS trading, CARBON offsetting, CARBON nanofibers
Abstract

In the context of environmental deterioration and people's growing environmental protection awareness, governments or regions have put forward corresponding carbon emission reduction policies. Among them, the carbon trading mechanism, as an effective means to promote enterprises to implement emission reduction measures, plays a crucial role in regulating enterprise behavior and promoting social sustainable development. Since various industries and sectors support each other in social and economic development, it is more reasonable to study the carbon emission reduction optimization decisions of society and enterprises from the perspective of the supply chain. To achieve the carbon reduction target of the supply chain system, manufacturing enterprises usually need to incur additional costs to invest in emission reduction technologies, and retail enterprises also need to conduct low-carbon publicity to increase product market share. On one hand, considering the impact of the government's emission reduction constraints and consumers' low-carbon preferences, manufacturers will take corporate social responsibility (CSR) into consideration to enhance product competitiveness. On the other hand, smaller retailers are more concerned about being treated fairly than about their own profits due to the extra cost of low-carbon advertising. In this paper, considering the background of carbon trading, the manufacturer's CSR and retailer's fairness concern behavior are introduced into the decision-making process of the low-carbon supply chain (LCSC), and the relevant emission reduction decision-making model is constructed by using Stackelberg game theory and backward derivation method. Through comparative analysis of relevant parameters, members' profits and utilities, this paper focuses on the influence of CSR and fairness concerns on system decision-making. The results show that the optimal way for LCSC decision-making is to cooperate with fair-concerned retailers and manufacturers with CSR. When manufacturers consider social responsibility within a certain range and retailers bear part of the cost of social responsibility as followers, it can not only effectively improve the emission reduction level of the supply chain and the profits of each entity, but also help to increase the enthusiasm of each entity for carbon emission reduction and the overall social welfare. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Study of amyloid-β peptide functional brain networks in AD, MCI and HC

Author

Jiang, Jiehui, Duan, Huoqiang, Huang, Zheming, Yu, Zhihua, and Initiative, The Alzheimer’s Disease Neuroimaging

Subjects
Engineering, Biomedical Engineering, Brain Disorders, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Alzheimer's Disease, Neurosciences, Acquired Cognitive Impairment, Neurodegenerative, Dementia, Biomedical Imaging, 2.1 Biological and endogenous factors, Neurological, Aged, Aged, 80 and over, Alzheimer Disease, Amyloid beta-Peptides, Brain, Cognitive Dysfunction, Female, Humans, Male, Positron-Emission Tomography, Alzheimer's disease, amyloid-beta peptide, carbon 11-labeled pittsburgh compound B, positron emission tomography, brain networks, Alzheimer's Disease Neuroimaging Initiative, Alzheimer’s disease, amyloid-β peptide, Biomedical engineering
Abstract

One medical challenge in studying the amyloid-β (Aβ) peptide mechanism for Alzheimer's disease (AD) is exploring the law of beta toxic oligomers' diffusion in human brains in vivo. One beneficial means of solving this problem is brain network analysis based on graph theory. In this study, the characteristics of Aβ functional brain networks of Healthy Control (HC), Mild Cognitive Impairment (MCI), and AD groups were compared by applying graph theoretical analyses to Carbon 11-labeled Pittsburgh compound B positron emission tomography (11C PiB-PET) data. 120 groups of PiB-PET images from the ADNI database were analyzed. The results showed that the small-world property of MCI and AD were lost as compared to HC. Furthermore, the local clustering of networks was higher in both MCI and AD as compared to HC, whereas the path length was similar among the three groups. The results also showed that there could be four potential Aβ toxic oligomer seeds: Frontal_Sup_Medial_L, Parietal_Inf_L, Frontal_Med_Orb_R, and Parietal_Inf_R. These four seeds are corresponding to Regions of Interests referred by physicians to clinically diagnose AD.

Published
2015

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