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Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation

Authors:

Jieping YeAuthors Info & Claims

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1335 - 1344

https://doi.org/10.1145/1557019.1557162

Published: 28 June 2009 Publication History

Abstract

Effective diagnosis of Alzheimer's disease (AD), the most common type of dementia in elderly patients, is of primary importance in biomedical research. Recent studies have demonstrated that AD is closely related to the structure change of the brain network, i.e., the connectivity among different brain regions. The connectivity patterns will provide useful imaging-based biomarkers to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. In this paper, we investigate the sparse inverse covariance estimation technique for identifying the connectivity among different brain regions. In particular, a novel algorithm based on the block coordinate descent approach is proposed for the direct estimation of the inverse covariance matrix. One appealing feature of the proposed algorithm is that it allows the user feedback (e.g., prior domain knowledge) to be incorporated into the estimation process, while the connectivity patterns can be discovered automatically. We apply the proposed algorithm to a collection of FDG-PET images from 232 NC, MCI, and AD subjects. Our experimental results demonstrate that the proposed algorithm is promising in revealing the brain region connectivity differences among these groups.

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Cited By

HEYER SDOYEN MVERGER A(2024)Metabolic brain connectivity reorganization in Alzheimer's disease patients: a systematic reviewThe Quarterly Journal of Nuclear Medicine and Molecular Imaging10.23736/S1824-4785.24.03570-2Online publication date: Jul-2024
https://doi.org/10.23736/S1824-4785.24.03570-2
Pan JZuo QWang BChen CLei BWang S(2024)DecGAN: Decoupling Generative Adversarial Network for Detecting Abnormal Neural Circuits in Alzheimer's DiseaseIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.34164205:10(5050-5063)Online publication date: Oct-2024
https://doi.org/10.1109/TAI.2024.3416420
Cao TLiu XDu ZZhou JZheng JXu L(2024)A Diagonal-Structured-State-Space-Sequence-Model-Based Deep Learning Framework for Effective Diagnosis of Mild Cognitive ImpairmentIEEE Sensors Journal10.1109/JSEN.2024.338710324:10(16734-16743)Online publication date: 15-May-2024
https://doi.org/10.1109/JSEN.2024.3387103
Show More Cited By

Index Terms

Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

June 2009

1426 pages

ISBN:9781605584959

DOI:10.1145/1557019

General Chairs:
John Elder
Elder Research, Inc., USA
,
Françoise Soulié Fogelman
KXEN, France
,
Program Chairs:
Peter Flach
University of Bristol, UK
,
Mohammed Zaki
RPI, USA

Copyright © 2009 ACM.

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Published: 28 June 2009

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KDD09

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

June 28 - July 1, 2009

Paris, France

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

HEYER SDOYEN MVERGER A(2024)Metabolic brain connectivity reorganization in Alzheimer's disease patients: a systematic reviewThe Quarterly Journal of Nuclear Medicine and Molecular Imaging10.23736/S1824-4785.24.03570-2Online publication date: Jul-2024
https://doi.org/10.23736/S1824-4785.24.03570-2
Pan JZuo QWang BChen CLei BWang S(2024)DecGAN: Decoupling Generative Adversarial Network for Detecting Abnormal Neural Circuits in Alzheimer's DiseaseIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.34164205:10(5050-5063)Online publication date: Oct-2024
https://doi.org/10.1109/TAI.2024.3416420
Cao TLiu XDu ZZhou JZheng JXu L(2024)A Diagonal-Structured-State-Space-Sequence-Model-Based Deep Learning Framework for Effective Diagnosis of Mild Cognitive ImpairmentIEEE Sensors Journal10.1109/JSEN.2024.338710324:10(16734-16743)Online publication date: 15-May-2024
https://doi.org/10.1109/JSEN.2024.3387103
Sadiq AYahya NTang THashim HNaseem I(2022)Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer’s DiseaseSensors10.3390/s2209310222:9(3102)Online publication date: 19-Apr-2022
https://doi.org/10.3390/s22093102
Abdelaziz MWang TElazab A(2022)Fusing Multimodal and Anatomical Volumes of Interest Features Using Convolutional Auto-Encoder and Convolutional Neural Networks for Alzheimer’s Disease DiagnosisFrontiers in Aging Neuroscience10.3389/fnagi.2022.81287014Online publication date: 28-Apr-2022
https://doi.org/10.3389/fnagi.2022.812870
Chai XDi YFeng ZGuan YZhang GLi ALuo Q(2022)Deep learning‐based large‐scale named entity recognition for anatomical region of mammalian brainQuantitative Biology10.15302/J-QB-022-030210:3(253-263)Online publication date: Sep-2022
https://doi.org/10.15302/J-QB-022-0302
Su YQian ZHe LKong XZhang ARangwala H(2022)ERNetProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539227(1666-1675)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539227
Xue YZhang YZhang LLee SQiao LShen D(2022)Learning Brain Functional Networks With Latent Temporal Dependency for MCI IdentificationIEEE Transactions on Biomedical Engineering10.1109/TBME.2021.310201569:2(590-601)Online publication date: Feb-2022
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Su YDai XHe LKong X(2022)ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00057(468-477)Online publication date: Nov-2022
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Tang SCao PHuang MLiu XZaiane O(2022)Dual feature correlation guided multi-task learning for Alzheimer's disease predictionComputers in Biology and Medicine10.1016/j.compbiomed.2021.105090140:COnline publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1016/j.compbiomed.2021.105090
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