Learning brain connectivity of Alzheimer’s disease by sparse inverse covariance estimation
By Shuai Huang, Jing Li, Liang Sun, Jieping Ye, Adam Fleisher, Teresa Wu, Kewei Chen, Eric M. Reiman. NeuroImage, 2010
346 citations recorded by the source corpus.
Rapid advances in neuroimaging techniques provide great potentials for study of Alzheimer’s disease (AD). Existing findings have shown that AD is closely related to alteration in the functional brain network, i.e., the functional connectivity between different brain regions. In this paper, we propose a method based on sparse inverse covariance estimation (SICE) to identify functional brain connectivity networks from PET data. Our method is able to identify both the connectivity network structure and strength for a large number o f brain regions with small sample sizes. We apply the proposed method to the PET data of AD, mild cognitive impairment (MCI), and normal control (NC) subjects. Compared with NC, AD shows decrease in the amount of inter-region functional connectivity within the temporal lobe especially between the area around hippocampus and other regions and increase in the amount of connectivity within the frontal lobe as well as between the parietal and occipital lobes. Also, AD shows weaker between-lobe connectivity than within-lobe connectivity and weaker between-hemisphere connectivity, compared with NC. In addition to being a method for knowledge discovery about AD, the proposed SICE method can also be used for classifying new subjects, which makes it a suitable approach for novel connectivity-based AD biomarker identification. Our experiments show that the best sensitivity and specificity our method can achieve in AD vs. NC classification are 88% and 88%, respectively.
Subfields: Cognitive Neuroscience, Psychiatry and Mental health, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/dc758bcee8cd75e8f9eec9eee24e9449df4eb34e
How researchers see this paper
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In Cognitive Neuroscience
- Lourens J. Waldorp
- Jonas M. B. Haslbeck
- Sacha Epskamp
- Denny Borsboom
- Richard J. McNally
- Hudson F. Golino
In Psychiatry and Mental health
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- The structural-functional connectome and the default mode network of the human brain (2014, neutral): “Similar approaches have been used on fMRI-data (Huang et al., 2010; Ryali et al., 2010; Varoquaux and Craddock, 2013; Varoquaux et al., 2010; Wee et al., in press) and in other domains, e.g. to analyze genetic datasets (Stifanelli et al., 2011; Witten and Tibshirani, 2009).”
- Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, neutral): “In previous studies, the critical regularization parameters which influence the selection of connections between brain regions were set arbitrarily (Huang et al., 2010; Smith et al., 2011).”
- mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2015, background): “A P ] 12 F e b 2020 Drake, Lusis, and Horvath 2006), neuroscience (Huang, Li, Sun, Ye, Fleisher, Wu, Chen, and Reiman 2010) and psychology (Borsboom and Cramer 2013).”
- How well do network models predict observations? On the importance of predictability in network models (2016, neutral): “…systems in a large variety of disciplines such as statistical mechanics (Albert & Barabasi, 2002), biology (Friedman, Linial, Nachman, & Pe’er, 2000), neuroscience (Huang et al., 2010), and are recently also applied in psychology (Costantini et al., 2015) and psychiatry (Borsboom & Cramer, 2013).”
- The Alzheimer’s Disease Neuroimaging Initiative: A review of papers published since its inception (2011, neutral): “1027 rs2075650 TOMM40 Translocase of outer mitochondrial membrane Protein transport across mitochondrial membrane ,1027 ADNI (96 AD, 176 MCI, 102 CN) Ab242, ptau181/ Ab242, t2tau/Ab242 Yes GWAS [149] rs439401 LOC100129500 Unknown function, overlaps with APOE Unknown ,1027 Ab242 rs2121433, rs1374441,”