Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder.

By Junran Zhang, Jinhui Wang, Qizhu Wu, W. Kuang, Xiaoqi Huang, Yong He, Qiyong Gong. Biological Psychiatry, 2011

937 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/e904681667a1b3ea1b1185ca5dbf9c23272b5081

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Yong He
2. Jinhui Wang
3. Alan C. Evans
4. Wojciech Samek
5. K. Müller
6. Olaf Sporns
7. Mingrui Xia
8. Xuhong Liao

In Experimental and Cognitive Psychology

1. Yong He
2. Jinhui Wang
3. Alan C. Evans
4. Kaustubh Supekar
5. Mingrui Xia
6. Srikanth Ryali
7. Xuhong Liao
8. Shelli R. Kesler

Citations in the corpus

• Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, methodology): “For example, several studies have used the pseudoinverse method for estimating partial correlations at the whole brain level (Balenzuela et al., 2010; Liang et al., 2011; Liu et al., 2008; Yu et al., 2011; Zhang et al., 2011).”
• GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, methodology): “Finally, GRETNA can also calculate the area under the curve (AUC) for each network measure to provide a scalar that does not depend on specific threshold selection (Wang et al., 2009; Zhang et al., 2011).”
• BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics (2013, background): “In addition, some of these figures were selected as cover images for several high-level neuroscience journals [51,84,87,92,93].”
• Interpretable LSTMs For Whole-Brain Neuroimaging Analyses (2018, background): “For example, to identify the complex patterns of brain activity underlying many psychiatric disorders (such as depression [59] or schizophrenia [60]).”
• Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, neutral): “, 2010a, 2012c), depression (Bai et al., 2012; Zhang et al., 2011), autism (Li et al.”
• GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, background): “In addition, resting state fMRI connectivity studies are sensitive to abnormal global network organization, revealing changes in several clinical populations with cognitive deficits including Alzheimer’s disease [5,9], schizophrenia [70,71], traumatic brain injury [72], temporal lobe epilepsy [73], ”