journal article·2023-03-01Open access

Depressed MEG causality analysis based on polynomial kernel Granger causality

Jing Qian, Wenpo Yao, Dengxuan Bai, Qiong Wang, Shuwang Wang, Ang Zhou, Wei Yan, Jun Wang

AIP Advances· Volume 13· Issue 3

Abstract

In this study, we employ the Granger causality of a polynomial kernel to identify the coupling causality of depressed magnetoencephalography (MEG). We collect MEG under positive, neutral, and negative emotional stimuli and focus on the β-band activities. According to test results, depressed people display stronger left–right symmetrical interconnection in their prefrontal and occipital lobes under nonpositive stimuli(namely neutral and negative stimuli), indicating that they are more sensitive to nonpositive stimuli. The intensity of the right occipital information flow is higher in depressed people. We also see the Granger causality index increased in the occipital–frontal areas of depressed patients under negative stimuli. In general, detecting the polynomial kernel Granger causality of the MEG can effectively characterize the strength of the interconnected brain regions in depressed patients, which can be used as a clinical diagnosis aid.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Functional Brain Connectivity Studies · Advanced MRI Techniques and Applications · Neural dynamics and brain function

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Geographic and Biologic Information, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications 1 , Nanjing 210023, China

School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications 2 , Nanjing 210003, China

Jinling Hospital, Medical School of Nanjing University 3 , Nanjing 210002, Jiangsu, China

Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University 4 , Nanjing 210029, China

Funding

Postgraduate Research and Practice Innovation Program of Jiangsu Province · Shandong Provincial Key Laboratory of Biophysics

References (38)

Show source-provided references
1. Clinical, functional, and biological correlates of cognitive dimensions in major depressive disorder–rationale, design, and characteristics of the cognitive function and mood study (CoFaM-study)10.3389/fpsyt.2016.00150 ↗
2. The detection and consequences of anxiety in clinical depression10.4088/jcp.v58n0307d ↗
3. The current status of the diagnosis of depression
4. Adherence to evidence-based guidelines for depression and anxiety disorders is associated with recording of the diagnosis10.1016/j.genhosppsych.2009.05.011 ↗
5. Structural brain abnormalities in major depressive disorder: A selective review of recent MRI studies10.1016/j.jad.2008.11.021 ↗
6. Research progress in brain imaging and nuclear brain imaging technology of depression10.12114/j.issn.1007-9572.2021.00.441 ↗
7. Beamformer analysis of meg data10.1016/s0074-7742(05)68006-3 ↗
8. Magnetoencephalography and magnetic source imaging: Technology overview and applications in psychiatric neuroimaging10.1017/s1092852900001218 ↗
9. Abnormal oscillatory brain dynamics in schizophrenia: A sign of deviant communication in neural network?10.1186/1471-244x-7-44 ↗
10. Source distribution of neuromagnetic slow wave activity in schizophrenic and depressive patients10.1016/s1388-2457(03)00210-4 ↗
11. Aberrant functional organization and maturation in early-onset psychosis: Evidence from magnetoencephalography10.1016/j.pscychresns.2007.01.004 ↗
12. Cortical excitability and rest activity properties in patients with depression
13. Small frontal gray matter volume in first-episode depression patients
14. Investigating causal relations by econometric models and cross-spectral methods10.2307/1912791 ↗
15. Granger causality and transfer entropy are equivalent for Gaussian variables10.1103/physrevlett.103.238701 ↗
16. Connectivity analysis of human functional MRI data: From linear to nonlinear and static to dynamic
17. Escaping the curse of dimensionality in estimating multivariate transfer entropy10.1103/PhysRevLett.108.258701 ↗
18. Nonlinear discriminant analysis using kernel functions
19. Geometric measures and properties of commonly used Kernel functions
20. Kernel method for nonlinear Granger causality10.1103/physrevlett.100.144103 ↗
21. Kernel-Granger causality and the analysis of dynamical networks10.1103/physreve.77.056215 ↗
22. Emotional face expressions are differentiated with brain oscillations10.1016/j.ijpsycho.2006.07.003 ↗
23. Event-related beta oscillations are affected by emotional eliciting stimuli10.1016/j.neulet.2010.08.002 ↗
24. Depression uncouples brain hate circuit10.1038/mp.2011.127 ↗
25. Frontal lobe executive function of patients with major depression and OCD
26. Information theory and an extension of the maximum likelihood principle10.1007/978-1-4612-1694-0_15 ↗
27. A new strategy for model order identification and its application to transfer entropy for EEG signals analysis10.1109/TBME.2012.2234125 ↗
28. Aberrant brain activation during a working memory task in psychotic major depression10.1176/appi.ajp.2010.09121718 ↗
29. Cognitive control and brain resources in major depression: An fMRI study using the n-back task10.1016/j.neuroimage.2005.02.048 ↗
30. Neural basis of depression related to a dominant right hemisphere: A resting-state fMRI study10.1155/2018/5024520 ↗
31. Interhemispheric cerebral asymmetry detected by VEPS in diabetic patients with recognized depression
32. Imbalance between left and right dorsolateral prefrontal cortex in major depression is linked to negative emotional judgment: An fMRI study in severe major depressive disorder10.1016/j.biopsych.2007.05.033 ↗
33. Neural mechanisms of the cognitive model of depression10.1038/nrn3027 ↗
34. Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field10.1016/j.clinph.2005.06.011 ↗
35. Quantifying time irreversibility using probabilistic differences between symmetric permutations10.1016/j.physleta.2018.11.043 ↗
36. Permutation-based time irreversibility in epileptic electroencephalograms10.1007/s11071-020-05506-9 ↗
37. Molecular mechanisms for synchronous, asynchronous, and spontaneous neurotransmitter release10.1146/annurev-physiol-021113-170338 ↗
38. The action potential in mammalian central neurons10.1038/nrn2148 ↗