Respective changes in resting-state linear and nonlinear measures in major depressive disorder (MDD) have been reported. However, few studies have used integrated measures of linear and nonlinear brain dynamics to explore the pathological mechanisms underlying MDD.
Method:
Forty-two patients with MDD and 42 sex- and age-matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging to calculate multiscale entropy (MSE) and regional homogeneity (ReHo). The MSE-ReHo coupling of the whole gray matter and the MSE/ReHo ratio (the complexity of intensity homogeneity per unit time series) of each voxel were compared between the two groups. To evaluate the discriminative capacity of ratio features between patients with MDD and HC, we employed the support vector machine (SVM) learning method.
Results:
We observed that patients with MDD displayed increased MSE/ReHo ratio mainly in the orbitofrontal cortex, sensorimotor areas, and visual cortex. Moreover, significant correlations were observed between MSE/ReHo ratio and clinical indicators, including depression severity and cognitive function tests. The SVM model demonstrated high accuracy in differentiating patients with MDD from HC, highlighting the potential of the MSE/ReHo ratio as a diagnostic and prognostic tool.
Conclusions:
The aberrant MSE/ReHo ratio implicated the underlying mechanisms of depressive symptoms and cognitive impairment in patients with MDD. It may represent a critical state of the brain region, reflecting the degree of chaos and order in the brain region. Integrating linear and nonlinear combinations of brain signals holds promise for diagnosing psychiatric disorders.
Impact Statement
Major depressive disorder (MDD) is a prevalent and severe mental illness that significantly impacts patients and their families. Conventional brain imaging techniques often fall short of revealing the pathogenesis of MDD. In this study, we employed resting-state functional magnetic resonance imaging alongside nonlinear and linear methods to identify abnormal multiscale entropy/regional homogeneity ratios in patients with MDD. This approach reflects the extent of brain state disarray and offers new insights into the pathogenesis, diagnosis, and treatment of mood disorders associated with MDD.
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References
1.
BennieJA, TeychenneMJ, De CockerK, et al.Associations between aerobic and muscle-strengthening exercise with depressive symptom severity among 17,839 U.S. adults. Prev Med, 2019; 121:121–127; doi: 10.1016/j.ypmed.2019.02.022
2.
BusaMA, van EmmerikREA. Multiscale entropy: A tool for understanding the complexity of postural control. J Sport Health Sci, 2016; 5(1):44–51; doi: 10.1016/j.jshs.2016.01.018
3.
CarlsonPJ, DiazgranadosN, NugentAC, et al.Neural correlates of rapid antidepressant response to ketamine in treatment-resistant unipolar depression: A preliminary positron emission tomography study. Biol Psychiatry, 2013; 73(12):1213–1221; doi: 10.1016/j.biopsych.2013.02.008
4.
ChenY, HuangL, ChenK, et al.White matter basis for the hub-and-spoke semantic representation: Evidence from semantic dementia. Brain, 2020; 143(4):1206–1219; doi: 10.1093/brain/awaa057
5.
ChenK, YangJ, LiF, et al.Molecular basis underlying default mode network functional abnormalities in postpartum depression with and without anxiety. Hum Brain Mapp, 2024; 45(5):e26657; doi: 10.1002/hbm.26657
6.
CooperCM, Chin FattCR, LiuP, et al.Discovery and replication of cerebral blood flow differences in major depressive disorder. Mol Psychiatry, 2020; 25(7):1500–1510; doi: 10.1038/s41380-019-0464-7
7.
Couvy-DuchesneB, StrikeLT, de ZubicarayGI, et al.Lingual gyrus surface area is associated with anxiety-depression severity in young adults: A genetic clustering approach. eNeuro, 2018; 5(1); doi: 10.1523/eneuro.0153-17.2017
8.
EhlersCL. Chaos and complexity. Can it help us to understand mood and behavior? Arch Gen Psychiatry, 1995; 52(11):960–964; doi: 10.1001/archpsyc.1995.03950230074010
9.
FerrarisM, CasselJC, Pereira de VasconcelosA, et al.The nucleus reuniens, a thalamic relay for cortico-hippocampal interaction in recent and remote memory consolidation. Neurosci Biobehav Rev, 2021; 125:339–354; doi: 10.1016/j.neubiorev.2021.02.025
10.
FristonK, BreakspearM, DecoG. Perception and self-organized instability. Front Comput Neurosci, 2012; 6:44; doi: 10.3389/fncom.2012.00044
11.
Frost BellgowanJ, MolfeseP, MarxM, et al.A neural substrate for behavioral inhibition in the risk for major depressive disorder. J Am Acad Child Adolesc Psychiatry, 2015; 54(10):841–848; doi: 10.1016/j.jaac.2015.08.001
12.
GoardMJ, PhoGN, WoodsonJ, et al.Distinct roles of visual, parietal, and frontal motor cortices in memory-guided sensorimotor decisions. Elife, 2016; 5; doi: 10.7554/eLife.13764
13.
GrayJP, MüllerVI, EickhoffSB, et al.Multimodal abnormalities of brain structure and function in major depressive disorder: A meta-analysis of neuroimaging studies. Am J Psychiatry, 2020; 177(5):422–434; doi: 10.1176/appi.ajp.2019.19050560
14.
HamazakiK, MaekawaM, ToyotaT, et al.Fatty acid composition of the postmortem prefrontal cortex of patients with schizophrenia, bipolar disorder, and major depressive disorder. Psychiatry Res, 2015; 227(2–3):353–359; doi: 10.1016/j.psychres.2015.01.004
15.
HanS, LiXX, WeiS, et al.Orbitofrontal cortex-hippocampus potentiation mediates relief for depression: A randomized double-blind trial and TMS-EEG study. Cell Rep Med, 2023; 4(6):101060; doi: 10.1016/j.xcrm.2023.101060
16.
HuangFF, YangXY, LuoJ, et al.Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods. BMC Psychiatry, 2023; 23(1):792; doi: 10.1186/s12888-023-05299-2
17.
JungJ, KangJ, WonE, et al.Impact of lingual gyrus volume on antidepressant response and neurocognitive functions in Major Depressive Disorder: A voxel-based morphometry study. J Affect Disord, 2014; 169:179–187; doi: 10.1016/j.jad.2014.08.018
18.
KorthauerK, KimesPK, DuvalletC, et al.A practical guide to methods controlling false discoveries in computational biology. Genome Biol, 2019; 20(1):118; doi: 10.1186/s13059-019-1716-1
19.
KumarN, ManningTF, OstryDJ. Somatosensory cortex participates in the consolidation of human motor memory. PLoS Biol, 2019; 17(10):e3000469; doi: 10.1371/journal.pbio.3000469
20.
LinC, LeeSH, HuangCM, et al.Increased brain entropy of resting-state fMRI mediates the relationship between depression severity and mental health-related quality of life in late-life depressed elderly. J Affect Disord, 2019; 250:270–277; doi: 10.1016/j.jad.2019.03.012
21.
LiuF, HuM, WangS, et al.Abnormal regional spontaneous neural activity in first-episode, treatment-naive patients with late-life depression: A resting-state fMRI study. Prog Neuropsychopharmacol Biol Psychiatry, 2012; 39(2):326–331; doi: 10.1016/j.pnpbp.2012.07.004
22.
LiuP, TuH, ZhangA, et al.Brain functional alterations in MDD patients with somatic symptoms: A resting-state fMRI study. J Affect Disord, 2021; 295:788–796; doi: 10.1016/j.jad.2021.08.143
23.
LuF, ChenY, CuiQ, et al.Shared and distinct patterns of dynamic functional connectivity variability of thalamo-cortical circuit in bipolar depression and major depressive disorder. Cereb Cortex, 2023; 33(11):6681–6692; doi: 10.1093/cercor/bhac534
MonroeSM, HarknessKL. Major depression and its recurrences: Life course matters. Annu Rev Clin Psychol, 2022; 18:329–357; doi: 10.1146/annurev-clinpsy-072220-021440
26.
NugentAC, DavisRM, ZarateCAJr, et al.Reduced thalamic volumes in major depressive disorder. Psychiatry Res, 2013; 213(3):179–185; doi: 10.1016/j.pscychresns.2013.05.004
27.
O’ConnellS, CannonDM, BroinP. Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks. Hum Brain Mapp, 2023; 44(18):6561–6574; doi: 10.1002/hbm.26521
28.
PardiMB, VogenstahlJ, DalmayT, et al.A thalamocortical top-down circuit for associative memory. Science, 2020; 370(6518):844–848; doi: 10.1126/science.abc2399
29.
PergolaG, DanetL, PitelAL, et al.The regulatory role of the human mediodorsal thalamus. Trends Cogn Sci, 2018; 22(11):1011–1025; doi: 10.1016/j.tics.2018.08.006
30.
PowerJD, BarnesKA, SnyderAZ, et al.Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 2012; 59(3):2142–2154; doi: 10.1016/j.neuroimage.2011.10.018
31.
RichmanJS, MoormanJR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol, 2000; 278(6):H2039–H2049; doi: 10.1152/ajpheart.2000.278.6.H2039
32.
RoyDS, ZhangY, AidaT, et al.Anterior thalamic circuits crucial for working memory. Proc Natl Acad Sci U S A, 2022; 119(20):e2118712119; doi: 10.1073/pnas.2118712119
33.
SantanaCP, de CarvalhoEA, RodriguesID, et al.rs-fMRI and machine learning for ASD diagnosis: A systematic review and meta-analysis. Sci Rep, 2022; 12(1):6030; doi: 10.1038/s41598-022-09821-6
34.
SchneckN, TuT, FalconeHR, et al.Large-scale network dynamics in neural response to emotionally negative stimuli linked to serotonin 1A binding in major depressive disorder. Mol Psychiatry, 2021; 26(6):2393–2401; doi: 10.1038/s41380-020-0733-5
35.
SelaY, FreimanM, DeryE, et al.fMRI-based hierarchical SVM model for the classification and grading of liver fibrosis. IEEE Trans Biomed Eng, 2011; 58(9):2574–2581; doi: 10.1109/tbme.2011.2159501
36.
ShangCY, LinHY, GauSS. The norepinephrine transporter gene modulates intrinsic brain activity, visual memory, and visual attention in children with attention-deficit/hyperactivity disorder. Mol Psychiatry, 2021; 26(8):4026–4035; doi: 10.1038/s41380-019-0545-7
37.
SmithRX, YanL, WangDJ. Multiple time scale complexity analysis of resting state FMRI. Brain Imaging Behav, 2014; 8(2):284–291; doi: 10.1007/s11682-013-9276-6
38.
TadayonnejadR, WilsonAC, ChuSA, et al.Use of right orbitofrontal repetitive transcranial magnetic stimulation (rTMS) augmentation for treatment-refractory obsessive-compulsive disorder with comorbid major depressive disorder. Psychiatry Res, 2022; 317:114856; doi: 10.1016/j.psychres.2022.114856
39.
ToaderAC, RegaladoJM, LiYR, et al.Anteromedial thalamus gates the selection and stabilization of long-term memories. Cell, 2023; 186(7):1369–1381.e1317; doi: 10.1016/j.cell.2023.02.024
40.
ToyodaH, TeraiH, YamadaK, et al.A decision tree analysis to predict clinical outcome of minimally invasive lumbar decompression surgery for lumbar spinal stenosis with and without coexisting spondylolisthesis and scoliosis. Spine J, 2023; 23(7):973–981; doi: 10.1016/j.spinee.2023.01.023
41.
Tzourio-MazoyerN, LandeauB, PapathanassiouD. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 2015; 122(1):1–5; doi: 10.1006/nimg.2001.0978
42.
WangL, WeiQ, WangC, et al.Altered functional connectivity patterns of insular subregions in major depressive disorder after electroconvulsive therapy. Brain Imaging Behav, 2020; 14(3):753–761; doi: 10.1007/s11682-018-0013-z
43.
WatkinsKE, CoweyA, AlexanderI, et al.Language networks in anophthalmia: Maintained hierarchy of processing in ‘visual’ cortex. Brain, 2012; 135(Pt 5):1566–1577; doi: 10.1093/brain/aws067
44.
XuH, ChenK, ZhuH, et al.Neurovascular coupling changes in patients with magnetic resonance imaging negative focal epilepsy. Epilepsy Behav, 2023; 138:109035; doi: 10.1016/j.yebeh.2022.109035
45.
YuX, ChenK, MaY, et al.Molecular basis underlying changes of brain entropy and functional connectivity in major depressive disorders after electroconvulsive therapy. CNS Neurosci Ther, 2024; 30(3):e14690; doi: 10.1111/cns.14690
46.
YuanJ, YuH, YuM, et al.Altered spontaneous brain activity in major depressive disorder: An activation likelihood estimation meta-analysis. J Affect Disord, 2022; 314:19–26; doi: 10.1016/j.jad.2022.06.014
47.
YunS, JeongB. Aberrant EEG signal variability at a specific temporal scale in major depressive disorder. Clin Neurophysiol, 2021; 132(8):1866–1877; doi: 10.1016/j.clinph.2021.05.011
48.
ZhangL, QiaoL, ChenQ, et al.Gray matter volume of the lingual gyrus mediates the relationship between inhibition function and divergent thinking. Front Psychol, 2016; 7:1532; doi: 10.3389/fpsyg.2016.01532
49.
ZhangQ, ShengJ, ZhangQ, et al.Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer’s disease. Comput Biol Med, 2023; 165:107392; doi: 10.1016/j.compbiomed.2023.107392
50.
ZhangC, YaoL, SongS, et al.Euler Elastica regularized logistic regression for whole-brain decoding of fMRI Data. IEEE Trans Biomed Eng, 2018; 65(7):1639–1653; doi: 10.1109/tbme.2017.2756665
51.
ZhuJ, ZhuoC, XuL, et al.Altered coupling between resting-state cerebral blood flow and functional connectivity in Schizophrenia. Schizophr Bull, 2017; 43(6):1363–1374; doi: 10.1093/schbul/sbx051
52.
ZuoXN, XuT, JiangL, et al.Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space. Neuroimage, 2013; 65:374–386; doi: 10.1016/j.neuroimage.2012.10.017
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