Share：
Share this content in WeChat
X
[Chinese][PDF]3861
• Review •
Advances in rs-fMRI combined with machine learning toward the gut-brain axis
JU Yan  WANG Song 

Cite this article as: JU Y, WANG S. Advances in rs-fMRI combined with machine learning toward the gut-brain axis[J]. Chin J Magn Reson Imaging, 2023, 14(5): 171-174, 180. DOI:10.12015/issn.1674-8034.2023.05.030.


[Abstract] The two-way communication between gut microbes and the brain is called the gut-brain axis. Disorders of the gut-brain axis are associated with many diseases. However, the current clinical diagnosis method is not perfect. Resting state functional magnetic resonance imaging (rs-fMRI) is an important imaging tool that helps provide information about changes in brain function; machine learning builds prediction models by selecting different feature extraction methods and classification algorithms. The combination of the two is often used in the diagnosis, classification and prognosis of diseases. This article reviews the application of rs-fMRI combined with machine learning to gastrointestinal and major neurological diseases related to the gut-brain axis, aims to provide technical reference for the establishment of relevant models, assist clinical diagnosis, and realize precision medicine.
[Keywords] gut-brain axis；magnetic resonance imaging；resting state functional magnetic resonance imaging；machine learning；deep learning

JU Yan   WANG Song*  

Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China

Corresponding author: Wang S, E-mail: songwangws@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Natural Science Foundation of Shanghai (No. 19ZR1457800).
Received  2022-09-07
Accepted  2023-05-06
DOI: 10.12015/issn.1674-8034.2023.05.030
Cite this article as: JU Y, WANG S. Advances in rs-fMRI combined with machine learning toward the gut-brain axis[J]. Chin J Magn Reson Imaging, 2023, 14(5): 171-174, 180. DOI:10.12015/issn.1674-8034.2023.05.030.

[1]
JARRET A, JACKSON R, DUIZER C, et al. Enteric Nervous System-Derived IL-18 Orchestrates Mucosal Barrier Immunity[J]. Cell, 2020, 180(1): 50-63. DOI: 10.1016/j.cell.2019.12.016">10.1016/j.cell.2019.12.016">10.1016/j.cell.2019.12.016.
[2]
LI B Z, CAO Y, ZHANG Y, et al. Relation of Decreased Functional Connectivity Between Left Thalamus and Left Inferior Frontal Gyrus to Emotion Changes Following Acute Sleep Deprivation[J]. Front Neurol, 2021, 12: 642411. DOI: 10.3389/fneur.2021.642411">10.3389/fneur.2021.642411">10.3389/fneur.2021.642411.
[3]
MAYER E A, SAVIDGE T, SHULMAN R J. Brain-gut microbiome interactions and functional bowel disorders[J]. Gastroenterology, 2014, 146(6): 1500-1512. DOI: 10.1053/j.gastro.2014.02.037">10.1053/j.gastro.2014.02.037">10.1053/j.gastro.2014.02.037.
[4]
MAO C P, CHEN F R, HUO J H, et al. Altered resting-state functional connectivity and effective connectivity of the habenula in irritable bowel syndrome: A cross-sectional and machine learning study[J]. Hum Brain Mapp, 2020, 41(13): 3655-3666. DOI: 10.1002/hbm.25038">10.1002/hbm.25038">10.1002/hbm.25038.
[5]
CEULEMANS M, JACOBS I, WAUTERS L, et al. Immune Activation in Functional Dyspepsia: Bystander Becoming the Suspect[J]. Front Neurosci, 2022, 16: 831761. DOI: 10.3389/fnins.2022.831761">10.3389/fnins.2022.831761">10.3389/fnins.2022.831761.
[6]
LI H, PAGE A J. Altered Vagal Signaling and Its Pathophysiological Roles in Functional Dyspepsia[J]. Front Neurosci, 2022, 16: 858612. DOI: 10.3389/fnins.2022.858612">10.3389/fnins.2022.858612">10.3389/fnins.2022.858612.
[7]
LIU P, FAN Y, WEI Y, et al. Altered structural and functional connectivity of the insula in functional dyspepsia[J/OL]. Neurogastroenterol Motil, 2018, 30(9): e13345 [2022-11-01]. https://doi.org/10.1111/nmo.13345. DOI: 10.1111/nmo.13345">10.1111/nmo.13345">10.1111/nmo.13345.
[8]
QI R, SHI Z, WENG Y, et al. Similarity and diversity of spontaneous brain activity in functional dyspepsia subtypes[J]. Acta Radiol, 2020, 61(7): 927-935. DOI: 10.1177/0284185119883391">10.1177/0284185119883391">10.1177/0284185119883391.
[9]
YIN T, SUN R, HE Z, et al. Subcortical-cortical functional connectivity as a potential biomarker for identifying patients with functional dyspepsia[J]. Cereb Cortex, 2022, 32(15): 3347-3358. DOI: 10.1093/cercor/bhab419">10.1093/cercor/bhab419">10.1093/cercor/bhab419.
[10]
YIN T, HE Z, CHEN Y, et al. Predicting acupuncture efficacy for functional dyspepsia based on functional brain network features: a machine learning study[J]. Cereb Cortex, 2023, 21, 33(7): 3511-3522. DOI: 10.1093/cercor/bhac288">10.1093/cercor/bhac288">10.1093/cercor/bhac288.
[11]
FORTE N, FERNÁNDEZ-RILO A C, Palomba L, et al. Obesity Affects the Microbiota-Gut-Brain Axis and the Regulation Thereof by Endocannabinoids and Related Mediators[J]. Int J Mol Sci, 2020, 21(5): 1554. DOI: 10.3390/ijms21051554">10.3390/ijms21051554">10.3390/ijms21051554.
[12]
DONG T S, MAYER E A, OSADCHIY V, et al. A Distinct Brain-Gut-Microbiome Profile Exists for Females with Obesity and Food Addiction[J]. Obesity (Silver Spring), 2020, 28(8): 1477-1486. DOI: 10.1002/oby.22870">10.1002/oby.22870">10.1002/oby.22870.
[13]
DONG T S, GUAN M, MAYER E A, et al. Obesity is associated with a distinct brain-gut microbiome signature that connects Prevotella and Bacteroides to the brain's reward center[J]. Gut Microbes, 2022, 14(1): 2051999. DOI: 10.1080/19490976.2022.2051999">10.1080/19490976.2022.2051999">10.1080/19490976.2022.2051999.
[14]
HENEKA M T, CARSON M J, EL K J, et al. Neuroinflammation in Alzheimer's disease[J]. Lancet Neurol, 2015, 14(4): 388-405. DOI: 10.1016/S1474-4422(15)70016-5">10.1016/S1474-4422(15)70016-5">10.1016/S1474-4422(15)70016-5.
[15]
KESIKA P, SUGANTHY N, SIVAMARUTHI B S, et al. Role of gut-brain axis, gut microbial composition, and probiotic intervention in Alzheimer's disease[J]. Life Sci, 2021, 264: 118627. DOI: 10.1016/j.lfs.2020.118627">10.1016/j.lfs.2020.118627">10.1016/j.lfs.2020.118627.
[16]
ZHANG Q, WANG Q, HE C, et al. Altered Regional Cerebral Blood Flow and Brain Function Across the Alzheimer's Disease Spectrum: A Potential Biomarker[J]. Front Aging Neurosci, 2021, 13: 630382. DOI: 10.3389/fnagi.2021.630382">10.3389/fnagi.2021.630382">10.3389/fnagi.2021.630382.
[17]
JIA H, WANG Y, DUAN Y, et al. Alzheimer's Disease Classification Based on Image Transformation and Features Fusion[J]. Comput Math Methods Med, 2021, 2021: 9624269. DOI: 10.1155/2021/9624269">10.1155/2021/9624269">10.1155/2021/9624269.
[18]
XU X, LI W, TAO M, et al. Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View[J]. Front Neurosci, 2020, 14: 577887. DOI: 10.3389/fnins.2020.577887">10.3389/fnins.2020.577887">10.3389/fnins.2020.577887.
[19]
LEI B, YU S, ZHAO X, et al. Diagnosis of early Alzheimer's disease based on dynamic high order networks[J]. Brain Imaging Behav, 2021, 15(1): 276-287. DOI: 10.1007/s11682-019-00255-9">10.1007/s11682-019-00255-9">10.1007/s11682-019-00255-9.
[20]
WU Y, ZHOU Y, SONG M. Classification of patients with AD from healthy controls using entropy-based measures of causality brain networks[J]. J Neurosci Methods, 2021, 361: 109265. DOI: 10.1016/j.jneumeth.2021.109265">10.1016/j.jneumeth.2021.109265">10.1016/j.jneumeth.2021.109265.
[21]
LIN K, JIE B, DONG P, et al. Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification[J]. Front Neurosci, 2022, 16: 933660. DOI: 10.3389/fnins.2022.933660">10.3389/fnins.2022.933660">10.3389/fnins.2022.933660.
[22]
LI X, YANG C, XIE P, et al. The diagnosis of amnestic mild cognitive impairment by combining the characteristics of brain functional network and support vector machine classifier[J]. J Neurosci Methods, 2021, 363: 109334. DOI: 10.1016/j.jneumeth.2021.109334">10.1016/j.jneumeth.2021.109334">10.1016/j.jneumeth.2021.109334.
[23]
ABROL A, FU Z, DU Y, et al. Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer's Disease Progression[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2019, 2019: 4409-4413. DOI: 10.1109/EMBC.2019.8856500">10.1109/EMBC.2019.8856500">10.1109/EMBC.2019.8856500.
[24]
ZHAO J, DING X, DU Y, et al. Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification[J/OL]. Brain Behav, 2019, 9(10): e1407 [2022-11-01]. https://doi.org/10.1002/brb3.1407. DOI: 10.1002/brb3.1407">10.1002/brb3.1407">10.1002/brb3.1407.
[25]
GAO Y, SENGUPTA A, LI M, et al. Functional connectivity of white matter as a biomarker of cognitive decline in Alzheimer's disease[J/OL]. PLoS One, 2020, 15(10): e240513 [2022-11-01]. https://doi.org/10.1371/journal.pone.0240513. DOI: 10.1371/journal.pone.0240513">10.1371/journal.pone.0240513">10.1371/journal.pone.0240513.
[26]
DUC N T, RYU S, QURESHI M, et al. 3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI[J]. Neuroinformatics, 2020, 18(1): 71-86. DOI: 10.1007/s12021-019-09419-w">10.1007/s12021-019-09419-w">10.1007/s12021-019-09419-w.
[27]
MILLAR P R, LUCKETT P H, GORDON B A, et al. Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease[J]. Neuroimage, 2022, 256: 119228. DOI: 10.1016/j.neuroimage.2022.119228">10.1016/j.neuroimage.2022.119228">10.1016/j.neuroimage.2022.119228.
[28]
TYSNES O B, STORSTEIN A. Epidemiology of Parkinson's disease[J]. J Neural Transm (Vienna), 2017, 124(8): 901-905. DOI: 10.1007/s00702-017-1686-y">10.1007/s00702-017-1686-y">10.1007/s00702-017-1686-y.
[29]
BRAAK H, RÜB U, GAI W P, et al. Idiopathic Parkinson's disease: possible routes by which vulnerable neuronal types may be subject to neuroinvasion by an unknown pathogen[J]. J Neural Transm (Vienna), 2003, 110(5): 517-536. DOI: 10.1007/s00702-002-0808-2">10.1007/s00702-002-0808-2">10.1007/s00702-002-0808-2.
[30]
CRYAN J F, O'RIORDAN K J, SANDHU K, et al. The gut microbiome in neurological disorders[J]. Lancet Neurol, 2020, 19(2): 179-194. DOI: 10.1016/S1474-4422(19)30356-4">10.1016/S1474-4422(19)30356-4">10.1016/S1474-4422(19)30356-4.
[31]
BAGGIO H C, ABOS A, SEGURA B, et al. Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level[J]. Neuroimage Clin, 2019, 22: 101720. DOI: 10.1016/j.nicl.2019.101720">10.1016/j.nicl.2019.101720">10.1016/j.nicl.2019.101720.
[32]
PANG H, YU Z, YU H, et al. Use of machine learning method on automatic classification of motor subtype of Parkinson's disease based on multilevel indices of rs-fMRI[J]. Parkinsonism Relat Disord, 2021, 90: 65-72. DOI: 10.1016/j.parkreldis.2021.08.003">10.1016/j.parkreldis.2021.08.003">10.1016/j.parkreldis.2021.08.003.
[33]
RUBBERT C, MATHYS C, JOCKWITZ C, et al. Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity[J]. Br J Radiol, 2019, 92(1101): 20180886. DOI: 10.1259/bjr.20180886">10.1259/bjr.20180886">10.1259/bjr.20180886.
[34]
SHI D, ZHANG H, WANG S, et al. Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis[J]. Front Aging Neurosci, 2021, 13: 624731. DOI: 10.3389/fnagi.2021.624731">10.3389/fnagi.2021.624731">10.3389/fnagi.2021.624731.
[35]
SHI D, YAO X, LI Y, et al. Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach[J]. Brain Imaging Behav, 2022, 16(5): 2150-2163. DOI: 10.1007/s11682-022-00685-y">10.1007/s11682-022-00685-y">10.1007/s11682-022-00685-y.
[36]
SHI D, ZHANG H, WANG G, et al. Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis[J]. Front Aging Neurosci, 2022, 14: 806828. DOI: 10.3389/fnagi.2022.806828">10.3389/fnagi.2022.806828">10.3389/fnagi.2022.806828.
[37]
BOUTET A, MADHAVAN R, ELIAS G, et al. Predicting optimal deep brain stimulation parameters for Parkinson's disease using functional MRI and machine learning[J]. Nat Commun, 2021, 12(1): 3043. DOI: 10.1038/s41467-021-23311-9">10.1038/s41467-021-23311-9">10.1038/s41467-021-23311-9.
[38]
KIM B H, KIM M K, JO H J, et al. Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features[J]. Sci Rep, 2022, 12(1): 13932. DOI: 10.1038/s41598-022-17769-w">10.1038/s41598-022-17769-w">10.1038/s41598-022-17769-w.
[39]
SHI Y, ZHANG L, WANG Z, et al. Multivariate Machine Learning Analyses in Identification of Major Depressive Disorder Using Resting-State Functional Connectivity: A Multicentral Study[J]. ACS Chem Neurosci, 2021, 12(15): 2878-2886. DOI: 10.1021/acschemneuro.1c00256">10.1021/acschemneuro.1c00256">10.1021/acschemneuro.1c00256.
[40]
DAI P, XIONG T, ZHOU X, et al. The alterations of brain functional connectivity networks in major depressive disorder detected by machine learning through multisite rs-fMRI data[J]. Behav Brain Res, 2022, 435: 114058. DOI: 10.1016/j.bbr.2022.114058">10.1016/j.bbr.2022.114058">10.1016/j.bbr.2022.114058.
[41]
CHUN J Y, SENDI M, SUI J, et al. Visualizing Functional Network Connectivity Difference between Healthy Control and Major Depressive Disorder Using an Explainable Machine-learning Method[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020: 1424-1427. DOI: 10.1109/EMBC44109.2020.9175685">10.1109/EMBC44109.2020.9175685">10.1109/EMBC44109.2020.9175685.
[42]
AN C, PARK Y W, AHN S S, et al. Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results[J/OL]. PLoS One, 2021, 16(8): e256152 [2022-11-01]. https://doi.org/10.1371/journal.pone.0256152. DOI: 10.1371/journal.pone.0256152.
[43]
BALKI I, AMIRABADI A, LEVMAN J, et al. Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review[J]. Can Assoc Radiol J, 2019, 70(4): 344-353. DOI: 10.1016/j.carj.2019.06.002.
[44]
MO J, LIU Z, SUN K, et al. Automated detection of hippocampal sclerosis using clinically empirical and radiomics features[J]. Epilepsia, 2019, 60(12): 2519-2529. DOI: 10.1111/epi.16392.
[45]
THOMAS R M, GALLO S, CERLIANI L, et al. Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data With 3D Convolutional Neural Networks[J]. Front Psychiatry, 2020, 11: 440. DOI: 10.3389/fpsyt.2020.00440.
[46]
YANG M, CAO M, CHEN Y, et al. Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model[J]. Front Hum Neurosci, 2021, 15: 687288. DOI: 10.3389/fnhum.2021.687288.
[47]
THOMPSON G J. Neural and metabolic basis of dynamic resting state fMRI[J]. Neuroimage, 2018, 180(Pt B): 448-462. DOI: 10.1016/j.neuroimage.2017.09.010.
[48]
LU Y, CHEN M, HUANG Z, et al. Antidepressants in the Treatment of Functional Dyspepsia: A Systematic Review and Meta-Analysis[J/OL]. PLoS One, 2016, 11(6): e157798 [2022-11-01]. https://doi.org/10.1371/journal.pone.0157798. DOI: 10.1371/journal.pone.0157798.
[49]
FU P, GAO M, YUNG K. Association of Intestinal Disorders with Parkinson's Disease and Alzheimer's Disease: A Systematic Review and Meta-Analysis[J]. ACS Chem Neurosci, 2020, 11(3): 395-405. DOI: 10.1021/acschemneuro.9b00607.
[50]
PRADILLO J M, HERNÁNDEZ-JIMÉNEZ M, FERNÁNDEZ-VALLE M E, et al. Influence of metabolic syndrome on post-stroke outcome, angiogenesis and vascular function in old rats determined by dynamic contrast enhanced MRI[J]. J Cereb Blood Flow Metab, 2021, 41(7): 1692-1706. DOI: 10.1177/0271678X20976412.

PREV Research progress of coronary artery calcification and cognitive impairment and brain magnetic resonance imaging
NEXT Diagnosis and prognosis prediction of glioma based on multimodal MRI radiomics and deep learning
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn