Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity
Table 1
The detailed characteristics of the included studies.
Participants Sample size, gender (M/F), age (Y)
Intervention
Modality
Feature
Purpose (C/R)
ML
Feature selection
Validation
Model assessment
MVPA findings
Univariate analysis results
Conclusion
López et al., 2013
Migraine Verum ACU: 18, 3/15, ; sham ACU: 18, 7/11,
One session of verum or sham ACU stimulation
Task-SPECT (function) Task: ACU when image data acquisition
Blood perfusion
C
Linear SVM
Filter: discarding voxels with intensity values under 25% of the maximum
LOOCV
ACC SPE SEN
The classifier performed better when the training data was extracted from the verum ACU group than from the sham ACU group.
Verum ACU yielded greater changes in the perfusion patterns than sham ACU. Verum ACU produced a more significant decrease in blood perfusion.
SVM can distinguish the SPECT images of pre- and post-ACU acquisitions. Changes in blood perfusion following verum ACU is greater than sham ACU.
Jung et al., 2019
HS 14, 14/0,
ACU at L HT7 or L PC6 for 20 blocks
Task-fMRI (function) Task: block design (16 s rest+6 s ACU+4 s stimulation location+4 s intensity report) 20
BOLD signal
C
Linear SVM
No feature selection steps
LOOCV
ACC
The classifier got an accuracy of 58.6% for classifying HT7 and PC6 with the features extracted from SI, MI, paraCL, anterior and posterior insula, SMG, ACG, vmPFC, PPC, and IPL. Using signal of ROI as feature, the classifier got higher accuracy (MI, 65%; SMA, 64%; SMG 62%; SI, 62%; and dlPFC, 62%).
No significant difference in BOLD signal alteration following HT7 and PC6 stimulation
Spatial localization of pain perceptions to ACU needle can be predicted by the neural response patterns in the somatosensory areas and the frontoparietal areas.
Yu et al., 2019
HS TR: 30, 16/14, 23-27; LT: 30, 18/12, 23-28
One session of TR or LT manipulation at ST36
Task-EEG (function) Task: ACU manipulation (TR or LT) for 3 min
Graph theory
C
DT NB SVM KNN LDA BP TSK
Selecting the features of interest
6-fold CV
ACC AUC
The classifier got an accuracy of 92.14% and AUC of 0.9570 with all graph theory features as inputs. With the increase of filter number, the accuracy was gradually improved. The highest accuracy was 92.37% with 6 filters in the TSK model.
PLV of TR was stronger than the baseline, while PLV of LT was weaker than the baseline. The value of all the six graph theory features of TR was significantly lower than that of LT.
Different ACU manipulations have different effects on functional brain networks. Classification of different ACU manipulations based on EEG with network features is feasible.
Liu et al., 2018
MWOA Responder: 38, /, ; nonresponder: 56, /,
24 sessions of sham ACU at NAP in 8 weeks
DTI (structure)
TABA
C
Linear SVM
Filter+wrapper: traversing the values of the two-sample test from 0.01 to 1 with a 0.01 interval to find the best for classifier
LOOCV
ACC SPE SEN PPV NPV
The single FA, MD, AD, and RD of the mPFC-amygdala fiber contributed to lackluster classification accuracy. The classifier got a higher accuracy with the combined features of FA, MD, and RD (in which ACC, SEN, SPE, PPV, and NPV were 84.0%, 90.2%, 76.7%, 82.1%, and 86.8%, respectively). The external capsule, ACG, and mPFC significantly contributed to the discrimination of responders and nonresponders.
The increased FA, decreased MD, decreased AD, and decreased RD of the mPFC-amygdala fiber were detected in MWOA patients than HS.
The variability of placebo treatment outcomes in migraineurs could be predicted from prior diffusion measures along the fiber pathways of the mPFC-amygdala.
12 sessions of ACU at GV20, GV24, bil-GB13, bil-GB8, and bil-GB20 in 4 weeks
T1 (structure)
GMV
C
Linear SVM
Filter+wrapper+embedded: traversing the values of the two-sample test from 0.0025 to 0.05 with a step of 0.0025 to select the best for classifier LASSO
10-fold CV
ACC SPE SEN AUC DSC
Using the clusters located at the frontal, temporal, parietal, precuneus, and cuneus gyri as features, the classifier got the SEN of 73%, SPE of 85%, ACC of 83%, and AUC of 0.7871.
The baseline GMV in all predictive regions significantly differed between responders and nonresponders. Alterations of migraine days were correlated with the baseline GMV of L cuneus, R MiFG/IFG, L IPL, and SPL/IPL. The responders achieved an increase in GMV of the L cuneus after ACU.
The pretreatment brain structure could be a novel predictor for ACU treatment of MWOA.
Tu et al., 2019
cLBP Real ACU: 24, 8/16, ; sham ACU: 26, 11/15,
6 sessions of ACU in 4 weeks, 8-12 effective acupoints were used in the real ACU group; 12 sham points were used in the sham ACU group.
Resting-fMRI (function)
ICA+rsFC
R
RBF SVR
Selecting the features of interest
5-fold CV
MAE
The prediction model obtained an of and an MAE of between actual and predicted treatment responses for real ACU. mPFC FC (mPFC-insula, mPFC-putamen, mPFC-caudate, and mPFC-AG) and other FC (PCC-MiFG, insula-IFG, insula-SPL, and caudate-AG) significantly contributed to prediction. The prediction model got an of and an MAE of for sham ACU. Connections of mPFC-dACG, mPFC-SPL, mPFC-paraCL, SFG-PreCG, SFG-MiFG, and ACG-paraCL provided significant information for prediction.
Changes of pain severity correlated with baseline mPFC-SN and mPFC-AG FC in the real ACU group. Baseline mPFC-dACG FC was correlated with changes in pain severity in the sham ACU group. Changes of FC between the mPFC and insula/AG were correlated with the relief of pain severity after real treatment, while changes of FC between the mPFC and paraCL/SPL were correlated with the relief of pain severity after sham ACU treatment.
Pretreatment rsFC could predict symptom changes for real and sham treatment, and the rsFC characteristics that were significantly predictive for real and sham treatment differed.
Xue et al., 2011
HS 12, 9/3, 21-26
ACU at GB40 or KI3 for 3 blocks, switching after a one-week interval
Task-fMRI (function) Task: on/off block design (1 min rest+1 min ACU) 3
BOLD signal
C
Linear SVM
Singular value decomposition
/
SDM
The performance of the classifier was not mentioned in this study. ACU stimulation at GB40 produced predominantly signal increases in the insula, red nucleus, thalamus, and amygdala. ACU at KI3 elicited more extensive decreased neural responses in the MFG, PCC, thalamus, and ACG.
ACU at GB40 and KI3 can both evoke similar widespread signal decreases in the limbic and subcortical structures.
Neural response patterns between ACU stimulation at GB40 and KI3 are distinct. Conventional GLM analysis is insensitive to detect neural activities evoked by ACU stimulation.
20 sessions of ACU in 4 weeks. One or two acupoints among CV12, ST36, and BL21 were used.
Resting-fMRI (function)
rsFC
C
Linear SVM
Wrapper: recursive feature elimination
LOOCV
ACC SPE SEN AUC
The classifier obtained an ACC of 84.9%, SEN of 78.6%, SPE of 89.5%, and AUC of 86.8%. The FC between R insula-L precuneus, L MiOFG-L thalamus, L insula-L ACG, R ACG-R temporal pole, R SOG-R cerebellum-3 contributed crucial information for prediction.
/
The whole-brain resting-state functional brain network has good predicting potential for ACU treatment to FD patients.
Hao et al., 2008
HS 60, /, 24-75
One session of electro-ACU at ST36
Task-EEG/ECG (function) Task: ACU when image data acquisition
BIS TPI LF/HF HR HRV
R
FNN
Selecting the features of interest
Validation with an independent set
AAE
With the FNN, the AAE of the estimation and true value is 10.2278.
/
The alteration of β-endorphin following electro-ACU can be predicted by monitoring EEG and ECG signal parameters.
Li et al., 2010
HS 11/11, ; GB37: 11, /, /; NAP: 11, /, /
ACU at GB37 or NAP for 2 blocks
Task-fMRI (function) Task: on/off block design (1 min rest+two 30 s ACU separated by a 50 s rest period+50 s rest)
BOLD signal
C
Linear SVM
Searchlight+singular value decomposition
LOOCV
ACC
The occipital cortex, limbic-cerebellar areas, and somatosensory cortex could help to differentiate the central neural response patterns induced by real or sham ACU stimulation with higher accuracy above the chance level.
Compared with the sham group, the ACU group induced higher signal intensity at some major regions of limbic-cerebellar system and small regions of the primary somatosensory cortex and supplementary motor area.
Neural response patterns of brain cortex to the ACU stimulation at GB37 and a nearby NAP could differ from each other effectively with the application of the MVPA approach.