High-Frequency rTMS of the Motor Cortex Modulates Cerebellar and Widespread Activity as Revealed by SVM

Wang, Jue; Deng, Xin‐Ping; Wu, Yun-Ying; Li, Xiaolong; Feng, Zi‐Jian; Wang, Hong-Xiao; Jing, Ying; Zhao, Na; Zang, Yu‐Feng; Zhang, Jian

doi:10.3389/fnins.2020.00186

Cited by 24 publications

(18 citation statements)

References 52 publications

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“…Against our expectations, we observed no effect of stimulation on multivoxel patterns of the targeted DLPFC. These results are in conflict with previous studies showing TMS effects on the multivariate brain patterns of various cortical targets - including the DLPFC - during resting-state but also during task practice 8,25–27,30 . It remains unclear why no such effects were observed in the current study.…”

Section: Discussioncontrasting

confidence: 99%

“…It remains unclear why no such effects were observed in the current study. Several methodological differences between our work and previous research might have contributed to these discrepancies (e.g., offline vs. online TMS 30 ; group vs. individual analyses 26 ; MVCS vs. classification 8,19,25 ; control task vs. control stimulation 8,25,27 ) but future research is certainly warranted to examine this question systematically.…”

Section: Discussionmentioning

confidence: 65%

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Prefrontal stimulation prior to motor sequence learning alters multivoxel patterns in the striatum and the hippocampus

Gann

King

Dolfen

et al. 2021

Preprint

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Motor sequence learning (MSL) is supported by dynamical interactions between hippocampal and striatal networks that are thought to be orchestrated by the prefrontal cortex. In the present study, we tested whether individually-tailored theta-burst stimulation of the dorsolateral prefrontal cortex (DLPFC) prior to MSL, can modulate multivoxel response patterns in the stimulated cortical area, the hippocampus and the striatum. Response patterns were assessed with multivoxel correlation structure analyses of functional magnetic resonance imaging data acquired during task practice and during resting-state scans before and after learning/stimulation. Results revealed that, across stimulation conditions, MSL induced greater modulation of task-related DLPFC multivoxel patterns than random practice. A similar learning-related modulatory effect was observed on sensorimotor putamen patterns under inhibitory stimulation. Furthermore, MSL as well as inhibitory stimulation affected (posterior) hippocampal multivoxel patterns at post-intervention rest. Exploratory analyses showed that MSL-related brain patterns in the posterior hippocampus persisted into post-learning rest preferentially after inhibitory stimulation. These results collectively show that prefrontal stimulation can alter multivoxel brain patterns in deep brain regions that are critical for the MSL process. They also suggest that stimulation influenced early offline consolidation processes as evidenced by a stimulation-induced modulation of the reinstatement of task pattern into post-learning wakeful rest.

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 65%

Prefrontal stimulation prior to motor sequence learning alters multivoxel patterns in the striatum and the hippocampus

Gann

King

Dolfen

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…These two indices can directly measure whether the local activity of the effective region is modulated by rTMS ( Ruan et al. 2019 ; Wang et al. 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Resting-State fMRI Functional Connectivity Strength Predicts Local Activity Change in the Dorsal Cingulate Cortex: A Multi-Target Focused rTMS Study

et al. 2021

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Previous resting state functional magnetic resonance imaging (RS-fMRI) studies suggested that repetitive transcranial magnetic stimulation (rTMS) can modulate local activity in distant areas via functional connectivity (FC). A brain region has more than one connection with the superficial cortical areas. The current study proposed a multi-target focused rTMS protocol for indirectly stimulating a deep region, and to investigate 1) whether FC strength between stimulation targets (right middle frontal gyrus [rMFG] and right inferior parietal lobule [rIPL]) and effective region (dorsal anterior cingulate cortex [dACC]) can predict local activity changes of dACC and 2) whether multiple stimulation targets can focus on the dACC via FC. A total of 24 healthy participants received rTMS with two stimulation targets, both showing strong FC with the dACC. There were four rTMS conditions (>1 week apart, 10 Hz, 1800 pulses for each): rMFG-target, rIPL-target, Double-targets (900 pulses for each target), and Sham. The results failed to validate the multi-target focused rTMS hypothesis. But rMFG-target significantly decreased the local activity in the dACC. In addition, stronger dACC-rMFG FC was associated with a greater local activity change in the dACC. Future studies should use stronger FC to focus stimulation effects on the deep region.

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“…The 8 min blocked design finger tapping task contained visual-guided (VG) blocks (40 s × 3), self-initiated blocks (40 s × 3), and rest blocks (40 s × 6) (Wang J. et al, 2020 ). The VG task presented a visual cue of “finger” image for every 2 s followed by a fixation cross.…”

Section: Methodsmentioning

confidence: 99%

Toward Precise Localization of Abnormal Brain Activity: 1D CNN on Single Voxel fMRI Time-Series

Wang

et al. 2022

Front. Comput. Neurosci.

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Functional magnetic resonance imaging (fMRI) is one of the best techniques for precise localization of abnormal brain activity non-invasively. Machine-learning approaches have been widely used in neuroimaging studies; however, few studies have investigated the single-voxel modeling of fMRI data under cognitive tasks. We proposed a hybrid one-dimensional (1D) convolutional neural network (1D-CNN) based on the temporal dynamics of single-voxel fMRI time-series and successfully differentiated two continuous task states, namely, self-initiated (SI) and visually guided (VG) motor tasks. First, 25 activation peaks were identified from the contrast maps of SI and VG tasks in a blocked design. Then, the fMRI time-series of each peak voxel was transformed into a temporal-frequency domain by using continuous wavelet transform across a broader frequency range (0.003–0.313 Hz, with a step of 0.01 Hz). The transformed time-series was inputted into a 1D-CNN model for the binary classification of SI and VG continuous tasks. Compared with the univariate analysis, e.g., amplitude of low-frequency fluctuation (ALFF) at each frequency band, including, wavelet-ALFF, the 1D-CNN model highly outperformed wavelet-ALFF, with more efficient decoding models [46% of 800 models showing area under the curve (AUC) > 0.61] and higher decoding accuracies (94% of the efficient models), especially on the high-frequency bands (>0.1 Hz). Moreover, our results also demonstrated the advantages of wavelet decompositions over the original fMRI series by showing higher decoding performance on all peak voxels. Overall, this study suggests a great potential of single-voxel analysis using 1D-CNN and wavelet transformation of fMRI series with continuous, naturalistic, steady-state task design or resting-state design. It opens new avenues to precise localization of abnormal brain activity and fMRI-guided precision brain stimulation therapy.

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High-Frequency rTMS of the Motor Cortex Modulates Cerebellar and Widespread Activity as Revealed by SVM

Cited by 24 publications

References 52 publications

Prefrontal stimulation prior to motor sequence learning alters multivoxel patterns in the striatum and the hippocampus

Prefrontal stimulation prior to motor sequence learning alters multivoxel patterns in the striatum and the hippocampus

Resting-State fMRI Functional Connectivity Strength Predicts Local Activity Change in the Dorsal Cingulate Cortex: A Multi-Target Focused rTMS Study

Toward Precise Localization of Abnormal Brain Activity: 1D CNN on Single Voxel fMRI Time-Series

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