Personalized High-Intensity Temporal Interference Stimulation Decouples Cerebellar Networks to Enhance Implicit Learning

Personalized High-Intensity Temporal Interference Stimulation Decouples Cerebellar Networks to Enhance Implicit Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Personalized High-Intensity Temporal Interference Stimulation Decouples Cerebellar Networks to Enhance Implicit Learning Dongsheng Tang, Lang Qin, Longfei Hu, Yixuan Jian, Siqi Gao, Zhenhe Huang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7114172/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted 8 You are reading this latest preprint version Abstract Objective Despite the central role of deep brain structures such as the striatum in motor learning, existing noninvasive stimulation methods are hindered by limited depth and precision. Temporal interference (TI) stimulation presents the potential for precise, individualized modulation of deep regions. However, how TI stimulation influences deep brain activity and large-scale network reorganization to facilitate motor learning is still unclear. Therefore, this study aimed to clarify these mechanisms by investigating how personalized, high-intensity TI targeting the striatum modulates neural activity and enhances motor learning. Methods Twenty-six healthy right-handed male participants were enrolled in a randomized, double-blind, sham-controlled crossover study. Each participant received both TI and sham stimulation targeting the right striatum (10 mA, Δf = 20 Hz) through individualized electrode montage. Resting-state functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and serial reaction time (SRT) task performance were assessed before and after each intervention. Neural analyses included static and dynamic fractional amplitude of low-frequency fluctuation (fALFF) in the target region, structure–function coupling (SC-FC) and topological metrics across six major brain networks, as well as brain-behavior correlations related to learning performance. Results ( 1 ) Target region activity: TI stimulation significantly increased both static and dynamic fALFF in the right striatum (p = 0.030 and p = 0.036). ( 2 ) Brain network reorganization: Compared with sham, the TI group exhibited significantly reduced SC-FC coupling in the cerebellar network (CN) (t=-2.279, p = 0.027), along with enhanced intra-network functional connectivity and increased nodal efficiency and degree in the CN and cingulo-opercular network (CON) (all FDR-corrected, p < 0.05). ( 3 ) Behavioral performance: The TI group demonstrated significant improvement in second-stage implicit learning (SIL) after stimulation (p < 0.01). ( 4 ) Brain-behavior correlation: Decreased SC-FC coupling in the CN was significantly negatively correlated with improvement in SIL (r=-0.372, p = 0.040). Conclusion Personalized high-intensity TI of the striatum enhances deep target activity and promotes selective network reorganization, particularly by reducing SC-FC coupling and strengthening intra-network connectivity in the CN. These network-level modulations underlie improved implicit learning performance, highlighting the potential of TI neuromodulation as a precise and effective approach for promoting motor learning by targeting deep nuclei and large-scale brain networks. Trial registration: ChiCTR2500098699 Temporal interference stimulation Noninvasive brain stimulation Cerebellar network Structure-Function couplings Implicit learning Network connection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction The striatum, as a core node of the basal ganglia, plays a pivotal role in complex cognitive functions such as motor learning and control mechanisms ( 1 , 2 ). In recent years, increasing evidence suggests that the striatum plays a crucial role in implicit learning, especially in unconscious skill acquisition and behavioral adaptation ( 3 , 4 ). Precise regulation of striatal function shows immense application potential in fields such as brain function research, neuropsychiatric intervention, and cognitive enhancement ( 5 , 6 ). However, due to the striatum's deep brain structural location, traditional non-invasive stimulation techniques such as transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS) have significant limitations in terms of stimulation depth and localization accuracy ( 7 , 8 ). Temporal Interference (TI) Stimulation, by forming a low-frequency interference field within brain tissue through two high-frequency currents, achieves precise, non-invasive regulation of deep neural structures (9). TI stimulation not only avoids over-activation of superficial tissues, significantly reduces side effects, but also possesses higher spatial resolution, thus being widely considered a powerful tool for non-invasive deep brain modulation ( 10 , 11 ). Although the safety and feasibility of TI technology have been initially verified, for example, Liu's study showed that TI stimulation of the globus pallidus interna in Parkinson's patients significantly improved motor function ( 12 ). Adam's study also showed that stimulating the subthalamic nucleus in Parkinson's patients could reverse abnormal brain discharge ( 13 ). However, there is currently no research providing direct evidence of blood oxygen level-dependent (BOLD) signal activation in deep brain target regions following TI stimulation. This lack of evidence may be attributed to insufficient electric field intensity at the target stimulation area. Cassarà et al indicated that although lower field strengths (0.2–0.5 V/m) can modulate neural activity under specific conditions, target stimulation field strength greater than 1V/m is preferred to achieve more robust and effective stimulation ( 14 ). Therefore, increasing the intensity of TI stimulation is a key factor in achieving effective stimulation. At the same time, there are significant differences in brain structure among individuals, and using the same stimulation parameters led to unsatisfactory results in previous studies ( 15 ). An electrostimulation scheme based on individual brain structure will greatly improve the effectiveness of stimulation ( 15 , 16 ). Furthermore, the systematic study of TI's regulation of striatal network function and its impact on implicit learning behavior remains unclear. Therefore, this study takes individualized high-intensity TI stimulation as a starting point, optimizing parameters based on individual brain structural differences, and combining multimodal brain imaging and behavioral indicators to systematically evaluate the causal effect of TI on the decoupling of striatum-related brain networks and the improvement of implicit learning performance. It hypothesize that personalized high-intensity TI stimulation of the striatum can effectively regulate the functional and structural networks of the brain closely associated with the striatum, and promote individual implicit learning ability, providing new theoretical and methodological bases for non-invasive precise regulation of deep brain regions and cognitive function intervention. 2. Methods 2.1 Participants 26 healthy male participants (mean age 20.31 ± 1.69 years) were included in the study. All participants were right-handed (mean laterality quotient 85.90 ± 17.44), had no history of neurological or severe psychiatric diseases, medication use, or metal implants. After quality-control procedures, three participants were excluded from serial reaction time (SRT) tasks analysis owing to explicit awareness of the task repetition. This study approved by the Medical Ethics Committee of Shenzhen University Health Science Center (project number 202400151) and pre-registered on the Chinese Clinical Trial Registry ( www.chictr.org.cn ; identifier: ChiCTR2500098699). All subjects provided written informed consent and the experimental procedures were performed in accordance with the Declaration of Helsinki. 2.2 Study design The study employed a randomized double-blind sham-controlled cross-over design. All participants underwent two experiments: the magnetic resonance imaging (MRI) experiment (Fig. 1 A) and the SRT task experiment (Fig. 1 B). In the MRI experiment, all participants underwent three experimental sessions. At the first session, demographic data were collected, and high-resolution T1-weighted MRI scans were acquired for personalized stimulation electrode protocol optimization. For the subsequent two sessions, participants were randomly assigned to either the TI group (TI stimulation) or the Sham group (sham stimulation). Resting-state fMRI and diffusion tensor imaging (DTI) data were collected before and after stimulation during each session. Additionally, participants completed the Stanford Sleepiness Scale (SSS) prior to stimulation and the Adverse Events Questionnaire (AEQ) following stimulation. The Montreal Cognitive Assessment (MoCA) was administered both before and after each stimulation session (Fig. 1 A). In the SRT task experiment, participants underwent the same cross-over design across two sessions, with SRT performance assessed before and after each stimulation intervention. The SRT task was performed on laptops using E-Prime V3.0 software (Psychology Software Tools, Inc., Pittsburgh, PA, USA). Participants were instructed to position their non-dominant left hand over the response pad, with the index, middle, ring, and little fingers placed over buttons 4, 3, 2, and 1, respectively. Participants were instructed to respond as rapidly as possible by pressing the corresponding keys with their pre-positioned fingers in response to the numbers indicated by asterisks displayed on the computer screen. The stimuli disappeared immediately after pressing any key and reappeared after a 500 ms interval ( 17 ). SRT task comprised eight blocks, each containing 120 trials. Blocks 1 and 6, termed random (“R”) blocks, presented the sequence of asterisks in a pseudorandom order. For both R blocks, the asterisks appeared with equal frequency at each position, and the sequence was constrained to prevent runs of four consecutive unique positions (e.g., 1234 or 4321) or trills of four elements (e.g., 1212). In contrast, Blocks 2–5 and 7–8, designated as sequence (“S”) blocks, featured a fixed 12-element sequence of asterisk positions (121423413243), which was repeated ten times per block. The task design encompassed two distinct learning phases. Denoting the reaction time of block n as RTn, the first implicit learning (FIL) was defined as FIL = RT1 – (RT2 + RT3 + RT4 + RT5)/4, while the second implicit learning (SIL) was defined as SIL = RT6 – (RT7 + RT8)/2. Prior to the experimental session, participants completed a 60-trial practice block presented in a random order to ensure comprehension of the task instructions. A 30-second rest interval was provided between each block. Importantly, participants were not informed about the presence of a repeating sequence. Upon completion of the experiment, participants were queried as to whether they had noticed any repeated sequences. Those who reported awareness of the sequence were excluded from the final analysis. **** Figure 1 insert here**** 2.3 Stimulation parameter and personalized stimulus montage protocol The intervention was conducted by the NervioX-1000 neuromodulation system (Suzhou Brain Dome Technology Co., Ltd., Suzhou, China). The stimulation region-of-interest (ROI) was located in the right striatum. Two channels of high-frequency alternating current were applied (I₁: 2 kHz and I₂: 2.02 kHz), generating a low frequency interference modulation of 20 Hz targeting the ROI (Fig. 2 ). The peak-to‐peak amplitude of the current was set at 10 mA. The duration of TI protocol was 20 min. The parameters of Sham group protocol were identical to those of TI group protocol, except that the current was only delivered at the first 30 s and the last 30 s of the whole stimulation session. The impedance was kept below 15 kΩ during stimulation. The safety profile of these stimulation parameters (exhibiting no adverse cognitive effects or side effects) and robust blinding efficacy have been validated in the present study. Detailed information is in Supplementary Section B. The electrode locations were optimized for each participant. This was done using the SimNIBS software to create a finite element model (FEM) of the brain from the structural images of the subject ( 18 ). Specifically, we segmented tissues and assigned conductivities, placed electrodes following the standard 10–10 EEG system of 64 channels, generated tetrahedral head meshes via Gmsh, performed FEM, and then calculated the electric field. The right striatum was targeted at MNI coordinates ( 28 , 4 , – 4 ) from Wessel et al ( 6 ), using a 10-mm spherical ROI to optimize electric field intensity. This approach achieved an average electric field intensity of 2.92 V/m in the target region, ensuring precise neuromodulation (for detailed electrode placement and electromagnetic computation data, see Supplementary Section A ). **** Figure 2 insert here**** 2.4 Image acquisition All MRI data were acquired at the Center for Magnetic Resonance Imaging, Shenzhen University using a Siemens Prisma 3.0-Tesla system (Erlangen, Germany) equipped with a 64-channel head coil. Rs-fMRI scans were acquired using gradient-echo echo-planar imaging (EPI) sequences with the following parameters: 3×3×3 mm 3 voxel size, repetition time (TR) = 1000 ms, echo time (TE) = 30 ms, flip angle (FA) = 66°, field of view (FOV) = 210×210 mm 2 , total acquisition time (TA) = 8.32 min, 488 volumes. T1 images were acquired using a 3D MPRAGE (magnetization-prepared rapid gradient echo) sequence with the following parameters: 1×1×1 mm 3 voxel size, TR = 2300 ms, TE = 2.26 ms, inverse time = 1000 ms, FA = 8°, FOV = 256×232 mm 2 , TA = 8.92 min, 192 volumes. A diffusion-weighted spin-echo echo-planar imaging sequence was acquired with the following parameters: 1.5×1.5×1.5 mm 3 voxel size, TR = 5600 ms, TE = 82.0 ms, FOV = 210×210 mm 2 , TA = 6.52 min, 66 volumes, phase encoding direction = P to A, 64 directions (b = 1500s/mm²), 1 b0). Participants wore earplugs for noise protection and were instructed to remain awake, still, and focused on a fixation cross with open eyes, avoiding directed thoughts during the scanning sessions. 2.5 Data preprocessing The rs-fMRI data were preprocessed using DPARSF V8.0 toolboxes ( 19 ). The first 8 volumes were discarded. Images then underwent slice timing, head motion correction, and spatial normalization to the Montreal Neurological Institute (MNI) space using the normalization parameters estimated during unified segmentation of structural T1 images. After normalization, the linear trends were removed and the nuisance variables, including Friston 24 head motion parameters, white matter (WM), cerebrospinal fluid signals (CSF) and global signal ( 20 ), were regressed out from the functional signal. Finally, band-pass filtering (0.01–0.1 Hz) was performed on the images to reserve low-frequency information. To mitigate head motion effects, volume-based frame-wise displacement (FD) was calculated ( 21 ). Timepoints with FD > 0.2 mm were marked as problematic and included as separate regressors during nuisance covariate regression ( 22 ). Participants with mean FD exceeding three standard deviations were excluded from analysis. Finally, 2 participants were excluded under the head motion control criteria, and 26 participants were included in the subsequent analysis. DWI data were pre-processed using Mrtrix3 ( http://www.mrtrix.org/ ). With the following operations: denoise, remove Gibbs Ringing artifacts. Using FSL’s eddy tool ( https://fsl.fmrib.ox.ac.uk/fsl/ ) inhomogeneity distortion correction, corrected for eddy currents and motion artifacts ( 23 ). Bias field correction using the N4 algorithm as provided in ANTs ( https://github.com/ANTsX/ANTs ) ( 24 ). Using dhollander method to estimate response function for spherical deconvolution and multi-shell multi-tissue CSD to estimate fiber orientation distributions from diffusion data using spherical deconvolution. Before performing streamlines tractography, conduct multi-tissue informed log-domain intensity normalization. Performing streamlines tractography using second-order integration over fiber orientation distributions option, 10 million streamlines are to be selected. Finally, tcksift was employed to filter the whole-brain fiber-tracking dataset, ensuring that the streamline densities matched the fiber orientation distribution lobe integrals. 2.6 Calculation of static and dynamic fALFF in target regions The analysis was conducted using the unfiltered, preprocessed data. A fast Fourier transform was performed on whole-brain voxels to convert the BOLD signal into the frequency-domain power spectrum. The square root of the power spectrum was calculated at each frequency, and the average value within the range of 0.01 to 0.10 Hz was used to calculate the static fALFF metric. The resulting static fALFF maps were further subjected to standardization and spatial smoothing with a full width at half maximum (FWHM) of 4 mm. The analysis of dynamic fALFF was performed using the DPABI-based dynamic analysis toolbox. The Hamming sliding window was selected for the whole-brain blood oxygenation level dependent signal time series :100TR (100s) window length and step width of 3 TR (3s) were selected for dynamic fALFF analysis ( 25 ). The mean dynamic fALFF metric was then calculated across all voxels within the 127 windows for each participant to assess the dynamic characteristics of fALFF. For regions of interest (ROIs), we extracted both static and mean dynamic fALFF values from a 5-mm radius sphere centered at the MNI coordinates ( 28 , 4 , – 4 ), corresponding to the right striatum (the target of stimulation). As a control region, we also derived measures from bilateral middle frontal gyrus and middle temporal gyrus, defined based on the AAL template, as well as from the area subjacent to the electrode placement. 2.7 Functional and structural network construction The structural and functional networks composed of 160 ROIs in the Dos-160 template. Brain edges were defined by connectivity between brain nodes. For each node, a sphere was created with a 5 mm radius, centered on the atlas coordinates. According to the study of Dosenbach et al. ( 26 ), the 160 ROIs have been assigned into six subnetworks, including cerebellum network (CN, 18 ROIs), cingulo-opercular network (CON, 32 ROIs), default mode network (DMN, 34 ROIs), fronto-parietal network (FPN, 21 ROIs), occipital network (ON, 22ROIs) and sensorimotor network (SMN, 33 ROIs). Subsequently, further network analyses were conducted on these six subnetworks (Fig. 3 ). After preprocessing, structure and function connectivity matrices were created for every individual using DPABINet V1.3 and MRtrix3 software ( 27 ). For SC matrices, weighted, undirected connectomes were constructed using the tck2connectome command while scaling each contribution to the connectome edge by the inverse of its two node volumes ( 28 ). This adjustment helps reduce bias caused by larger parcels having a higher probability of being intersected by any streamline ( 29 ). This method offers the advantage of covering anatomically compact or atrophic areas that inherently have a limited grey matter–white matter interface for streamline initiation. For FC matrices, Pearson correlation coefficients were calculated between all ROI pairs using their average regional BOLD time series, followed by Fisher's z-transformation. **** Figure 3 insert here**** 2.8 Network analysis To investigate the effects of TI stimulation on brain functional and structural networks, we first conducted structural-functional connectivity coupling analysis on six subnetworks. Pearson's correlation coefficients were computed between the SC matrix and FC matrix. The correlation coefficients for each participant represented the SC-FC coupling. Consistent with previous research ( 30 ), we only correlated the non-zero edges in the SC matrices with the FC matrices, and primarily focused on changes in SC-FC coupling values before and after intervention. Furthermore, we investigated the effects of TI stimulation on edge-based intra-network connectivity across six subnetworks by directly comparing differences in functional and structural network connectivity matrices both between and within groups. Finally, we calculated several common nodal topological brain network metrics for six functional and structural subnetworks using DPABINet V1.3. Regional nodal metrics included nodal efficiency, nodal degree and nodal betweenness. A sparsity threshold range of 0.05 < S < 0.29, with an interval of 0.01, selected in this study, which was checked by previous similar study ( 31 ). The area under the curve (AUC) across all sparsities was calculated for each network metric and fed into statistical analyses to avoid biases ( 32 ) ( 33 ). 2.9 Correlation analysis Pearson correlation analysis was conducted between the difference in second-stage implicit learning task performance (ΔSIL) and the difference in CN SC-FC coupling (ΔSC-FC), both showing significant changes following TI stimulation, to explore the neural mechanisms underlying TI stimulation-induced enhancement of implicit learning capacity. 2.10 Statistical analysis All statistical analyses were performed using SPSS version 26.0. For SC-FC coupling analysis, Pearson's correlation coefficient was calculated between nonzero edges of the SC network and corresponding elements of the FC matrix, with independent-sample t-tests comparing coupling values between groups. For the fALLL metric analysis of target regions, the focus was primarily on changes before and after stimulation, utilizing within-group paired t-tests. For the associated parameters of the serial reaction time task, within-group paired t-tests were employed, with independent-sample t-tests for between-group comparisons. Questionnaire data including blinding efficacy, AEQ, and SSS were assessed with Pearson's chi-square test, while generalized estimating equations (GEE) with Bonferroni-corrected post hoc comparisons were applied for non-normally distributed data such as MoCA scores. Statistical significance was defined as p < 0.05 across all analyses. In the edge-based intra-network connectivity and nodal topology comparison, DPABI software was applied. Using two-sample t-tests for between-group comparisons and paired t-tests for within-group assessments. The false discovery rate (FDR) was used for multiple comparisons, and the FDR- adjusted p values < 0.05 were considered statistically significant. 3. Results 3.1 Effects of TI stimulation on static and dynamic fALFF in target regions In static fALFF, within-group comparisons revealed that in the TI group, post-stimulation fALFF significantly increased in the right striatum, the target region (t = 2.299, p = 0.030). No significant differences were observed in the cortical control regions beneath electrode placement, including frontal and temporal areas ( p > 0.05). In the sham group, no significant differences were detected in striatal, frontal, or temporal fALFF following stimulation ( p > 0.05) (Fig. 4 A). In mean dynamic fALFF, the TI group similarly demonstrated significant enhancement in the right striatum (t = 2.219, p = 0.036), while cortical control regions beneath electrode placement showed no significant changes ( p > 0.05). In the sham group, post-stimulation mean dynamic fALFF significantly increased in frontal regions (t = 2.349, p = 0.027). No significant differences were observed in striatal and temporal regions ( p > 0.05) (Fig. 4 A). **** Figure 4 insert here**** 3.2 Effects of TI stimulation on SC-FC coupling and topological properties 3.2.1 Effects of SC-FC coupling Changes in the SC-FC coupling values of the CN network were significantly smaller in the TI group compared to the Sham group (t = -2.279, p = 0.027) (Fig. 5 A). No significant between-group differences were observed in the CON, DMN, FPN, ON, or SMN ( p >0.05) (Fig. 5 B-F). **** Figure 5 insert here**** 3.2.2 Effects of edge-based intra-network connectivity For functional networks, within-group comparison in the TI group revealed significantly increased intra-network FC within CN (med cerebellum to lat cerebellum, med cerebellum to med cerebellum) as well as within CON (thalamus to mid insula) following stimulation ( p < 0.05, FDR corrected) (Fig. 6 A, B). No significant differences were found in the between-group comparison. Additionally, no significant changes were observed in either within-group or between-group comparisons for the DMN, FPN, ON, or SMN. For structural networks, no significant changes were observed in either within-group or between-group comparisons for the CN, CON, DMN, FPN, ON, or SMN. **** Figure 6 insert here**** 3.2.3 Effects of network nodal topological metrics For functional networks, within-group comparison in the TI group revealed significantly increased nodal efficiency and nodal degree in the CN (med cerebellum) (Fig. 7 A), increased nodal efficiency in the CON (thalamus, basal ganglia and post insula), and increased nodal degree in the CON (basal ganglia) following stimulation ( p < 0.05, FDR corrected) (Fig. 7 B). Within-group comparison in the Sham group demonstrated increased nodal efficiency in the CON (mid insula), increased nodal efficiency in the SMN (mid insula), and increased nodal degree in the SMN (mid insula and poster insula) ( p < 0.05, FDR corrected) (Fig. S2). No significant differences were found in the between-group comparison. Additionally, no significant changes were observed in either within-group or between-group comparisons for the DMN, FPN, or ON. For structural networks, no significant changes were observed in either within-group or between-group comparisons for the CN, CON, DMN, FPN, ON, or SMN. **** Figure 7 insert here**** 3.3 Effects of TI stimulation on motor learning performance Within-group comparisons for FIL revealed significant improvements in both groups following stimulation, with the Sham group ( p < 0.01) and the TI group ( p 0.05). For SIL, within-group comparisons showed significant improvement only in the TI group following stimulation ( p 0.05). No significant between-group differences were detected ( p > 0.05). **** Figure 8 insert here**** 3.4 Effects of TI stimulation on brain-behavior correlations Pearson correlation analysis revealed a significant negative correlation between the change in CN SC-FC coupling (ΔSC-FC) and the change in second-stage implicit learning task performance (ΔSIL) following TI stimulation (r = -0.372, p = 0.040). **** Figure 9 insert here**** 4. Discussion This study is the first to elucidate the mechanism by which TI stimulation of the striatum optimizes motor learning through brain network reorganization: ( 1 ) At the target region activity level, TI selectively enhances static and dynamic fALFF in the deep right striatum without significant changes in cortical regions, confirming its precise targeting capability for deep nuclei; ( 2 ) At the brain network level, compared to the sham group, the TI group exhibited significantly reduced SC-FC coupling in the CN. Additionally, TI stimulation significantly enhanced functional intra-network connectivity in both the CN and CON. Nodal topological analysis showed that TI stimulation increased both nodal efficiency and degree in CN and CON; ( 3 ) At the learning level, the TI group exhibited specific improvement during the SIL phase (p < 0.01); ( 4 ) In brain-behavior correlations, the degree of SC-FC coupling reduction (ΔSC-FC) in the CN showed a significant negative correlation with SIL performance improvement (ΔSIL), revealing the regulatory role of network decoupling in learning consolidation. 4.1 Effects of TI stimulation on fALFF This study provides the first direct evidence of BOLD activation in deep brain regions following TI stimulation. Following TI stimulation, both static fALFF and mean dynamic fALFF in the right striatal target region showed significant increases, while no notable changes were observed in the frontal/temporal cortical control regions under electrode coverage, indicating that TI neuromodulation can precisely target deep brain regions without affecting superficial cortical layers. The significant fALFF enhancement in the striatal region reflects increased energy of spontaneous low-frequency oscillations (0.01–0.1 Hz) in local neural clusters, indicating improved synchronization of the stimulation target and striatum ( 34 , 35 ). Given the central role of the striatum in motor control and reward pathways, this oscillatory energy enhancement may optimize motor information processing efficiency through modulation of dopaminergic pathway activity, thereby affecting motor learning and executive functions ( 36 , 37 ). 4.2 Effects of TI stimulation on SC-FC coupling Our research findings indicate that TI stimulation targeting the right striatum significantly reduced SC-FC coupling only in the CN. The reduction in SC-FC coupling within the CN suggests that TI stimulation promotes functional organization that is no longer strictly constrained by underlying structural connections, potentially providing greater functional flexibility for cerebellar circuits ( 38 ). This observation aligns with established neuroscientific frameworks indicating that sensory-motor regions exhibit stronger structural-functional coupling to ensure rapid and reliable responses to external stimuli, whereas higher-level cognitive regions (particularly executive control networks) demonstrate lower structural-functional coupling to facilitate more adaptive neural processing for unpredictable complex cognitive tasks ( 38 ). 4.3 Effects of TI Stimulation on intra-network connectivity Further intra-network connectivity analysis demonstrated that following TI stimulation, there was significantly increased FC within the cerebellar network (between medial-lateral cerebellum and within medial cerebellar regions) and the cingulo-opercular network (between thalamus and middle insula). These findings demonstrate that TI stimulation targeting the striatum selectively modulates networks with direct structural connectivity or robust functional associations to the stimulation target, evoking network-specific neuroplastic reorganization. Our findings align with previous research on DBS combined with rs-fMRI. Hanssen et al. demonstrated that DBS significantly suppresses resting tremor in PD patients, an effect closely associated with enhanced FC within cerebellar networks ( 39 ). These results suggest that TI stimulation provides similar network modulation effects as DBS while offering the significant advantage of non-invasive neuromodulation with enhanced safety, potentially serving as an alternative therapeutic approach for PD and other disorders involving deep nuclear pathology ( 40 ). When these enhanced FCs are considered together with our findings of reduced structural-functional constraints in the CN, they likely reflect the cerebellum's newly discovered functional reorganization capability after breaking free from rigid structural determinism. This reorganization may enable more flexible sensory-motor integration and adaptive motor control ( 38 ). 4.3 Effects of TI Stimulation on nodal topological metrics Network nodal topological metrics analysis revealed that TI stimulation enhanced nodal efficiency and degree in the CN within the medial cerebellum, along with increased nodal efficiency in the CON including the thalamus, basal ganglia, and posterior insula in functional networks. No alterations were observed in the DMN, FPN, or ON. No changes in nodal topological properties were detected in structural networks. These findings suggest that TI stimulation specifically modulates functional integration within cerebellar and cingulo-opercular circuits without affecting structural network architecture. Nodes with high centrality can be categorized as network hubs ( 41 , 42 ). The observed elevations in nodal efficiency and degree centrality within the CN and CON networks indicate that TI stimulation potentiated the functional centrality of these regions, thereby facilitating enhanced information processing dynamics within their respective neural circuitry. It is noteworthy that the specific nodes exhibiting enhanced centrality metrics—medial cerebellum, thalamus, and basal ganglia (particularly the striatum)—collectively constitute critical components of the basal ganglia-cerebello-thalamo-cortical circuit. This circuit is highly integrated anatomically and functionally, playing decisive roles in motor control and cognitive processing ( 43 , 44 ). Moreover, clinical evidence has confirmed that the reduced nodal topological properties of the basal ganglia-cerebello-thalamo-cortical circuit are closely associated with motor symptoms in PD ( 45 ). The present study reveals the TI stimulation-induced modulation of nodal topological attributes in the striatum, providing a potential neurological network regulation strategy for PD treatment. 4.4 Effects of TI stimulation on motor learning performance This study found through analysis of implicit learning data that TI intervention demonstrated more significant facilitative effects during repeated implicit learning processes. Specifically, in the FIL, both the TI group and sham stimulation group achieved significant improvement following stimulation, indicating that initial task training itself can yield substantial learning benefits regardless of whether TI stimulation is received. This phenomenon is consistent with previous research findings regarding task familiarization and repeated practice promoting implicit learning ( 46 ). Notably, the difference between groups did not reach statistical significance, suggesting that in the initial learning phase, TI stimulation had not yet demonstrated effects beyond those of general training. In the SIL, the situation differed. Only the TI group showed significant improvement following stimulation, while the sham stimulation group exhibited no apparent changes. This suggests that TI intervention possesses additional facilitative effects in continuous or repeated learning, helping participants overcome potential "learning saturation" or plateau effects that may arise from simple repetitive training, thereby further stimulating neuroplasticity. Related neurostimulation studies similarly demonstrate that non-invasive stimulation techniques more readily exhibit their neuromodulatory advantages when learning task difficulty increases or traditional training effects approach a plateau ( 5 , 6 , 47 , 48 ). 4.5 Effects of TI stimulation on Brain-Behavior Correlations Correlation analysis revealed a significant negative correlation between changes in structure-function coupling within the cerebellar network following TI stimulation and improvements in implicit learning task performance. This finding provides robust empirical support for current theories of neural network plasticity—specifically, that decoupling helps free up neural resources and enhances behavioral performance ( 38 , 49 , 50 ). As a critical hub for motor control and cognitive coordination, the altered coupling state of the cerebellar network may render information processing more flexible, thereby facilitating improvements in implicit learning ability ( 38 , 49 , 50 ). From a theoretical perspective, this result aligns with the "neural dynamic plasticity" hypothesis. Appropriate network decoupling does not signify systemic imbalance but rather grants local brain regions greater autonomy while maintaining overall collaboration, enabling adaptation to environmental and task-related uncertainties ( 38 ). The structure-function decoupling induced by TI stimulation in the cerebellar network provides a neural foundation for individual cognitive flexibility and behavioral adaptation. It also suggests that dynamic brain-behavior regulation may stem from this self-organizing, self-regulating capacity of neural networks ( 51 , 52 ). 4.6 Limitations This study has several limitations. First, the limited sample size and ceiling effects observed in healthy participants may have compromised statistical power to detect significant between-group differences. Second, our analysis focused solely on immediate post-stimulation effects, precluding understanding of long-term neuroplastic changes. Third, while conducted on healthy participants, future research should prioritize clinical populations, particularly PD patients, to explore the potential therapeutic implications of TI stimulation. 5. Conclusion Personalized high-intensity targeted TI stimulation of the striatum enhances striatal activity and drives robust reorganization of brain networks, notably by decoupling SC-FC coupling within the CN and strengthening intra-network connectivity and nodal efficiency in both the CN and CON. These network-level modulations underlie improved implicit learning performance, highlighting the potential of TI stimulation as a precise and effective neuromodulation strategy for facilitating motor learning through deep nuclei and large-scale brain network reconfiguration. Declarations Ethics approval and consent to participate This study approved by the Medical Ethics Committee of Shenzhen University Health Science Center (project number 202400151) and pre-registered on the Chinese Clinical Trial Registry (www.chictr.org.cn; identifier: ChiCTR2500098699). All subjects provided written informed consent and the experimental procedures were performed in accordance with the Declaration of Helsinki. Consent for publication All authors have consented to the publication of this manuscript. Availability of data and materials The datasets generated during and/or analysed during the current study are available in the Mendeley Data repository, https://data.mendeley.com/datasets/pwfddp8m76/1 Competing interests The authors declare that no commercial or financial relationships could be construed as potential. Funding This work was supported by the National Natural Science Foundation of China (11932013), the Shenzhen Stability Support Program (20220810110849002), Guangdong Provincial Philosophy and Social Sciences Project (GD24XTY13), Shenzhen University Excellence Research Program (ZYQN2410). Authors' contributions D.T.: Conceptualization, Methodology, Software, Supervision, Visualization, Writing-Original Draft, Writing–review & editing; L.Q.: Data curation, Formal analysis, Writing-Original Draft, Visualization; L.H.: Investigation; Y.J.: Investigation; S.G.: Investigation; Z.H.: Conceptualization, Methodology; L.C.: Conceptualization, Methodology; S.S.: Conceptualization, Methodology, Project administration; G.Z.: Conceptualization, Methodology, Project administration; C.C.: Conceptualization, Methodology, Project administration; Z.Z: Conceptualization, Funding acquisition, Methodology, Writing-Review & Editing, Supervision, Project administration; All authors have read and agreed to the published version of the manuscript. Acknowledgments The authors thank all volunteers who participated in the study and the staff at the Magnetic Resonance Imaging (MRI) Center of Shenzhen University for their selfless and valuable assistance. References Cox J, Witten IB. 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Functional connectivity in Parkinson's disease candidates for deep brain stimulation. NPJ Parkinsons Dis. 2022;8(1):4. Doyon J, Benali H. Reorganization and plasticity in the adult brain during learning of motor skills. Curr Opin Neurobiol. 2005;15(2):161-7. Ma R, Xia X, Zhang W, Lu Z, Wu Q, Cui J, et al. High Gamma and Beta Temporal Interference Stimulation in the Human Motor Cortex Improves Motor Functions. Front Neurosci. 2021;15:800436. Qi S, Liu X, Yu J, Liang Z, Liu Y, Wang X. Temporally interfering electric fields brain stimulation in primary motor cortex of mice promotes motor skill through enhancing neuroplasticity. Brain Stimul. 2024;17(2):245-57. Boven E, Pemberton J, Chadderton P, Apps R, Costa RP. Cerebro-cerebellar networks facilitate learning through feedback decoupling. Nat Commun. 2023;14(1):51. Manto M, Bower JM, Conforto AB, Delgado-García JM, da Guarda SN, Gerwig M, et al. Consensus paper: roles of the cerebellum in motor control--the diversity of ideas on cerebellar involvement in movement. Cerebellum. 2012;11(2):457-87. Uddin LQ. Cognitive and behavioural flexibility: neural mechanisms and clinical considerations. Nat Rev Neurosci. 2021;22(3):167-79. Kupis L, Goodman ZT, Kornfeld S, Hoang S, Romero C, Dirks B, et al. Brain Dynamics Underlying Cognitive Flexibility Across the Lifespan. Cereb Cortex. 2021;31(11):5263-74. Additional Declarations No competing interests reported. Supplementary Files RevisedSupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2026 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted Editorial decision: Revision requested 01 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviews received at journal 27 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers invited by journal 28 Jul, 2025 Submission checks completed at journal 27 Jul, 2025 First submitted to journal 25 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7114172","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492091141,"identity":"a267c5bf-2f79-49a7-a302-c4a459aacfb9","order_by":0,"name":"Dongsheng Tang","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Tang","suffix":""},{"id":492091142,"identity":"86f9f9a4-a16e-4658-9fac-a6d558146220","order_by":1,"name":"Lang Qin","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Lang","middleName":"","lastName":"Qin","suffix":""},{"id":492091143,"identity":"df63929a-75e3-4ce1-90d1-cddb67eb3ff0","order_by":2,"name":"Longfei Hu","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Longfei","middleName":"","lastName":"Hu","suffix":""},{"id":492091144,"identity":"2dd92cd4-f007-4895-b0d7-d05026f9a194","order_by":3,"name":"Yixuan Jian","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Yixuan","middleName":"","lastName":"Jian","suffix":""},{"id":492091145,"identity":"60aa7023-fbb4-4155-bc2e-5c9faef6a3b3","order_by":4,"name":"Siqi Gao","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Gao","suffix":""},{"id":492091146,"identity":"8ec66b38-7954-4778-80a1-a3d3d616e93e","order_by":5,"name":"Zhenhe Huang","email":"","orcid":"","institution":"Affiliated Nanshan Hospital of Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Zhenhe","middleName":"","lastName":"Huang","suffix":""},{"id":492091147,"identity":"d821f052-8fcb-46f7-b776-a635316bb17e","order_by":6,"name":"Lingliang Cai","email":"","orcid":"","institution":"Affiliated Nanshan Hospital of Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Lingliang","middleName":"","lastName":"Cai","suffix":""},{"id":492091148,"identity":"3389c3a4-4f81-4de4-afdc-385098335892","order_by":7,"name":"Xueyun Shao","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Xueyun","middleName":"","lastName":"Shao","suffix":""},{"id":492091149,"identity":"f03abf31-f8f7-42e2-842d-c5749d82f4c8","order_by":8,"name":"Gang Zhao","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Zhao","suffix":""},{"id":492091150,"identity":"5182b70b-9055-4995-a940-dbc6406a53d3","order_by":9,"name":"Chunqi Chang","email":"","orcid":"","institution":"Shenzhen University Medical School, Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Chunqi","middleName":"","lastName":"Chang","suffix":""},{"id":492091151,"identity":"9b1469a0-f625-41ed-af8d-c61016d5c4f4","order_by":10,"name":"Zhiqiang Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIie3Rv2vCQBTA8RcO4vIg6yst5l944pjBfyUQMMsNEUFwLuiiu2D/idKlo+WgU+ZyYId0cXIRlwgZvDQ4xsRN8L7TcdyH+wVgs91jBCCAAc3QyaqpsD0RfAOpcqkV8deL/jFJghdvPf+Z5oUCryMZTp/1xHlLub/iGOk3nWwRFTwt9uws03oiSHKErBC0HG6BFLCWLJxZPXENUSXxDRnlrGDQRJBk77UkrONvwNDsQg2EaDgWaO7S01I848ZcKt0lX8srxF9FH0csgkFXx3+HvAi63jx6z05XyOUVyjP+/2T5p7BpBBXpZC0W2mw22yN2BkseSHzPBGeVAAAAAElFTkSuQmCC","orcid":"","institution":"Shenzhen University","correspondingAuthor":true,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-07-13 15:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7114172/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7114172/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12984-025-01865-9","type":"published","date":"2026-01-02T15:58:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88221820,"identity":"33b6717b-6aa1-4a27-b22f-280baf15b31a","added_by":"auto","created_at":"2025-08-04 08:01:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":357951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design. \u003c/strong\u003e(A)\u003cstrong\u003e \u003c/strong\u003eMagnetic resonance imaging experimental protocol. (B) Serial reaction time task protocol. Note: TI, temporal interference stimulation; rs-fMRI, resting-state functional magnetic resonance imaging; DTI, diffusion tensor imaging; SRT, serial reaction time task.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/668e85ee2735c7110eecf38c.png"},{"id":88223432,"identity":"3c198f58-a261-4a5a-856e-ac73eb9f1b99","added_by":"auto","created_at":"2025-08-04 08:09:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":381729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConcept map of electric field modeling with the right striatum.\u003c/strong\u003e Note: The colors show the temporal interference exposure (electric field modulation magnitude).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/d94771a2eaa163ccf077a9a4.png"},{"id":88221815,"identity":"f60ce71d-77a8-49c9-9282-7f80d374a205","added_by":"auto","created_at":"2025-08-04 08:01:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":527075,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe workflow of this study. \u003c/strong\u003eNote: CN, cerebellum network; CON, cingulo-opercular network; DMN, default mode network; FPM, fronto-parietal network; ON, occipital network; SMN, sensorimotor network.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/720eb360d6b1317c00431647.png"},{"id":88221830,"identity":"215a8ee1-b49a-498f-bb86-1aff29ef4fca","added_by":"auto","created_at":"2025-08-04 08:01:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":419390,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003efALFF and mean dynamic fALFF activity in stimulation targets and control regions (frontal and temporal lobes) beneath electrode placement in the TI and Sham groups. \u003c/strong\u003eNote: TI, temporal interference stimulation; Sham, sham stimulation; fALFF, fractional amplitude of low-frequency fluctuations; Pre, pre-stimulation; Post, post-stimulation; *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05; ns, not significant (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/2028a31b8c01780ebfe6f77b.png"},{"id":88223433,"identity":"3af75e6c-4829-4c05-9ef6-5f087a94c702","added_by":"auto","created_at":"2025-08-04 08:09:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":285293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in the SC-FC coupling values of brain networks in the TI and Sham groups. \u003c/strong\u003eThe violin figures show SC-FC coupling values in the: (A) Cerebellum, (B) Cingulo-Opercular, (C) Default Mode, (D) Fronto-Parietal, (E) Occipital, and (F) Sensorimotor networks. Note: TI, temporal interference stimulation; Sham, sham stimulation; SC-FC coupling, structural-functional connectivity coupling; *\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/6eb6e08be870ffe4030fc9ae.png"},{"id":88221823,"identity":"dc18e159-63ea-467d-bef8-8070bc80b92e","added_by":"auto","created_at":"2025-08-04 08:01:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":620891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEdge-based intra-network connectivity changes following stimulation in the TI group. \u003c/strong\u003e(A) Cerebellar network. (B) Cingulo-opercular network. Note: med-Cere, med cerebellum; lat-Cere, lat cerebellum; THA, thalamus; mid-INS, mid insula.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/df7d2d643b81de47ddd289fd.png"},{"id":88223431,"identity":"23c04587-7dc6-4874-aa3c-3181ecb3a762","added_by":"auto","created_at":"2025-08-04 08:09:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":622544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in nodal topological metrics in functional networks following stimulation in TI group.\u003c/strong\u003e (A) Cerebellar network. (B) Cingulo-opercular network. Note: Red dots indicate brain regions showing significantly increased nodal topological metrics post-stimulation compared to baseline.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/15d797007b9169527efc31f3.png"},{"id":88221838,"identity":"77160604-861e-423d-9984-cd139e9a1882","added_by":"auto","created_at":"2025-08-04 08:01:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":286820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReaction times for each block of the serial reaction time task in the TI group and Sham group. \u003c/strong\u003eNote: TI, temporal interference stimulation; Sham, sham stimulation; ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ns, not significant (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/9f10a5e0f123cc9f26cf32bf.png"},{"id":88221840,"identity":"f10578dc-d078-4d44-8d58-4c1eb521aabd","added_by":"auto","created_at":"2025-08-04 08:01:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":267451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePearson correlation analysis between changes in SIL following TI stimulation (ΔSIL) and changes in SC-FC coupling of the CN following TI stimulation (ΔSC-FC). \u003c/strong\u003eNote: TI, temporal interference stimulation; SIL, second-phase implicit learning; CN, cerebellar network; SC-FC coupling, structural-functional connectivity coupling.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/79a80940348012c402208d71.png"},{"id":99545299,"identity":"863ca212-6e80-43ed-8ebb-d08f80596cc8","added_by":"auto","created_at":"2026-01-05 16:05:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5181415,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/5dc75c0e-0065-4170-91eb-c109bae4af64.pdf"},{"id":88221841,"identity":"16fbc26f-c52b-4350-b3b9-8c922e2caab4","added_by":"auto","created_at":"2025-08-04 08:01:46","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":72756788,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7114172/v1/df4f7801f44ee3d75a49f1c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Personalized High-Intensity Temporal Interference Stimulation Decouples Cerebellar Networks to Enhance Implicit Learning","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe striatum, as a core node of the basal ganglia, plays a pivotal role in complex cognitive functions such as motor learning and control mechanisms (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In recent years, increasing evidence suggests that the striatum plays a crucial role in implicit learning, especially in unconscious skill acquisition and behavioral adaptation (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Precise regulation of striatal function shows immense application potential in fields such as brain function research, neuropsychiatric intervention, and cognitive enhancement (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, due to the striatum's deep brain structural location, traditional non-invasive stimulation techniques such as transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS) have significant limitations in terms of stimulation depth and localization accuracy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Temporal Interference (TI) Stimulation, by forming a low-frequency interference field within brain tissue through two high-frequency currents, achieves precise, non-invasive regulation of deep neural structures (9). TI stimulation not only avoids over-activation of superficial tissues, significantly reduces side effects, but also possesses higher spatial resolution, thus being widely considered a powerful tool for non-invasive deep brain modulation (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough the safety and feasibility of TI technology have been initially verified, for example, Liu's study showed that TI stimulation of the globus pallidus interna in Parkinson's patients significantly improved motor function (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Adam's study also showed that stimulating the subthalamic nucleus in Parkinson's patients could reverse abnormal brain discharge (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, there is currently no research providing direct evidence of blood oxygen level-dependent (BOLD) signal activation in deep brain target regions following TI stimulation. This lack of evidence may be attributed to insufficient electric field intensity at the target stimulation area. Cassar\u0026agrave; et al indicated that although lower field strengths (0.2\u0026ndash;0.5 V/m) can modulate neural activity under specific conditions, target stimulation field strength greater than 1V/m is preferred to achieve more robust and effective stimulation (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Therefore, increasing the intensity of TI stimulation is a key factor in achieving effective stimulation. At the same time, there are significant differences in brain structure among individuals, and using the same stimulation parameters led to unsatisfactory results in previous studies (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). An electrostimulation scheme based on individual brain structure will greatly improve the effectiveness of stimulation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Furthermore, the systematic study of TI's regulation of striatal network function and its impact on implicit learning behavior remains unclear.\u003c/p\u003e\u003cp\u003eTherefore, this study takes individualized high-intensity TI stimulation as a starting point, optimizing parameters based on individual brain structural differences, and combining multimodal brain imaging and behavioral indicators to systematically evaluate the causal effect of TI on the decoupling of striatum-related brain networks and the improvement of implicit learning performance. It hypothesize that personalized high-intensity TI stimulation of the striatum can effectively regulate the functional and structural networks of the brain closely associated with the striatum, and promote individual implicit learning ability, providing new theoretical and methodological bases for non-invasive precise regulation of deep brain regions and cognitive function intervention.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Participants\u003c/h2\u003e\u003cp\u003e26 healthy male participants (mean age 20.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69 years) were included in the study. All participants were right-handed (mean laterality quotient 85.90\u0026thinsp;\u0026plusmn;\u0026thinsp;17.44), had no history of neurological or severe psychiatric diseases, medication use, or metal implants. After quality-control procedures, three participants were excluded from serial reaction time (SRT) tasks analysis owing to explicit awareness of the task repetition. This study approved by the Medical Ethics Committee of Shenzhen University Health Science Center (project number 202400151) and pre-registered on the Chinese Clinical Trial Registry (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.chictr.org.cn\u003c/span\u003e\u003cspan address=\"http://www.chictr.org.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; identifier: ChiCTR2500098699). All subjects provided written informed consent and the experimental procedures were performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study design\u003c/h2\u003e\u003cp\u003eThe study employed a randomized double-blind sham-controlled cross-over design. All participants underwent two experiments: the magnetic resonance imaging (MRI) experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) and the SRT task experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In the MRI experiment, all participants underwent three experimental sessions. At the first session, demographic data were collected, and high-resolution T1-weighted MRI scans were acquired for personalized stimulation electrode protocol optimization. For the subsequent two sessions, participants were randomly assigned to either the TI group (TI stimulation) or the Sham group (sham stimulation). Resting-state fMRI and diffusion tensor imaging (DTI) data were collected before and after stimulation during each session. Additionally, participants completed the Stanford Sleepiness Scale (SSS) prior to stimulation and the Adverse Events Questionnaire (AEQ) following stimulation. The Montreal Cognitive Assessment (MoCA) was administered both before and after each stimulation session (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the SRT task experiment, participants underwent the same cross-over design across two sessions, with SRT performance assessed before and after each stimulation intervention. The SRT task was performed on laptops using E-Prime V3.0 software (Psychology Software Tools, Inc., Pittsburgh, PA, USA). Participants were instructed to position their non-dominant left hand over the response pad, with the index, middle, ring, and little fingers placed over buttons 4, 3, 2, and 1, respectively. Participants were instructed to respond as rapidly as possible by pressing the corresponding keys with their pre-positioned fingers in response to the numbers indicated by asterisks displayed on the computer screen. The stimuli disappeared immediately after pressing any key and reappeared after a 500 ms interval (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSRT task comprised eight blocks, each containing 120 trials. Blocks 1 and 6, termed random (\u0026ldquo;R\u0026rdquo;) blocks, presented the sequence of asterisks in a pseudorandom order. For both R blocks, the asterisks appeared with equal frequency at each position, and the sequence was constrained to prevent runs of four consecutive unique positions (e.g., 1234 or 4321) or trills of four elements (e.g., 1212). In contrast, Blocks 2\u0026ndash;5 and 7\u0026ndash;8, designated as sequence (\u0026ldquo;S\u0026rdquo;) blocks, featured a fixed 12-element sequence of asterisk positions (121423413243), which was repeated ten times per block. The task design encompassed two distinct learning phases. Denoting the reaction time of block n as RTn, the first implicit learning (FIL) was defined as FIL\u0026thinsp;=\u0026thinsp;RT1 \u0026ndash; (RT2\u0026thinsp;+\u0026thinsp;RT3\u0026thinsp;+\u0026thinsp;RT4\u0026thinsp;+\u0026thinsp;RT5)/4, while the second implicit learning (SIL) was defined as SIL\u0026thinsp;=\u0026thinsp;RT6 \u0026ndash; (RT7\u0026thinsp;+\u0026thinsp;RT8)/2. Prior to the experimental session, participants completed a 60-trial practice block presented in a random order to ensure comprehension of the task instructions. A 30-second rest interval was provided between each block. Importantly, participants were not informed about the presence of a repeating sequence. Upon completion of the experiment, participants were queried as to whether they had noticed any repeated sequences. Those who reported awareness of the sequence were excluded from the final analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003e****\u003c/b\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003einsert here****\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Stimulation parameter and personalized stimulus montage protocol\u003c/h2\u003e\u003cp\u003eThe intervention was conducted by the NervioX-1000 neuromodulation system (Suzhou Brain Dome Technology Co., Ltd., Suzhou, China). The stimulation region-of-interest (ROI) was located in the right striatum. Two channels of high-frequency alternating current were applied (I₁: 2 kHz and I₂: 2.02 kHz), generating a low frequency interference modulation of 20 Hz targeting the ROI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The peak-to‐peak amplitude of the current was set at 10 mA. The duration of TI protocol was 20 min. The parameters of Sham group protocol were identical to those of TI group protocol, except that the current was only delivered at the first 30 s and the last 30 s of the whole stimulation session. The impedance was kept below 15 kΩ during stimulation. The safety profile of these stimulation parameters (exhibiting no adverse cognitive effects or side effects) and robust blinding efficacy have been validated in the present study. Detailed information is in \u003cem\u003eSupplementary Section B.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe electrode locations were optimized for each participant. This was done using the SimNIBS software to create a finite element model (FEM) of the brain from the structural images of the subject (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Specifically, we segmented tissues and assigned conductivities, placed electrodes following the standard 10\u0026ndash;10 EEG system of 64 channels, generated tetrahedral head meshes via Gmsh, performed FEM, and then calculated the electric field. The right striatum was targeted at MNI coordinates (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) from Wessel et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), using a 10-mm spherical ROI to optimize electric field intensity. This approach achieved an average electric field intensity of 2.92 V/m in the target region, ensuring precise neuromodulation (for detailed electrode placement and electromagnetic computation data, see \u003cem\u003eSupplementary Section A\u003c/em\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003e****\u003c/b\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003einsert here****\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Image acquisition\u003c/h2\u003e\u003cp\u003eAll MRI data were acquired at the Center for Magnetic Resonance Imaging, Shenzhen University using a Siemens Prisma 3.0-Tesla system (Erlangen, Germany) equipped with a 64-channel head coil. Rs-fMRI scans were acquired using gradient-echo echo-planar imaging (EPI) sequences with the following parameters: 3\u0026times;3\u0026times;3 mm\u003csup\u003e3\u003c/sup\u003e voxel size, repetition time (TR)\u0026thinsp;=\u0026thinsp;1000 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;30 ms, flip angle (FA)\u0026thinsp;=\u0026thinsp;66\u0026deg;, field of view (FOV)\u0026thinsp;=\u0026thinsp;210\u0026times;210 mm\u003csup\u003e2\u003c/sup\u003e, total acquisition time (TA)\u0026thinsp;=\u0026thinsp;8.32 min, 488 volumes. T1 images were acquired using a 3D MPRAGE (magnetization-prepared rapid gradient echo) sequence with the following parameters: 1\u0026times;1\u0026times;1 mm\u003csup\u003e3\u003c/sup\u003e voxel size, TR\u0026thinsp;=\u0026thinsp;2300 ms, TE\u0026thinsp;=\u0026thinsp;2.26 ms, inverse time\u0026thinsp;=\u0026thinsp;1000 ms, FA\u0026thinsp;=\u0026thinsp;8\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;256\u0026times;232 mm\u003csup\u003e2\u003c/sup\u003e, TA\u0026thinsp;=\u0026thinsp;8.92 min, 192 volumes. A diffusion-weighted spin-echo echo-planar imaging sequence was acquired with the following parameters: 1.5\u0026times;1.5\u0026times;1.5 mm\u003csup\u003e3\u003c/sup\u003e voxel size, TR\u0026thinsp;=\u0026thinsp;5600 ms, TE\u0026thinsp;=\u0026thinsp;82.0 ms, FOV\u0026thinsp;=\u0026thinsp;210\u0026times;210 mm\u003csup\u003e2\u003c/sup\u003e, TA\u0026thinsp;=\u0026thinsp;6.52 min, 66 volumes, phase encoding direction\u0026thinsp;=\u0026thinsp;P to A, 64 directions (b\u0026thinsp;=\u0026thinsp;1500s/mm\u0026sup2;), 1 b0). Participants wore earplugs for noise protection and were instructed to remain awake, still, and focused on a fixation cross with open eyes, avoiding directed thoughts during the scanning sessions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data preprocessing\u003c/h2\u003e\u003cp\u003eThe rs-fMRI data were preprocessed using DPARSF V8.0 toolboxes (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The first 8 volumes were discarded. Images then underwent slice timing, head motion correction, and spatial normalization to the Montreal Neurological Institute (MNI) space using the normalization parameters estimated during unified segmentation of structural T1 images. After normalization, the linear trends were removed and the nuisance variables, including Friston 24 head motion parameters, white matter (WM), cerebrospinal fluid signals (CSF) and global signal (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), were regressed out from the functional signal. Finally, band-pass filtering (0.01\u0026ndash;0.1 Hz) was performed on the images to reserve low-frequency information. To mitigate head motion effects, volume-based frame-wise displacement (FD) was calculated (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Timepoints with FD\u0026thinsp;\u0026gt;\u0026thinsp;0.2 mm were marked as problematic and included as separate regressors during nuisance covariate regression (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Participants with mean FD exceeding three standard deviations were excluded from analysis. Finally, 2 participants were excluded under the head motion control criteria, and 26 participants were included in the subsequent analysis.\u003c/p\u003e\u003cp\u003eDWI data were pre-processed using Mrtrix3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mrtrix.org/\u003c/span\u003e\u003cspan address=\"http://www.mrtrix.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). With the following operations: denoise, remove Gibbs Ringing artifacts. Using FSL\u0026rsquo;s eddy tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) inhomogeneity distortion correction, corrected for eddy currents and motion artifacts (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Bias field correction using the N4 algorithm as provided in ANTs (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ANTsX/ANTs\u003c/span\u003e\u003cspan address=\"https://github.com/ANTsX/ANTs\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Using dhollander method to estimate response function for spherical deconvolution and multi-shell multi-tissue CSD to estimate fiber orientation distributions from diffusion data using spherical deconvolution. Before performing streamlines tractography, conduct multi-tissue informed log-domain intensity normalization. Performing streamlines tractography using second-order integration over fiber orientation distributions option, 10\u0026nbsp;million streamlines are to be selected. Finally, tcksift was employed to filter the whole-brain fiber-tracking dataset, ensuring that the streamline densities matched the fiber orientation distribution lobe integrals.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Calculation of static and dynamic fALFF in target regions\u003c/h2\u003e\u003cp\u003eThe analysis was conducted using the unfiltered, preprocessed data. A fast Fourier transform was performed on whole-brain voxels to convert the BOLD signal into the frequency-domain power spectrum. The square root of the power spectrum was calculated at each frequency, and the average value within the range of 0.01 to 0.10 Hz was used to calculate the static fALFF metric. The resulting static fALFF maps were further subjected to standardization and spatial smoothing with a full width at half maximum (FWHM) of 4 mm. The analysis of dynamic fALFF was performed using the DPABI-based dynamic analysis toolbox. The Hamming sliding window was selected for the whole-brain blood oxygenation level dependent signal time series :100TR (100s) window length and step width of 3 TR (3s) were selected for dynamic fALFF analysis (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The mean dynamic fALFF metric was then calculated across all voxels within the 127 windows for each participant to assess the dynamic characteristics of fALFF. For regions of interest (ROIs), we extracted both static and mean dynamic fALFF values from a 5-mm radius sphere centered at the MNI coordinates (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), corresponding to the right striatum (the target of stimulation). As a control region, we also derived measures from bilateral middle frontal gyrus and middle temporal gyrus, defined based on the AAL template, as well as from the area subjacent to the electrode placement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Functional and structural network construction\u003c/h2\u003e\u003cp\u003eThe structural and functional networks composed of 160 ROIs in the Dos-160 template. Brain edges were defined by connectivity between brain nodes. For each node, a sphere was created with a 5 mm radius, centered on the atlas coordinates. According to the study of Dosenbach et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), the 160 ROIs have been assigned into six subnetworks, including cerebellum network (CN, 18 ROIs), cingulo-opercular network (CON, 32 ROIs), default mode network (DMN, 34 ROIs), fronto-parietal network (FPN, 21 ROIs), occipital network (ON, 22ROIs) and sensorimotor network (SMN, 33 ROIs). Subsequently, further network analyses were conducted on these six subnetworks (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAfter preprocessing, structure and function connectivity matrices were created for every individual using DPABINet V1.3 and MRtrix3 software (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). For SC matrices, weighted, undirected connectomes were constructed using the tck2connectome command while scaling each contribution to the connectome edge by the inverse of its two node volumes (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This adjustment helps reduce bias caused by larger parcels having a higher probability of being intersected by any streamline (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This method offers the advantage of covering anatomically compact or atrophic areas that inherently have a limited grey matter\u0026ndash;white matter interface for streamline initiation. For FC matrices, Pearson correlation coefficients were calculated between all ROI pairs using their average regional BOLD time series, followed by Fisher's z-transformation.\u003c/p\u003e\u003cp\u003e\u003cb\u003e****\u003c/b\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003einsert here****\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Network analysis\u003c/h2\u003e\u003cp\u003eTo investigate the effects of TI stimulation on brain functional and structural networks, we first conducted structural-functional connectivity coupling analysis on six subnetworks. Pearson's correlation coefficients were computed between the SC matrix and FC matrix. The correlation coefficients for each participant represented the SC-FC coupling. Consistent with previous research (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), we only correlated the non-zero edges in the SC matrices with the FC matrices, and primarily focused on changes in SC-FC coupling values before and after intervention. Furthermore, we investigated the effects of TI stimulation on edge-based intra-network connectivity across six subnetworks by directly comparing differences in functional and structural network connectivity matrices both between and within groups. Finally, we calculated several common nodal topological brain network metrics for six functional and structural subnetworks using DPABINet V1.3. Regional nodal metrics included nodal efficiency, nodal degree and nodal betweenness. A sparsity threshold range of 0.05\u0026thinsp;\u0026lt;\u0026thinsp;S\u0026thinsp;\u0026lt;\u0026thinsp;0.29, with an interval of 0.01, selected in this study, which was checked by previous similar study (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The area under the curve (AUC) across all sparsities was calculated for each network metric and fed into statistical analyses to avoid biases (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Correlation analysis\u003c/h2\u003e\u003cp\u003ePearson correlation analysis was conducted between the difference in second-stage implicit learning task performance (ΔSIL) and the difference in CN SC-FC coupling (ΔSC-FC), both showing significant changes following TI stimulation, to explore the neural mechanisms underlying TI stimulation-induced enhancement of implicit learning capacity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using SPSS version 26.0. For SC-FC coupling analysis, Pearson's correlation coefficient was calculated between nonzero edges of the SC network and corresponding elements of the FC matrix, with independent-sample t-tests comparing coupling values between groups. For the fALLL metric analysis of target regions, the focus was primarily on changes before and after stimulation, utilizing within-group paired t-tests. For the associated parameters of the serial reaction time task, within-group paired t-tests were employed, with independent-sample t-tests for between-group comparisons. Questionnaire data including blinding efficacy, AEQ, and SSS were assessed with Pearson's chi-square test, while generalized estimating equations (GEE) with Bonferroni-corrected post hoc comparisons were applied for non-normally distributed data such as MoCA scores. Statistical significance was defined as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 across all analyses.\u003c/p\u003e\u003cp\u003eIn the edge-based intra-network connectivity and nodal topology comparison, DPABI software was applied. Using two-sample t-tests for between-group comparisons and paired t-tests for within-group assessments. The false discovery rate (FDR) was used for multiple comparisons, and the FDR- adjusted \u003cem\u003ep\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Effects of TI stimulation on static and dynamic fALFF in target regions\u003c/h2\u003e\u003cp\u003eIn static fALFF, within-group comparisons revealed that in the TI group, post-stimulation fALFF significantly increased in the right striatum, the target region (t\u0026thinsp;=\u0026thinsp;2.299, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030). No significant differences were observed in the cortical control regions beneath electrode placement, including frontal and temporal areas (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the sham group, no significant differences were detected in striatal, frontal, or temporal fALFF following stimulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn mean dynamic fALFF, the TI group similarly demonstrated significant enhancement in the right striatum (t\u0026thinsp;=\u0026thinsp;2.219, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), while cortical control regions beneath electrode placement showed no significant changes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the sham group, post-stimulation mean dynamic fALFF significantly increased in frontal regions (t\u0026thinsp;=\u0026thinsp;2.349, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027). No significant differences were observed in striatal and temporal regions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003cb\u003e****\u003c/b\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003einsert here****\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Effects of TI stimulation on SC-FC coupling and topological properties\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Effects of SC-FC coupling\u003c/h2\u003e\u003cp\u003eChanges in the SC-FC coupling values of the CN network were significantly smaller in the TI group compared to the Sham group (t = -2.279, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). No significant between-group differences were observed in the CON, DMN, FPN, ON, or SMN (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e****\u003c/b\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003einsert here****\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Effects of edge-based intra-network connectivity\u003c/h2\u003e\u003cp\u003eFor functional networks, within-group comparison in the TI group revealed significantly increased intra-network FC within CN (med cerebellum to lat cerebellum, med cerebellum to med cerebellum) as well as within CON (thalamus to mid insula) following stimulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR corrected) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). No significant differences were found in the between-group comparison. Additionally, no significant changes were observed in either within-group or between-group comparisons for the DMN, FPN, ON, or SMN.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor structural networks, no significant changes were observed in either within-group or between-group comparisons for the CN, CON, DMN, FPN, ON, or SMN.\u003c/p\u003e\u003cp\u003e\u003cb\u003e****\u003c/b\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003einsert here****\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Effects of network nodal topological metrics\u003c/h2\u003e\u003cp\u003eFor functional networks, within-group comparison in the TI group revealed significantly increased nodal efficiency and nodal degree in the CN (med cerebellum) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), increased nodal efficiency in the CON (thalamus, basal ganglia and post insula), and increased nodal degree in the CON (basal ganglia) following stimulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR corrected) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Within-group comparison in the Sham group demonstrated increased nodal efficiency in the CON (mid insula), increased nodal efficiency in the SMN (mid insula), and increased nodal degree in the SMN (mid insula and poster insula) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR corrected) (Fig. S2). No significant differences were found in the between-group comparison. Additionally, no significant changes were observed in either within-group or between-group comparisons for the DMN, FPN, or ON.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor structural networks, no significant changes were observed in either within-group or between-group comparisons for the CN, CON, DMN, FPN, ON, or SMN.\u003c/p\u003e\u003cp\u003e\u003cb\u003e****\u003c/b\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003einsert here****\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Effects of TI stimulation on motor learning performance\u003c/h2\u003e\u003cp\u003eWithin-group comparisons for FIL revealed significant improvements in both groups following stimulation, with the Sham group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and the TI group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant between-group differences were observed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eFor SIL, within-group comparisons showed significant improvement only in the TI group following stimulation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the Sham group exhibited no significant changes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). No significant between-group differences were detected (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003e****\u003c/b\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e \u003cb\u003einsert here****\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Effects of TI stimulation on brain-behavior correlations\u003c/h2\u003e\u003cp\u003ePearson correlation analysis revealed a significant negative correlation between the change in CN SC-FC coupling (ΔSC-FC) and the change in second-stage implicit learning task performance (ΔSIL) following TI stimulation (r = -0.372, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040).\u003c/p\u003e\u003cp\u003e\u003cb\u003e****\u003c/b\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e \u003cb\u003einsert here****\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first to elucidate the mechanism by which TI stimulation of the striatum optimizes motor learning through brain network reorganization: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) At the target region activity level, TI selectively enhances static and dynamic fALFF in the deep right striatum without significant changes in cortical regions, confirming its precise targeting capability for deep nuclei; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) At the brain network level, compared to the sham group, the TI group exhibited significantly reduced SC-FC coupling in the CN. Additionally, TI stimulation significantly enhanced functional intra-network connectivity in both the CN and CON. Nodal topological analysis showed that TI stimulation increased both nodal efficiency and degree in CN and CON; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) At the learning level, the TI group exhibited specific improvement during the SIL phase (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) In brain-behavior correlations, the degree of SC-FC coupling reduction (ΔSC-FC) in the CN showed a significant negative correlation with SIL performance improvement (ΔSIL), revealing the regulatory role of network decoupling in learning consolidation.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Effects of TI stimulation on fALFF\u003c/h2\u003e\u003cp\u003eThis study provides the first direct evidence of BOLD activation in deep brain regions following TI stimulation. Following TI stimulation, both static fALFF and mean dynamic fALFF in the right striatal target region showed significant increases, while no notable changes were observed in the frontal/temporal cortical control regions under electrode coverage, indicating that TI neuromodulation can precisely target deep brain regions without affecting superficial cortical layers. The significant fALFF enhancement in the striatal region reflects increased energy of spontaneous low-frequency oscillations (0.01\u0026ndash;0.1 Hz) in local neural clusters, indicating improved synchronization of the stimulation target and striatum (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Given the central role of the striatum in motor control and reward pathways, this oscillatory energy enhancement may optimize motor information processing efficiency through modulation of dopaminergic pathway activity, thereby affecting motor learning and executive functions (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Effects of TI stimulation on SC-FC coupling\u003c/h2\u003e\u003cp\u003eOur research findings indicate that TI stimulation targeting the right striatum significantly reduced SC-FC coupling only in the CN. The reduction in SC-FC coupling within the CN suggests that TI stimulation promotes functional organization that is no longer strictly constrained by underlying structural connections, potentially providing greater functional flexibility for cerebellar circuits (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This observation aligns with established neuroscientific frameworks indicating that sensory-motor regions exhibit stronger structural-functional coupling to ensure rapid and reliable responses to external stimuli, whereas higher-level cognitive regions (particularly executive control networks) demonstrate lower structural-functional coupling to facilitate more adaptive neural processing for unpredictable complex cognitive tasks (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Effects of TI Stimulation on intra-network connectivity\u003c/h2\u003e\u003cp\u003eFurther intra-network connectivity analysis demonstrated that following TI stimulation, there was significantly increased FC within the cerebellar network (between medial-lateral cerebellum and within medial cerebellar regions) and the cingulo-opercular network (between thalamus and middle insula). These findings demonstrate that TI stimulation targeting the striatum selectively modulates networks with direct structural connectivity or robust functional associations to the stimulation target, evoking network-specific neuroplastic reorganization. Our findings align with previous research on DBS combined with rs-fMRI. Hanssen et al. demonstrated that DBS significantly suppresses resting tremor in PD patients, an effect closely associated with enhanced FC within cerebellar networks (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). These results suggest that TI stimulation provides similar network modulation effects as DBS while offering the significant advantage of non-invasive neuromodulation with enhanced safety, potentially serving as an alternative therapeutic approach for PD and other disorders involving deep nuclear pathology (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhen these enhanced FCs are considered together with our findings of reduced structural-functional constraints in the CN, they likely reflect the cerebellum's newly discovered functional reorganization capability after breaking free from rigid structural determinism. This reorganization may enable more flexible sensory-motor integration and adaptive motor control (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Effects of TI Stimulation on nodal topological metrics\u003c/h2\u003e\u003cp\u003eNetwork nodal topological metrics analysis revealed that TI stimulation enhanced nodal efficiency and degree in the CN within the medial cerebellum, along with increased nodal efficiency in the CON including the thalamus, basal ganglia, and posterior insula in functional networks. No alterations were observed in the DMN, FPN, or ON. No changes in nodal topological properties were detected in structural networks. These findings suggest that TI stimulation specifically modulates functional integration within cerebellar and cingulo-opercular circuits without affecting structural network architecture.\u003c/p\u003e\u003cp\u003eNodes with high centrality can be categorized as network hubs (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The observed elevations in nodal efficiency and degree centrality within the CN and CON networks indicate that TI stimulation potentiated the functional centrality of these regions, thereby facilitating enhanced information processing dynamics within their respective neural circuitry. It is noteworthy that the specific nodes exhibiting enhanced centrality metrics\u0026mdash;medial cerebellum, thalamus, and basal ganglia (particularly the striatum)\u0026mdash;collectively constitute critical components of the basal ganglia-cerebello-thalamo-cortical circuit. This circuit is highly integrated anatomically and functionally, playing decisive roles in motor control and cognitive processing (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Moreover, clinical evidence has confirmed that the reduced nodal topological properties of the basal ganglia-cerebello-thalamo-cortical circuit are closely associated with motor symptoms in PD (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The present study reveals the TI stimulation-induced modulation of nodal topological attributes in the striatum, providing a potential neurological network regulation strategy for PD treatment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Effects of TI stimulation on motor learning performance\u003c/h2\u003e\u003cp\u003eThis study found through analysis of implicit learning data that TI intervention demonstrated more significant facilitative effects during repeated implicit learning processes. Specifically, in the FIL, both the TI group and sham stimulation group achieved significant improvement following stimulation, indicating that initial task training itself can yield substantial learning benefits regardless of whether TI stimulation is received. This phenomenon is consistent with previous research findings regarding task familiarization and repeated practice promoting implicit learning (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Notably, the difference between groups did not reach statistical significance, suggesting that in the initial learning phase, TI stimulation had not yet demonstrated effects beyond those of general training.\u003c/p\u003e\u003cp\u003eIn the SIL, the situation differed. Only the TI group showed significant improvement following stimulation, while the sham stimulation group exhibited no apparent changes. This suggests that TI intervention possesses additional facilitative effects in continuous or repeated learning, helping participants overcome potential \"learning saturation\" or plateau effects that may arise from simple repetitive training, thereby further stimulating neuroplasticity. Related neurostimulation studies similarly demonstrate that non-invasive stimulation techniques more readily exhibit their neuromodulatory advantages when learning task difficulty increases or traditional training effects approach a plateau (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Effects of TI stimulation on Brain-Behavior Correlations\u003c/h2\u003e\u003cp\u003eCorrelation analysis revealed a significant negative correlation between changes in structure-function coupling within the cerebellar network following TI stimulation and improvements in implicit learning task performance. This finding provides robust empirical support for current theories of neural network plasticity\u0026mdash;specifically, that decoupling helps free up neural resources and enhances behavioral performance (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). As a critical hub for motor control and cognitive coordination, the altered coupling state of the cerebellar network may render information processing more flexible, thereby facilitating improvements in implicit learning ability (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFrom a theoretical perspective, this result aligns with the \"neural dynamic plasticity\" hypothesis. Appropriate network decoupling does not signify systemic imbalance but rather grants local brain regions greater autonomy while maintaining overall collaboration, enabling adaptation to environmental and task-related uncertainties (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The structure-function decoupling induced by TI stimulation in the cerebellar network provides a neural foundation for individual cognitive flexibility and behavioral adaptation. It also suggests that dynamic brain-behavior regulation may stem from this self-organizing, self-regulating capacity of neural networks (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Limitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, the limited sample size and ceiling effects observed in healthy participants may have compromised statistical power to detect significant between-group differences. Second, our analysis focused solely on immediate post-stimulation effects, precluding understanding of long-term neuroplastic changes. Third, while conducted on healthy participants, future research should prioritize clinical populations, particularly PD patients, to explore the potential therapeutic implications of TI stimulation.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003ePersonalized high-intensity targeted TI stimulation of the striatum enhances striatal activity and drives robust reorganization of brain networks, notably by decoupling SC-FC coupling within the CN and strengthening intra-network connectivity and nodal efficiency in both the CN and CON. These network-level modulations underlie improved implicit learning performance, highlighting the potential of TI stimulation as a precise and effective neuromodulation strategy for facilitating motor learning through deep nuclei and large-scale brain network reconfiguration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study approved by the Medical Ethics Committee of Shenzhen University Health Science Center (project number 202400151) and pre-registered on the Chinese Clinical Trial Registry (www.chictr.org.cn; identifier: ChiCTR2500098699). All subjects provided written informed consent and the experimental procedures were performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have consented to the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available in the Mendeley Data repository, https://data.mendeley.com/datasets/pwfddp8m76/1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no commercial or financial relationships could be construed as potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (11932013), the Shenzhen Stability Support Program (20220810110849002), Guangdong Provincial Philosophy and Social Sciences Project (GD24XTY13), Shenzhen University Excellence Research Program (ZYQN2410).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.T.: Conceptualization, Methodology, Software,\u0026nbsp;Supervision, Visualization, Writing-Original Draft,\u0026nbsp;Writing\u0026ndash;review \u0026amp; editing; L.Q.: Data curation, Formal analysis, Writing-Original Draft, Visualization; L.H.: Investigation; Y.J.: Investigation; S.G.: Investigation; Z.H.: Conceptualization, Methodology; L.C.: Conceptualization, Methodology; S.S.: Conceptualization, Methodology, Project administration; G.Z.: Conceptualization, Methodology, Project administration; C.C.: Conceptualization, Methodology, Project administration; Z.Z: Conceptualization, Funding acquisition, Methodology, Writing-Review \u0026amp; Editing, Supervision, Project administration; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all volunteers who participated in the study and the staff at the Magnetic Resonance Imaging (MRI) Center of Shenzhen University for their selfless and valuable assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCox J, Witten IB. 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Cereb Cortex. 2021;31(11):5263-74.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Temporal interference stimulation, Noninvasive brain stimulation, Cerebellar network, Structure-Function couplings, Implicit learning, Network connection","lastPublishedDoi":"10.21203/rs.3.rs-7114172/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7114172/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eDespite the central role of deep brain structures such as the striatum in motor learning, existing noninvasive stimulation methods are hindered by limited depth and precision. Temporal interference (TI) stimulation presents the potential for precise, individualized modulation of deep regions. However, how TI stimulation influences deep brain activity and large-scale network reorganization to facilitate motor learning is still unclear. Therefore, this study aimed to clarify these mechanisms by investigating how personalized, high-intensity TI targeting the striatum modulates neural activity and enhances motor learning.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eTwenty-six healthy right-handed male participants were enrolled in a randomized, double-blind, sham-controlled crossover study. Each participant received both TI and sham stimulation targeting the right striatum (10 mA, Δf\u0026thinsp;=\u0026thinsp;20 Hz) through individualized electrode montage. Resting-state functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and serial reaction time (SRT) task performance were assessed before and after each intervention. Neural analyses included static and dynamic fractional amplitude of low-frequency fluctuation (fALFF) in the target region, structure\u0026ndash;function coupling (SC-FC) and topological metrics across six major brain networks, as well as brain-behavior correlations related to learning performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Target region activity: TI stimulation significantly increased both static and dynamic fALFF in the right striatum (p\u0026thinsp;=\u0026thinsp;0.030 and p\u0026thinsp;=\u0026thinsp;0.036). (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Brain network reorganization: Compared with sham, the TI group exhibited significantly reduced SC-FC coupling in the cerebellar network (CN) (t=-2.279, p\u0026thinsp;=\u0026thinsp;0.027), along with enhanced intra-network functional connectivity and increased nodal efficiency and degree in the CN and cingulo-opercular network (CON) (all FDR-corrected, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Behavioral performance: The TI group demonstrated significant improvement in second-stage implicit learning (SIL) after stimulation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Brain-behavior correlation: Decreased SC-FC coupling in the CN was significantly negatively correlated with improvement in SIL (r=-0.372, p\u0026thinsp;=\u0026thinsp;0.040).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003ePersonalized high-intensity TI of the striatum enhances deep target activity and promotes selective network reorganization, particularly by reducing SC-FC coupling and strengthening intra-network connectivity in the CN. These network-level modulations underlie improved implicit learning performance, highlighting the potential of TI neuromodulation as a precise and effective approach for promoting motor learning by targeting deep nuclei and large-scale brain networks.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e\u003cp\u003eChiCTR2500098699\u003c/p\u003e","manuscriptTitle":"Personalized High-Intensity Temporal Interference Stimulation Decouples Cerebellar Networks to Enhance Implicit Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 08:01:38","doi":"10.21203/rs.3.rs-7114172/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-01T11:32:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T07:53:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T11:16:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164147599892944473666129374085729924762","date":"2025-08-14T13:43:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122332324939305339539253806729607351559","date":"2025-08-06T08:21:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T13:58:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T03:23:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2025-07-26T02:34:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"06d79323-7c66-41a1-bc59-37e9185c55b1","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:01:03+00:00","versionOfRecord":{"articleIdentity":"rs-7114172","link":"https://doi.org/10.1186/s12984-025-01865-9","journal":{"identity":"journal-of-neuroengineering-and-rehabilitation","isVorOnly":false,"title":"Journal of NeuroEngineering and Rehabilitation"},"publishedOn":"2026-01-02 15:58:02","publishedOnDateReadable":"January 2nd, 2026"},"versionCreatedAt":"2025-08-04 08:01:38","video":"","vorDoi":"10.1186/s12984-025-01865-9","vorDoiUrl":"https://doi.org/10.1186/s12984-025-01865-9","workflowStages":[]},"version":"v1","identity":"rs-7114172","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7114172","identity":"rs-7114172","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}