Skip to main content
NCBI home page
NeuroImage : Clinical logoLink to NeuroImage : Clinical
. 2026 Jan 10;49:103948. doi: 10.1016/j.nicl.2026.103948

White matter alterations linked to social cognitive improvements following theta burst stimulation of the right inferior frontal gyrus in autism

Chun-Hung Yeh a,b, Jing-Ru Chen a, Yi-Ping Chao c,d, Chen-Te Wu e, Tai-Li Chou f,g,h, Susan Shur-Fen Gau g,i, Hsing-Chang Ni b,j,⁎, Hsiang-Yuan Lin k,l
PMCID: PMC12828818  PMID: 41534361

Highlights

  • •

    We examined white matter plasticity patterns with 8-week RIFG iTBS trial in autism.

  • •

    RIFG iTBS showed no significant white matter changes between active and sham iTBS.

  • •

    Social cognitive improvements correlated with white matter changes after RIFG iTBS.

  • •

    Further research is required to understand brain-behavior mechanisms of iTBS.

Keywords: Autism, Theta burst stimulation, Right inferior frontal gyrus, Diffusion MRI, White matter, Social cognition

Abstract

Objectives

Intermittent theta burst stimulation (iTBS) over the right inferior frontal gyrus (RIFG) has been shown to improve social cognitive function in autistic individuals. However, whether this intervention modulates underlying brain structure remains unknown. This study aimed to investigate the impact of iTBS over the RIFG on white matter macro- and micro-structure in intellectually able autistic children and young adults.

Materials and methods

In this 8-week double-blind, parallel, sham-controlled trial, autistic participants (aged 8–30 years) were randomized to receive twice-weekly neuro-navigated iTBS targeting the RIFG or sham stimulation using a sham coil applied over the same target. The social cognitive performance was measured with the Frith-Happé Animations Test. Diffusion MRI and behavioral assessments were acquired at baseline, week 8 (immediately after intervention), and week 12 (four-week follow-up). After quality control, data from 26 participants in the active group and 23 in the sham group were included in the final longitudinal whole-brain fixel-based analysis (FBA).

Results

No significant treatment-by-time interaction emerged: changes in the composite fiber-density-and-cross-section (FDC) metric did not differ between groups at either week 8 or week 12. Within the active group, however, a significant negative association was found between the change in FDC in the rostral body of the corpus callosum and the improvement in social cognitive performance from baseline to week 12.

Conclusions

An 8-week course of neuro-navigated RIFG iTBS did not produce significant group-level white matter macro/microstructural changes compared to sham stimulation in our autistic cohort. Nevertheless, the finding that individual improvements in social cognition correlated with specific white matter alterations in the active group suggests a potential link between treatment-induced benefits and neural plasticity. Future studies should investigate whether different TBS parameters could induce more pronounced or detectable structural changes on MRI in autistic individuals and further explore the intricate mechanisms underlying observed brain-behavior relationships.

1. Introduction

Theta burst stimulation (TBS), a novel non-invasive brain stimulation technique employing rapidly changing magnetic fields to modulate neuronal excitability and network connectivity (Huang et al., 2005), has emerged as a potential therapeutic tool. Its protocol, delivering bursts of three transcranial magnetic stimulation (TMS) pulses at 50 Hz repeated every 200 ms, allows for shorter stimulation sessions and lower intensities compared to traditional repetitive TMS (rTMS) (Huang et al., 2005). TBS has been investigated for various psychiatric conditions, including major depressive disorder (Kishi et al., 2024a), schizophrenia (Kishi et al., 2024b), and obsessive–compulsive disorder (Ni et al., 2024b) and its safety and tolerability have been established in adults (Oberman et al., 2011) and children and adolescents (Elmaghraby et al., 2022).

While the precise neurobiological mechanisms of TBS remain under investigation, its after-effects are thought to involve processes analogous to long-term depression (LTD) and long-term potentiation (LTP) (Huang et al., 2011). This view is generally supported by resting-state fMRI studies, which indicate that continuous TBS (cTBS) tends to decrease functional connectivity, whereas intermittent TBS (iTBS) tends to enhance it (Kirkovski et al., 2023). However, findings from task-based fMRI, particularly when targeting the prefrontal cortex, are more heterogeneous, potentially due to inter-individual variability and methodological factors (Kirkovski et al., 2023). Beyond these functional alterations, growing evidence indicates that TBS can also induce structural brain changes. For example, a single cTBS session over the anterior temporal lobe was shown to reduce gray matter volume in healthy adults (Jung and Lambon Ralph 2021), while in Parkinson's disease patients, multiple cTBS sessions over the left supplementary motor area increased the volume of the globus pallidus, correlating mildly with symptom improvement (Ji et al., 2021). Furthermore, diffusion MRI (dMRI) metrics have shown potential in predicting treatment response. For example, in patients with upper limb apraxia, the benefit of a single bilateral cTBS session over the inferior frontal gyrus correlated with the baseline microstructural integrity of the splenium based on diffusion tensor model (Vanbellingen et al., 2020).

Autism spectrum disorder (ASD; autism henceforth) is a prevalent neurodevelopment disorder characterized by social communication deficits and the presence of repetitive/restricted behaviors (Lord et al., 2022). An earlier consensus statement suggested that rTMS and TBS applied to key brain regions, including the dorsolateral prefrontal cortex (DLPFC), posterior superior temporal sulcus (pSTS) / temporoparietal junction and right inferior frontal gyrus (RIFG), may hold therapeutic potential for autism (Cole et al., 2019). The DLPFC plays a critical role of executive function (Friedman and Robbins 2022), language processing (Hertrich et al., 2021) and emotion processing (Nejati et al., 2021), all of which are implicated in ASD. Further studies demonstrated altered DLPFC function and structure in ASD, such as more neuron numbers (Courchesne et al., 2011), increased brain volumes (Carper and Courchesne 2005), and hypoactivity (Carlisi et al., 2017). The pSTS is critical in social perception processing, action observation and theory of mind (TOM) (Yang et al., 2015a). Neuroimaging studies have demonstrated the reduced functional activation and altered neurodevelopment of the morphometry of the pSTS in ASD (Hotier et al., 2017, Yang et al., 2015a). The RIFG is involved in several neuropsychological function including imitation (Hogeveen et al., 2014), empathy (Massey et al., 2017), emotion (Schurz et al., 2014), and perspective-taking (Kuang 2016). Several neuroimaging studies demonstrated altered neural correlates of RIFG in ASD including decreased gray matter volumes (Kosaka et al., 2010), hypoactivation during socially interfered cognitive control (Dichter and Belger 2007), functional hypoconnectivity (Villalobos et al., 2005), and reduced local intrinsic connectivity (Kennedy and Courchesne 2008).

Building on these findings, we have conducted a series of randomized controlled trials (RCT) exploring these targets in autistic individuals over the past decade. For the pSTS, we established the feasibility and safety of iTBS in autistic adults (Ni et al., 2017, Ni et al., 2022) and subsequently demonstrated that an 8-week course of twice-weekly iTBS could improve core symptoms in autistic children and adolescents (Ni et al., 2021). In contrast, while interventions targeting the DLPFC were found to be safe in both intellectually able (Ni et al., 2023b) and minimally verbal/intellectually disabled (Ni et al., 2025) autistic individuals, they did not yield significant improvements in multiple behavioral outcomes (Ni et al., 2023b). Regarding the RIFG, one study reported that 1 Hz rTMS (inhibitory) over the RIFG disrupted behavioral responses to infant stimuli in neurotypical adults (De Carli et al., 2019). Another study demonstrated that iTBS (excitatory) over the RIFG promoted a more efficient strategy for task goal activation, whereas cTBS (inhibitory) led to less efficient approach (Dippel and Beste 2015) in neurotypical adults. Given these findings, we hypothesized that iTBS (excitatory) over the RIFG could improve social cognition in autistic people. Most recently, our RCT targeting the RIFG revealed that twice-weekly iTBS for 8 consecutive weeks produced significant improvements in social cognition, as reflected by higher Feeling scores on the Frith-Happé Animations Test in autistic children and young adults, with effects sustained for at least four weeks post-treatment (Ni et al., 2024a).

Although TBS is emerging as a promising intervention for autism, the neuromodulatory mechanisms, particularly concerning structural plasticity, remain largely unknown. In our two previous sham-controlled RCTs, we did not find dMRI-based evidence of white matter (WM) change after four weeks of bilateral pSTS iTBS (Ni et al., 2023a) or after eight weeks of left DLPFC cTBS (Yeh et al., 2024). Whether iTBS targeting the RIFG, a region crucial for social‐cognitive processing, can induce WM plasticity has not yet been systematically investigated. Building on our previous findings of significant improvements in the Feeling scores of Frith-Happé Animations Test using a twice-weekly, eight-week iTBS protocol (Ni et al., 2024a), the present study investigates whether the same intervention induces measurable WM plasticity within the same cohort. Accordingly, the current study has two primary aims: 1) Treatment effect – to investigate WM alterations induced by an eight-week course of RIFG-targeted iTBS relative to sham stimulation; and 2) Brain-behavior relationships – to assess whether any iTBS-induced WM changes link to concurrent improvements in social cognition. To achieve this, we adopted whole-brain fixel-based analysis (FBA) to detect potential micro- and macro-structural effects of iTBS on WM. Given the limited prior literature, we did not make a priori predictions regarding the specific WM tracts that might be involved or the direction of any observed changes.

2. Methods

2.1. Design

This study was a parallel, double-blind, and sham-controlled RCT that delivered an 8-week course of iTBS to RIFG in intellectually able autistic children and young adults. Participants aged 8–30 years were enrolled at a single tertiary medical center in Taiwan between November 2019 and May 2023. Using a computer-generated, sex-stratified list, they were randomized (1:1) to active or sham stimulation. Each participant received two sessions per week for eight consecutive weeks (16 sessions in total) and was evaluated again four weeks after the final intervention. MRI and measures of clinical symptoms and social cognitive functions were conducted at three times points: baseline, week 8 (immediately after completion of the 16 intervention sessions) and week 12 (four weeks post-intervention). Full details of the blinding procedures and intervention protocol are reported elsewhere (Ni et al., 2024a).

Our study followed the 1964 Helsinki Declaration and ethical standards of Good Clinical Practice. We received ethical approval from the Research Ethics Committee at the Chang Gung Medical Foundation-Institutional Review Board (CGMF-IRB 201900713A0) and Taiwan Food and Drug Administration (TFDA 1086611101). Our trial was registered on ClinicalTrials.gov (NCT04987749). Written informed consent was obtained from all participants and their parents.

2.2. Participants

Participants aged 8–30 years were recruited from outpatient clinics and through online advertisements. An ASD diagnosis was established according to DSM-5 criteria and confirmed by the Autism Diagnostic Observation Schedule (Chang et al., 2023). We excluded participants with a full-scale intelligence quotient (FIQ) < 70 on the Wechsler Intelligence Scale-IV for Children or Adults, any lifetime major systemic or neurological illness, schizophrenia, bipolar disorder, current depressive disorder, or substance misuse. During the trial, all psychiatric medications remained unchanged.

2.3. Frith-Happé Animations Test

The Frith-Happé Animations Test was used to evaluate the mentalizing ability in autistic people (Castelli et al., 2002). Earlier studies demonstrated the robust activation during this task in the pSTS, precuneus, inferior frontal gyrus, medial prefrontal cortex and temporal poles (Moessnang et al., 2020). In this paradigm, two triangles are shown on the screen engaging in one of three interaction types: (1) complex social interactions requiring theory of mind (TOM, four questions), (2) goal-directed interactions reflecting agency (four questions), and (3) random movements without intentional meaning (four questions). Participants are first asked to judge whether an interaction occurs between the triangles (yielding categorization scores) (Livingston et al., 2021). When they respond correctly, they are then asked to select the words that best describe how the triangles “feel” during the interaction (yielding Feeling scores). In general, categorization scores measure the accuracy in identifying the type of interactions, whereas Feeling scores reflect the accuracy of inferring the emotional content. Higher scores represent better mentalizing ability. To improve contrast and reduce administration time, we adopted the shortened version of the Frith-Happé Animations Test that includes only the TOM and random conditions, omitting the goal-directed condition. This adapted version has been used in our previous TBS RCTs (Ni et al., 2023b, Ni et al., 2021).

2.4. Intervention

Biphasic TBS pulses were generated with the Magstim Super Rapid2 system (Magstim Company, Oxford, UK) and applied with the 70-mm figure-of-eight coil. Each participant’s active motor threshold (AMT) was determined as the minimum stimulation intensity needed to elicit motor evoked potentials ≥ 200 μV in at least 5 of 10 trials during 20 % of maximum voluntary contraction of the first dorsal interosseous muscle. The stimulus intensity was fixed at 90 % of AMT for both active and sham groups. Sham stimulation used a purpose-built sham coil which can produce tactile and auditory sensations of TMS without generating direct stimulation effect. Both active and sham stimulations were applied to the same target (i.e., RIFG), which was localized individually using MRI-guided coordinates.

We used the iTBS protocol proposed by Huang et al (Huang et al., 2005). Each iTBS train comprised a burst of three pulses delivered at 50 Hz at 200 ms intervals 10 times. The TBS trains were presented every 10 s, for a total of 20 trains per session. Therefore, there were 600 pulses for each iTBS course. Across the 16 sessions, each participant received 9600 pulses.

The RIFG target was localized individually by mapping the MNI coordinates (56, 10, 14) (Dapretto et al., 2006) onto each participant's baseline T1-weighted image using the Navigated Brain Stimulation system (Nexstim®, Helsinki, Finland). Comprehensive descriptions of the coordinate-transformation procedure (Ni et al., 2023a) and coil placement protocol (Ni et al., 2024a) are provided elsewhere.

Inter-individual differences in scalp-to-cortex distance of the motor cortex and RIFG mean that identical iTBS settings do not produce identical cortical electric fields. To account for this dosing variability, we created finite-element models with SimNIBS v3.2 (Saturnino et al., 2019) and calculated the normalized electric field at the RIFG target (Fig. 1). The full modeling details were reported elsewhere (Ni et al., 2023a). Across participants, the normalized electric field over the RIFG ranged from 43.2 to 87.3 V/m. For each subject, we also derived the ratio of normalized electric field induced at RIFG to that generated in primary motor cortex, which spanned from 0.61 to 1.27; this ratio was entered as a covariate in all subsequent statistical analyses (Supplementary Table 1).

Fig. 1.

Fig. 1

Open in a new tab

We used SimNIBS 3.2 to simulate the individualized electric field. The electric field was computed based on the figure-of-eight coil current and each participant’s active motor threshold. The coil was centered over the right inferior frontal gyrus (RIFG, MNI coordinates: 56, 10, 17) and oriented downward toward 56, 10, 14. A spherical region of interest with a 10-mm radius was defined to quantify the induced field. SimNIBS generated the normalized electric field (normE) map within the ROI. The figure showed the modeled normE distribution induced by iTBS over the RIFG in one representative participant, with warmer colors indicating higher field strength (V/m).

2.5. MRI acquisition

MRI scans were performed on all participants at three time points—baseline (t1), week 8 (t2), and week 12 (t3)—using a 3 T GE Discovery MR750 scanner with an 8-channel head coil at Chang Gung Memorial Hospital, Linkou.

Structural MRI. High-resolution anatomical T1-weighted images were acquired using GE’s 3D BRAVO sequence (fast spoiled gradient-echo with inversion recovery preparation), with parameters: repetition time (TR) = 8.2 ms, echo time (TE) = 3.2 ms, inversion time = 450 ms, flip angle = 12°, matrix = 256 × 256, and voxel size = 1 × 1 × 1 mm3.

Diffusion MRI. Diffusion imaging data were collected with a single-shot spin-echo echo-planar imaging sequence, using the following settings: TR/TE = 7,500/83 ms, acquisition matrix = 96 × 96, voxel resolution = 2.3 × 2.3 × 2.3 mm3, parallel imaging acceleration factor = 2, and 128 diffusion-encoding directions at b = 2,000 s mm−2, and 10 non-diffusion weighted volumes (b ≈ 0 s mm−2).

2.6. MRI preprocessing

DMRI data were processed in MRtrix3 with the recommended workflow (Tournier et al., 2019): data were (i) denoised with the Marchenko-Pastur PCA approach (Veraart et al., 2016); (ii) corrected for Gibbs ringing (Kellner et al., 2016); (iii) corrected for susceptibility-induced distortions (Schilling et al., 2019); (iv) corrected for eddy currents and inter-volume motion (Andersson et al., 2018, Andersson et al., 2017, Andersson and Sotiropoulos, 2016); (v) corrected for bias field (Tustison et al., 2010); and (vi) upsampled to a finer isotropic grid.

Per-subject quality control was performed with FSL’s eddy_quad utility (Bastiani et al., 2019), supplemented by visual inspection to exclude data with artifacts. We recorded the proportion of slice outliers and excluded datasets showing (a) excessive signal dropout or (b) mean relative frame-to-frame root-mean-square (RMS) displacement >1 mm. Two sham-treated participants missed the week 12 (t3) MRI scan. After quality check, 23 sham and 26 active participants met all criteria at every time point; only these datasets were carried forward to the longitudinal fixel-based analysis below.

2.7. Longitudinal FBA

To enable fixel-wise analysis through multiple time points, this study followed the design of longitudinal FBA conducted previously (Ni et al., 2023a, Yeh et al., 2024) as follows.

Reconstruction of fiber orientation distributions (FODs). For each preprocessed dMRI data at baseline (t1), group-average tissue response functions were estimated for WM, GM, and CSF with an unsupervised algorithm (Dhollander et al., 2019). Then, for each preprocessed dMRI data at each time point, FODs were reconstructed based on constrained spherical deconvolution (Tournier et al., 2008), using the multi-shell multi-tissue method to model WM and CSF compartments (Jeurissen et al., 2014). Intensity normalization was applied to correct for residual compartmental inhomogeneities (Tournier et al., 2019).

Reconstruction of fixel template. We first performed rigid, within-subject registration to co-align FODs from the three sessions (t1, t2, t3). Each participant’s aligned FODs were transformed into a midway space and averaged to create individual mean FODs. These subject-level means were then entered into MRtrix3′s population_template routine (Raffelt et al., 2017) to build a group template that served as the common spatial reference for all longitudinal analyses. The template’s FODs were subsequently segmented to produce the group fixel template (Smith et al., 2013).

Fixel metrics. Each session's FOD image was non-linearly warped to the group template with FOD-guided registration (Raffelt et al., 2011), establishing the correspondence between subject and template fixels. For every participant and time point we computed the fixel-wise fiber-density-and-cross-section (FDC) metric, which estimates local fiber connectivity by integrating changes in microscopic fiber density with macro-scale expansion or contraction of the bundle in the plane perpendicular to its principal orientation. The resulting FDC maps were re-projected onto the template fixels for downstream analyses.

Longitudinal FDC change was assessed using the absolute change from the baseline (t1) to a selected time point t (=t2 or t3 in this study), i.e.,

ΔFDC = FDCt – FDCt1 (1)

Fixel-wise statistical analysis. Whole-brain fixel metrics were evaluated with a general linear model (GLM) augmented by connectivity-based fixel enhancement (CFE) (Raffelt et al., 2015). The fixel-to-fixel connectivity matrix required for CFE was generated from a template-space whole-brain tractogram that had been post-processed with SIFT (Smith et al., 2013).

We carried out three complementary tests:

  • •

    Between-group comparisons – We evaluated the treatment-by-time effect to determine whether there were differences in ΔFDC between the active and sham groups from t1 to t2 or from t1 to t3.

  • •

    Brain-behavior relationships – To test the association between WM changes and clinical improvement, we evaluated the correlation between ΔFDC and changes in the Frith-Happé Animations Test (Total score and Feeling score) in both the active and the sham groups separately. We focused on the Frith-Happé Animations Test, as it was the only outcome to show a significant treatment-by-time interaction in the primary report from this trial (Ni et al., 2024a). The absolute change in Total or Feeling scores from t1 to a selected time point t (t2 or t3) was calculated as ΔS = St − St1. We then evaluated whether these changes correlated with ΔFDC as defined in Eq. (1).

  • •

    Baseline predictors of outcome – To assess if baseline WM structure predicted treatment response, we tested the association between baseline (t1) FDC and social cognition outcomes (scores at t2 and t3, and ΔS) in both the active and sham groups separately.

We utilized absolute change as our primary metric. While relative change metrics can sometimes normalize for baseline variability, they often exhibit poor statistical properties in clinical trials, potentially leading to inflated variance or spurious correlations, particularly when baseline values (the denominator) are small or highly variable (Vickers 2001). Further, the influence of baseline FDC was addressed through the predictive analysis described above.

Each GLM was controlled for age (in years), sex (coded as 0 = female, 1 = male), head motion during the scan (quantified by relative RMS displacement in millimeters, averaged if two time points were modeled), cumulative dose ratio (based on the ratio between the normalized induced electric fields of the RIFG and motor cortex), medication use, and the presence of co-occurring psychiatric conditions (binary variable; 1 = either on psychotropic medication or with a comorbid diagnosis, 0 = none). Intracranial volume underwent logarithmic transformation and was included as an additional covariate in the statistical modeling (Smith et al., 2019). All binary covariates were normalized, and continuous variables were demeaned prior to analysis. To account for multiple comparisons, statistical significance was determined using a family-wise error-corrected p-value (pFWE) < 0.05 for inter-group comparisons and pFWE < 0.025 for brain–behavior analyses, all with non-parametric testing with 20,000 permutations to provide robust and precise error estimates for each test.

To investigate potential developmental differences in treatment response, given prior evidence suggesting possible age-dependent responses to TBS in autistic people, we conducted an additional post-hoc age-stratified analysis. Participants were categorized into older (20 ≤ age ≤ 30 years; n = 26) and younger subgroups (8 ≤ age < 20 years; n = 23). The age cut-off was selected to ensure approximately balanced sample sizes between the two strata. We repeated the test for treatment-by-time interactions within each age-stratified subgroup.

3. Results

The demographic and baseline clinical features of the active (n = 26) and sham (n = 23) stimulation groups are presented in the Table 1. The groups did not significantly differ in key baseline demographic and clinical features, including age, sex, FIQ, clinical severity, co-occurring psychiatric conditions, or concurrent medication use. The only baseline discrepancy was a lower Frith-Happé Animations Feeling score in the active group (p = 0.01). Therefore, baseline performance on the Frith-Happé Animations was included in subsequent between-group analyses. The individual normalized induced electric field at the RIFG target, relative to the motor cortex, ranged from 61 to 127 %, with no significant between-group difference. The clinical data for both groups are presented in Supplementary Table 2.

Table 1.

Baseline demographics and clinical characteristics.

Active (n = 26) Sham (n = 23) P value
Age, mean (S.D.) 19.5 (6.8) 18.2 (6.8) 0.50
Male, n (%) 24 (92.3) 21 (91.3) 1
Full-Scale Intelligence Quotient, mean (S.D.) 84.0 (19.5) 92.3 (21.2) 0.16
Clinical symptoms
Social Responsiveness Scale, mean (S.D.) 112.7 (23.4) 115.4 (22.1) 0.67
Repetitive Behavior Scale-Revised, mean (S.D.) 36.4 (22.4) 33.4 (22.3) 0.65
Emotion Dysregulation Inventory, mean (S.D.) 23.0 (21.5) 23.7 (22.9) 0.91
Adaptive Behavior Assessment System –II –Social, mean (S.D.) 3.3 (2.65) 3.3 (2.4) 0.91
Social cognitive functions
Frith-Happé Animations_Total, mean (S.D.) 5.8 (1.6) 6.6 (1.6) 0.1
Frith-Happé Animations_Feeling, mean (S.D.) 2.4 (2.1) 3.9 (2.0) 0.01*
Comorbid with ADHD, n (%) 7 (26.9) 9 (39.1) 0.36
Concurrent Methylphenidate use, n (%) 1 (3.8) 3 (13.0) 0.33
Psychotherapy, n (%) 3 (11.5) 3 (13.0) 1
NormE (RIFG/motor), mean (S.D.) 0.85 (0.15) 0.83 (0.12) 0.598
Open in a new tab

*p < 0.05.

Abbreviation: ADHD = Attention-Deficit/Hyperactivity Disorder, NormE = normalized induced electric field, RIFG = right inferior frontal gyrus.

3.1. Treatment effects on WM

Whole-brain fixel-wise FDC was used to identify potential iTBS-induced WM changes. This analysis revealed no significant treatment-by-time interactions. Specifically, ΔFDC was not significantly greater in the active than in the sham group from baseline (t1) to either week 8 (t2) or week 12 (t3) (pFWE > 0.6916 and pFWE > 0.5868, respectively). Likewise, ΔFDC values were not significantly lower in the active group relative to the sham control (pFWE > 0.1082 for t1 → t2 and pFWE > 0.9674 for t1 → t3). For age-stratified analyses, we did not find significant treatment-by-time interactions for either the younger (8 ≤ age < 20 years) or older (20 ≤ age ≤ 30 years) subgroups (all pFWE > 0.05). This suggests that the null effect of iTBS on white matter structure was consistent across the developmental stages included in our sample.

3.2. Correlation with social cognition

In the active group, a significant negative correlation was observed between the change in FDC (i.e. ΔFDC) from baseline to week 12 (t1 → t3) and the change in Total scores (i.e. ΔTotal score, t1 → t3) on the Frith-Happé Animations Test. This correlation was localized to the rostral body of the corpus callosum (pFWE < 0.025; Fig. 2). No significant correlations were found between ΔFDC and changes in Feeling scores on the Frith-Happé Animations Test at either post-baseline time point (t1 → t2 or t1 → t3) in the active group.

Fig. 2.

Fig. 2

Open in a new tab

In the active iTBS group, a significant negative correlation was found between changes in fiber-density-and-cross-section (ΔFDC) from baseline to week 12 (t1 → t3) and normalized changes in Total scores on the Frith-Happé Animations Test (ΔTotal score, t1 → t3), at the rostral body of the corpus callosum (family-wise error corrected pFWE < 0.025). Panels a-c display streamlines where the correlation of ΔFDC and normalized ΔTotal score reach statistical significance from coronal, sagittal, and axial views, respectively. Panel d presents a scatter plot showing the relationship between mean ΔFDC (vertical axis) and normalized ΔTotal score (horizontal axis), based on fixels that met the statistical threshold. Data points are color-coded by age group (light blue: 8 ≤ age < 20 years; dark blue: 20 ≤ age ≤ 30 years) to illustrate the distribution across developmental stages. White matter fiber orientation is color-coded: red = left–right, blue = superior-inferior, green = anterior-posterior. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

In the sham group, a positive correlation was found between ΔFDC from baseline to week 8 (t1 → t2) and ΔFeeling score on the Frith-Happé Animations Test over the same period. This correlation was observed in the anterior midbody of the corpus callosum (pFWE < 0.025; Fig. 3).

Fig. 3.

Fig. 3

Open in a new tab

In the sham group, a significant positive correlation was observed between changes in fiber-density-and-cross-section (ΔFDC) from baseline to week 8 (t1 → t2) and normalized changes in Feeling scores on the Frith-Happé Animations Test (ΔFeeling score, t1 → t2), at the anterior midbody of the corpus callosum (family-wise error corrected pFWE < 0.025). Panels a-c display all streamlines where the correlation of ΔFDC and normalized ΔFeeling score reach statistical significance from coronal, sagittal, and axial views, respectively. Panel d shows the corresponding scatter plot of mean ΔFDC (vertical axis) versus normalized ΔFeeling score (horizontal axis), based on fixels that met the statistical threshold. White matter fiber orientation is color-coded: red = left–right, blue = superior-inferior, green = anterior-posterior. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.3. Baseline predictors of outcome

In the active group, baseline FDC was not significantly associated with social cognitive scores (Total or Feeling) at week 8 or week 12, nor with the change in scores over time (all pFWE > 0.05). In the sham group, a negative association was identified between baseline FDC in the anterior midbody of the corpus callosum and week 12 Feeling scores (pFWE < 0.05; Fig. 4); however, baseline FDC did not predict the change in Feeling scores.

Fig. 4.

Fig. 4

Open in a new tab

In the sham group, baseline (t1) FDC in the anterior midbody of the corpus callosum showed a significant negative correlation with week-12 (t3) Feeling scores (family-wise error corrected pFWE < 0.05). Panels a-c display all streamlines reaching statistical significance in coronal, sagittal, and axial views, respectively. White matter fiber orientation is color-coded: red = left–right, blue = superior-inferior, green = anterior-posterior. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

In this study, we employed whole-brain FBA to investigate the effects of an 8-week course of RIFG iTBS on WM structure in intellectually able autistic children and emerging adults. Our primary findings were twofold: first, we observed no significant group-level differences in WM macro- or microstructural changes (as assessed using FBA's FDC metric) between active and sham stimulation. Second, within the active group, we found a significant negative association between changes in FDC within the rostral body of the corpus callosum and concurrent improvements in social cognitive performance, as measured by the Frith-Happé Animations Test.

Our primary finding, the absence of significant group-level changes in WM FDC, is consistent with our previous dMRI studies investigating TBS effects targeting the left DLPFC (Yeh et al., 2024) and pSTS (Ni et al., 2023a) in autism. While the broader field suggests TBS can modulate neural structure (Ji et al., 2021, Jung and Lambon Ralph, 2021, Vanbellingen et al., 2020), our consistent null findings in this specific population raise important questions about the required stimulation parameters, individual variability, and sensitivity of dMRI metrics. The existing dMRI literature, though limited, offers some context. For instance, studies in stroke patients have shown that rTMS can enhance WM connectivity (Ueda et al., 2019, Yamada et al., 2018) and that these structural changes can correlate with clinical improvement (Yamada et al., 2018). Similarly, cTBS has been linked to microstructural improvements across several WM tracts in stroke survivors (Yang et al., 2015b), and animal models have shown increased fractional anisotropy following rTMS in healthy rats (Seewoo et al., 2022). Moreover, neural plasticity induced by brain stimulation is not confined to alterations in WM microstructure but may also involve other neural correlates such as changes in brain activation patterns and functional connectivity. WM structure alone is therefore unlikely to fully explain the observed clinical improvements. Concomitant gray matter modifications or functional alterations such as in regional activity or in inter-regional connectivity may also constitute critical substrates mediating the observed brain-behavior associations. Future investigations employing multimodal neuroimaging and complementary methodological approaches will be essential to further explore these complex neural processes underlying treatment-related effects.

The discrepancy between these findings and our results prompts several considerations that might explain why we did not detect significant WM changes in our autistic sample:

Firstly, the stimulation dosage may have been insufficient. Our protocol of two weekly TBS sessions, chosen based on feasibility and earlier open-label studies (Sokhadze et al., 2012, Sokhadze et al., 2009, Sokhadze et al., 2014, Sokhadze et al., 2018), is notably less frequent than typical depression treatment protocols (e.g., 5 sessions per week) (Voigt et al., 2021). Whether 600 pulses per session represents an optimal dose for inducing structural WM changes in autistic individuals warrants further debate. While some research has demonstrated dose-dependent effects of TBS on cortical excitability (Nettekoven et al., 2014), other studies do not consistently support this (Desforges et al., 2022, McCalley et al., 2021). Moreover, the interval between TBS sessions, or “spaced TBS”, can influence post-stimulation effects (Yu et al., 2020). The optimal interval for autistic individuals remains unknown, especially considering potential hyperplasticity phenomenon in this population (Desarkar et al., 2022, Oberman et al., 2012). Future research is needed to determine whether more frequent sessions or a higher number of pulses per session could yield more discernible impacts on neural correlates in autism.

Secondly, the stimulation intensity might be a factor. The adequate electric field threshold for TBS to induce lasting neural changes is still inconclusive. Although some studies suggest intensities of 35–50 V/m can elicit immediate electrophysiological effect (Zmeykina et al., 2020), required field strengths may vary across brain regions, with estimates around 56 V/m for the left DLPFC (Beynel et al., 2020) and 90–95 V/m for the right precuneus (Kraft et al., 2015). In this study, the average estimated electric field at the RIFG was approximately 62 V/m, corresponding to about 86 % of the electric field at the motor spot. It remains plausible that a higher electric field strength over the RIFG might be necessary to induce detectable WM structural changes, a hypothesis for future investigation. Beyond simply increasing stimulation intensity, future studies could calculate individualized cortical electric-field distributions in advance and adjust stimulation intensities accordingly (Saturnino et al., 2019). Such personalized, simulation-guided dosing approaches may help optimize stimulation efficacy and thus represent an important methodological direction to be considered in the future research.

Thirdly, high inter-individual variability in response to TBS protocols is a well-documented challenge (Vallence et al., 2015, Vernet et al., 2014). Although the original 600-pulse iTBS protocol was able to increase cortical excitability (measured by increase in motor evoked potential amplitude post intervention) via presumed LTP (Huang et al., 2005) mechanisms, subsequent studies have not always replicated this finding (Hamada et al., 2013, Lopez-Alonso et al., 2014), with some reporting no change or even opposite effects on motor evoked potential amplitude (Perellon-Alfonso et al., 2018, Vallence et al., 2015, Vernet et al., 2014). Thus, optimizing TBS protocol for individuals remains a critical step for advancing precision neuromodulation.

Despite the absence of a group-level effect of iTBS on WM structure, this study extends our previous findings which reported potential therapeutic benefits of 8 weeks of RIFG-targeted iTBS on social cognition in autistic individuals (Ni et al., 2024a). Here, we observed a significant negative association between changes in FDC in the rostral body of the corpus callosum and improvements in social cognitive performance (Frith-Happé Animations Test Total scores) within the active iTBS group. Importantly, baseline FDC in this region did not predict clinical improvement, suggesting that this association reflects dynamic changes occurring during or after the intervention rather than pre-existing structural differences. The corpus callosum is the principal WM tract connecting the cerebral hemispheres, and its rostral body, located in the anterior midsection, is crucial in interhemispheric communication between frontal cortical regions (Friedrich et al., 2020, Musiek, 1986). Frontal regions have been implicated in executive function (Jokinen et al., 2007), impulsivity and discriminability (Moeller et al., 2005).

Previous literature suggests alterations in the corpus callosum in autism. A meta-analysis reported reduced corpus callosum volumes in autistic individuals, particularly prominent in the rostral body (Frazier and Hardan 2009). Our previous study based on FBA also demonstrated reduced FDC in the premotor segment of corpus callosum in autistic individuals, with notable reductions in microscopic fiber density in the rostral body in intellectually impaired autistic participants (Yeh et al., 2022). Additionally, reviews (Marco Valenti et al., 2020) and diffusion tensor imaging studies (Alexander et al., 2007, Thomas et al., 2011) have reported decreased FA within various segments of the corpus callosum, including the rostral body, in autism compared to controls. Although the functional implications of these alterations remain unclear, decreased FA has been tentatively associated with slower processing speed (Alexander et al., 2007). In contrast, other research has not found clear relationships between rostral body volumes and performance on neuropsychological tests (Keary et al., 2009).

The negative correlation we observed—where a decrease in FDC was associated with greater improvement in social cognitive scores—appears counterintuitive if higher FDC is assumed to indicate “better” WM fiber connectivity. Several interpretations, though speculative, merit consideration. A decrease in FDC might reflect an increase in pathway efficiency or refinement, such as the pruning of less critical connections or changes in axonal organization (e.g., packing density or diameter) to improve signal transmission without necessarily increasing overall FDC linearly (Dhollander et al., 2021). The FDC metric itself integrates microscopic fiber density with macroscopic bundle cross-section; thus, a decrease could arise from changes in either component. The precise neurobiological underpinnings of this negative correlation are unclear and warrant further investigation, highlighting the complexities inherent in interpreting dMRI outcomes following neuromodulation in neurodevelopmental conditions. Interestingly, this structural-behavioral correlation was observed with the Total scores on the Frith-Happé Animations Test, whereas our previous clinical report on the same cohort highlighted more consistent iTBS effects on the Feeling scores (Ni et al., 2024a). This discrepancy suggests that different facets of social cognitive improvement might relate to distinct or less easily detectable neural changes at the FBA level. The relationship between TBS-induced dMRI alterations and specific behavioral or neuropsychological changes requires considerably more research.

It is also important to address the positive correlation found in the sham group between ΔFDC (t1 → t2) and ΔFeeling score on the Frith-Happé Animations Test (t1 → t2) in the anterior midbody of the corpus callosum. This finding contrasts with the active group, where a negative correlation was observed at a later time point (t1 → t3) in the rostral body, linked to the Total score. These differences in directionality, timing, location, and behavioral correlate suggest that the brain-behavior relationships observed in the active and sham groups likely reflect different underlying processes. The transient nature of the sham correlation (only at t2) might reflect non-specific factors, such as practice effects on the behavioral task co-occurring with natural fluctuations in white matter metrics, or potentially Type I error. In contrast, the counterintuitive negative correlation in the active group, emerging later in time, may reflect a specific mechanism of iTBS-induced plasticity, such as axonal refinement or increased pathway efficiency, rather than non-specific trial effects.

This study has several limitations. First, the wide age range of participants (8 to 30 years) could introduce variability, as younger brains may exhibit heightened neuroplasticity or distinct responses to TBS in autistic people (Jannati et al., 2020, Oberman and Benussi, 2024, Oberman et al., 2014). To address this, we performed age-stratified analyses (stratified at 20 years) and visual inspection of brain-behavior associations; neither approach revealed distinct age-related trends in our data. This aligns with our previous report on this cohort, which found no impact of age on social cognition outcomes (Ni et al., 2024a). However, we acknowledge that these age-stratified subgroup analyses may be underpowered, and the null results could reflect a Type II error. Future studies with larger samples should systematically investigate developmental trajectories in TBS response. Second, our sample included autistic youths with common co-occurring psychiatric disorders (e.g., anxiety, ADHD), some of whom were on stable psychiatric medications. The intricate interactions between comorbidities, medications, TBS after-effects, and WM fiber connectivity remain unknown. Third, while we employed sex-stratified randomization, this study did not achieve a sex-balanced sample. Given that sex assigned at birth may influence brain characteristics and responsiveness to neuromodulation in autism (Walsh et al., 2021), the potential moderating effect of sex on rTMS/TBS outcomes warrants future investigation.

In conclusion, our neuro-navigated iTBS intervention targeting RIFG did not result in significant group-level WM macro- or microstructural changes (as measured by FDC using FBA) compared to sham stimulation in intellectually able autistic children and young adults. Nevertheless, within the active treatment group, improvements in a measure of social cognition correlated negatively with FDC changes in the rostral body of the corpus callosum. These findings underscore the complex relationship between TBS effects, WM plasticity, and cognitive outcomes in autism. Future studies should investigate whether TBS parameters, such as higher stimulation intensities or alternative dosing schedules, might induce more pronounced or detectable brain changes on MRI in autistic individuals, and further explore the intricate mechanisms underlying observed brain-behavior relationships.

5. Funding information

This work is supported by grants from the National Science and Technology Council of Taiwan (108-2321-B-075-004-MY2; 113-2923-B-182-001; 114-2923-B-182-001) and Chang Gung Memorial Hospital (CMRPG3L0681-2). Hsiang-Yuan Lin is supported by the Azrieli Adult Neurodevelopmental Centre at Centre for Addiction and Mental Health, and an Academic Scholar Award from the Department of Psychiatry, University of Toronto. The funders had no role in the study's design, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

CRediT authorship contribution statement

Chun-Hung Yeh: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. Jing-Ru Chen: Writing – review & editing, Formal analysis. Yi-Ping Chao: Writing – review & editing, Methodology, Data curation. Chen-Te Wu: Writing – review & editing, Methodology, Data curation. Tai-Li Chou: Writing – review & editing, Resources. Susan Shur-Fen Gau: Writing – review & editing, Resources. Hsing-Chang Ni: Writing – review & editing, Writing – original draft, Funding acquisition, Formal analysis, Data curation, Conceptualization. Hsiang-Yuan Lin: Writing – review & editing, Writing – original draft, Supervision, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The authors would like to thank all of our participants and their family members for partaking in this study and the anonymous reviewers for comments that significantly improved the manuscript.

Authorship statement

All authors read and approved the final manuscript. Conceptualization and design: Hsing-Chang Ni, Hsiang-Yuan Lin; Methodology: Chun-Hung Yeh, Yi-Ping Chao, Chen-Te Wu; Clinical data collection: Hsing-Chang Ni; Neuroimaging data collection: Hsing-Chang Ni, Chun-Hung Yeh, Yi-Ping Chao, Chen-Te Wu; Formal analysis: Chun-Hung Yeh, Jing-Ru Chen, Hsing-Chang Ni, Hsiang-Yuan Lin; Interpretation: Chun-Hung Yeh, Jing-Ru Chen, Hsing-Chang Ni, Hsiang-Yuan Lin; Writing – original draft preparation: Chun-Hung Yeh, Hsing-Chang Ni, Hsiang-Yuan Lin; Figures: Chun-Hung Yeh, Jing-Ru Chen; Writing - review and editing: All authors; Funding acquisition: Hsing-Chang Ni; Resources: Tai-Li Chou and Susan Shur-Fen Gau; Supervision: Hsiang-Yuan Lin.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nicl.2026.103948.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary Data 1
mmc1.docx (35KB, docx)

Data availability

Data will be made available on request.

References

  1. Alexander A.L., Lee J.E., Lazar M., Boudos R., DuBray M.B., Oakes T.R., Miller J.N., Lu J., Jeong E.K., McMahon W.M., Bigler E.D., Lainhart J.E. Diffusion tensor imaging of the corpus callosum in Autism. Neuroimage. 2007;34(1):61–73. doi: 10.1016/j.neuroimage.2006.08.032. [DOI] [PubMed] [Google Scholar]
  2. Andersson J.L.R., Graham M.S., Drobnjak I., Zhang H., Campbell J. Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data. Neuroimage. 2018;171:277–295. doi: 10.1016/j.neuroimage.2017.12.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Andersson J.L.R., Graham M.S., Drobnjak I., Zhang H., Filippini N., Bastiani M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: within volume movement. Neuroimage. 2017;152:450–466. doi: 10.1016/j.neuroimage.2017.02.085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Andersson J.L.R., Sotiropoulos S.N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063–1078. doi: 10.1016/j.neuroimage.2015.10.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bastiani M., Cottaar M., Fitzgibbon S.P., Suri S., Alfaro-Almagro F., Sotiropoulos S.N., Jbabdi S., Andersson J.L.R. Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. Neuroimage. 2019;184:801–812. doi: 10.1016/j.neuroimage.2018.09.073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Beynel L., Davis S.W., Crowell C.A., Dannhauer M., Lim W., Palmer H., Hilbig S.A., Brito A., Hile C., Luber B., Lisanby S.H., Peterchev A.V., Cabeza R., Appelbaum L.G. Site-specific effects of online rTMS during a working memory task in healthy older adults. Brain Sci. 2020;10(5) doi: 10.3390/brainsci10050255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Carlisi C.O., Norman L.J., Lukito S.S., Radua J., Mataix-Cols D., Rubia K. Comparative multimodal meta-analysis of structural and functional brain abnormalities in autism spectrum disorder and obsessive-compulsive disorder. Biol. Psychiatry. 2017;82(2):83–102. doi: 10.1016/j.biopsych.2016.10.006. [DOI] [PubMed] [Google Scholar]
  8. Carper R.A., Courchesne E. Localized enlargement of the frontal cortex in early autism. Biol. Psychiatry. 2005;57(2):126–133. doi: 10.1016/j.biopsych.2004.11.005. [DOI] [PubMed] [Google Scholar]
  9. Castelli F., Frith C., Happé F., Frith U. Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes. Brain. 2002;125(8):1839–1849. doi: 10.1093/brain/awf189. [DOI] [PubMed] [Google Scholar]
  10. Chang J.-C., Lai M.-C., Chien Y.-L., Cheng C.-Y., Wu Y.-Y., Gau S.-S.-F. Psychometric properties of the Mandarin version of the autism diagnostic observation schedule-generic. J. Formos. Med. Assoc. 2023;122(7):574–583. doi: 10.1016/j.jfma.2023.01.008. [DOI] [PubMed] [Google Scholar]
  11. Cole E.J., Enticott P.G., Oberman L.M., Gwynette M.F., Casanova M.F., Jackson S.L.J., Jannati A., McPartland J.C., Naples A.J., Puts N.A.J., Albein-Urios N., Barnett C., Croarkin P., Elliot G., Feinstein C., Francis S., Lai M.-C., Levitt J., McCracken J., Mostofsky S., Pascual-Leone A., Rashtchy S., Rotenberg A., Schultz B., Taylor B., Taylor K.H., Burgt J.V., Veenstra-VanderWeele J., Wu W. The potential of repetitive transcranial magnetic stimulation for autism spectrum disorder: a consensus statement. Biol. Psychiatry. 2019;85(4):e21–e22. doi: 10.1016/j.biopsych.2018.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Courchesne E., Mouton P.R., Calhoun M.E., Semendeferi K., Ahrens-Barbeau C., Hallet M.J., Barnes C.C., Pierce K. Neuron number and size in prefrontal cortex of children with autism. J. Am. Med. Assoc. 2011;306(18):2001–2010. doi: 10.1001/jama.2011.1638. [DOI] [PubMed] [Google Scholar]
  13. Dapretto M., Davies M.S., Pfeifer J.H., Scott A.A., Sigman M., Bookheimer S.Y., Iacoboni M. Understanding emotions in others: mirror neuron dysfunction in children with autism spectrum disorders. Nat. Neurosci. 2006;9(1):28–30. doi: 10.1038/nn1611. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. De Carli P., Bakermans-Kranenburg M.J., Parolin L., Lega C., Zanardo B., Cattaneo Z., Riem M.M.E. A walk on the dark side: TMS over the right inferior frontal gyrus (rIFG) disrupts behavioral responses to infant stimuli. Soc. Neurosci. 2019;14(6):697–704. doi: 10.1080/17470919.2019.1574891. [DOI] [PubMed] [Google Scholar]
  15. Desarkar P., Rajji T.K., Ameis S.H., Blumberger D.M., Lai M.C., Lunsky Y., Daskalakis Z.J. Assessing and stabilizing atypical plasticity in autism spectrum disorder using rTMS: results from a proof-of-principle study. Clin. Neurophysiol. 2022;141:109–118. doi: 10.1016/j.clinph.2021.03.046. [DOI] [PubMed] [Google Scholar]
  16. Desforges M., Hadas I., Mihov B., Morin Y., Rochette Braun M., Lioumis P., Zomorrodi R., Theoret H., Lepage M., Daskalakis Z.J., Tremblay S. Dose-response of intermittent theta burst stimulation of the prefrontal cortex: a TMS-EEG study. Clin. Neurophysiol. 2022;136:158–172. doi: 10.1016/j.clinph.2021.12.018. [DOI] [PubMed] [Google Scholar]
  17. Dhollander T., Clemente A., Singh M., Boonstra F., Civier O., Duque J.D., Egorova N., Enticott P., Fuelscher I., Gajamange S., Genc S., Gottlieb E., Hyde C., Imms P., Kelly C., Kirkovski M., Kolbe S., Liang X., Malhotra A., Mito R., Poudel G., Silk T.J., Vaughan D.N., Zanin J., Raffelt D., Caeyenberghs K. Fixel-based analysis of diffusion MRI: methods, applications, challenges and opportunities. Neuroimage. 2021;241 doi: 10.1016/j.neuroimage.2021.118417. [DOI] [PubMed] [Google Scholar]
  18. Dhollander T., Mito R., Raffelt D., Connelly A. Improved white matter response function estimation for 3-tissue constrained spherical deconvolution. Proc ISMRM. 2019;555 [Google Scholar]
  19. Dichter G.S., Belger A. Social stimuli interfere with cognitive control in autism. Neuroimage. 2007;35(3):1219–1230. doi: 10.1016/j.neuroimage.2006.12.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Dippel G., Beste C. A causal role of the right inferior frontal cortex in implementing strategies for multi-component behaviour. Nat. Commun. 2015;6(1):6587. doi: 10.1038/ncomms7587. [DOI] [PubMed] [Google Scholar]
  21. Elmaghraby R., Sun Q., Ozger C., Shekunov J., Romanowicz M., Croarkin P.E. A systematic review of the safety and tolerability of theta burst stimulation in children and adolescents. Neuromodul. Technol. Neural Interface. 2022;25(4):494–503. doi: 10.1111/ner.13455. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Frazier T.W., Hardan A.Y. A meta-analysis of the corpus callosum in autism. Biol. Psychiatry. 2009;66(10):935–941. doi: 10.1016/j.biopsych.2009.07.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Friedman N.P., Robbins T.W. The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology. 2022;47(1):72–89. doi: 10.1038/s41386-021-01132-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Friedrich P., Forkel S.J., Thiebaut de Schotten M. Mapping the principal gradient onto the corpus callosum. Neuroimage. 2020;223 doi: 10.1016/j.neuroimage.2020.117317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hamada M., Murase N., Hasan A., Balaratnam M., Rothwell J.C. The role of interneuron networks in driving human motor cortical plasticity. Cereb. Cortex. 2013;23(7):1593–1605. doi: 10.1093/cercor/bhs147. [DOI] [PubMed] [Google Scholar]
  26. Hertrich I., Dietrich S., Blum C., Ackermann H. The role of the dorsolateral prefrontal cortex for speech and language processing. Front. Hum. Neurosci. 2021;15–2021 doi: 10.3389/fnhum.2021.645209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Hogeveen J., Obhi S.S., Banissy M.J., Santiesteban I., Press C., Catmur C., Bird G. Task-dependent and distinct roles of the temporoparietal junction and inferior frontal cortex in the control of imitation. Soc. Cogn. Affect. Neurosci. 2014;10(7):1003–1009. doi: 10.1093/scan/nsu148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hotier S., Leroy F., Boisgontier J., Laidi C., Mangin J.-F., Delorme R., Bolognani F., Czech C., Bouquet C., Toledano E., Bouvard M., Petit J., Mishchenko M., d'Albis M.-A., Gras D., Gaman A., Scheid I., Leboyer M., Zalla T., Houenou J. Social cognition in autism is associated with the neurodevelopment of the posterior superior temporal sulcus. Acta Psychiatr. Scand. 2017;136(5):517–525. doi: 10.1111/acps.12814. [DOI] [PubMed] [Google Scholar]
  29. Huang Y.-Z., Edwards M.J., Rounis E., Bhatia K.P., Rothwell J.C. Theta burst stimulation of the human motor cortex. Neuron. 2005;45(2):201–206. doi: 10.1016/j.neuron.2004.12.033. [DOI] [PubMed] [Google Scholar]
  30. Huang Y.-Z., Rothwell J.C., Chen R.-S., Lu C.-S., Chuang W.-L. The theoretical model of theta burst form of repetitive transcranial magnetic stimulation. Clin. Neurophysiol. 2011;122(5):1011–1018. doi: 10.1016/j.clinph.2010.08.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Jannati A., Block G., Ryan M.A., Kaye H.L., Kayarian F.B., Bashir S., Oberman L.M., Pascual-Leone A., Rotenberg A. Continuous theta-burst stimulation in children with high-functioning autism spectrum disorder and typically developing children. Front. Integr. Neurosci. 2020;14–2020 doi: 10.3389/fnint.2020.00013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Jeurissen B., Tournier J.-D., Dhollander T., Connelly A., Sijbers J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage. 2014;103:411–426. doi: 10.1016/j.neuroimage.2014.07.061. [DOI] [PubMed] [Google Scholar]
  33. Ji G.-J., Liu T., Li Y., Liu P., Sun J., Chen X., Tian Y., Chen X., Dahmani L., Liu H., Wang K., Hu P. Structural correlates underlying accelerated magnetic stimulation in Parkinson's disease. Hum. Brain Mapp. 2021;42(6):1670–1681. doi: 10.1002/hbm.25319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Jokinen H., Ryberg C., Kalska H., Ylikoski R., Rostrup E., Stegmann M.B., Waldemar G., Madureira S., Ferro J.M., van Straaten E.C., Scheltens P., Barkhof F., Fazekas F., Schmidt R., Carlucci G., Pantoni L., Inzitari D., Erkinjuntti T., group L Corpus callosum atrophy is associated with mental slowing and executive deficits in subjects with age-related white matter hyperintensities: the LADIS Study. J. Neurol. Neurosurg. Psychiatry. 2007;78(5):491–496. doi: 10.1136/jnnp.2006.096792. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Jung J., Lambon Ralph M.A. The immediate impact of transcranial magnetic stimulation on brain structure: short-term neuroplasticity following one session of cTBS. Neuroimage. 2021;240 doi: 10.1016/j.neuroimage.2021.118375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Keary C.J., Minshew N.J., Bansal R., Goradia D., Fedorov S., Keshavan M.S., Hardan A.Y. Corpus callosum volume and neurocognition in autism. J. Autism Dev. Disord. 2009;39(6):834–841. doi: 10.1007/s10803-009-0689-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kellner E., Dhital B., Kiselev V.G., Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn. Reson. Med. 2016;76(5):1574–1581. doi: 10.1002/mrm.26054. [DOI] [PubMed] [Google Scholar]
  38. Kennedy D.P., Courchesne E. The intrinsic functional organization of the brain is altered in autism. Neuroimage. 2008;39(4):1877–1885. doi: 10.1016/j.neuroimage.2007.10.052. [DOI] [PubMed] [Google Scholar]
  39. Kirkovski M., Donaldson P.H., Do M., Speranza B.E., Albein-Urios N., Oberman L.M., Enticott P.G. A systematic review of the neurobiological effects of theta-burst stimulation (TBS) as measured using functional magnetic resonance imaging (fMRI) Brain Struct. Funct. 2023;228(3):717–749. doi: 10.1007/s00429-023-02634-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kishi T., Ikuta T., Sakuma K., Hamanaka S., Nishii Y., Hatano M., Kito S., Iwata N. Theta burst stimulation protocols for schizophrenia: a systematic review and network meta-analysis. JAMA Netw. Open. 2024;7(10):e2441159–e. doi: 10.1001/jamanetworkopen.2024.41159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kishi T., Ikuta T., Sakuma K., Hatano M., Matsuda Y., Wilkening J., Goya-Maldonado R., Tik M., Williams N.R., Kito S., Iwata N. Theta burst stimulation for depression: a systematic review and network and pairwise meta-analysis. Mol. Psychiatry. 2024;29(12):3893–3899. doi: 10.1038/s41380-024-02630-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Kosaka H., Omori M., Munesue T., Ishitobi M., Matsumura Y., Takahashi T., Narita K., Murata T., Saito D.N., Uchiyama H., Morita T., Kikuchi M., Mizukami K., Okazawa H., Sadato N., Wada Y. Smaller insula and inferior frontal volumes in young adults with pervasive developmental disorders. Neuroimage. 2010;50(4):1357–1363. doi: 10.1016/j.neuroimage.2010.01.085. [DOI] [PubMed] [Google Scholar]
  43. Kraft A., Dyrholm M., Kehrer S., Kaufmann C., Bruening J., Kathmann N., Bundesen C., Irlbacher K., Brandt S.A. TMS over the right precuneus reduces the bilateral field advantage in visual short term memory capacity. Brain Stimul. 2015;8(2):216–223. doi: 10.1016/j.brs.2014.11.004. [DOI] [PubMed] [Google Scholar]
  44. Kuang S. Two polarities of attention in social contexts: from attending-to-others to attending-to-self. Front. Psychol. 2016;7–2016 doi: 10.3389/fpsyg.2016.00063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Livingston L.A., Shah P., White S.J., Happé F. Further developing the Frith–Happé animations: a quicker, more objective, and web-based test of theory of mind for autistic and neurotypical adults. Autism Res. 2021;14(9):1905–1912. doi: 10.1002/aur.2575. [DOI] [PubMed] [Google Scholar]
  46. Lopez-Alonso V., Cheeran B., Rio-Rodriguez D., Fernandez-Del-Olmo M. Inter-individual variability in response to non-invasive brain stimulation paradigms. Brain Stimul. 2014;7(3):372–380. doi: 10.1016/j.brs.2014.02.004. [DOI] [PubMed] [Google Scholar]
  47. Lord C., Charman T., Havdahl A., Carbone P., Anagnostou E., Boyd B., Carr T., de Vries P.J., Dissanayake C., Divan G., Freitag C.M., Gotelli M.M., Kasari C., Knapp M., Mundy P., Plank A., Scahill L., Servili C., Shattuck P., Simonoff E., Singer A.T., Slonims V., Wang P.P., Ysrraelit M.C., Jellett R., Pickles A., Cusack J., Howlin P., Szatmari P., Holbrook A., Toolan C., McCauley J.B. The Lancet Commission on the future of care and clinical research in autism. Lancet. 2022;399(10321):271–334. doi: 10.1016/S0140-6736(21)01541-5. [DOI] [PubMed] [Google Scholar]
  48. Marco Valenti M.C.P., Mazza M., Panzarino G., Di Paolantonio C., Verrotti A. Abnormal structural and functional connectivity of the corpus callosum in autism spectrum disorders: a review. Rev. J. Autism Dev. Disord. 2020;7:46–62. doi: 10.1007/s40489-019-00176-9. [DOI] [Google Scholar]
  49. Massey S.H., Stern D., Alden E.C., Petersen J.E., Cobia D.J., Wang L., Csernansky J.G., Smith M.J. Cortical thickness of neural substrates supporting cognitive empathy in individuals with schizophrenia. Schizophr. Res. 2017;179:119–124. doi: 10.1016/j.schres.2016.09.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. McCalley D.M., Lench D.H., Doolittle J.D., Imperatore J.P., Hoffman M., Hanlon C.A. Determining the optimal pulse number for theta burst induced change in cortical excitability. Sci. Rep. 2021;11(1):8726. doi: 10.1038/s41598-021-87916-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Moeller F.G., Hasan K.M., Steinberg J.L., Kramer L.A., Dougherty D.M., Santos R.M., Valdes I., Swann A.C., Barratt E.S., Narayana P.A. Reduced anterior corpus callosum white matter integrity is related to increased impulsivity and reduced discriminability in cocaine-dependent subjects: diffusion tensor imaging. Neuropsychopharmacology. 2005;30(3):610–617. doi: 10.1038/sj.npp.1300617. [DOI] [PubMed] [Google Scholar]
  52. Moessnang C., Baumeister S., Tillmann J., Goyard D., Charman T., Ambrosino S., Baron-Cohen S., Beckmann C., Bölte S., Bours C., Crawley D., Dell’Acqua F., Durston S., Ecker C., Frouin V., Hayward H., Holt R., Johnson M., Jones E., Lai M.-C., Lombardo M.V., Mason L., Oldenhinkel M., Persico A., Cáceres A.S.J., Spooren W., Loth E., Murphy D.G.M., Buitelaar J.K., Banaschewski T., Brandeis D., Tost H., Meyer-Lindenberg A., the EUALg Social brain activation during mentalizing in a large autism cohort: the Longitudinal European Autism Project. Mol. Autism. 2020;11(1):17. doi: 10.1186/s13229-020-0317-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Musiek F.E. Neuroanatomy, neurophysiology, and central auditory assessment. Part III: Corpus callosum and efferent pathways. Ear Hear. 1986;7(6):349–358. doi: 10.1097/00003446-198612000-00001. [DOI] [PubMed] [Google Scholar]
  54. Nejati V., Majdi R., Salehinejad M.A., Nitsche M.A. The role of dorsolateral and ventromedial prefrontal cortex in the processing of emotional dimensions. Sci. Rep. 2021;11(1):1971. doi: 10.1038/s41598-021-81454-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Nettekoven C., Volz L.J., Kutscha M., Pool E.M., Rehme A.K., Eickhoff S.B., Fink G.R., Grefkes C. Dose-dependent effects of theta burst rTMS on cortical excitability and resting-state connectivity of the human motor system. J. Neurosci. 2014;34(20):6849–6859. doi: 10.1523/JNEUROSCI.4993-13.2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Ni H.-C., Chao Y.-P., Tseng R.-Y., Wu C.-T., Cocchi L., Chou T.-L., Chen R.-S., Gau S.-S.-F., Yeh C.-H., Lin H.-Y. Lack of effects of four-week theta burst stimulation on white matter macro/microstructure in children and adolescents with autism. NeuroImage: Clin. 2023;37 doi: 10.1016/j.nicl.2023.103324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Ni H.-C., Chen Y.-L., Chao Y.-P., Wu C.-T., Chen R.-S., Chou T.-L., Gau S.-S.-F., Lin H.-Y. A lack of efficacy of continuous theta burst stimulation over the left dorsolateral prefrontal cortex in autism: a double blind randomized sham-controlled trial. Autism Res. 2023;16(6):1247–1262. doi: 10.1002/aur.2954. [DOI] [PubMed] [Google Scholar]
  58. Ni H.-C., Chen Y.-L., Chao Y.-P., Wu C.-T., Wu Y.-Y., Liang S.-H.-Y., Chin W.-C., Chou T.-L., Gau S.-S.-F., Huang Y.-Z., Lin H.-Y. Intermittent theta burst stimulation over the posterior superior temporal sulcus for children with autism spectrum disorder: a 4-week randomized blinded controlled trial followed by another 4-week open-label intervention. Autism. 2021;25(5):1279–1294. doi: 10.1177/1362361321990534. [DOI] [PubMed] [Google Scholar]
  59. Ni H.-C., Chen Y.-L., Hsieh M.-Y., Wu C.-T., Chen R.-S., Juan C.-H., Li C.-T., Gau S.-S.-F., Lin H.-Y. Improving social cognition following theta burst stimulation over the right inferior frontal gyrus in autism spectrum: an 8-week double-blind sham-controlled trial. Psychol. Med. 2024;54(12):3261–3272. doi: 10.1017/S0033291724001387. [DOI] [PubMed] [Google Scholar]
  60. Ni H.-C., Chen Y.-L., Lin H.-Y. Feasibility and tolerability of daily theta burst stimulation in autistic youth with intellectual disabilities and minimally speaking status: a pilot double-blind randomized sham-controlled trial. J. Autism Dev. Disord. 2025;55(12):4262–4271. doi: 10.1007/s10803-024-06477-1. [DOI] [PubMed] [Google Scholar]
  61. Ni H.-C., Hung J., Wu C.-T., Wu Y.-Y., Chang C.-J., Chen R.-S., Huang Y.-Z. The impact of single session intermittent theta-burst stimulation over the dorsolateral prefrontal cortex and posterior superior temporal sulcus on adults with autism spectrum disorder. Front. Neurosci. 2017;11–2017 doi: 10.3389/fnins.2017.00255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Ni H.-C., Lin H.-Y., Chen Y.-L., Hung J., Wu C.-T., Wu Y.-Y., Liang H.-Y., Chen R.-S., Gau S.-S.-F., Huang Y.-Z. 5-day multi-session intermittent theta burst stimulation over bilateral posterior superior temporal sulci in adults with autism-a pilot study. Biomed. J. 2022;45(4):696–707. doi: 10.1016/j.bj.2021.07.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Ni R., Liu Y., Jiang J., Zhang W., Chen X., Liu J., Tang W., Wang K., Zhu C., Bu J. Continuous theta burst stimulation to relieve symptoms in patients with moderate obsessive-compulsive disorder: a preliminary study with an external validation. Transl. Psych. 2024;14(1):321. doi: 10.1038/s41398-024-03041-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Oberman L., Edwards D., Eldaief M., Pascual-Leone A. Safety of theta burst transcranial magnetic stimulation: a systematic review of the literature. J. Clin. Neurophysiol. 2011;28(1):67–74. doi: 10.1097/WNP.0b013e318205135f. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Oberman L., Eldaief M., Fecteau S., Ifert-Miller F., Tormos J.M., Pascual-Leone A. Abnormal modulation of corticospinal excitability in adults with Asperger's syndrome. Eur. J. Neurosci. 2012;36(6):2782–2788. doi: 10.1111/j.1460-9568.2012.08172.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Oberman L.M., Benussi A. Transcranial magnetic stimulation across the lifespan: impact of developmental and degenerative processes. Biol. Psychiatry. 2024;95(6):581–591. doi: 10.1016/j.biopsych.2023.07.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Oberman L.M., Pascual-Leone A., Rotenberg A. Modulation of corticospinal excitability by transcranial magnetic stimulation in children and adolescents with autism spectrum disorder. Front. Hum. Neurosci. 2014;8–2014 doi: 10.3389/fnhum.2014.00627. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Perellon-Alfonso R., Kralik M., Pileckyte I., Princic M., Bon J., Matzhold C., Fischer B., Slahorova P., Pirtosek Z., Rothwell J., Kojovic M. Similar effect of intermittent theta burst and sham stimulation on corticospinal excitability: a 5-day repeated sessions study. Eur. J. Neurosci. 2018;48(4):1990–2000. doi: 10.1111/ejn.14077. [DOI] [PubMed] [Google Scholar]
  69. Raffelt D., Tournier J.D., Fripp J., Crozier S., Connelly A., Salvado O. Symmetric diffeomorphic registration of fibre orientation distributions. Neuroimage. 2011;56(3):1171–1180. doi: 10.1016/j.neuroimage.2011.02.014. [DOI] [PubMed] [Google Scholar]
  70. Raffelt D.A., Smith R.E., Ridgway G.R., Tournier J.D., Vaughan D.N., Rose S., Henderson R., Connelly A. Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres. Neuroimage. 2015;117:40–55. doi: 10.1016/j.neuroimage.2015.05.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Raffelt D.A., Tournier J.D., Smith R.E., Vaughan D.N., Jackson G., Ridgway G.R., Connelly A. Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage. 2017;144:58–73. doi: 10.1016/j.neuroimage.2016.09.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Saturnino G.B., Puonti O., Nielsen J.D., Antonenko D., Madsen K.H., Thielscher A. In: Brain and Human Body Modeling: Computational Human Modeling at EMBC 2018. Makarov S., Horner M., Noetscher G., editors. Springer; Cham (CH): 2019. SimNIBS 2.1: a comprehensive pipeline for individualized electric field modelling for transcranial brain stimulation; pp. 3–25. Copyright 2019, The Author(s) [DOI] [PubMed] [Google Scholar]
  73. Schilling K.G., Blaber J., Huo Y., Newton A., Hansen C., Nath V., Shafer A.T., Williams O., Resnick S.M., Rogers B., Anderson A.W., Landman B.A. Synthesized b0 for diffusion distortion correction (Synb0-DisCo) Magn. Reson. Imaging. 2019;64:62–70. doi: 10.1016/j.mri.2019.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Schurz M., Radua J., Aichhorn M., Richlan F., Perner J. Fractionating theory of mind: a meta-analysis of functional brain imaging studies. Neurosci. Biobehav. Rev. 2014;42:9–34. doi: 10.1016/j.neubiorev.2014.01.009. [DOI] [PubMed] [Google Scholar]
  75. Seewoo B.J., Feindel K.W., Won Y., Joos A.C., Figliomeni A., Hennessy L.A., Rodger J. White matter changes following chronic restraint stress and neuromodulation: a diffusion magnetic resonance imaging study in young male rats. Biol. Psych. Global Open Sci. 2022;2(2):153–166. doi: 10.1016/j.bpsgos.2021.08.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Smith R., Dhollander T., Connelly A. On the regression of intracranial volume in fixel-based analysis. Proc. Int. Soc. Magn. Reson. Med. Sci. Meet. Exhib. 2019 [Google Scholar]
  77. Smith R.E., Tournier J.-D., Calamante F., Connelly A. SIFT: spherical-deconvolution informed filtering of tractograms. Neuroimage. 2013;67:298–312. doi: 10.1016/j.neuroimage.2012.11.049. [DOI] [PubMed] [Google Scholar]
  78. Sokhadze E.M., Baruth J.M., Sears L., Sokhadze G.E., El-Baz A.S., Casanova M.F. Prefrontal neuromodulation using rTMS improves error monitoring and correction function in autism. Appl. Psychophysiol. Biofeedback. 2012;37(2):91–102. doi: 10.1007/s10484-012-9182-5. [DOI] [PubMed] [Google Scholar]
  79. Sokhadze E.M., El-Baz A., Baruth J., Mathai G., Sears L., Casanova M.F. Effects of low frequency repetitive transcranial magnetic stimulation (rTMS) on gamma frequency oscillations and event-related potentials during processing of illusory figures in autism. J. Autism Dev. Disord. 2009;39(4):619–634. doi: 10.1007/s10803-008-0662-7. [DOI] [PubMed] [Google Scholar]
  80. Sokhadze E.M., El-Baz A.S., Sears L.L., Opris I., Casanova M.F. rTMS neuromodulation improves electrocortical functional measures of information processing and behavioral responses in autism. Front. Syst. Neurosci. 2014;8:134. doi: 10.3389/fnsys.2014.00134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Sokhadze E.M., Lamina E.V., Casanova E.L., Kelly D.P., Opris I., Tasman A., Casanova M.F. Exploratory study of rTMS neuromodulation effects on electrocortical functional measures of performance in an oddball test and behavioral symptoms in autism. Front. Syst. Neurosci. 2018;12:20. doi: 10.3389/fnsys.2018.00020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Thomas C., Humphreys K., Jung K.J., Minshew N., Behrmann M. The anatomy of the callosal and visual-association pathways in high-functioning autism: a DTI tractography study. Cortex. 2011;47(7):863–873. doi: 10.1016/j.cortex.2010.07.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Tournier J.D., Smith R., Raffelt D., Tabbara R., Dhollander T., Pietsch M., Christiaens D., Jeurissen B., Yeh C.-H., Connelly A. MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage. 2019;202 doi: 10.1016/j.neuroimage.2019.116137. [DOI] [PubMed] [Google Scholar]
  84. Tournier J.D., Yeh C.-H., Calamante F., Cho K.-H., Connelly A., Lin C.-P. Resolving crossing fibres using constrained spherical deconvolution: Validation using diffusion-weighted imaging phantom data. Neuroimage. 2008;42(2):617–625. doi: 10.1016/j.neuroimage.2008.05.002. [DOI] [PubMed] [Google Scholar]
  85. Tustison N.J., Avants B.B., Cook P.A., Zheng Y., Egan A., Yushkevich P.A., Gee J.C. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging. 2010;29(6):1310–1320. doi: 10.1109/TMI.2010.2046908. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Ueda R., Yamada N., Abo M., Senoo A. Relationship between motor function improvements and white matter structure after low-frequency repetitive transcranial magnetic stimulation plus intensive occupational therapy in chronic subcortical stroke patients. Neuroreport. 2019;30(7):485–490. doi: 10.1097/wnr.0000000000001227. [DOI] [PubMed] [Google Scholar]
  87. Vallence A.M., Goldsworthy M.R., Hodyl N.A., Semmler J.G., Pitcher J.B., Ridding M.C. Inter- and intra-subject variability of motor cortex plasticity following continuous theta-burst stimulation. Neuroscience. 2015;304:266–278. doi: 10.1016/j.neuroscience.2015.07.043. [DOI] [PubMed] [Google Scholar]
  88. Vanbellingen T., Pastore-Wapp M., Kübel S., Nyffeler T., Schüpfer A.-C., Kiefer C., Zizlsperger L., Lutz K., Luft A.R., Walther S., Bohlhalter S. Interhemispheric facilitation of gesturing: a combined theta burst stimulation and diffusion tensor imaging study. Brain Stimul. 2020;13(2):457–463. doi: 10.1016/j.brs.2019.12.013. [DOI] [PubMed] [Google Scholar]
  89. Veraart J., Novikov D.S., Christiaens D., Ades-aron B., Sijbers J., Fieremans E. Denoising of diffusion MRI using random matrix theory. Neuroimage. 2016;142:394–406. doi: 10.1016/j.neuroimage.2016.08.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Vernet M., Bashir S., Yoo W.K., Oberman L., Mizrahi I., Ifert-Miller F., Beck C.J., Pascual-Leone A. Reproducibility of the effects of theta burst stimulation on motor cortical plasticity in healthy participants. Clin. Neurophysiol. 2014;125(2):320–326. doi: 10.1016/j.clinph.2013.07.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Vickers A.J. The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study. BMC Med. Res. Method. 2001;1(1):6. doi: 10.1186/1471-2288-1-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Villalobos M.E., Mizuno A., Dahl B.C., Kemmotsu N., Müller R.-A. Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism. Neuroimage. 2005;25(3):916–925. doi: 10.1016/j.neuroimage.2004.12.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Voigt J.D., Leuchter A.F., Carpenter L.L. Theta burst stimulation for the acute treatment of major depressive disorder: a systematic review and meta-analysis. Transl. Psychiatry. 2021;11(1):330. doi: 10.1038/s41398-021-01441-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Walsh M.J.M., Wallace G.L., Gallegos S.M., Braden B.B. Brain-based sex differences in autism spectrum disorder across the lifespan: a systematic review of structural MRI, fMRI, and DTI findings. NeuroImage: Clinical. 2021;31 doi: 10.1016/j.nicl.2021.102719. [DOI] [PMC free article] [PubMed] [Google Scholar]
  95. Yamada N., Ueda R., Kakuda W., Momosaki R., Kondo T., Hada T., Sasaki N., Hara T. Senoo a and Abo M (2018) diffusion tensor imaging evaluation of neural network development in patients undergoing therapeutic repetitive transcranial magnetic stimulation following stroke. Neural Plast. 2018;1 doi: 10.1155/2018/3901016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Yang D.Y.J., Rosenblau G., Keifer C., Pelphrey K.A. An integrative neural model of social perception, action observation, and theory of mind. Neurosci. Biobehav. Rev. 2015;51:263–275. doi: 10.1016/j.neubiorev.2015.01.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Yang W., Liu T.-T., Song X.-B., Zhang Y., Li Z.-H., Cui Z.-H., Hao Q., Liu H.L., Lei C.L., Liu J. Comparison of different stimulation parameters of repetitive transcranial magnetic stimulation for unilateral spatial neglect in stroke patients. J. Neurol. Sci. 2015;359(1):219–225. doi: 10.1016/j.jns.2015.08.1541. [DOI] [PubMed] [Google Scholar]
  98. Yeh C.-H., Lin P.-C., Tseng R.-Y., Chao Y.-P., Wu C.-T., Chou T.-L., Chen R.-S., Gau S.-S.-F., Ni H.-C., Lin H.-Y. Lack of effects of eight-week left dorsolateral prefrontal theta burst stimulation on white matter macro/microstructure and connection in autism. Brain Imaging Behav. 2024;18(4):794–807. doi: 10.1007/s11682-024-00874-x. [DOI] [PubMed] [Google Scholar]
  99. Yeh C.-H., Tseng R.-Y., Ni H.-C., Cocchi L., Chang J.-C., Hsu M.-Y., Tu E.-N., Wu Y.-Y., Chou T.-L., Gau S.-S.-F., Lin H.-Y. White matter microstructural and morphometric alterations in autism: implications for intellectual capabilities. Mol. Autism. 2022;13(1):21. doi: 10.1186/s13229-022-00499-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  100. Yu F., Tang X., Hu R., Liang S., Wang W., Tian S., Wu Y., Yuan T.F., Zhu Y. The after-effect of accelerated intermittent theta burst stimulation at different session intervals. Front. Neurosci. 2020;14:576. doi: 10.3389/fnins.2020.00576. [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Zmeykina E., Mittner M., Paulus W., Turi Z. Weak rTMS-induced electric fields produce neural entrainment in humans. Sci. Rep. 2020;10(1):11994. doi: 10.1038/s41598-020-68687-8. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Data 1
mmc1.docx (35KB, docx)

Data Availability Statement

Data will be made available on request.

Articles from NeuroImage : Clinical are provided here courtesy of Elsevier

RESOURCES