Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-22T04:48:31.095Z Has data issue: false hasContentIssue false

Improving social cognition following theta burst stimulation over the right inferior frontal gyrus in autism spectrum: an 8-week double-blind sham-controlled trial

Published online by Cambridge University Press:  06 September 2024

Hsing-Chang Ni*
Affiliation:
Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan College of Medicine, Chang Gung University, Taoyuan, Taiwan
Yi-Lung Chen
Affiliation:
Department of Healthcare Administration, Asia University, Taichung, Taiwan Department of Psychology, Asia University, Taichung, Taiwan
Meng-Ying Hsieh
Affiliation:
Deparment of Pediatrics, Chang Gung Memorial Hospital at Taipei, Taipei, Taiwan Department of Pediatric Neurology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
Chen-Te Wu
Affiliation:
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
Rou-Shayn Chen
Affiliation:
Department of Neurology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
Chi-Hung Juan
Affiliation:
Institue of Cognitive Neuroscience, National Central University, Jhongli, Taiwan
Cheng-Ta Li
Affiliation:
Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
Susan Shur-Fen Gau
Affiliation:
Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
Hsiang-Yuan Lin*
Affiliation:
Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
*
Corresponding author: Hsing-Chang Ni; Email: [email protected]; [email protected]; Hsiang-Yuan Lin; Email: [email protected]; [email protected]
Corresponding author: Hsing-Chang Ni; Email: [email protected]; [email protected]; Hsiang-Yuan Lin; Email: [email protected]; [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Background

The right inferior frontal gyrus (RIFG) is a potential beneficial brain stimulation target for autism. This randomized, double-blind, two-arm, parallel-group, sham-controlled clinical trial assessed the efficacy of intermittent theta burst stimulation (iTBS) over the RIFG in reducing autistic symptoms (NCT04987749).

Methods

Conducted at a single medical center, the trial enrolled 60 intellectually able autistic individuals (aged 8–30 years; 30 active iTBS). The intervention comprised 16 sessions (two stimulations per week for eight weeks) of neuro-navigated iTBS or sham over the RIFG. Fifty-seven participants (28 active) completed the intervention and assessments at Week 8 (the primary endpoint) and follow-up at Week 12.

Results

Autistic symptoms (primary outcome) based on the Social Responsiveness Scale decreased in both groups (significant time effect), but there was no significant difference between groups (null time-by-treatment interaction). Likewise, there was no significant between-group difference in changes in repetitive behaviors and exploratory outcomes of adaptive function and emotion dysregulation. Changes in social cognition (secondary outcome) differed between groups in feeling scores on the Frith-Happe Animations (Week 8, p = 0.026; Week 12, p = 0.025). Post-hoc analysis showed that the active group improved better on this social cognition than the sham group. Dropout rates did not vary between groups; the most common adverse event in both groups was local pain. Notably, our findings would not survive stringent multiple comparison corrections.

Conclusions

Our findings suggest that iTBS over the RIFG is not different from sham in reducing autistic symptoms and emotion dysregulation. Nonetheless, RIFG iTBS may improve social cognition of mentalizing others' feelings in autistic individuals.

Type
Original Article
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

Introduction

Autism spectrum is a neurodevelopmental condition with social communication difficulties and repetitive/restricted behaviors (Lord, Elsabbagh, Baird, & Veenstra-Vanderweele, Reference Lord, Elsabbagh, Baird and Veenstra-Vanderweele2018). Despite the increasing prevalence (Zeidan et al., Reference Zeidan, Fombonne, Scorah, Ibrahim, Durkin, Saxena and Elsabbagh2022), there has yet to be an effective biological intervention to reduce autistic symptoms and associated adaptive function.

In the past three decades, noninvasive brain stimulation has shown the potential to improve several psychiatric disorders (Dayan, Censor, Buch, Sandrini, & Cohen, Reference Dayan, Censor, Buch, Sandrini and Cohen2013). Repetitive transcranial magnetic stimulation (rTMS), a common noninvasive brain stimulation method, can deliver various focused electromagnetic pulses to modulate brain function and neuroplasticity (Klomjai, Katz, & Lackmy-Vallee, Reference Klomjai, Katz and Lackmy-Vallee2015). Theta burst stimulation (TBS) is a modified variant of rTMS (Huang, Edwards, Rounis, Bhatia, & Rothwell, Reference Huang, Edwards, Rounis, Bhatia and Rothwell2005). The TBS effect may be related to long-term depression/potentiation-like aftereffects, glutamatergic/GABAergic neurotransmission, and synaptic plasticity (Huang, Rothwell, Chen, Lu, & Chuang, Reference Huang, Rothwell, Chen, Lu and Chuang2011; Li et al., Reference Li, Huang, Bai, Tsai, Su and Cheng2019). Compared to conventional rTMS, TBS is delivered faster with lower stimulation intensity (Chung, Hill, Rogasch, Hoy, & Fitzgerald, Reference Chung, Hill, Rogasch, Hoy and Fitzgerald2016; Huang et al., Reference Huang, Edwards, Rounis, Bhatia and Rothwell2005) while achieving non-inferior efficacy in depression (Blumberger et al., Reference Blumberger, Vila-Rodriguez, Thorpe, Feffer, Noda, Giacobbe and Downar2018, Reference Blumberger, Mulsant, Thorpe, McClintock, Konstantinou, Lee and Downar2022). Previous studies demonstrated the safety and tolerability of TBS from children to older adults (Hong et al., Reference Hong, Wu, Pedapati, Horn, Huddleston, Laue and Gilbert2015; Rossi et al., Reference Rossi, Antal, Bestmann, Bikson, Brewer and Brockmoller2021). With shorter stimulation duration and comparable therapeutic efficacy, TBS thus is a favorable protocol for clinical populations.

The previous neuroscience-informed consensus and meta-analysis suggest the dorsolateral prefrontal cortex (DLPFC), posterior superior temporal sulcus (pSTS)/temporoparietal junction and right inferior frontal gyrus (RIFG) could be a potential stimulation target for autism (Barahona-Correa, Velosa, Chainho, Lopes, & Oliveira-Maia, Reference Barahona-Correa, Velosa, Chainho, Lopes and Oliveira-Maia2018; Cole et al., Reference Cole, Enticott, Oberman, Gwynette, Casanova and Jackson2019). However, the published results are mixed. Most studies in the past decade applied rTMS/TBS over the DLPFC in autism. Earlier open-label trials without a sham-controlled group demonstrated that low-frequency rTMS (an inhibitory protocol) over the DLPFC might reduce autistic and inattentive symptoms in autistic individuals (Casanova et al., Reference Casanova, Hensley, Sokhadze, El-Baz, Wang, Li and Sears2014; Sokhadze et al., Reference Sokhadze, El-Baz, Baruth, Mathai, Sears and Casanova2009, Reference Sokhadze, Baruth, Tasman, Mansoor, Ramaswamy, Sears and Casanova2010, Reference Sokhadze, Lamina, Casanova, Kelly, Opris, Tasman and Casanova2018). However, our recent double-blind, randomized, sham-controlled trial (RCT) of an inhibitory TBS (continuous TBS, cTBS) over the DLPFC did not show group differences in changes in autistic symptoms and social cognition between the active and sham conditions in autistic people (Ni et al., Reference Ni, Chen, Chao, Wu, Chen, Chou and Lin2023b). Similarly, the other double-blind RCT trial did not support the efficacy of high-frequency rTMS over the DLPFC on executive function in autistic youth (Ameis et al., Reference Ameis, Blumberger, Croarkin, Mabbott, Lai, Desarkar and Daskalakis2020). On the other hand, our earlier RCT demonstrated a beneficial potential of intermittent TBS (iTBS, an excitatory protocol) over the pSTS in autistic adults (Ni et al., Reference Ni, Hung, Wu, Wu, Chang, Chen and Huang2017, Reference Ni, Lin, Chen, Hung, Wu, Wu and Huang2022). Likewise, our further RCT in autistic youth demonstrated the safety, feasibility, and beneficial potentials of pSTS iTBS (Ni et al., Reference Ni, Chen, Chao, Wu, Wu, Liang and Lin2021). However, this pSTS iTBS cannot modulate white matter properties in autistic individuals (Ni et al., Reference Ni, Chao, Tseng, Wu, Cocchi, Chou and Lin2023a).

Based on the consensus (Cole et al., Reference Cole, Enticott, Oberman, Gwynette, Casanova and Jackson2019), the RIFG is also a potential stimulation target for autism. RIFG is involved with a wide range of social cognition, including imitation (Hogeveen et al., Reference Hogeveen, Obhi, Banissy, Santiesteban, Press, Catmur and Bird2015), response inhibition (Duque, Greenhouse, Labruna, & Ivry, Reference Duque, Greenhouse, Labruna and Ivry2017), empathy (Massey et al., Reference Massey, Stern, Alden, Petersen, Cobia, Wang and Smith2017), emotion (Schurz, Radua, Aichhorn, Richlan, & Perner, Reference Schurz, Radua, Aichhorn, Richlan and Perner2014), social touch (Peled-Avron, Glasner, Gvirts, & Shamay-Tsoory, Reference Peled-Avron, Glasner, Gvirts and Shamay-Tsoory2019), and perspective-taking (Kuang, Reference Kuang2016). One-session RIFG rTMS could modulate several neuropsychological functions in neurotypical adults, such as artificial syntax processing (Udden et al., Reference Udden, Folia, Forkstam, Ingvar, Fernandez, Overeem and Petersson2008), deductive reasoning (Tsujii, Sakatani, Masuda, Akiyama, & Watanabe, Reference Tsujii, Sakatani, Masuda, Akiyama and Watanabe2011), action stopping (Sundby, Jana, & Aron, Reference Sundby, Jana and Aron2021), and emotional prosody processing (Hoekert, Vingerhoets, & Aleman, Reference Hoekert, Vingerhoets and Aleman2010). Moreover, RIFG stimulation also modulates social cognition in neurotypical adults. For example, one study demonstrated that one-session 1 Hz rTMS (inhibitory) over RIFG could disrupt behavioral responses to infant stimuli (De Carli et al., Reference De Carli, Bakermans-Kranenburg, Parolin, Lega, Zanardo, Cattaneo and Riem2019). Another study found that one-session 10 Hz rTMS (inhibitory) would temporarily disrupt RIFG activity and slow the pain-related empathy processing (Li et al., Reference Li, Li, Zhang, Zhang, Jin and Li2021). The other study demonstrated that RIFG iTBS could induce a more efficient strategy of task goal activation, while RIFG cTBS led to an inefficient approach (Dippel & Beste, Reference Dippel and Beste2015). Clinically, excitatory RIFG rTMS may benefit attention issues in Alzheimer's disease (Eliasova, Anderkova, Marecek, & Rektorova, Reference Eliasova, Anderkova, Marecek and Rektorova2014), language performance in poststroke aphasia (Winhuisen et al., Reference Winhuisen, Thiel, Schumacher, Kessler, Rudolf, Haupt and Heiss2007), smoking behaviors (Upton, Brown, Golzy, Garland, & Froeliger, Reference Upton, Brown, Golzy, Garland and Froeliger2023), and inhibitory control (Newman-Norlund, Gibson, McConnell, & Froeliger, Reference Newman-Norlund, Gibson, McConnell and Froeliger2020) in nicotine dependence.

Several magnetic resonance imaging (MRI) studies found alterations of RIFG in autism, including decreased gray matter volumes (Kosaka et al., Reference Kosaka, Omori, Munesue, Ishitobi, Matsumura, Takahashi and Wada2010), hypoactivation during response inhibition (Kana, Keller, Minshew, & Just, Reference Kana, Keller, Minshew and Just2007) and socially interfered cognitive control (Dichter & Belger, Reference Dichter and Belger2007), functional hypoconnectivity (Villalobos, Mizuno, Dahl, Kemmotsu, & Muller, Reference Villalobos, Mizuno, Dahl, Kemmotsu and Muller2005), and reduced local intrinsic connectivity (Kennedy & Courchesne, Reference Kennedy and Courchesne2008). Despite the evidence of RIFG ‘hypo-ness’ in autism, the ‘enhancement’ by excitatory rTMS/TBS, and the tolerability, only one double-blind RCT applied 10-session high-frequency RIFG rTMS (excitatory) in a small sample of 10 autistic children (4 active) (Kaokhieo et al., Reference Kaokhieo, Tretriluxana, Chaiyawat, Siripornpanich, Permpoonputtana, Tretriluxana and Nanbancha2023). They found improving adaptive functions in the active group but did not find significant active-v.-sham-by-time interaction in any outcomes. This caveat prompts further investigations of RIFG stimulation for autism in a sufficiently powered and statistically rigorous design.

In this context, we conducted this double-blind, two-arm, parallel-group sham-controlled RCT to investigate the efficacy of RIFG iTBS in a sufficiently powered sample of intellectually able autistic children, adolescents and young adults. Because there has been no established rule to localize the RIFG (c.f., the ‘5-cm rule’ of localizing the dorsolateral prefrontal cortex [Cash et al., Reference Cash, Weigand, Zalesky, Siddiqi, Downar, Fitzgerald and Fox2021]) without a need for MRI navigation, we decided not to include autistic people with intellectual disabilities, who generally have difficulties in complying with the MRI assessment. The objectives of this paper included investigating (1) the efficacy of RIFG iTBS in reducing autistic symptoms (primary outcome), improving social cognition (secondary outcome), and improving adaptive function and emotion dysregulation (exploratory outcome) following the 8-week (two sessions per week) iTBS course (primary endpoint); (2) whether the efficacy (if there would be any) could last for four weeks after the last stimulation (Week 12, the secondary endpoint); (3) the safety and feasibility of this protocol. We hypothesized that RIFG iTBS would improve autistic symptoms and social cognition in autistic individuals without intellectual disabilities, and its effect could last four weeks. This protocol would be safe and feasible.

Methods

Design

This study was a randomized, double-blind, parallel, sham-controlled trial comparing active iTBS v. sham over RIFG twice per week for continuous eight weeks (16 sessions in total) in intellectually able autistic individuals. This trial was conducted at a single tertiary medical center in Taiwan (Linkou Chang Gung Memorial Hospital) from November 2019 to May 2023.

Participants were randomly assigned to the active or sham group following a computer-generated randomization list with 1:1 allocation, stratified by sex. Then, participants would receive active RIFG iTBS or sham intervention for eight consecutive weeks and be observed four weeks after the last stimulation. Considering Taiwan's tight curriculum and inflexible on-leave arrangement, participants could choose two non-fixed days for intervention per week, with at least 48 h apart between sessions. Assessments of symptoms, social cognition, MRI (including structural, diffusion, and functional MRI), and electroencephalography (EEG) happened at baseline, Week 8, and Week 12. We only reported clinical and social cognition results herein. MRI and EEG data will be analyzed and reported subsequently in the future.

The blinding involved a research assistant (not involved in the enrollment) who concealed the randomization allocation. Throughout the study, only the neuromodulation technicians knew the active/sham allocation but were not involved in any assessments or analyses. All participants, parents/caregivers, and the principal investigator were blind to the allocation until Week 12. Data analysis was conducted by an independent statistician.

Our trial followed the 1964 Helsinki Declaration, its later amendments, and ethical standards of Good Clinical Practice and received ethical approval from the Research Ethics Committee at Chang Gung Medical Foundation-Institutional Review Board (CGMF-IRB 201900713A0) and Taiwan Food and Drug Administration (TFDA 1086611101). We also registered the trial on ClinicalTrials.gov (NCT04987749). Written informed consent was obtained from participants and their parents (substitute decision-makers). Whether a given participant assented or consented to the study depended on his/her/their capacity of consent to participate in the research as per the clinical assessment (Barstow, Shahan, & Roberts, Reference Barstow, Shahan and Roberts2018).

Participants

Study participants were recruited either from outpatient clinics or through online advertisements. The inclusion criteria were age 8–30 years and a clinical diagnosis of autism spectrum disorder based on DSM-5 and confirmed by the Autism Diagnosis Objective Schedule (ADOS) (Lord et al., Reference Lord, Risi, Lambrecht, Cook, Leventhal, DiLavore and Rutter2000). The exclusion criteria were any lifetime major systemic or neurological illness (especially seizure), schizophrenia, bipolar disorder, current depressive disorder, substance misuse, and full-scale intelligence Quotient (FIQ) <70 based on the Wechsler Intelligence Scale-IV for children or adults. Medications remained unchanged throughout the study. Information about comorbidities and medications is detailed in online Supplementary Table S1. Because all of the participants had to comply with MRI scans to precisely localize the RIFG, the baseline functional level of autistic people was either level 1 (requiring support) or level 2 (requiring substantial support) based on the DSM-5 criteria (Table 1).

Table 1. Baseline demographics and clinical characteristics

Abbreviation: ADHD, Attention-Deficit/Hyperactivity Disorder; OCD, Obsessive-compulsive Disorder.

*p < 0.05, **p < 0.01.

Intervention

The biphasic pulses for TBS were generated with the Magstim Super Rapid2 system (Magstim Company, Oxford, UK) and applied with the 70-mm figure-of-eight coil. Active motor threshold (AMT) was determined based on standard methods. The stimulus intensity was 90% AMT for both groups. The sham stimulation was executed with a sham coil, producing tactile and auditory stimulation without direct brain effects.

The present iTBS protocol (Huang et al., Reference Huang, Edwards, Rounis, Bhatia and Rothwell2005) was adopted as (1) Each iTBS train comprised a burst of 3 TMS pulses at 50 Hz at 200 ms intervals 10 times; (2) the TBS train was delivered every 10 s for 20 times to have 600 pulses for each iTBS course; (3) the total cumulative pulses were 9600 (16 sessions). Finally, iTBS was delivered when participants were in a state of rest.

The RIFG was localized by transforming from MNI coordinates (56, 10, 14) (Dapretto et al., Reference Dapretto, Davies, Pfeifer, Scott, Sigman, Bookheimer and Iacoboni2006) to the individual's baseline T1-weighted image using the Navigated Brain Stimulation system (Nexstim®, Helsinki, Finland). Information about localization transformation is detailed elsewhere (Ni et al., Reference Ni, Chao, Tseng, Wu, Cocchi, Chou and Lin2023a). The coil was targeted at the individual's MNI coordinates and applied tangentially against the skull, with the coil handle pointing upwardly and slightly posteriorly to prevent the stimulation blockage by the ear.

Outcomes

The primary outcomes were core autistic symptoms measured by the subjective caregiver-rated Social Responsiveness Scale (SRS) (Gau, Liu, Wu, Chiu, & Tsai, Reference Gau, Liu, Wu, Chiu and Tsai2013) and the Repetitive Behavior Scale-Revised (RBS-R) (Bodfish, Symons, Parker, & Lewis, Reference Bodfish, Symons, Parker and Lewis2000; Yang et al., Reference Yang, Lu, Jeng, Tsao, Cheong, Li and Wu2019) and objective ADOS-2 calibrated severity scores (ADOS-2-CSS) (Hus & Lord, Reference Hus and Lord2014; Lord, DiLavore, Risi, Gotham, & Bishop, Reference Lord, DiLavore, Risi, Gotham and Bishop2012). Higher scores on those scales represent greater severity.

Because of the essential role of RIFG in social cognition, we used two common social tasks, the Reading the Mind in the Eye Test (RMET) (Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, Reference Baron-Cohen, Wheelwright, Hill, Raste and Plumb2001) and Frith-Happe Animations (White, Coniston, Rogers, & Frith, Reference White, Coniston, Rogers and Frith2011), as the secondary outcome. The RMET was developed to evaluate social cognition for autism (Baron-Cohen et al., Reference Baron-Cohen, Wheelwright, Hill, Raste and Plumb2001). The RMET requires matching visual information from photographs of a pair of eyes along with the surrounding part of a face and a pair of words describing affective or mental states (4 items). There are 36 items in the RMET. The total scores (ranging from 0 to 36) on the RMET represent how many correct answers the participant infers from the eye expressions of different emotions, which captures facial affect reading and language command (Pavlova & Sokolov, Reference Pavlova and Sokolov2022). A recent meta-analysis (Penuelas-Calvo, Sareen, Sevilla-Llewellyn-Jones, & Fernandez-Berrocal, Reference Penuelas-Calvo, Sareen, Sevilla-Llewellyn-Jones and Fernandez-Berrocal2019) has demonstrated autistic people exhibit difficulties on the RMET in comparison to typical control (d = 1.15). The Frith-Happe Animations task was developed to evaluate mentalizing (theory of mind) in autistic people (Castelli, Frith, Happe, & Frith, Reference Castelli, Frith, Happe and Frith2002). In the Frith-Happe Animations, two triangles are presented on the screen with three different interaction patterns: perception of complex mental states involving the theory of mind (TOM; 4 questions), perception of agency for goal-directed behaviors (Goal-directed; 4 questions) or random movement with no attribution of mental states (Random; 4 questions). Participants are asked to infer whether an interaction between two triangles exists (Livingston, Shah, White, & Happe, Reference Livingston, Shah, White and Happe2021). If the participant answers correctly, they will be asked to select the words that best describe how two triangles feel during the interaction (feeling scores). The categorical scores on Frith-Happe Animations represent how participants correctly judge the interactions between two triangles; the feeling scores represent how participants select the correct words that best describe how two triangles feel during the interaction. To enhance contrast and reduce assessment time, we adopted the shortened Frith–Happe Animations that only includes TOM and Random and drops the Goal-directed. This adapted version has been used in the Human Connectome Project (Barch et al., Reference Barch, Burgess, Harms, Petersen, Schlaggar, Corbetta and Consortium2013) and our previous rTMS/TBS RCTs (Ni et al., Reference Ni, Chen, Chao, Wu, Wu, Liang and Lin2021, Reference Ni, Chen, Chao, Wu, Chen, Chou and Lin2023b). Based on this modification, the total categorical scores of the Frith-Happe Animations Task will be 0–8. The feeling scores of the Frith-Happe Animations Task range from 0 to 8. Higher scores in the Frith-Happe Animations Task represent better mentalizing ability. A recent meta-analysis (Wilson, Reference Wilson2021) has demonstrated autistic people largely do not perform as well as typically developing controls in this task (d = 0.25–0.62).

Because the RIFG is involved in response inhibition, which may be associated with emotion regulation and adaptive function, we also included these two domains as parent/caregiver-rated exploratory outcomes. Emotion dysregulation (Mazefsky et al., Reference Mazefsky, Herrington, Siegel, Scarpa, Maddox, Scahill and White2013) and lower adaptive function (Kenworthy, Case, Harms, Martin, & Wallace, Reference Kenworthy, Case, Harms, Martin and Wallace2010) are common in autistic people. Emotion dysregulation was estimated using the caregiver-rated Emotion Dysregulation Inventory (EDI) (Mazefsky, Yu, White, Siegel, & Pilkonis, Reference Mazefsky, Yu, White, Siegel and Pilkonis2018b). EDI comprises 30 items in two factors (Emotion Reactivity and Dysphoria) and can capture common emotional dysregulation issues in autistic youth with good test-retest reliability and sensitivity to change. The original psychometric sample during the development of EDI aged 4–20 years (Mazefsky et al., Reference Mazefsky, Day, Siegel, White, Yu and Pilkonis2018a, Reference Mazefsky, Yu, White, Siegel and Pilkonis2018b). Seldom published studies recruited samples of young adults aged beyond 20 years. Nonetheless, Dr Mazefsky and the team suggest that ‘the EDI can be used by caregivers of adults with ASD’ (Mazefsky, Reference Mazefsky2021). Higher scores of EDI represent worse emotion regulation. The adaptive function was measured with Adaptive Behavior Assessment System-II (ABAS-II) (Harrison & Oakland, Reference Harrison and Oakland2003). The ABAS-II is a caregiver-rated measurement for adaptive function, including communication, community use, functional academics, home living, health/safety, leisure, self-care, self-direction and social (Harrison & Oakland, Reference Harrison and Oakland2003). Higher ABAS-II scores represent better adaptive functions.

All questionnaires were assessed at baseline, Week 8 (within three days after the last iTBS) and Week 12. The social tasks were measured within one hour after the last iTBS in Week 8.

Adverse events were checked at each session using open-ended questions regarding physical discomfort, followed by close-ended questions studying the most common side effects of TBS, such as ‘pain at the application site,’ ‘headache/dizziness,’ and ‘tinnitus’ (Elmaghraby et al., Reference Elmaghraby, Sun, Ozger, Shekunov, Romanowicz and Croarkin2022). A neurologist evaluated the EEG data of each time point to assess any neurophysiologic changes.

Sample size

The sample size and the post-hoc power estimation were calculated with G*Power 3.1 (Faul, Erdfelder, Buchner, & Lang, Reference Faul, Erdfelder, Buchner and Lang2009). A minimum sample size of 44 was estimated with 95% power and 0.05 significance to detect a standardized effect of 0.50 (based on the previous meta-analysis [Barahona-Correa et al., Reference Barahona-Correa, Velosa, Chainho, Lopes and Oliveira-Maia2018]) from the within-between interaction of the repeated-measure analysis of the variance model. Considering dropouts (30%), the total sample size was 60. The mean attrition rate in RCTs for chronic pediatric mental health conditions is suggested to be 20% (Karlson & Rapoff, Reference Karlson and Rapoff2009). Because of the location of the RIFG, we expected that there might be a substantial proportion of participants would drop out because of the intolerable local pain induced by stimulation. When we calculated the sample size, the expected attrition rate was thus set at 30% (higher than the mean of 20%).

Statistical analysis

Demographic and clinical characteristics at baseline and adverse events between the active and sham groups were compared using an independent t test and χ2 test. We used the intent-to-treat (ITT) approach to account for dropouts. The proportion of missing data is 2% (4/180) in the SRS, RBS-R, EDI, and ABAS-II and 7% (12/180) in the RMET and Frith-Happe Animations. The Expected-Maximization algorithm was used to impute these missing data based on variables that were part of the planned analysis, comprising demographics (sex and age), intervention status, visit status, and all outcome variables (SRS, RMET, EDI, and ABAS-II). The generalized estimating equations (GEE) model was used to explore the treatment effect, time effect, and treatment-by-time effect from baseline to Week 8 and from baseline to Week 12, respectively. A working correlation matrix with a compound symmetry (exchangeable) autocorrelation was used with the robust standard error estimator to account for correlations between individuals' repeated measurements between visits. In the GEE model, baseline scores on the outcome of interest, age, and medication use were taken as covariates in our analysis. A paired t test was used as a post-hoc analysis within the active and sham groups, with the baseline as the reference. Notably, because earlier studies highlighted a possible age-dependent response to TBS in autistic people (Jannati et al., Reference Jannati, Block, Ryan, Kaye, Kayarian, Bashir and Rotenberg2020; Oberman, Pascual-Leone, & Rotenberg, Reference Oberman, Pascual-Leone and Rotenberg2014), we did an additional age-stratified GEE as shown in online Supplementary Information.

The statistical analysis was performed using the SAS Version 9.4 software (SAS Institute, Cary, NC, USA). The results from the GEE and post-hoc analyses were considered statistically significant at p value ⩽ 0.05. As the first preregistered clinical trial of RIFG TBS in autistic people, we aimed to investigate all potentially relevant effects caused by RIFG iTBS extensively. Given the exploratory nature, we did not correct for multiple comparisons in our study.

Results

Feasibility

Sixty-eight participants were screened by the principal investigator (H.-C.N.) (Fig. 1). Sixty participants completed the baseline assessments and were randomly assigned to the active or sham group (30 active). Two participants in the active group withdrew (one withdrew after 13 sessions for personal reasons, and the other withdrew after 16 sessions because of refusing to complete assessments). One participant in the sham group was withdrawn because of incidental findings (right frontal encephalomalacia) in the baseline MRI. Eventually, fifty-seven participants completed the study, with a retention rate of 95%. The post-hoc power calculation achieved 98.8%, with the conditions of two-sided alpha = 0.05, effect size d = 0.50, nonsphericity correction ɛ = 1, and correlation among repeated measures = 0.5.

Figure 1. Flow diagram.

Both groups did not differ in the baseline demographic and clinical features, including age, sex, FIQ, clinical severity, psychiatric comorbidity, and concurrent treatment (Table 1). However, total scores on the RMET (p = 0.008) and feeling scores of Frith-Happe Animations (p = 0.011) were lower in the active group than in the sham. Therefore, baseline performance was adjusted in the GEE.

Adverse events during iTBS comprised local pain (63%), dizziness (10%) and eye pain (3%) in the active group, and local pain (3%), dizziness (3%), and headache (3%) in the sham group (online Supplementary Table S2). All adverse events were transitory. No seizure was reported during the intervention and at Week 12. No abnormal findings on EEG were identified. All participants could tolerate iTBS, and none withdrew due to adverse events. No participants actively mentioned or questioned their allocation status during the study.

Primary outcome

The subjective SRS, RBS-R, and objective ADOS-2 CSS outcomes are reported in Table 2 and online Supplementary Fig. S1. Although we detected a significant time effect on changes in SRS (Week 8, Z = −2.96, p = 0.003; Week 12, Z = −3.19, p = 0.002; Table 3 and online Supplementary Table S3) and ADOS-2 CSS (Week 12, Z = −3.56, p < 0.001; online Supplementary Table S3), there was no significant treatment-by-time interaction effect on SRS (p Week 8 = 0.984; p Week 12 = 0.399), RBS-R (p Week 8 = 0.146; p Week 12 = 0.247) and ADOS-2 CSS (p Week 8 = 0.240; p Week 12 = 0.580), indicating both groups did not differ in changes in autistic symptoms from baseline to Week 8 and Week 12. The post-hoc within-group analysis (Table 2) revealed that total SRS scores significantly reduced from baseline to Week 8 (active: p < 0.001, Z = −3.60, Cohen's d = 0.66; sham: p = 0.004, Z = −2.90, Cohen's d = 0.53) and Week 12 (active: p = 0.025, Z = −2.24, Cohen's d = 0.41; sham: p = 0.002, Z = −3.12, Cohen's d = 0.57) in both groups, respectively. Total RBS-R scores decreased from baseline to Week 8 (p = 0.003, Z = −2.94, Cohen's d = 0.52) and Week 12 (p = 0.048, Z = −1.98, Cohen's d = 0.35) in the active group. ADOS-2 CSS scores decreased from baseline to Week 12 in the sham group (p < 0.001, Z = −3.52, Cohen's d = 0.64).

Table 2. Primary, secondary, and exploratory outcomes before and after intervention for Active and Sham groups

SRS, Social Responsiveness Scale; RBS-R, Repetitive Behavior Scale-Revised; EDI, Emotion Dysregulation Inventory; ABAS-II, Adaptive Behavior Assessment System-II; RMET, Reading the Mind in the Eyes Test.

* p < 0.05, ** p < 0.01, *** p < 0.001.

Table 3. Adjusted estimates of primary, secondary, and exploratory outcomes based on GEE model from baseline to week 8 for Active and Sham groups

The Sham group serves as the reference group.

SRS, Social Responsiveness Scale; RBS-R, Repetitive Behavior Scale-Revised; EDI, Emotion Dysregulation Inventory; ABAS-II, Adaptive Behavior Assessment System-II; RMET, Reading the Mind in the Eyes Test.

* p < 0.05, ** p < 0.01.

a Cohen's d effect size was calculated using the mean difference between groups with difference score as the mean score at follow-up minus the mean score at baseline score for each group to take the difference at baseline into account.

Secondary outcome

From baseline to week 8 (Table 3 and Fig. 2), we found a significant time effect (p = 0.020, Z = −2.32) and treatment-by-time interaction effect (p = 0.023, Z = 2.28, Cohen's d = 0.58) on categorical scores of Frith-Happe Animations. Post-hoc analysis revealed that categorical scores significantly decreased in the sham group (p = 0.018, Z = −2.36, Cohen's d = 0.42) but did not change in the active group (Table 2). There was also a significant treatment-by-time interaction effect on feeling scores of Frith-Happe Animations from baseline to Week 8 (p = 0.026, Z = 2.22, Cohen's d = 0.57) and Week 12 (p = 0.025, Z = 2.24, Cohen's d = 0.57), respectively (Table 3 and Fig. 2). Post-hoc analyses showed feeling scores increased from baseline to Week 8 (p = 0.051, Z = 1.95, Cohen's d = 0.36) and Week 12 (p = 0.014, Z = 2.46, Cohen's d = 0.45) in the active group, while there was no significant change in the sham group. As for the RMET, there was no significant time or treatment-by-time effect. Nonetheless, total scores significantly increased from baseline to Week 8 in the active group (p < 0.001, Z = 4.03, Cohen's d = 0.75; Table 2 and online Supplementary Fig. S2).

Figure 2. The individual and average scores of Frith-Happe Animations in Active and Sham groups at Baseline, Week 8, and Week 12, (a) categorical scores, (b) feeling scores. To better illustrate the trend of changes of categorical and feeling scores of Frith-Happe Animations, symmetrized percentage changes from the baseline are compared between both groups. The error bars denote standard errors.

Exploratory outcome

There was no significant treatment-by-time interaction effect on emotion dysregulation or adaptive function from baseline to Week 8 and Week 12 (online Supplementary Figs S3 and S4), but a significant time effect existed from baseline to Week 8 on emotion dysregulation (p = 0.032, Z = −2.15; Table 3 and online Supplementary Table S3). Post-hoc analysis showed adaptive function (p = 0.005, Z = 2.81, Cohen's d = 0.50 from baseline to Week 8) and emotion dysregulation (p = 0.003, Z = −3.02, Cohen's d = 0.55 from baseline to Week 12) significantly improved in the active group. At the same time, emotion dysregulation improved in the Sham group from baseline to Week 8 (Table 2).

Discussion

This sufficiently powered clinical trial sought to address the clinical efficacy of multi-session prefrontal iTBS in reducing autistic symptoms. Sixteen sessions of neuro-navigated iTBS delivered over the RIFG had no superior effect over sham in reducing autistic symptoms, emotion dysregulation, or adaptive function difficulties. Nonetheless, active iTBS, relative to sham, was associated with significant improvement in social cognition of inferring others' feelings (estimated by the Animations task) in autistic individuals, and this benefit could still be noted four weeks following the last stimulation. Notably, these results would not survive the stringent multiple comparison corrections. Neurophysiological data and adverse event profiles suggest RIFG iTBS is safe and tolerable in autistic individuals. If replicated in another independent dataset, results from our multimodal trial provide evidence to support the beneficial potential of RIFG iTBS to improve social cognition in autistic people.

Consistent with most of the published sham-controlled blind rTMS/TBS RCTs (Ameis et al., Reference Ameis, Blumberger, Croarkin, Mabbott, Lai, Desarkar and Daskalakis2020; Ni et al., Reference Ni, Chen, Chao, Wu, Wu, Liang and Lin2021, Reference Ni, Chen, Chao, Wu, Chen, Chou and Lin2023b), we did not find significant treatment-by-time interactions on any core or associated clinical symptoms, indicating no group differences between RIFG iTBS and sham. Nonetheless, also in line with effect sizes reported in previous open-label rTMS trials (Sokhadze et al., Reference Sokhadze, Lamina, Casanova, Kelly, Opris, Tasman and Casanova2018) and within-group post-pretreatment analyses in earlier blind RCTs (Kaokhieo et al., Reference Kaokhieo, Tretriluxana, Chaiyawat, Siripornpanich, Permpoonputtana, Tretriluxana and Nanbancha2023), our post-hoc within-group analysis revealed that autistic symptoms, adaptive function, and emotion dysregulation improved with time in both groups. Generally, we observed a more positive extent in the active group than in the sham group (in terms of larger effect sizes, which nonetheless were not robust enough to result in significant treatment-by-time interactions), except for the pattern of the ADOS-2 CSS (i.e. more reduction from the baseline to week 12 in the sham relative to active). This within-group pattern may reconcile the ostensible discrepancy between our findings (i.e. no group differences in clinical symptoms as quantified by treatment-by-time effects) and results from an earlier RIFG rTMS RCT (Kaokhieo et al., Reference Kaokhieo, Tretriluxana, Chaiyawat, Siripornpanich, Permpoonputtana, Tretriluxana and Nanbancha2023). Namely, Kaokhieo et al. (Kaokhieo et al. Reference Kaokhieo, Tretriluxana, Chaiyawat, Siripornpanich, Permpoonputtana, Tretriluxana and Nanbancha2023) showed that excitatory 5 Hz RIFG rTMS is associated with improving adaptive function in four autistic children in the active group, as evidenced by a significant time effect, but not by a significant treatment-by-time interaction. We cannot rule out the possibility that active RIFG iTBS/rTMS might still be superior to the sham in reducing clinical difficulties associated with autism, considering the sampling heterogeneity (Masi, Lampit, Glozier, Hickie, & Guastella, Reference Masi, Lampit, Glozier, Hickie and Guastella2015). Further, despite being sufficiently powered for the whole sample analysis, the current sample size is not big enough for valid sub-group analyses stratified by severity, age, or sex, precluding the detection of hidden changes due to inter-individual variability in response (Ni et al., Reference Ni, Chen, Chao, Wu, Wu, Liang and Lin2021, Reference Ni, Lin, Chen, Hung, Wu, Wu and Huang2022). Given the current intellectually able sample, relatively lower severity may also affect sensitivity to detect changes in clinical trials for autism spectrum (King et al., Reference King, Dukes, Donnelly, Sikich, McCracken, Scahill and Hirtz2013). This limitation merits more endeavors (e.g. NCT04987541) to include the autistic subpopulation with more severe symptoms and high support needs (i.e. those with minimally verbal status and/or intellectual disabilities) in the rTMS/TBS trials. Despite these possible explanations and caveats, our findings of null treatment-by-time interaction but significant time effects on changes in autistic symptoms following RIFG iTBS echo the concern that clinical rTMS/TBS studies (Burke et al., Reference Burke, Romanella, Mencarelli, Greben, Fox, Kaptchuk and Santarnecchi2022) involving neurodevelopmental populations are susceptible to generalized/placebo effects (Masi et al., Reference Masi, Lampit, Glozier, Hickie and Guastella2015).

We found that 16-session RIFG iTBS, relative to sham, was associated with improved social cognition (feeling scores on Frith-Happe Animations, indicating mentalizing others' feelings) in autistic individuals. This group difference in social cognition change can continue for four weeks after the last stimulation. However, this iTBS-associated change in social cognition was not meaningfully noted in category scores on the Animations task and RMET. This discrepancy could be explained as follows. The categorical score on the Animations task simply estimates whether a participant can judge the presence of an intention to interact between two agencies (i.e. two triangles in this task). On the other hand, the feeling score involves a more complex and subtle process of mental state inference, which requires a participant to correctly infer what these agencies are feeling during interactions to score (Livingston et al., Reference Livingston, Shah, White and Happe2021). Thus, the categorical score may be associated with ceiling effects, limiting sensitivity to capture sufficient variance in reflecting changes (Livingston et al., Reference Livingston, Shah, White and Happe2021). Regarding the double dissociation between changes in the Animations and RMET, this finding largely matches the theory and empirical findings of social cognition. Specifically, the RMET relies on facial affect reading, language command, and memory (Pavlova & Sokolov, Reference Pavlova and Sokolov2022), which differs from the mentalizing process reflected by Frith-Happe Animations. Further, RMET performance is supported by ensembles of circuits beyond the social brain (Pavlova & Sokolov, Reference Pavlova and Sokolov2022). In contrast, the RIFG is considered a gateway into a mentalizing network (Tettamanti et al., Reference Tettamanti, Vaghi, Bara, Cappa, Enrici and Adenzato2017). RIFG stimulation in neurotypical adults has also been shown to modulate mentalizing processes (De Carli et al., Reference De Carli, Bakermans-Kranenburg, Parolin, Lega, Zanardo, Cattaneo and Riem2019; Dippel & Beste, Reference Dippel and Beste2015; Li et al., Reference Li, Li, Zhang, Zhang, Jin and Li2021).

Despite this preceding neurobiological endorsement, several psychometric issues are considered. Specifically, there were between-group baseline differences in the RMET and feeling scores on Frith-Happe Animations, thus included as covariates. However, this baseline difference is considered a random artifact that could exist even in perfect randomization (Altman, Reference Altman1985) and is a ‘ubiquitous error’ in published RCTs (Austin, Manca, Zwarenstein, Juurlink, & Stanbrook, Reference Austin, Manca, Zwarenstein, Juurlink and Stanbrook2010). Therefore, the CONSORT recommends that ‘significance testing of baseline differences in randomized controlled trials (RCTs) should not be performed because it is superfluous and can mislead investigators and their readers’ (Moher et al., Reference Moher, Hopewell, Schulz, Montori, Gotzsche and Devereaux2010). Including covariates of the baseline scores in the models can minimize this concern of randomization (Austin et al., Reference Austin, Manca, Zwarenstein, Juurlink and Stanbrook2010). Notably, we acknowledge there might be a ceiling effect on the RMET result, despite the fact that the current RMET mean and standard deviation were similar to those reported in a meta-analysis (Penuelas-Calvo et al., Reference Penuelas-Calvo, Sareen, Sevilla-Llewellyn-Jones and Fernandez-Berrocal2019). The other concern is whether there is a learning effect in these two social cognition tasks. Previous studies have reported good test-retest reliability of the RMET with a 4-week interval (intraclass correlation coefficient 0.833) (Vellante et al., Reference Vellante, Baron-Cohen, Melis, Marrone, Petretto, Masala and Preti2013) and the Animations task with a 3-month interval (intraclass correlation coefficient 0.78) (Altschuler & Faja, Reference Altschuler and Faja2022). The good test-retest stability suggests that the learning effect for these two tasks may be minimal. In our study, the intervals between the time points were 8 weeks and 4 weeks, respectively, similar to these test-retest investigations. Moreover, the inference of whether active v. sham difference exists is based on the treatment-by-time interaction effect. A learning effect is more associated with a time effect, for which we did not find any significant change from baseline to Week 8 (Table 3) and baseline to Week 12 (online Supplementary Table S3) for most RMET and Animations results. The only exception was the categorical score of Frith-Happe Animations from baseline to Week 8 (p = 0.020), with an average worse performance at follow-up in the whole sample. If there had been a learning effect, this change would have been better with time, and the effect should have existed evenly in the two treatment groups, given the current proper randomization. We thus consider the learning effect in these two social cognition tasks to be minimal. These preceding reasons, the objective nature of social cognitive tasks, and lasting changes at the follow-up of Week 12 endorse the robustness of this RIFG iTBS-associated improvement in inferring others' feelings in autistic people.

Notably, RIFG iTBS-associated social cognition change cannot be translated into changes in autistic symptoms. Several explanations may account for this conceivable discrepancy. First, autism-associated social-communication symptoms involve interactions between a wide range of social cognition beyond mentalizing and non-social mental processes (Leekam, Reference Leekam2016). Correlations between social cognition and social difficulties in autistic people are thus just small to medium (Bottema-Beutel, Kim, & Crowley, Reference Bottema-Beutel, Kim and Crowley2019). Second, social cognitive tasks may not represent ecologically valid interpersonal processes involved in real-life interactions (Konstantin, Nordgaard, & Henriksen, Reference Konstantin, Nordgaard and Henriksen2023). Third, cognition and symptoms in autism sometimes have a paradoxical relationship (Geurts, Corbett, & Solomon, Reference Geurts, Corbett and Solomon2009). Fourth, the present primary outcome measures are parent/caregiver-rated and might not be sensitive to capture changes in RCT. Lastly, the current stimulation frequency/intensity might not be sufficient to induce broad social functioning improvement in autistic people.

The last argument brings forward general methodological considerations of rTMS/TBS. Specifically, optimal stimulation intensity, frequency, and cumulative doses, which could generate clinically meaningful changes in autism spectrum (among other psychiatric disorders), are far from being conclusive. Further, although theoretical rationales and empirical evidence justify the present implementation of RIFG iTBS (excitatory) for autism, neurotypical literature suggests that there exists high inter-individual variability in behavioral and neurobiological responses to TBS paradigms (Chung et al., Reference Chung, Hill, Rogasch, Hoy and Fitzgerald2016; Corp et al., Reference Corp, Bereznicki, Clark, Youssef, Fried, Jannati and Big2020). This idiosyncratic response might be even more pronounced in autistic people, evidenced by a small study showing that some autistic people had excitatory. In contrast, others had inhibitory cumulative aftereffects following cTBS (generally considered an inhibitory protocol) (Jannati et al., Reference Jannati, Block, Ryan, Kaye, Kayarian, Bashir and Rotenberg2020). This inter-individual variability in cTBS aftereffects may be driven by unique genotypes (Jannati et al., Reference Jannati, Block, Ryan, Kaye, Kayarian, Bashir and Rotenberg2020), idiosyncrasy in GABAergic system dysfunction in autism (Zhao et al., Reference Zhao, Mao, Zhu, Zou, Peng, Yang and Cui2021), shared and different synaptic mechanisms between iTBS and cTBS (Lee et al., Reference Lee, Chu, Wu, Chung, Hsieh, Chang and Lin2023), or differential regional neurobiological properties resulting in variable responses (Gollo, Roberts, & Cocchi, Reference Gollo, Roberts and Cocchi2017). These methodological issues prompt extensive investigations in the future.

The feasibility and safety of RIFG iTBS for autistic youth accords with most rTMS RCT trials (Ameis et al., Reference Ameis, Blumberger, Croarkin, Mabbott, Lai, Desarkar and Daskalakis2020; Enticott et al., Reference Enticott, Fitzgibbon, Kennedy, Arnold, Elliot, Peachey and Fitzgerald2014; Kaokhieo et al., Reference Kaokhieo, Tretriluxana, Chaiyawat, Siripornpanich, Permpoonputtana, Tretriluxana and Nanbancha2023). The current retention rate was 95%, compatible with previous RCTs on the pSTS (91%) and DLPFC (98%) (Ni et al., Reference Ni, Chen, Chao, Wu, Wu, Liang and Lin2021, Reference Ni, Chen, Chao, Wu, Chen, Chou and Lin2023b). Regarding the adverse events, 63% of participants in the active group felt local pain, which is higher than our previous DLPFC (27%) and pSTS (10%) trials. Nevertheless, all these discomforts were transitory and did not lead to dropouts. Moreover, we are the first to provide EEG evidence to prove that no seizure was induced by TBS in autistic people.

This study has some other limitations. First, considering the real-world generalization, we did not exclude participants with psychiatric comorbidities and concurrent medications. However, we acknowledge that hidden complex relationships between comorbidities/medications and treatment responses might exist. Second, the sex ratio of participants was quite uneven (5 females), precluding the investigation of sex/gender effects. Third, although we tried our best to maintain the integrity of blinding, the considerably higher incidence of reported pain in the active group might partly compromise the blinding. Future studies need to formally adopt the assessment of blinding in the protocol (e.g. Bang Blinding indexes) to assess whether caregivers/participants and raters could correctly guess to which arm participants are assigned. Lastly, although the measure developer team has suggested that the EDI can be rated by caregivers of autistic adults, there has been no caregiver norm for the EDI. Self-report perspectives might be different from the informant's response (Sandercock, Lamarche, Klinger, & Klinger, Reference Sandercock, Lamarche, Klinger and Klinger2020), while psychometrics of self-report measures for emotion dysregulation in autistic adults have not been validated yet.

Conclusion

In a sufficiently powered and rigorously conducted RCT, our MRI-guided RIFG iTBS intervention failed to reduce autistic symptoms more than the sham. Nonetheless, we showed that RIFG iTBS, relative to sham, could improve social cognition of inferring others' feelings in autistic people. Future studies are required to verify if the present promising results are robust to differences in the neuromodulation parameters. Our results also prompt investigations if protocol optimization (such as stimulation intensities and doses), together with other intervention modalities, could confer additional clinical benefits.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291724001387

Acknowledgements

The authors would like to thank all of our participants and their family members for partaking in this study, Ms. Chiu-Fen Lin, Ms. Yi-Chun Liu, Mr. Wei-Cheng Lin, and Mr. Hung Chien for carrying out the recruitment and data management, CGMH for equipment support, and the anonymous reviewers for comments that significantly improved the manuscript. We would like to pay tribute to the late Professor Ying-Zu Huang, who guided and inspired this study with wisdom, insight, and tenacity.

Author contributions

Hsing-Chang Ni: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing-original draft, Writing-review & editing, Visualization, Supervision, Project administration, Funding acquisition; Yi-Lung Chen: Formal analysis, Writing-review & editing; Meng-Ying Hsieh: Resources; Yi-Ping Chao: Resources; Chen-Te Wu: Resources; Rao-Shayn Chen: Resources; Chi-Hung Juan: Conceptualization; Cheng-Ta Li: Conceptualization; Susan Shur-Fen Gau: Resources, Conceptualization, Writing-editing; Hsiang-Yuan Lin: Conceptualization, Methodology, Software, Validation, Writing-original draft, Writing-review & editing, Visualization, Supervision.

Funding statement

This study was supported by the Ministry of Science and Technology of Taiwan (108-2321-B-075-004-MY2) and Chang Gung Memorial Hospital (CMRPG3L0681-2). H-YL is supported by the Azrieli Adult Neurodevelopmental Centre, 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 design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Competing interests

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

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

References

Altman, D. G. (1985). Comparability of randomised groups. Journal of the Royal Statistical Society. Series D (The Statistician), 34(1), 125136.Google Scholar
Altschuler, M. R., & Faja, S. (2022). Brief report: Test-retest reliability of cognitive, affective, and spontaneous theory of mind tasks among school-aged children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 52(4), 18901895. doi: 10.1007/s10803-021-05040-6CrossRefGoogle ScholarPubMed
Ameis, S. H., Blumberger, D. M., Croarkin, P. E., Mabbott, D. J., Lai, M. C., Desarkar, P., … Daskalakis, Z. J. (2020). Treatment of executive function deficits in autism spectrum disorder with repetitive transcranial magnetic stimulation: A double-blind, sham-controlled, pilot trial. Brain Stimulation, 13(3), 539547. doi: 10.1016/j.brs.2020.01.007CrossRefGoogle ScholarPubMed
Austin, P. C., Manca, A., Zwarenstein, M., Juurlink, D. N., & Stanbrook, M. B. (2010). A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: A review of trials published in leading medical journals. Journal of Clinical Epidemiology, 63(2), 142153. doi: 10.1016/j.jclinepi.2009.06.002CrossRefGoogle ScholarPubMed
Barahona-Correa, J. B., Velosa, A., Chainho, A., Lopes, R., & Oliveira-Maia, A. J. (2018). Repetitive transcranial magnetic stimulation for treatment of autism spectrum disorder: A systematic review and meta-analysis. Frontiers in Integrative Neuroscience, 12, 27. doi: 10.3389/fnint.2018.00027CrossRefGoogle ScholarPubMed
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., ... Consortium, W. U.-M. H. (2013). Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage, 80, 169189. doi: 10.1016/j.neuroimage.2013.05.033CrossRefGoogle ScholarPubMed
Baron-Cohen, S., Wheelwright, S., Hill, J., Raste, Y., & Plumb, I. (2001). The “Reading the Mind in the Eyes” test revised version: A study with normal adults, and adults with Asperger syndrome or high-functioning autism. Journal of Child Psychology and Psychiatry, 42(2), 241251.CrossRefGoogle ScholarPubMed
Barstow, C., Shahan, B., & Roberts, M. (2018). Evaluating medical decision-making capacity in practice. American Family Physician, 98(1), 4046.Google ScholarPubMed
Blumberger, D. M., Mulsant, B. H., Thorpe, K. E., McClintock, S. M., Konstantinou, G. N., Lee, H. H., … Downar, J. (2022). Effectiveness of standard sequential bilateral repetitive transcranial magnetic stimulation vs bilateral theta burst stimulation in older adults with depression: The FOUR-D randomized noninferiority clinical trial. JAMA Psychiatry, 79(11), 10651073. doi: 10.1001/jamapsychiatry.2022.2862CrossRefGoogle ScholarPubMed
Blumberger, D. M., Vila-Rodriguez, F., Thorpe, K. E., Feffer, K., Noda, Y., Giacobbe, P., … Downar, J. (2018). Effectiveness of theta burst versus high-frequency repetitive transcranial magnetic stimulation in patients with depression (THREE-D): A randomised non-inferiority trial. Lancet (London, England), 391(10131), 16831692. doi: 10.1016/S0140-6736(18)30295-2CrossRefGoogle ScholarPubMed
Bodfish, J. W., Symons, F. J., Parker, D. E., & Lewis, M. H. (2000). Varieties of repetitive behavior in autism: Comparisons to mental retardation. Journal of Autism and Developmental Disorders, 30(3), 237243. doi: 10.1023/a:1005596502855CrossRefGoogle ScholarPubMed
Bottema-Beutel, K., Kim, S. Y., & Crowley, S. (2019). A systematic review and meta-regression analysis of social functioning correlates in autism and typical development. Autism Research, 12(2), 152175. doi: 10.1002/aur.2055CrossRefGoogle ScholarPubMed
Burke, M. J., Romanella, S. M., Mencarelli, L., Greben, R., Fox, M. D., Kaptchuk, T. J., … Santarnecchi, E. (2022). Placebo effects and neuromodulation for depression: A meta-analysis and evaluation of shared mechanisms. Molecular Psychiatry, 27(3), 16581666. doi: 10.1038/s41380-021-01397-3CrossRefGoogle ScholarPubMed
Casanova, M. F., Hensley, M. K., Sokhadze, E. M., El-Baz, A. S., Wang, Y., Li, X., & Sears, L. (2014). Effects of weekly low-frequency rTMS on autonomic measures in children with autism spectrum disorder. Frontiers in Human Neuroscience, 8, 851. doi: 10.3389/fnhum.2014.00851CrossRefGoogle ScholarPubMed
Cash, R. F. H., Weigand, A., Zalesky, A., Siddiqi, S. H., Downar, J., Fitzgerald, P. B., & Fox, M. D. (2021). Using brain imaging to improve spatial targeting of transcranial magnetic stimulation for depression. Biological Psychiatry, 90(10), 689700. doi: 10.1016/j.biopsych.2020.05.033CrossRefGoogle ScholarPubMed
Castelli, F., Frith, C., Happe, F., & Frith, U. (2002). Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes. Brain, 125(Pt 8), 18391849. doi: 10.1093/brain/awf189CrossRefGoogle ScholarPubMed
Chung, S. W., Hill, A. T., Rogasch, N. C., Hoy, K. E., & Fitzgerald, P. B. (2016). Use of theta-burst stimulation in changing excitability of motor cortex: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 63, 4364. doi: 10.1016/j.neubiorev.2016.01.008CrossRefGoogle ScholarPubMed
Cole, E. J., Enticott, P. G., Oberman, L. M., Gwynette, M. F., Casanova, M. F., Jackson, S. L. J., … r, T. M. S. i. A. S. D. C. G. (2019). The potential of repetitive transcranial magnetic stimulation for autism spectrum disorder: A consensus statement. Biological Psychiatry, 85(4), e21e22. doi: 10.1016/j.biopsych.2018.06.003CrossRefGoogle ScholarPubMed
Corp, D. T., Bereznicki, H. G. K., Clark, G. M., Youssef, G. J., Fried, P. J., Jannati, A., … Big, T. M. S. D. C. (2020). Large-scale analysis of interindividual variability in theta-burst stimulation data: Results from the ‘Big TMS Data Collaboration’. Brain Stimulation, 13(5), 14761488. doi: 10.1016/j.brs.2020.07.018CrossRefGoogle ScholarPubMed
Dapretto, M., Davies, M. S., Pfeifer, J. H., Scott, A. A., Sigman, M., Bookheimer, S. Y., & Iacoboni, M. (2006). Understanding emotions in others: Mirror neuron dysfunction in children with autism spectrum disorders. Nature Neuroscience, 9(1), 2830. doi: 10.1038/nn1611CrossRefGoogle ScholarPubMed
Dayan, E., Censor, N., Buch, E. R., Sandrini, M., & Cohen, L. G. (2013). Noninvasive brain stimulation: From physiology to network dynamics and back. Nature Neuroscience, 16(7), 838844. doi: 10.1038/nn.3422CrossRefGoogle ScholarPubMed
De Carli, P., Bakermans-Kranenburg, M. J., Parolin, L., Lega, C., Zanardo, B., Cattaneo, Z., & Riem, M. M. E. (2019). A walk on the dark side: TMS over the right inferior frontal gyrus (rIFG) disrupts behavioral responses to infant stimuli. Social Neuroscience, 14(6), 697704. doi: 10.1080/17470919.2019.1574891CrossRefGoogle ScholarPubMed
Dichter, G. S., & Belger, A. (2007). Social stimuli interfere with cognitive control in autism. Neuroimage, 35(3), 12191230. doi: 10.1016/j.neuroimage.2006.12.038CrossRefGoogle ScholarPubMed
Dippel, G., & Beste, C. (2015). A causal role of the right inferior frontal cortex in implementing strategies for multi-component behaviour. Nature Communications, 6, 6587. doi: 10.1038/ncomms7587CrossRefGoogle ScholarPubMed
Duque, J., Greenhouse, I., Labruna, L., & Ivry, R. B. (2017). Physiological markers of motor inhibition during human behavior. Trends in Neurosciences, 40(4), 219236. doi: 10.1016/j.tins.2017.02.006CrossRefGoogle ScholarPubMed
Eliasova, I., Anderkova, L., Marecek, R., & Rektorova, I. (2014). Non-invasive brain stimulation of the right inferior frontal gyrus may improve attention in early Alzheimer's disease: A pilot study. Journal of the Neurological Sciences, 346(1-2), 318322. doi: 10.1016/j.jns.2014.08.036CrossRefGoogle ScholarPubMed
Elmaghraby, R., Sun, Q., Ozger, C., Shekunov, J., Romanowicz, M., & Croarkin, P. E. (2022). A systematic review of the safety and tolerability of theta burst stimulation in children and adolescents. Neuromodulation, 25(4), 494503. doi: 10.1111/ner.13455CrossRefGoogle ScholarPubMed
Enticott, P. G., Fitzgibbon, B. M., Kennedy, H. A., Arnold, S. L., Elliot, D., Peachey, A., … Fitzgerald, P. B. (2014). A double-blind, randomized trial of deep repetitive transcranial magnetic stimulation (rTMS) for autism spectrum disorder. Brain Stimulation, 7(2), 206211. doi: 10.1016/j.brs.2013.10.004CrossRefGoogle ScholarPubMed
Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 11491160. doi: 10.3758/BRM.41.4.1149CrossRefGoogle Scholar
Gau, S. S. F., Liu, L. T., Wu, Y. Y., Chiu, Y. N., & Tsai, W. C. (2013). Psychometric properties of the Chinese version of the Social Responsiveness Scale. Research in Autism Spectrum Disorders, 7(2), 349360. doi: 10.1016/j.rasd.2012.10.004CrossRefGoogle Scholar
Geurts, H. M., Corbett, B., & Solomon, M. (2009). The paradox of cognitive flexibility in autism. Trends in Cognitive Sciences, 13(2), 7482. doi: 10.1016/j.tics.2008.11.006CrossRefGoogle ScholarPubMed
Gollo, L. L., Roberts, J. A., & Cocchi, L. (2017). Mapping how local perturbations influence systems-level brain dynamics. Neuroimage, 160, 97112. doi: 10.1016/j.neuroimage.2017.01.057CrossRefGoogle ScholarPubMed
Harrison, P. L., & Oakland, T. (2003). Adaptive behavior assessment system® second edition ABAS®-II. San Antonio: Harcourt.Google Scholar
Hoekert, M., Vingerhoets, G., & Aleman, A. (2010). Results of a pilot study on the involvement of bilateral inferior frontal gyri in emotional prosody perception: An rTMS study. BMC Neuroscience, 11, 93. doi: 10.1186/1471-2202-11-93CrossRefGoogle ScholarPubMed
Hogeveen, J., Obhi, S. S., Banissy, M. J., Santiesteban, I., Press, C., Catmur, C., & Bird, G. (2015). Task-dependent and distinct roles of the temporoparietal junction and inferior frontal cortex in the control of imitation. Social Cognitive and Affective Neuroscience, 10(7), 10031009. doi: 10.1093/scan/nsu148CrossRefGoogle ScholarPubMed
Hong, Y. H., Wu, S. W., Pedapati, E. V., Horn, P. S., Huddleston, D. A., Laue, C. S., & Gilbert, D. L. (2015). Safety and tolerability of theta burst stimulation vs. single and paired pulse transcranial magnetic stimulation: A comparative study of 165 pediatric subjects. Frontiers in Human Neuroscience, 9, 29. doi: 10.3389/fnhum.2015.00029CrossRefGoogle ScholarPubMed
Huang, Y. Z., Edwards, M. J., Rounis, E., Bhatia, K. P., & Rothwell, J. C. (2005). Theta burst stimulation of the human motor cortex. Neuron, 45(2), 201206. doi: 10.1016/j.neuron.2004.12.033CrossRefGoogle ScholarPubMed
Huang, Y. Z., Rothwell, J. C., Chen, R. S., Lu, C. S., & Chuang, W. L. (2011). The theoretical model of theta burst form of repetitive transcranial magnetic stimulation. Clinical Neurophysiology, 122(5), 10111018. doi: 10.1016/j.clinph.2010.08.016CrossRefGoogle ScholarPubMed
Hus, V., & Lord, C. (2014). The autism diagnostic observation schedule, module 4: Revised algorithm and standardized severity scores. Journal of Autism and Developmental Disorders, 44(8), 19962012. doi: 10.1007/s10803-014-2080-3CrossRefGoogle ScholarPubMed
Jannati, A., Block, G., Ryan, M. A., Kaye, H. L., Kayarian, F. B., Bashir, S., … Rotenberg, A. (2020). Continuous theta-burst stimulation in children with high-functioning autism spectrum disorder and typically developing children. Frontiers in Integrative Neuroscience, 14, 13. doi: 10.3389/fnint.2020.00013CrossRefGoogle ScholarPubMed
Kana, R. K., Keller, T. A., Minshew, N. J., & Just, M. A. (2007). Inhibitory control in high-functioning autism: Decreased activation and underconnectivity in inhibition networks. Biological Psychiatry, 62(3), 198206. doi: 10.1016/j.biopsych.2006.08.004CrossRefGoogle ScholarPubMed
Kaokhieo, J., Tretriluxana, J., Chaiyawat, P., Siripornpanich, V., Permpoonputtana, K., Tretriluxana, S., & Nanbancha, A. (2023). Effects of repetitive transcranial magnetic stimulation combined with action-observation-execution on social interaction and communication in autism spectrum disorder: Feasibility study. Brain Research, 1804, 148258. doi: 10.1016/j.brainres.2023.148258CrossRefGoogle ScholarPubMed
Karlson, C. W., & Rapoff, M. A. (2009). Attrition in randomized controlled trials for pediatric chronic conditions. Journal of Pediatric Psychology, 34(7), 782793. doi: 10.1093/jpepsy/jsn122CrossRefGoogle ScholarPubMed
Kennedy, D. P., & Courchesne, E. (2008). The intrinsic functional organization of the brain is altered in autism. Neuroimage, 39(4), 18771885. doi: 10.1016/j.neuroimage.2007.10.052CrossRefGoogle ScholarPubMed
Kenworthy, L., Case, L., Harms, M. B., Martin, A., & Wallace, G. L. (2010). Adaptive behavior ratings correlate with symptomatology and IQ among individuals with high-functioning autism spectrum disorders. Journal of Autism and Developmental Disorders, 40(4), 416423. doi: 10.1007/s10803-009-0911-4CrossRefGoogle ScholarPubMed
King, B. H., Dukes, K., Donnelly, C. L., Sikich, L., McCracken, J. T., Scahill, L., … Hirtz, D. (2013). Baseline factors predicting placebo response to treatment in children and adolescents with autism spectrum disorders: A multisite randomized clinical trial. JAMA Pediatrics, 167(11), 10451052. doi: 10.1001/jamapediatrics.2013.2698CrossRefGoogle ScholarPubMed
Klomjai, W., Katz, R., & Lackmy-Vallee, A. (2015). Basic principles of transcranial magnetic stimulation (TMS) and repetitive TMS (rTMS). Annals of Physical and Rehabilitation Medicine, 58(4), 208213. doi: 10.1016/j.rehab.2015.05.005CrossRefGoogle ScholarPubMed
Konstantin, G. E., Nordgaard, J., & Henriksen, M. G. (2023). Methodological issues in social cognition research in autism spectrum disorder and schizophrenia spectrum disorder: A systematic review. Psychological Medicine, 53(8), 32813292. doi: 10.1017/S0033291723001095CrossRefGoogle ScholarPubMed
Kosaka, H., Omori, M., Munesue, T., Ishitobi, M., Matsumura, Y., Takahashi, T., … Wada, Y. (2010). Smaller insula and inferior frontal volumes in young adults with pervasive developmental disorders. Neuroimage, 50(4), 13571363. doi: 10.1016/j.neuroimage.2010.01.085CrossRefGoogle ScholarPubMed
Kuang, S. (2016). Two polarities of attention in social contexts: From attending-to-others to attending-to-self. Frontiers in Psychology, 7, 63. doi: 10.3389/fpsyg.2016.00063CrossRefGoogle ScholarPubMed
Lee, C. W., Chu, M. C., Wu, H. F., Chung, Y. J., Hsieh, T. H., Chang, C. Y., … Lin, H. C. (2023). Different synaptic mechanisms of intermittent and continuous theta-burst stimulations in a severe foot-shock induced and treatment-resistant depression in a rat model. Experimental Neurology, 362, 114338. doi: 10.1016/j.expneurol.2023.114338CrossRefGoogle Scholar
Leekam, S. (2016). Social cognitive impairment and autism: What are we trying to explain? Philosophical Transactions of the Royal Society B, 371(1686), 20150082. doi: 10.1098/rstb.2015.0082CrossRefGoogle ScholarPubMed
Li, C. T., Huang, Y. Z., Bai, Y. M., Tsai, S. J., Su, T. P., & Cheng, C. M. (2019). Critical role of glutamatergic and GABAergic neurotransmission in the central mechanisms of theta-burst stimulation. Human Brain Mapping, 40(6), 20012009. doi: 10.1002/hbm.24485CrossRefGoogle ScholarPubMed
Li, Y., Li, W., Zhang, T., Zhang, J., Jin, Z., & Li, L. (2021). Probing the role of the right inferior frontal gyrus during pain-related empathy processing: Evidence from fMRI and TMS. Human Brain Mapping, 42(5), 15181531. doi: 10.1002/hbm.25310CrossRefGoogle ScholarPubMed
Livingston, L. A., Shah, P., White, S. J., & Happe, F. (2021). Further developing the frith-happe animations: A quicker, more objective, and web-based test of theory of mind for autistic and neurotypical adults. Autism Research, 14(9), 19051912. doi: 10.1002/aur.2575CrossRefGoogle ScholarPubMed
Lord, C., Elsabbagh, M., Baird, G., & Veenstra-Vanderweele, J. (2018). Autism spectrum disorder. Lancet (London, England), 392(10146), 508520. doi: 10.1016/S0140-6736(18)31129-2CrossRefGoogle ScholarPubMed
Lord, C., Risi, S., Lambrecht, L., Cook, E. H. Jr., Leventhal, B. L., DiLavore, P. C., … Rutter, M. (2000). The autism diagnostic observation schedule-generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders, 30(3), 205223.CrossRefGoogle ScholarPubMed
Lord, C. R. M., DiLavore, P. C., Risi, S., Gotham, K., & Bishop, S. L. (2012). Autism diagnostic observation schedule, second edition (ADOS-2) manual (part I): Modules 1–4. Torrance, CA: Western Psychological Services.Google Scholar
Masi, A., Lampit, A., Glozier, N., Hickie, I. B., & Guastella, A. J. (2015). Predictors of placebo response in pharmacological and dietary supplement treatment trials in pediatric autism spectrum disorder: A meta-analysis. Translational Psychiatry, 5(9), e640. doi: 10.1038/tp.2015.143CrossRefGoogle ScholarPubMed
Massey, S. H., Stern, D., Alden, E. C., Petersen, J. E., Cobia, D. J., Wang, L., … Smith, M. J. (2017). Cortical thickness of neural substrates supporting cognitive empathy in individuals with schizophrenia. Schizophrenia Research, 179, 119124. doi: 10.1016/j.schres.2016.09.025CrossRefGoogle ScholarPubMed
Mazefsky, C. A. (2021). Encyclopedia of autism Spectrum disorders. Cham, Switzerland: SpringerLink.Google Scholar
Mazefsky, C. A., Day, T. N., Siegel, M., White, S. W., Yu, L., Pilkonis, P. A., … Developmental Disabilities Inpatient Research, C. (2018a). Development of the emotion dysregulation inventory: A PROMIS(R)ing method for creating sensitive and unbiased questionnaires for autism spectrum disorder. Journal of Autism and Developmental Disorders, 48(11), 37363746. doi: 10.1007/s10803-016-2907-1CrossRefGoogle Scholar
Mazefsky, C. A., Herrington, J., Siegel, M., Scarpa, A., Maddox, B. B., Scahill, L., & White, S. W. (2013). The role of emotion regulation in autism spectrum disorder. Journal of the American Academy of Child Psychiatry, 52(7), 679688. doi: 10.1016/j.jaac.2013.05.006CrossRefGoogle ScholarPubMed
Mazefsky, C. A., Yu, L., White, S. W., Siegel, M., & Pilkonis, P. A. (2018b). The emotion dysregulation inventory: Psychometric properties and item response theory calibration in an autism spectrum disorder sample. Autism Research, 11(6), 928941. doi: 10.1002/aur.1947CrossRefGoogle Scholar
Moher, D., Hopewell, S., Schulz, K. F., Montori, V., Gotzsche, P. C., Devereaux, P. J., … Consolidated Standards of Reporting Trials, G. (2010). CONSORT 2010 Explanation and elaboration: Updated guidelines for reporting parallel group randomised trials. Journal of Clinical Epidemiology, 63(8), e137. doi:10.1016/j.jclinepi.2010.03.004CrossRefGoogle ScholarPubMed
Newman-Norlund, R. D., Gibson, M., McConnell, P. A., & Froeliger, B. (2020). Dissociable effects of theta-burst repeated transcranial magnetic stimulation to the inferior frontal gyrus on inhibitory control in nicotine addiction. Frontiers in Psychiatry, 11, 260. doi: 10.3389/fpsyt.2020.00260CrossRefGoogle Scholar
Ni, H. C., Chao, Y. P., Tseng, R. Y., Wu, C. T., Cocchi, L., Chou, T. L., … Lin, H. Y. (2023a). Lack of effects of four-week theta burst stimulation on white matter macro/microstructure in children and adolescents with autism. NeuroImage: Clinical, 37, 103324. doi: 10.1016/j.nicl.2023.103324CrossRefGoogle ScholarPubMed
Ni, H. C., Chen, Y. L., Chao, Y. P., Wu, C. T., Chen, R. S., Chou, T. L., … Lin, H. Y. (2023b). 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 Research, 16(6), 12471262. doi: 10.1002/aur.2954CrossRefGoogle ScholarPubMed
Ni, H. C., Chen, Y. L., Chao, Y. P., Wu, C. T., Wu, Y. Y., Liang, S. H., … Lin, H. Y. (2021). 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, 25(5), 12791294. doi: 10.1177/1362361321990534CrossRefGoogle ScholarPubMed
Ni, H. C., Hung, J., Wu, C. T., Wu, Y. Y., Chang, C. J., Chen, R. S., & Huang, Y. Z. (2017). 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. Frontiers in Neuroscience, 11, 255. doi: 10.3389/fnins.2017.00255CrossRefGoogle ScholarPubMed
Ni, H. C., Lin, H. Y., Chen, Y. L., Hung, J., Wu, C. T., Wu, Y. Y., … Huang, Y. Z. (2022). 5-day multi-session intermittent theta burst stimulation over bilateral posterior superior temporal sulci in adults with autism-a pilot study. Biomedical Journal, 45(4), 696707. doi: 10.1016/j.bj.2021.07.008CrossRefGoogle ScholarPubMed
Oberman, L. M., Pascual-Leone, A., & Rotenberg, A. (2014). Modulation of corticospinal excitability by transcranial magnetic stimulation in children and adolescents with autism spectrum disorder. Frontiers in Human Neuroscience, 8, 627. doi: 10.3389/fnhum.2014.00627CrossRefGoogle ScholarPubMed
Pavlova, M. A., & Sokolov, A. A. (2022). Reading language of the eyes. Neuroscience & Biobehavioral Reviews, 140, 104755. doi: 10.1016/j.neubiorev.2022.104755CrossRefGoogle ScholarPubMed
Peled-Avron, L., Glasner, L., Gvirts, H. Z., & Shamay-Tsoory, S. G. (2019). The role of the inferior frontal gyrus in vicarious social touch: A transcranial direct current stimulation (tDCS) study. Developmental Cognitive Neuroscience, 35, 115121. doi: 10.1016/j.dcn.2018.04.010CrossRefGoogle Scholar
Penuelas-Calvo, I., Sareen, A., Sevilla-Llewellyn-Jones, J., & Fernandez-Berrocal, P. (2019). The “Reading the Mind in the Eyes” test in autism-spectrum disorders comparison with healthy controls: A systematic review and meta-analysis. Journal of Autism and Developmental Disorders, 49(3), 10481061. doi: 10.1007/s10803-018-3814-4CrossRefGoogle Scholar
Rossi, S., Antal, A., Bestmann, S., Bikson, M., Brewer, C., Brockmoller, J., … basis of this article began with a Consensus Statement from the Ifcn Workshop on “Present, F. o. T. M. S. S. E. G. S. O. u. t. A. (2021). Safety and recommendations for TMS use in healthy subjects and patient populations, with updates on training, ethical and regulatory issues: Expert guidelines. Clinical Neurophysiology, 132(1), 269306. doi: 10.1016/j.clinph.2020.10.003CrossRefGoogle ScholarPubMed
Sandercock, R. K., Lamarche, E. M., Klinger, M. R., & Klinger, L. G. (2020). Assessing the convergence of self-report and informant measures for adults with autism spectrum disorder. Autism, 24(8), 22562268. doi: 10.1177/1362361320942981CrossRefGoogle ScholarPubMed
Schurz, M., Radua, J., Aichhorn, M., Richlan, F., & Perner, J. (2014). Fractionating theory of mind: A meta-analysis of functional brain imaging studies. Neuroscience & Biobehavioral Reviews, 42, 934. doi: 10.1016/j.neubiorev.2014.01.009CrossRefGoogle ScholarPubMed
Sokhadze, E., Baruth, J., Tasman, A., Mansoor, M., Ramaswamy, R., Sears, L., … Casanova, M. F. (2010). Low-frequency repetitive transcranial magnetic stimulation (rTMS) affects event-related potential measures of novelty processing in autism. Applied Psychophysiology and Biofeedback, 35(2), 147161. doi: 10.1007/s10484-009-9121-2CrossRefGoogle ScholarPubMed
Sokhadze, E. M., El-Baz, A., Baruth, J., Mathai, G., Sears, L., & Casanova, M. F. (2009). Effects of low frequency repetitive transcranial magnetic stimulation (rTMS) on gamma frequency oscillations and event-related potentials during processing of illusory figures in autism. Journal of Autism and Developmental Disorders, 39(4), 619634. doi: 10.1007/s10803-008-0662-7CrossRefGoogle ScholarPubMed
Sokhadze, E. M., Lamina, E. V., Casanova, E. L., Kelly, D. P., Opris, I., Tasman, A., & Casanova, M. F. (2018). Exploratory study of rTMS neuromodulation effects on electrocortical functional measures of performance in an oddball test and behavioral symptoms in autism. Frontiers in Systems Neuroscience, 12, 20. doi: 10.3389/fnsys.2018.00020CrossRefGoogle Scholar
Sundby, K. K., Jana, S., & Aron, A. R. (2021). Double-blind disruption of right inferior frontal cortex with TMS reduces right frontal beta power for action stopping. Journal of Neurophysiology, 125(1), 140153. doi: 10.1152/jn.00459.2020CrossRefGoogle ScholarPubMed
Tettamanti, M., Vaghi, M. M., Bara, B. G., Cappa, S. F., Enrici, I., & Adenzato, M. (2017). Effective connectivity gateways to the theory of mind network in processing communicative intention. Neuroimage, 155, 169176. doi: 10.1016/j.neuroimage.2017.04.050CrossRefGoogle Scholar
Tsujii, T., Sakatani, K., Masuda, S., Akiyama, T., & Watanabe, S. (2011). Evaluating the roles of the inferior frontal gyrus and superior parietal lobule in deductive reasoning: An rTMS study. Neuroimage, 58(2), 640646. doi: 10.1016/j.neuroimage.2011.06.076CrossRefGoogle ScholarPubMed
Udden, J., Folia, V., Forkstam, C., Ingvar, M., Fernandez, G., Overeem, S., … Petersson, K. M. (2008). The inferior frontal cortex in artificial syntax processing: An rTMS study. Brain Research, 1224, 6978. doi: 10.1016/j.brainres.2008.05.070CrossRefGoogle ScholarPubMed
Upton, S., Brown, A. A., Golzy, M., Garland, E. L., & Froeliger, B. (2023). Right inferior frontal gyrus theta-burst stimulation reduces smoking behaviors and strengthens fronto-striatal-limbic resting-state functional connectivity: A randomized crossover trial. Frontiers in Psychiatry, 14, 1166912. doi: 10.3389/fpsyt.2023.1166912CrossRefGoogle ScholarPubMed
Vellante, M., Baron-Cohen, S., Melis, M., Marrone, M., Petretto, D. R., Masala, C., & Preti, A. (2013). The “Reading the Mind in the Eyes” test: Systematic review of psychometric properties and a validation study in Italy. Cognitive Neuropsychiatry, 18(4), 326354. doi: 10.1080/13546805.2012.721728CrossRefGoogle Scholar
Villalobos, M. E., Mizuno, A., Dahl, B. C., Kemmotsu, N., & Muller, R. A. (2005). Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism. Neuroimage, 25(3), 916925. doi: 10.1016/j.neuroimage.2004.12.022CrossRefGoogle ScholarPubMed
White, S. J., Coniston, D., Rogers, R., & Frith, U. (2011). Developing the frith-happe animations: A quick and objective test of theory of mind for adults with autism. Autism Research, 4(2), 149154. doi: 10.1002/aur.174CrossRefGoogle ScholarPubMed
Wilson, A. C. (2021). Do animated triangles reveal a marked difficulty among autistic people with reading minds? Autism, 25(5), 11751186. doi: 10.1177/1362361321989152CrossRefGoogle ScholarPubMed
Winhuisen, L., Thiel, A., Schumacher, B., Kessler, J., Rudolf, J., Haupt, W. F., & Heiss, W. D. (2007). The right inferior frontal gyrus and poststroke aphasia: A follow-up investigation. Stroke, 38(4), 12861292. doi: 10.1161/01.STR.0000259632.04324.6cCrossRefGoogle ScholarPubMed
Yang, Y. C., Lu, L., Jeng, S. F., Tsao, P. N., Cheong, P. L., Li, Y. J., … Wu, Y. T. (2019). Multidimensional developments and free-play movement tracking in 30- to 36-month-old toddlers with autism spectrum disorder who were full term. Physical Therapy, 99(11), 15351550. doi: 10.1093/ptj/pzz114CrossRefGoogle ScholarPubMed
Zeidan, J., Fombonne, E., Scorah, J., Ibrahim, A., Durkin, M. S., Saxena, S., … Elsabbagh, M. (2022). Global prevalence of autism: A systematic review update. Autism Research, 15(5), 778790. doi: 10.1002/aur.2696CrossRefGoogle ScholarPubMed
Zhao, H., Mao, X., Zhu, C., Zou, X., Peng, F., Yang, W., … Cui, R. (2021). GABAergic system dysfunction in autism Spectrum disorders. Frontiers in Cell and Developmental Biology, 9, 781327. doi: 10.3389/fcell.2021.781327CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Baseline demographics and clinical characteristics

Figure 1

Figure 1. Flow diagram.

Figure 2

Table 2. Primary, secondary, and exploratory outcomes before and after intervention for Active and Sham groups

Figure 3

Table 3. Adjusted estimates of primary, secondary, and exploratory outcomes based on GEE model from baseline to week 8 for Active and Sham groups

Figure 4

Figure 2. The individual and average scores of Frith-Happe Animations in Active and Sham groups at Baseline, Week 8, and Week 12, (a) categorical scores, (b) feeling scores. To better illustrate the trend of changes of categorical and feeling scores of Frith-Happe Animations, symmetrized percentage changes from the baseline are compared between both groups. The error bars denote standard errors.

Supplementary material: File

Ni et al. supplementary material

Ni et al. supplementary material
Download Ni et al. supplementary material(File)
File 1.3 MB