Post-stroke fatigue has a significant impact on stroke survivors’ mental and physical well-being. Our recent clinical trial showed significant reduction of post-stroke fatigue with modafinil treatment, however functional connectivity changes in response to modafinil have not yet been explored in stroke survivors with post-stroke fatigue. Twenty-eight participants  had MRI scans at baseline, and during modafinil and placebo treatment. Resting-state functional MRI data were obtained, and independent component analysis was used to extract functional networks. Resting-state functional connectivity (rsFC) was examined between baseline, modafinil and placebo treatment using permutation testing with threshold-free cluster enhancement. Overall twenty-eight participants  were included. During modafinil treatment, increased rsFC was observed in the right hippocampus  compared to placebo. This coincided with lower rsFC in the left frontoparietal , somatosensory  and mesolimbic network . In conclusion, modafinil treatment induces significant changes in rsFC in post-stroke fatigue. This modulation of rsFC may relate to a reduction of post-stroke fatigue; however, the relationship between sensory processing, neurotransmitter expression and fatigue requires further exploration. Although symptom resolution is observed in many stroke survivors, around 40% of patients experience persistent fatigue for months or years2. Excessive tiredness limits the ability of stroke survivors to perform activities of daily living3, partake in social events4, and to return to work5. Post-stroke fatigue has a negative impact on rehabilitation, quality of life6, and is a predictor of mortality two years after stroke onset7.Fatigue is a highly prevalent post-stroke symptom with 60–70% of patients reporting fatigue in the first days after stroke8. Therefore, it has been suggested that more general mechanisms may contribute to post-stroke fatigue such as decreased physical activity9, decreased cortical excitability10, or immune activity12, which decreases the availability of neurotransmitters such as dopamine and serotonin13.The mechanisms underlying early and non-resolving chronic fatigue after stroke are poorly understood, and previous studies could not conclusively show a link between stroke-specific characteristics such as clinical stroke severity, stroke lesion location or lesion volume to the onset or prevalence of fatigue14. Additionally, non-pharmacological therapies consisting of physical therapy and cognitive behavioural therapy are available; however, their efficacy has not been established in large, high-quality trials14. These non-pharmacological interventions may also not be viable for patients with severe fatigue, as the fatigue itself may interfere with their ability to participate in such a treatment15, indicating a need for pharmacological management of fatigue.Treatment of post-stroke fatigue is an ongoing challenge, as pharmacological treatments (e.g. fluoxetine) have failed to show positive results in the reduction of post-stroke fatigue16. Modafinil is a weak dopamine-reuptake inhibitor that upregulates expression of orexin, a neuropeptide involved in wakefulness and attention. Depletion of orexin in the brain has been associated with narcolepsy and its associated bouts of excessive sleepiness17. Upregulation of expression of orexin in response to modafinil treatment is therefore thought to increase wakefulness, which is suggested to be beneficial in the management of fatigue.A previous small phase II trial of 200 mg of modafinil given daily for six weeks showed promising results by significantly reducing self-reported fatigue and increasing quality of life18. We investigated whether modafinil modulates rsFC in attention and cognition-related networks in patients with post-stroke fatigue. The primary analysis in this study was to compare functional connectivity within networks comparing modafinil treatment and placebo treatment. We hypothesized that modafinil would increase rsFC in cognition and attention-related networks, based on studies in healthy subjects20.Global changes in communication between brain areas can be assessed using resting-state functional connectivity (rsFC) analysis using functional MRI (fMRI). To investigate whether modafinil treatment and the reduction in post-stroke fatigue coincides with modulation of rsFC in the brain; we collected resting-state fMRI data in a group of patients with severe post stroke fatigue who were treated with modafinil during a clinical trial16. The study protocol was registered with the Australian New Zealand Clinical Trials Registry 18. As part of the MIDAS trial, patients were randomized (1:1) to either modafinil (200 mg) or placebo for 6 weeks , and as part of the cross-over design, the study medication was swapped for another 6 weeks following a one-week washout period  trial, a phase-II, single-centre, placebo-controlled, double-blind, randomized crossover trialClinical measures taken at all study time points included the multi-dimensional fatigue inventory (MFI-20), the Montreal Cognitive Assessment (MoCA), the Stroke Specific Quality of Life Scale (SSQoL), and the Depression, Anxiety and Stress Scales (DASS-42).Participants underwent multi-modal MRI scanning sessions in a 3-Tesla Magnetom Prisma . During scanning sessions, a high-resolution T1-weighted anatomical scan  was obtained. Structural images were used for registration purposes and to create lesion masks needed to optimize registration. Additionally, rs-fMRI BOLD (blood oxygenation level dependent) T2*-weighted series were obtained with a gradient echo-planar-imaging (EPI) acquisition . During resting-state series acquisition, patients received instructions to lie still with eyes opened, and to not fall asleep. Total scanning time was approximately 30 minutes.21. Resting-state series were visually checked for quality and the first 10 volumes of the time-series were deleted to remove any T1-attenuation effects. Volumes were consecutively de-spiked using 3dDespike from the Analysis of Functional NeuroImages (AFNI) program, slice-time corrected with respect to the middle slice using Statistical Parametric Mapping , and motion-corrected using the 24-parameter model22. Subsequently, the component-based noise correction (aCompCor) method was applied to remove signal from cerebrospinal fluid and white matter masks23. This was achieved by segmenting the structural images to create white matter, grey matter and cerebrospinal fluid masks and which were registered to the EPI images using SPM. The resting-state time-series were registered to the MNI-template using AntsRegistration package from the Advanced Normalization Tools 24. To improve registration lesion masks  were included in the registration process. Then, registered resting-state series were band-pass filtered (0.08–0.1 Hz) using a Fourier filter and smoothed with a Gaussian kernel of 6 mm full-width at half-maximum (SPM).Imaging data were preprocessed within the Nipype framework25. Data were reduced to 8 independent components, which were subsequently supplied to dual regression in FSL to obtain participant-specific networks27. We selected the components corresponding to the networks-of-interest based on their visual concordance with previously defined resting-state networks30.To identify resting-state networks we performed independent component analysis using temporal concatenation of all included scans using melodic from the fMRI of the brain software library For the pooled analysis, scans were included in the modafinil treatment group irrespective of whether the patients received the active agent in treatment arm one or two, and similarly for the placebo group. We worked under the assumption that functional connectivity would return to the baseline state following the one week wash out period given that the half-life of modafinil is 10 to 12 hours. The imaging analysis required that data was available at least two time points. Given that not all participants completed MRI scanning at each time point  imaging analysis was restricted to those that participants with available MRI scans.n). Family-wise errors where corrected using the threshold-free cluster enhancement and the significance level was set at p < 0.05. rsFC data are presented using the maximum observed t-value, maximum p-value under the null distribution and location of the maxima in MNI-space (in a 3 mm-isotropic template). Brain areas were obtained in FSL’s FSLeyes using the Harvard Oxford brain atlas and the Juelich anatomical atlas.Within-subject differences were assessed using FSL’s randomise with the maximum number of possible sign-flips  for each respective analysis  participants who received modafinil then placebo, (2) participants with a large ‘treatment response’ who had an MFI score of <50 at their assessment while on modafinil and (3) treatment non-responders who had an MFI score of ≥60 during their assessment while on modafinil. The MFI score of 50 represents the general population mean16, however we investigated within-subject differences for each analysis individually, to ensure that clinical changes in MFI-20 occurred in the respective analyses. Data are presented with the mean and standard deviations, and assumptions for statistical testing were met unless stated otherwise. Statistical tests on the clinical data were two-tailed and significance level was set at p = 0.05. We performed statistical analyses using the R Statistical Package (version 3.3.2)32.Within-subject differences of the clinical measurements for the primary and secondary analyses were assessed using paired t-tests if assumptions for parametric testing were met, and otherwise the Wilcoxon signed-rank test was used. Statistics presented in the current study are not a formal assessment of the efficacy of modafinil for the overall crossover trialFrom the clinical trial, twenty-eight of the thirty-six (77.8%) participants included in MIDAS were eligible for and consented to the MRI-component of the MIDAS study. Participants who underwent the additional MRI scanning had a mean age of 63 years SD = 14.3), median 3.0 years post-stroke (IQR = 1.5–4.6 years) and ten participants (36%) were female. 15 participants (54.6%) had a stroke in the right hemisphere and 13 participants (46.4%) in the left hemisphere. This cohort had a mean baseline fatigue score on the MFI-20 of 72.3 (SD = 9.24), mean baseline MoCA of 22.1 (SD = 5.21), mean DASS-42 score of 45.3 . Two participants did not partake in baseline scanning due to the possible presence of MRI contraindications, but were confirmed to be eligible for subsequent scanning sessions. One participant could not complete baseline scanning due to claustrophobia. Fourteen participants opted out of one or more scanning sessions  during the treatment arms, either as they did not tolerate MRI scanning well, or due to illness. Resting-state data from five participants were excluded due to excessive motion artefacts. Altogether, twenty-one baseline scans, seventeen scans during modafinil treatment, and seventeen scans during placebo were available at each respective time point. Each analysis was based on participants that had an eligible scan available at both time points of interest, and therefore the number of participants in each analysis varies . Self-reported fatigue on the MFI-20 was significantly lower during modafinil treatment compared to during placebo treatment  and SSQoL was significantly higher . In participants who were taken off active therapy as part of the crossover study design, there was a significant increase in the self-reported fatigue on the MFI-20 during placebo treatment  and a significant decrease in SSQoL . After the wash-out period of 1 week, fatigue scores within this group were significantly increased . The removal of modafinil was associated with lower rsFC during placebo within the left inferior parietal lobule in the left frontoparietal network , the primary somatosensory cortex within the somatosensory network , and the left temporal pole in the mesolimbic system .Responders (n = 6). In the subset of patients with an MFI score lower than 50 during modafinil treatment, MFI-20 scores were significantly lower during modafinil treatment compared to during placebo  and SSQoL was significantly higher . A significant decrease in rsFC was observed in this group in the somatosensory network , in the left frontoparietal network , and in the mesolimbic network .Non-responders (n = 5). MFI scores did not significantly differ during modafinil treatment and baseline , nor did SSQoL scores . Within the ventral default mode network, connectivity within the left posterior cingulate gyrus was significantly lower during modafinil treatment compared to placebo . MFI-20 scores were significantly reduced by 33.6 points (p < 0.001) during modafinil treatment compared to baseline. Additionally, SSQoL was significantly increased by 39.6 points during modafinil treatment (p = 0.043), and DASS-42 score was lower by 24.6 points (p = 0.006). This coincided with a significant increase of rsFC within the left premotor cortex in the ventral default mode network .Non-responders (n = 7). MFI-scores were significantly lower during modafinil therapy compared to baseline , and SSQoL was significantly higher . A significant increase of rsFC was observed in the left thalamus within the thalamic network  of the left frontoparietal, somatosensory, mesolimbic and thalamic networks occur during modafinil treatment. This coincided with an alleviation of self-reported fatigue and an increase in quality of life. We hypothesized that modafinil would increase rsFC in attentional and cognitive networks due to the mechanisms of modafinil, however, our current findings suggest that modafinil treatment increases in the thalamic network and decreases in the frontoparietal, somatosensory and mesolimbic networks.33. When healthy subjects performed a visuo-sensorimotor task after a single-dose (200 mg) of modafinil, task-induced deactivation decreased in the left inferior parietal lobule, indicating that these findings may relate to modafinil administration rather than fatigue-resolution19. Dopamine has shown to regulate rsFC of the frontoparietal network with the default mode network35, and therefore we hypothesize that our findings may be a reflection of altered rsFC in response to modafinil-induced dopaminergic changes. Although modafinil is thought to primarily upregulate orexin, dopamine and its receptors have been shown to be important for the effect of modafinil36. Furthermore, orexin is involved in the regulation of dopamine38, which indicates that our findings may reflect resting-state changes due to dopamine changes which are in turn driven by modafinil-induced upregulation of orexin.The inferior parietal lobe is a brain area associated with sensory integration relating to visuospatial information39. The mesolimbic network contains the mesolimbic pathway, which is one of the major dopaminergic pathways in the brain associated with the reward response and motivation40. Qualitative data showed that motivation is a major theme in post-stroke fatigue41, and data from the main MIDAS trial indicated a significant improvement in the motivation sub-domain of the MFI-2016. Given that modafinil has shown to influence dopamine levels in the brain42, these results may be a reflection modafinil-induced upregulation of dopamine in the mesolimbic system, which may be associated with increase in reported motivation.We additionally observed decreased rsFC in the temporal pole within the mesolimbic network. The temporal pole has been suggested to be involved in social and emotional processing as well as semantic memory processing43. The changes in the inferior parietal lobule and the primary somatosensory cortex suggest a possible role of altered sensory processing in post-stroke fatigue; however, as we did not measure sensory processing across multiple domains, this relationship needs further exploration.We also showed a decrease of rsFC in the primary somatosensory cortex of the somatosensory network. Using independent component analysis, we were able to construct a distinct motor and somatosensory network, rather than a combined sensorimotor network. This indicates that our findings reflect somatosensory changes, rather than functional changes in the motor network. Limited data is available on the role of somatosensory processing in mental fatigue, however, a study in healthy participants indicated that somatosensory-evoked potentials measured using electroencephalography (EEG) were modulated after administration of modafinil and hypothesized that these changes related to alertness and arousal of the somatosensory system45. Dysregulation of dopamine was shown in not only MS-related fatigue46, but also in cancer-related fatigue47, indicating a possible overlap in the mechanisms of fatigue disorders. However, it was shown that dopamine is associated with increased rsFC in the somatosensory network48, indicating that dopamine may not be the underlying substrate of our findings, where we showed a decrease in rsFC in this network.In this study we report alterations of rsFC in networks that can potentially be influenced by dopamine, however we cannot definitively state that modafinil-induced dopamine modulation is responsible for the network changes, nor for the resolution of fatigue. The mesolimbic network, associated with the dopaminergic mesolimbic pathway, has been previously implicated in MS-related fatigue48, indicating that modafinil-associated fatigue reductions may be related to serotonin, rather than dopamine.In contrast, dopamine has shown to be associated with decreased rsFC of the default mode network, indicating differential effects of dopamine on specific networks. Additionally, decreases of rsFC have been shown in relation to the serotonin signalling pathway12. Modafinil has shown to suppress the expression of proinflammatory cytokines49, and therefore modafinil may reduce neuro-inflammation and thereby reduce fatigue. Inflammatory markers in turn influence the availability of serotonin and dopamine and could thereby alter rsFC50. Thus, the effects of dopamine and serotonin on rsFC need to be further explored to improve our understanding of their role in pathological fatigue.Studies have shown that post-stroke fatigue is associated with the presence of inflammatory markers C-reactive protein and interferon-α51, the posterior insula52, and the anterior cingulate cortex20. Our findings showed a decrease in rsFC in several brain networks, which may be indicative of post-stroke fatigue related hyper-connectivity, which normalizes after modafinil administration. Increases of rsFC in the posterior cingulate cortex, primary and supplementary motor cortex have been reported in patients with multiple sclerosis-related fatigue compared to non-fatigued MS patients53. The question of whether increased rsFC patterns occur in post-stroke fatigue would need to be addressed in a future study comparing brain networks in fatigued stroke survivors to those of non-fatigued stroke survivors.We mainly observed decreases in rsFC, which was in contrast to our a-priori hypothesis. Studies on rsFC after modafinil administration are scarce, however, those available indicate an increase of connectivity in areas such as the frontal cortex54. This could lead to increased noise within the signal, which could theoretically obscure any of the effects of interest. However, in the current study we included only within-subject comparisons and worked under the assumption that these factors would show minimal change in the time course of the study, as patients were more than three months post-stroke and would therefore have a marginal impact on our findings. We had sufficient participants to extract the resting-state networks of interest, indicating that there was enough shared variance between the participants. However, sample sizes were small (ranging from five to twelve), as this study was limited by the number of participants that were eligible for the neuroimaging component of the study and therefore we may have had insufficient power to show further smaller effects. Additionally, we applied permutation tests to assess differences, which is a more suitable method for smaller sample sizes. Currently, no formal methods for the calculation of sample sizes are available for resting-state fMRI studies; however, in studies with stroke populations where effectiveness of treatment and concurrent rsFC changes have been investigated, sample sizes fluctuate between 8 to 12 participants57.Ischaemic stroke is likely to cause changes in neurovascular coupling, cerebral blood flow, and cerebral blood volume, which are factors that influence the blood-oxygen level dependent signal on which resting-state fMRI are based58.A further limitation of this study was that the imaging assessor was not blinded to the study outcomes and the group allocation. However, data acquisition was performed by a blinded assessor. Since this study was not a formal investigation on the efficacy of modafinil, we believe that this does not impact our current findings. Furthermore, we selected participants that were eligible (MRI safety) and consented to the imaging arm of this study. There were no significant differences in fatigue scores between those participants that were included in the imaging study and those who only participated in the main MIDAS trialModafinil treatment-associated alleviation of post-stroke fatigue coincided with altered rsFC in the left frontoparietal network, somatosensory network, thalamic and mesolimbic network. We cannot make inferences regarding causal relationships between fatigue and rsFC, but the neuroimaging data does indicate a potential role for sensory processing and motivational deficits in post-stroke fatigue. Modafinil may aid in the alleviation of these deficits, and thereby reduce perceived fatigue. Changes in networks susceptible to dopamine expression indicate a possible role for this neurotransmitter in post-stroke fatigue. Future studies are needed to identify differences in sensory processing and dopamine levels between stroke survivors and their non-fatigued peers, and how they relate to rsFC in functional networks.


Surface EMGs were recorded from the VL and biceps femoris (BF) muscles. Simultaneous recordings of the knee joint angle by electro-goniometer and of the VL fascicle length by ultrasonography were used to calculate the muscle-tendon unit and tendinous length of VL . In the dolphin-kicking, the stretching and shortening amplitudes of VL LMTU did not differ significantly between the two kicking speed conditions. However, both stretching and shortening amplitudes of the VL fascicle length were lower at FAST than at NORMAL speed whereas the opposite was found for the VL LTT values. At FAST, the contribution of the VL tendinous length to the entire VLMTU length changes increased. The EMG analysis revealed at FAST higher agonist VL activation from the late up-beat (MTU stretching) to the early down-beat phases as well as increased muscle co-activation of VL and BF muscles from the late down-beat to early up-beat phases of dolphin-kicking. These results suggest that at increasing kicking speeds, the VL fascicles and tendinous tissues during aquatic movements can utilize tendon elasticity in a similar way than in terrestrial forms of locomotion. However, these activation profiles of VL and BF muscles may differ from their activation pattern in terrestrial locomotion.Without high impact forces, it is not clear how humans can utilize tendon elasticity during low-impact activities. The purpose of the present study was to examine the muscle-tendon behavior together with the electromyographic (EMG) activities of the vastus lateralis (VL) muscle during the human dolphin-kicking. In a swimming pool, each subject ( In terrestrial locomotion, the actively stretched skeletal muscle can enhance the power and/or efficiency, following the stretch-shortening cycle (SSC) concept  volunteered to participate in the study . All subIn an outdoor swimming pool (25 m), each subject was requested to swim 25 m underwater using the dolphin-kick (legs only) without breathing. After warming-up, the following devices were attached on the right leg: the surface electrodes for recording thigh electromyographic activities (EMG), a goniometer  for the knee joint angle and an ultrasound probe  for the VL muscle length changes. After preparation of the measurements, the subjects swam the dolphin-kicking at two different speeds . First, Muscle activities were recorded from the VL and biceps femoris (BF) muscles of the right leg using bipolar surface electrodes  with a multi-telemeter system . The electrode placements followed the SENIAM (Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles) guidelines  was used for all subjects. Similar to prior investigations during locomotion . Moreover, the up-beat and down-beat kick phases were divided equally into the early and late parts. From the 8–10 recorded dolphin-kick cycles, all of them were kept for the knee joint angle and EMG analyses whereas the ultrasonography analyses concentrated on 3 successive dolphin-kick cycles. The knee joint angle data was filtered with a Butterworth fourth-order filter (cut-off frequency 10 Hz). The obtained knee joint angles were used to calculate the instantaneous VL muscle-tendon unit length (VL LMTU) with the model of Visser et al. (TT), which was defined as the sum of the proximal and distal tendinous structures and aponeuroses, was calculated by subtracting VL fascicle length multiplied by the cosine of its pennation angle from LMTU  and full wave rectified. These signals and joint angular data of 8–10 stable cycle kicks were averaged for each cycle to get the averaged time course data for each subject whereas the ultrasonography analyses concentrated on three successive cycles. Thereafter, the averaged EMG amplitudes (aEMG) were calculated individually and separately for the four different phases during the dolphin-kicking (see above definitions). The recorded EMG values for VL and BF muscles during swimming were, respectively, normalized relative to knee extension/flexion MVC EMG values for each subject to consider the muscle activation level for swimming.t-test for comparing the stretching and shortening amplitude of the VL LMTU, fascicle length and LTT between NORMAL and FAST conditions. For EMG data, normalized average EMG amplitudes were compared using two-way repeated measures ANOVA and Bonferroni post-hoc tests when applicable were used to examine the main effects of each parameter and interaction between the conditions (NORMAL and FAST) and phases . A criterion alpha level of P < 0.05 was used to determine statistical significance for all data. SPSS 23.0 software was used for statistical analyses.All values are presented as means ± standard deviations in the text and figures. Statistical analyses were performed using paired −1 at FAST and 0.9 ± 0.1 m s−1 at NORMAL. The 1 cycle times of the dolphin-kicking for NORMAL and FAST were 647 ± 104 and 468 ± 46 ms, respectively. The separated phase times during the up-beat kick phase were 370 ± 76 and 235 ± 23 ms, respectively, and those during the down-beat kick phase were 278 ± 32 and 233 ± 31 ms, respectively. These times  were significantly shorter at FAST than at NORMAL . The transition timing from the up-beat to down-beat kick phases of the 1 cycle dolphin-kicking was significantly shifted earlier in FAST (50.3 ± 2.9%) than in NORMAL (56.7 ± 3.3%) (p < 0.001).The actual mean dolphin-kick speeds were 1.3 ± 0.1 m sTT as well as VL LMTU at both NORMAL and FAST during the dolphin-kicking underwent a stretching and shortening behavior from the up-beat to down-beat kicking  . Although the stretching and shortening speeds of the VL fascicle length did not show any significant differences between NORMAL and FAST, the stretching and shortening speeds of the VL LTT were faster at FAST than at NORMAL  . InverseThe VL EMG activity at NORMAL increased gradually during the late up-beat kick phase and then reached its maximal muscle activation during the early down-beat kick phase , 4. On tTT were increased. The contribution of VL tendinous tissues to the entire VL MTU length changes increased at the faster speed of the dolphin-kicking. Therefore, not only on land but also under water, the efficient muscle force output prevents the increasing speeds of the muscle fascicle stretching and shortening. The increased utilization of the VL tendinous elasticity is considered as the common muscle-tendon strategy to enhance power output for faster human movements, such as running, jumping, and swimming. In the muscle activation patterns, however, not only the VL activation from the late up-beat (MTU stretching) to the early down-beat kick phases, but also specific muscle co-activation between VL and BF from the late down-beat to early up-beat kick phases of dolphin-kicking may play important roles to enhance kicking speeds.The results of the present study clearly show that the VL muscle fascicles and tendinous tissues as well as the MTU present a SSC behavior during the knee joint flexion and extension during human dolphin-kicking. At faster speeds of the dolphin-kicking, the stretching and shortening amplitudes of the VL fascicle length were decreased and inversely, those of the VL LMTU and LTT increased with the increased LTT stretching and shortening amplitudes, although the speeds of VL fascicle length were unchanged and the amplitudes of VL fascicle length were decreased. These muscle fascicle and tendinous tissue interactions for enhancing performance of swimming are in line with those of previous studies in terrestrial locomotion  can not only decelerate the knee extension at the end of the range of motion (ROM) but also enhance the kicking movements of the leg segment, causing the hyperextension of the swimmer knee. In the following early up-beat phase, both VL and BF were additionally activated at faster speed. BF then functions as an agonist  to initiate knee flexion and inversely VL functions as an antagonist  to prevent knee flexion and fascicle lengthening. This increased antagonist VL activation concomitantly with the increased BF activation during the knee flexion at FAST can result in an increased muscle stiffness and function as the preprogramed muscle activation in SSC for complementing the low impact load dolphin kicking. On the other hand, a very small amount of co-activation occurred during the late up-beat and early down-beat phases. These different activation profiles among phases may be related to the gravitational loading and dominant kicking direction. Such co-activation during the transition phase has been observed in animal swimming    and EMG activities were recorded simultaneously during human underwater swimming. We have measured only one knee extensor and flexor muscles during dolphin-kicking. However, other muscles at the knee, ankle, and hip joints can also contribute to dolphin-kicking and should be measured in the future studies. Secondly, the attached wired ultrasound probe and goniometers in the present study may influence the dolphin-kicking speeds. However, the maximal kicking speed value (1.3 ± 0.1 m s-1) for the FAST condition was similar in the speeds previously reported for male and female national level swimmers .KS, MI, and PK designed the experiment and wrote the manuscript. KS, TS, YD, and RN verified the analytical methods, performed the analyses, and interpreted the results. All authors performed the experiments, discussed the results, and commented on the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Transcranial magnetic stimulation (TMS) can non-invasively modulate neural activity in humans. Despite three decades of research, the spatial extent of the cortical area activated by TMS is still controversial. Moreover, how TMS interacts with task-related activity during motor behavior is unknown. Here, we applied single-pulse TMS over macaque parietal cortex while recording single-unit activity at various distances from the center of stimulation during grasping. The spatial extent of TMS-induced activation is remarkably restricted, affecting the spiking activity of single neurons in an area of cortex measuring less than 2 mm in diameter. In task-related neurons, TMS evokes a transient excitation followed by reduced activity, paralleled by a significantly longer grasping time. Furthermore, TMS-induced activity and task-related activity do not summate in single neurons. These results furnish crucial experimental evidence for the neural effects of TMS at the single-cell level and uncover the neural underpinnings of behavioral effects of TMS. Transcranial Magnetic Stimulation (TMS) can modulate human brain activity, but the extent of the cortical area activated by TMS is unclear. Here, the authors show that TMS affects monkey single neuron activity in an area less than 2 mm diameter, while TMS-induced activity and task-related activity do not summate. The most attractive feature of these NIBS techniques is that they allow excitation or inhibition of neural tissue through the skull, so that causal inferences about the role of cortical areas can be made in healthy volunteers. Of all NIBS techniques, transcranial magnetic stimulation (TMS) has been successfully and widely used in volunteers and patients for over 30 years11, but a number of fundamental questions remain unresolved. We know very little about the spread of the induced currents in the brain and their excitatory or inhibitory effects on neural activity at the single-cell level, both locally (i.e. immediately below the TMS coil) and at a distance . Inferences about the causal role of a cortical area in behavior critically depend on the ability to manipulate neural activity with high spatial selectivity, since interfering with multiple areas simultaneously could lead to erroneous conclusions. Selective spatial targeting also involves focusing on specific neuronal populations within a cortical area, but again, direct single-cell evidence for the effect of TMS on different neuronal subpopulations is lacking. Furthermore, numerous studies have demonstrated that the application of TMS can cause behavioral effects18, yet how TMS-induced activity interacts with ongoing neural activity during behavior is entirely unknown.A host of noninvasive brain stimulation (NIBS) techniques are widely used in neuroscience research23 or indirect measurements of the effect of TMS on neural activity, including behavior , noninvasive electrophysiology (EEG32) or functional imaging40. Other studies have investigated the effect of repetitive TMS (rTMS) on hemodynamic, local field potential (ECoG) and single-cell responses in anesthetized animals45. However, none of these approaches provide the spatial and temporal resolution necessary to examine the impact of single-pulse TMS on individual neurons, the spatial extent of TMS effects, nor the interaction between TMS-induced and task-related activity during behavior. Recently, Mueller et al.46 demonstrated the feasibility of measuring single-cell activity during single-pulse TMS, and Ortuño et al.47 recorded extracellular activity in remote subcortical brain structures in awake behaving monkeys immediately after rTMS. The monkey model offers several important advantages for TMS studies because of its brain size, the pattern of sulci and gyri (which affects the current spread in the brain19) comparable to that of the human brain, and the possibility to measure single-cell activity during complex visuomotor tasks48.Previous studies have employed computational modeling19), simulating a distance of 15 mm between our TMS coil and the cortical surface . First, we modelled the spatial spread of the TMS-induced current over the macaque parietal cortex using existing software (simNIBS50) based on anatomical MRI and CT imaging , which is smaller than the TMS coils traditionally used in humans , to take into account the smaller size of the monkey’s skull. The coil was positioned over the parietal cortex while recording the spiking activity of neurons located under the center of stimulation . We analyzed the spatial spread of TMS applied at different task epochs . Also, we applied TMS at two different intensities: 60% (low-intensity TMS) and 120% (high-intensity TMS) of the rMT, and recorded the activity of 538 parietal neurons  in two rhesus monkeys. To record as closely as possible to the skull, we precisely positioned the TMS coil as to target the parietal convexity , whereas low-intensity stimulation did not induce any effect . Single-pulse TMS did not exclusively induce excitatory effects in single neurons. The second example neuron . Overall, the example neuron in Fig. Single-pulse TMS evoked a variety of effects on individual neurons in parietal cortex, as illustrated in Fig. ron Fig.  showed ap = 0.04, Wilcoxon, Supplementary Fig. 52. In addition, we tested the effect of an intermediate stimulation intensity (100% rMT) in 18 neurons. The TMS-evoked activity at 100% rMT differed significantly  from the activity during stimulation at 60% rMT . Importantly, these results indicate that our standard stimulation intensity was sufficiently above threshold to activate neurons in parietal cortex.We performed two additional experiments to investigate the effect of coil orientation and stimulation intensity on single neurons. In 15 neurons showing a TMS effect, we inverted the coil so that the current flow was anterior-posterior (AP) instead of posterior-anterior (PA), and recorded the TMS-evoked activity under those conditions. Although we could elicit a TMS-evoked burst of activity with an inverted coil orientation, the magnitude of this response was significantly lower than that recorded with the standard orientation . Of those 132 neurons, a large majority (77%) showed facilitation , a smaller fraction (17%) showed inhibition , and an even smaller fraction (7%) showed both facilitation and inhibition .Out of 476 neurons recorded in the standard experiment in two monkeys, 132 (28%) were significantly affected by single-pulse TMS Fig. , i.e., sp = 0.019 and 0.014), whereas the surrounding recording sites were barely affected by TMS . In the second monkey , which was surrounded by recording sites showing no effect of TMS. The interaction between grid position and TMS was significant in a nested ANOVA analysis (with cells as a nested variable) in both animals (p = 0.0002 in monkey Y and p = 0.0001 in monkey P). In addition, we observed a second marginally significant  focus 3 mm away from the center , single-pulse TMS evoked barely any response at the neighboring depths. Our results estimate the spatial selectivity of the TMS effect as a volume of cortex measuring ~2 mm on a side, at least one order of magnitude smaller than the simulation results , which was not the case in the low stimulation trials  showed significant TMS-induced responses  and significant task-related activity . The example neuron Fig.  and the N = 62). To assess the extent to which TMS-evoked activity and task-related activity summate, we aligned the activity to the onset of the TMS pulse, and compared the average activity in trials when TMS occurred at hand lift  to the activity when TMS occurred 400 ms after hand lift , indicating that the TMS-evoked response did not summate with task-related activity. Later in the trial (40–260 ms after TMS onset), the activity difference reappeared . These observations strongly suggest that TMS-induced activity and task-related activity do not linearly summate in PFG neurons.Since task-related activity appeared relatively late in the trial (in the later part of the transport phase of the hand and around the lift of the object) and the TMS-evoked response was short, we could not determine whether task-related activity and TMS-induced activity would linearly summate in PFG neurons. Therefore, we ran a control experiment in which we applied single-pulse TMS 400 ms after the hand lift, at a moment when the average task-related activity was high . We also simulated the electric field induced by TMS in the macaque brain based on state-of-the-art software (simNIBS), and found a predicted spread of at least 10 times broader compared to our single-cell results. However, TMS of primary motor cortex can elicit individual finger movements in humans53 and in monkeys (our own observations). Near-threshold intracortical microstimulation studies in monkeys54 estimate that the area in primary motor cortex for finger flexion and extension measures ~4 mm2, which is remarkably consistent with our single-cell results. Thus, the focality of the TMS effect on individual neurons is in fact entirely consistent with previous behavioral results obtained with single-pulse TMS, especially in experiments where the TMS coil orientation is precisely aligned with the central sulcus for mapping different hand muscles55. Our data also confirm the limited penetration of TMS with a figure-of-eight coil. We measured robust TMS-evoked responses in the parietal convexity close to the skull, but very little if any effect further from the TMS coil, in more medial recording positions.The spatial selectivity of the TMS effect was unexpected based on previous studies modeling the electric field induced by TMS in humans . However, at least three possible factors could explain this apparent discrepancy. Firstly, the EEG signal mainly reflects synaptic activations59, whereas we performed extracellular recordings of action potentials in individual neurons. Secondly, we recorded the effect of single-pulse TMS in parietal cortex, and these parietal activations can certainly activate remote areas, e.g., in frontal cortex , which could be detected by EEG. Finally, despite the absence of significant TMS-induced spiking responses at recording positions located 2 mm or more from the center of stimulation, TMS did induce strong oscillations (mainly in the low-frequency band) in the firing rate of these neurons, which may also contribute to the EEG signal. It is therefore worth noting that the spatial spread of TMS-induced spiking activity is clearly dissociable from TMS-induced oscillatory activity, which spreads more remotely. Overall, it is most likely impossible to identify the exact location of a cortical activation on a millimeter scale with EEG, as we did in our single-cell recording experiments. Thus, concurrent EEG-TMS recordings would have probably shown a TMS-evoked potential across a large part of parietal cortex. A similar reasoning may apply to the apparent discrepancy between our results and TMS-fMRI studies62. A previous study combining fMRI and single-cell recordings in parietal cortex of monkeys63 has demonstrated that very restricted clusters of active neurons can drive an extensive fMRI activation.Our results may also be surprising in the light of numerous TMS-EEG studies, which showed oscillatory activity in the EEG induced by single-pulse TMS  were not uncommon in our data set. We even observed secondary activations in recording sites further away from the center of stimulation. All these observations corroborate the idea that single-pulse TMS exerts robust effects on networks of neurons, which cannot be appreciated with population measurements such as fMRI, EEG or behavioral measurements.Two technical factors may have influenced our findings. TMS may have induced a current in the microelectrode under the coil, causing direct electrical stimulation and consequently neuronal activation around the tip of the electrode. However, we did not observe such a neuronal activation at a distance of merely one millimeter from the center of stimulation Fig. , althoug50. However, TMS also influenced multi-unit activity characterized by small spikes, and—given the transient inhibition periods we observed in neurons with large spikes—also of small inhibitory interneurons. In contrast, even at the center of stimulation, not every neuron recorded showed an effect of single-pulse TMS. This heterogeneity in the susceptibility of individual neurons to TMS pulses may be related to differences in the local orientation of neurons with respect to the induced electric field65, which is difficult to estimate with our electrode approach. Thus, TMS influences a wide variety of cell types and evokes a variety of effects at the center of stimulation, the sum of which is a transient excitation lasting less than 100 ms.We measured the effect of single-pulse TMS on the activity of single neurons generating large action potentials that we could record for an extended period of time (up to an hour) during an active motor task. These spikes most likely originated from large to medium-sized pyramidal neurons typical of area PFG67. Notably, we did not observe long-lasting excitation (up to 300 ms after TMS onset) or suppression (up to two seconds after TMS onset), as reported by Moliadze and colleagues66. Undoubtedly, the absence of anesthesia and the active engagement in a motor task in our study could explain these differences. The active engagement in a task is also crucial for the similarity of our study to TMS studies carried out in human volunteers .The effect of single-pulse TMS on the spiking activity of individual neurons we observed in awake behaving monkeys differed markedly from previous observations in anesthetized animals50 and during grasping observation70, but no reversible inactivation of PFG during grasping has been published. In our experiments, task-related activity in PFG during the hand movement was suppressed after high-intensity single-pulse TMS. The increase in grasping time we observed may arise from this decreased task-related activity in PFG. Alternatively, the fast burst of action potentials immediately after TMS onset may have interfered with task-related activity in downstream cortical areas involved in grasping such as ventral premotor cortex, with which PFG is connected60. However, applying TMS at 400 ms after the hand lift  did not affect the grasping time, suggesting that the suppression of task-related activity following TMS may have been the most important factor. Our behavioral data strongly resemble previous findings in humans. TMS applied over the anterior IPS in human volunteers disrupts grasping in the presence13 or absence71 of a perturbation in object orientation requiring online adjustments. Additionally, TMS applied over the anterior IPS also prolongs reaching times when subjects have to correct for visual perturbations during the reaching movement72. Because the position of the TMS coil was fixed in our experiments, we could not stimulate control sites elsewhere in parietal cortex, but the single-cell data clearly showed that single-pulse TMS activated a patch of cortex in area PFG, at the parietal convexity. Thus, our study also represents the first causal evidence for area PFG playing a role in VGG using a reversible perturbation .A unique advantage of our approach is that we could investigate the relationship between TMS-induced activity, task-related activity and behavior. TMS applied after the onset of the hand movement significantly prolonged the total object grasping time in both monkeys. Previous studies have demonstrated that neurons in area PFG are frequently active during object graspingSingle-pulse TMS during task-related activity could not excite the neurons above their normal activity levels (in the absence of TMS), indicating sublinear summation. This result is puzzling, since the average activity in our population of neurons may not have been maximal. Future studies will have to investigate the relationship between the neuronal ‘state’ and its excitability by TMS.76 and the clinical practice79. Invasive animal studies combined with behavioral measurements can uncover new and unprecedented insights into the effects of brain stimulation on individual neurons and on behavior, and will undoubtedly contribute to the refinement and development of novel stimulation protocols80.For several decades, NIBS techniques have proven to be major assets for both systems neuroscienceTwo male rhesus monkeys  were trained to sit in a primate chair. A head post (Crist Instruments) was then implanted on the skull with ceramic screws and dental acrylic. For this and all other surgical protocols, monkeys were kept under propofol anesthesia (10 mg/kg/h) and strict aseptic conditions. All experimental procedures were performed in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and the EU Directive 2010/63/EU, and were approved by the Ethical Committee at KU Leuven. Intensive training in passive fixation and VGG began after 6 weeks of recovery. Once the monkeys had achieved an adequate level of performance, a craniotomy was made, guided by anatomical magnetic resonance imaging (MRI), over area PFG of the left hemisphere in monkey Y and the right hemisphere in monkey P. The recording chamber was implanted at an angle of approximately 45 deg with respect to the vertical, allowing oblique penetrations into the parietal convexity Fig. . To conf23) and a standard monkey brain template . The meshes consisted of five different volumes obtained from MRI images  by the progressive segmentation of five tissue types: white matter (WM), grey matter (GM), cerebro-spinal fluid (CSF), skull, and scalp23. The assigned conductivity values were fixed: 0.126 S/m (WM), 0.275 S/m (GM), 1.654 S/m (CSF), 0.01 S/m (skull), 0.465 S/m . Based on those volumes, simNIBS obtained surface reconstructions of the brain. Tissue segmentation was performed in a semi-automatic way using FSL tools  which were manually corrected when required. The electric fields were calculated assuming a quasi-static regime83, according to the following equation:E is the electric field vector and φ denotes the electric potential. The time derivative of the magnetic vector potential A of the chosen TMS coil was given as input to the FEM calculations, and the rate of change of the coil current (dI/dt) was set to 1 A/μs. Our model was applied using two different coil files: a Magstim 70 mm figure-of-eight coil (D70) and a Magstim 55 mm  figure-of-eight coil . The distance between the coil and the cortex was set to 15 mm, as measured in our MRI images, and the coil handle was oriented following the coordinates used in the actual experiment . The simulations obtained from the two coil types produced similar results, although the total spread was smaller with the smallest coil size  was created using simNIBS (simulation of Non-invasive Brain Stimulation MethodsTMS was applied at 60 and 120% of the rMT (see below for details) by means of a 55 mm figure-of-eight coil. We determined the rMT for each animal as the lowest stimulus intensity at which TMS of the primary motor cortex produced a contralateral finger or wrist movement, (right hemisphere on monkey Y and left hemisphere on monkey P) while the monkey held its hand in the resting position. In these experiments, the TMS coil was handheld over the primary motor cortex of the hemisphere contralateral to the recordings, at a distance of approximately 15 mm from the surface of the brain, similar to the distance in the recording experiments. The parietal recording chamber was implanted but no electrode was inserted in the brain. In monkey Y, we used a Magstim 200 Stimulator , which delivered monophasic pulses , whereas in monkey P, we used a Magstim Rapid Stimulator , which delivered biphasic pulses . In a series of control experiments, we also used the Magstim Rapid Stimulator in monkey Y, and could reproduce the basic findings obtained with the Magstim 200. For the duration of the experiment, the coil was placed over the guiding rods tangentially to the skull. To provide additional stability, the TMS coil was held in place by means of an adjustable metal arm. Since our recording sessions averaged 4 h in duration, gel-filled cool packs were placed around the coil to prevent overheating .46, we used diodes and serial low-gain amplification to clip the artifact generated by the magnetic pulses and to prevent amplifier saturation. For this, we modified a regular BAK Electronics preamplifier (Model A-1) by connecting two leakage diodes (BAS45A) anti-parallel between the signal lines and ground before each stage of amplification. The initial front-end of the headstage remained unmodified to maintain the high-input impedance. Also, we covered the tungsten microelectrodes with polyamide tubing  to minimize vibration inside the guide tube. With these settings, the evoked TMS artifact in our signal ranged from 8 to 12 ms  inserted through the dura by means of a 23-gauge stainless steel guide tube and a hydraulic microdrive (FHC). Mimicking the artifact reduction strategy proposed by Mueller and colleaguesDuring the experiments, the monkeys were sitting upright with the head fixed. For each recording session, the hand ipsilateral to the recording chamber remained restrained within the chair, while the contralateral hand was placed on a resting device. A single grasping object  was located in front of the monkey at a distance of 30 cm from the resting position. The resting position of the hand, the start of the reach to grasp movement and the pull of the object were detected by fiber-optic cables. Our motor task consisted of a visually guided grasping task (VGG), in which the monkey had to place the hand contralateral to the recorded hemisphere in the resting position in complete darkness to initiate the sequence. After a variable time , a red laser projected a fixation spot under the object. If the animal maintained its gaze inside the electronically-defined fixation window (+/−2.5 deg) for 500 ms, the object was illuminated. Following a variable delay (900–1100 ms), a visual GO cue (dimming of the laser) instructed the monkey to lift the hand from the resting position, and reach, grasp, lift and hold the sphere for a variable interval (holding time: 500–900 ms). Whenever the monkey performed the whole sequence correctly, it received a drop of juice as reward. During the task, we measured both the time between the go-signal and the onset of the hand movement (reaction time), and the time between the start of the movement and the lift of the object (grasping time).To address the influence of single-pulse TMS on neuronal activity, we interleaved trials with  and without  stimulation, and applied single magnetic pulses in different task epochs. These TMS pulses were applied in blocks of either low (60% rMT) or high (120% rMT) intensity TMS, randomly interleaved with no-stimulation trials. In both monkeys, we applied single-pulse TMS at two different task epochs: at light onset above the object, and immediately (within 2 ms) after the onset of the hand movement. In monkey P, we also run an experimental control to investigate the effect of TMS on task-related activity. For this, we applied high and low-intensity TMS at both hand lift and at hand lift + 400 ms. Across multiple experimental sessions, we measured the TMS-induced changes in the spiking activity of individual neurons located in a region under the center of the coil extending 7 mm in the antero-posterior direction and 3 mm in the medio-lateral direction . For the high- and low stimulation trials, the neural activity was aligned on the TMS pulse. Also, for comparison, the no-stimulation trials were aligned on the same time bin (either light onset or hand lift). Net neural responses were then calculated as the average firing rate recorded after TMS minus the baseline .We created line and raster plots comparing the average response (spikes/sec) of every cell during no stimulation, low stimulation and high stimulation trials at different task epochs. A bin-by-bin analysis  comparing the spike rate in high stimulation bins with that in the corresponding no-stimulation bins was performed to classify the neurons according to their functional properties . Similarly, we also ran a two-sided Wilxocoxon ranksum test to identify those neurons with significant task-related activity. For this, we first aligned the neural activity on the lift of the hand and then compared the cell’s responses obtained during the delay (−200–0 ms before the lifting of the hand) and the reaching-to-grasp phases of the task (10–410 ms after lifting the hand/TMS event).stimulation and task epoch. To estimate the spatial spread of the observed TMS effect, we calculated the average response of all neurons recorded at each recording position during high-, low- and no-stimulation trials. For each recording grid position, we determined the significance of the TMS-evoked effect by performing a Wilcoxon ranksum test comparing the average spike rate across all neurons recorded at that position. In this analysis, we compared the bin showing the maximum response to stimulation  to the corresponding bin in the no stimulation trials (bin size: 20 ms). Next, we tested whether the TMS-evoked effect differed significantly between the center of stimulation  and four neighboring positions located 1 mm away  using a three-way nested ANOVA with position and TMS condition (high versus no stimulation) as factors, and cells as nested factors. To normalize the activity across all neurons showing task-related activity, we used the normr function in Matlab.To determine the significance of the TMS-evoked effect in individual cells, we compared the cell responses observed between 10 and 50 ms post-TMS in the high stimulation condition to those in the no-stimulation condition (two-sided Wilxocoxon ranksum test). A two-way ANOVA was performed to quantify the interaction between the factors Finally, we measured the behavioral effect of TMS by directly comparing the reaction times and grasping times obtained for the high stimulation and no stimulation conditions with single-pulse TMS applied at light onset and at the onset of the hand movement. To adjust for multiple testing, we applied the Bonferroni correction.Further information on research design is available in the Supplementary InformationTransparent Peer Review FileReporting Summary


Optimising the use of antibiotic agents is a pressing challenge to overcoming the rapid emergence and spread of multidrug-resistant pathogens in intensive care units (ICUs). Although Gram staining may possibly provide immediate information for predicting pathogenic bacteria, Gram stain-guided initial antibiotic treatment is not well established in the ICU setting. We planned the GRam stain-guided Antibiotics ChoicE for Ventilator-Associated Pneumonia (GRACE-VAP) trial to investigate whether Gram staining can safely restrict the use of broad-spectrum antibiotics in patients with ventilator-associated pneumonia (VAP), which is one of the most common hospital-acquired infections in ICUs.Staphylococcus aureus (anti-MRSA) agents as initial antibiotic therapies, 28-day all-cause mortality, ICU-free days, ventilator-free days and adverse events. Patients are randomly assigned to receive Gram stain-guided treatment or guidelines-based treatment at a ratio of 1:1. In the Gram stain group, results of Gram staining of endotracheal aspirate are used to guide the selection of antibiotics. In the guidelines group, the combination of an anti-pseudomonal agent and an anti-MRSA agent is administered. A total sample size of 200 was estimated to provide a power of 80% with a one-sided alpha level of 2.5% and a non-inferiority margin of 20%, considering 10% non-evaluable patients.The GRACE-VAP trial is a multicentre, randomised, open-label parallel-group trial to assess the non-inferiority of Gram stain-guided initial antibiotic treatment to guidelines-based initial antibiotic treatment for the primary endpoint of clinical response rate in patients with VAP. Secondary endpoints include the coverage rates of initial antibiotic therapies, the selected rates of anti-pseudomonal agents and anti-methicillin-resistant The GRACE-VAP trial is expected to reveal whether Gram staining can reduce the use of broad-spectrum antibiotics without impairing patient outcomes and thereby provide evidence for an antibiotic selection strategy in patients with VAP.NCT03506113. Registered on 29 March 2018.Clinicaltrials.gov, University Hospital Medical Information Network, UMIN000031933. Registered on 26 March 2018.The online version of this article (10.1186/s13063-018-2971-2) contains supplementary material, which is available to authorized users. The increasing prevalence of infections caused by multidrug-resistant (MDR) organisms in intensive care units (ICUs) is recognised as a significant health threat all over the world , 2. HoweStaphylococcus aureus (MRSA) and Pseudomonas aeruginosa. Although empirical broad-spectrum treatments help to ensure that infections are treated effectively, overuse of broad-spectrum antibiotic agents leads to an increase in antimicrobial-resistant organisms. Thus, there is an increasing need to establish methods to safely reduce the use of broad-spectrum antibiotic agents for VAP.Antibiotics are extensively used in ICUs because of the increased risks of infection due to underlying diseases or conditions, impaired immunity, and exposure to multiple invasive devices. In fact, the Extended Prevalence of Infection in Intensive Care II study showed that 51% of patients were considered to be infected during their stay in ICUs . VentilaIn this trial, we will adopt a traditional but credible method, the Gram stain, to estimate pathogens of VAP. Gram staining of respiratory samples is potentially useful to guide appropriate initial antibiotic therapy. Several studies have evaluated the correlation between the results of Gram staining and culture, but the results were conflicting –16. FurtWe hypothesise that antibiotic treatment based on Gram stain results can restrict the administration of broad-spectrum antibiotic agents for VAP without detrimental effects on patient outcomes. Thus, we planned the GRam stain-guided Antibiotics ChoicE for VAP (GRACE-VAP) trial to investigate whether Gram stain-guided antibiotic treatment is non-inferior to guidelines-based treatment with respect to the clinical response rate in patients with VAP.We will conduct a multicentre, open-label, randomised, non-inferiority trial with blinded endpoint assessment that will compare the Gram stain-guided treatment with standard antibiotic treatment for patients with VAP. The final trial report will follow the Consolidated Standards of Reporting Trials (CONSORT) statement and its extension to non-inferiority and equivalence trials . This stThe study will be conducted in the ICUs of ten tertiary referral hospitals in Japan Table .Table 1PAll patients admitted to the ICU will be screened every day during their ICU stay by the study investigators. The study participants will be enrolled in this study if they meet all of the inclusion criteria and none of the exclusion criteria. Patients will be included if they (1) are aged 15 years or older; (2) have undergone mechanical ventilation for at least 48 h; and (3) are diagnosed as having VAP, which is defined by a modified clinical pulmonary infection score of 5 or more .Patients will be excluded if they (1) have an allergy to study medications, (2) are pregnant, (3) have already been discharged from the ICU, (4) are diagnosed as having heart failure or atelectasis, (5) have been administered antibiotics for more than 24 h when they meet the inclusion criteria, (6) are declined to provide full life support, or (7) are judged as inappropriate for inclusion at the discretion of the study physician.The clinical trial will be carried out according to the principles of the Declaration of Helsinki and Ethical Guidelines for Medical and Health Research Involving Human Subjects published by the Ministry of Health, Labour and Welfare of Japan and the Japanese Ministry of Education, Culture, Sports, Science and Technology. The study protocol was approved by the ethics committees of each participating hospital. Written informed consent will be obtained from all patients or their representatives.Patients are randomly allocated to each treatment arm at a 1:1 ratio no earlier than 1 day before starting antibiotic therapy for VAP. Randomisation will be performed with the use of a stochastic minimisation procedure stratified by study centre, presence of chronic obstructive pulmonary disease, presence of traumatic brain injury, presence of post-cardiopulmonary arrest syndrome, and prior antibiotic therapy during the hospitalisation. We will use an electronic data capture system to conduct randomisation and data collection.Treatment of patients in this trial follows a pragmatic approach. All study medications are commercially available and approved in Japan. They are used as marketed in standard dosage regimens. According to their treatment group, patients will receive either antibiotics selected according to the results of Gram staining or standard therapy based on the IDSA/ATS guidelines  Fig. 1)1).Fig. 1In the Gram stain group, the results of Gram staining of endotracheal aspirate are used to guide the selection of antibiotics. The results of the Gram stains are categorised as Gram-positive cocci (GPC) chains, GPC clusters, Gram-positive bacilli (GPB), Gram-negative rods (GNR), or a combination of these. A non-pseudomonal β-lactam antibiotic is selected when the Gram stain of the endotracheal aspirate shows only GPC chains and/or GPB. An anti-MRSA agent is selected when the Gram stain results show GPC clusters without GNR. An anti-pseudomonal agent is selected when the Gram stain results show GNR without GPC clusters. The combination of an anti-pseudomonal agent and an anti-MRSA agent is selected when the Gram stain results show both GPC clusters and GNR. We escalate an initial treatment selection process to cover pathogens isolated from respiratory samples before the onset of VAP if the Gram stain results suggest their involvement.S. aureus isolates are MRSA in Japanese ICUs (https://janis.mhlw.go.jp/report/open_report/2016/3/1/ken_Open_Report_201600.pdf). If drug-resistant pathogens are isolated from respiratory samples before the onset of VAP, we escalate an initial treatment selection process to cover them. In both groups, specific antibiotic agents are selected according to previously recorded antimicrobial resistance patterns in each ICU.In the standard group, patients are administered the combination of an anti-pseudomonal agent and an anti-MRSA agent according to IDSA/ATS guidelines, because 47.7% of The study medication can be de-escalated or escalated to a definitive treatment level according to the results of the pathogens isolated from respiratory samples. Dose adjustments in individual patients are performed as judged appropriate by the site investigator. Study medications are continued for at least 7 days and discontinued on the basis of the site investigator’s judgement.Clinical assessments are performed at baseline and daily throughout the study treatment, at the end of therapy (EOT) and at the follow-up/test of cure (FU/TOC) visit Fig. . LaboratThe primary outcome is the clinical response rate at FU/TOC. Clinical response is defined as that fulfilling all four of the following components: completion of antibiotic therapy within 14 days, improvement or lack of progression of baseline radiographic findings at EOT, and resolution of signs and symptoms of pneumonia at FU/TOC. The signs and symptoms of pneumonia are defined as body temperature ≥ 38 °C, increase in purulent sputum and deterioration of oxygenation. The site investigators will collect information relevant to the clinical response described above. On the basis of these individual data, blinded evaluators on the event adjudication committee will judge the achievement of primary outcome.Clostridium difficile infection, skin rash and seizure.The secondary outcomes are the coverage rate of initial antibiotic therapies, the selected rates of anti-pseudomonal agents and anti-MRSA agents as initial antibiotic therapies, 28-day mortality, 28-day ICU-free days, 28-day ventilator-free days, and adverse events. Therapy will be considered appropriate when all pathogens isolated with at least 1+ semi-quantitative growth from endotracheal aspirates are covered by the selected antibiotic agents. Safety outcomes are death during the study period and any adverse events reported at FU/TOC, including renal impairment, thrombocytopenia, diarrhoea, The primary efficacy analysis will assess the non-inferiority of the clinical response of Gram stain-guided antibiotic therapy compared with standard therapy. The margin of non-inferiority is set at 20% on the basis of statistically acceptable tolerance and clinically acceptable margin. This margin was used in previous large clinical trials in the field of critical care –21. ThenBecause this is a non-inferiority trial, we will compare the proportion of patients meeting the definition of clinical response using per-protocol analysis as a primary analysis. The per-protocol analysis population will consist of all randomised patients who are not lost to follow-up and have no major protocol deviations. We will also conduct an intention-to-treat analysis as a secondary analysis. We will attest the non-inferiority of the primary outcome on the basis of the normal theory test for binomial proportions. We will conduct the primary analysis without adjustment of potential confounders. We will construct multivariable logistic models or Cox proportional models including the stratified variables as the secondary analysis. We will conduct other post hoc exploratory analyses based on the recommendations of the steering committee. Because of the exploratory nature of these analyses for other than the primary endpoint, no correction for multiplicity is made.Secondary outcomes will be analysed under a superiority or non-inferiority assumption, as appropriate. Pre-defined subgroup analyses for the primary and secondary endpoints include (1) patients who received prior antibiotic therapy during the hospitalisation versus those who did not, (2) length of ICU stay ≥ 5 days versus < 5 days before randomisation, and (3) septic shock versus no septic shock.P values are two-sided, and P < 0.05 is considered significant other than for the non-inferiority test for clinical response, for which a one-sided P < 0.025 is considered significant. All statistical analyses will be performed using JMP 13.1 and SAS 9.4 software .All The trial will be managed by the Division of Trauma and Surgical Critical Care, Osaka General Medical Center, Osaka, Japan. The data centre is located at the Institute for Clinical Effectiveness, Kyoto, Japan, and data managers will centrally monitor the data through the study period. A steering committee was involved in protocol development and will oversee study progress Table . A Data The GRACE-VAP trial will assess whether initial Gram stain-based restriction of antibiotic therapy is non-inferior to guidelines-based initial broad-spectrum antibiotic therapy in patients with VAP in terms of the clinical response rate. If non-inferiority is achieved, the Gram stain-guided antibiotic choice will be considered to have several advantages in clinical settings. First, Gram staining can be a point-of-care test for selecting antibiotic agents because it should be easy to access at the bedside clinically. Second, it is an inexpensive examination and easy to install in ICUs all over the world, including developing countries. Third, it is easy to learn how to evaluate the results of Gram staining by the method indicated in this trial because we apply merely a simple classification of bacteria.To the best of our knowledge, this trial will be the first study to answer the clinical question of whether Gram staining can reduce the use of broad-spectrum antibiotics without impairing patient outcomes. We hope that this trial will have a great impact on the establishment of a novel strategy to optimise the use of antibiotic agents safely and restrict the overuse of broad-spectrum antibiotic agents.The first patient was recruited on 1 April 2018. The last patient is expected to be recruited in March 2021. Osaka General Medical Center provides central trial management and coordination.Additional file 1:SPIRIT checklist. (DOC 121 kb)


A stroke often results in gait impairments, activity limitations and restricted participation in daily life. Virtual reality (VR) has shown to be beneficial for improving gait ability after stroke. Previous studies regarding VR focused mainly on improvements in functional outcomes. As participation in daily life is an important goal for rehabilitation after stroke, it is of importance to investigate if VR gait training improves participation. The primary aim of this study is to examine the effect of VR gait training on participation in community-living people after stroke.The ViRTAS study comprises a single-blinded, randomized controlled trial with two parallel groups. Fifty people between 2 weeks and 6 months after stroke, who experience constraints with walking in daily life, are randomly assigned to the virtual reality gait training (VRT) group or the non-virtual reality gait training (non-VRT) group. Both training interventions consist of 12 30-min sessions in an outpatient rehabilitation clinic during 6 weeks. Assessments are performed at baseline, post intervention and 3 months post intervention. The primary outcome is participation measured with the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P). Secondary outcomes are subjective physical functioning, functional mobility, walking ability, walking activity, fatigue, anxiety and depression, falls efficacy and quality of life.The results of the study provide insight into the effect of VR gait training on participation after stroke.NTR6215. Registered on 3 February 2017.Netherlands National Trial Register, Identifier The online version of this article (10.1186/s13063-018-3165-7) contains supplementary material, which is available to authorized users. Stroke is the third most common cause of disability worldwide . GloballRecent research has increasingly focused on the use of virtual reality (VR) in stroke rehabilitation, including to enhance walking –16. RehaAlthough multiple studies have promising results showing that gait training using VR can improve balance and walking ability after stroke , 21, 22,The primary aim of the ViRTAS  study is to examine the effect of VR gait training on participation in community-living people between 2 weeks and 6 months after stroke. VR gait training is compared with a non-VR gait training consisting of conventional treadmill training and functional gait exercises. Both treadmill training and task-oriented gait exercises are commonly used rehabilitation interventions that have been demonstrated to be effective in people after stroke –25. We hThe study is a single-blinded, randomized controlled trial with two parallel groups that investigates the effects of VR gait training on participation, subjective physical functioning and walking activity in people after stroke. Participants are allocated to the virtual reality gait training (VRT) group or non-virtual reality gait training (non-VRT) group. The protocol is described according to the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) Checklist for clinical trials  see Add.The training sessions and assessments for the study are conducted in outpatient rehabilitation clinic, Revant Rehabilitation Centres, Breda, The Netherlands.Potential participants are included if they meet the following inclusion criteria: (1) diagnosed with stroke according to the World Health Organization (WHO) definition , (2) a tParticipants are primarily recruited from the rehabilitation center by their physician or physiotherapist who provide patients a brief description of the study. If patients are interested, the clinician obtains permission to pass contact details of the patient to the research team. The researcher then contacts the patient by telephone to give them more information about participation in the study and to verify whether all inclusion criteria are met (eligibility screening). After this contact with the researcher, potential participants can decide whether to participate. If patients are willing to participate, written informed consent is obtained and patient details are passed to an independent person for randomization. Besides recruitment from the rehabilitation center, participants are recruited via flyers at the neurology department of the local hospital, physiotherapy practices and general practices in the area. People after stroke are then invited to contact the research team by telephone call, email or post. All participants provide written informed consent, and anonymity is assured. The protocol of the ViRTAS study has been approved by the Medical Ethics Review Committee of Slotervaart Hospital and Reade, Amsterdam, The Netherlands  and the study is registered in the Netherlands National Trial Register (NTR6215).Participants in both the VRT and non-VRT group follow a training intervention of 2 30-min sessions per week for 6 weeks (12 sessions). Assessments are taken at baseline (T0), post intervention  and follow-up  is blinded to treatment allocation. Due to the nature of the intervention participants and physiotherapists providing the training intervention cannot be blinded to treatment allocation. Participants are explicitly asked not to disclose group allocation to the researcher. The assigned intervention is only revealed for the researcher when this is necessary to manage serious adverse events.Participants who are allocated to the VRT group receive 2 30-min sessions of VRT on the Gait Real-time Analysis Interactive Lab  per week for 6 weeks (12 sessions). The GRAIL consists of a dual-belt treadmill with force platform, a motion-capture system  and a 180° semi-cylindrical screen for the projection of environments with optic flow . The non-VRT consists of two stages: (1) conventional treadmill training (10–15 min) and (2) functional gait exercises (15 min). During the conventional treadmill training the speed is increased progressively. Also, the inclination angle of the treadmill may be increased. Functional gait exercises include six different exercises: (1) tapping or stepping up and down a step, (2) walking and picking up various objects from the ground, (3) walking on non-level surface, (4) walking a slalom, (5) stepping in hoops (increasing step length) and (6) stepping over a stick that is fixed between two pylons. The exercises are based on the exercises used in the FIT Stroke trial . TraininDuring the baseline assessment several demographic, injury-related and therapy-related variables are identified. These variables are presented in Table An overview of the measurement instruments used to assess the primary and secondary outcome variables is given in Table The effect of the intervention on participation is measured with the Utrecht Scale for Evaluation of Rehabilitation-Participation (USER-P). Participation can be defined as a person’s involvement in all life situations, whereby participation restrictions are problems one may experience in involvement in daily life situations , 30. TheThe Frequency subscale of the USER-P is divided in parts A and B. Part A measures the time that an individual has spent on paid work, unpaid work, volunteer work and housekeeping using scores from 0  up to 5 (36 h or more). Part B registers the frequency of leisure and social activities in the past 4 weeks with scores ranging from 0  to 5 (19 times or more). Furthermore, the Satisfaction subscale measures how satisfied someone is with vocational activities, leisure activities and social relationships. Items are scored on a scale of 0 (very dissatisfied) to 4 (very satisfied). The sum scores for the Frequency and Satisfaction scales are converted into a 0–100 scale. Higher scores represent a higher frequency and satisfaction .The SIS-16 is a stroke-specific instrument for measuring subjective physical functioning and consists of 16 from the 28 items of the physical domain of the original SIS version 3.0. The items are scored on a 5-point scale, from “not difficult at all” to “cannot do it at all.” The SIS-16 is an appropriate instrument to monitor physical limitations over time in subacute patients after stroke. The SIS-16 demonstrates good instrument reliability and concurrent validity .The TUG measures functional mobility . ParticiThe 6-MWT is a commonly used valid and reliable test to assess walking ability in people after stroke . ParticiThe FSS measures the level of fatigue and the impact of fatigue on daily functioning. This questionnaire consists of 9 items that are scored on a 7-point scale from 1 (completely disagree) to 7 (completely agree). The total score is calculated by the mean of the nine items . FatigueThe HADS is used to assess anxiety and depression. This questionnaire consists of 14 items  and all items are scored on a 4-point scale from 0 to 3 . In the The FES-I consists of 16 items about the person’s level of confidence in avoiding falling during essential, non-hazardous activities of daily living . The scoThe SS-QOL is used to measure quality of life. This questionnaire is designed for use in clinical stroke trials and consists of 49 items divided over 12 domains: energy, family roles, language, mobility, mood, personality, self-care, social roles, thinking, upper extremity function, vision and work/productivity. Each item is scored on a 5-point Likert scale and a total score is calculated by a mean of the 12 domains , 51.Participants wear a tri-axial accelerometer  to measure daily-life walking activity. The accelerometer (55 g) is worn for five consecutive days at baseline (T0), post intervention (T1) and follow-up . Five consecutive days of monitoring are necessary to obtain reliable walking activity data . The meaTo monitor the intensity of the training sessions in both the VRT and non-VRT group the BORG-RPE scale (CR-10) and a pedometer  were used. The BORG-RPE scale (CR-10) asks participants about the rate of perceived exertion and workload during the training sessions and can be scored from 0 (no exertion) to 10  , 54. ThiThe sample size calculations are based on the primary outcome measure, the USER-P Restrictions subscale. A difference of 18.2 points on the USER-P Restrictions subscale is regarded as clinically relevant . The staGait activity data monitored with the accelerometer is analyzed using a validated stroke-specific algorithm for gait detection and gait quantification in Matlab  . This alt test or non-parametric equivalent, the Mann-Whitney U test. A Χ2 test is used to examine categorical variables. Furthermore, to compare the intensity of the training sessions in the VRT and non-VRT groups, the mean number of steps measured with the pedometer and the mean BORG-RPE score are analyzed with an independent t test or the non-parametric equivalent. Results are considered significant when P values are < 0.05.The effectiveness of the intervention on the primary outcome measure, USER-P Restrictions subscale, is assessed using random coefficient analysis. We include time of assessment, group assignment (intervention and comparison group) and the interaction between time of assessment and group assignment in the multi-level regression model. Because random coefficient analysis can handle missing data, the analysis is performed with all available data, including data from participants with incomplete datasets . IntentiThe ViRTAS study examines the effect of VR gait training on participation in community-living people between 2 weeks and 6 months after stroke. Also, the effect of VR gait training on secondary outcome measures including subjective physical functioning, functional mobility, walking ability, walking activity, fatigue, anxiety and depression, falls efficacy and quality of life is discussed.VR can be defined as a computer-based technology that simulates a real environment and provides the user with opportunities to interact with objects and events , 58. In A foreseen difficulty of the study is that participants continue to receive usual care and rehabilitation which might interfere with the effect of the studied interventions. From an ethical perspective, it is not an option to withheld care or rehabilitation from subacute stroke survivors. As participants are within 6 months after stroke, they may receive other therapies focusing on gait. However, due to the randomization it is expected that there is no noticeable difference between the VRT and non-VRT group in the potential interference of usual care and rehabilitation. Still, the frequency and duration of the gait-related therapies are registered.To the authors’ knowledge this is the first study to investigate the effect of a VR gait training intervention on the level of participation in people after stroke. VR gait training might be a great potential for rehabilitation after stroke.The trial is still ongoing. The first participant was included in April 2017 and the patient recruitment will be completed around August 2019.Additional file 1:Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Checklist: recommended items to address in a clinical trial protocol and related documents. (DOC 125 kb)


N = 64/116) of participants (ages 6–16 years) with a diagnosis of autism spectrum disorder, who participated in a previously-published randomized controlled trial of therapeutic horseback riding (THR) compared to a no-horse contact active control. The objective of this study was to examine whether significant improvements of irritability, hyperactivity, social, and communication behaviors observed in participants randomized to receive a 10-week manual-based THR intervention were sustained 6 months after the intervention conclusion. Participants' caregivers from both the THR (n = 36) and active control (n = 28) groups completed a measure of irritability and hyperactivity behaviors (primary outcome variables). Additionally, only the THR group participants completed the full battery of study outcomes assessments. Between group comparisons examining the extended interval from baseline (1-month pre-intervention assessment) to 6-months after the intervention revealed that the THR group maintained reductions in irritability behavior at a 0.1 level . , which was 73% of efficacy preserved from the primary post-intervention endpoint (within 1-month post-intervention). Hyperactivity behaviors did not sustain this same trend. Comparisons from baseline and 6-months after the intervention revealed that the THR group sustained significant initial improvements made in social and communication behaviors, along with number of words and different words spoken during a standard language sample. This is the first known study to examine and demonstrate the longer-term effects of THR for individuals with ASD and warrants a more thorough evaluation of whether the effects of THR are maintained for at least 6-months after the intervention compared to a control.This paper presents 6-month follow-up data of 44% (http://clinicaltrials.gov; NCT02301195.Clinical Trial Registration Information: Trial of Therapeutic Horseback Riding in Children and Adolescents with Autism Spectrum Disorder;  Along with the diagnostic social, communication, restricted, and repetitive behavior features of autism spectrum disorder (ASD) this population has particular difficulties with emotion regulation . EmotionEmerging evidence for the benefits of animals on the health and well-being of individuals with ASD is highlighted by recent systematic literature reviews , 11. ResA systematic mapping review of equine-assisted activities and interventions (EAAT) studies with the ASD population revealed a wide variety of intervention methods ranging from equine assisted activities (EAA)  involving riding instructors, coaches or trainers, and equine-assisted therapies (EAT)  involving therapists  and therapeutic riding instructors . In 31 oN = 127) randomized controlled trial (RCT) of THR compared to a no-horse activity control with children ages 6–16 years diagnosed with ASD (n = 58) compared to the control (n = 58) on measures of irritability, hyperactivity, social cognition, social communication, and total words and new words spoken during a standardized language sample.Although THR appears to be a wide-spread practice that has become popular for individuals with ASD, few studies have systematically validated the effects of THR for individuals with ASD following recommended guidelines for ASD research of therapeutic interventions . Such a with ASD . Resultsn = 22) compared to the control (n = 15) . On(n = 15) . That st(n = 15) . ExaminiThis article presents 6-month follow-up data from a subset of participants who were randomized as part of a previously published RCT to receive either 10-weeks of THR intervention or 10-weeks of a barn activity (BA) control group with no exposure to horses . In thisThe following summarizes the methods of our previously published RCT , which wn = 36; control group n = 28). Of the 32 participants who did not complete this third study phase, two were no longer living with the same caregivers, rather were living in community-based placements, and the remainder were otherwise unable to schedule and/or complete study forms. Participating families received compensation for travel to study visits and for completing and returning the study questionnaire.At the onset of the RCT , all of Following institutional review board-approved informed consent/assent procedures for the RCT , particin = 36) and control (n = 28) groups, completed the 6-month follow-up study measures. Only the THR group participants (n = 36) were scheduled to come into the study clinic site for a re-administration of all baseline assessment measures . The speech therapist, who had conducted the baseline and post-intervention evaluations as a blinded evaluator, was unblinded at the 6-month follow-up, because only THR participants received the 6-month follow-up language assessment. However, baseline and post-intervention assessments were not available to the speech therapist during the 6-month evaluation period.Six months following the completion of the intervention phase of the study, the same caregiver who completed baseline (pre-intervention) study forms for THR   were theThe persistence of the previously-reported  improvemTo evaluate the persistence of word fluency improvements made in the THR group compared to the control from baseline to post-intervention , the subt-test or chi square test as appropriate. The outcome analysis used a linear mixed effects model (LMM). For irritability and hyperactivity, the fixed effects of LMM included the classification variables of evaluation time  and group indicator (THR or BA) as well as their interaction. Statistical test of the interaction of group by time was used to examine the significance of efficacy. Cohen's D effect size for efficacy is estimated based on this interaction test using 2 times t value divided by degree of freedom. Other outcome variables collected only for THR participants were also analyzed by LMM. Compound symmetry was the covariance structure used for all LMM analyses.SAS 9.4 was used for all the analyses n = 64) and those participants (n = 52) who did not follow-up are listed in Table Summary statistics of demographic and baseline clinical characteristics for the 64 participants who chose to follow-up 6-months post the initial THR intervention , the THR group experienced significantly more improvements  on the ABC-C (p = 0.07) results favoring the THR group for the ABC-C (p = 0.08), in contrast to the significant finding in the original RCT study (p = 0.61), indicating efficacy of THR on hyperactivity was not sustained.For the subgroup of participants from the RCT  who comphe ABC-C  Irritabihe ABC-C . Examinihe ABC-C  Irritabihe ABC-C  HyperactCT study . There wp < 0.01). These post-intervention changes sustained from 1-month post-intervention to the 6-month follow-up period  between baseline and 6 months' post-intervention, while there was not a significant difference between 1-month post-intervention and 6 months' post-intervention.Consistent with the original RCT study , signifin = 64) in a previously published RCT , participants in the THR group showed significantly more improvements on the in irritability and hyperactivity behaviors compared to the BA control . The THRThese results suggest that THR may be an effective complementary intervention to enhance social and verbal core symptoms of ASD, and to reduce irritability behaviors. Given the results of this study, particularly the lingering effects on irritability behaviors, we hypothesize that our THR manual-based approach may induce a reduction in arousal states, dampening stress/anxiety, in youth with ASD. Therefore, an important next step might be to examine the physiological regulation mechanisms involved in THR may explain  improved outcomes in youth with ASD. Additionally, this study's finding of the long-term sustained improvements in irritability behaviors have clinical practice implications for youth with ASD that can add to the current standard of practice of administering anti-psychotic medications  to reduce symptoms of irritability in this population (ages 5–16 years and 6–17 years) –33. For The limitations of this study include its small sample size due to the high dropout rate 6 months following the initial intervention phase. This limits the validity of the results, as findings may not represent the greater population of youth with ASD. Another limitation is the fact that for those participants who did not chose to follow-up, attrition might be because they lived farther away from the study site. This also presents a possible selection bias. Even though we only included participants who refrained from engaging in THR for 6-months following the initial intervention, we did not specifically assess what other, if any, contact with horses was made during that time, which is a limitation. Another limitation is the fact that this study did not assess the efficacy of THR compared to the BA control group on all outcome measures employed in the previously published RCT . This liDespite these limitations, this study provides useful preliminary data to both support and extend the significant findings from our previously published pilot study and RCT , 35. ThiRG was the principal investigator of this study and contributed to writing the majority of this manuscript. ZP served as the statistical expert for this study and wrote the result section of this manuscript. GM served as a consultant for this study and provided editorial edits to this manuscript. BD served as the clinical coordinator for this study and provided editorial edits to this manuscript. NG assisted in writing the introduction of this manuscript.RG is a co-author of the book, Growing Up with Autism: Working with School-aged Children and Adolescents (Guilford Press) and the book, Autism from Research to Individualized Practice (Jessica Kingsley Publishers), from which she receives royalties. Current grant funding for RG provided by Simons and Lurie Foundations and The Human-Animal Bond Research Institute (HABRI) Foundation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.