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SummaryData analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed.

The field of neurostimulation of the cerebellum either with transcranial magnetic stimulation (TMS; single pulse or repetitive (rTMS)) or transcranial direct current stimulation (tDCS; anodal or cathodal) is gaining popularity in the scientific community, in particular because these stimulation techniques are non-invasive and provide novel information on cerebellar functions. There is a consensus amongst the panel of experts that both TMS and tDCS can effectively influence cerebellar functions, not only in the motor domain, with effects on visually guided tracking tasks, motor surround inhibition, motor adaptation and learning, but also for the cognitive and affective operations handled by the cerebro-cerebellar circuits. Verbal working memory, semantic associations and predictive language processing are amongst these operations. Both TMS and tDCS modulate the connectivity between the cerebellum and the primary motor cortex, tuning cerebellar excitability. Cerebellar TMS is an effective and valuable method to evaluate the cerebello-thalamo-cortical loop functions and for the study of the pathophysiology of ataxia. In most circumstances, DCS induces a polarity-dependent site-specific modulation of cerebellar activity. Paired associative stimulation of the cerebello-dentato-thalamo-M1 pathway can induce bidirectional long-term spike-timing-dependent plasticity-like changes of corticospinal excitability. However, the panel of experts considers that several important issues still remain unresolved and require further research. In particular, the role of TMS in promoting cerebellar plasticity is not established. Moreover, the exact positioning of electrode stimulation and the duration of the after effects of tDCS remain unclear. Future studies are required to better define how DCS over particular regions of the cerebellum affects individual cerebellar symptoms, given the topographical organization of cerebellar symptoms. The long-term neural consequences of non-invasive cerebellar modulation are also unclear. Although there is an agreement that the clinical applications in cerebellar disorders are likely numerous, it is emphasized that rigorous large-scale clinical trials are missing. Further studies should be encouraged to better clarify the role of using non-invasive neurostimulation techniques over the cerebellum in motor, cognitive and psychiatric rehabilitation strategies.

SummaryThe human cerebellum plays an important role in language, amongst other cognitive and motor functions [1], but a unifying theoretical framework about cerebellar language function is lacking. In an established model of motor control, the cerebellum is seen as a predictive machine, making short-term estimations about the outcome of motor commands. This allows for flexible control, on-line correction, and coordination of movements [2]. The homogeneous cytoarchitecture of the cerebellar cortex suggests that similar computations occur throughout the structure, operating on different input signals and with different output targets [3]. Several authors have therefore argued that this ‘motor’ model may extend to cerebellar nonmotor functions [3–5], and that the cerebellum may support prediction in language processing [6]. However, this hypothesis has never been directly tested. Here, we used the ‘Visual World’ paradigm [7], where on-line processing of spoken sentence content can be assessed by recording the latencies of listeners' eye movements towards objects mentioned. Repetitive transcranial magnetic stimulation (rTMS) was used to disrupt function in the right cerebellum, a region implicated in language [8]. After cerebellar rTMS, listeners showed delayed eye fixations to target objects predicted by sentence content, while there was no effect on eye fixations in sentences without predictable content. The prediction deficit was absent in two control groups. Our findings support the hypothesis that computational operations performed by the cerebellum may support prediction during both motor control and language processing.

BackgroundWhile transcranial magnetic stimulation (TMS) coil geometry has important effects on the evoked magnetic field, no study has systematically examined how different coil designs affect the effectiveness of cerebellar stimulation.HypothesisThe depth of the cerebellar targets will limit efficiency. Angled coils designed to stimulate deeper tissue are more effective in eliciting cerebellar stimulation.MethodsExperiment 1 examined basic input–output properties of the figure-of-eight, batwing and double-cone coils, assessed with stimulation of motor cortex. Experiment 2 assessed the ability of each coil to activate cerebellum, using cerebellar-brain inhibition (CBI). Experiment 3 mapped distances from the scalp to cerebellar and motor cortical targets in a sample of 100 subjects' structural magnetic resonance images.ResultsExperiment 1 showed batwing and double-cone coils have significantly lower resting motor thresholds, and recruitment curves with steeper slopes than the figure-of-eight coil. Experiment 2 showed the double-cone coil was the most efficient for eliciting CBI. The batwing coil induced CBI only at higher stimulus intensities. The figure-of-eight coil did not elicit reliable CBI. Experiment 3 confirmed that cerebellar tissue is significantly deeper than primary motor cortex tissue, and we provide a map of scalp-to-target distances.ConclusionsThe double-cone and batwing coils designed to stimulate deeper tissue can effectively stimulate cerebellar targets. The double-cone coil was found to be most effective. The depth map provides a guide to the accessible regions of the cerebellar volume. These results can guide coil selection and stimulation parameters when designing cerebellar TMS studies.

Reinforcement learning (RL) theory posits that learning is driven by discrepancies between the predicted and actual outcomes of actions (prediction errors [PEs]). In social environments, learning is often guided by similar RL mechanisms. For example, teachers monitor the actions of students and provide feedback to them. This feedback evokes PEs in students that guide their learning. We report the first study that investigates the neural mechanisms that underpin RL signals in the brain of a teacher. Neurons in the anterior cingulate cortex (ACC) signal PEs when learning from the outcomes of one's own actions but also signal information when outcomes are received by others. Does a teacher's ACC signal PEs when monitoring a student's learning? Using fMRI, we studied brain activity in human subjects (teachers) as they taught a confederate (student) action-outcome associations by providingpositiveornegativefeedback.Weexaminedactivitytime-lockedtothestudents'responses,whenteachersinferstudentpredictionsandknow actual outcomes. We fitted a RL-based computational model to the behavior of the student to characterize their learning, and examined whether a teacher'sACCsignalswhenastudent'spredictionsarewrong.Inlinewithourhypothesis,activityintheteacher'sACCcovariedwiththePEvaluesinthe model. Additionally, activity in the teacher's insula and ventromedial prefrontal cortex covaried with the predicted value according to the student. Our findings highlight that the ACC signals PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning. These results suggest that RL mechanisms, processed vicariously, may underpin and facilitate teaching behaviors.

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clinicaltrials.gov Identifier: NCT00830739.