This methods paper (Hamoline et al., 2025) in the field of Transcranial Magnetic Stimulation (TMS) compares three approaches to measuring the resting motor threshold (RMT). The RMT is defined as the minimal TMS intensity that elicits a muscle twitch. It is measured in nearly all TMS studies, including those not focused on the motor system, to calibrate stimulation intensity. Hence, researchers are interested in methods that provide quick and reliable RMT measurements.

The standard procedure involves delivering ten stimulation trials, counting the number of motor responses, and adjusting the TMS intensity up or down until responses occur in five out of ten trials. The corresponding intensity is taken as the RMT.

The central question addressed in this paper concerns how a motor response should be defined. Since the early days of TMS, electromyography (EMG) has been the gold standard for RMT measurement. EMG is objective, as responses can be automatically detected, and sensitive, as it can register even minor muscle activations that do not produce visible movement. However, setting up EMG requires additional time and equipment, which may be impractical, particularly in clinical contexts.

An alternative approach is visual observation of finger twitches as a proxy for motor responses. This is simpler but subjective. A third option is to attach an accelerometer to the finger, providing an objective yet technically straightforward method compared with EMG.

The study rigorously compared these three methods to determine whether they measure the same phenomenon and how variable their estimates are. The authors analysed TMS data from five participants and used ratings from 69 online observers who judged whether finger movements occurred in video recordings of the TMS sessions.

The paper makes two principal points. First, RMT values derived using EMG are lower than those obtained through the other two methods. This is expected, as EMG can detect small muscle action potentials insufficient to cause visible motion. Second, accelerometry offers more reliable detection of movement, whereas visual observation produces widely variable estimates. This finding is also intuitive.

Although the work does not introduce a novel concept, it provides valuable evidence and could be cited to justify methodological decisions in TMS research. Importantly, it demonstrates that RMTs defined using EMG are not interchangeable with those determined without EMG. Simple intensity scaling is unreliable, as even in the small sample of five participants, the ratios between EMG- and non-EMG-based thresholds varied considerably (see Figure 2 in the paper).

References

Gautier Hamoline, Antoine E Lunardi, Marcos Moreno-Verdú, Baptiste M Waltzing, Elise E Van Caenegem, Siobhan MacAteer, Robert M Hardwick (2025) Comparing electromyography, accelerometry, and visual inspection to assess the resting motor threshold for transcranial magnetic stimulation. bioRxiv, ver.2 peer-reviewed and recommended by PCI Neuroscience https://doi.org/10.1101/2025.06.04.657787

An important way to reduce animal use in research is to design adequately statistically powered experiments. Critically, one needs to select an experimental task that is not too easy to solve by chance, and an appropriate number of repetitions each animal is required to perform. Optimising such parameters allows selecting the lowest number of animals required to get a robust result. While there are some estimation tools available for optimising these parameters, behavioural paradigms in neuroscience are highly diverse, and not all available tools are easily amenable for the design of all types of experiments. One such example is spatial memory paradigms such as mazes where the animal must remember the correct path to a reward (e.g. plus-maze, or radial arm maze). Desachy et al (Desachy et al., 2025) report a new freely available online tool for this type of cognitive tasks. 

The authors provide a description of the statistical analyses done to design the SuccessRatePower tool, where the modification of three main parameters in the statistical design (i.e., number of trials, use of lower chance level or use of test suited for proportion comparison) allows for the increment of statistical power without the need of increasing sample size. This way, by running simulations multiple times, with different sample sizes, users can reach the experimental design that will have a specific statistical power. 

The authors also include different analysis approaches, including defining the unit of measurement as either all trials across a cohort, or average performance of each animal. In addition, they include a choice of different statistical testing approaches, including summary statistics (t-test) and multilevel modelling. These are highly useful illustrations as each approach has been used in the literature, and has advantages and limitations in hierarchically clustered datasets where multiple measurements are from one animal (see, (Galbraith et al., 2010; McNabb & Murayama, 2021; Bloom et al., 2022; Eleftheriou et al., 2025)). 

We also wish to highlight the journey of peer-review with this specific article. First of all, we praise the dedication and time invested by all reviewers, and in particular statistics expert Daniël Lakens. The development of this preprint throughout the process is a testimony of the usefulness of peer-review. The dialogue, exchange of knowledge and acknowledgement of other colleagues’ work, all focused on achieving the common goal of ensuring the conclusions of the manuscript are consistent with its results and methodology, is the essence of what we are working for in this community initiative. At the same time, the process also evidenced to us a need for discussion and common ground between expert statisticians and wet-lab neuroscientists, which could be achieved by better training in statistical testing (Lakens, 2021; Alger, 2022). 

While there may be an element of ‘it’s a matter of taste’ involved in selecting the statistical test, it is important to consider different approaches as this helps develop an intuition of the data-generating process (Bloom et al., 2022), and to avoid pitfalls, such as pseudoreplication, which has become increasingly prevalent despite increasingly rigorous statistical reporting guidelines (Eleftheriou et al., 2025).

References

1. Theo Desachy, Marc Thevenet, Samuel Garcia, Anistasha Lightning, Anne Didier, Nathalie Mandairon, Nicola Kuczewski (2025) Enhancing Statistical Power While Maintaining Small Sample Sizes in Behavioral Neuroscience Experiments Evaluating Success Rates. bioRxiv, ver.6 peer-reviewed and recommended by PCI Neuroscience https://doi.org/10.1101/2024.07.25.605060
2. Alger BE (2022) Neuroscience Needs to Test Both Statistical and Scientific Hypotheses. The Journal of Neuroscience, 42, 8432–8438. https://doi.org/10.1523/JNEUROSCI.1134-22.2022 
3. Bloom PA, Thieu MKN, Bolger N (2022) Commentary on Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices. Current Research in Neurobiology, 3, 100041. https://doi.org/10.1016/j.crneur.2022.100041 
4. Desachy T, Thevenet M, Garcia S, Lightning A, Didier A, Mandairon N, Kuczewski N (2025) Enhancing Statistical Power While Maintaining Small Sample Sizes in Behavioral Neuroscience Experiments Evaluating Success Rates. , 2024.07.25.605060. https://doi.org/10.1101/2024.07.25.605060 
5. Eleftheriou C, Giachetti S, Hickson R, Kamnioti-Dumont L, Templaar R, Aaltonen A, Tsoukala E, Kim N, Fryer-Petridis L, Henley C, Erdem C, Wilson E, Maio B, Ye J, Pierce JC, Mazur K, Landa-Navarro L, Petrović NG, Bendova S, Woods H, Rizzi M, Salazar-Sanchez V, Anstey N, Asiminas A, Basu S, Booker SA, Harris A, Heyes S, Jackson A, Crocker-Buque A, McMahon AC, Till SM, Wijetunge LS, Wyllie DJ, Abbott CM, O’Leary T, Kind PC (2025) Better statistical reporting does not lead to statistical rigour: lessons from two decades of pseudoreplication in mouse-model studies of neurological disorders. Molecular Autism, 16, 30. https://doi.org/10.1186/s13229-025-00663-3 
6. Galbraith S, Daniel JA, Vissel B (2010) A Study of Clustered Data and Approaches to Its Analysis. Journal of Neuroscience, 30, 10601–10608. https://doi.org/10.1523/JNEUROSCI.0362-10.2010 
7. Lakens D (2021) The Practical Alternative to the p Value Is the Correctly Used p Value. Perspectives on Psychological Science, 16, 639–648. https://doi.org/10.1177/1745691620958012 
8. McNabb CB, Murayama K (2021) Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices. Current Research in Neurobiology, 2, 100024. https://doi.org/10.1016/j.crneur.2021.100024 

We need to keep track of our heading direction, and head direction cells in various brain regions represent exactly that; therefore, they have been identified as key substrates of direction coding. But how is such a variable computed? Head direction representation is thought to arise from the integration of angular velocity, suggesting the existence of angular head velocity (AHV) neurons. Theoretically, vestibular, visual, proprioceptive and motor command signals can all contribute to angular velocity computations, while the actual coding mechanisms have remained unclear.Keshavarzi et al. addressed this by separately controlling vestibular and visual cues in a head-fixed configuration (Keshavarzi et al., 2022b). They targeted the retrosplenial cortex (RSC), a multisensory associational area known to be important for the integration of egocentric and allocentric information during navigation (Solari & Hangya, 2018). The use of high channel-count multielectrode arrays (silicon probes) allowed the authors to track a large number of neurons through different conditions, including free exploration of an open field arena. They identified subsets of neurons representing head direction, AHV, locomotion speed or a combination of these variables.

A large fraction of AHV neurons showed similar tuning properties during free exploration and passive rotation in the dark, demonstrating the importance of vestibular input. In agreement, lesioning the semi-circular canals largely reduced these responses. Next, mice were presented with visual motion stimuli while remaining stationary. This showed that visual signals also contributed to AHV coding and specifically suggested that they increased the gain and improved the signal-to-noise ratio of AHV representations. Mice trained on discriminating rotational stimuli showed improved performance when both visual and vestibular information was available compared to either vestibular-only or visual-only stimuli, further demonstrating the integration of the two types of sensory cues. Finally, the availability of both vestibular and visual information improved decoding accuracy of angular speed from ensembles of retrosplenial cortex neurons compared to single modality stimuli, at least at the beginning of motion.

Keshavarzi et al. provide compelling evidence for the critical role of vestibular input in encoding AHV within the RSC. While the widespread presence of vestibular signals in rodent cortical circuits is well-documented (Rancz et al., 2015), this study significantly advances our understanding by demonstrating that RSC neurons can also encode AHV. These findings align with research that identified AHV representations in the RSC and adjacent cortical regions, including primary visual, secondary visual, posterior parietal, primary motor, secondary motor and primary somatosensory cortices (Hennestad et al., 2021), and with later work that showed AHV in parahippocampal circuits (Spalla et al., 2022).

Notably, the relative contributions of vestibular and visual signals to AHV encoding likely depend on the specific cortical area and the AHV amplitude. Visual flow may be more prominent at lower AHV ranges (0-90 deg/s), while vestibular input likely dominates AHV representation at higher speeds (Stahl, 2004; Hennestad et al., 2021). This suggests a complementary contribution of vestibular and visual information, enabling encoding a broader range of angular velocity and driving a widespread AHV signal across cortical areas.

This is an elegant study in which a creative and clear experimental design helps teasing apart different contributors of a specific computation that normally appear linked during natural behaviors. This way it also demonstrates the power of precise experimental control, while immediately extrapolating to natural behavior by examining the same neurons during free exploration. It additionally demonstrates a non-trivial multisensory integration in the retrosplenial cortex that can directly contribute to egocentric spatial representations during navigation (Alexander & Nitz, 2015, 2017).

Editorial note: A preprint version of this article was peer-reviewed by PCI Neuroscience. The refereed preprint can be found through the cited link here (Keshavarzi et al., 2022a), and the peer-review process here.

References

Alexander AS, Nitz DA (2015) Retrosplenial cortex maps the conjunction of internal and external spaces. Nature Neuroscience, 18, 1143–1151. https://doi.org/10.1038/nn.4058

Alexander AS, Nitz DA (2017) Spatially Periodic Activation Patterns of Retrosplenial Cortex Encode Route Sub-spaces and Distance Traveled. Current Biology, 27, 1551-1560.e4. https://doi.org/10.1016/j.cub.2017.04.036

Hennestad E, Witoelar A, Chambers AR, Vervaeke K (2021) Mapping vestibular and visual contributions to angular head velocity tuning in the cortex. Cell Reports, 37, 110134. https://doi.org/10.1016/j.celrep.2021.110134

Keshavarzi S, Bracey EF, Faville RA, Campagner D, Tyson AL, Lenzi SC, Branco T, Margrie TW (2022a) The retrosplenial cortex combines internal and external cues to encode head velocity during navigation. https://doi.org/10.5281/zenodo.5834392

Keshavarzi S, Bracey EF, Faville RA, Campagner D, Tyson AL, Lenzi SC, Branco T, Margrie TW (2022b) Multisensory coding of angular head velocity in the retrosplenial cortex. Neuron, 110, 532-543.e9. https://doi.org/10.1016/j.neuron.2021.10.031

Rancz EA, Moya J, Drawitsch F, Brichta AM, Canals S, Margrie TW (2015) Widespread vestibular activation of the rodent cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 35, 5926–5934. https://doi.org/10.1523/JNEUROSCI.1869-14.2015

Solari N, Hangya B (2018) Cholinergic modulation of spatial learning, memory and navigation. European Journal of Neuroscience, 48, 2199–2230. https://doi.org/10.1111/ejn.14089

Spalla D, Treves A, Boccara CN (2022) Angular and linear speed cells in the parahippocampal circuits. Nature Communications, 13, 1907. https://doi.org/10.1038/s41467-022-29583-z

Stahl JS (2004) Using eye movements to assess brain function in mice. Vision Research, 44, 3401–3410. https://doi.org/10.1016/j.visres.2004.09.011

The study conducted by Carneiro and colleagues (Carneiro et al., 2023) seeks to explore the specific circumstances that influence the impact of protein synthesis inhibitors on the formation and endurance of fear-related memories. This investigation takes the form of a comprehensive meta-analysis of existing literature, making two significant contributions. Firstly, it enhances our understanding of fear conditioning by corroborating established interpretations (Schroyens et al., 2019, 2021) while also introducing novel insights. Secondly, it contributes to the ongoing discourse within behavioral neuroscience regarding the practicality and challenges of applying systematic reviews and meta-analyses to pre-clinical research (Prinz et al., 2011; Errington et al., 2021).

To delve deeper into the subject, the authors conducted distinct meta-analyses for different injection sites and target sessions, thus examining the intervention's effects under varying conditions. Their findings highlight the robust influence of protein synthesis inhibitors on memory consolidation and reconsolidation, but suggest a lack of significant impact on extinction, potentially attributed to the limited number of studies on this topic. Notably, their analysis pinpoints certain well-recognized influencing factors, such as intervention timing and re-exposure duration. However, other proposed boundary conditions, such as memory age and training strength, do not appear to significantly influence the effect size, possibly due to a limited number of studies. This leads to the conclusion that while meta-analyses are valuable for consolidating existing knowledge, substantiation through well-powered, confirmatory experiments is imperative.

Moreover, the research underlines the substantial heterogeneity among individual experiments, particularly within studies, which poses challenges for meta-analysis. Aggregating studies using various methodologies increases the capacity to identify influencing factors, emphasizing the importance of these approaches. The study also addresses the limitations of existing meta-analysis methods and suggests that additional sources of variability and difficulties in replication may exist, extending beyond the usual boundary conditions. These challenges could be attributed to biases within the literature, random error, or variations in experimental protocols.

The paper highlights the significance of rigorous and reproducible research practices in pre-clinical investigations, emphasizing the need for an iterative process that combines data synthesis with empirical testing. While meta-analyses serve as valuable tools for knowledge consolidation, the authors stress that they cannot replace high-powered, confirmatory replication studies. Consequently, they advocate for a more holistic and interconnected approach to experimental science, incorporating data synthesis with empirical validation to enhance the reliability of research findings.

References

Carneiro CFD, Amorim FE, Amaral OB (2023) A meta-analysis of the effect of protein synthesis inhibitors on rodent fear conditioning. , 2022.10.11.509645. https://doi.org/10.1101/2022.10.11.509645

Errington TM, Mathur M, Soderberg CK, Denis A, Perfito N, Iorns E, Nosek BA (2021) Investigating the replicability of preclinical cancer biology. eLife, 10, e71601. https://doi.org/10.7554/eLife.71601

Prinz F, Schlange T, Asadullah K (2011) Believe it or not: how much can we rely on published data on potential drug targets? Nature Reviews. Drug Discovery, 10, 712. https://doi.org/10.1038/nrd3439-c1

Schroyens N, Alfei JM, Schnell AE, Luyten L, Beckers T (2019) Limited replicability of drug-induced amnesia after contextual fear memory retrieval in rats. Neurobiology of Learning and Memory, 166, 107105. https://doi.org/10.1016/j.nlm.2019.107105

Schroyens N, Sigwald EL, Van Den Noortgate W, Beckers T, Luyten L (2021) Reactivation-Dependent Amnesia for Contextual Fear Memories: Evidence for Publication Bias. eNeuro, 8, ENEURO.0108-20.2020. https://doi.org/10.1523/ENEURO.0108-20.2020

Up to now, the automatic approach-avoidance tendency towards physical activity and sedentary stimuli has been studied in various categories of the population. Previous studies showed faster reaction times (RTs) when approaching physical activity stimuli and avoiding sedentary stimuli, especially in healthy young individuals (Cheval et al., 2014; Locke & Berry, 2021). However, the whole spectrum of adulthood has never been tested within the same experiment.

A first strength of the study of Farajzadeh et al. (2023) is that they constructed an online paradigm and analyzed the results of 130 participants aged between 21 and 77 years. It should be noted that this study has been pre-registered (https://doi.org/10.17605/OSF.IO/7GXZR). Overall the authors performed the experiment as first described. They updated their a priori power analysis, including the highest number of predictors (six tested predictors including two interaction effects and a total of eleven predictors). This increased the planned number of participants recruited from 85 to 144. Also, in addition to planned hypotheses, exploratory analyses were conducted to test whether automatic approach-avoidance tendencies toward physical activity and sedentary behaviors were associated with explicit attitudes and the intention to be physically active across aging.

The authors recorded RTs and errors when participants had to approach/move an avatar towards/away from physical activity and sedentary stimuli.

Another strength lies in data analysis and statistical design. To avoid any misinterpretation of the results, which could arise from an increase in RTs relative to age, the authors had the foresight to measure RTs when approaching/avoiding neutral stimuli (ellipses and rectangles).

Taking into account such individuals differences, Farajzadeh et al. confirmed a main tendency to approach physical activity stimuli and to avoid sedentary stimuli throughout the lifespan. 

When the participants considered physical activity as the most pleasant and enjoyable (explicit affective attitude toward physical activity), the RTs were shorter when approaching physical activity and avoiding sedentary stimuli, irrespective of age. However, the intention to be physically active did not influence the individual's RTs. 

Altogether, the study by Farajzadeh et al. suggests that age and explicit attitudes modulate the time to respond to physical activity and sedentary stimuli.

References

Cheval, B., Sarrazin, P., and Pelletier, L. (2014). Impulsive approach tendencies toward physical activity and sedentary behaviors, but not reflective intentions, prospectively predict non-exercise activity thermogenesis. PLoS One, 9(12), e115238. https://doi.org/10.1371/journal.pone.0115238

Farajzadeh, A., Goubran, M., Beehler, A., Cherkawi, N., Morrison, P., de Chanaleilles, M., Maltagliati, S., Cheval, B., Miller, M.W., Sheehy, L., Bilodeau, M., Orsholits, D., and Boisgontier, M.P. (2023) Automatic approach-avoidance tendency toward physical activity, sedentary, and neutral stimuli as a function of age, explicit affective attitude, and intention to be active. MedRxiv. https://doi.org/10.1101/2022.09.05.22279509

Locke, S. R., and Berry, T. R. (2021). Examining the relationship between exercise-related cognitive errors, exercise schema, and implicit associations. Journal of Sport and Exercise Psychology, 43(4), 345–352. https://doi.org/10.1123/jsep.2021-0031