NeuroMat - Research Center for Neuromathematics

28/08/2026

The 6th and final episode of 4th season of the podcast A Matemática do Cérebro, by CEPID NeuroMat, has been released today.

In this episode, host Felipe Parlato talks with NeuroMat researchers Antonio C. Roque and Flavio Rusch about a study that investigates the role of hierarchical modular neuronal networks in maintaining critical behavior.

Listen here: https://podcast.numec.prp.usp.br/

17/08/2026

The fifth episode of 4th season of the podcast A Matemática do Cérebro, by CEPID NeuroMat, has been released today.

In this episode, host Felipe Parlato talks with NeuroMat researchers Oswaldo Baffa, NeuroMat's principal investigator, and Victor H. Souza about the robotic–electronic platform for transcranial magnetic stimulation developed at NeuroMat.

Listen here:

Podcast A Matemática do Cérebro, apresentada pelo CEPID Neuromat

11/08/2026

The fourth episode of 4th season of the podcast A Matemática do Cérebro, by CEPID NeuroMat, has been released today.

In this episode, host Felipe Parlato talks with NeuroMat researchers Maria Elisa Pimentel Piemonte e Gabriel Venas ( in memorian ) about an index capable of showing early postural instability in people with Parkinson’s disease.

Listen here:

Podcast A Matemática do Cérebro, apresentada pelo CEPID Neuromat

07/08/2026

In the “Call for papers” page, we read:
“Antonio Galves was a leading figure of contemporary probability and statistics” [1]. Driven by the desire to promote concrete applications to the knowledge he persistently helped to build, Antonio Galves founded 13 years ago in Sao Paulo the NeuroMat center “meant not only to push forward the development of mathematics for neuroscience but also to promote interdisciplinary, experimental and clinical research”.

The excerpt appears in “Probabilistic processes and their applications: from linguistics to neuroscience, passing through Non-Markovianness and Context Tree Models. A tribute to Antonio Galves”, a special edition of the “Stochastic Processes and their Applications” journal that is open for submissions since August-2024. That was about a year after NeuroMat’s founder passed.

About a year ago, we started this series about some of NeuroMat’s remarkable contributions to science, technology, and our society. In “Waltz in the dark” [2], our first post, we invited those interested in having a look inside the black-box of science to follow us through this journey. Since then, we have been discussing practices, tools, methods, knowledge, and much more that occur inside and around science construction, with some of NeuroMat’s papers filling the background to our discussion.

Today, we conclude this cycle remembering what is in the core of the scientific enterprise that allows us to learn, day after day, a little more about the universe: people.
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“Antonio Galves was always interested in concrete applications, and he spent several years of his life doing research in mathematical linguistics and lately, working for the development of mathematical neuroscience.” [1]
Researchers get used to submission pages, although they are rarely discussed during classes. Still, a keen eye may find in them much more than instructions and technical guidance. There is sometimes evidence that knowledge is built not only from defiant questions and established methods. We are as motivated by inspiring people as we are by the world around us.
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The effect of Galves and Löcherbach’s 2008 study [3] can be seen in the foundation of NeuroMat itself, and it has been inspiring outstanding works. Hernández and colleagues’ “Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data” [4], for example, was pictured as “waltz in the dark” in the first post of our series.
Cabral-Passos and colleagues [5] conjectured whether “the distribution of response times in a given context would be affected by errors in previous predictions” made by the human brain.

Other contributions are still being released. For example, more recent work from Galves, Najman, Svarc and Vargas, in which they introduce a new statistical method for grouping sets of functional data, has just been published [6]. It may help researchers compare complex signals more reliably across experiments or populations. We might see unfoldings from this work in the near future.
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Method, technology and theory can all constitute the legacy of a project, an institution, a researcher. Likewise, one’s commitment to serious work on science can inspire many others along the way — for an unpredictable amount of time.

NeuroMat itself is part of Antonio Galves’ legacy. What comes next can be exciting, and keeping in our minds how we got here is as important as looking forward.
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Stochastc Processes and their Applications’ is open for submissions until 31 December 2026. Guest editors are Dr. Roberto Fernandez, Dr. Claudia D. Vargas and Dr. Eva E. Löcherbach. The special edition “aims at collecting articles showing recent progress on the many topics addressed by Antonio Galves during his rich and important scientific career”. See the link below [1].

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𝐋𝐞𝐠𝐚𝐜𝐲, 𝐩𝐚𝐫𝐭 𝟒: 𝐀 𝐭𝐫𝐢𝐛𝐮𝐭𝐞 — The “Black-Box of Science” Series Finale

References:
[1] Elsevier. (2024). Probabilistic processes and their applications: From linguistics to neuroscience, passing through non-Markovianness and context tree models – A tribute to Antonio Galves [Call for papers – Special issue]. Stochastic Processes and Their Applications. https://www.sciencedirect.com/special-issue/313492/probabilistic-processes-and-their-applications-from-linguistics-to-neuroscience-passing-through-non-markovianness-and-context-tree-models-a-tribute-to-antonio-galves
[2] NeuroMat (2025) - "Waltz in the dark | The “Black-Box of
Science” Series", Facebook post.
[3] Galves, A. & Löcherbach, E. (2008) - Stochastic chains with memory of variable length. TICSP Ser. 38, 117–133.
[4] Hernández, N., Duarte, A., Ost, G. et al. (2021) - Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data. Sci Rep 11, 3520 . https://doi.org/10.1038/s41598-021-83119-x
[5] Cabral-Passos et al. (2024) - Response times are affected by mispredictions in a
stochastic game. https://doi.org/10.1038/s41598-024-58203-7
[6] Galves A.; Najman, F. A.; Svarc, M.; Vargas, C. D. (2026). Clustering functional data sets by law

28/07/2026

The third episode of 4th season of the podcast A Matemática do Cérebro, by CEPID NeuroMat, has been released today.

In this episode, host Felipe Parlato talks with NeuroMat researchers Fernanda Figueiredo Torres and Cláudia Vargas about a study that aims to understand changes in facial and hand sensorimotor functions associated with brachial plexus injury.

Listen here:

Podcast A Matemática do Cérebro, apresentada pelo CEPID Neuromat

21/07/2026

When we talk about legacy, application quickly comes to mind. After all, how can the knowledge from neuromathematics take form in our daily lives?
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In a biological context, patterns may not appear in a linear fashion. At NeuroMat, this idea was already tested by Pavão and colleagues a decade ago (see 1). In another study from 2012, Pavão, Helene, and Xavier (2) asked the same question in the context of the progression of Parkinson’s Disease (PD). They performed a systematic review of the literature considering functional impairments and dopaminergic medication effects to draw a picture about the disease trajectory and the possible correlations between these factors.

Their explanatory model revealed that “cognitive impairments arise at early stages of PD and stabilize, while disruption of implicit knowledge acquisition and motor impairments are still in progression”. The model, they concluded, could turn out to become a useful tool for understanding the multifaceted characteristics of PD”.

The NeuroMat’s legacy, as we have been discussing here, includes the application of knowledge in many forms and areas. Today, we’re going to recall two fruitful initiatives that took form starting with questions such as the one above.

The second post of our series (see 3) described how the Goalkeeper’s Game has been transformed into a tool with potential contributions to people with Parkinson’s Disease. Quoting the early post, “a person plays the Goalkeeper’s Game (GG) and their score — now transformed by the elegant statistical model — could (plausibly) be correlated with a clinical variable: gait performance.”

Stern and colleagues (4) hypothesized that the game would achieve a gait performance prediction level similar to a well-established cognitive assessment test, which came to be true: they registered 56% accuracy with the test, while their model achieved 65%. Hence, it became plausible to suppose that GG would be able to predict the level of impairment and fall risk in daily living activity in people with PD. Further studies could take the lead in that direction.
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In a different field, with collaboration of some of NeuroMat’s researchers, independent studies on fatigue have offered meaningful insights to areas such as civil aviation. As revealed by Rodrigues, Fischer, Helene and colleagues in 2023 (see 5), a bio-mathematical model can be efficiently applied to approach risk factors, leading to a “few safe recommendations to the aviation sector regarding regulatory reviews and fatigue risk management”, including mitigation policies.

Similarly, in 2025, Rodrigues, Furlan, Helene and colleagues (see 6) investigated relationships between workload and fatigue of sleepiness on Brazilian aircrews. The study proposes “a novel approach to investigate the complex relationships between workload metrics and self-rated fatigue and sleepiness scores”, offering specific insights on best practices and safety recommendations.
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These are examples of how the most theoretical advances can be expanded and taken further into different areas of application. Whether in neighboring projects inside the research center, e.g. our Parkinson’s Disease initiatives at NeuroMat, or outside, the legacy of science can benefit the whole society.

See you next week in the farewell of the series. Check out the cited papers and posts below.
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Legacy, part 3 — The “Black-Box of Science” Series

References:
1. Pavão, Savietto, Sato, Xavier & Helene (2016) - On Sequence Learning Models: Open-loop Control Not Strictly Guided by Hick’s Law. Sci Rep 6, 23018. https://doi.org/10.1038/srep23018
2. Pavão, Helene, & Xavier (2012) - Parkinson’s Disease Progression: Implicit Acquisition, Cognitive and Motor Impairments, and Medication Effects. Front. Integr. Neurosci. 6:56. https://doi.org/10.3389/fnint.2012.00056
3. NeuroMat (2025) - "A Person-Environment Model | The “Black-Box of Science” Series", Facebook post. https://www.facebook.com/photo/?fbid=1410985133726709&set=a.609782380513659
4. Stern, d'Alencar, Uscapi, Gubitoso, Roque, Helene & Piemonte (2020) - Goalkeeper Game: A New Assessment Tool for Prediction of Gait Performance Under Complex Condition in People With Parkinson's Disease. Front. Aging Neurosci. 12:50. doi: 10.3389/fnagi.2020.00050
5. Rodrigues, Fischer, Helene et al. (2023) - Modelling the root causes of fatigue and associated risk factors in the Brazilian regular aviation industry. Safety Science, 157, 105905. https://doi.org/10.1016/j.ssci.2022.105905
6. Rodrigues, Furlan, Helene et al. (2025) - Aircrew rostering workload patterns and associated fatigue and sleepiness scores in short and medium haul flights in Brazil. Sci Rep 15, 37845. https://doi.org/10.1038/s41598-025-21705-z

Parkinson’s disease (PD) symptoms have been collectively ascribed to malfunctioning of dopamine-related nigro-striatal and cortico-striatal loops. However, s...

10/07/2026

Something special started when the first patient filled out the electronic form. Years later, thanks to the effort of NeuroMat’s multidisciplinary team—and the collaboration of many other patients—, a refined version of the tool became part of a newly released resource: a public database on traumatic brachial plexus injury (ref. 1).

As we discussed here before (2), more than 80% of traumatic brachial plexus injuries (TBPI) are due to motorcycle accidents. The digital database developed by Cristiane Patroclo, Bia Ramalho and colleagues (2026) made public interesting data from 170 individuals with TBPI with varying degrees of functional impairment. The initiative encourages data sharing and reuse, fostering clinical improvements and the development of new investigative tools to assess the subject.

Such a database reinforces the importance of open science and democratic access to knowledge all around the world. Besides, the work was possible thanks to another tool developed in our research center: the Neuroscience Experiment System (NES), a key part of this legacy (see our previous post in ref. 3)
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A well-structured, long-term project provides us with the opportunity to see a single seed flourishing and developing into a tree with several branches, each one with its own treasures. This legacy can take different forms: theoretical transformation, practical application, technology transfer, professional qualification and so on.

The research effort at NeuroMat has been fruitful to the scientific community and the society as a whole. Perhaps the ABRAÇO initiative, also mentioned in this series, is where most of these results appear in a tangible form.
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While a scientific career demands a lot of creativity to tackle variable questions, results make it worth—for the team, for society. The future, as always, is mostly unpredictable. Still, questions are part of this legacy and give us a hint.

Since 2025, for example, the ABRAÇO team has been conducting experimental protocols with human subjects that employ both the Goalkeeper Game and TMS pulses to investigate brain plasticity mechanisms following TBPI, aiming to develop new assessment and rehabilitation strategies. The next years will show how these achievements can impact people's lives.

More information can be found in the ABRAÇO website (4). If you see a scientific career in your future, we hope these projects help you picture how diverse can be the results of our daily work. See you soon!

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Legacy, part 2 – The “Black-Box of Science” Series

References:
1. Patroclo et al. (2026) - A public database on traumatic brachial plexus injury: data collection and sharing. BMC Research Notes.
2. NEUROMAT (2026) - Blindfolded | The “Black Box of Science” series”. Facebook post.
3. NEUROMAT (2026) - Research project management | The “Black Box of Science” series”. Facebook post.
4. ABRAÇO initiative website:

A missão da iniciativa ABRAÇO é acolher e orientar pacientes que sofreram uma lesão traumática do plexo braquial e seus familiares, oferecer treinamento e capacitação para profissionais interessados e divulgar pesquisa em desenvolvimento sobre a lesão e suas consequências funcionais.

24/06/2026

Power law can efficiently describe forest fires, city sizes, species extinction, and even income distribution. In computational neuroscience, neuronal avalanches are, by definition, a cascade of activations with power law distributions. (2). It means one quantity varies depending on the variation of the other.

One of its most interesting properties is scale invariance: it applies for a system in its different scales, often resulting in fractals, structures whose parts are similar to the whole. The heavily tailed distribution is also characteristic, meaning extreme values are more likely.

The curve is actually very interesting. In spite of its high values early in time, the long tail makes us wonder how many meaningful events may occur in a relatively distant future.

The unpredictable legacy of a complex system.
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Thirteen years ago, NeuroMat started its activities aiming at developing new explanations for the brain, considering its many scales of complexity and the power of Galves–Löcherbach model that was encoded in the Institute’s DNA. Goals have been formalized. Strategies and infrastructure followed.

“(...) to test and construct large-scale computational implementations of the models developed by [our] scientific team, NeuroMat has established a High-Performance Computational Center that allows for the simulation of large-scale network models.” (3).

Over the last 13 years, a bulk of impactful work has been done by our dedicated scientific team. For example, the recently published paper by Flavio Rusch, Osame Kinouchi, and Antonio Roque (4) assess the “influence of topology on the critical behavior of hierarchical modular neuronal network”.

Previous works have discussed whether Self-Organized Criticality (SOC) could allow brain networks to operate close to critical points. As we explained in another post (4), that tipicaly results in optimal performance for complex tasks, efficient memory usage, health behavior, among other benefits. However, brains are non-conservative systems, so they would be better described as Self-Organized quasicritical (SOqC) systems.

Now, “how can a neuronal network reach and stay near a critical region regardless of perturbations?”, Rusch, Kinouchi and Roque ask. The paper describes their investigation on how the possible influence of hierarchical modular network topology in the emergence of critical behavior in this type of network.
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“Our findings suggest that hierarchical modularity strengthens the robustness of quasicritical behavior and shapes how the network transitions through critical regimes”, they explain. From the discussion, they conclude that “that heterogeneous networks combined with adaptive self-organized criticality dynamics can exhibit critical behavior”.
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The legacy of an institution is multidimensional. From infrastructure to the very knowledge produced, several things can have a meaningful impact in the future. Let’s highlight that with a question and an invitation.
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The paper by Rusch, Kinouchi and Roque (2025) offers a hint for the future in the field: to explore “how even more heterogeneous networks, such as hierarchical modular networks of stochastic excitatory-inhibitory neurons, may also display criticality”.

Those willing to know more about this work and others can listen to Prof. Roque himself, which brings us to the invitation.
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On June 24-25, 2026, The Research, Innovation and Dissemination Center for Neuromathematics will hold its closing event, “Celebrating 13 Years of Research at NeuroMat: A Look at the Past and the Future”.

The 2-day workshop will gather fellows to celebrate the Institute’s outstanding achievements and to reflect on research perspectives and new directions. More information on the link below:
https://neuromat.numec.prp.usp.br/content/neuromat-2026-06-24/index.html
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In the next few weeks, the final chapters of our series will feature aspects of NeuroMat’s history and legacy. See you soon!

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Legacy, part 1 – The “Black-Box of Science” Series

References:
(1) Wikipedia. Power law. https://en.wikipedia.org/wiki/Power_law
(2) Begs, J. (2007) Neuronal avalanches. Scholarpedia, 2(1):1344. doi:10.4249/scholarpedia.1344
(3) Neuromat. Scientific project.
https://neuromat.numec.prp.usp.br/scientific-project/
(4) Rusch, F.R., Kinouchi, O. & Roque, A.C. (2025) Influence of topology on the critical behavior of hierarchical modular neuronal networks. Commun Phys 8, 168. https://doi.org/10.1038/s42005-025-02074-5
(5) Neuromat. Sandpiles and Neuronal Networks | The “Black-Box of Science” series. Facebook post. https://www.facebook.com/neuromathematics/posts/pfbid0zrW6axbRiekhyURwyYcpCvaDNvU9mtLtRLQSoeNXKa5hoD32uapQwhcxSJ7MCEv2l

The mission of the Center is to develop the new mathematics needed to construct a Theory of the Brain accounting for the full experimental data gathered by neuroscience research.

15/06/2026

There is something interesting about how we perceive the complexity of the brain. The more we study its elements and processes, the more we realise it can be exponentially more complicated—meaning, interesting—than we thought. This happens in two ways.

First: The object itself. Take a neuron. Once we learn the basics about a single cell, we realise it is capable of doing several things in multiple ways. There are several combinations through which it can compose a large system, and multiple functionalities it can contribute to.
Second: variations. As soon as we learn the very basics of a neuron, we also discover that there are types of neurons. They vary in impressive ways, from the number of ramifications to size and electrochemical dynamics.

Taken together, these two aspects of studying the brain make the process increasingly interesting. Also, it gives us some idea, not exactly about the complexity of the brain itself, but how much more complex it can be than we could have assumed.

In this sense, trying to break the system down to individual pieces in a reductionist approach turned out to constrain our comprehension of the brain and its complex dynamics. Sometimes, a single neuron—of a population of them—does not underlie a single cognitive function. As Tardelli and colleagues show us in their 2022 paper, a close look at a small area of the cortical surface can reveal an overlap of motor representations with major implications.

After signing a consent form, each of the 12 right-handed young male volunteers went through a MRI scan. The gray matter surface was segmented for guiding the next step.

As the participant sat in a reclining chair and was instructed to stay fully relaxed with their right hand in a neutral posture, the navigated Transcranial Magnetic Stimulation coil was positioned on their head. Surface EMG electrodes were placed: one on the right forearm muscle and two on hand muscles.

‘Interestingly’—a participant may have thought—’there’s a lot of tech devices, but I just need to stay relaxed and wait’.
EMG data were continuously recorded from the three muscles. There were pseudo TMS biphasic pulses: three consecutive in each of the 20 sites around the muscles. A coil navigation software controlled everything.

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The primary motor cortex, also called M1, is organized in a very interesting way. Located in the top of the head, it presents a sort of a map to body parts. A cortical site control a specific muscle. However, multiple reports describe an overlapping among motor representations. That would be needed to execute movements in a synergistic arrangement.

Using nTMS, Tardelli and colleagues evaluated the muscle coactivation and representation overlapping between specific movements in the forearm and hand.
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“We hypothesized that these representations would be highly overlapped due to the muscles’ extensive coactivation in several hand movements, e.g. grasping”, they state in the 2022 paper. “Also the overlap degree would differ between adjacent and target muscles from different body parts and would increase with the TMS intensity”.

While the nTMS stimulated certain neuronal populations in the M1, the electrodes in the forearm and hand collected the data to measure the impact of stimuli.
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The substantial overlapping found in their results “is possibly explained by how the M1 seems to encode movements and muscle recruitments”, in their words. If we look at the premotor cortex, we are not expected to find point-to-point connectivity between these areas and muscles. As the authors explain, that implies a “synergetic spatial organization”, which makes the brain capable of maintaining representations of complex movements as well as of related muscles.

Importantly, these findings can have immediate clinical impact, “when defining the eloquent brain regions during pre-surgical planning”, for example, since “avoiding highly overlapped areas associated with muscular synergy would minimize deficits in the patients’ motor functions”, they conclude.
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The brain is a universe that can’t be described or explained just in terms of individual elements. Now, how may other cognitive domains benefit from a synergic organization like this? Let us know your thoughts!
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Synergy | The “Black Box of Science” series
References:
Tardelli, G. P., Souza, V. H., Matsuda, R. H., Garcia, M. A. C., Novikov, P. A., Nazarova, M. A., & Baffa, O. (2022). Forearm and Hand Muscles Exhibit High Coactivation and Overlapping of Cortical Motor Representations. Brain topography, 35(3), 322–336. DOI:

Most of the motor mapping procedures using navigated transcranial magnetic stimulation (nTMS) follow the conventional somatotopic organization of the primary motor cortex (M1) by assessing the representation of a particular target muscle, disregarding the possible coactivation of synergistic muscles...

01/06/2026

Imaginary worlds | The “Black Box of Science” series

Imagine that you are performing the movement. Try to stay focused during the whole experiment. Keep your eyes closed until the end of each block.
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Healthy, right-handed participants sit in front of a 17-inch LCD monitor 60cm away and aligned to their eyes. They had no history of neurological, orthopedic, vascular, or muscular dysfunctions, nor spinal fractures or muscular issues affecting the upper limb.
The room remained silent. They received instructions from the researcher before the task started, then they heard a voice dictating numbers in a random sequence via headphones.
1, 2, 3, 1, 2, 2, 1, 2, 3…
The other group was instructed to press one of the three keys on the keyboard, as quickly as possible upon identifying the number.
This group should just imagine. The movement. The sensation. The visual experience.
There was a familiarization phase. Then the test phase started.
1, 2, 2, 1, 2, 3, 1…
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At some point, the participant hears two consecutive beeps. The first block is finished, time for some rest.
So, let’s take a short break.
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Motor imagery is “the mental practice of a motor task”. It involves both visual and kinesthetic components. Besides, evidence shows that it activates neural patterns that resemble those of motor ex*****on.

Similar mechanisms, some similar outcomes, such as learning. The essence of mental practice, through the lens of motor emulation theory, is “to induce an experience without the original input of external stimuli”.

“Evidence shows that motor imagery and motor ex*****on share brain mechanisms and provide activation of the same motor pathways”, explain Patricia Camargo, Paulo Cabral-Passos, and André Frazão Helene in their just-published paper (2026; see ref. 1).
“Improvements in motor function have been found in healthy subjects, and subjects in motor rehabilitation as a consequence of the practice”. Hence, the research “can help produce guidelines for participants to obtain the most from the practice”.

For example, training Brain-Machine Interfaces controlled by motor imagery can be improved. The same applies to motor imagery used in clinical conditions and even sports, as reported by studies with professional football players undergoing rehabilitation after injury (see ref. 2).
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About a year before, a professional football player concludes the first two segments of the physical rehabilitation program, and a 20-minute guided motor imaginary session. After the initial relaxation phase, the headphone repeats the instructions for a mental rehearsal of the balance tasks.

The routine was repeated with dozens of players over six sessions. The study contributed to our understanding of the impact of motor imagery vividness as part of rehabilitation protocols.
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The study investigates the similarities and differences between motor imagery and real ex*****on during a probabilistic sequence-learning task, using context trees — a familiar term in our series.

So, our brains learn probabilistic structures. That is done based on the preceding context, reflecting—in Duarte and colleagues’ 2019 experiment—the regularities of a context tree.

So, how closely motor imagery and real ex*****on would be, considering their own performance signature in face of this particular learning task?

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The rest period works to minimize fatigue and maintain attention. There were 5 blocks in total, 150 trials each. Quite a lot to imagine.
Context trees sustained stimuli sequences. Participants repeatedly pressed the key. Others worked with their imagination. Response Times were collected and compared in a series of analyses.
After all, Camargo and colleagues observed that imagery triggered learning along the blocks, progressively achieving lower response times. These results highlight how fruitful it can be when integrated to physical therapy and recovery, for example.

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Some theories propose that the emulation would provide our mental “planner” with predictions, enabling corrections and adjustment. Hence improving performance, preventing errors.

What is the difference between real and imaginary? Most of the time healthy, typical brains know how to differ. Even though, several parts of neural networks work similarly on both “situations”.

What other signatures can we find in our brain? Comment below, let us know your thoughts!

REFERENCES:
1. Camargo et al. (2026). Different factors determining motor ex*****on and motor imagery performance in a serial reaction time task with intrinsic variability. Brain Sciences, v. 16, n. 2, p. 147. DOI: 10.3390/brainsci16020147
2. Plakoutsis et al. (2025). Motor imagery ability and motor imagery perspective among professional football players. Healthcare, 13, 3045.

Background: Motor Imagery (MI) refers to the mental simulation of movement without physical ex*****on and activates brain areas involved in motor control. Its use in sports rehabilitation is growing due to its potential to promote recovery, reduce fear of re-injury, and maintain neuromuscular engage...