Figure 1 - uploaded by Alessandro Santuz
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Schematic representation of a spinal reflex arc. A pin in the skin produces an input signal which travels through the afferent pathway in the spinal nerve until the dorsal root of the spinal cord. The relevant motor output is exiting the spinal cord from the ventral root and finally reaches the muscle. The connection between the afferent (input) and efferent (output) pathways is mediated by the interneuron.
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The need to move over uneven, continuously changing terrains is part of our daily life. Thus, the central nervous system must integrate an augmented amount of information in order to be able to cope with the unpredictability of external disturbances. A consequence of this increased demand might be a flexible recombination of the modular organisatio...
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... spinal segment gives rise to paired spinal nerves (i.e. bundles of neuronal axons) joined in dorsal and ventral roots (see Figure 1 for a graphical representation). In the spinal cord both white and grey matter tissues are present. ...
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... the pressure values of the individual foot recorded from the plate, the code evaluates the footprint length (calculated length) along the treadmill's anterior-posterior axis. If the calculated value differs more than 5% from the measured one (e.g. for TS cases), the footprint is corrected with the "real" value, like shown in Figure 10. The most important assumption underlying this step is that, during the toe-off phase, the tip of the shoe or of the foot always touches the ground. ...
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... the algorithm this is considered as the first nonzero pressure matrix after the last toe-off. In Figure 11, a flowchart shows the logic of the FSP determination algorithm, emphasizing the individual steps and their interconnections. ...
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... fundamental synergy can be defined as an activation pattern whose motor primitive shows a single peak of activation. When two (or more) fundamental synergies are blended into one, a combined synergy appears, like shown in Figure 13. The recognition can be done by manual selection of the fundamental primitives associated to a specific synergy. ...
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... actual number of fundamental synergies recognizable in the factorization results is the same using any of the two NMF algorithms (i.e. 5 for walking and 6 for running). The typical NMF outputs for a specific filtering condition are reported in Figure 14 (level walking), Comparing the two NMF algorithms, the minimum number of synergies necessary to reconstruct the original signals is on average five for the GNMF and four for the IGNMF method (Table 9). These results represent the mean values across the filtered and non-filtered data sets and were obtained by employing the R 2 criterion. ...
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... are significant (p<0.001). Figure 18 shows the influence of the filtering conditions on the minimum number of synergies needed by the GNMF and IGNMF models to reconstruct the original signal. For both methods, the only consistently significant factor affecting the number of synergies is the HP frequency (p<0.001; Figure 18). ...
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... both methods, the only consistently significant factor affecting the number of synergies is the HP frequency (p<0.001; Figure 18). Other influencing factors are the LP frequency in the GNMF (p=0.026) and the filter order in the IGNMF results (p=0.001). ...
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... of 20 participants, 14 (7 male, 7 female) transitioned from RS (shod) to MFS (barefoot). Figure 21 depicts the average spatiotemporal spinal motor output for shod and barefoot running. The two-way ANOVA identified statistically significant differences in the FWHM of the mapped EMG activities when comparing shod and barefoot running for both the stance (p=0.018) and swing (p=0.019) ...
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... but this number is species-specific 2 . Each spinal segment gives rise to paired spinal nerves (i.e. bundles of neuronal axons) joined in dorsal and ventral roots (see Figure 1 for a graphical representation). In the spinal cord both white and grey matter tissues are present. The former contains ascending (to the brain) and descending (to the periphery) myelinated fibres, including sensory and motor neuronal axons. The latter encloses unmyelinated neuronal cells (called interneurons) as well as the somas, axons and dendrites of motor neurons, embedding simple (e.g. reflex arcs) and more complex (e.g. CPGs) spinal circuits. Simple, locally-mediated reflex pathways such as the Ia, Ib and FRA (flexion reflex afferent) reflex arcs are, in order of decreasing simplicity as to the number of synaptic connections, well described in literature 2 . The excitatory Ia (monosynaptic) and Ib (disynaptic) reflex arcs are thought to be major contributors for muscular contraction during locomotion by mediating afferent inputs from muscle spindles and Golgi tendon organs (GTOs), respectively 2,10,11 . The FRA, even if 3 typically involved in limb's withdrawal when receiving information from cutaneous nociceptors (receptors of potential harm), is also an important reflex for locomotion. In fact, the FRA can enhance postural control and has been shown, if pharmacologically stimulated, to reset the gait cycle to flexion 10,11 . Several white matter tracts have the function of relaying information between the brain and the spinal cord. Ascending pathways that travel to the brain guide sensory-related signals such as touch, proprioception, pressure and vibration. These large myelinated fibres decussate (travel contralaterally) through the spinal cord until they reach the upper levels of the thalamus and sensory cortex. Descending pathways that travel from the brain are the corticospinal (or pyramidal) and the extrapyramidal tracts. These pathways carry the information associated with complex movements, such as fine skilled motion, dynamic stability maintenance and stereotyped motor behaviours like locomotion. The scientific attention given to the cellular organisation and function of the spinal grey matter is relatively recent. On the one hand, somatic (i.e. related to skeletal muscle) reflex arcs have been known and studied for almost two centuries 1,12 . In his book published in 1824, On the other hand, more complex spinal networks such as the CPG for locomotion have been intensively analysed only with the advent of intracellular recordings, in the late 1960s 2 ...
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... pressure plate-data elaboration revolves around the concept of strike index (SI). The SI, as originally defined by Cavanagh and Lafortune 201 and then adopted from several other authors 204,214,220,221,225,[236][237][238][239][240] , is the distance from the heel to the center of pressure at impact relative to total foot length. However, the most important assumption made from this method is to know the foot (or shoe) length. The plate itself cannot measure this quantity accurately, since in cases like the TS the entire foot does not touch the treadmill. Therefore, for a clear identification of the FSPs from the pressure distribution data, the first step is to determine for each participant the footprint lengths. The measured lengths with and without shoes (for shod and barefoot trials, respectively) have been used as a reference to carry on the analyses. To identify the forefoot, the midfoot and the rearfoot, the footprint is divided into three geometrically equal parts, each representing one third of the total length. Using the pressure values of the individual foot recorded from the plate, the code evaluates the footprint length (calculated length) along the treadmill's anterior-posterior axis. If the calculated value differs more than 5% from the measured one (e.g. for TS cases), the footprint is corrected with the "real" value, like shown in Figure 10. The most important assumption underlying this step is that, during the toe-off phase, the tip of the shoe or of the foot always touches the ...
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... proportion of explained variation R 2 (obtained as described in the methods section using an increasing number of synergies in the model) was independent of the investigated condition. The average values, proven to be significantly different (p<0.001), were 0.94 ? 0.02 for the GNMF and 0.99 ? 0.01 for the IGNMF algorithm. This metric can be used to 63 assess the ability of each algorithm to reconstruct the original signal. Another metric useful for determining the differences in performance is the computation time. Using an Intel? Xeon? X5650 @ 2.66 GHz with 48 GB RAM on Windows 7 64-bit, the computation times for walking and running were significantly lower for the GNMF (1.4 s for every recorded second in both walking and running) compared to the IGNMF (2.0 s for walking and 1.7 s for running). Differences are significant (p<0.001). Figure 18 shows the influence of the filtering conditions on the minimum number of synergies needed by the GNMF and IGNMF models to reconstruct the original signal. For both methods, the only consistently significant factor affecting the number of synergies is the HP frequency (p<0.001; Figure 18). Other influencing factors are the LP frequency in the GNMF (p=0.026) and the filter order in the IGNMF results ...
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... proportion of explained variation R 2 (obtained as described in the methods section using an increasing number of synergies in the model) was independent of the investigated condition. The average values, proven to be significantly different (p<0.001), were 0.94 ? 0.02 for the GNMF and 0.99 ? 0.01 for the IGNMF algorithm. This metric can be used to 63 assess the ability of each algorithm to reconstruct the original signal. Another metric useful for determining the differences in performance is the computation time. Using an Intel? Xeon? X5650 @ 2.66 GHz with 48 GB RAM on Windows 7 64-bit, the computation times for walking and running were significantly lower for the GNMF (1.4 s for every recorded second in both walking and running) compared to the IGNMF (2.0 s for walking and 1.7 s for running). Differences are significant (p<0.001). Figure 18 shows the influence of the filtering conditions on the minimum number of synergies needed by the GNMF and IGNMF models to reconstruct the original signal. For both methods, the only consistently significant factor affecting the number of synergies is the HP frequency (p<0.001; Figure 18). Other influencing factors are the LP frequency in the GNMF (p=0.026) and the filter order in the IGNMF results ...
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... width of the foot is considered as the widest footprint recorded. The footprint is thus localized within its real boundaries: this is done by expanding each pressure matrix in length and width with the appropriate number of zero elements in order to reach the correct length and width. The calculation of the SI, then, automatically provides one of the three FSPs (RS, MS or FS, being the TS case included in the FS). To temporally locate the impact, the first recorded data after the swing phase has been taken as a reference, thus defining "impact" as "initial contact" 201 . In the algorithm this is considered as the first nonzero pressure matrix after the last toe-off. In Figure 11, a flowchart shows the logic of the FSP determination algorithm, emphasizing the individual steps and their interconnections. ...
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... fundamental synergy can be defined as an activation pattern whose motor primitive shows a single peak of activation. When two (or more) fundamental synergies are blended into one, a combined synergy appears, like shown in Figure 13. The recognition can be done by manual selection of the fundamental primitives associated to a specific synergy. Due to the great amount of data produced by the different calculated data sets, we implemented a learning algorithm based on a curve-fitting model. The first implementation step consists in choosing some examples of single-peak activation patterns, which might represent a fundamental primitive. The code is then fed by these manually-picked examples of fundamental primitives and a search of similar shapes is done across the whole dataset of 57 factorized curves. With a first iteration, the primitives that have a high similarity (R 2 > 0.95) with the ones present in the manually-created database are added to the set. The number of fundamental primitives is then selected by looking at the motor modules and merging possible repetitions. After updating the database, the code starts the recognition across the entire dataset searching, synergy-by-synergy, for similar primitives (we found R 2 > 0.5 to be a good threshold in this phase). Non-recognized curves can then be visually inspected with an interactive routine or automatically identified as new fundamental or combined primitives. This approach, validated in a pilot study, can reproduce the results of a completely manual selection of the curves with a margin of error of ? 5%. ...
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... actual number of fundamental synergies recognizable in the factorization results is the same using any of the two NMF algorithms (i.e. 5 for walking and 6 for running). The typical NMF outputs for a specific filtering condition are reported in Figure 14 (level walking), Comparing the two NMF algorithms, the minimum number of synergies necessary to reconstruct the original signals is on average five for the GNMF and four for the IGNMF method (Table 9). These results represent the mean values across the filtered and non-filtered data sets and were obtained by employing the R 2 criterion. Using the AIC method, though, produced the same results only for the IGNMF algorithm, while for the GNMF created in all cases a minimum at 1 (the same behavior has already been reported by Devarajan and Cheung 78 ). The minimum number of synergies was significantly lower in the level compared to the incline walking condition, independently of the factorization method. The largest 60 difference in the frequency of appearance of fundamental synergies was observed for the fourth synergy. On this matter, no differences could be noted between the two running conditions. ...
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... of 20 participants, 14 (7 male, 7 female) transitioned from RS (shod) to MFS (barefoot). Figure 21 depicts the average spatiotemporal spinal motor output for shod and barefoot running. The two-way ANOVA identified statistically significant differences in the FWHM of the mapped EMG activities when comparing shod and barefoot running for both the stance (p=0.018) and swing (p=0.019) phase of the gait cycle (Table 11). The post-hoc analysis showed significantly lower FWHM in the barefoot condition of segment L4's spinal motor output, innervating the muscles ME, AL, FL, RF, VM, VL, ST, TA and PL. The CoA was not significantly different between conditions in neither the stance (p=0.107) or the swing (p=0.091) phase (Table 11). the maximum of each condition). The stance and swing phases have been temporally normalized to the same amount of data points (100 each). Values are the means across all subjects and all ...
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... De manière intéressante, les primitives motrices suivent également une architecture fractale. Bien que celles-ci ne suivent pas strictement un schéma dit auto-similaire, on dit qu'elles sont statistiquement auto-similaires ou auto-affines (Mandelbrot, 1985 ;Santuz et Akay, 2020). ...
... Les primitives motrices (indiquées par la lettre H) sont les coefficients dépendants du temps. La multiplication de W et H permet la reconstruction (approximée) de la matrice initiale V (Adapté deSantuz, 2018). ...
... Si deux ou plusieurs synergies fondamentales étaient fusionnées en une seule, la synergie était classée comme "combinée". L'origine des synergies combinées est encore fortement incomprise aujourd'hui et fortement influencée par le traitement du signal effectué(Santuz, 2018).Pour la comparaison des primitives motrices, nous avons utilisé deux métriques linéaires : la largeur totale à mi-hauteur (FWHM) et le centre d'activité (CoA). Le CoA a été défini comme l'angle du vecteur (en coordonnées polaires) qui pointe vers le centre de masse de cette distribution circulaire(Cappellini et al., 2006). ...
Après une course d'endurance, le patron de récupération fonctionnelle est décrit comme biphasique, étant caractérisé par des déficits fonctionnels immédiats, suivis d'une récupération partielle à 2 h, avant de nouveaux déficits 1 à 2 jours plus tard ne s'atténuant que progressivement sur plusieurs jours. En raison du potentiel effet protecteur des hormones œstrogènes, notamment au niveau musculaire, les femmes pourraient mieux résister à la fatigue et récupérer plus rapidement. La littérature s’est néanmoins focalisée sur la récupération des hommes et principalement en phase aiguë. La phase retardée se caractérise pourtant par un phénomène inflammatoire lié à la régénérescence des microlésions musculaires causées par la course. Cette phase s’accompagne de courbatures musculaires qui disparaissent avant que la récupération ne soit complète, ce qui constitue un risque potentiellement accru de blessure à la reprise de la pratique. L’objectif principal de ce travail de thèse était d’établir et de comparer la cinétique de récupération structurale et fonctionnelle de coureurs féminins et masculins après une course d’endurance de 20 km avec dénivelé. Nos résultats soulignent l‘interaction entre le sexe et le test d’évaluation utilisé. Les femmes ont présenté plus de courbatures et d’altérations structurales (tant à l’échographie que par imagerie par résonnance magnétique) des muscles ischio-jambiers que les hommes. Par contre, leurs déficits fonctionnels étaient moindres et leur récupération plus précoce dans certains tests. Ce travail souligne la faiblesse des liens entre les altérations structurales et les déficits fonctionnels, autant que la richesse des ajustements neuromusculaires en situations dynamiques pluri-articulaires et musculaires. Les différences fonctionnelles observées entre les sexes semblent fortement influencées par l’organisation spécifique des synergies musculaires propres à chaque sexe.
... The root mean square (RMS) is considered as the main method in the time domain [29]. Considering that movements are not the result of independent muscle activations, but rather of linear combined patterns of activations (called muscle synergies) [30], complex mathematical models, including linear machine learning such as non-negative matrix factorization (NMF), seem well-suited to assess such a dynamic task. This method has been used to quantify muscle synergies in a wide variety of tasks [31,32,33,34] and seems particularly suitable for revealing potential sex-differences in muscle fatigue and intermuscular compensations during the recovery period. ...
... η2 ¼ 0.06), 14 participants in each sex group were required to obtain a statistical power of 80%. Due to the loss of three participants (two because of back pain and torn muscle during the race and one for noisy EMG recordings), the final group included only 9 female (age: 35 AE 7 years (26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45), body mass: 59.8 AE 8.7 kg, height: 1.66 AE 0.08 m) and 8 male (age: 29 AE 7 years (21-38), body mass: 70.9 AE 6.2 kg, height: 1.76 AE 0.06 m) runners. Two women were in the follicular phase, 4 in the luteal phase and 3 were amenorrhoeic. ...
... Finally, the 20% BW condition differed from the others by showing for both sexes only two synergies at PRE, but three synergies at both D2 and D4 that could reflect a fatigue effect for both sexes. However, as about 30% of combined synergies were found for the 20% BW at PRE for both men and women compared to about 15% for the other loads, this result may still be attributed to the variability of the computation method [30]. ...
The acute and delayed phases of the functional recovery pattern after running exercise have been studied mainly in men. However, it seems that women are less fatigable and/or recover faster than men, at least when tested in isometric condition. After a 20 km graded running race, the influence of sex on the delayed phase of recovery at 2–4 days was studied using a horizontal ballistic force-velocity test. Nine female and height male recreational runners performed maximal concentric push-offs at four load levels a week before the race (PRE), 2 and 4 days (D2 and D4) later. Ground reaction forces and surface electromyographic (EMG) activity from 8 major lower limb muscles were recorded. For each session, the mechanical force-velocity-power profile (i.e. theoretical maximal values of force (F¯¯ 0), velocity (V¯¯¯ 0), and power (P¯¯¯ max)) was computed. Mean EMG activity of each recorded muscle and muscle synergies (three for both men and women) were extracted. Independently of the testing sessions, men and women differed regarding the solicitation of the bi-articular thigh muscles (medial hamstring muscles and rectus femoris). At mid-push-off, female made use of more evenly distributed lower limb muscle activities than men. No fatigue effect was found for both sexes when looking at the mean ground reaction forces. However, the force-velocity profile varied by sex throughout the recovery: only men showed a decrease of both V¯¯¯ 0 (p < 0.05) and P¯¯¯ max (p < 0.01) at D2 compared to PRE. Vastus medialis activity was reduced for both men and women up to D4, but only male synergies were impacted at D2: the center of activity of the first and second synergies was reached later. This study suggests that women could recover earlier in a dynamic multi-joint task and that sex-specific organization of muscle synergies may have contributed to their different recovery times after such a race.
... An Illustration of Neuron Functional Classification[26]. ...
As a follow-up tutorial article of [29], in this paper, we will introduce the basic compositional units of the human brain, which will further illustrate the cell-level bio-structure of the brain. On average, the human brain contains about 100 billion neurons and many more neuroglia which serve to support and protect the neurons. Each neuron may be connected to up to 10,000 other neurons, passing signals to each other via as many as 1,000 trillion synapses. In the nervous system, a synapse is a structure that permits a neuron to pass an electrical or chemical signal to another neuron or to the target effector cell. Such signals will be accumulated as the membrane potential of the neurons, and it will trigger and pass the signal pulse (i.e., action potential) to other neurons when the membrane potential is greater than a precisely defined threshold voltage. To be more specific, in this paper, we will talk about the neurons, synapses and the action potential concepts in detail. Many of the materials used in this paper are from wikipedia and several other neuroscience introductory articles, which will be properly cited in this paper. This is the second of the three tutorial articles about the brain (the other two are [29] and [28]). The readers are suggested to read the previous tutorial article [29] to get more background information about the brain structure and functions prior to reading this paper.
A novel look at the defining moments of personhood - the development of the reflex pathway.
