Tendons are made of elastic tissue and also play a key role in the functioning of joints. They connect muscle to bone. A coating of another fibrous tissue called cartilage covers the bone surface and keeps the bones from rubbing directly against each other. Some joints move and some don't. Joints in the skull don't move.
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Synovial joints are movable joints. They make up most of the joints in the body and are located mostly in the limbs, where mobility is critical. They contain synovial fluid, which helps them to move freely. Ball and socket joints, such as hip and shoulder joints, are the most mobile type of joint. They allow you to move your arms and legs in many different directions. Ellipsoidal joints, such as the one at the base of the index finger, allow bending and extending.
Gliding joints are found between flat bones that are held together by ligaments. Some bones in the wrists and ankles move by gliding against each other.
Hinge joints are those in the knee and elbow. They enable movement similar to the way a hinged door moves.
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Medical Records Pay Hospital Bill. If you are experiencing a medical emergency, call From this set of solutions, we identified a robust representative consensus community structure [ 24 ]. S1 Fig illustrates how the detected communities change as a function of the resolution parameter for the muscle-centric network. We use rewired graphs as a null model against which to compare the empirical data. Specifically, we constructed a null hypergraph by rewiring muscles that are assigned the same category Table 3 , defined below uniformly at random.
In this way, muscles of the little finger will only be rewired within the little finger, and similarly for muscles in other categories. Importantly, this method also preserves the degree of each muscle as well as the degree distribution of the entire hypergraph. Categories were assigned to muscles such that the overall topology of the musculoskeletal system was grossly preserved, and changes were spatially localized.
Specifically, we partitioned the muscles into communities of roughly size 3, such that each muscle was grouped with the two muscles that are most topologically related. We then permuted only within these small groups. This is a data-driven way of altering connections only within very small groups of related muscles. To partition muscles into communities, we took a greedy approach to modularity maximization, similar to prior work [ 25 ]. Furthermore, where K indicates the total number of communities. This term penalizes determining a set of communities that are highly unequal in size.
To conduct multidimensional scaling MDS on the muscle-centric network, the weighted muscle-centric adjacency matrix was simplified to a binary matrix all nonzero elements set equal to 1. From this data, a distance matrix D was constructed, the elements D ij of which are equal to the length of the shortest path between muscles i and j, or are equal to 0 if no path exists.
To construct the binary matrix, a threshold of 0 was set, and all values above that threshold were converted to 1.outer-edge-design.com/components/install/1791-the-best-smartphone.php
Functions of Bones
However, to make analysis robust to this choice, we explored a range of threshold values to verify that results are invariant with respect to threshold. The upper bound of the threshold range was established by determining the maximal value that would maintain a fully connected matrix; otherwise, the distance matrix D would have entries of infinite weight. In our case, this value was 0. Within this range of thresholds i. As a supplementary analysis, we also employed a method of constructing a distance matrix from a weighted adjacency matrix in order to preclude thresholding S5 Fig , and we again observed qualitatively consistent results.
We calculated the correlation between impact score and muscle injury recovery times. The recovery times and associated citations, listed in Table 4 , are average recovery times gathered from population studies. If the literature reported a range of different severity levels and associated recovery times for a particular injury, the least severe level was selected.
If the injury was reported for a group of muscles rather than a single muscle, the impact score deviation for that group was averaged together. Data points for muscle groups were weighted according to the number of muscles in that group for the purpose of the linear fit. We calculated the correlation between impact score deviation and the area of somatotopic representation devoted to a particular muscle group. The areas of representation were collected from two separate sources [ 38 , 39 ]. The volumes and associated citations are listed in Table 5.
In both studies, subjects were asked to articulate a joint repetitively, and the volumes of the areas of primary motor cortex that underwent the greatest change in BOLD signal were recorded. We then calculated the correlation coefficient between cortical volumes and the mean impact of all muscles associated with that joint, as determined by the Hosford Muscle tables.
To examine the structural interconnections of the human musculoskeletal system, we used a hypergraph approach. Drawing from recent advances in network science [ 5 ], we examined the musculoskeletal system as a network in which bones network nodes are connected to one another by muscles network hyperedges. A hyperedge is an object that connects multiple nodes; muscles link multiple bones via origin and insertion points. The degree, k, of a hyperedge is equal to the number of nodes it connects; thus, the degree of a muscle is the number of bones it contacts.
For instance, the trapezius is a high-degree hyperedge that links 25 bones throughout the shoulder blade and spine; conversely, the adductor pollicis is a low-degree hyperedge that links 7 bones in the hand Fig 2a and 2b. High-degree hyperedges are most heavily concentrated at the core. Data available for e at DOI : The representation of the human musculoskeletal system as a hypergraph facilitates a quantitative assessment of its structure Fig 2c.
We observed that the distribution of hyperedge degree is heavy-tailed: most muscles link 2 bones, and a few muscles link many bones Fig 2d and 2e. To probe the functional role of muscles within the musculoskeletal network, we employed a simplified model of the musculoskeletal system and probed whether the model could generate useful clinical correlates. We implemented a physical model in which bones form the core scaffolding of the body, while muscles fasten this structure together.
Each node bone is represented as a mass, whose spatial location and movement are physically constrained by the hyperedges muscles to which it is connected. Next, we perturbed each of muscles in the body and calculated their impact score on the network see Materials and methods and Fig 1c and 1d. As a muscle is physically displaced, it causes a rippling displacement of other muscles throughout the network. The impact score of a muscle is the mean displacement of all bones and indirectly, muscles resulting from its initial displacement. Muscles with a larger number of insertion and origin points have a greater impact on the musculoskeletal system when perturbed than muscles with few insertion and origin points [ 42 ].
In S11 Fig , we show that the network function as measured by the impact score was significantly correlated with the average shortest path length. While the network statistics are static in nature, their functional interpretation is provided by the perturbative simulations of system dynamics. Data available at DOI : To guide interpretation, it is critical to note that the impact score, while significantly correlated with muscle degree, is not perfectly predicted by it Fig 3a.
Fun Facts About Bones and Joints
Instead, the local network structure surrounding a muscle also plays an important role in its functional impact and ability to recover. To better quantify the effect of this local network structure, we asked whether muscles existed that had significantly higher or significantly lower impact scores than expected in a null network. We defined a positive negative impact score deviation that measures the degree to which muscles are more less impactful than expected in a network null model see Materials and methods.
This calculation resulted in a metric that expresses the impact of a particular muscle, relative to muscles of identical hyperedge degree in the null model. In other words, this metric accounts for the complexity of a particular muscle Table 1. Is this mathematical model clinically relevant?
Does the body respond differently to injuries to muscles with higher impact score than to muscles with lower impact score? To answer this question, we assessed the potential relationship between muscle impact and recovery time following injury.