Musculoskeletal Network Analysis for Physiological Data • PhysioMSKNet

Musculoskeletal Network Analysis for Physiological Data

PhysioMSKNet applies network science to the human musculoskeletal system. It implements hypergraph representations of muscle-bone connections, perturbation-based impact scoring, community detection, cross-modal mapping (EMG, IMU, MoCap), clinical outcome prediction, and neuromechanical coupling analysis. The theoretical foundation follows Murphy et al. (2018) doi:10.1371/journal.pbio.2002811.

The package exports 88 functions organized into 10 functional categories, providing a comprehensive toolkit for translational musculoskeletal research spanning biomechanics, rehabilitation, and neuroscience.

Features

Hypergraph Representation

The MSKHypergraph class models the musculoskeletal system as a hypergraph where bones are vertices and muscles are hyperedges connecting their origin and insertion sites. This representation preserves the many-to-many topology lost in standard graph projections.

Function	Description
`MSKHypergraph()`	Construct a hypergraph from an incidence matrix
`vertexDegree()`	Number of muscles attached to each bone
`hyperedgeDegree()`	Number of bones spanned by each muscle
`projectMuscleGraph()`	Project to muscle co-activation graph
`projectBoneGraph()`	Project to bone adjacency graph
`mskNullHypergraph()`	Generate a degree-preserving null model
`mskNullEnsemble()`	Generate an ensemble of null models
`loadIncidenceMatrix()`	Load incidence matrix from file
`loadMSKData()`	Load bundled MSK dataset
`loadMuscleMetadata()`	Load bundled muscle metadata
`loadValidationData()`	Load validation dataset

Network Metrics

Function	Description
`mskNetworkMetrics()`	Compute all metrics in one call
`mskBetweenness()`	Betweenness centrality
`mskCloseness()`	Closeness centrality
`degreeDistribution()`	Degree distribution analysis
`mskModularity()`	Network modularity score
`mskShortestPaths()`	Shortest path computation

Physical Simulation

Function	Description
`mskSimulate()`	Damped harmonic oscillator simulation
`mskReproducePaper()`	Reproduce Murphy et al. (2018) results

Perturbation-Based Impact Analysis

Function	Description
`mskImpactScore()`	Perturbation impact for a single vertex
`mskImpactScoreAll()`	Impact scores for all vertices
`mskImpactDeviation()`	Deviation from null model expectation
`mskImpactRecoveryModel()`	Recovery dynamics after perturbation

Community Detection

Function	Description
`mskCommunityDetect()`	Detect musculoskeletal communities
`mskConsensusPartition()`	Consensus partition from multiple runs
`mskCommunityProfile()`	Detailed profile of each community
`mskZRand()`	z-score of the Rand index for partition comparison

Bridge musculoskeletal network topology to physiological signals from EMG, IMU, and motion capture.

EMG Integration:

Function	Description
`emgToMSKMapping()`	Map EMG channels to MSK network nodes
`emgCommunityCompare()`	Compare EMG synergies to MSK communities
`emgMSKEnrichment()`	Test enrichment of EMG patterns in MSK modules
`emgStructuralCoherence()`	Coherence between EMG and structural topology

IMU Integration:

Function	Description
`imuToMSKMapping()`	Map IMU sensors to MSK network nodes
`imuCommunityDynamics()`	Time-varying community structure from IMU
`imuImpactPrediction()`	Predict impact scores from IMU signals
`imuNetworkKinematics()`	Network-informed kinematic analysis

MoCap Integration:

Function	Description
`mocapToMSKMapping()`	Map motion capture markers to MSK network
`mocapCommunityDynamics()`	Time-varying community structure from MoCap
`mocapImpactPrediction()`	Predict impact scores from MoCap data
`mocapNetworkKinematics()`	Network-informed kinematic analysis

Clinical Prediction

Function	Description
`mskClinicalPredictor()`	Build predictive model from network features
`mskPredictFunctionalOutcome()`	Predict functional outcome scores
`mskDetectResponderStatus()`	Classify responders vs. non-responders
`mskDetectCompensation()`	Detect compensatory movement patterns
`mskDetectRecoveryPlateau()`	Identify recovery plateaus
`mskClinicalEvidence()`	Generate clinical evidence summary
`mskInjuryRiskProfile()`	Compute injury risk from network topology
`mskFunctionalMilestones()`	Track functional milestones

Compensation Analysis

Function	Description
`mskCompensationNetwork()`	Build compensation network from deviations
`mskCompensationSummary()`	Summarize compensation patterns
`mskCompensationEvolution()`	Track compensation changes over time
`mskCompensationRiskScore()`	Quantify compensation-related risk

Recovery and Rehabilitation

Function	Description
`mskOutcomeReport()`	Generate outcome report
`mskOutcomeSummary()`	Concise outcome summary
`mskOutcomeConfidenceInterval()`	Confidence intervals for outcome metrics
`mskRecoveryTrajectoryFit()`	Fit recovery trajectory models
`mskRecoveryTimeline()`	Projected recovery timeline
`mskLongitudinalTracker()`	Track network metrics over time
`mskMinimalDetectableChange()`	Compute MDC for network metrics
`mskRehabProtocol()`	Generate network-informed rehab protocol
`mskAdaptProtocol()`	Adapt protocol based on progress data
`mskReassess()`	Reassess patient status
`mskCoordinationQualityScore()`	Coordination quality from network metrics

Neuromechanics Bridge

Function	Description
`neuromechSummary()`	Integrated neuromechanical summary
`neuromechMuscleSynergy()`	Muscle synergy extraction
`neuromechDirectionalCoupling()`	Directional coupling between signals
`neuromechCorticomuscularCoupling()`	Corticomuscular coherence
`neuromechJointTorque()`	Joint torque estimation
`neuromechMotorDriveTopography()`	Spatial motor drive mapping
`neuromechElectromechanicalDelay()`	EMD estimation
`neuromechIntegratedVulnerability()`	Multi-factor vulnerability index
`mskNeuralAdaptationIndex()`	Neural adaptation quantification
`mskSynergyChangeIndex()`	Synergy change over time

Annotation and Knowledge Graph

Function	Description
`mskAnnotate()`	Annotate network nodes with anatomical metadata
`mskEnrichKG()`	Knowledge graph enrichment analysis
`mskKGSummary()`	Knowledge graph summary for a network
`mskPathwayQuery()`	Query anatomical pathways
`mskHomuncCorrelation()`	Correlation with somatotopic organization

OpenSim Integration

Function	Description
`opensimToMSKHypergraph()`	Convert OpenSim model to MSK hypergraph
`opensimNetworkAnalysis()`	Full network analysis from an OpenSim model
`opensimForceToImpact()`	Map OpenSim muscle forces to impact scores
`opensimCompareModels()`	Compare network topology across models

Visualization

Function	Description
`plotMSKNetwork()`	Network graph visualization
`plotDegreeDistribution()`	Degree distribution plot
`plotHomunculus()`	Body map with network overlay
`plotCommunityStructure()`	Community detection results
`plotImpactVsDegree()`	Impact score vs. degree scatter
`plotImpactVsRecovery()`	Impact vs. recovery time

Statistics

Function	Description
`mskRobustRegression()`	Robust regression for network predictors

Installation

From R-universe

install.packages("PhysioMSKNet",
                  repos = c("https://x-biosignal.r-universe.dev",
                            "https://cloud.r-project.org"))

From GitHub

# install.packages("remotes")
remotes::install_github("x-biosignal/PhysioMSKNet")

Quick Start

library(PhysioMSKNet)

# --- Build a hypergraph from bundled data ---
inc <- loadIncidenceMatrix()
hg <- MSKHypergraph(inc)
print(hg)

# --- Compute network metrics ---
metrics <- mskNetworkMetrics(hg)
metrics$degree
metrics$betweenness
metrics$modularity

# --- Perturbation-based impact analysis ---
impact <- mskImpactScoreAll(hg)
head(sort(impact, decreasing = TRUE))

# --- Detect communities ---
comm <- mskCommunityDetect(hg)
profile <- mskCommunityProfile(hg, comm)

# --- Visualize ---
plotMSKNetwork(hg, community = comm)
plotImpactVsDegree(hg, impact)
plotHomunculus(hg, values = impact)

# --- Cross-modal: map EMG to MSK network ---
emg_map <- emgToMSKMapping(
  emg_channels = c("TA", "SOL", "GM", "GL", "RF", "VL", "VM", "BF"),
  hypergraph = hg
)
emgCommunityCompare(emg_map, comm)

# --- Clinical prediction ---
predictor <- mskClinicalPredictor(
  features = metrics,
  outcome = patient_scores,
  method = "lasso"
)
mskPredictFunctionalOutcome(predictor, new_features)

# --- Neuromechanics ---
synergies <- neuromechMuscleSynergy(emg_data, n_synergies = 4)
neuromechSummary(hg, emg_data, imu_data)

Dependencies

R (>= 4.2)
Matrix (sparse matrix operations)

Optional (Suggests)

Package	Purpose
igraph	Graph algorithms for advanced network analysis
MASS	Robust statistical methods
ggplot2	Enhanced visualization
readxl	Excel data import
xml2	OpenSim XML parsing
PhysioAnnotationHub	Anatomical annotation and knowledge graph
PhysioCrossModal	Cross-modal signal integration
PhysioEMG	EMG signal processing
PhysioMoCap	Motion capture data handling
PhysioOpenSim	Native OpenSim model access
SummarizedExperiment	Bioconductor data containers

Theoretical Background

The musculoskeletal network model is based on:

Murphy AC, Muldoon SF, Baker D, Lastowka A, Bennett B, Yang M, Bhatt P, Bassett DS (2018). “Structure, function, and control of the human musculoskeletal network.” PLOS Biology, 16(1): e2002811. doi:10.1371/journal.pbio.2002811

The human musculoskeletal system is modeled as a hypergraph where bones are vertices and muscles are hyperedges connecting their attachment sites. This representation enables network-theoretic analysis of structural vulnerability, functional modularity, and injury impact propagation.

Ecosystem

PhysioMSKNet is part of the PhysioExperiment ecosystem, a suite of R packages for multi-modal physiological signal analysis.

Related packages:

Package	Role
PhysioExperiment	Core data model and signal processing
PhysioOpenSim	Native OpenSim C++ integration
PhysioAnnotationHub	Anatomical knowledge graph
PhysioMoCap	Motion capture I/O and analysis
PhysioEMG	EMG signal processing

Author

Yusuke Matsui

License

MIT

Governance & support

Part of the Physio ecosystem. Community and policy documents live in the umbrella repository:

PhysioMSKNet