Dataset Information

In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function.

ABSTRACT: Human motor function emerges from the interaction between the neuromuscular and the musculoskeletal systems. Despite the knowledge of the mechanisms underlying neural and mechanical functions, there is no relevant understanding of the neuro-mechanical interplay in the neuro-musculo-skeletal system. This currently represents the major challenge to the understanding of human movement. We address this challenge by proposing a paradigm for investigating spinal motor neuron contribution to skeletal joint mechanical function in the intact human in vivo. We employ multi-muscle spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate ?-motor neuron discharges across five lumbosacral segments in the human spinal cord. We use complete ?-motor neuron discharge series to drive forward subject-specific models of the musculoskeletal system in open-loop with no corrective feedback. We perform validation tests where mechanical moments are estimated with no knowledge of reference data over unseen conditions. This enables accurate blinded estimation of ankle function purely from motor neuron information. Remarkably, this enables observing causal associations between spinal motor neuron activity and joint moment control. We provide a new class of neural data-driven musculoskeletal modeling formulations for bridging between movement neural and mechanical levels in vivo with implications for understanding motor physiology, pathology, and recovery.

SUBMITTER: Sartori M

PROVIDER: S-EPMC5647446 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function.

Sartori Massimo M Yavuz Utku Ş UŞ Farina Dario D

Scientific reports 20171018 1

Human motor function emerges from the interaction between the neuromuscular and the musculoskeletal systems. Despite the knowledge of the mechanisms underlying neural and mechanical functions, there is no relevant understanding of the neuro-mechanical interplay in the neuro-musculo-skeletal system. This currently represents the major challenge to the understanding of human movement. We address this challenge by proposing a paradigm for investigating spinal motor neuron contribution to skeletal j ...[more]

PMID: 29044165

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Project description:Clinical research based on neuroimaging data has benefited from machine learning methods, which have the ability to provide individualized predictions and to account for the interaction among units of information in the brain. Application of machine learning in structural imaging to investigate diseases that involve brain injury presents an additional challenge, especially in conditions like stroke, due to the high variability across patients regarding characteristics of the lesions. Extracting data from anatomical images in a way that translates brain damage information into features to be used as input to learning algorithms is still an open question. One of the most common approaches to capture regional information from brain injury is to obtain the lesion load per region (i.e. the proportion of voxels in anatomical structures that are considered to be damaged). However, no systematic evaluation has yet been performed to compare this approach with using patterns of voxels (i.e. considering each voxel as a single feature). In this paper we compared both approaches applying Gaussian Process Regression to decode motor scores in 50 chronic stroke patients based solely on data derived from structural MRI. For both approaches we compared different ways to delimit anatomical areas: regions of interest from an anatomical atlas, the corticospinal tract, a mask obtained from fMRI analysis with a motor task in healthy controls and regions selected using lesion-symptom mapping. Our analysis showed that extracting features through patterns of voxels that represent lesion probability produced better results than quantifying the lesion load per region. In particular, from the different ways to delimit anatomical areas compared, the best performance was obtained with a combination of a range of cortical and subcortical motor areas as well as the corticospinal tract. These results will inform the appropriate methodology for predicting long term motor outcomes from early post-stroke structural brain imaging.

Project description:The motor neuron (MN)–hexamer complex consisting of LIM homeobox 3, Islet-1, and nuclear LIM interactor is a key determinant of motor neuron specification and differentiation. To gain insights into the transcriptional network in motor neuron development, we performed a genome-wide ChIP-sequencing analysis and found that the MN–hexamer directly regulates a wide array of motor neuron genes by binding to the HxRE (hexamer response element) shared among the target genes. Interestingly, STAT3-binding motif is highly enriched in the MN–hexamer–bound peaks in addition to the HxRE. We also found that a transcriptionally active form of STAT3 is expressed in embryonic motor neurons and that STAT3 associates with the MN–hexamer, enhancing the transcriptional activity of the MN–hexamer in an upstream signal-dependent manner. Correspondingly, STAT3 was needed for motor neuron differentiation in the developing spinal cord. Together, our studies uncover crucial gene regulatory mechanisms that couple MN–hexamer and STAT-activating extracellular signals to promote motor neuron differentiation in vertebrate spinal cord. To explain our experimental scheme briefly, we are interested in finding target sites for the dimer of transcription factors Isl1 and Lhx3. To mimic the biological activity of Isl1/Lhx3 dimer, we made Isl1-Lhx3 fusion and found that Isl1-Lhx3 has a potent biological activity in multiple systems (i.e. generation of ectopic motor neurons). Then we made ES cell line that induces Flag-tagged Isl1-Lhx3 expression upon Dox treatment. These *mouse* ES cells differentiate to motor neurons (iMN-ESCs) when treated with Dox following EB formation. To identify genomic binding sites of Isl1-Lhx3 (Flag-tagged), we performed ChIP with Flag antibody (pull down of Flag-Isl1-Lhx3) in ES cells treated with Dox. ChIP with Flag antibody in ES cells treated with vehicle (no Dox) was done as a negative control in parallel, and sequenced along with +Dox sample. We have done these experiments twice (two sets).

Dataset Information

In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function.

Publications

In Vivo Neuromechanics: Decoding Causal Motor Neuron Behavior with Resulting Musculoskeletal Function.

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