From decision to action: Detailed modelling of frog tadpoles reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming movements in response to sensory signals

How does the brain process sensory stimuli, and decide whether to initiate locomotor behaviour? To investigate this question we develop two whole body computer models of a tadpole. The “Central Nervous System” (CNS) model uses evidence from whole-cell recording to define 2000 neurons in 12 classes to study how sensory signals from the skin initiate and stop swimming. In response to skin stimulation, it generates realistic sensory pathway spiking and shows how hindbrain sensory memory populations on each side can compete to initiate reticulospinal neuron firing and start swimming. The 3-D “Virtual Tadpole” (VT) biomechanical model with realistic muscle innervation, body flexion, body-water interaction, and movement is then used to evaluate if motor nerve outputs from the CNS model can produce swimming-like movements in a volume of “water”. We find that the whole tadpole VT model generates reliable and realistic swimming. Combining these two models opens new perspectives for experiments.

Neurophysiological Factors Affecting Muscle Innervation Zone Estimation Using Surface EMG: A Simulation Study

Surface electromyography (EMG) recorded by a linear or 2-dimensional electrode array can be used to estimate the location of muscle innervation zones (IZ). There are various neurophysiological factors that may influence surface EMG and thus potentially compromise muscle IZ estimation. The objective of this study was to evaluate how surface-EMG-based IZ estimation might be affected by different factors, including varying degrees of motor unit (MU) synchronization in the case of single or double IZs. The study was performed by implementing a model simulating surface EMG activity. Three different MU synchronization conditions were simulated, namely no synchronization, medium level synchronization, and complete synchronization analog to M wave. Surface EMG signals recorded by a 2-dimensional electrode array were simulated from a muscle with single and double IZs, respectively. For each situation, the IZ was estimated from surface EMG and compared with the one used in the model for performance evaluation. For the muscle with only one IZ, the estimated IZ location from surface EMG was consistent with the one used in the model for all the three MU synchronization conditions. For the muscle with double IZs, at least one IZ was appropriately estimated from interference surface EMG when there was no MU synchronization. However, the estimated IZ was different from either of the two IZ locations used in the model for the other two MU synchronization conditions. For muscles with a single IZ, MU synchronization has little effect on IZ estimation from electrode array surface EMG. However, caution is required for multiple IZ muscles since MU synchronization might lead to false IZ estimation.

Diabetic Foot

Diabetic foot syndrome is a complex complex of anatomical and functional changes that occur in 40-60% of patients with diabetes mellitus. It is believed that a high blood glucose content reduces its fluidity, impairs arterial and capillary blood circulation (angiopathy), leads to damage to the vessels and nerves of the lower extremities, and to a disorder of muscle innervation processes (neuropathy). At first, gangrene develops on one leg, which can be seen from the swelling and color difference of the skin of the legs, the appearance of a feeling of "foot in a trap", when its squeezing is felt, the temperature of the tissues rises

Whole animal modelling reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming in frog tadpoles

Animal behaviour is based on interaction between nervous, musculoskeletal and environmental systems. How does an animal process sensory stimuli, use it to decide whether and how to respond, and initiate the locomotor behaviour? We build the whole body computer models of a simple vertebrate with a complete chain of neural circuits and body units for sensory information processing, decision-making, generation of spiking activities, muscle innervation, body flexion, body-water interaction, and movement. Our Central Nervous System (CNS) model generates biologically-realistic spiking and reveals that sensory memory populations on two hindbrain sides compete for swimming initiation and first body flexion. Biomechanical 3-dimensional "Virtual Tadpole" (VT) model is constructed to evaluate if motor outputs of CNS model can produce swimming-like movements in a volume of "water". We find that whole animal modelling generates reliable and realistic swimming. The combination of CNS and VT models opens a new perspective for experiments with immobilised tadpoles.

Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods

This paper focuses on the statistical analysis of mimetic muscle rehabilitation after head and neck surgery causing facial paresis in patients after head and neck surgery. Our work deals with a classificationan evaluation problem of mimetic muscle rehabilitation that is observed by a Kinect stereo-vision camera. After a specific brain surgery, patients are often affected by face palsy, and rehabilitation to renew mimetic muscle innervation takes several months. It is important to be able to observe the rehabilitation process in an objective way. The most commonly used House–Brackmann (HB) scale is based on the clinician’s subjective opinion. This paper compares different methods of supervised learning classification that should be independent of the clinician’s opinion. We compare a parametric model (based on logistic regression), non-parametric model (based on random forests), and neural networks. The classification problem that we have studied combines a limited dataset (it contains only 122 measurements of 93 patients) of complex observations (each measurement consists of a collection of time curves) with an ordinal response variable (four HB grades are considered). To balance the frequencies of the considered classes in our data set, we reclassified the samples from HB4 to HB3 and HB5 to HB6—it means that only four HB grades are used for classification algorithm. The parametric statistical model was found to be the most suitable thanks to its stability, tractability, and reasonable performance in terms of both accuracy and precision.

Lumbrical Muscles Neural Branching Patterns: A Cadaveric Study With Potential Clinical Implications

Background Lumbrical muscles originate in the palm from the 4 tendons of the flexor digitorum profundus and course distally along the radial side of the corresponding metacarpophalangeal joints, in front of the deep transverse metacarpal ligament. The first and second lumbrical muscles are typically innervated by the median nerve, and third and fourth by the ulnar nerve. A plethora of lumbrical muscle variants has been described, ranging from muscles’ absence to reduction in their number or presence of accessory slips. The current cadaveric study highlights typical and variable neural supply of lumbrical muscles. Materials Eight (3 right and 5 left) fresh frozen cadaveric hands of 3 males and 5 females of unknown age were dissected. From the palmar wrist crease, the median and ulnar nerve followed distally to their terminal branches. The ulnar nerve deep branch was dissected and lumbrical muscle innervation patterns were noted. Results The frequency of typical innervations of lumbrical muscles is confirmed. The second lumbrical nerve had a double composition from both the median and ulnar nerves, in 12.5% of the hands. The thickest branch (1.38 mm) originated from the ulnar nerve and supplied the third lumbrical muscle, and the thinnest one (0.67 mm) from the ulnar nerve and supplied the fourth lumbrical muscle. In 54.5%, lumbrical nerve bifurcation was identified. Conclusion The complex innervation pattern and the peculiar anatomy of branching to different thirds of the muscle bellies are pointed out. These findings are important in dealing with complex and deep injuries in the palmar region, including transmetacarpal amputations.