Carina Marconi Germer

@article{Germer2025,

title = {NeuroNella: Automatic identification of neural activity from multielectrode arrays with blind source separation},

author = {Carina Marconi Germer and Dario Farina and Stuart N Baker and Alessandro Del Vecchio},

doi = {10.1088/1741-2552/adc5a4},

issn = {1741-2552},

year  = {2025},

date = {2025-03-26},

journal = {J. Neural Eng.},

publisher = {IOP Publishing},

abstract = {Abstract

               Objective: The identification of individual neuronal activity from multielectrode arrays poses significant challenges, including handling data from numerous electrodes, resolving overlapping action potentials and tracking activity across long recordings. This study introduces NeuroNella, an automated algorithm developed to address these challenges. Approach: NeuroNella employs blind source separation to leverage the sparsity of action potentials in multichannel recordings. It was validated using three datasets, including two publicly available ones: (1) in vitro recordings (252 channels) of retinal ganglion cells from mice with simultaneous ground-truth loose patch data to assess accuracy; (2) a Neuropixel recording from an awake mouse, comprising 374 channels spanning different brain areas, to demonstrate scalability with dense multielectrode configurations in in vivo recordings; and (3) data (32 channels) recorded from the medullary reticular formation in a terminally anaesthetised macaque, to showcase decomposition over long periods of time. Main Results: The algorithm exhibited an error rate of less than 1% compared to ground-truth data. It reliably identified individual neurons, detected neuronal activity across a wide amplitude range, and tolerated minor probe shifts, maintaining robustness in prolonged experimental sessions. Significance: NeuroNella provides an automated and efficient method for neuronal activity identification. Its adaptability to diverse dataset, species, and recording configurations underscores its potential to advance studies of neuronal dynamics and facilitate real-time neuronal decoding systems.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Nuccio2024,

title = {Neuroplastic alterations in common synaptic inputs and synergistic motor unit clusters controlling the vastii muscles of individuals with ACL reconstruction},

author = {Stefano Nuccio and Carina M Germer and Andrea Casolo and Riccardo Borzuola and Luciana Labanca and Jacopo E. Rocchi and Pier Paolo Mariani and Francesco Felici and Dario Farina and Deborah Falla and Andrea Macaluso and Paola Sbriccoli and Alessandro Del Vecchio},

doi = {10.1152/japplphysiol.00056.2024},

issn = {1522-1601},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-01},

journal = {Journal of Applied Physiology},

volume = {137},

number = {4},

pages = {835--847},

publisher = {American Physiological Society},

abstract = {<jats:p> Chronic quadriceps dysfunction is common after anterior cruciate ligament reconstruction (ACLR). We investigated voluntary force control strategies by estimating common inputs to motor neurons innervating the vastii muscles. Our results showed attenuated common inputs to the vastus lateralis and plastic rearrangements in functional clusters of motor neurons modulating knee extension forces in the reconstructed limb. These findings suggest neuroplastic adjustments following ACLR that may occur to fine-tune the control of quadriceps forces. </jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid36922028,

title = {The Forces Generated by Agonist Muscles during Isometric Contractions Arise from Motor Unit Synergies},

author = {Alessandro Del Vecchio and Carina Marconi Germer and Thomas M Kinfe and Stefano Nuccio and François Hug and Bjoern Eskofier and Dario Farina and Roger M Enoka},

doi = {10.1523/JNEUROSCI.1265-22.2023},

issn = {1529-2401},

year  = {2023},

date = {2023-04-01},

journal = {J Neurosci},

volume = {43},

number = {16},

pages = {2860--2873},

abstract = {The purpose of our study was to identify the low-dimensional latent components, defined hereafter as motor unit modes, underlying the discharge rates of the motor units in two knee extensors (vastus medialis and lateralis, eight men) and two hand muscles (first dorsal interossei and thenars, seven men and one woman) during submaximal isometric contractions. Factor analysis identified two independent motor unit modes that captured most of the covariance of the motor unit discharge rates. We found divergent distributions of the motor unit modes for the hand and vastii muscles. On average, 75% of the motor units for the thenar muscles and first dorsal interosseus were strongly correlated with the module for the muscle in which they resided. In contrast, we found a continuous distribution of motor unit modes spanning the two vastii muscle modules. The proportion of the muscle-specific motor unit modes was 60% for vastus medialis and 45% for vastus lateralis. The other motor units were either correlated with both muscle modules (shared inputs) or belonged to the module for the other muscle (15% for vastus lateralis). Moreover, coherence of the discharge rates between motor unit pools was explained by the presence of shared synaptic inputs. In simulations with 480 integrate-and-fire neurons, we demonstrate that factor analysis identifies the motor unit modes with high levels of accuracy. Our results indicate that correlated discharge rates of motor units that comprise motor unit modes arise from at least two independent sources of common input among the motor neurons innervating synergistic muscles. It has been suggested that the nervous system controls synergistic muscles by projecting common synaptic inputs to the engaged motor neurons. In our study, we reduced the dimensionality of the output produced by pools of synergistic motor neurons innervating the hand and thigh muscles during isometric contractions. We found two neural modules, each representing a different common input, that were each specific for one of the muscles. In the vastii muscles, we found a continuous distribution of motor unit modes spanning the two synergistic muscles. Some of the motor units from the homonymous vastii muscle were controlled by the dominant neural module of the other synergistic muscle. In contrast, we found two distinct neural modules for the hand muscles.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34802707,

title = {Influence of central and peripheral motor unit properties on isometric muscle force entropy: A computer simulation study},

author = {Jakob Dideriksen and Leonardo Abdala Elias and Ellen Pereira Zambalde and Carina Marconi Germer and Ricardo Gonçalves Molinari and Francesco Negro},

doi = {10.1016/j.jbiomech.2021.110866},

issn = {1873-2380},

year  = {2022},

date = {2022-06-01},

journal = {J Biomech},

volume = {139},

pages = {110866},

abstract = {Approximate entropy of isometric force is a popular measure to characterize behavioral changes across muscle contraction conditions. The degree to which force entropy characterizes the randomness of the motor control strategy, however, is not known. In this study, we used a computational model to investigate the correlation between approximate entropy of the synaptic input to a motor neuron pool, the neural drive to muscle (cumulative spike train; CST), and the force. This comparison was made across several simulation conditions, that included different synaptic command signal bandwidths, motor neuron pool sizes, and muscle contractile properties. The results indicated that although force entropy to some degree reflects the entropy of the synaptic command to motor neurons, it is biased by changes in motor unit properties. As a consequence, there was a low correlation between approximate entropy of force and the motor neuron input signal across all simulation conditions (r = 0.13). Therefore, force entropy should only be used to compare motor control strategies across conditions where motor neuron properties can be assumed to be maintained. Instead, we recommend that the entropy of the descending motor commands should be estimated from CSTs comprising spike trains of multiple motor units.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid34236246,

title = {Surface EMG cross talk quantified at the motor unit population level for muscles of the hand, thigh, and calf},

author = {Carina M Germer and Dario Farina and Leonardo A Elias and Stefano Nuccio and François Hug and Alessandro Del Vecchio},

doi = {10.1152/japplphysiol.01041.2020},

issn = {1522-1601},

year  = {2021},

date = {2021-08-01},

urldate = {2021-08-01},

journal = {J Appl Physiol (1985)},

volume = {131},

number = {2},

pages = {808--820},

abstract = {Cross talk is an important source of error in interpreting surface electromyography (EMG) signals. Here, we aimed at characterizing cross talk for three groups of synergistic muscles by the identification of individual motor unit action potentials. Moreover, we explored whether spatial filtering (single and double differential) of the EMG signals influences the level of cross talk. Three experiments were conducted. Participants (total 25) performed isometric contractions at 10% of the maximal voluntary contraction (MVC) with digit muscles and knee extensors and at 30% MVC with plantar flexors. High-density surface EMG signals were recorded and decomposed into motor unit spike trains. For each muscle, we quantified the cross talk induced to neighboring muscles and the level of contamination by the nearby muscle activity. We also estimated the influence of cross talk on the EMG power spectrum and intermuscular correlation. Most motor units (80%) generated significant cross-talk signals to neighboring muscle EMG in monopolar recording mode, but this proportion decreased with spatial filtering (50% and 42% for single and double differential, respectively). Cross talk induced overestimations of intermuscular correlation and has a small effect on the EMG power spectrum, which indicates that cross talk is not reduced with high-pass temporal filtering. Conversely, spatial filtering reduced the cross-talk magnitude and the overestimations of intermuscular correlation, confirming to be an effective and simple technique to reduce cross talk. This paper presents a new method for the identification and quantification of cross talk at the motor unit level and clarifies the influence of cross talk on EMG interpretation for muscles with different anatomy. We proposed a new method for the identification and quantification of cross talk at the motor unit level. We show that surface EMG cross talk can lead to physiological misinterpretations of EMG signals such as overestimations in the muscle activity and intermuscular correlation. Cross talk had little influence on the EMG power spectrum, which indicates that conventional temporal filtering cannot minimize cross talk. Spatial filter (single and double differential) effectively reduces but not abolish cross talk.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Germer2020,

title = {Assessment of force control improvement induced by sinusoidal vibrotactile stimulation in dominant and non-dominant hands},

author = {Carina Marconi Germer and Luciana Sobral Moreira and Leonardo Abdala Elias},

doi = {10.1007/s42600-020-00111-6},

issn = {2446-4740},

year  = {2021},

date = {2021-03-00},

journal = {Res. Biomed. Eng.},

volume = {37},

number = {1},

pages = {95--103},

publisher = {Springer Science and Business Media LLC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}