Participants: Sebastián Villafañe, Oscar Filevich, Daniela Andres
Plug & play software developed at our lab to classify spikes and isolate single cell activity from multi-neuronal microelectrode recordings. We use threshold detection, wavelet transformations and a genetic algorithm for the classification of spikes.
Participants: Daniela Andres, Oscar Filevich, Sebastián Villafañe.
Objective: To understand and characterize information coding by the nervous system in a quantitative and formal manner. To model neuronal activity and information transmission in nervous tissue in the spatio-temporal domain. To use this knowledge in the development of applications and technology that offers new solutions in neurology and neurophysiology.
• D. S. Andres On the motion of spikes: turbulent-like neuronal activity in the human basal ganglia. Frontiers in Human Neuroscience doi: 10.3389/fnhum.2018.00429, 2018.
Computer simulations. Left column: Time evolution of the velocity of spikes, u(x,t), as the diffusion coefficient δ increases (from top to bottom), with time on the vertical axis and space on the horizontal axis. White areas represent the parts of the integration domain where the module of the velocity of spikes is below an arbitrary limit (108), in opposition to black areas, where it is higher than this limit. As the diffusion coefficient increases, white areas are enlarged, as the total velocity diminishes across the integration domain. Middle column: Sample temporal multifractal spectra ζτ(q) obtained from temporal structure functions of increasing order, at fixed spatial points. Non-linearity indicates temporal multifractality. Right column: Sample spatial multifractal spectra ζx(q) obtained from spatial structure functions of increasing order, at fixed times. Non-linearity indicates spatial multifractality.
• D.S. Andres, O. Darbin. Complex dynamics in the basal ganglia: health and disease beyond the motor system. The Journal of Neuropsychiatry and Clinical Neurosciences doi: 10.1176/appi.neuropsych.17020039, 2018.
• F. Nanni, D.S. Andres. Structure function revisited: a simple tool for complex analysis of neuronal activity. Frontiers in Human Neuroscience doi: 10.3389/fnhum.2017.00409, 2017
Transformation from a raw neuronal recording into a temporal structure function. (Upper) Sample raw extracellular microelectrode recording of neuronal activity. This recording was obtained from the entopeduncular nucleus of a healthy rat (technical details can be found in Andres et al., 2014a). The vertical axis indicates electric potential (mV) and the horizontal axis indicates time (s). The inlet at the lower right shows the whole recording, from which a zoom is shown in the bigger window. Individual spikes are marked with a red arrow. Once spikes are classified as belonging to a single neuron's activity, interspike intervals (ISI) are calculated as shown (ISI = time elapsed between the occurrence of a spike and the next). (Middle) Sample time series of interspike intervals, obtained from a neuronal recording like the one shown in the upper panel. The vertical axis indicates ISI duration (ms) and the horizontal axis indicates ISI number (position in the time series). Notice the high variability of the ISI, typical of complex systems. (Lower) Temporal structure function obtained from a time series of ISI like the one shown in the middle panel. The vertical axis is the value of the function S(τ) and the horizontal axis is the scale τ. In pallidal neurons it is common to observe a positive slope of the function at lower scales, followed by a breakpoint and a plateau at higher scales, also typical of complex systems. The double logarithmic scale helps visualization of smaller τ.
• D.S. Andres, D.F. Cerquetti, M. Merello. Neural code alterations and abnormal time patterns in Parkinson's disease. Journal of Neural Engineering 12:026004 (9pp), 2015.
• DS. Andres, F. Gomez, F.S. Ferrari, D.F. Cerquetti, M. Merello, R. Viana, R. Stoop. Multiple-time-scale framework for understanding the progression of Parkinson's disease. Physical Review E 90:062709, 2014
• D.S. Andres, D.F. Cerquetti, M. Merello, R. Stoop. Neuronal entropy depends on the level of alertness in the parkinsonian Globus Pallidus in vivo. Frontiers in Neurology, 5, 96:1-9, 2014
• D.S. Andres, D.F. Cerquetti, M. Merello. Finite dimensional structure of the GPi discharge in patients with Parkinson’s disease. International Journal of Neural Systems 21(3): 175-186, 2011
• D.S. Andres, D.F. Cerquetti, M. Merello. Turbulence in Globus pallidum neurons in patients with Parkinson's disease: Exponential decay of the power spectrum. Journal of Neuroscience Methods 197(1): 14-20, 2011
Participants: Gianfranco Bianchi, Camila Reinaldo, Daniela Andrés
Explanation: Parkinson’s disease is a neurodegenerative disorder with complex symptoms, which makes diagnosis extremely difficult.
Objective: To create technology that brings medical solutions to people who need them.
Advances: This project consists of the creation of an integral system for quantitative diagnosis and follow-up of patients with movement disorders, like Parkinson’s disease, ataxia and Huntington’s chorea. We measure movement with wearable devices and process information with mobile applications, to gain objective measures of movement. This helps non-specialized medical centers to evaluate the motor state of patients, transmitting the results to be evaluated by experts in the field.
Presentations: FENS 2018, Berlin “Quantitative diagnosis of Parkinson´s disease based on scale invariance of acceleration signals”
Participants: Mariano Paladino, Oscar Filevich, Daniela Andres.
Objective: To develop a surgical tool to recanalize the biliary tract in pediatric patients with hepatic transplant when this tract suffers fibrosis.
Advances: Currently there are studies going on about the technique employed to recanalize the biliary tract in patients with hepatic transplant. In a collaborative work with the team of surgical interventionism of Garrahan Hospital, directed by Dr. Matías Garriga, we are developing a new tool that combines electronics and AI techniques to help solving this problem. Considering the growing number of hepatic transplantations worldwide and the elevated number of patients that suffer under the complication of biliary tract fibrosis, this device will be of great help for professionals.
Participants: Daniela Andres, Gustavo Vinci
Objective: To get quantitative measures of the echocardiogram, sound recordings of heart activity, ECG or other diagnostic devices that allow to better diagnose and treat cardiac disease.
Advances: Myocardial ischemia is a pathology caused by lack of oxygen in the cardiac tissue, being myocardial infarction the most severe form of this pathology. Using the public database Physionet we revised and classified ECG to obtain isolated cardiac cycles representative of healthy and ischemic cases. We applied a custom developed algorithm to detect and isolate cardiac cycles, and then trained a neural network to detect ischemia based on a single cardiac cycle. We obtained a precision of 86%. This kind of analysis can be applied to early detection of ischemia, in stages when myocardial infarction can be prevented or treated without complications.
Participants: Daniela Andres, Gianfranco Bianchi, Sebastián Villafañe, Mariano Paladino
Start-Up originated at LabNIng, dedicated to the development of medical technology.