Young Investigators

Alejandro Cámera (1), Mariano Belluscio, Daniel Tomsic
(1) University of Western Australia, Australia

Escape responses to danger stimuli have been studied across many groups of animals. One of these animals is the crab Neohelice granulata. On this model the MLG2 neuron has been shown to be involved in the running speed of the crab during the escape response execution. The neural response of the MLG2 to looming stimuli of various dynamics match the running speed of the crabs to those same stimuli. Via mathematical modeling a hypothesis was proposed that MLG2 performs the visuo-motor transformation that controls the animals’ escape velocity (1). However, this was achieved using a combination of behavioral experiments made with a tracking ball device and intracellular recordings obtained from immobile animals on an electrophysiology setup, i.e., the behavioral and neuronal responses were obtained from different animals. In the last few years, we began to perform extracellular recordings while the crab is running on a treadmill device to record both neural and behavioral data simultaneously. Supporting our initial hypothesis, we found that both the initial response of the MLG2 and its’ spontaneous firing rate are enough to anticipate the running speed and the time of escape of the animal. But we also show that after the stimuli stops moving the MLG2 activity is modulated by the animals running speed. This suggests that the MLG2 does not only rely on visual stimuli to control the animal’s velocity but can integrate proprioceptive information as well.

(1) Oliva & Tomsic, JEB (2016)

Castro-Pascual, Ivanna1; das Neves Oliveira, Angela2; van Helvoort Lengert, Andre2; Cargnelutti,Ethelina1; Lacoste, María Gabriela1; Ferramola, Mariana1; Delgado, Silvia Marcela1; Melendez,Matías2 Anzulovich, Ana1.

1Laboratory of Chronobiology, IMIBIO-SL, CONICET-UNSL. Faculty of Chemistry, Biochemistry and Pharmacy, National University of San Luis (UNSL), San Luis, Argentina.
2Centro de Pesquisa em Oncologia Molecular, Hospital de Câncer de Barretos, São Paulo, Brasil.

Disruption of circadian rhythms and alterations in the DNA repair systems, constitute part of the biological and molecular basis of motor and cognitive aging (Lacoste et al., 2017; Langie et al., 2017). Azis Sancar et al. (2015) reported that the DNA nucleotide excision repair is regulated by the cellular
clock. Given cerebellum is very susceptible to oxidative stress and DNA damage, we analyzed the aging consequences on the circadian regulation of the DNA base excision repair (BER) system and the daily profiles of BER-related epigenetic factors, in the cerebellum. We also evaluated the effect of caloric restriction (CR) and investigated the mechanism of Ogg1 and Ape1 circadian regulation. Three- and 22-mo-old rats treated or not with a 40% CR diet and maintained under constant darkness, were used in this study. We observed that BMAL1 protein, as well as Sirt1 and Dnmt1 expression display circadian rhythms(p<0.05) in the cerebellum. Of note, Ogg1 and Ape1 expression is arrhythmic in this tissue. Aging disrupts clock and epigenetic factors circadian rhythms and makes rhythmic the BER enzymes expression. CR partially restored the temporal patterns. Transient transfection experiments showed that Ogg1 and Ape1 expression is regulated by the BMAL1:CLOCK transcription factor. We expect our results contribute to the understanding of the circadian regulation of the DNA BER system and how nonpharmacological strategies could improve aging- related circadian decline in the cerebellum.


The environment is complex and continuously changing whereby brains need to be able to adapt and quickly shift between resting, working or arousal states in order to allow adaptative behaviors. These global state shifts are intimately linked to the brain-wide release of the neuromodulators. Although the neurons that release neuromodulators generally have projections throughout the whole brain, there are only studies showing neuromodulators effects in specific functions and/or specific brain areas and still remains unclear what is the effect in the whole brain dynamics and how neuromodulators affect the information flow and computations in the entire brain.

In order to disentangle the specific circuit involved in the brain dynamic changes associated with noradrenaline release We used zebrafish larva as experimental model in combination with light-sheet microscopy. Whole-brain dynamics with single-neuron resolution was monitored while simultaneously recording free tail movement as a behavioral output. In addition optogenetic manipulation and cell type identification were performed.

Results show a noradrenergic neurons mediated switch in the brain state when animals perform a strong scape behavior. The switch is characterized by the shutting down of a vast majority of active neurons at the same time that the inactive neurons start up. The activation of neurons located in the Locus Coeruleus (LC) seem to trigger the switch. In addition we found that, when the switch is spontaneous, before the LC activation there is a ramping activity in a neuronal subpopulation located in another noradrinergic area, the NE-MO. I will present a characterization of the brain dynamic before and after the shift (by using spontaneous, stimuli and optogenetically triggered events) together with a components, role and dynamics description of the noradrenergic neuronal circuit involved in the brain state and behavior switch..

Subirada, PV1; Tovo, A2; Vaglienti, MV2; Vicentin D3; Ribotta NA3; Luna Pinto, JD3; Sánchez, MC2; Anastasía, A1; Barcelona, PF2.

1Instituto Ferreyra, INIMEC-CONICET-UNC.
2Departamento de Bioquímica Clínica, CIBICI-CONICET, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba.
3Centro Privado de Ojos Romagosa. Fundación VER. Córdoba, Argentina.

Purpose: Age related macular degeneration (AMD) is one of the leading causes of blindness in adults over 60 years. In wet AMD, abnormal vascular tufts (termed choroidal neovascularization, CNV) invade the retina inducing photoreceptor degeneration. The p75 neurotrophin receptor (p75NTR) is involved in the transduction of neuronal death signals and it also participates in vascular changes. Here, we aim to determine the p75NTR role during retinal neurodegeneration in a mouse model of CNV.

Methods: 2-months old WT and p75NTR KO mice were injured in the retina using a photocoagulation laser. Mice with sham procedure were used as controls. 7 days after laser, mice were sacrificed. Retinas and retinal pigmented epithelium (RPE)-Choroid were processed separately.

Results: Western blot of neural retinas showed increased expression of p75NTR after the laser in CNV mice respect to control. Confocal images evidenced expression of p75NTR in Muller glial cells but not in pericytes, neurons nor in choroidal vessels. p75NTR KO mice with CNV showed decreased GFAP protein levels, reduced number of pycnotic nuclei, decreased TNFα mRNA, lower percentage of mononuclear phagocytic cells (MPCs) infiltrated and partial preservation of the retinal functionality, respect to WT CNV mice. The neovascular area was reduced in p75NTR KO CNV mice, although no changes in VEGF levels were detected.

Conclusions: Our results suggest that p75NTR is involved in retinal and vascular alterations in the CNV mice.

WAGNER Paula M1,2, GUIDO Mario E1,2

1CIQUIBIC-CONICET, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Córdoba 5000, Argentina
2Departamento de Química Biológica Ranwel Caputto, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Córdoba 5000, Argentina.

The circadian timekeeping regulates diverse cellular processes in organs, tissues, and even in individual cells, including tumor cells. Nowadays, it is known that the cellular clock is composed of the transcripcional machine and the metabolic/cytosolic oscillator which work together to maintain the cellular homeostasis. However, habits of the modern life have severely altered the cellular temporal organization and can cause an increased risk of cancer. In particular, glioblastoma (GBM) is the most common and aggressive type of brain tumor of the central nervous system. Due to its great resistance to conventional therapies, it is necessary new chemotherapeutic approaches considering the impact of the circadian clock on tumor biology (review in Wagner et al 2021). Previous results evidenced a strong interaction between the metabolic oscillator and the transcriptional machinery in T98G cells (Wagner et al 2018). Here, we investigate how the metabolic/cytosolic oscillator modulation can be used as a novel strategy for GBM treatment using a selective pharmacological inhibitor of glycogen synthase kinase 3β (CHIR99021). The results showed a cytotoxic effect, a delayed wound closure, alterations on lipid droplets oscillations and redox state in CHIR-treated cells compared to control cells. Understanding and delving into tumor regulation from a chronobiological viewpoint will further help to design new treatments that maximize therapeutic benefits.

– Wagner, P. M., Sosa Alderete, L. G., Gorné, L. D., Gaveglio, V., Salvador, G., Pasquaré, S., and Guido, M. E.
(2018) Proliferative Glioblastoma Cancer Cells Exhibit Persisting Temporal Control of Metabolism and Display Differential
Temporal Drug Susceptibility in Chemotherapy. Mol. Neurobiol.
– Wagner, P. M., Prucca, C. G., Caputto, B. L., and Guido, M. E. (2021) Adjusting the Molecular Clock: The
Importance of Circadian Rhythms in the Development of Glioblastomas and Its Intervention as a Therapeutic Strategy. Int.
J. Mol. Sci. 2021, Vol. 22, Page 8289 22, 8289

Dr. Rodrigo Echeveste

Abstract: Employing tools from machine learning for modeling in computational neuroscience is an area of great expansion in recent years, and has become mutually beneficial for both fields. One of the central ideas behind this approach is that by optimizing artificial neural networks under biological constraints for tasks which are relevant for the brain, it is possible to find models that imitate different aspects of visual cortical processing. Deep convolutional neural networks (DNNs) were originally inspired by visual sensory processing, and are currently the best predictors of mean responses in multiple areas of the cortex. However, these models are not designed to faithfully represent the uncertainty of their predictions, which is central in the context of perception, where the information we receive from our senses is always noisy and incomplete. Moreover, these models lack dynamics, and do not capture the huge variability in cortical responses both over time and trials.

In this talk I’ll first give a short overview of the state of the art in ML methods based on DNNs as models of cortical visual processing, to then focus on models which incorporate recurrent connections to go beyond mean responses and capture stereotypical features of cortical dynamics, such as transients and oscillations. Finally I’ll show an example of how these types of models can be used to bridge current knowledge between physiology and perception in disorders such as autism.

Jose A. Fernandez-Leon1,2,3,*, Ahmet K. Uysal1, Daoyun Ji1

1 Dept. of Neuroscience, Baylor College of Medicine, Houston, Texas, United States;
2 CIFICEN (CONICET-CICPBA-UNCPBA) and INTIA (UNCPBA-CICPBA), Exact Sciences Faculty-UNCPBA, Tandil, Argentina;
3 National Scientific and Technical Research Council (CONICET), Argentina
* Corresponding author/Editorial correspondence: Jose A. Fernandez-Leon Fellenz (;

Keywords: place cells; grid cells; continuous attractor model; path integration; dynamics

Abstract: Grid cells (GCs) in the medial entorhinal cortex (MEC) use speed and direction to map the environment during spatial navigation. Hippocampal place cells (PCs) encode place and seem to minimize the accumulated error of GCs for path integration. However, the dynamic relationship between both cell types and the involved mechanism for error minimization is yet to be understood. Recent theoretical studies have also suggested the possibility of a network of
loops between the Hippocampus and MEC. The dynamical coupling between these cell types could coordinate the integration of velocity input to the GCs network and update the network’s estimated position using PC network signals. A realistic toroidal topology model of GCs was implemented based on path integration to address this issue. Place cell-like neurons were modeled by defining their PFs through visual flow detection and proximity information during the animal’s exploration of a squared arena. PFs appeared mostly during early exploration, helping to decrease the path integration error of GCs. Relatively slow-emergent PCs enabled anchoring signals for a precise GCs path integration. Consistent with experimental observations that place cells can retrieve spatial information from grid-like cells to create a more accurate spatial representation, the dynamic coupling between PCs and GCs may be one of the key components of the brain’s navigational system.


López Steinmetz, Lorena Cecilia a,b*

a Instituto de Investigaciones Psicológicas, Universidad Nacional de Córdoba – Consejo Nacional de Investigaciones Científicas y Técnicas (IIPsi-UNC-CONICET), Córdoba, Argentina
b Universidad Siglo 21, Córdoba, Argentina

This study aimed to develop and compare machine learning (ML) classifiers to predict depression in Argentinean college students during COVID-19 quarantine. The target variable was binarized. Psychological inventory scores, clinical information, quarantine sub- periods, and demographics were the input features. The data was randomly split into training and test sets. Quantile transformation and principal component analysis were used.
Longitudinal data (N=1492), previously analyzed using mixed effects modeling, was re- examined with ML algorithms (logistic regression, random forest, and support vector machine (SVM)). Uniform random, most frequent, and stratified random dummy models were used for baseline comparisons. Performance was assessed using area under the precision-recall curve, area under the receiver operating characteristic curve, balanced accuracy, precision, recall, F1 score, Brier score, and average Hamming loss. The classifiers outperformed baseline models. SVM and logistic regression performing similarly and better than random forest. Permutation feature importance analysis revealed that depression and anxiety scores at T1 were the most influential predictors. Surprisingly, traditional depression-related features (e.g., suicide behavior history) had little impact on model performance. Traditional statistical methods in psychology, such as mixed effects modeling, prioritize inference over prediction, while ML focus on prediction, potentially causing

KEYWORDS: machine learning; classifiers; depression; college students; COVID-19 pandemic

Macchione, AF 1-2
1Instituto de Investigaciones Psicológicas, IIPsi-CONICET-UNC
2Facultad de Psicología, UNC

Maternal ethanol (EtOH) intake during pregnancy and lactation is a highly frequent “social” behavior in Argentine, exposing fetus or neonates to moderate EtOH intoxication through the amniotic fluid and placenta. Early EtOH exposure triggers a spectrum of neurobehavioral dysfunctions affecting, also, the breathing response. In an animal model equivalent to the 3 rd trimester of the human gestation we explore the early ethanol exposure effects on the ventilatory responses in normoxic and hypoxic-air conditions. We also study central areas involved in breathing modulation as the solitary tract nucleus and the medullary raphe system. Our results show that a brief and early ethanol exposure alters both basal and hypoxia-induced breathing frequencies and apneas through modifications in the activation patterns of central areas of study. Actually, early ethanol exposure induces a basal breathing depression in normoxic conditions but, against a hypoxic challenge, ethanol triggers two consecutive altered events: first a lower hyperventilation rate during the hypoxic event itself and then, during the post-hypoxic period, ethanol elicit the emergency of an adaptive phenomenon, the ventilatory long-term facilitation. Alterations in the activation patterns in the NTS and raphe obscurus, and an increase in the 5HT levels in the medullary raphe nuclei (magnus, obscurus y pallidus) were observed as a function of different ways of early ethanol exposure.

Funding by MINCyT-Cba; FONCyT and SECyT-UNC.

Victoria Peterson – Instituto de Matemática Aplicada del Litoral, IMAL, FIQ-UNL, CONICET, Santa Fe, Argentina

Brain-computer interfaces (BCIs) can be thought of as co-adaptive systems, in which the user learns to control the computer while the computer learns to decode the user’s brain activity. When used across multiple days, such as in motor imagery (MI) BCIs for rehabilitation, the recorded brain activity exhibits high variability. To enable adaptive and supportive machine learning systems for decoding the brain activity, we proposed an adaptation algorithm named BOTDA [1] that can avoid recalibration of the whole system across BCI sessions. Although the method showed promising results in offline experiments with real MI-BCI datasets, it remains to be determined whether its success depends on the subject’s ability to perform the MI task or on the model’s adaptive capabilities. Thus, we hypothesize that the adaptation based on BOTDA is successful only when: H1) the patterns provided by the user correspond to the mental task to be performed and H2) the calibration data used to train the decoding model is discriminative enough from the decoding system’s viewpoint. Results from realistic simulations suggest that BOTDA could be a valuable tool for developing co-adaptive MI-BCI systems.

[1] Peterson, Victoria, et al. “Transfer learning based on optimal transport for motor imagery brain-computer interfaces.” IEEE Transactions on
Biomedical Engineering 69.2 (2021): 807-817.