Biology subjects

Rigotti, M.

Publications and source records attributed to Rigotti, M..

4 recordsLinked to original sources

bioRxiv2024

Effects of transcranial photobiomodulation on peripheral biomarkers associated with oxidative stress and complex IV activity in the prefrontal cortex in rats subjected to chronic mild stress

BackgroundThis study addresses the urgent need for effective alternatives to treat major depressive disorder (MDD) in patients who do not respond to conventional therapies Transcranial photobiomodulation therapy (tPBM) shows promise by enhancing mitochondrial function and reducing oxidative stress, as demonstrated in the chronic mild stress (CMS) rat model. To analyze the impact of tPBM with two wavelengths (red and infrared) on behavioral and biological parameters related to MDD in a CMS model. MethodsMale Wistar rats were subjected to CMS for five weeks and categorized into resilient (CMS-R) and susceptible (CMS-S) groups using the sucrose consumption test (SCT). The CMS-S group received tPBM treatment (600nm and 840nm) for five weeks. Biological measures included lipid damage (TBARS), antioxidant defense (TEAC), mitochondrial complex IV activity (CCO), and nitric oxide (NO) concentration in the prefrontal cortex and blood were measured. ResultsAs expected, post-tPBM treatment (both red and infrared groups) exhibited increased sucrose consumption compared to the sham (Kruskal Wallis chi-squared=26.131; p<0.001). The red and infrared presented higher serum TEAC levels than the sham and control groups, but these effects did not reach statistical significance (p=0.306). In contrast, the red group showed lower peripheral TBARS levels (M = 9.50, SD = 2.87) than the sham group (M = 13.66, SD = 2.20) (p=0.0048); such effect was similar to the control non-stress group. The infrared group showed higher NO levels within the hippocampus than the sham group (Mean = 107.83; SD = 6.48, Dunn Test p = 0.0134) and higher prefrontal CCO activity levels than the red group (p=0.012), which was similar to the control non-stress group. ConclusionsOur study demonstrated that tPBM using both red and infrared wavelengths significantly improved behavioral and biological parameters in the chronic mild stress (CMS) rat model. In particular, tPBM may offer therapeutic benefits by ameliorating oxidative stress and enhancing mitochondrial function, thereby presenting a promising alternative for the management of MDD.

neuroscience↗

bioRxiv2018

Signatures and mechanisms of low-dimensional neural predictive manifolds

Artificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the tasks low-dimensional latent structure in the network activity - i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data.

neuroscience↗

bioRxiv2018

The geometry of abstraction in hippocampus and prefrontal cortex

The curse of dimensionality plagues models of reinforcement learning and decision-making. The process of abstraction solves this by constructing abstract variables describing features shared by different specific instances, reducing dimensionality and enabling generalization in novel situations. Here we characterized neural representations in monkeys performing a task where a hidden variable described the temporal statistics of stimulus-response-outcome mappings. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training. This type of generalization requires a particular geometric format of neural representations. Neural ensembles in dorsolateral pre-frontal cortex, anterior cingulate cortex and hippocampus, and in simulated neural networks, simultaneously represented multiple hidden and explicit variables in a format reflecting abstraction. Task events engaging cognitive operations modulated this format. These findings elucidate how the brain and artificial systems represent abstract variables, variables critical for generalization that in turn confers cognitive flexibility.

neuroscience↗

bioRxiv2017

Hebbian Learning in a Random Network Captures Selectivity Properties of Prefrontal Cortex

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by prefrontal cortex (PFC). Neural activity in PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear mixed selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which PFC exhibits computationally relevant properties such as mixed selectivity and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data shows significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and allows the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results give intuition about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training.\n\nSignificance StatementPrefrontal cortex (PFC) is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli--and in particular, to combinations of stimuli (\"mixed selectivity\")--is a topic of interest. Despite the fact that models with random feedforward connectivity are capable of creating computationally-relevant mixed selectivity, such a model does not match the levels of mixed selectivity seen in the data analyzed in this study. Adding simple Hebbian learning to the model increases mixed selectivity to the correct level and makes the model match the data on several other relevant measures. This study thus offers predictions on how mixed selectivity and other properties evolve with training.

neuroscience↗