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Balzani, E.

Publications and source records attributed to Balzani, E..

5 recordsLinked to original sources

Hippocampal neuronal activity is aligned with action plans

Neurons in the hippocampus are correlated with different variables, including space, time, sensory cues, rewards, and actions, where the extent of tuning depends on ongoing task demands. However, it remains uncertain whether such diverse tuning corresponds to distinct functions within the hippocampal network or if a more generic computation can account for these observations. To disentangle the contribution of externally driven cues versus internal computation, we developed a task in mice where space, auditory tones, rewards, and context were juxtaposed with changing relevance. High-density electrophysiological recordings revealed that neurons were tuned to each of these modalities. By comparing movement paths and action sequences, we observed that external variables had limited direct influence on hippocampal firing. Instead, spiking was influenced by online action plans modulated by goal uncertainty. Our results suggest that internally generated cell assembly sequences are selected and updated by action plans toward deliberate goals. The apparent tuning of hippocampal neuronal spiking to different sensory modalities might emerge due to alignment to the afforded action progression within a task rather than representation of external cues.

neuroscience↗

A common computational and neural anomaly across mouse models of autism

Computational psychiatry has suggested that humans within the autism spectrum disorder (ASD) inflexibly update their expectations (i.e., Bayesian priors). Here, we leveraged high-yield rodent psychophysics (n = 75 mice), extensive behavioral modeling (including principled and heuristics), and (near) brain-wide single cell extracellular recordings (over 53k units in 150 brain areas) to ask (1) whether mice with different genetic perturbations associated with ASD show this same computational anomaly, and if so, (2) what neurophysiological features are shared across genotypes in subserving this deficit. We demonstrate that mice harboring mutations in Fmr1, Cntnap2, and Shank3B show a blunted update of priors during decision-making. Neurally, the differentiating factor between animals flexibly and inflexibly updating their priors was a shift in the weighting of prior encoding from sensory to frontal cortices. Further, in mouse models of ASD frontal areas showed a preponderance of units coding for deviations from the animals long-run prior, and sensory responses did not differentiate between expected and unexpected observations. These findings demonstrate that distinct genetic instantiations of ASD may yield common neurophysiological and behavioral phenotypes.

neuroscience↗

Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex

Natural behaviors occur in closed action-perception loops and are supported by dynamic and flexible beliefs abstracted away from our immediate sensory milieu. How this real-world flexibility is instantiated in neural circuits remains unknown. Here we have macaques navigate in a virtual environment by primarily leveraging sensory (optic flow) signals, or by more heavily relying on acquired internal models. We record single-unit spiking activity simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and the dorso-lateral prefrontal cortex (dlPFC). Results show that while animals were able to maintain adaptive task-relevant beliefs regardless of sensory context, the fine-grain statistical dependencies between neurons, particularly in 7a and dlPFC, dynamically remapped with the changing computational demands. In dlPFC, but not 7a, destroying these statistical dependencies abolished the areas ability for cross-context decoding. Lastly, correlation analyses suggested that the more unit-to-unit couplings remapped in dlPFC, and the less they did so in MSTd, the less were population codes and behavior impacted by the loss of sensory evidence. We conclude that dynamic functional connectivity between prefrontal cortex neurons maintains a stable population code and context-invariant beliefs during naturalistic behavior with closed action-perception loops.

neuroscience↗

Flexible neural coding in sensory, parietal, and frontal cortices during goal-directed virtual navigation

We do not understand how neural nodes operate and coordinate within the recurrent action-perception loops that characterize naturalistic self-environment interactions. Here, we record single-unit spiking activity and local field potentials (LFPs) simultaneously from the dorsomedial superior temporal area (MSTd), parietal area 7a, and dorsolateral prefrontal cortex (dlPFC) as monkeys navigate in virtual reality to "catch fireflies." This task requires animals to actively sample from a closed-loop virtual environment while concurrently computing continuous latent variables: (i) the distance and angle travelled (i.e., path integration) and (ii) the distance and angle to a memorized firefly location (i.e., a hidden spatial goal). We observed a patterned mixed selectivity, with the prefrontal cortex most prominently coding for latent variables, parietal cortex coding for sensorimotor variables, and MSTd most often coding for eye-movements. However, even the traditionally considered sensory area (i.e., MSTd) tracked latent variables, demonstrating path integration and vector-coding of hidden spatial goals. Further, global encoding profiles and unit-to-unit coupling (i.e., noise correlations) suggested a functional subnetwork composed by MSTd and dlPFC, and not between these and 7a, as anatomy would suggest. We show that the greater the unit-to-unit coupling between MSTd and dlPFC, the more the animals gaze position was indicative of the ongoing location of the hidden spatial goal. We suggest this MSTd-dlPFC subnetwork reflects the monkeys natural and adaptive task strategy wherein they continuously gaze toward the location of the (invisible) target. Together, these results highlight the distributed nature of neural coding during closed action-perception loops and suggest that fine-grain functional subnetworks may be dynamically established to subserve (embodied) task strategies.

neuroscience↗

Paternally expressed imprinted Snord116 and Peg3 regulate hypothalamic orexin neurons

Imprinted genes are highly expressed in the hypothalamus; however, whether specific imprinted genes affect hypothalamic neuromodulators and their functions is unknown. It has been suggested that Prader-Willi syndrome (PWS), a neurodevelopmental disorder caused by lack of paternal expression at chromosome 15q11-q13, is characterised by hypothalamic insufficiency. Here, we investigate the role of the paternally expressed Snord116 gene within the context of sleep and metabolic abnormalities of PWS, and we report a novel role of this imprinted gene in the function and organisation of the two main neuromodulatory systems of the lateral hypothalamus (LH), namely, the orexin (OX) and melanin concentrating hormone (MCH) systems. We observe that the dynamics between neuronal discharge in the LH and the sleep-wake states of mice with paternal deletion of Snord116 (PWScrm+/p-) are compromised. This abnormal state-dependent neuronal activity is paralleled by a significant reduction in OX neurons in the LH of mutants. Therefore, we propose that an imbalance between OX- and MCH-expressing neurons in the LH of mutants reflects a series of deficits manifested in the PWS, such as dysregulation of rapid eye movement (REM) sleep, food intake and temperature control.\n\nHighlightsO_LISnord116 regulates neuronal activity in the lateral hypothalamus (LH), which is time-locked with cortical states of sleep.\nC_LIO_LILoss of Snord116 reduces orexin neurons in the LH and affects sleep homeostasis and thermoregulation in mice.\nC_LIO_LISnord116 and Peg3 independently control orexin expression in the LH.\nC_LIO_LIPaternally expressed alleles maximize the patrilineal effects in the control of REM sleep by the LH in mammals.\nC_LI

neuroscience↗