Visual thalamocortical circuits in parvalbumin-deficient mice

Lintas, Alessandra; Schwaller, Beat; Villa, Alessandro E. P.

doi:10.1016/j.brainres.2013.04.048

Cited by 10 publications

(9 citation statements)

References 79 publications

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“…As previously described (Wiggins et al, 1982;Meeren et al, 1998;Sanz-Rodriguez et al, 2018), flash stimulations evoke an early positive-negative-positive field potential followed by some late oscillatory components in the dLGN (Fig 6C). The latency values for the first positive component in WT mice are in line with reported mouse field potentials and unitary recordings evoked by photic stimulation (Lintas et al, 2013;Sanz-Rodriguez et al, 2018). Notably, HET animals display a significantly (t = À6.385 with 22 degrees of freedom; P < 0.001) longer mean conduction velocity (15.79 AE 048 ms) for the first component of the evoked field potential than WT mice (12.38 AE 0.25 ms; Fig 6C and D), suggesting decreased transmission of retinal impulses upon Nr2f1 reduced dosage.…”

Section: Reduced Axonal Conduction Velocity In the Visual Pathway Of supporting

confidence: 88%

Mouse Nr2f1 haploinsufficiency unveils new pathological mechanisms of a human optic atrophy syndrome

et al. 2019

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Optic nerve atrophy represents the most common form of hereditary optic neuropathies leading to vision impairment. The recently described Bosch‐Boonstra‐Schaaf optic atrophy ( BBSOA ) syndrome denotes an autosomal dominant genetic form of neuropathy caused by mutations or deletions in the NR 2F1 gene. Herein, we describe a mouse model recapitulating key features of BBSOA patients—optic nerve atrophy, optic disc anomalies, and visual deficits—thus representing the only available mouse model for this syndrome. Notably, Nr2f1 ‐deficient optic nerves develop an imbalance between oligodendrocytes and astrocytes leading to postnatal hypomyelination and astrogliosis. Adult heterozygous mice display a slower optic axonal conduction velocity from the retina to high‐order visual centers together with associative visual learning deficits. Importantly, some of these clinical features, such the optic nerve hypomyelination, could be rescued by chemical drug treatment in early postnatal life. Overall, our data shed new insights into the cellular mechanisms of optic nerve atrophy in BBSOA patients and open a promising avenue for future therapeutic approaches.

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Section: Reduced Axonal Conduction Velocity In the Visual Pathway Of supporting

confidence: 88%

Mouse Nr2f1 haploinsufficiency unveils new pathological mechanisms of a human optic atrophy syndrome

et al. 2019

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“…A change in P1 might be related to a change in the early modulation of attention, such to improve the sensory-perceptual level of processing that is necessary to improve the decisional process. There is evidence that modulation of neural activity by selective attention may occur at the subcortical level [101], where inhibitory gating mechanisms take place [102][103][104]. Then, an increase of the P1-like wave, in our paradigm, could be associated with a more effective processing of the decision due to a greater inhibition of potentially competing and task irrelevant networks.…”

Section: Discussionmentioning

confidence: 70%

Early Attentional Modulation by Working Memory Training in Young Adult ADHD Patients during a Risky Decision-Making Task

et al. 2020

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Background: Working memory (WM) deficits and impaired decision making are among the characteristic symptoms of patients affected by attention deficit/hyperactivity disorder (ADHD). The inattention associated with the disorder is likely to be due to functional deficits of the neural networks inhibiting irrelevant sensory input. In the presence of unnecessary information, a good decisional process is impaired and ADHD patients tend to take risky decisions. This study is aimed to test the hypothesis that the level of difficulty of a WM training (WMT) is affecting the top-down modulation of the attentional processes in a probabilistic gambling task. Methods: Event-related potentials (ERP) triggered by the choice of the amount wagered in the gambling task were recorded, before and after WMT with a the dual n-back task, in young ADHD adults and matched controls. For each group of participants, randomly assigned individuals were requested to perform WMT with a fixed baseline level of difficulty. The remaining participants were trained with a performance-dependent adaptive n-level of difficulty. Results: We compared the ERP recordings before and after 20 days of WMT in each subgroup. The analysis was focused on the time windows with at least three recording sites showing differences before and after training, after Bonferroni correction ( p < 0.05 ). In ADHD, the P1 wave component was selectively affected at frontal sites and its shape was recovered close to controls’ only after adaptive training. In controls, the strongest contrast was observed at parietal level with a left hemispheric dominance at latencies near 900 ms, more after baseline than after adaptive training. Conclusion: Partial restoration of early selective attentional processes in ADHD patients might occur after WMT with a high cognitive load. Modified frontal sites’ activities might constitute a neural marker of this effect in a gambling task. In controls, conversely, an increase in late parietal negativity might rather be a marker of an increase in transfer effects to fluid intelligence.

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“…Sudden transitions are accounted for at the edges of the fold, for example as the stochastic dynamics moves along the surface toward the pleat, at some point a small change in control parameters may cause a sudden shift such that, after a long interval without cyclic activity, quasi-random activity develops into quasi-attractors and long cycles may suddenly appear containing many constitutive cycles and many alternations between spurious and meaningful attractors, e.g. tuning thalamic activity by corticofugal activity [123] , [124] .…”

Section: Discussionmentioning

confidence: 99%

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

Cabessa

Villa

2014

PLoS ONE

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We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.

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Visual thalamocortical circuits in parvalbumin-deficient mice

Abstract: The dorsal lateral geniculate nucleus (dLGN) is considered as the visual gateway to the visual cortex (VC) and sends collaterals to the thalamic reticular nucleus (RTN) that in turn receives collaterals of the corticofugal feedback projections. At all levels of this thalamo- .

Cited by 10 publications

References 79 publications

Mouse Nr2f1 haploinsufficiency unveils new pathological mechanisms of a human optic atrophy syndrome

Mouse Nr2f1 haploinsufficiency unveils new pathological mechanisms of a human optic atrophy syndrome

Early Attentional Modulation by Working Memory Training in Young Adult ADHD Patients during a Risky Decision-Making Task

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

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