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Ambrosi, P.

Publications and source records attributed to Ambrosi, P..

2 recordsLinked to original sources

Striatonigrostriatal Circuit Architecture for Disinhibition of Dopamine Signaling

SUMMARY/ABSTRACTThe basal ganglia operate largely in closed parallel loops, including an associative circuit for goal-directed behavior originating from the dorsomedial striatum (DMS) and a somatosensory circuit important for habit formation originating from the dorsolateral striatum (DLS). An exception to this parallel circuit organization has been proposed to explain how information might be transferred between striatal subregions, for example from DMS to DLS during habit formation. The "ascending spiral hypothesis" proposes that DMS disinhibits dopamine signaling in DLS through a tri-synaptic, open-loop striato-nigro-striatal circuit. Here, we used transsynaptic and intersectional genetic tools to investigate both closed- and open-loop striato-nigro-striatal circuits. We found strong evidence for closed loops, which would allow striatal subregions to regulate their own dopamine release. We also found evidence for functional synapses in open loops. However, these synapses were unable to modulate tonic dopamine neuron firing, questioning the prominence of their role in mediating crosstalk between striatal subregions.

neuroscience

Modeling Brain Connectivity Dynamics in Functional Magnetic Resonance Imaging via Particle Filtering

Interest in the studying of functional connections in the brain has grown considerably in the last decades, as many studies have pointed out that these interactions can play a role as markers of neurological diseases. Most studies in this field treat the brain network as a system of connections stationary in time, but dynamic features of brain connectivity can provide useful information, both on physiology and pathological conditions of the brain. In this paper, we propose the application of a computational methodology, named Particle Filter (PF), to study non-stationarities in brain connectivity in functional Magnetic Resonance Imaging (fMRI). The PF algorithm estimates time-varying hidden parameters of a first-order linear time-varying Vector Autoregressive model (VAR) through a Sequential Monte Carlo strategy. On simulated time series, the PF approach effectively detected and enabled to follow time-varying hidden parameters and it captured causal relationships among signals. The method was also applied to real fMRI data, acquired in presence of periodic tactile or visual stimulations, in different sessions. On these data, the PF estimates were consistent with current knowledge on brain functioning. Most importantly, the approach enabled to detect statistically significant modulations in the cause-effect relationship between brain areas, which correlated with the underlying visual stimulation pattern presented during the acquisition.

neuroscience