Biology subjects

Di Ianni, T.

Publications and source records attributed to Di Ianni, T..

2 recordsLinked to original sources

bioRxiv2022

Sex dependence of opioid-mediated responses to subanesthetic ketamine

Subanesthetic ketamine rapidly and robustly reduces depressive symptoms in patients with treatment-resistant depression. While it is commonly classified as an N-methyl D-aspartate receptor (NMDAR) antagonist, our picture of ketamines mechanistic underpinnings is incomplete. Recent clinical evidence has indicated, controversially, that a component of the efficacy of ketamine in depression may be opioid dependent. Using pharmacological functional ultrasound imaging in rats, we found that blocking opioid receptors suppressed neurophysiologic changes evoked by ketamine, but not by a more selective NMDAR antagonist, in regions implicated in the pathophysiology of depression and in reward processing. Importantly, this opioid-dependent response was strongly sex dependent, as it was not evident in female subjects and was fully reversed by surgical removal of the male gonads. We observed similar opioid-mediated sex-dependent effects in ketamine-evoked structural plasticity and behavioral sensitization. Together, these results underscore the potential for ketamine to induce its affective responses via opioid signaling, and indicate that this opioid dependence may be strongly influenced by subject sex. These factors should be more directly assessed in future clinical trials. One-Sentence SummarySubanesthetic ketamine evokes opioid-mediated behavioral and neurophysiological effects in male, but not female, rats.

neuroscience↗

bioRxiv2020

Deep-fUS: functional ultrasound imaging of the brain using deep learning and sparse data

Functional ultrasound (fUS) is a rapidly emerging modality that enables whole-brain imaging of neural activity in awake and mobile rodents. To achieve sufficient blood flow sensitivity in the brain microvasculature, fUS relies on long ultrasound data acquisitions at high frame rates, posing high demands on the sampling and processing hardware. Here we develop an end-to-end image reconstruction approach based on deep learning that significantly reduces the amount of data necessary while retaining the imaging performance. We trained a convolutional neural network to learn the power Doppler reconstruction function from sparse sequences of ultrasound data with a compression factor up to 95%, using high-quality images from in vivo acquisitions in rats. We tested the imaging performance in a functional neuroimaging application. We demonstrate that time series of power Doppler images can be reconstructed with sufficient accuracy to detect the small changes in cerebral blood volume (~10%) characteristic of task-evoked cortical activation, even though the network was not formally trained to reconstruct such image series. The proposed platform may facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or in clinical scanners.

bioengineering↗