Biology subjects

Di Ianni, T.

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

4 recordsLinked to original sources

bioRxiv2026

Bayesian-enhanced closed-loop optimization of ultrasound protocols for targeted and precise neuromodulation

Low-intensity focused ultrasound (LIFU) is a promising neuromodulation modality, but challenges related to high response variability and the poorly understood parameter space undermine progress in clinical applications. To facilitate the development of therapeutic LIFU protocols, we developed an approach for Bayesian-enhanced adaptive control of ultrasound neuromodulation (BEACUN). BEACUN enables efficient, data-driven parameter mapping using a limited number of stimulation-response evaluations. We used functional ultrasound imaging (fUSI) to measure the neural responses to LIFU stimulation in real time, and we carried out in vivo experiments in rats to optimize and validate the performance of the BEACUN search. In live optimizations, we show that BEACUN produces more effective inhibitory LIFU neuromodulation protocols than conventional parameter exploration methods and converges to the optimal solution in 23 {+/-} 3.67 stimulation-response evaluations. Our approach realizes a platform for efficient optimization of neuromodulation parameters that could pave the way for personalized LIFU protocol development in patients.

bioengineering↗

bioRxiv2025

Towards individualized deep brain stimulation: A stereoencephalography-based workflow for unbiased neurostimulation target identification

ObjectivesDeep brain stimulation (DBS) is increasingly being used to treat a variety of neuropsychiatric conditions, many of which exhibit idiosyncratic symptom presentations and neural correlates across individuals. Thus, we have utilized inpatient stereoelectroencephalography (sEEG) to identify personalized therapeutic stimulation sites for chronic implantation of DBS. Informed by our experience, we have developed a statistics-driven framework for stimulation testing to identify therapeutic targets. Materials and MethodsFourteen participants (major depressive disorder = 6, chronic pain = 6, obsessive-compulsive disorder = 2) underwent inpatient testing using sEEG and symptom monitoring to identify personalized stimulation targets for subsequent DBS implantation. We present a structured approach to this sEEG testing, integrating a Stimulation Testing Decision Tree with power analysis and effect size considerations to inform adequately powered results to detect therapeutic stimulation sites with statistical rigor. ResultsEffect sizes (Hedges g) of stimulation-induced symptom score changes ranged from -1.5 to +2.39. The standard deviation of sham trial responses was a strong predictor of stimulation response variability, as confirmed by a leave-one-out cross-validated linear regression (R2 = 0.67, permutation p<0.001). Thus, early sham trial data could be used to estimate the variability of stimulation responses for power analysis calculations. We show that approximately 10 sham trials were needed to robustly estimate sham variability. Power analysis (using a paired-t test) showed that for effect sizes [≥] 1.1, roughly 10 trials should be used per stimulation site for sufficiently powered results. ConclusionsThe presented workflow is adaptable to multiple indications and is specifically designed to overcome key challenges experienced during stimulation site testing. Through incorporating sham trials, effect size calculations, and tolerability testing, the described approach can be used to identify personalized and clinically efficacious stimulation sites.

neuroscience↗

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↗