Biology subjects

Dehghani, H.

Publications and source records attributed to Dehghani, H..

5 recordsLinked to original sources

bioRxiv2024

Time-Domain Diffuse Optical Tomography for Precision Neuroscience

AbstractRecent years have witnessed a rise in research utilizing neuroimaging for precision neuromedicine, but clinical translation has been hindered by scalability and cost. Time Domain functional Near Infrared Spectroscopy (TD-fNIRS), the gold standard of optical neuroimaging techniques, offers a unique opportunity in this domain since it provides superior depth sensitivity and enables resolution of absolute properties unlike its continuous wave counterparts. However, current TD systems have limited commercial availability, slow sampling rates, and sparse head coverage. Our team has overcome the technical challenges involved in developing a whole-head time-domain diffuse optical tomography (TD-DOT) system. Here, we present the system characterization results using standardized protocols and compare them to the state-of-the-art. Furthermore, we showcase the system performance in retrieving cortical activation maps during standard hemodynamic, sensory, and motor tasks. A combination of the system performance, signal quality, and ease-of-use can enable future studies aimed at investigating TD-DOT clinical applications.

bioengineering↗

bioRxiv2023

In vivo characterization of the optical and hemodynamic properties of the human sternocleidomastoid muscle through ultrasound-guided hybrid near-infrared spectroscopies.

The non-invasive monitoring of the hemodynamics and metabolism of the sternocleidomastoid muscle (SCM) during respiration became a topic of increased interest partially due to the increased use of mechanical ventilation during the COVID-19 pandemic. Near-infrared diffuse optical spectroscopies were proposed as potential practical monitors of increased recruitment of SCM during respiratory distress. They can provide clinically relevant information on the degree of the patients respiratory effort that is needed to maintain an optimal minute ventilation, with potential clinical application ranging from evaluating chronic pulmonary diseases to more acute settings, such as acute respiratory failure, or to determine the readiness to wean from invasive mechanical ventilation. In this paper, we present a detailed characterization of the optical properties (wave-length dependent absorption and reduced scattering coefficients) and hemodynamic properties (oxy-, deoxy- and total hemoglobin concentrations, blood flow, blood oxygen saturation and metabolic rate of oxygen extraction) of the human SCM, obtained by measuring sixty-five subjects through ultrasound-guided near-infrared time-resolved and diffuse correlation spectroscopies. We provide detailed tables of the results related to SCM baseline (i.e. muscle at rest) properties, and reveal significant differences on the measured parameters due to variables such as side of the neck, sex, age, body mass index and thickness of the overlaying tissues, allowing future clinical studies to take into account such dependencies.

physiology↗

bioRxiv2022

DeepRetroMoCo: Deep neural network-based Retrospective Motion Correction Algorithm for Spinal Cord functional MRI

There are unique challenges in the preprocessing of spinal cord fMRI data, particularly voluntary or involuntary movement artifacts during image acquisition. Despite advances in data processing techniques for movement detection and correction, there are challenges in extrapolating motion correction algorithm developments in the brain cortex to the brainstem and spinal cord. We trained a Deep Learning-based convolutional neural network (CNN) via an unsupervised learning algorithm, called DeepRetroMoCo, to detect and correct motions in axial T2*-weighted spinal cord data. Spinal cord fMRI data from 27 participants were used for training of the network (135 runs for training and 81 runs for testing). We used average temporal signal-to-noise-ratio (tSNR) and Delta Variation Signal (DVARS) of raw and motion-corrected images to compare the outcome of DeepRetroMoco with sct_fmri_moco implemented in the spinal cord toolbox. The average tSNR in the cervical cord was significantly higher when DeepRetroMoco was used for motion correction compared to sct_fmri_moco method. Average DVARS was lower in images corrected by DeepRetroMoco than those corrected by sct_fmri_moco. The average processing time for DeepRetroMoco was also significantly shorter than sct_fmri_moco. Our results suggest that DeepRetroMoCo improves motion correction procedures in fMRI data acquired from the cervical spinal cord.

neuroscience↗

bioRxiv2022

Latent human herpesvirus 6 is reactivated in chimeric antigen receptor T cells

Cell therapies have yielded durable clinical benefits for patients with cancer, but the risks associated with the development of therapies from manipulated human cells are still being understood. For example, we currently lack a comprehensive understanding of the mechanisms of neurotoxicity observed in patients receiving T cell therapies, including recent reports of encephalitis caused by human herpesvirus 6 (HHV-6) reactivation1. Here, via petabase-scale viral RNA data mining, we examine the landscape of human latent viral reactivation and demonstrate that HHV-6B can become reactivated in human CD4+ T cells in standard in vitro cultures. Using single-cell sequencing, we identify a rare population of HHV-6 super-expressors (~1 in 300-10,000 cells) that possess high viral transcriptional activity in chimeric antigen receptor (CAR) T cell culture before spreading to infect other cells in vitro. Through the analysis of single-cell sequencing data from patients receiving cell therapy products that are FDA-approved2 or used in clinical studies3,4, we identify the presence of CAR+, HHV-6 super-expressor T cells in vivo. Together, our study implicates cell therapy products as a source of lytic HHV-6 reported in clinical trials1,5-7 and has broad implications for the design, production, and monitoring of cell therapies.

immunology↗

bioRxiv2020

Evaluation and Optimization of Motion Correction in Spinal Cord fMRI Preprocessing

Motion correction is an essential step in the preprocessing of functional magnetic resonance imaging (fMRI) data, improving the temporal signal to noise ratio (tSNR) and removing unwanted variance. Because of the characteristics of the spinal cord (non-rigidity, surrounded by moving organs), motion correction becomes especially challenging. We compared the efficiency of different motion correction protocols and suggest a preferred method for spinal cord fMRI data. Here we acquired gradient-echo echo-planar-imaging axial lumbar spinal cord fMRI data during painful mechanical stimulation of the left lower extremity of 15 healthy volunteers on a 3T scanner. We compared multiple motion correction techniques: 2D and 3D FLIRT realignment with and without slice-wise regulation, SliceCorr (implemented in the Spinal Cord Toolbox) and proposed a method 3D FLIRT in addition to Slice Regulation (SLiceReg) along the spinal cord. TSNR, image entropy, DVARS, image Sum of Absolute Differences and number of activated voxels in the spinal cord from GLM analysis to evaluate the performance of multiple motion correction procedures. The tSNR and DVARS 3D FLIRT + SLiceReg were significantly improved over other realignment methods (p<0.001). In comparison, tSNR=14.20{+/-}0.02 and DVARS=165.77{+/-}1.54 were higher than other methods. Additionally, the number of activated voxels of the statistical map in our suggested method was higher than the other realignment methods (p<0.05). Our results illustrated the proposed motion correction algorithm that integrated 3D motion correction and 2D slicewise regularization along spinal cord curvature could improve subject-level processing outputs by reducing the effects of motions. Our proposed protocols can improve subject-level analysis, especially in lumbar region that suffers from involuntary motions and signal loss due to susceptibility effect more than other spinal cord regions.

neuroscience↗