Oral Delta-9-Tetrahydrocannabinol (THC) Increases Parasympathetic Activity and Supraspinal Conditioned Pain Modulation in Chronic Neuropathic Pain Male Patients: A Crossover, Double-Blind, Placebo-Controlled Trial.

Background: Disordered autonomic nervous system regulation and supraspinal pain inhibition have been repeatedly described in chronic pain. We aimed to explore the effects of δ-9-tetrahydrocannabinol (THC), an emerging treatment option, on autonomic nervous system and central pain modulation measures in patients with chronic pain.

Methods: Twelve male patients with chronic radicular neuropathic pain participated in a randomized, double-blind, crossover, placebo-controlled, single-administration trial.

Microdosing ketamine in Drosophila does not block serotonin reuptake, but causes complex behavioral changes mediated by glutamate and serotonin receptors.

Microdosing ketamine is a novel antidepressant for treatment-resistant depression. Traditional antidepressants, like selective serotonin reuptake inhibitors (SSRIs), inhibit serotonin reuptake, but it is not clear if ketamine shows a similar mechanism. Here, we tested the effects of feeding ketamine and SSRIs to Drosophila melanogaster larvae, which has a similar serotonin system to mammals and is a good model to track depressive behaviors, such as locomotion and feeding.

Microdosing ketamine in does not inhibit SERT like SSRIs, but causes behavioral changes mediated by glutamate and serotonin receptors.

Recently, the FDA approved microdosing ketamine for treatment resistant depression. Traditional antidepressants, like serotonin selective reuptake inhibitors (SSRIs), block serotonin reuptake, but it is not clear if ketamine blocks serotonin reuptake. Here, we tested the effects of feeding ketamine and SSRIs to larvae, which has a similar serotonin system to mammals, and is a good model to track depression behaviors, such as locomotion and feeding.

Abnormal Visual Evoked Responses to Emotional Cues Correspond to Diagnosis and Disease Severity in Fibromyalgia.

Background: Chronic pain disorders are often associated with cognitive-emotional dysregulation. However, the relations between such dysregulation, underlying brain processes, and clinical symptom constellations, remain unclear. Here, we aimed to characterize the abnormalities in cognitive-emotional processing involved in fibromyalgia syndrome (FMS) and their relation to disease severity.

Coupled Dictionary Learning for Multi-Contrast MRI Reconstruction.

Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities in either structure level or gray level. In this paper, we propose a coupled dictionary learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage the dependency correlation between different contrasts for guided or joint reconstruction from their under-sampled k -space data.

Cannabis analgesia in chronic neuropathic pain is associated with altered brain connectivity.

Objective: To characterize the functional brain changes involved in δ-9-tetrahydrocannabinol (THC) modulation of chronic neuropathic pain.

Methods: Fifteen patients with chronic radicular neuropathic pain participated in a randomized, double-blind, placebo-controlled trial employing a counterbalanced, within-subjects design. Pain assessments and functional resting state brain scans were performed at baseline and after sublingual THC administration.

Low-rank magnetic resonance fingerprinting.

Purpose: Magnetic resonance fingerprinting (MRF) is a relatively new approach that provides quantitative MRI measures using randomized acquisition. Extraction of physical quantitative tissue parameters is performed offline, without the need of patient presence, based on acquisition with varying parameters and a dictionary generated according to the Bloch equation simulations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore, a high undersampling ratio in the sampling domain (k-space) is required for reasonable scanning time.

PEAR: PEriodic and ApeRiodic signal separation for fast FMRI.

Undersampling of functional MRI (fMRI) data leads to increased temporal resolution, as it allows shorter acquisition time per frame. High quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the fMRI data. Recent publications suggest that the fMRI signal is a superposition of periodic and aperiodic signals.

PEAR: PEriodic And fixed Rank separation for fast fMRI.

Purpose: In functional MRI (fMRI), faster acquisition via undersampling of data can improve the spatial-temporal resolution trade-off and increase statistical robustness through increased degrees-of-freedom. High-quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the data. We present an fMRI reconstruction approach based on modeling the fMRI signal as a sum of periodic and fixed rank components, for improved reconstruction from undersampled measurements.

Low rank magnetic resonance fingerprinting.

Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required.

Reference-based MRI.

Purpose: In many clinical MRI scenarios, existing imaging information can be used to significantly shorten acquisition time or to improve Signal to Noise Ratio (SNR). In this paper the authors present a framework, referred to as FASTMER, for fast MRI by exploiting a reference image.

Methods: The proposed approach utilizes the possible similarity of the reference image to the acquired image, which exists in many clinical MRI imaging scenarios.

Fast reference based MRI.

In many clinical MRI scenarios, existing imaging information can be used to significantly shorten acquisition time or improve Signal to Noise Ratio (SNR). In those cases, a previously acquired image can serve as a reference image, that may exhibit similarity in some sense to the image being acquired. Examples include similarity between adjacent slices in high resolution MRI, similarity between various contrasts in the same scans and similarity between different scans of the same patients.

Compressed sensing for longitudinal MRI: An adaptive-weighted approach.

Purpose: Repeated brain MRI scans are performed in many clinical scenarios, such as follow up of patients with tumors and therapy response assessment. In this paper, the authors show an approach to utilize former scans of the patient for the acceleration of repeated MRI scans.

Methods: The proposed approach utilizes the possible similarity of the repeated scans in longitudinal MRI studies.

The effect of chemotherapy on optic pathway gliomas and their sub-components: A volumetric MR analysis study.

Background: Optic pathway gliomas (OPG) represent 5% of pediatric brain tumors and compose a major therapeutic dilemma to the treating physicians. While chemotherapy is widely used for these tumors, our ability to predict radiological response is still lacking. In this study, we use volumetric imaging to examine in detail the long-term effect of chemotherapy on the tumor as well as its various sub-components.

Tumor burden evaluation in NF1 patients with plexiform neurofibromas in daily clinical practice.

Background: Existing volumetric measurements of plexiform neurofibromas (PNs) are time consuming and error prone, as they require delineation of PN boundaries, a procedure that is not practical in the typical clinical setting. The aim of this study is to assess the Plexiform Neurofibroma Instant Segmentation Tool (PNist), a novel semi-automated segmentation program that we developed for PN delineation in a clinical context. PNist was designed to greatly simplify volumetric assessment of PNs through use of an intuitive user interface while providing objectively consistent results with minimal interobserver and intraobserver variabilities in reasonable time.

Exploiting similarity in adjacent slices for compressed sensing MRI.

Due to fundamental characteristics of MRI that limit scan speedup, sub-sampling techniques such as compressed sensing (CS) have been developed for rapid MRI. Current CS MRI approaches utilize sparsity of the image in the wavelet or other transform domains to speed-up acquisition. Another drawback of MRI is its poor signal-to-noise ratio (SNR), which is proportional to the image slice thickness.

Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies.

Purpose: Tracking the progression of low grade tumors (LGTs) is a challenging task, due to their slow growth rate and associated complex internal tumor components, such as heterogeneous enhancement, hemorrhage, and cysts. In this paper, the authors show a semiautomatic method to reliably track the volume of LGTs and the evolution of their internal components in longitudinal MRI scans.

Methods: The authors' method utilizes a spatiotemporal evolution modeling of the tumor and its internal components.

PNist: interactive volumetric measurements of plexiform neurofibromas in MRI scans.

Purpose: Volumetric measurements of plexiform neurofibromas (PNs) are time consuming and error prone, as they require the delineation of the PN boundaries, which is mostly impractical in the daily clinical setup. Accurate volumetric measurements are seldom performed for these tumors mainly due to their great dispersion, size and multiple locations. This paper presents a semiautomatic method for segmentation of PN from STIR MRI scans.

Prediction of brain MR scans in longitudinal tumor follow-up studies.

We present a new method for the estimation of the next brain MR scan in a longitudinal tumor follow-up study. Our method effectively incorporates information of the past scans in the time series to predict the future scan of the patient. Its advantages are that it requires no user intervention and does not assume any particular tumor growth model.

Interactive segmentation of plexiform neurofibroma tissue: method and preliminary performance evaluation.

Plexiform neurofibromas (PNs) are a major manifestation of neurofibromatosis-1 (NF1), a common genetic disease involving the nervous system. Treatment decisions are mostly based on a gross assessment of changes in tumor using MRI. Accurate volumetric measurements are rarely performed in this kind of tumors mainly due to its great dispersion, size, and multiple locations.

Carotid vasculature modeling from patient CT angiography studies for interventional procedures simulation.

Objective: A practical method for patient-specific modeling of the aortic arch and the entire carotid vasculature from computed tomography angiography (CTA) scans for morphologic analysis and for interventional procedure simulation.

Materials And Methods: The method starts with the automatic watershed-based segmentation of the aorta and the construction of an a-priori intensity probability distribution function for arteries. The carotid arteries are then segmented with a graph min-cut method based on a new edge weighting function that adaptively couples voxel intensity, intensity prior, and local vesselness shape prior.

Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI.

This paper presents an automatic method for the segmentation, internal classification and follow-up of optic pathway gliomas (OPGs) from multi-sequence MRI datasets. Our method starts with the automatic localization of the OPG and its core with an anatomical atlas followed by a binary voxel classification with a probabilistic tissue model whose parameters are estimated from the MR images. The method effectively incorporates prior location, tissue characteristics, and intensity information for the delineation of the OPG boundaries in a consistent and repeatable manner.

MRI internal segmentation of optic pathway gliomas: clinical implementation of a novel algorithm.

Purpose: Optic pathway gliomas (OPGs) are diagnosed based on typical MR features and require careful monitoring with serial MRI. Reliable, serial radiological comparison of OPGs is a difficult task, where accuracy becomes very important for clinical decisions on treatment initiation and results. Current radiological methodology usually includes linear measurements that are limited in terms of precision and reproducibility.