Clinical utility of fMRI in evaluating of LSD effect on pain-related brain networks in healthy subjects.

Objective: We aimed to evaluate the effect of Lysergic acid diethylamide (LSD) on the pain neural network (PNN) in healthy subjects using functional magnetic resonance imaging (fMRI). Methods: Twenty healthy volunteers participated in a balanced-order crossover study, receiving intravenous administration of LSD and placebo in two fMRI scanning sessions. Brain regions associated with pain processing were analyzed by amplitude of low-frequency fluctuation (ALFF), independent component analysis (ICA), functional connectivity and dynamic casual modeling (DCM). Results: ALFF analysis demonstrated that LSD effectively relieves pain due to modulation in the neural network associated with pain processing. ICA analysis showed more active voxels in anterior cingulate cortex (ACC), thalamus (THL)-left, THL-right, insula cortex (IC)-right, parietal operculum (PO)-left, PO-right and frontal pole (FP)-right in the placebo session than the LSD session. There were more active voxels in FP-left and IC-left in the LSD session compared to the placebo session. Functional brain connectivity was observed between THL-left and PO-right and between PO-left with FP-left, FP-right and IC-left in the placebo session. In the LSD session, functional connectivity of PO-left with FP-left and FP-right was observed. The effective connectivity between left anterior insula cortex (lAIC)-lAIC, lAIC-dorsolateral prefrontal cortex (dlPFC) and secondary somatosensory cortex (SII)-dlPFC were significantly different. Finally, the correlation between fMRI biomarkers and clinical pain criteria was calculated. Conclusion: This study enhances our understanding of the LSD effect on the architecture and neural behavior of pain in healthy subjects and provides great promise for future research in the field of cognitive science and pharmacology.

Anhedonia symptoms: The assessment of brain functional mechanism following music stimuli using functional magnetic resonance imaging.

PURPOSE: This study aimed to investigate the effect of music stimulation on the brain functional mechanism of depressed patients with anhedonia symptoms using functional magnetic resonance imaging (fMRI). METHODS: Participants in this study included 20 healthy subjects as the control group, 25 subjects with depression and no anhedonia as the intervention group A, and 24 subjects with depression and anhedonia as the intervention group B. The safely emotional stimulation was done by Iranian music. To investigate the effect of music therapy on the brain, a task including 50 tracks of 12 s Iranian music (traditional and pop) was used. Finally, the data were analyzed using SPM Toolbox in MATLAB software. RESULTS: The results showed that brain patterns in depressed patients with and without anhedonia could be distinguished based on positive and negative musical stimuli (p < 0.05). Important fMRI biomarker such as effective connectivity strength related to the fronto-limbic network, including the supragenual ACC, subgenual ACC, AMYG, and FFG were evaluated in depressed patients with anhedonia. CONCLUSION: This was the first study to investigate the neural circuits involved in music-related emotional processing in patients with anhedonia symptoms. These findings could help advance neurological understandings of anhedonia and suggest new treatments.

Novel FMRI-Compatible wrist robotic device for brain activation assessment during rehabilitation exercise.

Magnetic Resonance Imaging (MRI) can be applied to study the effects of rehabilitation strategies for neuroscience research. An MRI-wrist robot is designed and used as a clinical tool to examine the process of the brain plasticity changes. In this robot, the patient actuation is accomplished with two standard air cylinders, located inside the MRI chamber with two degrees of freedom (flexion-extension and ulna-radial deviation) with pneumatic air transmission, consisting of simple mechanism converting rotary motion to linear independently. A pilot study of brain image aiming at revealing more effective therapeutic strategies carried out to confirm the technical aspects of the development and validation. In a healthy subject, both wrist movement of robot and subject demonstrated brain activity in the contralateral primary somatosensory cortex. Because the robot does not move during the patient's body, a stand was designed to allow the wrist robot and patient to fit comfortably within the MRI machine. While all the parts of the robot were carefully selected with strict MRI compatibility requirements, the robot was tested by presenting some pilot imaging data with null effects on the image quality, as well. Finally, the possible further development of the robot has been introduced for a rehabilitation assessment.

A Novel Classification Method using Effective Neural Network and Quantitative Magnetization Transfer Imaging of Brain White Matter in Relapsing Remitting Multiple Sclerosis.

Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets. Objective: We classified patients with relapsing-remitting multiple sclerosis (RRMS) from healthy subjects using QMTI and T1 longitudinal relaxation time data of brain white matter, then the performance of three ANN-based classifiers have been investigated. Materials and Methods: The input features of ANN algorithms, including multilayer perceptron (MLP), radial basis function (RBF) and ensemble neural networks based on Akaike information criterion (ENN-AIC) were extracted in the form of QMTI and T1 mean values from parametric maps. The ANNs quantitative performance is measured by the standard evaluation of confusion matrix criteria. Results: The results indicate that ENN-AIC-based classification method has achieved 90% accuracy, 92% sensitivity and 86% precision compared to other ANN models. NPV, FPR and FDR values were found to be 0.933, 0.125 and 0.133, respectively, according to the proposed ENN-AIC model. A graphical representation of how to track actual data by the predictive values derived from ANN algorithms, was also presented. Conclusion: It has been demonstrated that ENN-AIC as an effective neural network improves the quality of classification results compared to MLP and RBF.In addition, this research provides a new direction to classify a large amount of quantitative MRI data that can help the physician in a correct MS diagnosis.