Diagnosis of traumatic liver injury on computed tomography using machine learning algorithms and radiomics features: The role of artificial intelligence for rapid diagnosis in emergency rooms.

Background: The initial assessment of trauma is a time-consuming and challenging task. The purpose of this research is to examine the diagnostic effectiveness and usefulness of machine learning models paired with radiomics features to identify blunt traumatic liver injury in abdominal computed tomography (CT) images.

Materials And Methods: In this study, 600 CT scan images of people with mild and severe liver damage due to trauma and healthy people were collected from the Kaggle dataset.

Investigating resting-state functional connectivity changes within procedural memory network across neuropsychiatric disorders using fMRI.

Background: Cognitive networks impairments are common in neuropsychiatric disorders like Attention Deficit Hyperactivity Disorder (ADHD), bipolar disorder (BD), and schizophrenia (SZ). While previous research has focused on specific brain regions, the role of the procedural memory as a type of long-term memory to examine cognitive networks impairments in these disorders remains unclear. This study investigates alterations in resting-state functional connectivity (rs-FC) within the procedural memory network to explore brain function associated with cognitive networks in patients with these disorders.

Brain mapping, biomarker identification and using machine learning method for diagnosis of anxiety during emotional face in preschool children.

Background: Due to the importance and the consequences of anxiety, the goals of the current study are brain mapping, biomarker identification and the use of an assessment method for diagnosis of anxiety during emotional face in preschool children.

Method: 45 preschool children participated in this study. Functional Magnetic Resonance Imaging (fMRI) data were taken in fearful and angry conditions.

Deciphering brain activation during wrist movements: comparative fMRI and fNIRS analysis of active, passive, and imagery states.

Understanding the complex activation patterns of brain regions during motor tasks is crucial. Integrated functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) offers advanced insights into how brain activity fluctuates with motor activities. This study explores neuronal activation patterns in the cerebral cortex during active, passive, and imagined wrist movements using these functional imaging techniques.

Radiomics-based machine learning for automated detection of Pneumothorax in CT scans.

The increasing complexity of diagnostic imaging often leads to misinterpretations and diagnostic errors, particularly in critical conditions such as pneumothorax. This study addresses the pressing need for improved diagnostic accuracy in CT scans by developing an intelligent model that leverages radiomics features and machine learning techniques. By enhancing the detection of pneumothorax, this research aims to mitigate diagnostic errors and accelerate the process of image interpretation, ultimately improving patient outcomes.

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).

Characterizing the Visual Cortex Function in Cognitive Task-Induced Mental Load: An Functional Magnetic Resonance Imaging Study.

The mental load caused by simultaneous multitasking can affect visual information processing and reduce its ability. This study investigated the effect of mental load caused by cognitive tasks simultaneously with visual task on the number of active voxels in the visual cortex. This study recruited 22 individuals with a mean age of 24.

The effect of virtual reality on reducing patients' anxiety and pain during dental implant surgery.

Background: Dental anxiety and pain pose serious problems for both patients and dentists. One of the most stressful and frightening dental procedures for patients is dental implant surgery; that even hearing its name causes them stress. Virtual reality (VR) distraction is an effective intervention used by healthcare professionals to help patients cope with unpleasant procedures.

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.

Brain Cortical Activation during Imagining of the Wrist Movement Using Functional Near-Infrared Spectroscopy (fNIRS).

Background: fNIRS is a useful tool designed to record the changes in the density of blood's oxygenated hemoglobin (oxyHb) and deoxygenated hemoglobin (deoxyHb) molecules during brain activity. This method has made it possible to evaluate the hemodynamic changes of the brain during neuronal activity in a completely non-aggressive manner.

Objective: The present study has been designed to investigate and evaluate the brain cortex activities during imagining of the execution of wrist motor tasks by comparing fMRI and fNIRS imaging methods.

Assessment of Motor Cortex in Active, Passive and Imagery Wrist Movement Using Functional MRI.

Background: Functional Magnetic resonance imaging (fMRI) measures the small fluctuation of blood flow happening during task-fMRI in brain regions.

Objective: This research investigated these active, imagery and passive movements in volunteers design to permit a comparison of their capabilities in activating the brain areas.

Material And Methods: In this applied research, the activity of the motor cortex during the right-wrist movement was evaluated in 10 normal volunteers under active, passive, and imagery conditions.

Brain Functional Mechanisms in Attentional Processing Following Modified Conflict Stroop Task.

Background: Cognitive control of brain regions can be determined by the tasks involving the cognitive control such as the color word Stroop task. Stroop task define the reduction in function in incongruent condition, which requires more attention and control of competitive responses.

Objective: The purpose of this study was to evaluate the activity of brain using the Modified Conflict Stroop Task in Military Personnel.

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 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.