جستجوی مقالات مرتبط با کلیدواژه "functional magnetic resonance imaging (fmri)" در نشریات گروه "پزشکی"

Cue-induced craving is central to addictive disorders. Most cue-reactivity functional magnetic resonance imaging studies are analyzed statically and report averaged signals, disregarding the dynamic nature of craving and task fatigue. Accordingly, this study investigates temporal dynamics of the neural response to drug cues as a functional magnetic resonance imaging study among methamphetamine users. 

A total of 32 early abstinent methamphetamine users underwent functional magnetic resonance imaging while viewing visual methamphetamine cues. A craving > neutral contrast was obtained in regions of interest. To explore the changes over time, the pre-processed signal was divided into three intervals. Contrast estimates were calculated within each interval, and were compared using the analysis of variance followed by the post hoc t-tests. The results were compared with those from a static analysis across all blocks.

A priori expected activations in the prefrontal cortex, insula, and striatum not detected by static analysis were discovered by the dynamic analysis. Post hoc tests revealed distinct temporal activation patterns in several regions. Most patterns showed rapid activation (including both ventral/dorsal striata and most regions in the prefrontal, insular, and cingulate cortices), whereas some had delayed activation (the right anterior insula, left middle frontal gyrus, and left dorsal anterior cingulate cortex).

This study provided preliminary insights into the temporal dynamicity of cue-reactivity, and the potential of a conventional blocked-design task to consider it as a simple dynamic analysis. We highlight regional activations that were only uncovered by dynamic analysis and discuss the interesting and theoretically expected early versus late regional activation patterns. Rapidly activated regions are mostly those involved in the earlier stages of cue reactivity, while regions with later activation participate in cognitive functions relevant later, such as reappraisal, interoception, and executive control.

Independent Component Analysis (ICA) is the most common and standard technique used in functional neuroscience data analysis.  In this study, two of the significant functional brain techniques are introduced as a model for neuroscience data analysis. In this experimental and analytical study, Electroencephalography (EEG) signal and functional Magnetic Resonance Imaging (fMRI) were analyzed and managed by the developed tool. The introduced package combines Independent Component Analysis (ICA) to recognize significant dimensions of the data in neuroscience. This study combines EEG and fMRI in the same package for analysis and comparison results.  The findings of this study indicated the performance of the ICA, which can be dealt with the presented easy-to-use and learn intuitive toolbox. The user can deal with EEG and fMRI data in the same module. Thus, all outputs were analyzed and compared at the same time; the users can then import the neurofunctional datasets easily and select the desired portions of the functional biosignal for further processing using the ICA method.   A new toolbox and functional graphical user interface, running in cross-platform MATLAB, was presented and applied to biomedical engineering research centers.

Finding neural correlates underlying deception may have implementations in judicial, security, and financial settings. Telling a successful lie may activate different brain regions associated with risk evaluation, subsequent reward/punishment possibility, decision-making, and theory of mind (ToM). Many other protocols have been developed to study individuals who proceed with deception under instructed laboratory conditions. However, no protocol has practiced lying in a real-life environment. We performed a functional MRI using a 3Tesla machine on 31 healthy individuals to detect the participants who successfully lie in a previously-designed game to earn or lose the monetary reward. The results revealed that lying results in an augmented activity in the right dorsolateral and right dorsomedial prefrontal cortices, the right inferior parietal lobule, bilateral inferior frontal gyri, and right anterior cingulate cortex. The findings would contribute to forensic practices regarding the detection of a deliberate lie. They may also have implications for guilt detection, social cognition, and the societal notions of responsibility.

Studies show that weakness in hazard perception is a major cause of traffic accidents, leading to high consequences.

This study aimed to design a valid and reliable driver’s Hazard Perception Test (HPT) based on neural imaging, reaction time, and miss rate in two groups of experienced and inexperienced drivers.

Different roads, including urban, intercity, and rural, were filmed from drivers’ visual angles to examine the real road conditions. All videos were screened according to some quality factors. Then, hazard onset was determined for screened videos. The validity of the test was performed in three steps. Miss rates and reaction times to hazardous situations were measured. In the second step, 35 selected videos were broadcasted to 16 experienced and 16 novice drivers on a functional magnetic resonance imaging (fMRI). Finally, using 18 videos with statistically significant differences in neuro-cerebral neuronal activity, miss rate and reaction time were picked up for driver’s HPT.

The mean differences in reaction time, miss rate, and active neurons in the task of perceiving hazards in two groups of drivers were equal to 1.58 seconds, 29.55%, and 5248 neurons, respectively. There was a significant correlation between active neurons and miss rate (r = 0.556, P < 0.001). Eventually, the 18-videos of the valid test became HPT software.

Application of this valid test is suggested for assessing the hazard perception of drivers, particularly those who are responsible for transporting staff and goods in the studied country.

about 20% to 30% of patients with epilepsy are diagnosed with drug-resistant epilepsy and one third of these are candidates for epilepsy surgery. Surgical resection of the epileptogenic tissue is a well-established method for treating patients with intractable focal epilepsy. Determining language laterality and locality is an important part of a comprehensive epilepsy program before surgery. Functional Magnetic Resonance Imaging (fMRI) has been increasingly employed as a non-invasive alternative method for the Wada test and cortical stimulation. Sensitive and accurate language tasks are essential for any reliable fMRI mapping. 

The present study reviews the methods of presurgical fMRI language mapping and their dedicated fMRI tasks, specifically for patients with epilepsy.

Different language tasks including verbal fluency are used in fMRI to determine language laterality and locality in different languages such as Persian. there are some considerations including the language materials and technical protocols for task design that all presurgical teams should take into consideration.

Accurate presurgical language mapping is very important to preserve patients language after surgery. This review was the first part of a project for designing standard tasks in Persian to help precise presurgical evaluation and in Iranian PWFIE.

Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman’s general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and resting-state fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.

Functional magnetic resonance imaging (fMRI) methods have been used to study sensorimotor processing in the spinal cord. However, these techniques confront unwanted noises to the measured signal from the physiological fluctuations. In the spinal cord imaging, most of the challenges are consequences of cardiac and respiratory movement artifacts that are considered as significant sources of noise, especially in the thoracolumbar region. In this study, we investigated the effect of each source of physiological noise and their contribution to the outcome of the analysis of the blood-oxygen-level-dependent signal in the human thoracolumbar spinal cord.

Fifteen young healthy male volunteers participated in the study, and pain stimuli were delivered on the L5 dermatome between the two malleoli. Respiratory and cardiac signals were recorded during the imaging session, and the generated respiration and cardiac regressors were included in the general linear model for quantification of the effect of each of them on the task-analysis results.  The sum of active voxels of the clusters was calculated in the spinal cord in three correction states (respiration correction only, cardiac correction only, and respiration and cardiac noise corrections) and analyzed with analysis of variance statistical test and receiver operating characteristic curve.

The results illustrated that cardiac noise correction had an effective role in increasing the active voxels (Mean±SD= 23.46±9.46) compared to other noise correction methods. Cardiac effects were higher than other physiological noise sources

In summary, our results indicate great respiration effects on the lumbar and thoracolumbar spinal cord fMRI, and its contribution to the heartbeat effect can be a significant variable in the individual fMRI data analysis. Displacement of the spinal cord and the effects of this noise in the thoracolumbar and lumbar spinal cord fMRI results are significant and cannot be ignored.

A fixed hemodynamic response function (HRF) is commonly used for functional magnetic resonance imaging (fMRI) analysis. However, HRF may vary from region to region and subject to subject. We investigated the effect of locally estimated HRF (in functionally homogenous parcels) on activation detection sensitivity in a heroin cue reactivity study.
We proposed a novel exploratory method for brain parcellation based on a probabilistic model to segregate the brain into spatially connected and functionally homogeneous components. Then, we estimated HRF and detected activated regions in response to an experimental task in each parcel using a joint detection estimation (JDE) method. We compared the proposed JDE method with the general linear model (GLM) that uses a fixed HRF and is implemented in FEAT (as a part of FMRIB Software Library, version 4.1).
1) Regions detected by JDE are larger than those detected by fixed HRF, 2) In group analysis, JDE found areas of activation not detected by fixed HRF. It detected drug craving a priori “regions-of-interest” in the limbic lobe (anterior cingulate cortex [ACC], posterior cingulate cortex [PCC] and cingulate gyrus), basal ganglia, especially striatum (putamen and head of caudate), and cerebellum in addition to the areas detected by the fixed HRF method, 3) JDE obtained higher Z-values of local maxima compared to those obtained by fixed HRF.
In our study of heroin cue reactivity, our proposed method (that estimates HRF locally) outperformed the conventional GLM that uses a fixed HRF.