Archives of Neuroscience
Volume:6 Issue: 1, Jan 2019

Near one-third of epileptic cases never achieve remission, despite optimal medication use. In these patients, various surgical procedures can be helpful. However, surgical success is directly associated with the ability to localize precisely the region of seizure onset. The goal of this study was to evaluate the role of susceptibility weighted imaging (SWI) in detecting intracerebral lesions and localizing epileptogenic zones in addition to conventional MRI with routine epilepsy protocol in drug-resistant epileptic patients. The study was carried out at an academic medical center in a major metropolitan city, and all participants underwent conventional MRI assessment with seizure protocol and SWI evaluation. During the study period, 59 cases met the criteria. Thirty-four were male (57.6%) and twenty-five were female. Mean age of participants was 30 years, and mean age at the time of epilepsy onset was 11. In 50 cases (85%), there was evidence of brain abnormalities in conventional MRI and/or SWI. Brain abnormalities were also evident in conventional MRI evaluation of 47 cases (79%). In three out of twelve cases with normal conventional MRI with routine epilepsy protocol, SWI showed brain abnormalities. In 20 cases (40%), the same lateralization or localized lesions were detected in EEG and MRI. More information from SWI was reported in 13 patients (22%). In two cases, in which EEG showed evidence of partial seizures, conventional MRI showed no abnormality while SWI showed abnormal vascular cluster. In one of these two cases, caput medusa, in agreement with developmental venous anomaly, was reported. In one case, conventional MRI was normal while SWI showed evidence of cavernoma. In another patient, in addition to the lesion detectable in conventional MRI, SWI found two other lesions in agreement with cavernomas. Susceptibility weighted imaging can be helpful in localizing epileptogenic zones, which are not detectable by conventional MRI with routine epilepsy protocol, in patients with drug-resistant epilepsy.

Dear Editor, Electromagnetic fields (EMFs), physical energy, are non-ionic waves with speed, frequency, wavelength, and amplitude characteristics, emitted by electrical machines and industry equipments. According to frequency, these fields are divided to low, intermediate, high, and static radiations. In previous studies, it has been claimed that EMFs (duration of exposure) have noxious outcomes on biological systems of humans and rodent, based on frequency and other properties (1, 2). Most researches have been done on EMFs and central nervous systems (CNS) with different exposures involved in vital portions (3, 4). Hippocampus formations, as the main compartment of the limbic system, are principal members of CNS at emotional, behaviors, memory, and regulating of endocrine systems, which could be one of the high sensitive portions to EMFs waves. According to previous studies, it is estimated that EMFs have remarkable effects on neurological compartments (5). A previous study by the current authors showed that 30 mT electromagnetic fields has negative impacts on rat's hippocampus (6). Increased free radicals and created oxidative stress via EMFs waves may be one of the most important reasons (7). This condition leads to promotion of cell's apoptosis pathway in the main parts of hippocampus, such as the dentate gyrus (6). The EMFs have a potential role in the starting of intrinsic and extrinsic pathways of apoptotic process. Breaking of DNA in brain's cells, chromosome abnormalities, genetic mutations, intracellular enzymes dysfunction, and altered expression level of neurotransmitters in various parts of the brain are side effects of electromagnetic fields (8, 9). Furthermore, it is assumed that EMFs have a major effect on high metabolic and proliferative organs, such as hippocampus formations. The produced free radicals in the hippocampus and similar structures can be defined as sensitive organs to EMFs (Figure 1). However, in addition to some researches on the relationship between electromagnetic fields and CNS, there is no suitable strategy to remove electrical devices in the society's life style. In this case, general global designs should be made in order to prevent abnormal births and the risk of neurological disorders

 Medical imaging technologies are an indispensable tool in medicine today developed to satisfy the significant demand for information on medical imaging by visualizing internal organs for clinical analysis. This enables the radiologists and clinicians to accurately understand the patient’s condition and makes medical practices easier, more effective for patients, and cheaper for the healthcare system. The current study aimed at presenting a comprehensive review on the recent classification and segmentation techniques of brain tumors in magnetic resonance image (MRI).  Google Scholar, ScienceDirect, Web of Knowledge, Springer, and manual search of reference lists from 1990 to 2018.  The current study considered brain tumors since they are relatively less common and more important compared with other tumors due to their high morbidity rate. Many automated brain tumors segmentation algorithms of magnetic resonance imaging (MRI) were reviewed and discussed including their advantages and limitations to provide a clear insight into these algorithms. The review concentratedon the state-of-art methods of segmentation of MRI brain tumors since they attracted a significant attention in the recent two decades resulting in many algorithms being developed for automated, semi-automated, and interactive segmentation of brain tumors. While there is a significant development of segmentation algorithms, they are rarely used clinically due to lack of interaction between developers and clinicians. Most studies did not consider grading of brain tumors and did not distinguish to which grade the brain tumor belonged. This enables the developers to understand how the margins of brain tumors appear in medical images.  The most important limitations that make brain tumors segmentation remaina challenging task are the variety of the shape and intensity of tumors in addition to the probability of inhomogeneity of tumorous tissue.

Detection of actual residual tumor extent after resection of gliomas is important for further treatment implications. Conventional MRI features such as T1 weighted contrast enhancement or T2 weighted hyperintensity are not strong indicators of the tumor. Therefore, it is needed to use advanced metabolic imaging such as magnetic resonance spectroscopy (MRS). This work reports the contrast between MRS defining metabolic alteration and imaging features of residual tumor after glioma resection. Eighteen patients with glioma after tumor resection were included in the study. Routine MRI sequences and multi-voxel MRS were obtained. Metabolic regions of interest (ROI) were defined for Cho/NAA and Cho/Cr in different thresholds. Imaging ROI for residual tumor (ROI-t) was defined on conventional MR images. Area of each ROI, the distance between ROI centers, and dice coefficient for the evaluation of similarity between imaging and metabolic ROIs were calculated. Maximum similarity and minimum distance of ROI centers were determined between ROI of Cho/NAA > 1.7 and ROI-t. For Cho/Cr, the maximum similarity was determined in > 1.5. Findings of the present study propose that MRS could be a proper detector for residual tumor after surgical treatment of glioma.

General anesthesia is a widely used medical procedure. However, its underlying physiological mechanisms are still unknown. Current research has identified bottom-up mechanisms in the brain involving subcortical sleep-promoting and arousal structures and top-downmechanisms comprising corticocortical and corticothalamic circuits. The current work presents a neuralmodel considering both mechanisms. Its numerical simulation yields frontal and occipital cortical activity that exhibits the characteristic spectral changes as observed experimentally. In addition, increasing the anesthetic level enhances local synchrony and weakens distant synchrony. This represents a fragmentation of the brain as observed in experimental data under anesthesia.

This study was conducted to determine the morphometric alterations of the corpus callosum in stroke patients using MRI in northern Iran.

This case-control study was carried out on 40 right-handed men and women referring to an MRI center in Gorgan city, North of Iran. The subjects were divided into case and control groups. The case group included 20 male and female patients with stroke and the control group comprised 20 healthy people with no neurological signs and intracranial lesions on MRI. The widths of the rostrum, body, and splenium, the anterior to posterior length, and the maximum height of the corpus callosum were measured for each subject. The ratios of the body to length and body to the height of the corpus callosum were also calculated. Student’s unpaired t-test and regression analysis were used for data analysis at the significance level of 95%.

The mean width ± SD of the rostrum, splenium, and body of the corpus callosum was significantly lower in the stroke patients than in controls (9.84 ± 1.7 vs. 11.20 ± 1.3 mm, P = 0.01; 10.32 ± 1.9 vs. 11.98 ± 0.9 mm, P = 0.002; and 6.20 ± 1.0 vs. 6.84 ± 0.6 mm, P = 0.03, respectively). The widths of the rostrum, splenium, and body were significantly lower in male stroke patients than in controls (9.45 ± 1.7 vs. 11.65 ± 1.2 mm, P = 0.003; 9.6 ± 1.9 vs. 11.98 ± 0.8 mm, P = 0.003; and 5.64 ± 0.9 vs. 6.44 ± 0.7 mm, P = 0.05, respectively). However, these indices were not significantly lower in female stroke patients than in controls.

This study showed that stroke reduces morphometric indices of the corpus callosum, particularly in the splenium of men

Optical imaging has attracted the researcher’s attention in recent years as an uncompromising and efficient method to measure the changes in brain cortex activity. Functional Near-Infrared Spectroscopy (fNIRS) is a method that measures hemodynamic changes in the brain cerebral cortex based on optical principles.

The current study aimed to evaluate the activities of the brain cortex during wrist movement using fNIRS.

In this study, the activity of the brain motor cortex was investigated during right wrist movement in 10 young righthanded volunteers. Data were collected using a 48-channel fNIRS device with two wavelengths of 855 nm and 765 nm. For this experiment, 20 channels were used and the sampling frequency was set at 10 Hz.

Signal intensity in the motor cortex was significantly higher during movement than in the rest (P ≤ 0.05). The activation map of wrist movements was separated spatially in the motor cortex. The highest activity was recorded in the primary motor cortex (M1). There was a significant difference in the focus of the maximum activation of the brain between the four main directions.

It is possible to differentiate between different directions of movement using near-infrared signals. The presence of directional activation in the cerebral cortex helps confirm the notion that this part of the brain participates in the processing of complex information besides controlling the movement of different parts of the body.

Determining the activated brain areas due to different activities is one of the most common targets in functional magnetic resonance imaging (fMRI) data analysis, which could be carried out by hemodynamic response function (HRF) evaluation. The HR functions reflect changes of cerebral blood flow (CBF) in response to neural activity.

In this study, five models of HRF estimation were evaluated based on a simulated dataset. Models with higher accuracy were used to determine HRF parameters of the block-design fMRI data.

The fMRI data were acquired in a 3 Tesla scanner. For block-design fMR imaging, CO2 gas was administered using a facemask under physiological monitoring. Three patients with brain tumors were scanned. The fMRI data analysis was performed using the SPM 12 MATLAB toolbox. Akaike’s information criterion (AIC), Schwarz’ Bayesian (SBC), and mean square error (MSE) criteria were used to select the best HRF estimation model.

In simulation studies, the estimated HRFs by the canonical HRF plus its temporal derivative (TD), finite impulse response (FIR), and inverse logistic (IL) models were almost equal to the standard HRF. Mean square error, AIC, and SBC indices were ignorable for TD, FIR, and IL models (MSE/AIC/SBC magnitudes for TD, FIR, and IL models were 0.052/-1235.1/-1223.9, 0.055/-1206.4/-1194.9, and 0.068/-1091.5/-1049.2, respectively), which indicates that these models could accurately estimate HRF in block design fMRI studies.

The HRF models could non-invasively evaluate the change of MR signal intensity under cerebrovascular reactivity (CVR) conditions and they might be helpful to investigate changes in human cerebral blood flow.

Promising therapeutic agents for the symptoms in animal models of fragile X syndrome (FXS) have not resulted in similar advances in clinical trials of humans with FXS due to the dearth of tools to quantify their key cognitive and behavioral outcomemeasures with optimal validity and reliability. Therefore, experts strongly recommended an effort to develop and implement use of biomarkers in unfolding clinical trials in FXS. Molecular imaging provides a spectrum of agents to serve as biomarkers to confirm that humans with FXS exhibit the molecular abnormalities of animal models of FXS. Thus, molecular imaging provides the mechanism to establish target engagement in humans for clinical trials of novel agents for FXS.