FASB: an integrated processing pipeline for Functional Analysis of simultaneous Spinal cord-Brain fMRI.

Simultaneous functional magnetic resonance imaging (fMRI) of the spinal cord and brain represents a powerful method for examining both ascending sensory and descending motor pathways in humans in vivo . However, its image acquisition protocols, and processing pipeline are less well established. This limitation is mainly due to technical difficulties related to spinal cord fMRI, and problems with the logistics stemming from a large field of view covering both brain and cervical cord. Here, we propose an acquisition protocol optimized for both anatomical and functional images, as well as an optimized integrated image processing pipeline, which consists of a novel approach for automatic modeling and mitigating the negative impact of spinal voxels with low temporal signal to noise ratio (tSNR). We validate our integrated pipeline, named FASB, using simultaneous fMRI data acquired during the performance of a motor task, as well as during resting-state conditions. We demonstrate that FASB outperforms the current spinal fMRI processing methods in three domains, including motion correction, registration to the spinal cord template, and improved detection power of the group-level analysis by removing the effects of participant-specific low tSNR voxels, typically observed at the disk level. Using FASB, we identify significant task-based activations in the expected sensorimotor network associated with a unilateral handgrip force production task across the entire central nervous system, including the contralateral sensorimotor cortex, thalamus, striatum, cerebellum, brainstem, as well as ipsilateral ventral horn at C5-C8 cervical levels. Additionally, our results show significant task-based functional connectivity between the key sensory and motor brain areas and the dorsal and ventral horns of the cervical cord. Overall, our proposed acquisition protocol and processing pipeline provide a robust method for characterizing the activation and functional connectivity of distinct cortical, subcortical, brainstem and spinal cord regions in humans.

Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach.

PURPOSE: Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE). METHODS: Six global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE. RESULTS: Our results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%. CONCLUSION: Accounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.

Evolution of Graph Theory in Dynamic Functional Connectivity for Lateralization of Temporal Lobe Epilepsy.

Resting-state functional magnetic resonance imaging (rsfMRI) has described the functional architecture of the human brain in the absence of any task or stimulus. Since the functional connectivity (FC), has non-stationary nature, it is evidenced to be varying over time. Using dynamic functional connectivity, six graph theoretical characteristics were measured and compared between left and right temporal lobe epilepsy (TLE). We also obtain a trend for each characteristic in the time course of experiments. The results demonstrated that the static connectivity analysis failed to fully separate the left and right TLE patients for some characteristics, whereby the dynamic analysis has been shown capable of identifying the laterality. Furthermore, the results suggest that the temporal trend of some graph theoretical characteristics can be exploited as a novel marker for TLE laterality.

The effect of Zataria multiflora Boiss hydroalcoholic extract and fractions in pentylenetetrazole-induced kindling in mice.

OBJECTIVE: At present, there are many antiepileptic drugs with a wide range of side effects on the human body. It was assumed that Zataria multiflora Boiss (Z. multiflora) with sedative, anti-spasmodic and anti-inflammatory activity may be effective in the treatment of epilepsy. The aim of the present study was to elucidate the effect of Z. multiflora hydroalcoholic extract and its fraction extracts on pentylenetetrazole (PTZ)-induced chemical kindling. MATERIALS AND METHODS: In this experimental study, eight separate groups of male albino mice were used. All groups received 11 separate intraperitoneal injections of PTZ (35 mg/kg) with two-day intervals. 30 min before the injection of PTZ, mice received vehicle, Z. multiflora hydroalcoholic extract (300 and 600 mg/kg), n-hexane, acetone, methanol fraction extracts (150 mg/kg), or diazepam (10 mg/kg). RESULTS: The kindled mice that were pretreated with vehicle showed a gradual increase in their seizure scores up to the end of the study. The hydroalcoholic extract of Z. multiflora (300 and 600 mg/kg) reduced seizure scores significantly. However, n-hexane, acetone and methanol extracts did not affect seizure scores significantly. CONCLUSION: The present findings demonstrate that the hydroalcoholic extract of Z. multiflora did reduce the severity of seizure attacks in PTZ-induced chemical kindling in mice.

The prevalence of internet addiction among students of Rafsanjan University of Medical Sciences

Internet addiction is defined as mismanagement of internet use that causes mental, social and occupational problems. Thus, the assessment of prevalence of this disorder can lead to preventive measures and appropriate treatment to prevent its spread. Methods: In this cross-sectional study, prevalence of internet addiction disorder was assessed in Rafsanjan University of Medical Sciences, Rafsanjan, Iran in 2012. Stratified random sample was used to select 224 students. Demographic data were recorded and Internet Addiction test Test (IAT) questionnaire was administrated. Results: Out off 224 students participating in the study, 86 (38.4%) were males and 138 (61.6%) were female with a mean age of 21.05± 0.1 years. Most of the students (42.4%) were using the internet "under one hour" and the lowest (4%) "More than six hours". Mean test score of IAT was 24.81 ± 1.08 (mild addiction). In terms of internet addiction, 95 (42.4%) cases were normal users, 115 (51.3%) had mild addiction, 12 (5.4%) showed moderate addiction and 2 (0.9%) were cases of severe addiction which are lower compared to previous studies. Conclusion: The rate of internet addiction among students of Rafsanjan University of Medical Sciences in Iran is lower than the previous reports. It is still necessary to curb the spread of this problem due to its complications.