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Background: Common carotid artery intima-media thickness (cIMT) has been associated with cardiovascular diseases, including stroke and coronary heart disease. There is a limited number of studies that specifically address the variability of ultrasound imaging modes in measuring cIMT. Therefore, this study aimed to assess the agreement-level and inter-observer reproducibility of cIMT measurements using B-mode and M-mode. Materials and Methods: B-mode and M-mode ultrasound imaging were used in measuring cIMT of healthy subjects using linear-array transducer. Inter-imaging mode agreement and inter-observer reproducibility of B-mode and M-mode were assessed using Intraclass correlation coefficient (ICC). Differences in cIMT measurements between imaging modes and observers were determined using paired t-test Results: There was significant difference between B-mode and M-mode in measuring cIMT (mean difference (MD), 0.03 mm, 95% CI 0.02 - 0.05, p

The coronavirus (COVID-2019) has spread very rapidly over many countries around the world, producing an outbreak of acute infectious pneumonia Medical imaging devices play a vital role in early diagnosis, evaluating severity and disease prognosis of confirmed patients with COVID-19 The aim of the study is to evaluate the awareness level of radiology students about COVID-19 and their understanding of the role of radiology devices in diagnosing COVID-19 An online cross-sectional questionnaire was conducted Seventy-one students participated in this study This study showed that students were properly aware of the basic knowledge of the COVID-19, 87 % of the students believed that radiology had an important role in diagnosing COVID-19 In addition, 50% of the students believed that CT was the most important modality in diagnosing COVID-19 and 32 35% believed that x-ray was the most important one Lack of knowledge was found regarding the biomarkers that appear in radiological images in patients with COVID-19 66 18 % of the students acquired the knowledge from their study in the radiology field and from social media (Twitter) The level of awareness among radiology students is high There were two main reasons for this high level of awareness: Gained education and social media, especially Twitter The outbreak of the COVID-19 was not only about its health effects but also how the population persevered this pandemic However, this level of awareness tended to be lower when discussing detailed findings of radiological images such as detailed biomarkers © 2021 by the authors

The cytoarchitectonic properties of the primary motor cortex have shown two distinct sub-regions: Anterior Broadmann area 4 (BA4a) and Posterior Broadmann area 4 (BA4p). Some previous studies have suggested that these two sub-regions are functionally different and showed that in few fMRI experiments, these sub-regions may have different roles in brain functions. Resting-state fMRI (rsfMRI) is advanced technique that allows investigating in detail the functional connectivity and provides a greater understanding of the physiological behavior of different brain regions. In this study, 198 healthy subjects were examined using a region-based rsfMRI analysis to investigate whether BA4a and BA4p have similar or different connections to other brain networks. The finding shows that indeed these two sub-regions have distinct connectivity to different brain networks. BA4a has a greater connection to motor-related areas while BA4p has connections to nonmotor-related areas (such as sensory, attentional, and higher order regions), suggesting that these two sub-regions should be considered as two separate regions of interests.

This study highlights the importance of looking beyond the main effect of movement to study alterations in functional response in the presence of central nervous system pathologies such as multiple sclerosis (MS). Data show that MS selectively affects regional BOLD (Blood Oxygenation Level Dependent) responses to variable grip forces (GF). It is known that the anterior and posterior BA 4 areas (BA 4a and BA 4p) are anatomically and functionally distinct. It has also been shown in Healthy volunteers that there are linear (1st order, typical of BA 4a) and non-linear (2nd-4th order, typical of BA 4p) BOLD responses to different levels of GF applied during a dynamic motor paradigm. After modelling the BOLD response with a polynomial expansion of the applied GFs, the particular case of BA 4a and BA 4p were investigated in Healthy Volunteers (HV) and MS subjects. The main effect of movement (0th order) analysis showed that the BOLD signal is greater in MS compared to healthy volunteers within both BA 4 sub-regions. At higher order, BOLD-GF responses were similar in BA 4a but showed a marked alteration in BA 4p of MS subjects, with those with greatest disability showing the greatest deviations from the healthy response profile. Therefore, the different behaviour in HV and MS could only be uncovered through a polynomial analysis looking beyond the main effect of movement into the two BA 4 sub-regions. Future studies will investigate the source of this pathophysiology, combining the present fMRI paradigm with blood perfusion and non-linear neuronal response analysis.

Spatial smoothing is an essential pre-processing step in the process of analysing functional magnetic resonance imaging (fMRI) data, both during an experimental task or during resting-state fMRI (rsfMRI). The main benefit of this spatial smoothing step is to artificially increase the signal-to-noise ratio of the fMRI signal. Previous fMRI studies have investigated the impact of spatial smoothing on task fMRI data, while rsfMRI studies usually apply the same analytical process used for the task data. However, this study investigates changes in different rsfMRI analyses, such as ROI-to-ROI, seed-to-voxels and ICA analyses. Nineteen healthy volunteers were scanned using rsfMRI with three applied smoothing kernels: 0 mm, 4 mm and 8 mm. Appropriate statistical comparisons were made. The findings showed that spatial smoothing has a greater effect on rsfMRI data when analysed using seed-to-voxel-based analysis. The effect was less pronounced when analysing data using ROI-ROI or ICA analyses. The results demonstrated that even when analysing the data without the application of spatial smoothing, the results were significant compared with data analysed using a typical smoothing kernel. However, data analysed with lower-smoothing kernels produced greater negative correlations, particularly with the ICA analysis. The results suggest that a medium smoothing kernel (around 4 mm) may be preferable, as it is comparable with the 8 mm kernel in all of the analyses performed. It is also recommended that the researchers consider analysing the data using two different smoothing kernels, as this will help to confirm the significance of the results and avoid overestimating the findings.

Resting state functional MRI(rsfMRI) is an established tool for investigating cognitive impairment(CI) (affecting 40–70% dependent on criteria) in Multiple Sclerosis(MS). This study used rsfMRI to investigate the pattern of functional connectivity (FC) in cognitively impaired(CI) and cognitively preserved(CP) secondary progressive MS(SPMS) patients based on criteria.70 SPMS subjects (age54±7.1, disease duration 22±9.1 years, EDSS 6.0 (range 4.0–6.5), progression duration8.03±5.2 years) were recruited locally. Cognition was assessed by a battery of neuropsychometric tests and defined using the conservative criteria (z-score of -1.96 standard deviations (SD) on ≥2 tests) and lenient criteria (z-score of -1.5SDs on ≥2 tests). RsfMRI obtained using a 3T Philips scanner was pre-processed and underwent Independent Component Analysis(ICA) to identify the Resting State Networks(RSNs). Dual regression compared group-specific maps(i.e. CI vs CP according to criteria).We found that the proportion of SPMS-CI changes by 17.1% when using the more stringent conservative(30%) versus the lenient(47.1%) criteria. Overall, 14 RSNs were identified. The greatest FC differences between CI and CP patients within RSNs occurred with the conservative criteria. Independent of the CI criteria used, the left ventral attention network(LVAN), involved in attention processing, is overall the most impaired network in SPMS-CI suggesting that pathological changes in this RSN may underpin global cognitive problems in SPMS.

The relationship between the BOLD response and an applied force was quantified in the cerebellum using a power grip task. To investigate whether the cerebellum responds in an on/off way to motor demands or contributes to motor responses in a parametric fashion, similarly to the cortex, five grip force levels were investigated under visual feedback. Functional MRI data were acquired in 13 healthy volunteers and their responses were analyzed using a cerebellum‐optimized pipeline. This allowed us to evaluate, within the cerebellum, voxelwise linear and non‐linear associations between cerebellar activations and forces. We showed extensive non‐linear activations (with a parametric design), covering the anterior and posterior lobes of the cerebellum with a BOLD‐force relationship that is region‐dependent. Linear responses were mainly located in the anterior lobe, similarly to the cortex, where linear responses are localized in M1. Complex responses were localized in the posterior lobe, reflecting its key role in attention and executive processing, required during visually guided movement. Given the highly organized responses in the cerebellar cortex, a key question is whether deep cerebellar nuclei show similar parametric effects. We found positive correlations with force in the ipsilateral dentate nucleus and negative correlations on the contralateral side, suggesting a somatotopic organization of the dentate nucleus in line with cerebellar and cortical areas. Our results confirm that there is cerebellar organization involving all grey matter structures that reflect functional segregation in the cortex, where cerebellar lobules and dentate nuclei contribute to complex motor tasks with different BOLD response profiles in relation to the forces. Hum Brain Mapp 38:2566–2579, 2017. © 2017 Wiley Periodicals, Inc.