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Background: Changes in both the vascular system and brain tissues can occur after a prior episode of coronavirus disease 2019 (COVID-19), detectable through modifications in diffusion parameters using magnetic resonance imaging (MRI) techniques. These changes in diffusion parameters may be particularly prominent in highly organized structures such as the corpus callosum (CC), including its major components, which have not been adequately studied following COVID-19 infection. Therefore, the study aimed to evaluate microstructural changes in whole-brain (WB) diffusion, with a specific focus on the CC., Methods: A total of 101 probands (age range from 18 to 69 years) participated in this retrospective study, consisting of 55 volunteers and 46 post-COVID-19 patients experiencing neurological symptoms. The participants were recruited from April 2022 to September 2023 at the Institute for Clinical and Experimental Medicine in Prague, Czech Republic. All participants underwent MRI examinations on a 3T MR scanner with a diffusion protocol, complemented by additional MRI techniques. Two volunteers and five patients were excluded from the study due to motion artefacts, severe hypoperfusion or the presence of lesions. Participants were selected by a neurologist based on clinical examination and a serological test for COVID-19 antibodies. They were then divided into three groups: a control group of healthy volunteers (n=28), an asymptomatic group (n=25) with a history of infection but no symptoms, and a symptomatic group (n=41) with a history of COVID-19 and neurological symptoms. Symptomatic patients did not exhibit neurological symptoms before contracting COVID-19. Diffusion data underwent eddy current and susceptibility distortion corrections, and fiber tracking was performed using default parameters in DSI studio. Subsequently, various diffusion metrics, were computed within the reconstructed tracts of the WB and CC. To assess the impact of COVID-19 and its associated symptoms on diffusion indices within the white matter of the WB and CC regions, while considering age, we employed a statistical analysis using a linear mixed-effects model within the R framework., Results: Statistical analysis revealed a significant difference in mean diffusivity (MD) between the symptomatic and control groups in the forceps minor (P=0.001) and CC body (P=0.003). In addition to changes in diffusion, alterations in brain perfusion were observed in two post-COVID-19 patients who experienced a severe course. Furthermore, hyperintense lesions were identified in subcortical and deep white matter areas in the vast majority of symptomatic patients., Conclusions: The main finding of our study was that post-COVID-19 patients exhibit increased MD in the forceps minor and body of the CC. This finding suggests a potential association between microstructural brain changes in post-COVID-19 patients and reported neurological symptoms, with significant implications for research and clinical applications., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-162/coif). The authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Background: Negative symptoms (NS) represent a detrimental symptomatic domain in schizophrenia affecting social and occupational outcomes., Aims: We aimed to identify factors from the baseline visit (V1) - with a mean illness duration of 0.47 years (SD = 0.45) - that predict the magnitude of NS at the follow-up visit (V3), occurring 4.4 years later (mean +/- 0.45)., Method: Using longitudinal data from 77 first-episode schizophrenia spectrum patients, we analysed eight predictors of NS severity at V3: (1) the age at disease onset, (2) age at V1, (3) sex, (4) diagnosis, (5) NS severity at V1, (6) the dose of antipsychotic medication at V3, (7) hospitalisation days before V1 and; (8) the duration of untreated psychosis /DUP/). Secondly, using a multiple linear regression model, we studied the longitudinal relationship between such identified predictors and NS severity at V3 using a multiple linear regression model., Results: DUP (Pearson's r = 0.37, p = 0.001) and NS severity at V1 (Pearson's r = 0.49, p < 0.001) survived correction for multiple comparisons. The logarithmic-like relationship between DUP and NS was responsible for the initial stunning incremental contribution of DUP to the severity of NS. For DUP < 6 months, with the sharpest DUP/NS correlation, prolonging DUP by five days resulted in a measurable one-point increase in the 6-item negative symptoms PANSS domain assessed 4.9 (+/- 0.6) years after the illness onset. Prolongation of DUP to 14.7 days doubled this NS gain, whereas 39 days longer DUP tripled NS increase., Conclusion: The results suggest the petrification of NS during the early stages of the schizophrenia spectrum and a crucial dependence of this symptom domain on DUP. These findings are clinically significant and highlight the need for primary preventive actions., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Aging negatively influences the structure of the human brain including the white matter. The objective of our study was to identify, using fixel-based morphometry, the age induced changes in the pathways connecting several regions of the central auditory system (inferior colliculus, Heschl's gyrus, planum temporale) and the pathways connecting these structures with parts of the limbic system (anterior insula, hippocampus and amygdala). In addition, we were interested in the extent to which the integrity of these pathways is influenced by hearing loss and tinnitus., Methods: Tractographic data were acquired using a 3 T MRI in 79 volunteers. The participants were categorized into multiple groups in accordance with their age, auditory thresholds and tinnitus status. Fixel-based analysis was utilized to identify alterations in the subsequent three parameters: logarithm of fiber cross-section, fiber density, fiber density and cross-section. Two modes of analysis were used: whole brain analysis and targeted analysis using fixel mask, corresponding to the pathways connecting the aforementioned structures., Results: A significantly negative effect of aging was present for all fixel-based metrics, namely the logarithm of the fiber cross-section, (7 % fixels in whole-brain, 14% fixels in fixel mask), fiber density (5 % fixels in whole-brain, 15% fixels in fixel mask), fiber density and cross section (7 % fixels in whole-brain, 19% fixels in fixel mask). Expressed age-related losses, exceeding 30% fixels, were particularly present in pathways connecting the auditory structures with limbic structures. The effect of hearing loss and/or tinnitus did not reach significance., Conclusions: Our results show that although an age-related reduction of fibers is present in pathways connecting several auditory regions, the connections of these structures with limbic structures are even more reduced. To what extent this fact influences the symptoms of presbycusis, such as decreased speech comprehension, especially in noise conditions, remains to be elucidated., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Svobodová, Profant, Škoch, Tintěra, Tóthová, Chovanec, Čapková and Syka.)

Background: The main aim of the present study is to determine the role of metabolites observed using proton magnetic resonance spectroscopy (1H-MRS) in obsessive-compulsive disorder (OCD). As the literature describing biochemical changes in OCD yields conflicting results, we focused on accurate metabolite quantification of total N-acetyl aspartate (tNAA), total creatine (tCr), total choline-containing compounds (tCh), and myo-inositol (mI) in the anterior cingulate cortex (ACC) to capture the small metabolic changes between OCD patients and controls and between OCD patients with and without medication., Methods: In total 46 patients with OCD and 46 healthy controls (HC) matched for age and sex were included in the study. The severity of symptoms in the OCD was evaluated on the day of magnetic resonance imaging (MRI) using the Yale-Brown Obsessive-Compulsive Scale (YBOCS). Subjects underwent 1H-MRS from the pregenual ACC (pgACC) region to calculate concentrations of tNAA, tCr, tCho, and mI. Twenty-eight OCD and 28 HC subjects were included in the statistical analysis. We compared differences between groups for all selected metabolites and in OCD patients we analyzed the relationship between metabolite levels and symptom severity, medication status, age, and the duration of illness., Results: Significant decreases in tCr (U = 253.00, p = 0.022) and mI (U = 197.00, p = 0.001) in the pgACC were observed in the OCD group. No statistically significant differences were found in tNAA and tCho levels; however, tCho revealed a trend towards lower concentrations in OCD patients (U = 278.00, p = 0.062). Metabolic concentrations showed no significant correlations with the age and duration of illness. The correlation statistics found a significant negative correlation between tCr levels and YBOCS compulsions subscale (cor = -0.380, p = 0.046). tCho and YBOCS compulsions subscale showed a trend towards a negative correlation (cor = -0.351, p = 0.067). Analysis of subgroups with or without medication showed no differences., Conclusions: Patients with OCD present metabolic disruption in the pgACC. The decrease in tCr shows an important relationship with OCD symptomatology. tCr as a marker of cerebral bioenergetics may also be considered as a biomarker of the severity of compulsions. The study failed to prove that metabolic changes correlate with the medication status or the duration of illness. It seems that a disruption in the balance between these metabolites and their transmission may play a role in the pathophysiology of OCD., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Despite the rising global burden of stroke and its socio-economic implications, the neuroimaging predictors of subsequent cognitive impairment are still poorly understood. We address this issue by studying the relationship of white matter integrity assessed within ten days after stroke and patients' cognitive status one year after the attack. Using diffusion-weighted imaging, we apply the Tract-Based Spatial Statistics analysis and construct individual structural connectivity matrices by employing deterministic tractography. We further quantify the graph-theoretical properties of individual networks. The Tract-Based Spatial Statistic did identify lower fractional anisotropy as a predictor of cognitive status, although this effect was mostly attributable to the age-related white matter integrity decline. We further observed the effect of age propagating into other levels of analysis. Specifically, in the structural connectivity approach we identified pairs of regions significantly correlated with clinical scales, namely memory, attention, and visuospatial functions. However, none of them persisted after the age correction. Finally, the graph-theoretical measures appeared to be more robust towards the effect of age, but still were not sensitive enough to capture a relationship with clinical scales. In conclusion, the effect of age is a dominant confounder especially in older cohorts, and unless appropriately addressed, may falsely drive the results of the predictive modelling., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Bučková et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Motor disability is a dominant and restricting symptom in multiple sclerosis, yet its neuroimaging correlates are not fully understood. We apply statistical and machine learning techniques on multimodal neuroimaging data to discriminate between multiple sclerosis patients and healthy controls and to predict motor disability scores in the patients. We examine the data of sixty-four multiple sclerosis patients and sixty-five controls, who underwent the MRI examination and the evaluation of motor disability scales. The modalities used comprised regional fractional anisotropy, regional grey matter volumes, and functional connectivity. For analysis, we employ two approaches: high-dimensional support vector machines run on features selected by Fisher Score (aiming for maximal classification accuracy), and low-dimensional logistic regression on the principal components of data (aiming for increased interpretability). We apply analogous regression methods to predict symptom severity. While fractional anisotropy provides the classification accuracy of 96.1% and 89.9% with both approaches respectively, including other modalities did not bring further improvement. Concerning the prediction of motor impairment, the low-dimensional approach performed more reliably. The first grey matter volume component was significantly correlated (R = 0.28-0.46, p < 0.05) with most clinical scales. In summary, we identified the relationship between both white and grey matter changes and motor impairment in multiple sclerosis. Furthermore, we were able to achieve the highest classification accuracy based on quantitative MRI measures of tissue integrity between patients and controls yet reported, while also providing a low-dimensional classification approach with comparable results, paving the way to interpretable machine learning models of brain changes in multiple sclerosis., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Magnetic resonance (MR) tractography of the brachial plexus (BP) is challenging due to different factors such as motion artifacts, pulsation artifacts, signal-to-noise ratio, spatial resolution; eddy currents induced geometric distortions, sequence parameters and choice of used coils. Notably challenging is the separation of the peripheral nerve bundles and skeletal muscles as both structures have similar fractional anisotropy values. We proposed an algorithm for robust visualization and assessment of BP root bundles using the segmentation of the spinal cord (SSC, C4-T1) as seed points for the initial starting area for the fibre tracking algorithm., Methods: Twenty-seven healthy volunteers and four patients with root avulsions underwent magnetic resonance imaging (MRI) examinations on a 3T MR scanner with optimized measurement protocols for diffusion-weighted images and coronal T2 weighted 3D short-term inversion recovery sampling perfection with application optimized contrast using varying flip angle evaluation sequences used for BP fibre reconstruction and MR neurography (MRN). The fibre bundles reconstruction was optimized in terms of eliminating the skeletal muscle fibres contamination using the SSC and the tracking threshold of the normalized quantitative anisotropy (NQA) on reconstruction of the BP. In our study, the NQA parameter has been used for fiber tracking instead of fractional anisotropy (FA). The diffusion data were processed in individual C4-T1 root bundles using the generalized q-sampling imaging (GQI) algorithm. Calculated diffusion parameters were statistically analysed using the two-sample t -test. The MRN was performed in MedINRIA and post-processed using the maximum intensity projection (MIP) method to demonstrate BP root bundles in multiple planes., Results: In control subjects, no significant effect of laterality in diffusion parameters was found (P>0.05) in the BP. In the central part of the BP, a significant difference between control subjects and patients at P=0.02 was found in the NQA values. Other diffusion parameters were not significantly different., Conclusions: Using NQA instead of FA in the proposed algorithm allowed for a better separation of muscle and root nerve bundles. The presented algorithm yields a high quality reconstruction of the BP bundles that may be helpful both in research and clinical practice., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-30/coif). II, AŠ, IH, JB, MJ and JT report that this work was supported by scientific grants from the Ministry of Health of the Czech Republic (No. 17-28587A; No. IKEM, IN 00023001). The other authors have no conflicts of interest to declare., (2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

The human brain represents a complex computational system, the function and structure of which may be measured using various neuroimaging techniques focusing on separate properties of the brain tissue and activity. We capture the organization of white matter fibers acquired by diffusion-weighted imaging using probabilistic diffusion tractography. By segmenting the results of tractography into larger anatomical units, it is possible to draw inferences about the structural relationships between these parts of the system. This pipeline results in a structural connectivity matrix, which contains an estimate of connection strength among all regions. However, raw data processing is complex, computationally intensive, and requires expert quality control, which may be discouraging for researchers with less experience in the field. We thus provide brain structural connectivity matrices in a form ready for modelling and analysis and thus usable by a wide community of scientists. The presented dataset contains brain structural connectivity matrices together with the underlying raw diffusion and structural data, as well as basic demographic data of 88 healthy subjects., (© 2022. The Author(s).)

Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Gerster, Taher, Škoch, Hlinka, Guye, Bartolomei, Jirsa, Zakharova and Olmi.)

Abstract: MR tractography of the lumbosacral plexus (LSP) is challenging due to the difficulty of acquiring high quality data and accurately estimating the neuronal tracts. We proposed an algorithm for an accurate visualization and assessment of the major LSP bundles using the segmentation of the cauda equina as seed points for the initial starting area for the fiber tracking algorithm.Twenty-six healthy volunteers underwent MRI examinations on a 3T MR scanner using the phased array coils with optimized measurement protocols for diffusion-weighted images and coronal T2 weighted 3D short-term inversion recovery sampling perfection with application optimized contrast using varying flip angle evaluation sequences used for LSP fiber reconstruction and MR neurography (MRN).The fiber bundles reconstruction was optimized in terms of eliminating the muscle fibers contamination using the segmentation of cauda equina, the effects of the normalized quantitative anisotropy (NQA) and angular threshold on reconstruction of the LSP. In this study, the NQA parameter has been used for fiber tracking instead of fractional anisotropy (FA) and the regions of interest positioning was precisely adjusted bilaterally and symmetrically in each individual subject.The diffusion data were processed in individual L3-S2 nerve fibers using the generalized Q-sampling imaging algorithm. Data (mean FA, mean diffusivity, axial diffusivity and radial diffusivity, and normalized quantitative anisotropy) were statistically analyzed using the linear mixed-effects model. The MR neurography was performed in MedINRIA and post-processed using the maximum intensity projection method to demonstrate LSP tracts in multiple planes.FA values significantly decreased towards the sacral region (P < .001); by contrast, mean diffusivity, axial diffusivity, radial diffusivity and NQA values significantly increased towards the sacral region (P < .001).Fiber tractography of the LSP was feasible in all examined subjects and closely corresponded with the nerves visible in the maximum intensity projection images of MR neurography. Usage of NQA instead of FA in the proposed algorithm enabled better separation of muscle and nerve fibers.The presented algorithm yields a high quality reconstruction of the LSP bundles that may be helpful both in research and clinical practice., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Age related hearing loss (presbycusis) is a natural process represented by elevated auditory thresholds and decreased speech intelligibility, especially in noisy conditions. Tinnitus is a phantom sound that also potentially leads to cortical changes, with its highest occurrence coinciding with the clinical onset of presbycusis. The aim of our project was to identify age, hearing loss and tinnitus related structural changes, within the auditory system and associated structures. Groups of subjects with presbycusis and tinnitus (22 subjects), with only presbycusis (24 subjects), young tinnitus patients with normal hearing (10 subjects) and young controls (17 subjects), underwent an audiological examination to characterize hearing loss and tinnitus. In addition, MRI (3T MR system, analysis in Freesurfer software) scans were used to identify changes in the cortical and subcortical structures. The following areas of the brain were analyzed: Heschl gyrus (HG), planum temporale (PT), primary visual cortex (V1), gyrus parahippocampus (PH), anterior insula (Ins), amygdala (Amg), and hippocampus (HP). A statistical analysis was performed in R framework using linear mixed-effects models with explanatory variables: age, tinnitus, laterality and hearing. In all of the cortical structures, the gray matter thickness decreased significantly with aging without having an effect on laterality (differences between the left and right hemispheres). The decrease in the gray matter thickness was faster in the HG, PT and Ins in comparison with the PH and V1. Aging did not influence the surface of the cortical areas, however there were differences between the surface size of the reported regions in the left and right hemispheres. Hearing loss caused only a borderline decrease of the cortical surface in the HG. Tinnitus was accompanied by a borderline decrease of the Ins surface and led to an increase in the volume of Amy and HP. In summary, aging is accompanied by a decrease in the cortical gray matter thickness; hearing loss only has a limited effect on the structure of the investigated cortical areas and tinnitus causes structural changes which are predominantly within the limbic system and insula, with the structure of the auditory system only being minimally affected., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Profant, Škoch, Tintěra, Svobodová, Kuchárová, Svobodová Burianová and Syka.)

We study patterns of partial synchronization in a network of FitzHugh-Nagumo oscillators with empirical structural connectivity measured in human subjects. We report the spontaneous occurrence of synchronization phenomena that closely resemble the ones seen during epileptic seizures in humans. In order to obtain deeper insights into the interplay between dynamics and network topology, we perform long-term simulations of oscillatory dynamics on different paradigmatic network structures: random networks, regular nonlocally coupled ring networks, ring networks with fractal connectivities, and small-world networks with various rewiring probability. Among these networks, a small-world network with intermediate rewiring probability best mimics the findings achieved with the simulations using the empirical structural connectivity. For the other network topologies, either no spontaneously occurring epileptic-seizure-related synchronization phenomena can be observed in the simulated dynamics, or the overall degree of synchronization remains high throughout the simulation. This indicates that a topology with some balance between regularity and randomness favors the self-initiation and self-termination of episodes of seizure-like strong synchronization.

Background: Omega-3 (n-3) fatty acids (FA) play and important role in neural development and other metabolic diseases such as obesity and diabetes. The knowledge about the in vivo content and distribution of n-3 FA in human body tissues is not well established and the standard quantification of FA is invasive and costly., Purpose: To detect omega-3 (n-3 CH 3 ) and non-omega-3 (CH 3 ) methyl group resonance lines with echo times up to 1200 msec, in oils, for the assessment of n-3 FA content, and the n-3 FA fraction in adipose tissue in vivo., Study Type: Prospective technical development., Population: Three oils with different n-3 FA content and 24 healthy subjects., Field Strength/sequence: Single-voxel MR spectroscopy (SVS) with a point-resolved spectroscopy (PRESS) sequence with an echo time (TE) of 1000 msec at 7 T., Assessment: Knowledge about the J-coupling evolution of both CH 3 resonances was used for the optimal detection of the n-3 CH 3 resonance line at a TE of 1000 msec. The accuracy of the method in oils and in vivo was validated from a biopsy sample with gas chromatography analysis., Statistical Tests: SVS data were compared to gas chromatography with the Pearson correlation coefficient., Results: T 2 relaxation times in oils were assessed as follows: CH 2 , 65 ± 22 msec; CH 3 , 325 ± 7 msec; and n-3 CH 3 , 628 ± 34 msec. The n-3 FA fractions from oil phantom experiments (n = 3) were in agreement with chromatography analysis and the comparison of in vivo obtained data with the results of chromatography analysis (n = 5) yielded a significant correlation (P = 0.029)., Data Conclusion: PRESS with ultralong-TE can detect and quantify the n-3 CH 3 signal in vivo at 7 T., Level of Evidence: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:71-82., (© 2018 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

Background: Augmented reality (AR) glasses with GPS navigation represent the rapidly evolving technology which spares (and externalizes) navigational capacities. Regarding the expected everyday usage of this device, its impact on neuroplastic brain changes and navigation abilities should be evaluated., Aims: This study aimed to assess possible changes in functional connectivity (FC) of hippocampus and other brain regions involved in spatial navigation., Methods: Thirty-three healthy participants completed two resting state functional magnetic resonance imaging (rsfMRI) measurements at the baseline and after 3 months. For this period, the experimental group (n = 17) has had used AR device (Vuzix M100) with incorporated GPS guidance system during navigation in real world. Participants from the control group (n = 16) have not used any GPS device while navigating during walking. The rsfMRI FC of right and left hippocampi was analyzed using a seed-driven approach. Virtual city task was used to test navigational abilities both before and after the usage of AR device., Results: We identified strong functional coupling of right and left hippocampi at the baseline (p < 0.05, FDR corrected). Mild changes in bilateral hippocampal FC (p < 0.05, FDR uncorrected) were observed in both assessed groups mainly between the bilateral hippocampi and between each hippocampus and temporal regions and cerebellum. However, the experimental group showed FC decrease after three months of using GPS navigation implemented in AR glasses in contrast to FC increase in the control group without such intervention. Importantly, no effect of intervention on navigational abilities was observed., Discussion: Our observation supports the assumption that externalization of spatial navigation to technological device (GPS in AR glasses) can decrease the functional coupling between hippocampus and associated brain regions. Considering some limitations of the present study, further studies should elucidate the mechanism of the observed changes and their impact on cognitive abilities.

Ectopic lipid accumulation in skeletal muscle is associated with insulin resistance. Telmisartan improves metabolic parameters in type 2 diabetic patients. The aim of our study was to evaluate the in vivo effect of telmisartan on intramyocellular lipid content (IMCL) in subjects with impaired fasting glucose (IFG) by magnetic resonance spectroscopy (MRS). We enrolled 10 subjects with IFG in a cross-over, placebo-controlled, randomized, double-blind trial, treated with 3 weeks of telmisartan (160 mg daily) or placebo. After completing each treatment, a hyperinsulinaemic euglycaemic clamp (1 mU/kg per min; 5 mmol/l; 120 min) to assess insulin action (metabolic clearance rate of glucose, MCR) and (1)H MRS of the m. tibialis anterior using a MR Scanner Siemens Vision operating at 1.5 T to evaluate IMCL content, were performed. Plasma adipokine levels were determined simultaneously. Telmisartan treatment resulted in a lower fasting plasma glucose (FPG) (p < 0.05), but insulin action was comparable to after placebo. Telmisartan did not affect IMCL content. After placebo, IMCL correlated negatively with total cholesterol (p < 0.001), MCR (p < 0.05) and adiponectin (p < 0.05) and positively with FPG (p < 0.05). After telmisartan treatment there was only a positive correlation between IMCL and TNFα (p < 0.05). IMCL content is related to parameters of glucose metabolism and insulin action in sedentary IFG subjects. A short telmisartan treatment did not affect the IMCL content despite its positive effect on FPG. The improvement in FPG was probably mediated through interference with other metabolic pathways., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Entropy is a measure of information content or complexity. Information-theoretic modeling has been successfully used in various biological data analyses including functional magnetic resonance (fMRI). Several studies have tested and evaluated entropy measures on simulated datasets and real fMRI data. The efficiency of entropy algorithms has been compared to classical methods based on the linear model. Here we explain and summarize entropy algorithms that have been used in fMRI analysis, their advantages over classical methods and their potential use in event-related and block design fMRI.