Top-down and bottom-up modulation of language related areas – An fMRI Study

BACKGROUND: One major problem for cognitive neuroscience is to describe the interaction between stimulus and task driven neural modulation. We used fMRI to investigate this interaction in the human brain. Ten male subjects performed a passive listening and a semantic categorization task in a factorial design. In both tasks, words were presented auditorily at three different rates. RESULTS: We found: (i) as word presentation rate increased hemodynamic responses increased bilaterally in the superior temporal gyrus including Heschl's gyrus (HG), the planum temporale (PT), and the planum polare (PP); (ii) compared to passive listening, semantic categorization produced increased bilateral activations in the ventral inferior frontal gyrus (IFG) and middle frontal gyrus (MFG); (iii) hemodynamic responses in the left dorsal IFG increased linearly with increasing word presentation rate only during the semantic categorization task; (iv) in the semantic task hemodynamic responses decreased bilaterally in the insula with increasing word presentation rates; and (v) in parts of the HG the hemodynamic response increased with increasing word presentation rates during passive listening more strongly. CONCLUSION: The observed "rate effect" in primary and secondary auditory cortex is in accord with previous findings and suggests that these areas are driven by low-level stimulus attributes. The bilateral effect of semantic categorization is also in accord with previous studies and emphasizes the role of these areas in semantic operations. The interaction between semantic categorization and word presentation in the left IFG indicates that this area has linguistic functions not present in the right IFG. Finally, we speculate that the interaction between semantic categorization and word presentation rates in HG and the insula might reflect an inhibition of the transfer of unnecessary information from the temporal to frontal regions of the brain

Erratum to: Amino acid PET and MR perfusion imaging in brain tumours

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Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Robust estimator framework in diffusion tensor imaging

Diffusion of water molecules in the human brain tissue has strong similarities with diffusion in porous media. It is affected by different factors such as restrictions and compartmentalization, interaction with membrane walls, strong anisotropy imposed by cellular microstructure, etc. However, multiple artefacts abound in in vivo measurements either from subject motions, such as cardiac pulsation, bulk head motion, respiratory motion, and involuntary tics and tremor, or hardware related problems, such as table vibrations, etc. All these artefacts can substantially degrade the resulting images and render postprocessing diffusion analysis difficult or even impossible. In order to overcome these problems, we have developed a robust and efficient approach based on the least trimmed squares algorithm that works well with severely degraded datasets with low signal-to-noise ratio. This approach has been compared with other diffusion imaging post-processing algorithms using simulations and in vivo experiments. We demonstrate that the least trimmed squares algorithm can be easily adopted for multiple non-Gaussian diffusion models such as the biexponential model. The developed approach is shown to exhibit a high efficiency and accuracy and can, in principle, be exploited in other diffusion studies where artefact/outlier suppression is demanded

Robust estimator framework in diffusion tensor imaging

Diffusion of water molecules in the human brain tissue has strong similarities with diffusion in porous media. It is affected by different factors such as restrictions and compartmentalization, interaction with membrane walls, strong anisotropy imposed by cellular microstructure, etc. However, multiple artefacts abound in in vivo measurements either from subject motions, such as cardiac pulsation, bulk head motion, respiratory motion, and involuntary tics and tremor, or hardware related problems, such as table vibrations, etc. All these artefacts can substantially degrade the resulting images and render postprocessing diffusion analysis difficult or even impossible. In order to overcome these problems, we have developed a robust and efficient approach based on the least trimmed squares algorithm that works well with severely degraded datasets with low signal-to-noise ratio. This approach has been compared with other diffusion imaging post-processing algorithms using simulations and in vivo experiments. We demonstrate that the least trimmed squares algorithm can be easily adopted for multiple non-Gaussian diffusion models such as the biexponential model. The developed approach is shown to exhibit a high efficiency and accuracy and can, in principle, be exploited in other diffusion studies where artefact/outlier suppression is demanded

Advances in neuro-oncology imaging

Despite the fact that MRI has evolved to become the standard method for diagnosis and monitoring of patients with brain tumours, conventional MRI sequences have two key limitations: the inability to show the full extent of the tumour and the inability to differentiate neoplastic tissue from nonspecific, treatment-related changes after surgery, radiotherapy, chemotherapy or immunotherapy. In the past decade, PET involving the use of radiolabelled amino acids has developed into an important diagnostic tool to overcome some of the shortcomings of conventional MRI. The Response Assessment in Neuro-Oncology working group - an international effort to develop new standardized response criteria for clinical trials in brain tumours - has recommended the additional use of amino acid PET imaging for brain tumour management. Concurrently, a number of advanced MRI techniques such as magnetic resonance spectroscopic imaging and perfusion weighted imaging are under clinical evaluation to target the same diagnostic problems. This Review summarizes the clinical role of amino acid PET in relation to advanced MRI techniques for differential diagnosis of brain tumours; delineation of tumour extent for treatment planning and biopsy guidance; post-treatment differentiation between tumour progression or recurrence versus treatment-related changes; and monitoring response to therapy. An outlook for future developments in PET and MRI techniques is also presented

Automatic segmentation of human cortical layer-complexes and architectural areas using ex vivo diffusion MRI and its validation

Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology

Connectivity Patterns in the Core Resting-State Networks and Their Influence on Cognition

Introduction: Three prominent resting-state networks (rsNW) (default mode network [DMN], salience network [SN], and central executive network [CEN]) are recognized for their important role in several neuropsychiatric conditions. However, our understanding of their relevance in terms of cognition remains insufficient.Materials and Methods: In response, this study aims at investigating the patterns of different network properties (resting-state activity [RSA] and short- and long-range functional connectivity [FC]) in these three core rsNWs, as well as the dynamics of age-associated changes and their relation to cognitive performance in a sample of healthy controls (N = 74) covering a large age span (20–79 years). Using a whole-network based approach, three measures were calculated from the functional magnetic resonance imaging (fMRI) data: amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and degree of network centrality (DC). The cognitive test battery covered the following domains: memory, executive functioning, processing speed, attention, and visual perception.Results: For all three fMRI measures (ALFF, ReHo, and DC), the highest values of spontaneous brain activity (ALFF), short- and long-range connectivity (ReHo, DC) were observed in the DMN and the lowest in the SN. Significant age-associated decrease was observed in the DMN for ALFF and DC, and in the SN for ALFF and ReHo. Significant negative partial correlations were observed for working memory and ALFF in all three networks, as well as for additional cognitive parameters and ALFF in CEN.Discussion: Our results show that higher RSA in the three core rsNWs may have an unfavorable effect on cognition. Conversely, the pattern of network properties in healthy subjects included low RSA and FC in the SN. This complements previous research related to the three core rsNW and shows that the chosen approach can provide additional insight into their function