Search

Several studies predicting Functional Connectivity (FC) from Structural Connectivity (SC) at individual level have been published in recent years, each promising increased performance and utility. We investigated three of these studies, analyzing whether the results truly represent a meaningful individual-level mapping from SC to FC. Using data from the Human Connectome Project shared accross the three studies, we constructed a predictor by averaging FC of training data and analyzed its performance in the same way. In each case, we found that group average FC is an equivalent or better predictor of individual FC than the predictive models in terms of raw prediction performance. Furthermore, we showed that additional analyses performed by the authors of the three studies, in which they attempt to show that their predicted FC has value beyond raw prediction performance, could also be reproduced using the group average FC predictor. This makes it unclear whether any of the three methods represent a meaningful individual-level predictive model. We conclude that either the methods are not appropriate for the data, that the sample size is too small, or that the data does not contain sufficient information to learn a mapping from SC to FC. We advise future individual-level studies to explicitly report results in comparison to the performance of the group average, and carefully demonstrate that their predictions contain meaningful individual-level information. Finally, we believe that investigating alternatives for the construction of SC and FC may improve the chances of developing a meaningful individual-level mapping from SC to FC.

Patients with meningiomas frequently exhibit impairments in executive functioning. There are few studies specifically examining the role of frontal meningioma localization in executive functioning impairments. This study examines whether frontally located meningiomas are specifically associated with executive functioning impairments in a large sample of meningioma patients before treatment, using an axis-wise and lobe-based approach to meningioma localization. We retrospectively examined cognitive performances in 353 patients with frontal, frontally-involved and non-frontal meningiomas on a battery of tests including tests of executive functioning. We applied an axis-based approach to meningioma location, in addition to qualitative lobe-based localization. We examined the association between meningioma coordinates on an anterior-posterior axis and continuous cognitive performance scores in univariate correlations and linear regression analyses. We also examined the association between meningioma coordinates on an anterior-posterior axis with cognitive impairments in multivariable logistic regression analyses. Meningioma position on the anterior-posterior axis was only univariately associated with mean performance on the Stroop test Interference ratio and Symbol Digit Coding task. There was no (multivariable) association with impairments on tests of executive or non-executive domains. Increased odds of impairment on executive functioning tasks were associated with left-localization (Verbal Fluency) and larger meningioma volumes (Shifting Attention). We did not find a specific relation between a frontal meningioma location and executive functioning impairments, which may be explained by widespread organization of executive functioning throughout the brain, diffuse cognitive effects of the mass of meningiomas, functional reorganization due to neuroplasticity, or functional involvement of less-anteriorly located frontal areas.

Contains fulltext : 287706.pdf (Publisher’s version ) (Open Access)

The relation between structure and function of the brain, and how behavior arises from it, is a central topic of interest in neuroscience. This problem can be formulated in terms of Structural Connectivity (SC) and Functional Connectivity (FC), respectively representing anatomical connections and functional interactions between regions in the brain. Recently, a study by Sarwar and colleagues has demonstrated individualized prediction of FC from SC using machine learning, additionally showing that variation in cognitive performance is explained by simulated FC (sFC) almost as well as by empirical FC (eFC). We investigated how decisions made to predict cognition differ between the models based on eFC and sFC. We predicted cognitive performance with Lasso regression in 100 cross-validation loops from both eFC and sFC separately, using FC between each of the 2278 pairs of regions in the 68-region Desikan-Killiany parcellation as features. We identified relevant predictors of cognition by inspecting permutation importance scores and keeping only features whose importance scores were consistently high across validation loops. 13 eFC features and 21 sFC features survived this procedure. Of these, only one feature overlapped between eFC and sFC. Analyzing overlap between regions corresponding to important features and functional systems known to support cognition revealed no patterns for either eFC or sFC features. In conclusion, we found that while cognition can be predicted from sFC almost as well as from eFC, different features are used in the models, and these features were not found to follow any structure in terms of functional systems. This shows that while machine learning models provide a theoretical upper bound on how accurately function can be predicted from structure, they do not necessarily produce output that can be interpreted in the same way as the data the models were trained on.

In healthy subjects, activity in the default mode network (DMN) and the frontoparietal network (FPN) has consistently been associated with working memory (WM). In particular, the dorsolateral prefrontal cortex (DLPFC) is important for WM. The functional-anatomical basis of WM impairment in glioma patients is, however, still poorly understood. We investigated whether WM performance of glioma patients is reflected in resting-state functional connectivity (FC) between the DMN and FPN, additionally focusing on the DLPFC. Resting-state functional MRI data were acquired from 45 glioma patients prior to surgery. WM performance was derived from a pre-operative N-back task. Scans were parcellated into ROIs using both the Gordon and Yeo atlas. FC was calculated as the average Pearson correlation between functional time series. The FC between right DLPFC and DMN was inversely related to WM performance for both the Gordon and Yeo atlas (p = .010). No association was found for FC between left DLPFC and DMN, nor between the whole FPN and DMN. The results are robust and not dependent on atlas choice or tumor location, as they hold for both the Gordon and Yeo atlases, and independently of location variables. Our findings show that WM performance of glioma patients can be quantified in terms of interactions between regions and large-scale networks that can be measured with resting-state fMRI. These group-based results are a necessary step toward development of biomarkers for clinical management of glioma patients, and provide additional evidence that global disruption of the DMN relates to cognitive impairment in glioma patients.

Background Manual segmentation of brain tumors requires expertise, is time-consuming, and is subject to inter-rater variability. Fully automatic brain tumor segmentation is possible for glioma and meningioma when volumetric T1, T1 contrast-enhanced (T1c), T2, and Fluid-attenuated inversion recovery (FLAIR) MRIs are available. In clinical care of meningiomas, however, often only volumetric T1c scans are available. In this work, we trained a deep learning network to segment meningiomas using only T1c scans for use in clinical research. Material and Methods NnU-Net, a deep learning model that is optimized for medical image segmentation, was trained to segment meningiomas from T1c images. This was performed on a large clinically collected meningioma dataset (n=374) of T1c scans with semi-automatically generated enhancing tumor masks and additional data from the BraTS2020 glioma dataset. Model performance was compared against inter-rater reliability, between different models, between anatomical tumor locations, and against models using multiple MRI modalities. Results The best performing model obtained a Dice score of 0.90. This performance was 0.03 points lower when compared to inter-rater reliability (Dice=0.93) and almost equal to models using multiple MRI modalities. Model performance split over anatomical tumor locations was between 0.90 and 0.97 (Dice). Conclusion Fully automatic meningioma segmentation using only T1c images is possible with an accuracy that is similar to inter-rater reliability and models using multiple imaging modalities.

Background Patients with primary brain tumors frequently suffer from cognitive impairments in multiple domains, leading to serious consequences for socio-professional functioning and quality of life. The functional-anatomical basis of these impairments is still poorly understood. The study of correlated BOLD activity in the brain (i.e. functional connectivity) has greatly contributed to our understanding of how brain activity supports cognitive function. In particular, activity observed during the execution of specific tasks can be related to various distributed functional networks, stressing the importance of interactions between remote brain regions. Among these networks, the Default Mode Network (DMN) and the Fronto-Parietal Network (FPN) have consistently been associated with working memory performance. Recently, using task-fMRI in glioma patients, poor performance in a working memory task was associated with less deactivation of the DMN during this task and to a lack of task-evoked changes in the DMN-FPN structure. In this study, we investigated whether these effects are reflected in the resting-state (RS) functional connectivity of the same patient group, i.e. when no task was performed during fMRI. We additionally zoomed in on the part of the FPN located in the dorsolateral Prefrontal Cortex (dlPFC), since this region is believed to be mainly responsible for DMN deactivation. Material and Methods Resting-state functional MRI data were acquired pre-operatively from 45 brain tumor patients (20 low- and 25 high-grade glioma patients). Results of a pre-operative in-scanner N-back working memory fMRI task were used to assess working memory performance. Patient brains were parcellated into ROIs using both the Gordon and Yeo atlas, which have the FPN and DMN network identities readily available. The dlPFC was defined based on masks retrieved from NeuroSynth. To measure DMN-FPN functional connectivity the average P

Item does not contain fulltext, It is widely accepted that psychosocial prognostic factors should be addressed by clinicians in their assessment and management of patient suffering from low back pain (LBP). On the other hand, an overview is missing how these factors are addressed in clinical LBP guidelines. Therefore, our objective was to summarize and compare recommendations regarding the assessment and management of psychosocial prognostic factors for LBP chronicity, as reported in clinical LBP guidelines. We performed a systematic search of clinical LBP guidelines (PROSPERO registration number 154730). This search consisted of a combination of previously published systematic review articles and a new systematic search in medical or guideline-related databases. From the included guidelines, we extracted recommendations regarding the assessment and management of LBP which addressed psychosocial prognostic factors (ie, psychological factors ["yellow flags"], perceptions about the relationship between work and health, ["blue flags"], system or contextual obstacles ["black flags") and psychiatric symptoms ["orange flags"]). In addition, we evaluated the level or quality of evidence of these recommendations. In total, we included 15 guidelines. Psychosocial prognostic factors were addressed in 13 of 15 guidelines regarding their assessment and in 14 of 15 guidelines regarding their management. Recommendations addressing psychosocial factors almost exclusively concerned "yellow" or "black flags," and varied widely across guidelines. The supporting evidence was generally of very low quality. We conclude that in general, clinical LBP guidelines do not provide clinicians with clear instructions about how to incorporate psychosocial factors in LBP care and should be optimized in this respect. More specifically, clinical guidelines vary widely in whether and how they address psychosocial factors, and recommendations regarding these factors generally require better evidence support. This emphasizes a need f

Leaf fungal pathogens alter their host species’ performance and, thus, changes in fungal species composition can translate into effects at the tree community scale. Conversely, the functional diversity of tree species in a host tree’s local neighbourhood can affect the host’s foliar fungal infestation. Therefore, understanding the factors that affect fungal infestations is important to advance our understanding of biodiversity-ecosystem functioning (BEF) relationships. Here we make use of the largest BEF tree experiment worldwide, the BEF-China experiment, where we selected tree host species with different neighbour species. Identifying fungal taxa by microscopy and by high-throughput DNA sequencing techniques based on the internal transcribed spacer (ITS) rDNA region, we analysed the fungal richness and infestation rates of our target trees as a function of local species richness. Based on the visual microscopic assessment, we found that a higher tree diversity reduced fungal richness and host-specific fungal infestation in the host’s local neighbourhood, while molecular fungal richness was unaffected. This diversity effect was mainly explained by the decrease in host proportion. Thus, the dilution of host species in the local neighbourhood was the primary mechanism in reducing the fungal disease severity. Overall, our study suggests that diverse forests will suffer less from foliar fungal diseases compared to those with lower diversity.

Fungal pathogens have the potential to affect plant biogeography and ecosystem processes through their influence on the fitness and functioning of their plant hosts. Simultaneously, changes in plant communities can influence fungal pathogen communities. Exactly which host plant attributes determine the composition of fungal pathogen communities on the leaves remains poorly understood.Here, we characterized foliar fungal pathogen communities in subtropical tree communities along an experimental diversity gradient in Jiangxi, South‐East China. On 32 tree species, we identified all visible fungal structures and symptoms microscopically and studied fungi‐specific traits such as conidia or spores in detail. We asked how different facets of biodiversity, including taxonomic, phylogenetic and functional tree diversity, shape fungal diversity and fungal infestation at different scales.We found a positive relationship between tree richness and fungal richness at the plot level. At the level of individual trees of the same species, the relationship between tree species richness and fungal species richness was marginally significantly negative. Importantly, the fungal infestation rates decreased as tree species richness increased, suggesting that colonization of hosts by specialist fungi was impeded by dilution of the pool of available hosts. Moreover, we found evidence for similar topologies between phylogenetic topologies of trees and fungal genera which is a precondition for coevolution and we identified leaf traits, including leaf habit (deciduous/evergreen), leaf magnesium content and stomata size, that predicted fungal richness and infestation.Synthesis Our study indicates that host tree species harbour different foliar fungal pathogens, which sums up to the highest fungal pathogen richness in more diverse forest stands. At the same time, the foliar fungal pathogen infestation rate for each tree species decreased with increasing tree richness in forests. We identified le

Global change exposes forest ecosystems to many risks including novel climatic conditions, increased frequency of climatic extremes and sudden emergence and spread of pests and pathogens. At the same time, forest landscape restoration has regained global attention as an integral strategy for climate change mitigation. Owing to unpredictable future risks and the need for new forests that provide multiple ecosystem services, mixed-species forests have been advocated for this purpose. However, the successful establishment of mixed forests requires intrinsic knowledge of biodiversity's role for forest ecosystem functioning. In this respect, a better understanding of tree-tree interactions and how they contribute to observed positive tree species richness effects on key ecosystem functions is critical. Here, we review the current knowledge of the underlying mechanisms of tree-tree interactions and argue that positive net biodiversity effects at the community scale may emerge from the dominance of positive over negative interactions at the local neighbourhood scale. In a second step, we demonstrate how tree-tree interactions and the immediate tree neighbourhood's role can be systematically assessed in a tree diversity experiment. The expected results will improve predictions about the effects of tree interactions on ecosystem functioning based on general principles. We argue that this knowledge is urgently required to guide the design of tree species mixtures for the successful establishment of newly planted forests.

It is widely accepted that psychosocial prognostic factors should be addressed by clinicians in their assessment and management of patient suffering from low back pain (LBP). On the other hand, an overview is missing how these factors are addressed in clinical LBP guidelines. Therefore, our objective was to summarize and compare recommendations regarding the assessment and management of psychosocial prognostic factors for LBP chronicity, as reported in clinical LBP guidelines. We performed a systematic search of clinical LBP guidelines (PROSPERO registration number 154730). This search consisted of a combination of previously published systematic review articles and a new systematic search in medical or guideline-related databases. From the included guidelines, we extracted recommendations regarding the assessment and management of LBP which addressed psychosocial prognostic factors (ie, psychological factors ["yellow flags"], perceptions about the relationship between work and health, ["blue flags"], system or contextual obstacles ["black flags") and psychiatric symptoms ["orange flags"]). In addition, we evaluated the level or quality of evidence of these recommendations. In total, we included 15 guidelines. Psychosocial prognostic factors were addressed in 13 of 15 guidelines regarding their assessment and in 14 of 15 guidelines regarding their management. Recommendations addressing psychosocial factors almost exclusively concerned "yellow" or "black flags," and varied widely across guidelines. The supporting evidence was generally of very low quality. We conclude that in general, clinical LBP guidelines do not provide clinicians with clear instructions about how to incorporate psychosocial factors in LBP care and should be optimized in this respect. More specifically, clinical guidelines vary widely in whether and how they address psychosocial factors, and recommendations regarding these factors generally require better evidence support. This emphasizes a need f