Working memory performance is associated with functional connectivity between the right dlPFC and DMN in glioma patients

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.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 Pearson correlation between the activation time series in the regions belonging to the FPN and the DMN was calculated. Functional connectivity between the DMN and the dlPFC was calculated in a similar way.The average correlation between the resting-state fMRI activity in the right dlPFC and in the DMN was negatively associated with working memory performance for both the Gordon atlas (p \\< 0.003) and Yeo atlas (p \\< 0.007). No association was found for the correlation between activity in the left dlPFC and the DMN, nor for the correlation between the activity in the whole FPN and the DMN.Our findings show that working memory performance of glioma patients is related to interactions between networks that can be measured with resting-state fMRI. Furthermore, the results provide further evidence that not only specific brain regions are important for cognitive performance, but that also the interactions between large-scale networks should be considered

Interpreting predictions of cognition from simulated versus empirical resting state functional connectivity

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

Cognitive complaints in patients with untreated obstructive sleep apnea versus patients with neurological and respiratory diseases:Prevalence, severity and risk factors

PurposeLittle is known about cognitive complaints (self-reported problems in cognitive functioning) in patients with Obstructive Sleep Apnea (OSA). We compared the prevalence and severity of cognitive complaints in patients with untreated OSA to patients with neurological and respiratory diseases. We also studied risk factors for cognitive complaints across these diseases, including OSA.MethodsWe used a convenience sample to compare untreated OSA patients (N = 86) to patients with stroke (N = 166), primary brain tumor (N = 197) and chronic obstructive pulmonary disease (COPD, N = 204) on cognitive complaints (Cognitive Failure Questionnaire, CFQ), anxiety and depression (Hospital Anxiety and Depression Scale, HADS) and cognitive impairments using neuropsychological tests. We combined all patient groups (OSA, stroke, brain tumor and COPD) and studied potential risk factors (demographic variables, anxiety, depression and cognitive impairments) for cognitive complaints across all patient groups using regression analysis.ResultsThe prevalence of cognitive complaints was higher in OSA patients and complaints of forgetfulness and distractibility were more severe compared to stroke and primary brain tumor patients, but similar to or lower than COPD patients. Regression analysis for the combined sample of all patient groups showed that cognitive complaints were most strongly associated with symptoms of anxiety and depression.ConclusionA high rate of OSA reported clinically significant cognitive complaints, comparable to other respiratory and neurological patients. Symptoms of anxiety and depression are important risk factors for cognitive complaints in patients with various neurological and respiratory diseases. Future studies should examine the relation between anxiety, depression and cognitive complaints in patients with OSA

Factors associated with the local control of brain metastases:A systematic search and machine learning application

Background: Enhancing Local Control (LC) of brain metastases is pivotal for improving overall survival, which makes the prediction of local treatment failure a crucial aspect of treatment planning. Understanding the factors that influence LC of brain metastases is imperative for optimizing treatment strategies and subsequently extending overall survival. Machine learning algorithms may help to identify factors that predict outcomes. Methods: This paper systematically reviews these factors associated with LC to select candidate predictor features for a practical application of predictive modeling. A systematic literature search was conducted to identify studies in which the LC of brain metastases is assessed for adult patients. EMBASE, PubMed, Web-of-Science, and the Cochrane Database were searched up to December 24, 2020. All studies investigating the LC of brain metastases as one of the endpoints were included, regardless of primary tumor type or treatment type. We first grouped studies based on primary tumor types resulting in lung, breast, and melanoma groups. Studies that did not focus on a specific primary cancer type were grouped based on treatment types resulting in surgery, SRT, and whole-brain radiotherapy groups. For each group, significant factors associated with LC were identified and discussed.. As a second project, we assessed the practical importance of selected features in predicting LC after Stereotactic Radiotherapy (SRT) with a Random Forest machine learning model. Accuracy and Area Under the Curve (AUC) of the Random Forest model, trained with the list of factors that were found to be associated with LC for the SRT treatment group, were reported.Results: The systematic literature search identified 6270 unique records. After screening titles and abstracts, 410 full texts were considered, and ultimately 159 studies were included for review. Most of the studies focused on the LC of the brain metastases for a specific primary tumor type or after a specific treatment type. Higher SRT radiation dose was found to be associated with better LC in lung cancer, breast cancer, and melanoma groups. Also, a higher dose was associated with better LC in the SRT group, while higher tumor volume was associated with worse LC in this group. The Random Forest model predicted the LC of brain metastases with an accuracy of 80% and an AUC of 0.84. Conclusion: This paper thoroughly examines factors associated with LC in brain metastases and highlights the translational value of our findings for selecting variables to predict LC in a sample of patients who underwent SRT. The prediction model holds great promise for clinicians, offering a valuable tool to predict personalized treatment outcomes and foresee the impact of changes in treatment characteristics such as radiation dose.<br/

Fully automatic meningioma segmentation using T1-weighted contrast-enhanced MR images only

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

Perioperative executive functioning in patients with low-grade gliomas near the Frontal Aslant Tract

Background A tract potentially involved in important executive cognitive processes is the Frontal Aslant Tract (FAT). In particular the right FAT has been associated with executive functioning (EF). In neurosurgery, it remains unclear if patients with tumors near the FAT demonstrate EF impairments after resection. This study investigated whether low grade gliomas (LGG) that affect the core white matter and/or structural integrity of the FAT predict preoperative and 3 months postoperative EF, when controlled for tumor volume and the integrity of other nearby tracts (SLF II and SLF III). Material and Methods Data was analyzed from patients with frontal and parietal LGG who underwent surgery between 2010-2021. Probabilistic tractography was performed prior to surgery to generate preoperative tracts of the FAT, SLF II and SLF III. The core of the FAT was defined as the white matter between the seed and the target region. Average mean diffusivity for each tract was taken as a measure of structural integrity. EF was assessed one day before and 3 months post-surgery with the following tests: Stroop test, symbol digit coding test (SDC), shifting attention test (SAT), and letter fluency test (LF). We performed linear mixed models and linear regression analyses to investigate the relationship between presurgical tumor overlap with the core of the FAT and FAT integrity with pre- and postsurgical executive test performances. Results Seventy-five patients were included (left tumor N=39, right tumor N=36). Mean pre-surgical Z-scores were within 0.5 standard deviation from a healthy control group for all tests, but with substantial variance between patients (Z-score range:-3.59 to 2.4). The results demonstrated that core overlap of the right FAT predicted preoperative performance on the SAT (p<.01, β= -.473), Stroop (p<.01, β= -.519), and SDC (p<.01, β= -.519). Right or left core overlap did not significantly predict performance three months after surgery. FAT integrity did not predict preoperative EF performance, whereas it did predict SAT performance at three months post-surgical (p<.01, β= -.694) when controlled for SLF II, III integrity and tumor volume. Conclusion Although patients with frontal or parietal LGG showed no dysfunction on tests of EF before surgery on group level, they demonstrated large variability between patients. Tumor overlap with the core of the right FAT predicted worse presurgical EF performances, but not short-term post-surgical performances. Right FAT integrity predicted short-term post-surgical performance on cognitive flexibility. These results are in line with previous findings that the right FAT is involved in EF and indicate that preoperative FAT integrity might predict which patients will perform worse after surgery

A quantitative comparison of cognitive performance and patient-reported symptoms in preoperative lower-grade glioma patients from two Dutch Hospitals

Background Protocols for assessment of (neuro)psychological outcomes in lower-grade glioma patients vary between hospitals. This potentially complicates generalization of these outcomes. We compared standardized scores on tests of two frequently impaired cognitive domains (attention and executive functioning (EF)), and two relevant patient-reported outcomes (PROs; depression and fatigue) of two neuro-oncological hospitals that use different measurement instruments. Material and Methods Data were used from preoperative assessments of patients with (IDH-mut) WHO grade II/III glioma tested between 2007 and 2021 at Amsterdam UMC (AMS) or at Elisabeth-Tweesteden Hospital Tilburg (ETZ). AMS patients were referred for (neuro)psychological assessment based on physician and patient preference (paper and pencil tests), whereas all ETZ patients routinely undergo screening (computerized tests). To compare scores of the different attention and EF tests we converted patients’ performances to z-scores based on normative data. For cognitive performance, we compared scores of different cognitive flexibility tests (CST vs SAT), processing speed tests (SDC vs LDMT), and Stroop tests (Stroop I and Stroop III). PROs included the CES-D vs HADS-D and the CIS-fatigue vs MVI-general fatigue (AMS vs ETZ, resp.). Differences were tested using Fisher's, χ 2, and Mann-Whitney U tests. Results Assessments were done median 4 weeks (AMS, n=97, range 19-0 weeks) and 1 day (ETZ, n=106; range 14-0 days) preoperatively. Age, sex, tumor location and histology were comparable between cohorts (p>0.05), but the AMS cohort showed significantly more grade III tumors (36% vs 16%) and more awake surgeries (84% vs 46%). Z-scores measuring attention and EF (n=94 and n=95, AMS vs ETZ) were not significantly different (CST vs SAT, percentage with a disorder (z <-1.5SD) 15% vs 13%; SDC vs LDMT 13% vs 14%; Stroop I 11% vs 18%; Stroop III 13% vs 16% at AMS and ETZ, resp.). Percentages of patients with possible depression (CES-D≥16, n=88 and HADS-D≥8, n=106) did not differ significantly between hospitals (28% vs 26%), nor did percentages of patients with severe fatigue (CIS-fatigue≥35, n=88 and MVI-general fatigue (z <-1.5SD), n=38, 42% vs 24% at AMS and ETZ, resp.). Conclusion Standardized scores of glioma patients on cognitive domains (attention and EF) and PROs (depression and fatigue) did not differ between two centers with slightly different samples using different testing protocols. This cautiously suggests that study findings on cognitive functioning and symptoms could be generalized. For research purposes, conjoint use of pooled populations for outcome evaluation could be explored with different samples from other centers using different instruments

The Timbre Perception Test (TPT): A new interactive musical assessment tool to measure timbre perception ability

To date, tests that measure individual differences in the ability to perceive musical timbre are scarce in the published literature.The lack of such tool limits research on how timbre, a primary attribute of sound, is perceived and processed among individuals.The current paper describes the development of the Timbre Perception Test (TPT), in which participants use a slider to reproduce heard auditory stimuli that vary along three important dimensions of timbre: envelope, spectral flux, and spectral centroid. With a sample of 95 participants, the TPT was calibrated and validated against measures of related abilities and examined for its reliability. The results indicate that a short-version (8 minutes) of the TPT has good explanatory support from a factor analysis model, acceptable internal reliability (α=.69,ωt = .70), good test–retest reliability (r= .79) and substantial correlations with self-reported general musical sophistication (ρ= .63) and pitch discrimination (ρ= .56), as well as somewhat lower correlations with duration discrimination (ρ= .27), and musical instrument discrimination abilities (ρ= .33). Overall, the TPT represents a robust tool to measure an individual’s timbre perception ability. Furthermore, the use of sliders to perform a reproductive task has shown to be an effective approach in threshold testing. The current version of the TPT is openly available for research purposes

Bringing the real world into the fMRI scanner: Repetition effects for pictures versus real objects

Our understanding of the neural underpinnings of perception is largely built upon studies employing 2-dimensional (2D) planar images. Here we used slow event-related functional imaging in humans to examine whether neural populations show a characteristic repetition-related change in haemodynamic response for real-world 3-dimensional (3D) objects, an effect commonly observed using 2D images. As expected, trials involving 2D pictures of objects produced robust repetition effects within classic object-selective cortical regions along the ventral and dorsal visual processing streams. Surprisingly, however, repetition effects were weak, if not absent on trials involving the 3D objects. These results suggest that the neural mechanisms involved in processing real objects may therefore be distinct from those that arise when we encounter a 2D representation of the same items. These preliminary results suggest the need for further research with ecologically valid stimuli in other imaging designs to broaden our understanding of the neural mechanisms underlying human vision

Preference for facial averageness: evidence for a common mechanism in human and macaque infants

Human adults and infants show a preference for average faces, which could stem from a general processing mechanism and may be shared among primates. However, little is known about preference for facial averageness in monkeys. We used a comparative developmental approach and eye-tracking methodology to assess visual attention in human and macaque infants to faces naturally varying in their distance from a prototypical face. In Experiment 1, we examined the preference for faces relatively close to or far from the prototype in 12-month-old human infants with human adult female faces. Infants preferred faces closer to the average than faces farther from it. In Experiment 2, we measured the looking time of 3-month-old rhesus macaques (Macaca mulatta) viewing macaque faces varying in their distance from the prototype. Like human infants, macaque infants looked longer to faces closer to the average. In Experiments 3 and 4, both species were presented with unfamiliar categories of faces (i.e., macaque infants tested with adult macaque faces; human infants and adults tested with infant macaque faces) and showed no prototype preferences, suggesting that the prototypicality effect is experience-dependent. Overall, the findings suggest a common processing mechanism across species, leading to averageness preferences in primates