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

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

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 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. Results 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. Conclusion 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

Working memory performance in glioma patients is associated with functional connectivity between the right dorsolateral prefrontal cortex and default mode network

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.</p

Working memory performance in glioma patients is associated with functional connectivity between the right dorsolateral prefrontal cortex and default mode network

In healthy subjects, activity in the Default Mode Network (DMN) and the Fronto-Parietal Network (FPN) has consistently been associated with Working Memory (WM). Particularly, 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 zooming in 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 = 0.010). 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 towards 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

P13.03.B Predicting functional connectivity from structural connectivity in glioma patients

BackgroundThe mechanism by which activity in the brain relates to its anatomical structure is a central topic of research in neuroscience. In recent years, this relation has been studied in the context of network science, where anatomical white-matter connections are captured in Structural Connectivity (SC) and statistical dependence between grey-matter activity in Functional Connectivity (FC). The relation between SC and FC is poorly understood at an individual level, especially in glioma patients. Developing methods to predict FC from SC in these patients could improve our understanding of how gliomas affect brain function, and consequently cognition. Furthermore, a model accurate at individual patient level could aid in clinical decision-making by identifying eloquent areas and connections in individuals.Material and methodsBuilding on earlier work predicting FC from SC in healthy subjects, we trained a deep learning model on tractography and resting-state fMRI data of 288 glioma patients to predict FC at an individual level. Since our sample size is modest in the context of deep learning, we attempted to improve accuracy by pre-training the model on a large set of healthy subjects. This way, the model should learn baseline patterns in SC and FC before learning the specific patterns associated with glioma patients. We used data from 1052 subjects of the Human Connectome Project and 633 subjects of the Cambridge Centre for Ageing and Neuroscience. Finally, we analyzed the effect of several factors on prediction accuracy: tumor volume, location and grade and patient age.ResultsTraining on only the patient set, the prediction model achieved an accuracy, measured as correlation between predicted and empirical FC, of 0.53 ± 0.03 at individual patient level. This is comparable with accuracy achieved in studies on healthy subjects. Pre-trained models did not improve accuracy. The model had more difficulty predicting FC in patients with a glioma in the insular region (p = 0.0014). No other factors were associated with accuracy.ConclusionAlthough further research is required to investigate the utility of FC predictions made by our model, our work is a first step towards predicting activity from brain structure in glioma patients at an individual level, since earlier research in patient populations has focused on group-level effects. We observed that predictions are less accurate in patients with a glioma in the insular region, which indicates that the relation between the structural and functional organization of the brain in this group is different from that in patients with tumors in other locations. Improving our methods to achieve higher accuracy could provide us, as a next step, with a tool to predict functional consequences of resective surgery on glioma patients, and consequently improve decision making by more accurately identifying eloquent regions at an individual level