scholarly journals Dynamic Reconfiguration of Functional Topology in Human Brain Networks: From Resting to Task States

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Wenhai Zhang ◽  
Fanggui Tang ◽  
Xiaolin Zhou ◽  
Hong Li
Keyword(s):  
Working Memory ◽  
Resting State ◽  
Dynamic Reconfiguration ◽  
Distributed Networks ◽  
Task Demands ◽  
Functional Networks ◽  
High Threshold ◽  
Long Distance ◽  
Topological Features ◽  
And Task

Task demands evoke an intrinsic functional network and flexibly engage multiple distributed networks. However, it is unclear how functional topologies dynamically reconfigure during task performance. Here, we selected the resting- and task-state (emotion and working-memory) functional connectivity data of 81 health subjects from the high-quality HCP data. We used the network-based statistic (NBS) toolbox and the Brain Connectivity Toolbox (BCT) to compute the topological features of functional networks for the resting and task states. Graph-theoretic analysis indicated that under high threshold, a small number of long-distance connections dominated functional networks of emotion and working memory that exhibit distinct long connectivity patterns. Correspondently, task-relevant functional nodes shifted their roles from within-module to between-module: the number of connector hubs (mainly in emotional networks) and kinless hubs (mainly in working-memory networks) increased while provincial hubs disappeared. Moreover, the global properties of assortativity, global efficiency, and transitivity decreased, suggesting that task demands break the intrinsic balance between local and global couplings among brain regions and cause functional networks which tend to be more separated than the resting state. These results characterize dynamic reconfiguration of large-scale distributed networks from resting state to task state and provide evidence for the understanding of the organization principle behind the functional architecture of task-state networks.

Moving Toward Accurate Assessment of Working Memory in Adults With Neurogenically Based Communication Disorders

2021 ◽  
pp. 1-9
Author(s):  
Bijoyaa Mohapatra ◽  
Jacqueline Laures-Gore
Keyword(s):  
Working Memory ◽  
Experimental Testing ◽  
Communication Disorders ◽  
Test Construction ◽  
Task Demands ◽  
Future Research ◽  
Modality Effects ◽  
Memory Assessment ◽  
Response Modality ◽  
And Task

Purpose This article presents a viewpoint highlighting concerns regarding currently available assessments of working memory in adults with neurogenic communication disorders. Additionally, we provide recommendations for improving working memory assessment in this population. Method This viewpoint includes a critique of clinical and experimental working memory tests relevant to speech-language pathologists. We consider the terminology used to describe memory, as well as discuss language demands and test construction. Results Clinical and experimental testing of working memory in adults with neurogenic communication disorders is challenged due to theoretical, methodological, and practical limitations. The major limitations are characterized as linguistic and task demands, presentation and response modality effects, test administration, and scoring parameters. Taking these limitations into consideration, several modifications to working memory testing and their relevance to neurogenic populations are discussed. Conclusions The recommendations provided in this article can better guide clinicians and researchers to advocate for improved tests of working memory in adults with neurogenic communication disorders. Future research should continue to address these concerns and consider our recommendations.

scholarly journals Anterior cingulate cortex differently modulates fronto-parietal functional connectivity between resting-state and working memory tasks

2019 ◽  
Author(s):  
Xin Di ◽  
Heming Zhang ◽  
Bharat B Biswal
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Anterior Cingulate Cortex ◽  
Resting State ◽  
Cingulate Cortex ◽  
Task Demands ◽  
Anterior Cingulate ◽  
Functional Coupling ◽  
The Right ◽  
State 1

AbstractThe brain fronto-parietal regions and the functional communications between them are critical in supporting working memory and other executive functions. The functional connectivity between fronto-parietal regions are modulated by working memory loads, and are shown to be modulated by a third brain region in resting-state. However, it is largely unknown that whether the third-region modulations remain the same during working memory tasks or were largely modulated by task demands. In the current study, we collected functional MRI (fMRI) data when the subjects were performing n-back tasks and in resting-state. We first used a block-designed localizer to define the fronto-parietal regions that showed higher activations in the 2-back than the 1-back condition. Next, we performed physiophysiological interaction (PPI) analysis using left and right middle frontal gyrus (MFG) and superior parietal lobule (SPL) regions, respectively, in three continuous-designed runs of resting-state, 1-back, and 2-back conditions. No regions showed consistent modulatory interactions with the seed pairs in the three conditions. Instead, the anterior cingulate cortex (ACC) showed different modulatory interactions with the right MFG and SPL among the three conditions. While increased activity of the ACC was associated with decreased functional coupling between the right MFG and SPL in resting-state, it was associated with increased functional coupling in the 2-back condition. The observed task modulations support the functional significance of the modulations of the ACC on fronto-parietal connectivity.

NIMG-14. RESTING STATE EXECUTIVE CONTROL AND SALIENCE NETWORK CONNECTIVITY IN CLINICALLY STABLE LOWER GRADE GLIOMA COVARIES WITH COGNITIVE PERFORMANCE

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi130-vi131
Author(s):  
Tracy Luks ◽  
Javier Villanueva-Meyer ◽  
Christina Weyer-Jamora ◽  
Melissa Brie ◽  
Ellen Smith ◽  
...  
Keyword(s):  
Working Memory ◽  
Executive Control ◽  
Resting State ◽  
Digit Span ◽  
Network Connectivity ◽  
Cognitive Outcomes ◽  
Cognitive Test ◽  
Functional Networks ◽  
Salience Network ◽  
Lower Grade

Abstract BACKGROUND Survival outcomes for patients with lower grade gliomas (LrGG) are improving. However, injury from tumor growth and consequences of treatment often leads to impaired cognition, particularly in cognitive domains reliant on distributed functional networks and intact white-matter tracts. Resting state functional MRI (rsfMRI) is a method of investigating the integrity of these functional networks. METHODS This study investigated rsfMRI connectivity in 21 patients with clinically stable LrGG compared to age- and gender-matched healthy controls, and associated imaging measures with cognitive outcomes. Data were acquired for 12 cognitive tests administered within one week of imaging. RsfMRI and T1-weighted images for 21 research controls were acquired from OpenNeuro datasets. RsfMRI data were processed and analyzed using the CONN toolbox using CONN’s standard regions of interest (ROI) for the 8 canonical networks as seeds, and cognitive test scores as covariates, with a threshold for T tests of p< .001 uncorrected. RESULTS Median age was 48 years old (range 27-67). There were 6 astrocytomas, IDHmut; 3 astrocytomas IDH-wt, 8 oligodendrogliomas, and 4 NOS. Thirteen had left hemisphere tumors (8 frontal, 3 parietal, 2 temporal), and 6 right (5 frontal, 1 temporal). Fourteen had previously recieved radiotherapy. There was significantly lower connectivity in frontoparietal executive control and the salience networks in LrGG patients versus controls. Within patients, lower executive control network connectivity covaried with worse performance on executive measures (FAS, Tower of London, Trails-A, Animal Naming, FrSBe), and attention and working memory measures (Digit Symbol, HVLT). Lower salience network connectivity covaried with poorer performance on executive measures (FrSBe, FAS) and attention and working memory measures (Digit Span, HVLT, WAIS-WM). CONCLUSION In clinically stable LrGG, rsfMRI measures of network connectivity are potentially useful markers to monitor and track, given the concordance with cognition, and could help guide cognitive assessment and rehabilitation.

scholarly journals Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

2021 ◽  
Author(s):  
Ru Kong ◽  
Qing Yang ◽  
Evan Gordon ◽  
Aihuiping Xue ◽  
Xiaoxuan Yan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Distributed Networks ◽  
Resting State Functional Connectivity ◽  
Out Of Sample ◽  
Spatially Distributed ◽  
Behavioral Prediction ◽  
Better Than ◽  
And Task ◽  
Task Activation

AbstractResting-state functional MRI (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, i.e., should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple non-contiguous components, therefore we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10min of data generalized better than other approaches using 150min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity (RSFC) derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM (cMS-HBM) exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM (gMS-HBM) was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (GITHUB_LINK).

scholarly journals Hemisphere-Specific Functional Remodeling and Its Relevance to Tumor Malignancy of Cerebral Glioma Based on Resting-State Functional Network Analysis

2021 ◽  
Vol 14 ◽  
Author(s):  
Siqi Cai ◽  
Zhifeng Shi ◽  
Chunxiang Jiang ◽  
Kai Wang ◽  
Liang Chen ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Functional Networks ◽  
Cerebral Glioma ◽  
Individual Level ◽  
Topological Features ◽  
Functional Remodeling ◽  
Functional Features ◽  
Malignant Grade ◽  
The Individual

Background: Functional remodeling may vary with tumor aggressiveness of glioma. Investigation of the functional remodeling is expected to provide scientific relevance of tumor characterization and disease management of glioma. In this study, we aimed to investigate the functional remodeling of the contralesional hemisphere and its utility in predicting the malignant grade of glioma at the individual level with multivariate logistic regression (MLR) analysis. Subjects and Methods: One hundred and twenty-six right-handed subjects with histologically confirmed cerebral glioma were included with 80 tumors located in the left hemisphere (LH) and 46 tumors located in the right hemisphere (RH). Resting-state functional networks of the contralesional hemisphere were constructed using the human brainnetome atlas based on resting-state fMRI data. Functional connectivity and topological features of functional networks were quantified. The performance of functional features in predicting the glioma grade was evaluated using area under (AUC) the receiver operating characteristic curve (ROC). The dataset was divided into training and validation datasets. Features with high AUC values in malignancy classification in the training dataset were determined as predictive features. An MLR model was constructed based on predictive features and its classification performance was evaluated on the training and validation datasets with 10-fold cross validation. Results: Predictive functional features showed apparent hemispheric specifications. MLR classification models constructed with age and predictive functional connectivity features (AUC of 0.853 ± 0.079 and 1.000 ± 0.000 for LH and RH group, respectively) and topological features (AUC of 0.788 ± 0.150 and 0.897 ± 0.165 for LH and RH group, respectively) achieved efficient performance in predicting the malignant grade of gliomas. Conclusion: Functional remodeling of the contralesional hemisphere was hemisphere-specific and highly predictive of the malignant grade of glioma. Network approach provides a novel pathway that may innovate glioma characterization and management at the individual level.

How Effective Feedback for Training Depends on Learner Resources and Task Demands

2006 ◽  
Author(s):  
Anne Collins McLaughlin ◽  
Wendy A. Rogers ◽  
Arthur D. Fisk
Keyword(s):  
Task Demands ◽  
Effective Feedback ◽  
And Task

scholarly journals Brainwide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wei-Tang Chang ◽  
Stephanie K. Langella ◽  
Yichuan Tang ◽  
Sahar Ahmad ◽  
Han Zhang ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
A Priori ◽  
Longitudinal Axis ◽  
Functional Networks ◽  
Resting State Functional Connectivity ◽  
Underlying Assumption ◽  
Isotropic Resolution ◽  
Hippocampal Subfields ◽  
Fine Grained

AbstractThe hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies have observed similar resting-state functional connectivity (FC) profiles between aHPSFs. Alternatively, data-driven approaches investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may result in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7 T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2 s and brain-wide coverage to (1) investigate the functional organization within hippocampus at rest, and (2) compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). This study showed that fHPSFs were arranged along the longitudinal axis that were not comparable to the lamellar structures of aHPSFs. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. Different functional networks also showed preferential connections with different portions of hippocampal subfields.

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