scholarly journals Exploring Anti-correlated Resting State BOLD Signals Through Dynamic Functional Connectivity and Whole-brain Computational Modeling

2016 ◽  
Author(s):  
Murat Demirtaş ◽  
Matthieu Gilson ◽  
John D. Murray ◽  
Dina Popovic ◽  
Eduard Vieta ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Computational Modeling ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Hemodynamic Response Function ◽  
Dynamic Functional Connectivity ◽  
Whole Brain ◽  
Global Average ◽  
Functional Connections

AbstractResting-state functional magnetic resonance imaging and diffusion weight imaging became a conventional tool to study brain connectivity in healthy and diseased individuals. However, both techniques provide indirect measures of brain connectivity leading to controversies on their interpretation. Among these controversies, interpretation of anti-correlated functional connections and global average signal is a major challenge for the field. In this paper, we used dynamic functional connectivity to calculate the probability of anti-correlations between brain regions. The brain regions forming task-positive and task-negative networks showed high anti-correlation probabilities. The fluctuations in anti-correlation probabilities were significantly correlated with those in global average signal and functional connectivity. We investigated the mechanisms behind these fluctuations using whole-brain computational modeling approach. We found that the underlying effective connectivity and intrinsic noise reflect the static spatiotemporal patterns, whereas the hemodynamic response function is the key factor defining the fluctuations in functional connectivity and anti-correlations. Furthermore, we illustrated the clinical implications of these findings on a group of bipolar disorder patients suffering a depressive relapse (BPD).

scholarly journals Functional connectivity predicts the dispositional use of expressive suppression but not cognitive reappraisal

2019 ◽  
Author(s):  
Daisy A. Burr ◽  
Tracy d'Arbeloff ◽  
Maxwell Elliott ◽  
Annchen R. Knodt ◽  
Bartholomew D. Brigidi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Cognitive Reappraisal ◽  
Predictive Performance ◽  
Brain Regions ◽  
Expressive Suppression ◽  
Whole Brain ◽  
Functional Connections ◽  
Neural Signature

Previous research has identified specific brain regions associated with regulating emotion using common strategies such as expressive suppression and cognitive reappraisal. However, most research focuses on a priori regions and directs participants how to regulate, which may not reflect how people naturally regulate outside the laboratory. Here, we used a data-driven approach to investigate how individual differences in distributed intrinsic functional brain connectivity predict emotion regulation tendency. Specifically, we used connectome-based predictive modeling to extract functional connections in the brain significantly related to the dispositional use of suppression and reappraisal. These edges were then used in a predictive model and cross-validated in novel participants to identify a neural signature that reflects individual differences in the tendency to suppress and reappraise emotion. We found a significant neural signature for the dispositional use of suppression, but not reappraisal. Within this whole-brain signature, the intrinsic connectivity of the default mode network was most informative of suppression tendency. In addition, the predictive performance of this model was significant in males, but not females. These findings help inform how whole-brain networks of functional connectivity characterize how people tend to regulate emotion outside the laboratory.

scholarly journals Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

2020 ◽  
Author(s):  
Behnaz Yousefi ◽  
Shella Keilholz
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Spatial Organization ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Intrinsic Activity ◽  
Resting State Networks ◽  
Whole Brain ◽  
Functional Connections ◽  
Driving Mechanisms

The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as resting-state networks (RSNs) and functional connectivity gradients (FCGs). Dynamic analysis techniques have shown that the time-averaged maps captured by functional connectivity are mere summaries of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain intrinsic activity that cannot be inferred by functional connectivity, RSNs or FCGs. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale FCG, simultaneously across the cortical sheet and in most other brain regions. In some areas, like the thalamus, the propagation suggests previously-undescribed FCGs. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between brain regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between RSNs is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

scholarly journals A Whole-Brain Computational Modeling Approach to Explain the Alterations in Resting-State Functional Connectivity during Progression of Alzheimer’s Disease

2016 ◽  
Author(s):  
Murat Demirtaş ◽  
Carles Falcon ◽  
Alan Tucholka ◽  
Juan Domingo Gispert ◽  
José Luis Molinuevo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Computational Modeling ◽  
Resting State ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Modeling Approach

AbstractUnderstanding the mechanisms behind Alzheimer’s disease (AD) is one of the most challenging problems in neuroscience. Recent efforts provided valuable insights on the genetic, biochemical and neuronal correlates of AD. The advances in structural and functional neuroimaging provided massive evidence for the AD related alterations in brain connectivity. In this study, we investigated the whole-brain resting state functional connectivity (FC) and variability in dynamic functional connectivity (v-FC) of the subjects with preclinical condition (PC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The synchronization in the whole-brain was monotonously decreasing during the course of the progression. However, only in the AD group the reduced synchronization produced significant widespread effects in FC. Furthermore, we found elevated variability of FC in PC group, which was reversed in AD group. We proposed a whole-brain computational modeling approach to study the mechanisms behind these alterations. We estimated the effective connectivity (EC) between brain regions in the model to reproduce observed FC of each subject. First, we compared ECs between groups to identify the changes in underlying connectivity structure. We found that the significant EC changes were restricted to temporal lobe. Then, based on healthy control subjects we systematically manipulated the dynamics in the model to investigate its effect on FC. The model showed FC alterations similar to those observed in clinical groups providing a mechanistic explanation to AD progression.

scholarly journals Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

2021 ◽  
Author(s):  
Andrew Lynn ◽  
Eric D. Wilkey ◽  
Gavin Price
Keyword(s):  
Functional Connectivity ◽  
Predictive Modeling ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Math Skills ◽  
Whole Brain ◽  
Building Models ◽  
State Dependent ◽  
Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

scholarly journals Linear Input as a Fundamental Motif of Brain Connectivity Organization

2019 ◽  
Author(s):  
Jonathan F. O’Rawe ◽  
Hoi-Chung Leung
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Fundamental Aspect ◽  
Biased Sampling ◽  
Whole Brain ◽  
3 Dimensional ◽  
Face Area ◽  
Learning Techniques

AbstractDescribing the pattern of region-to-region functional connectivity is an important step towards understanding information transfer and transformation between brain regions. Although fMRI data are limited in spatial resolution, recent advances in technology afford more precise mapping. Here, we extended previous methods, connective field mapping, to 3 dimensions to provide a more concise estimate of the organization and potential information transformation from one region to another. We first replicated previous work with the 3 dimensional model by showing that the topology of functional connectivity between early visual regions maintained along their eccentricity axis or the anterior-posterior dimension. We then examined higher order visual regions (e,g, fusiform face area) and showed that their pattern of connectivity, the convergence and biased sampling, seem to contribute to some of their core receptive field properties. We further demonstrated that linearity of input is a fundamental aspect of functional connectivity of the whole brain, with higher linearity between regions within a network than across networks; that is, high connective linearity was evident between early visual areas, and between prefrontal areas, but less evident between them. By decomposing the whole brain linearity matrix with manifold learning techniques, we found that the principle mode of the linearity maps onto decompositions in both functional connectivity and genetic expression reported in previous studies. The current work provides evidence supporting that linearity of input is likely a fundamental motif of functional connectivity between regions for information processing across the brain, with high linearity preserving the integrity of information from one region to another within a network.

scholarly journals Brain Connectivity Tracks Effects of Chemotherapy Separately from Behavioral Measures

2018 ◽  
Author(s):  
Omid Kardan ◽  
Mary K. Askren ◽  
Misook Jung ◽  
Scott Peltier ◽  
Bratislav Misic ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Performance ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Self Report ◽  
Breast Cancer Patients ◽  
Resting State Functional Connectivity ◽  
Behavioral Measures ◽  
Cognitive Health

AbstractSeveral studies in cancer research have suggested that cognitive dysfunction following chemotherapy, referred to in lay terms as “chemobrain”, is a serious problem. At present, the changes in integrative brain function that underlie such dysfunction remains poorly understood. Recent developments in neuroimaging suggest that patterns of functional connectivity can provide a broadly applicable neuromarker of cognitive performance and other psychometric measures. The current study used multivariate analysis methods to identify patterns of disruption in resting state functional connectivity of the brain due to chemotherapy and the degree to which the disruptions can be linked to behavioral measures of distress and cognitive performance. Sixty two women (22 healthy control, 18 patients treated with adjuvant chemotherapy, and 22 treated without chemotherapy) were evaluated with neurocognitive measures followed by self-report questionnaires and open eyes resting-state fMRI scanning at three time points: diagnosis (M0, pre-adjuvant treatment), at least 1 month (M1), and 7 months (M7) after treatment. The results indicated deficits in cognitive health of breast cancer patients immediately after chemotherapy that improved over time. This psychological trajectory was paralleled by a disruption and later recovery of resting-state functional connectivity, mostly in the parietal and frontal brain regions. The functional connectivity alteration pattern seems to be a separable treatment symptom from the decreased cognitive health. More targeted support for patients should be developed to ameliorate these multi-faceted side effects of chemotherapy treatment on neural functioning and cognitive health.

scholarly journals Heterogeneity of EEG resting-state brain networks in absolute pitch

2020 ◽  
Author(s):  
Marielle Greber ◽  
Carina Klein ◽  
Simon Leipold ◽  
Silvano Sele ◽  
Lutz Jäncke
Keyword(s):  
Functional Connectivity ◽  
Auditory Cortex ◽  
Resting State ◽  
Large Scale ◽  
Absolute Pitch ◽  
Brain Regions ◽  
Brain Network ◽  
Higher Order ◽  
Neural Basis ◽  
Whole Brain

AbstractThe neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks.Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability.In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

scholarly journals Resting-State Functional Connectivity Associated With Extraversion and Agreeableness in Adolescence

2022 ◽  
Vol 15 ◽  
Author(s):  
Leehyun Yoon ◽  
Angelica F. Carranza ◽  
Johnna R. Swartz
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Neural Connectivity ◽  
Connectivity Analysis ◽  
Whole Brain ◽  
Personality Dimensions ◽  
Attention Network ◽  
Negatively Associated ◽  
Postcentral Gyrus

Although adolescence is a period in which developmental changes occur in brain connectivity, personality formation, and peer interaction, few studies have examined the neural correlates of personality dimensions related to social behavior within adolescent samples. The current study aims to investigate whether adolescents’ brain functional connectivity is associated with extraversion and agreeableness, personality dimensions linked to peer acceptance, social network size, and friendship quality. Considering sex-variant neural maturation in adolescence, we also examined sex-specific associations between personality and functional connectivity. Using resting-state functional magnetic resonance imaging (fMRI) data from a community sample of 70 adolescents aged 12–15, we examined associations between self-reported extraversion and agreeableness and seed-to-whole brain connectivity with the amygdala as a seed region of interest. Then, using 415 brain regions that correspond to 8 major brain networks and subcortex, we explored neural connectivity within brain networks and across the whole-brain. We conducted group-level multiple regression analyses with the regressors of extraversion, agreeableness, and their interactions with sex. Results demonstrated that amygdala connectivity with the postcentral gyrus, middle temporal gyrus, and the temporal pole is positively associated with extraversion in girls and negatively associated with extraversion in boys. Agreeableness was positively associated with amygdala connectivity with the middle occipital cortex and superior parietal cortex, in the same direction for boys and girls. Results of the whole-brain connectivity analysis revealed that the connectivity of the postcentral gyrus, located in the dorsal attention network, with regions in default mode network (DMN), salience/ventral attention network, and control network (CON) was associated with extraversion, with most connections showing positive associations in girls and negative associations in boys. For agreeableness, results of the within-network connectivity analysis showed that connections within the limbic network were positively associated with agreeableness in boys while negatively associated with or not associated with agreeableness in girls. Results suggest that intrinsic functional connectivity may contribute to adolescents’ individual differences in extraversion and agreeableness and highlights sex-specific neural connectivity patterns associated with the two personality dimensions. This study deepens our understanding of the neurobiological correlates of adolescent personality that may lead to different developmental trajectories of social experience.

scholarly journals Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Frigyes Samuel Racz ◽  
Orestis Stylianou ◽  
Peter Mukli ◽  
Andras Eke
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Spatial Organization ◽  
Brain Activity ◽  
Surrogate Data ◽  
Brain Regions ◽  
Local Dynamic ◽  
Dynamic Functional Connectivity ◽  
Multifractal Scaling ◽  
The Brain

Abstract Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.

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