A Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex Using Intrinsic Individual Brain Connectivity.
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ABSTRACT:
SUBMITTER: Menon SS
PROVIDER: S-EPMC6450922 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Mild traumatic brain injury (mTBI) is the most common neurological disorder and is typically characterized by temporally limited cognitive impairment and emotional symptoms. Previous examinations of intrinsic resting state networks in mTBI have primarily focused on abnormalities in static functional connectivity, and deficits in dynamic functional connectivity have yet to be explored in this population. Resting-state data was collected on 48 semi-acute (mean = 14 days post-injury) mTBI patients and 48 matched healthy controls. A high-dimensional independent component analysis (N = 100) was utilized to parcellate intrinsic connectivity networks (ICN), with a priori hypotheses focusing on the default-mode network (DMN) and sub-cortical structures. Dynamic connectivity was characterized using a sliding window approach over 126 temporal epochs, with standard deviation serving as the primary outcome measure. Finally, distribution-corrected z-scores (DisCo-Z) were calculated to investigate changes in connectivity in a spatially invariant manner on a per-subject basis. Following appropriate correction for multiple comparisons, no significant group differences were evident on measures of static or dynamic connectivity within a priori ICN. Reduced (HC > mTBI patients) static connectivity was observed in the DMN at uncorrected (p < 0.005) thresholds. Finally, a trend (p = 0.07) for decreased dynamic connectivity in patients across all ICN was observed during spatially invariant analyses (DisCo-Z). In the semi-acute phase of recovery, mTBI was not reliably associated with abnormalities in static or dynamic functional connectivity within the DMN or sub-cortical structures.
| S-EPMC4504345 | biostudies-literature
Project description:BackgroundStudies have revealed that intrinsic neural activity varies over time. However, the temporal variability of brain local connectivity in internet gaming disorder (IGD) remains unknown. The purpose of this study was to explore the alterations of static and dynamic intrinsic brain local connectivity in IGD and whether the changes were associated with clinical characteristics of IGD.MethodsResting-state functional magnetic resonance imaging (rs-fMRI) scans were performed on 36 individuals with IGD (IGDs) and 44 healthy controls (HCs) matched for age, gender and years of education. The static regional homogeneity (sReHo) and dynamic ReHo (dReHo) were calculated and compared between two groups to detect the alterations of intrinsic brain local connectivity in IGD. The Internet Addiction Test (IAT) and the Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the severity of online gaming addiction and sleep quality, respectively. Pearson correlation analysis was used to evaluate the relationship between brain regions with altered sReHo and dReHo and IAT and PSQI scores. Receiver operating characteristic (ROC) curve analysis was used to reveal the potential capacity of the sReHo and dReHo metrics to distinguish IGDs from HCs.ResultsCompared with HCs, IGDs showed both increased static and dynamic intrinsic local connectivity in bilateral medial superior frontal gyrus (mSFG), superior frontal gyrus (SFG), and supplementary motor area (SMA). Increased dReHo in the left putamen, pallidum, caudate nucleus and bilateral thalamus were also observed. ROC curve analysis showed that the brain regions with altered sReHo and dReHo could distinguish individuals with IGD from HCs. Moreover, the sReHo values in the left mSFG and SMA as well as dReHo values in the left SMA were positively correlated with IAT scores. The dReHo values in the left caudate nucleus were negatively correlated with PSQI scores.ConclusionsThese results showed impaired intrinsic local connectivity in frontostriatothalamic circuitry in individuals with IGD, which may provide new insights into the underlying neuropathological mechanisms of IGD. Besides, dynamic changes of intrinsic local connectivity in caudate nucleus may be a potential neurobiological marker linking IGD and sleep quality.
| S-EPMC10410779 | biostudies-literature
Project description:Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.
| S-EPMC6866312 | biostudies-literature
Project description:Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer's disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity.
| S-EPMC7691406 | biostudies-literature
Project description:Using resting-state functional magnetic resonance imaging, we test the hypothesis that subjects with post-traumatic stress disorder (PTSD) are characterized by reduced temporal variability of brain connectivity compared to matched healthy controls. Specifically, we test whether PTSD is characterized by elevated static connectivity, coupled with decreased temporal variability of those connections, with the latter providing greater sensitivity toward the pathology than the former. Static functional connectivity (FC; nondirectional zero-lag correlation) and static effective connectivity (EC; directional time-lagged relationships) were obtained over the entire brain using conventional models. Dynamic FC and dynamic EC were estimated by letting the conventional models to vary as a function of time. Statistical separation and discriminability of these metrics between the groups and their ability to accurately predict the diagnostic label of a novel subject were ascertained using separate support vector machine classifiers. Our findings support our hypothesis that PTSD subjects have stronger static connectivity, but reduced temporal variability of connectivity. Further, machine learning classification accuracy obtained with dynamic FC and dynamic EC was significantly higher than that obtained with static FC and static EC, respectively. Furthermore, results also indicate that the ease with which brain regions engage or disengage with other regions may be more sensitive to underlying pathology than the strength with which they are engaged. Future studies must examine whether this is true only in the case of PTSD or is a general organizing principle in the human brain. Hum Brain Mapp 38:4479-4496, 2017. © 2017 Wiley Periodicals, Inc.
| S-EPMC6866943 | biostudies-literature
Project description:Recent efforts to evaluate the heritability of the brain's functional connectome have predominantly focused on static connectivity. However, evaluating connectivity changes across time can provide valuable insight about the inherent dynamic nature of brain function. Here, the heritability of Human Connectome Project resting-state fMRI data was examined to determine whether there is a genetic basis for dynamic fluctuations in functional connectivity. The dynamic connectivity variance, in addition to the dynamic mean and standard static connectivity, was evaluated. Heritability was estimated using Accelerated Permutation Inference for the ACE (APACE), which models the additive genetic (h2), common environmental (c2), and unique environmental (e2) variance. Heritability was moderate (mean h2: dynamic mean = 0.35, dynamic variance = 0.45, and static = 0.37) and tended to be greater for dynamic variance compared to either dynamic mean or static connectivity. Further, heritability of dynamic variance was reliable across both sessions for several network connections, particularly between higher-order cognitive and visual networks. For both dynamic mean and static connectivity, similar patterns of heritability were found across networks. The findings support the notion that dynamic connectivity is genetically influenced. The flexibility of network connections, not just their strength, is a heritable endophenotype that may predispose trait behavior.
| S-EPMC8325018 | biostudies-literature
Project description:Previous studies have implicated abnormal functional coordination in brain regions of smokers. Neuroimaging studies demonstrated alternations in brain connectivity by using the resting-state functional connectivity (rsFC) method which arbitrarily chooses specific networks or seed regions as priori selections and cannot provide a full picture of the FC changes in chronic smokers. The aim of this study was to investigate the whole-brain functional coordination measured by functional connectivity density (FCD). As the variance of brain activity, dynamic FCD (dFCD) was performed to investigate dynamic changes of whole-brain integration in chronic smokers. In total, 120 chronic smokers and 56 nonsmokers were recruited, and static FCD and dFCD were performed to investigate aberrance of whole-brain functional coordination. Shared aberrance in visual areas has been found in both static and dFCD study in chronic smokers. Furthermore, the results exhibited that both heavy and light smokers demonstrated decreased dFCD in the visual cortex and left precuneus, and also increased dFCD in the right orbitofrontal cortex, left caudate, right putamen, and left thalamus compared with nonsmokers. In addition, alternations of dFCD have been found between heavy and light smokers. Furthermore, the dFCD variations showed significant positive correlation with smoking-related behaviors. The results demonstrated that chronic smokers not only have some initial areas, but also have some regions associated with severity of cigarette smoking. Lastly, dFCD could provide more subtle variations in chronic smokers, and the combination of static and dFCD may deepen our understanding of the brain alternations in chronic smokers.
| S-EPMC9068985 | biostudies-literature
Project description:When people sense that another person tries to control their decisions, some people will act against the control, whereas others will not. This individual tendency to control-averse behavior can have far-reaching consequences, such as engagement in illegal activities or noncompliance with medical treatments. Although individual differences in control-averse behavior have been well documented in behavioral studies, their neurological basis is less well understood. Here, we use a neural trait approach to examine whether individual differences in control-averse behavior might be linked to stable brain-based characteristics. To do so, we analyze the association between intrinsic connectivity networks as measured by resting state functional magnetic resonance imaging and control-averse behavior in an economic exchange game. In this game, subjects make choices that are either free or controlled by another person, with real consequences to both interaction partners. We find that the individual level of control-averse behavior can be positively predicted by intrinsic connectivity within the salience network, but not the central executive network or the default mode network. Specifically, subjects with a more prominent connectivity hub in the dorsal anterior cingulate cortex show greater levels of control-averse behavior. This finding provides the first evidence that the heterogeneity in control-averse behavior might originate in systematic differences of the stable functional brain organization.
| S-EPMC6866545 | biostudies-literature
Project description:Neuroimaging-based functional connectivity (FC) analyses have revealed significant developmental trends in specific intrinsic connectivity networks linked to cognitive and behavioral maturation. However, knowledge of how brain functional maturation is associated with FC dynamics at rest is limited. Here, we examined age-related differences in the temporal variability of FC dynamics with data publicly released by the Nathan Kline Institute (NKI; n = 183, ages 7-30) and showed that dynamic inter-region interactions can be used to accurately predict individual brain maturity across development. Furthermore, we identified a significant age-dependent trend underlying dynamic inter-network FC, including increasing variability of the connections between the visual network, default mode network (DMN) and cerebellum as well as within the cerebellum and DMN and decreasing variability within the cerebellum and between the cerebellum and DMN as well as the cingulo-opercular network. Overall, the results suggested significant developmental changes in dynamic inter-network interaction, which may shed new light on the functional organization of typical developmental brains.
| S-EPMC4503925 | biostudies-literature
Project description:BackgroundCerebrovascular reactivity (CVR) is an important aspect of brain function, and as such it is important to understand relationship between CVR and functional connectivity.MethodsThis research studied the role of CVR, or the brain's ability to react to vasoactive stimuli on brain functional connectivity by scanning subjects with blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) while they periodically inhale room air and a CO 2-enriched gas mixture. We developed a new metric to measure the effect of CVR on each intrinsic connectivity network (ICN), which contrasts to voxel-wise CVR. We also studied the changes in whole-brain connectivity patterns using both static functional network connectivity (sFNC) and dynamic FNC (dFNC).ResultsWe found that network connectivity is generally weaker during vascular dilation, which is supported by previous research. The dFNC analysis revealed that participants did not return to the pre-CO 2 inhalation state, suggesting that one-minute periods of room-air inhalation is not enough for the CO 2 effect to fully dissipate.ConclusionsCerebrovascular reactivity is one tool that the cerebrovascular system uses to ensure the constant, finely-tuned flow of oxygen to function properly. Understanding the relationship between CVR and brain dynamism can provide unique information about cerebrovascular diseases and general brain function. We observed that CVR has a wide, but consistent relationship to connectivity patterns between functional networks.
| S-EPMC7303385 | biostudies-literature