Functional Connectivity in the Resting Brain: An Analysis Based on ICA
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The concept of resting state functional magnetic resonance imaging (fMRI) is built onto an original finding in 1995 that brain hemispheres present synchronous signal fluctuations with distinct patterns. fMRI measurements rely on blood oxygenation changes that indirectly mirror neural activity. Therefore, the origin of certain functional connectivity patterns, resting state networks (RSNs), has been a widely debated research question and numerous contributing factors have been identified. According to current understanding the fluctuations reflect maintenance of the system integrity in addition to spontaneous thought and action processes in the resting state. A popular method to study the functional connectivity in resting state fMRI is spatial independent component analysis (ICA) that decomposes signal sources into statistically independent components. The dichotomy of functional specialization versus functional integration has a correspondence in fMRI studies where RSNs play the integrative viewpoint of brain function. Although canonical large-scale RSNs are broadly distributed they also express modularity that can be accomplished by ICA with a high number of estimated components. The characteristics of high ICA dimensionality are broadly investigated in the thesis. An enduring issue in resting state research has been the confounding noise sources like motion and cardiorespiratory processes which may hamper the analysis. In this thesis the ability of ICA to separate these noise sources from the default mode network, a major RSN, is studied. Additionally, the suitability of ICA for full frequency spectrum analysis, a relatively rare setting in biosignal analysis, is investigated. The results of the thesis support the viewpoint of ICA as a robust analysis method for functional connectivity analysis. Cardiorespiratory and motion induced noise did not confound the functional connectivity analyses with ICA. High dimensional ICA provided better signal source separation, revealed the modular structure of the RSNs and pinpointed the specific aberrations in the autism spectrum disorder population. ICA was also found applicable for fully explorative analysis in both the spatial and temporal domains and indicated functional connectivity changes induced by transcranial bright light stimulation.
Communication noise
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Functional near-infrared spectroscopy (fNIRS) is an emerging brain imaging technique. Recent fNIRS studies confirm that the intrinsic fluctuation can be robustly detected by functional near infrared spectroscopy (fNIRS), the phenomenon is also termed as resting state functional connectivity (RSFC). However, functional connectivity exists not only during the resting state. Therefore, one important question is that whether the functional connectivity patterns during both states are consistent. In this paper, we investigate the functional connectivity during both resting state and task state. The comparison result suggests that the functional connectivity patterns revealed by fNIRS signal during both states are similar.
Functional near-infrared spectroscopy
Functional Imaging
Dynamic functional connectivity
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Autism spectrum traits exist on a continuum and are more common in males than in females, but the basis for this sex difference is unclear. To this end, the present study draws on the extreme male brain theory, investigating the relationship between sex difference and the default mode network (DMN), both known to be associated with autism spectrum traits. Resting-state functional magnetic resonance imaging (MRI) was carried out in 42 females (mean age ± standard deviation, 22.4 ± 4.2 years) and 43 males (mean age ± standard deviation, 23.8 ± 3.9 years) with typical development. Using a combination of different analyses (viz., independent component analysis (ICA), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and seed-based analyses), we examined sex differences in the DMN and the relationship to autism spectrum traits as measured by autism-spectrum quotient (AQ) scores. We found significant differences between female and male subjects in DMN brain regions, with seed-based analysis revealing a significant negative correlation between default-mode resting state functional connectivity of the anterior medial prefrontal cortex seed (aMPFC) and AQ scores in males. However, there were no relationships between DMN sex differences and autism spectrum traits in females. Our findings may provide important insight into the skewed balance of functional connectivity in males compared to females that could serve as a potential biomarker of the degree of autism spectrum traits in line with the extreme male brain theory.
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The default-mode network (DMN) is a set of functionally connected regions that play crucial roles in internal cognitive processing. Previous resting-state fMRI studies have demonstrated that the intrinsic functional organization of the DMN undergoes remarkable reconfigurations during childhood and adolescence. However, these studies have mainly focused on cross-sectional designs with small sample sizes, limiting the consistency and interpretations of the findings. Here, we used a large sample of longitudinal resting-state fMRI data comprising 305 typically developing children (6-12 years of age at baseline, 491 scans in total) and graph theoretical approaches to delineate the developmental trajectories of the functional architecture of the DMN. For each child, the DMN was constructed according to a prior parcellation with 32 brain nodes. We showed that the overall connectivity increased in strength from childhood to adolescence and became spatially similar to that in the young adult group (N = 61, 18-28 years of age). These increases were primarily located in the midline structures. Global and local network efficiency in the DMN also increased with age, indicating an enhanced capability in parallel information communication within the brain system. Based on the divergent developmental rates of nodal centrality, we identified three subclusters within the DMN, with the fastest rates in the cluster mainly comprising the anterior medial prefrontal cortex and posterior cingulate cortex. Together, our findings highlight the developmental patterns of the functional architecture in the DMN from childhood to adolescence, which has implications for the understanding of network mechanisms underlying the cognitive development of individuals.
Posterior cingulate
Task-positive network
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Task-positive network
Human brain
Network Analysis
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Task-positive network
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Abstract The effects of non-concussive impacts in contact-sports such as in Australian rules football (ARF) are still largely unexplored. These impacts are often but not always lower in intensity, but occur more frequently than actual concussions. Since non-concussive impacts are often asymptomatic, their significance may be underestimated. Acute or subacute measurement of non-concussive injury is challenging as the pathological response and injury is poorly described. There is therefore a need for a greater understanding of the pathological consequences of exposure. Growing evidence indicates that resting-state functional connectivity (rs-fMRI) changes in the Default Mode Network (DMN) may be an important biomarker that is sensitive to characterize these impacts. In this work, we examined functional connectivity changes within the DMN of ARF players to evaluate its potential as an early biomarker for non-concussive impacts. Based on rs-fMRI, we compare the DMN of 47 sub-elite ARF players (mean age 21.5±2.7 years [SD], males 57%) and 42 age-matched healthy controls (mean age 23.2±2.3 years [SD], males 48%) using Independent Component Analysis (ICA) and Dual Regression. This approach permits an unbiased decomposition of brain activity into networks with principled handling of statistical error. An 83% increase in DMN connectivity (as measured by the Strictly Standardized Mean Difference on values derived from Dual Regression) was observed in ARF players in the left retrosplenial cingulate cortex compared to healthy controls (FDR-corrected p-value from dual regression = 0.03, 95% CI computed via bootstrapping was 58% to 116%). The AUC for distinguishing ARF players from controls was 0.80 (95% CI; [0.71, 0.89]), equating to a PPV of 78% and a NPV of 74%. These results are preliminary; future work could investigate robustness to different random initializations of ICA and validate the findings on an independent testing set, as well as investigate longitudinal changes in ARF players over the course of a playing season.
Football players
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