Top PDF Evidence for Hubs in Human Functional Brain Networks

Evidence for Hubs in Human Functional Brain Networks

Evidence for Hubs in Human Functional Brain Networks

correlations (RSFC graphs are commonly formed using Pearson correlations between BOLD time series, and unless otherwise specified, ‘‘correlation’’ signifies Pearson correlation in this paper), node degree is substantially explained by subnetwork size. The second argument is concerned with amplifications of the first argument that can occur when systems are not modeled at their inherent levels of organization, such as when brains (cortically organized at levels of columns, areas, and systems [ Churchland and Sejnowski, 1988; Felleman and Van Essen, 1991 ]) are modeled as voxels (an arbitrary volumetric element). Since some classic methods of hub identification are confounded in correlation networks, we develop two alternative methods for identifying hubs that are more suited to RSFC cor- relation networks. Both methods aim to identify regions of the brain that are well-situated to support and/or integrate multiple types of information. Both methods leverage the correspon- dence between functional brain systems (e.g., dorsal attention system) and graph subnetworks observed in recently described RSFC graphs ( Power et al., 2011 ; see also Yeo et al., 2011 ). First, using a model of the brain at the level of functional areas, we identify nodes that participate in many subnetworks of the brain (e.g., a node that has relationships with members of multiple brain systems, such as visual, default mode, or frontoparietal control systems). These nodes are candidate brain hubs. We identify these candidate hubs using the established measure of participation coefficients ( Guimera` and Nunes Amaral, 2005 ). Second, we examine a high-resolution brain network to identify spatial locations where many subnetworks are present within a small volume (e.g., finding, within a small sphere, voxels repre- senting the dorsal attention, visual, frontoparietal control, and default mode systems). We call these locations articulation points—they are not hubs in the traditional graph theoretic sense, but they are locations where such hubs might be situated. Both methods identify similar sets of brain regions in the anterior insula, anterior, middle and superior frontal cortex, medial supe- rior frontal cortex, medial parietal cortex, inferior parietal, and temporo-occipital cortex. Notably, these regions do not empha- size the default mode system.
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Abnormal intrinsic functional hubs in alcohol dependence: evidence from a voxelwise degree centrality analysis

Abnormal intrinsic functional hubs in alcohol dependence: evidence from a voxelwise degree centrality analysis

Our study provides new insight into understanding the dysfunction and pathophysiology of AD using graph-theoretic whole-brain network analysis with unbiased opportunity to search abnormalities within the entire connectivity matrix of full-brain functional connectome without priori hypothesis and a priori definition of ROIs. The present study showed that the pattern of negative functional deficits appears to be disrupted by alcohol intoxication, which implicates at least three principal neural systems: cerebellar-executive control-visual cortex, which further affected normal visuo- motor behaviors such as an explicit type of impaired driving behavior. These findings highlight the role of functional connectivity in the pathophysiology of AD and expand our understanding of the functional characteristics of AD.
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Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks

Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks

Most of the previous evidence for biological validity of human MRI connectomes has therefore rested on analogical rather than reductionist logic. Informative analogies make comparisons between a poorly understood system and a more certainly or completely understood system of the same type. There have been several studies recently supporting the value of this approach for comparative connectomics— comparing the topology of human neuroimaging networks to the topology of more biologically specified nervous systems [50]. For example, MRI networks generally comprise a rich club of densely inter-connected high-degree hubs. Human brain network-rich clubs are topologically integrative by mediating many of the shortest paths between pairs of more peripheral nodes in different modules [51]. Rich clubs of fMRI co- activation networks are associated with diverse cognitive functions including higher order executive tasks [16], and are biologically expensive in terms of wiring cost [52,53]. These MRI observations are suggestive of an economical trade-off, between minimizing biological cost and maximizing topologi- cal value, and have been affirmed by demonstration of analogous findings in more certainly known nervous systems. In particular, the anatomical network of axonal projections and synapses between the 302 neurons of Caenorhabditis elegans [53] includes a topologically integrative rich club that is expensively wired and comprises command interneurons known to be functionally important for coordinated movement and adapt- ive behaviours [54]. Indeed, high-cost, high-value rich clubs have now been demonstrated across a wide range of scales, species, experimental techniques for network mapping, and computational models of network generation [55–58]. It seems plausible, on this basis, that the topological properties of human fMRI networks are not idiosyncratic epiphenomena but are instead representative of a general class of brain net- works that have been naturally selected by the same competitive pressures for relatively low biological cost and high topological integration [18].
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The structural connectivity of higher order association cortices reflects human functional brain networks

The structural connectivity of higher order association cortices reflects human functional brain networks

Graph-theory a b s t r a c t Human higher cognition arises from the main tertiary association cortices including the frontal, temporal and parietal lobes. Many studies have suggested that cortical functions must be shaped or emerge from the pattern of underlying physical (white matter) connec- tivity. Despite the importance of this hypothesis, there has not been a large-scale analysis of the white-matter connectivity within and between these associative cortices. Thus, we explored the pattern of intra- and inter-lobe white matter connectivity between multiple areas defined in each lobe. We defined 43 regions of interest on the lateral associative cortex cytoarchitectonically (6 regions of interest e ROIs in the frontal lobe and 17 ROIs in the parietal lobe) and anatomically (20 ROIs in the temporal lobe) on individuals' native space. The results demonstrated that intra-region connectivity for all 3 lobes was dense and graded generally. In contrary, the inter-lobe connectivity was relatively discrete and regionally specific such that only small sub-regions exhibited long-range connections to another lobe. The long-range connectivity was mediated by 6 major associative white matter tracts, consistent with the notion that these higher cognitive functions arises from brain-wide distributed connectivity. Using graph-theory network analysis we revealed five physically-connected sub-networks, which correspond directly to five known functional networks. This study provides strong and direct evidence that core functional brain networks mirror the brain's structural connectivity. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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Intracranial electrophysiology reveals reproducible intrinsic functional connectivity within human brain networks

Intracranial electrophysiology reveals reproducible intrinsic functional connectivity within human brain networks

A B C D E F Figure 1. Correspondence of seed-based functional connectivity between resting state BOLD and ECoG (HFB envelope) within 5 subjects (S1–S5). On the cortical surface projections, the seed electrode location is shown as a black dot. Other electrode locations are shown as circles filled with a color representing the degree of ECoG-HFB envelope correlation with the seed (from one representative ECoG run). The degree of BOLD correlation with the seed (expressed as Z-score from a general linear model analysis) is shown on the pial surface. Color scales are anchored at minimum and maximum r values and Z-scores for ECoG and BOLD, respectively (disregarding electrodes immediately neighboring the seed in ECoG). In A), C), and D), respectively, individual results are shown from seed locations within the default mode network (DMN), dorsal attention network (DAN), and frontoparietal control network (FPCN), including scatter plots of the spatial correlation between ECoG versus BOLD seed-based functional connectivity across target electrode locations (unanalyzed electrodes not shown). B) Example BOLD and ECoG-HFB envelope time courses extracted from the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) within the DMN. E) Reproducibility of the relationship between BOLD and ECoG functional connectivity across two independent ECoG resting state runs (each subject indicated with a different marker shape, and each network labeled with a different color). F ) An example of BOLD versus ECoG-HFB connectivity of the mPFC on the inflated medial and lateral surfaces for two independent ECoG runs (patient S3, BOLD FC thresholded at Z-score ⬎ 兩10兩).
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Imaging structural and functional brain networks in temporal lobe epilepsy

Imaging structural and functional brain networks in temporal lobe epilepsy

In humans, two major indirect approaches have been employed to map structural networks: diffusion MRI tractography and structural MRI covariance (see Figures 2A–D). Structural networks derived from diffusion-weighted MRI data provide an approximation of the underlying white matter architecture (Le Bihan et al., 1986, 1996; Johansen-Berg and Behrens, 2006; Jbabdi and Johansen-Berg, 2011) by describing the direction- ality and magnitude of water diffusion at each imaging voxel. These data can be further processed by tractography algo- rithms (Mori et al., 1999; Behrens et al., 2003), which recon- struct fiber pathways running along plausible diffusion trajec- tories in voxel-space (Figure 2A). While somewhat challenged in regions where different fiber populations intersect (Behrens et al., 2003; Jones et al., 2012), such as the cortical gray mat- ter, tractography can generate consistent results, particularly in deep white matter. Findings have shown overall a good cor- respondence with the animal tracing literature, and have been cross-validated by comparative sacrificial tracing studies in non- human primates (Mori et al., 1999; Parker et al., 2002; Dauguet et al., 2007). Moreover, it has been shown that factors such as fiber diameter and density, membrane permeability, myeli- nation, as well as fiber packing (Beaulieu, 2002; Concha et al., 2010) can influence the directionality and magnitude of water displacement at a given voxel. Diffusion imaging may, thus, be used to assess microstructural and architectural integrity
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Network-Based Investigations of Human Functional Brain Dynamics

Network-Based Investigations of Human Functional Brain Dynamics

locally greedy modularity maximization algorithm. Following this reasoning, community detection is performed independently across a range of γ values, and the mean z-score of the Rand index between each pair of partitions generated by 100 algorithm runs at each γ value is computed, providing a quantitative measure of similarity across the par- titions [186]. The Rand z-score is chosen because it inherently provides a comparison to a null distribution that takes the number and size of communities in each partition into account; it can be calculated analytically; and its behavior in the context of modularity maximization on functional brain networks has been previously characterized [35, 186]. The optimal γ value is that giving the highest average Rand z-score across pairs of algo- rithm runs and across participants, indicating the most consistent community partitions. When there is no clear choice (i.e., when the γ landscape is relatively flat), a near-optimal value is chosen based upon the expected number of functional communities [113]. After choosing a spatial scale, a temporal scale (ω value) is determined by choosing the value that maximizes the variance in flexibility across network nodes, where a node’s flexibility measures the number of times it switches community assignments between adjacent time windows (see Eq 5.2). This ensures that the algorithm will resolve high-flexibility nodes from those that remain within the same community throughout the experiment. See Section 5.4 for further treatment of resolution parameters.
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Functional brain networks for learning predictive statistics

Functional brain networks for learning predictive statistics

Statistical learning a b s t r a c t Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the functional brain networks that mediate this type of statistical learning. Here, we test whether changes in the processing and connectivity of functional brain networks due to training relate to our ability to learn temporal regularities. By combining behavioral training and functional brain connectivity analysis, we demonstrate that individuals adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. Further, we show that individual learning of temporal structures relates to decision strategy. Our fMRI results demonstrate that learning-dependent changes in fMRI activation within and functional connectivity be- tween brain networks relate to individual variability in strategy. In particular, extracting the exact sequence statistics (i.e., matching) relates to changes in brain networks known to be involved in memory and stimulus-response associations, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to changes in frontal and striatal networks. Thus, our findings provide evidence that dissociable brain networks mediate individual ability in learning behaviorally-relevant statistics.
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Reorganization of brain networks in aging: a review of functional connectivity studies

Reorganization of brain networks in aging: a review of functional connectivity studies

Connectivity changes have been further explored using higher- level analysis methods. Tomasi and Volkow (2012) found that long-range connectivity decreased with age whereas short-range connections were stronger. These results were interpreted under the hypothesis that some brain regions, with key roles in whole- brain connectivity, named hubs ( Buckner et al., 2009; Crossley et al., 2013 ), could experiment strengthening of functional con- nectivity with their closest regions, leading to an increase in local connectivity ( Ferreira and Busatto, 2013 ). In addition, another line of research refers to the study of functional connectivity within and between the main large-scale networks. In this regard, it has been described that the age-related decreases in connectivity between regions of a network are accompanied by increases in the connectivity of these network toward regions of other RSNs, affecting the overall functional connectivity architecture ( Betzel et al., 2014; Geerligs et al., 2014 ).
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Vascular physiology drives functional brain networks

Vascular physiology drives functional brain networks

Pillai, J. J., & Mikulis, D. J. (2015). Cerebrovascular Reactivity Mapping: An Evolving Standard for Clinical Functional Imaging. AJNR. American Journal of Neuroradiology, 36(1), 7–13. http://doi.org/10.3174/ajnr.A3941 Porret, C. A., Stergiopulos, N., Hayoz, D., Brunner, H. R., & Meister, J. J. (1995). Simultaneous ipsilateral and contralateral measurements of vasomotion in conduit arteries of human upper limbs. The American Journal of Physiology, 269(6 Pt 2), H1852–8.

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A review of structural and functional brain networks: small world and atlas

A review of structural and functional brain networks: small world and atlas

The methods to construct structural brain networks can be divided into two categories: cerebral cortex correlation and white fiber tracking. Many types of brain morpho- logical measurements are used to calculate cerebral cortex correlation, including the commonly used cortical thick- ness and volume [27–29]. Anatomical networks can be obtained by calculating correlations of cortical thickness (or volume) between all pairs of regions in a predetermined anatomical parcellation scheme, such as in the AAL and ANIMAL atlases [12, 30]. Strong interregional correlation of cortical thickness measurements may be the axonal connection, which might be caused by mutual nutrition [31–33]. Structural networks can also be constructed by the data of diffusion tensor imaging (DTI) via tracking the white matter fiber bundles [34]. Some summation indices, such as the trace apparent diffusion coefficient or the fractional anisotropy (FA), can be extracted using tensor decomposition from DTI [35–37]. DTI has become the preferred choice for detecting white matter alterations in the human brain [38]. However, the partial volume effect and inability of the model to cope with nonGaussian dif- fusion are the two main drawbacks of DTI [39], which limit its application.
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Effects of chronic peripheral olfactory loss on functional brain networks

Effects of chronic peripheral olfactory loss on functional brain networks

Olfaction is the phylogenetically and ontogenetically oldest sense ( Breiphol and Apfelbach, 1986 ) and stands out among all other human sensory systems, especially in the domain of central processing, in that peripheral input can be processed without a thalamic relay. Further- more, the olfactory network responsible for the sense of smell projects largely ipsilaterally within the brain ( Lascano et al., 2010; Iannilli et al., 2013 ). The spatial organization of the olfactory network is much more dis- persed compared to other senses (for a review see Lundstro¨m et al. (2011) ). Secondary and tertiary areas of olfactory processing involve parts of the limbic system, and are thus closely linked to memory and emotional states ( Arshamian et al., 2013 ). Moreover, the olfactory system holds the unique ability to be activated by the sen- sorimotor act of sniffing, without the presentation of an odor ( Sobel et al., 1998a ). Due to this fact, the sense of smell is a powerful model for investigating the effects of peripheral sensory loss on central processing.
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Brain Functional and Structural Networks Underpinning Musical Creativity

Brain Functional and Structural Networks Underpinning Musical Creativity

3 BRAIN ACTIVITY AND FUNCTIONAL NETWORK INTERACTION 3.1 Introduction Musical improvisation is an excellent model to study human creativity in which the output is created in real-time and revision impossible. Similar to innovative verbalizations or movement sequences, musical improvisation is only possible because choices are constrained by stylistic rules and physical limitations [45]. Expert practitioners who have internalized these rules and practiced the related motor movements can produce amazingly intricate improvisations. Despite some previous studies, the neural underpinnings of expert’s improvising performance; what and how brain areas are involved during musical improvisation are not clearly understood. Here, we designed a new functional magnetic resonance imaging (fMRI) study, in which, while being in the MRI scanner, advanced jazz improvisers performed improvisatory vocalization and imagination as main tasks and performed a pre-learned melody as a control task. We
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Control Theoretic Analysis of Human Brain Networks

Control Theoretic Analysis of Human Brain Networks

The quest to understand and predict brain dynamics from the architecture of underlying structural connectivity is certainly not a new one. In fact, there have been concerted efforts over the last decade and more to identify structural predictors of the resting state BOLD signal. Seminal contributions have included the observations of statistically significant correlations between structural connectivity estimated from diffusion imaging data and functional connectivity estimated from fMRI [Honey et al. (2009)], as well as extensions of these correlations that account for long distance paths along white matter tracts [Go˜ ni et al. (2014)] and spectral properties of structural matrices [Becker et al. (2016)]. The question of how brain structure constrains a wide range of brain states (beyond simply the resting state) is a very open area of inquiry. Moreover, this question is particularly challenging to address with empirical data because it is difficult to obtain data from humans in more than a handful of task states [Cole et al. (2014)]. For this reason, computational models play a very important role in offering testbeds for the development of theories linking structure to ensembles of brain states, which can in turn offer testable predictions. Our results suggest that an understanding of the relationship between brain structure and function is perhaps ill-constrained when examining connectivity alone. The additional assumption of energy conservation produces a set of brain states that cannot be simply predicted from statistics of structural connectivity, perhaps offering a mechanism for the large amount of unexplained variance in prior predictions [Honey et al. (2009); Go˜ ni et al. (2014)].
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Reorganisation of Brain Hubs across Altered States of Consciousness.

Reorganisation of Brain Hubs across Altered States of Consciousness.

hubs was observed across the two levels of propofol-induced sedation as compared to the baseline resting state condition. κ was calculated as the slope of a linear fit to the scatterplot of average functional connectivity strength between a chosen condition (light or moderate) and the difference between this condition and baseline resting state recording for each participant (panels A,B). When directly assessing sedation-level-dependent disruptions to the hub structure between moderate and light sedation conditions, regions belonging to the transmodal default mode (e.g. RAG = right angular gyrus), as well as the unimodal visual (e.g. RIOG = right inferior occipital gyrus), auditory (e.g. LSMG = left supramarginal gyrus) and sensory/somatomotor networks showed a marked decrease in their hubness, while those assigned to the subcortical (LTHAL = left thalamus, LPALL = left pallidum) and salience (RaINS = right anterior insula) networks displayed a strong increase in their nodal strength (panel C). Network nodes are colour-coded based on the Power et al. 61 parcellation scheme.
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Identifying Changes of Functional Brain Networks using Graph Theory

Identifying Changes of Functional Brain Networks using Graph Theory

with a heavy involvement of the caudate area (Poldrack and Packard, 2003). We found that the dynamics on a whole-brain level was further reduced in older subjects. These results shed new light onto earlier findings of reduced fMRI signal variability in older subjects (Garrett et al., 2011). In general our findings give further evidence for a non- artifactual origin of dynamic resting state functional connectivity. This is important, as dynamic functional connectivity methods might become a very valuable tool for clinical purposes (Hutchison et al., 2013a). In this context, these methods might enable to increase the reliability and or sensitivity of future diagnostic tools. This additional benefit will come without additional costs, as the same dataset can be analyzed with both, static and dynamic functional connectivity methods. Thus, the investigation of differences in the dynamics of pathologically changed brains might substantially enhance our understanding of brain diseases but also of healthy brain function.
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The Contribution of Functional Brain Networks and Oscillations to the Development of Cognitive Control

The Contribution of Functional Brain Networks and Oscillations to the Development of Cognitive Control

We sought to characterize the spatiotemporal profile of task switching and its protracted development in adolescence and early adulthood. Our results provide evidence that: (1) task switching is characterized by a network of brain regions that comprise core regions of cognitive control networks, including the ACC, bilateral insula, bilateral DLPFC, and bilateral parietal association cortices; (2) theta band ACC scales linearly and positively with average reaction time but not trial-to-trial reaction time, indicating theta band oscillations in the ACC underlie conflict signaling arising from an exogenous cue switch signal; (3) a network of brain regions oscillating in the beta/gamma band, including the bilateral insula, IPL, DLPFC demonstrate greater activity preceding more rapid motor response during task switching at the single trial level (4) slower frequency power, specifically in the theta and alpha band (4-14 Hz) within the DLPFC, IPL and midline regions decrease into adulthood; (5) the relationship between ACC theta power and average RT is stronger during adolescence than in adulthood, suggesting adolescence require greater recruitment of ACC neurons to resolve conflict when switching between tasks 133,191,202 . These findings suggest that foundational aspects of oscillatory activity underlying task switching are present by adolescence. However, before adulthood it is more effortful to task switch requiring greater instantiation of cognitive control reflected in greater theta power through adolescence compared to adulthood. See gen discussion
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Functional and structural connectivity of reading networks in the adult brain

Functional and structural connectivity of reading networks in the adult brain

In addition, the white matter connections observed here agrees with previous DTI studies of the white matter connectivity between these regions (e.g. Saur et al., 2008; Makris & Pandya, 2009). These findings are in agreement with theoretical views that suggest a dual stream model for auditory language processing (e.g. Hickok & Poeppel, 2004; Warren et al., 2005). According to the dual stream model, initial auditory processing in the STG proceeds via a dorsal stream to the inferior parietal lobule and then to the IFG for auditory-motor integration, which is necessary for articulatory output. This dorsal stream has since been proposed to be the arcuate fasciculus/superior longitudinal fasciculus (e.g. Friderieci, 2011; Friederici and Gierhan, 2013). The arcuate fasciculus (AF) is a critical pathway of language and supports auditory-motor integration, including phonological processing (Friederici, 2011). Evidence comes from lesion studies showing that damage to the AF leads to difficulties repeating words, as well as electrocortical stimulation studies, where people substitute incorrect sounds when the AF direct segment was stimulated intraoperatively (Duffau, 2008).
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FROM REST TO TASK: Functional brain networks in achizophrenia

FROM REST TO TASK: Functional brain networks in achizophrenia

6. Canonical correlation analysis (CCA) was used as a blind source separation technique to remove broadband or electromyography (EMG) noise from single trial data using a method similar to that used by others (De Clercq, Vergult et al. 2006, Ries, Janssen et al. 2013) with some important differences. The CCA de-noising procedure involves correlating time series data from all channels with the one-sample time-lagged series from all channels, which is the multivariate equivalent of auto-regressive time series correlation. Each set of canonical correlation coefficients (one for each electrode resulting in 31 for this study) has an associated time series (i.e., linear function of the coefficients and raw data called canonical variates). The Fast Fourier Transformed (FFT) power spectra of these canonical variate time series have been used to identify EMG components by taking the ratio of high (e.g., 15 to 30Hz) to low (e.g., <15Hz) power and removing components with ratios greater than a pre-determined limit (e.g., if high/low > 1/5 in (Ries, Janssen et al. 2013). This is a very rough heuristic for determining if a canonical variate’s power spectrum has Power-law scaling (e.g., 1/f β or f α , where -β=α) where log-transformed power decreases linearly with increasing log- transformed frequency. Previous studies (Pereda, Gamundi et al. 1998, Freeman, Holmes et al. 2003) have suggested that the exponent, α, is less than -1 in human EEG, while white noise or EMG would have an exponent of approximately zero. Using simple linear regression, we estimated α by predicting log-power with log-frequency. For each trial and canonical variate, a bootstrap confidence interval was constructed for the estimated α by randomly sampling, without replacement, half of the frequency bins between 1-125Hz from the FFT one thousand times to avoid potential contamination by a few frequencies (i.e., 60Hz or alpha-band). If the interval contained values less than -1, the component was retained while all others were algebraically removed during back-projection to the original EEG epoch space.
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Functional brain networks constructed in subjects’ native spaces

Functional brain networks constructed in subjects’ native spaces

Network neuroscience is a new discipline but already achieved impor- tant results. For example it was outlined the presence of hubs in the brain, i.e. higly connected nodes which can be crucial for information transfer and processing [26, 27, 93]. It was observed that in the brain, hubs tend to be strongly connected each other in what are called rich clubs [29]. Furthermore, the other nodes, which form the perifery, are connected to the hubs which are found in the center. This property is called core-periphery organization [29] (this concept should not be confounded with the concept of assortativity, which measure the tendency of similar-degree nodes to connect each others, see 2.2.4 for details). A more global results tells us that brain is organized in hierarchical communities, i.e. strongly connected subnetworks which can be further divided into smaller communities [29]. It was also observed that brain structure changes with age or because neurological deseases such as Alzheimer’s or autism spectrum disorder and this could be crucial in order to find a remediation for those illnesses [30, 31].
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