scholarly journals On the brain networks organization of individuals with high versus average fluid intelligence: a combined DTI and MEG study

2021 ◽  
Author(s):  
Silvia EP Bruzzone ◽  
Massimo Lumaca ◽  
Elvira Brattico ◽  
Peter Vuust ◽  
Morten L Kringelbach ◽  
...  
Keyword(s):  
Degree Distribution ◽  
Fluid Intelligence ◽  
Diffusion Tensor ◽  
Functional Integration ◽  
Structural Connectivity ◽  
Brain Network ◽  
Opposite Pattern ◽  
Structural Brain Network ◽  
Fast Oscillations ◽  
The Brain

The neural underpinning of human fluid intelligence (Gf) has gathered a large interest in the scientific community. Nonetheless, previous research did not provide a full understanding of such intriguing topic. Here, we studied the structural (from diffusion tensor imaging, DTI) and functional (from magnetoencephalography (MEG) resting state) connectivity in individuals with high versus average Gf scores. Our findings showed greater values in the brain areas degree distribution and higher proportion of long-range anatomical connections for high versus average Gfs. Further, the two groups presented different community structures, highlighting the structural and functional integration of the cingulate within frontal subnetworks of the brain in high Gfs. These results were consistently observed for structural connectivity and functional connectivity of delta, theta and alpha. Notably, gamma presented an opposite pattern, showing more segregation and lower degree distribution and connectivity in high versus average Gfs. Our study confirmed and expanded previous perspectives and knowledge on the small-worldness of the brain. Further, it complemented the widely investigated structural brain network of highly intelligent individuals with analyses on fast-scale functional networks in five frequency bands, highlighting key differences in the integration and segregation of information flow between slow and fast oscillations in groups with different Gf.

scholarly journals Integrative Structural Brain Network Analysis In Diffusion Tensor Imaging

2017 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Node Degree ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Structural Brain Network ◽  
Structural Networks

AbstractIn diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length and FA-values into the connectivity model. Using various node-degree based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in post-institutional settings before being adopted by families in the US.

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

scholarly journals A Computational Network Control Theory Analysis of Depression Symptoms

2018 ◽  
Vol 1 ◽  
Author(s):  
Yoed N. Kenett ◽  
Roger E. Beaty ◽  
John D. Medaglia
Keyword(s):  
Control Theory ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Brain Network ◽  
Network Control ◽  
Depression Symptoms ◽  
Theory Approach ◽  
Imaging Data ◽  
The Brain ◽  
Subclinical Depression

AbstractRumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

scholarly journals Integrative Structural Brain Network Analysis in Diffusion Tensor Imaging

2017 ◽  
Vol 7 (6) ◽  
pp. 331-346 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Network Analysis ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Structural Brain Network ◽  
Structural Brain

scholarly journals Local and Distant responses to single pulse electrical stimulation reflect different forms of connectivity

2020 ◽  
Author(s):  
Britni Crocker ◽  
Lauren Ostrowski ◽  
Ziv M. Williams ◽  
Darin D. Dougherty ◽  
Emad N. Eskandar ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Resting State ◽  
Diffusion Tensor ◽  
Effective Connectivity ◽  
Clinical Care ◽  
Structural Connectivity ◽  
Intractable Epilepsy ◽  
Single Pulse ◽  
Intracranial Electrode ◽  
The Brain

AbstractBackgroundMeasuring connectivity in the human brain can involve innumerable approaches using both noninvasive (fMRI, EEG) and invasive (intracranial EEG or iEEG) recording modalities, including the use of external probing stimuli, such as direct electrical stimulation.Objective/HypothesisTo examine how different measures of connectivity correlate with one another, we compared ‘passive’ measures of connectivity during resting state conditions map to the more ‘active’ probing measures of connectivity with single pulse electrical stimulation (SPES).MethodsWe measured the network engagement and spread of the cortico-cortico evoked potential (CCEP) induced by SPES at 53 total sites across the brain, including cortical and subcortical regions, in patients with intractable epilepsy (N=11) who were undergoing intracranial recordings as a part of their clinical care for identifying seizure onset zones. We compared the CCEP network to functional, effective, and structural measures of connectivity during a resting state in each patient. Functional and effective connectivity measures included correlation or Granger causality measures applied to stereoEEG (sEEGs) recordings. Structural connectivity was derived from diffusion tensor imaging (DTI) acquired before intracranial electrode implant and monitoring (N=8).ResultsThe CCEP network was most similar to the resting state voltage correlation network in channels near to the stimulation location. In contrast, the distant CCEP network was most similar to the DTI network. Other connectivity measures were not as similar to the CCEP network.ConclusionsThese results demonstrate that different connectivity measures, including those derived from active stimulation-based probing, measure different, complementary aspects of regional interrelationships in the brain.

scholarly journals Intra- and Inter-Modular Connectivity Alterations in the Brain Structural Network of Spinocerebellar Ataxia Type 3

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 317 ◽  
Author(s):  
Chi-Wen Jao ◽  
Bing-Wen Soong ◽  
Tzu-Yun Wang ◽  
Hsiu-Mei Wu ◽  
Chia-Feng Lu ◽  
...  
Keyword(s):  
Spinocerebellar Ataxia ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Three Dimensional ◽  
Brain Network ◽  
Spinocerebellar Ataxia Type ◽  
Compensatory Mechanisms ◽  
Spinocerebellar Ataxia Type 3 ◽  
Type 3 ◽  
The Brain

In addition to cerebellar degeneration symptoms, patients with spinocerebellar ataxia type 3 (SCA3) exhibit extensive involvements with damage in the prefrontal cortex. A network model has been proposed for investigating the structural organization and functional mechanisms of clinical brain disorders. For neural degenerative diseases, a cortical feature-based structural connectivity network can locate cortical atrophied regions and indicate how their connectivity and functions may change. The brain network of SCA3 has been minimally explored. In this study, we investigated this network by enrolling 48 patients with SCA3 and 48 healthy subjects. A novel three-dimensional fractal dimension-based network was proposed to detect differences in network parameters between the groups. Copula correlations and modular analysis were then employed to categorize and construct the structural networks. Patients with SCA3 exhibited significant lateralized atrophy in the left supratentorial regions and significantly lower modularity values. Their cerebellar regions were dissociated from higher-level brain networks, and demonstrated decreased intra-modular connectivity in all lobes, but increased inter-modular connectivity in the frontal and parietal lobes. Our results suggest that the brain networks of patients with SCA3 may be reorganized in these regions, with the introduction of certain compensatory mechanisms in the cerebral cortex to minimize their cognitive impairment syndrome.

scholarly journals Heritability of hierarchical structural brain network

2017 ◽  
Author(s):  
Moo K. Chung ◽  
Zhan Luo ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Davidson J. Richard ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Imaging Study ◽  
Brain Network ◽  
Genetic Contribution ◽  
Whole Brain ◽  
Diffusion Weighted ◽  
Structural Brain Network ◽  
Structural Brain

ABSTRACTWe present a new structural brain network parcellation scheme that can subdivide existing parcellations into smaller subregions in a hierarchically nested fashion. The hierarchical parcellation was used to build multilayer convolutional structural brain networks that preserve topology across different network scales. As an application, we applied the method to diffusion weighted imaging study of 111 twin pairs. The genetic contribution of the whole brain structural connectivity was determined. We showed that the overall heritability is consistent across different network scales.

scholarly journals Transitions in brain-network level information processing dynamics are driven by alterations in neural gain

2019 ◽  
Author(s):  
Mike Li ◽  
Yinuo Han ◽  
Matthew J. Aburn ◽  
Michael Breakspear ◽  
Russell A. Poldrack ◽  
...  
Keyword(s):  
Information Processing ◽  
Information Transfer ◽  
Large Scale ◽  
Functional Integration ◽  
Brain Network ◽  
Information Storage ◽  
Transfer Entropy ◽  
Arousal System ◽  
Neural Gain ◽  
The Brain

AbstractA key component of the flexibility and complexity of the brain is its ability to dynamically adapt its functional network structure between integrated and segregated brain states depending on the demands of different cognitive tasks. Integrated states are prevalent when performing tasks of high complexity, such as maintaining items in working memory, consistent with models of a global workspace architecture. Recent work has suggested that the balance between integration and segregation is under the control of ascending neuromodulatory systems, such as the noradrenergic system. In a previous large-scale nonlinear oscillator model of neuronal network dynamics, we showed that manipulating neural gain led to a ‘critical’ transition in phase synchrony that was associated with a shift from segregated to integrated topology, thus confirming our original prediction. In this study, we advance these results by demonstrating that the gain-mediated phase transition is characterized by a shift in the underlying dynamics of neural information processing. Specifically, the dynamics of the subcritical (segregated) regime are dominated by information storage, whereas the supercritical (integrated) regime is associated with increased information transfer (measured via transfer entropy). Operating near to the critical regime with respect to modulating neural gain would thus appear to provide computational advantages, offering flexibility in the information processing that can be performed with only subtle changes in gain control. Our results thus link studies of whole-brain network topology and the ascending arousal system with information processing dynamics, and suggest that the constraints imposed by the ascending arousal system constrain low-dimensional modes of information processing within the brain.Author summaryHigher brain function relies on a dynamic balance between functional integration and segregation. Previous work has shown that this balance is mediated in part by alterations in neural gain, which are thought to relate to projections from ascending neuromodulatory nuclei, such as the locus coeruleus. Here, we extend this work by demonstrating that the modulation of neural gain alters the information processing dynamics of the neural components of a biophysical neural model. Specifically, we find that low levels of neural gain are characterized by high Active Information Storage, whereas higher levels of neural gain are associated with an increase in inter-regional Transfer Entropy. Our results suggest that the modulation of neural gain via the ascending arousal system may fundamentally alter the information processing mode of the brain, which in turn has important implications for understanding the biophysical basis of cognition.

scholarly journals Hemispheric Asymmetry of Human Brain Anatomical Network Revealed by Diffusion Tensor Tractography

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ni Shu ◽  
Yaou Liu ◽  
Yunyun Duan ◽  
Kuncheng Li
Keyword(s):  
Human Brain ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Functional Integration ◽  
Brain Regions ◽  
Hemispheric Asymmetries ◽  
Characteristic Path Length ◽  
Topological Measures ◽  
Structural Connections ◽  
The Brain

The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. However, few studies have investigated the hemispheric asymmetries of the human brain from the perspective of the network model, and little is known about the asymmetries of the connection patterns of brain regions, which may reflect the functional integration and interaction between different regions. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 72 right-handed healthy adult subjects. We established the existence of structural connections between any pair of the 90 cortical and subcortical regions using deterministic tractography. To investigate the hemispheric asymmetries of the brain, statistical analyses were performed to reveal the brain regions with significant differences between bilateral topological properties, such as degree of connectivity, characteristic path length, and betweenness centrality. Furthermore, local structural connections were also investigated to examine the local asymmetries of some specific white matter tracts. From the perspective of both the global and local connection patterns, we identified the brain regions with hemispheric asymmetries. Combined with the previous studies, we suggested that the topological asymmetries in the anatomical network may reflect the functional lateralization of the human brain.

scholarly journals Age-Related Compensatory Reconfiguration of PFC Connections during Episodic Memory Retrieval

2020 ◽  
Author(s):  
Lifu Deng ◽  
Mathew L Stanley ◽  
Zachary A Monge ◽  
Erik A Wing ◽  
Benjamin R Geib ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Memory Retrieval ◽  
Medial Temporal Lobe ◽  
Memory Performance ◽  
Functional Integration ◽  
Brain Network ◽  
Age Related ◽  
Connectivity Patterns ◽  
The Brain ◽  
Related Increase

Abstract During demanding cognitive tasks, older adults (OAs) frequently show greater prefrontal cortex (PFC) activity than younger adults (YAs). This age-related increase in PFC activity is often associated with enhanced cognitive performance, suggesting functional compensation. However, the brain is a complex network of interconnected regions, and it is unclear how network connectivity of PFC regions differs for OAs versus YAs. To investigate this, we examined the age-related difference on the functional brain networks mediating episodic memory retrieval. YAs and OAs participants encoded and recalled visual scenes, and age-related differences in network topology during memory retrieval were investigated as a function of memory performance. We measured both changes in functional integration and reconfiguration in connectivity patterns. The study yielded three main findings. First, PFC regions were more functionally integrated with the rest of the brain network in OAs. Critically, this age-related increase in PFC integration was associated with better retrieval performance. Second, PFC regions showed stronger performance-related reconfiguration of connectivity patterns in OAs. Finally, the PFC reconfiguration increases in OAs tracked reconfiguration reductions in the medial temporal lobe (MTL)—a core episodic memory region, suggesting that PFC connectivity in OAs may be compensating for MTL deficits.

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