Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.

Download Full-text

Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

Brain Sciences ◽

10.3390/brainsci11010118 ◽

2021 ◽

Vol 11 (1) ◽

pp. 118

Author(s):

Blake R. Neyland ◽

Christina E. Hugenschmidt ◽

Robert G. Lyday ◽

Jonathan H. Burdette ◽

Laura D. Baker ◽

...

Keyword(s):

Older Adults ◽

Graph Theory ◽

Community Structure ◽

Resting State ◽

Brain Networks ◽

Brain Network ◽

Functional Brain ◽

Network Community ◽

Functional Brain Networks ◽

The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

Download Full-text

Brain network dynamics correlate with personality traits

10.1101/702266 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aya Kabbara ◽

Veronique Paban ◽

Arnaud Weill ◽

Julien Modolo ◽

Mahmoud Hassan

Keyword(s):

Functional Connectivity ◽

Personality Traits ◽

Resting State ◽

Brain Networks ◽

Neural Substrates ◽

Brain Network ◽

Functional Brain ◽

Human Connectome Project ◽

Functional Brain Networks ◽

The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

Download Full-text

Electroencephalography-based functional brain network analysis according to lesion location and motor function in stroke patients

10.21203/rs.2.20942/v1 ◽

2020 ◽

Author(s):

Da-Hye Kim ◽

Gyu Hyun Kwon ◽

Wanjoo Park ◽

Yun-Hee Kim ◽

Seong-Whan Lee ◽

...

Keyword(s):

Brain Networks ◽

Brain Network ◽

Control Group ◽

Stroke Patients ◽

Functional Brain ◽

Lesion Location ◽

Functional Brain Network ◽

Functional Brain Networks ◽

Stroke Lesion ◽

The Brain

Abstract Background. While numerous studies have investigated changes in brain activation after stroke, limited information exists on the association between functional brain networks and lesion location in stroke patients. Methods. We compared the characteristics of brain networks among patients with cortico-subcortical lesions (n = 5), subcortical lesions (n = 7), and age-matched healthy controls (n = 12) during the execution of hand movements. Functional brain networks were analyzed based on network parameters in beta frequency electroencephalography (EEG) bands. Results. Our results indicated that while the healthy control group had appropriate compensatory patterns on the brain network with an aging effect, the two stroke lesion groups exhibited different hyper-connected characteristics in the brain network within the sensorimotor regions, particularly the contralesional M1, during motor execution. In addition, the betweenness centrality on the contralesional motor area was identified as a promising biomarker for motor functional ability associated with stroke. Our findings further allowed us to identify the characteristics of the stroke lesion that could not be found with EEG power by using the EEG brain network on the cerebral cortex. Conclusions. We anticipate that our study will improve the understanding of the complex changes that occur in the brain network as a result of stroke, and support the development of more effective and efficient rehabilitation programs based on lesion location for stroke patients.

Download Full-text

Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE

Journal of Alzheimer s Disease ◽

10.3233/jad-215156 ◽

2021 ◽

pp. 1-11

Author(s):

Yi Liu ◽

Zhuoyuan Li ◽

Xueyan Jiang ◽

Wenying Du ◽

Xiaoqi Wang ◽

...

Keyword(s):

Graph Theory ◽

Cognitive Decline ◽

Path Length ◽

Brain Networks ◽

Brain Network ◽

Clustering Coefficient ◽

Subjective Cognitive Decline ◽

Functional Brain ◽

Functional Brain Network ◽

Functional Brain Networks

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p < 0.001). The above results appeared in both the whole brain and DMN. Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

Download Full-text

Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal

International Journal of Neural Systems ◽

10.1142/s0129065720500513 ◽

2020 ◽

Vol 30 (10) ◽

pp. 2050051

Author(s):

Feng Fang ◽

Thomas Potter ◽

Thinh Nguyen ◽

Yingchun Zhang

Keyword(s):

Emotion Regulation ◽

Large Scale ◽

Brain Networks ◽

Cognitive Reappraisal ◽

Brain Network ◽

Affective Processing ◽

Functional Brain ◽

Affective Stimuli ◽

Functional Brain Network ◽

Functional Brain Networks

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

Download Full-text

Focused attention meditation changes the boundary and configuration of functional networks in the brain

Scientific Reports ◽

10.1038/s41598-020-75396-9 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Shogo Kajimura ◽

Naoki Masuda ◽

Johnny King L. Lau ◽

Kou Murayama

Keyword(s):

Network Architecture ◽

Single Case ◽

Brain Networks ◽

Brain Network ◽

Focused Attention ◽

Community Size ◽

Whole Brain ◽

Motor Network ◽

Functional Brain Networks ◽

The Brain

Abstract Research has shown that focused attention meditation not only improves our cognitive and motivational functioning (e.g., attention, mental health), it influences the way our brain networks [e.g., default mode network (DMN), fronto-parietal network (FPN), and sensory-motor network (SMN)] function and operate. However, surprisingly little attention has been paid to the possibility that meditation alters the architecture (composition) of these functional brain networks. Here, using a single-case experimental design with intensive longitudinal data, we examined the effect of mediation practice on intra-individual changes in the composition of whole-brain networks. The results showed that meditation (1) changed the community size (with a number of regions in the FPN being merged into the DMN after meditation) and (2) led to instability in the community allegiance of the regions in the FPN. These results suggest that, in addition to altering specific functional connectivity, meditation leads to reconfiguration of whole-brain network architecture. The reconfiguration of community architecture in the brain provides fruitful information about the neural mechanisms of meditation.

Download Full-text

Multi-Task Brain Network Reconfiguration is Inversely Associated with Human Intelligence

10.1101/2021.07.31.454563 ◽

2021 ◽

Author(s):

Jonas Alexander Thiele ◽

Joshua Faskowitz ◽

Olaf Sporns ◽

Kirsten Hilger

Keyword(s):

Cognitive Ability ◽

Network Architecture ◽

Brain Networks ◽

Brain Network ◽

Task Demands ◽

Human Intelligence ◽

Network Reconfiguration ◽

General Intelligence ◽

Functional Brain ◽

Functional Brain Networks

Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated. We used fMRI data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different tasks as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system, and replicates in two independent samples (N = 138, N = 184). Our findings suggest that the intrinsic network architecture of individuals with higher general intelligence scores is closer to the network architecture as required by various cognitive demands. Multi-task brain network reconfiguration may, therefore, reflect the neural equivalent of the behavioral positive manifold, i.e., the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multi-task brain network.

Download Full-text

Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle

10.1101/2020.06.29.178152 ◽

2020 ◽

Author(s):

Joshua M. Mueller ◽

Laura Pritschet ◽

Tyler Santander ◽

Caitlin M. Taylor ◽

Scott T. Grafton ◽

...

Keyword(s):

Menstrual Cycle ◽

Community Detection ◽

Brain Networks ◽

Brain Network ◽

Functional Brain ◽

Default Mode ◽

Hormone Interactions ◽

Dynamic Community Detection ◽

Functional Brain Networks ◽

Dynamic Community

AbstractSex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intra-network increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that ovulation results in a temporary, localized patterns of brain network reorganization.Author SummarySex steroid hormones influence the central nervous system across multiple spatiotemporal scales. Estrogen and progesterone concentrations rise and fall throughout the menstrual cycle, but it remains poorly understood how day-to-day fluctuations in hormones shape human brain dynamics. Here, we assessed the structure and stability of resting-state brain network activity in concordance with serum hormone levels from a female who underwent fMRI and venipuncture for 30 consecutive days. Our results reveal that while network structure is largely stable over the menstrual cycle, there is temporary reorganization of several largescale functional brain networks during the ovulatory window. In particular, a default mode subnetwork exhibits increased connectivity with itself and with regions from temporoparietal and limbic networks, providing novel perspective into brain-hormone interactions.

Download Full-text

Vestibular Agnosia is linked to abnormal functional brain networks

10.1101/2021.12.03.471139 ◽

2021 ◽

Author(s):

Zaeem Hadi ◽

Yuscah Pondeca ◽

Elena Calzolari ◽

Mariya Chepisheva ◽

Rebecca M Smith ◽

...

Keyword(s):

Motion Perception ◽

Brain Network ◽

Functional Brain ◽

Inferior Longitudinal Fasciculus ◽

Functional Brain Networks ◽

Vestibular Perception ◽

Postural Imbalance ◽

Self Motion ◽

The Brain ◽

Anterior Posterior

AbstractActivation of the peripheral vestibular apparatus simultaneously elicits a reflex vestibular nystagmus and the vestibular perception of self-motion (vestibular-motion perception) or vertigo. In a newly characterised condition called Vestibular Agnosia found in conditions with disrupted brain network connectivity, e.g. traumatic brain injury (TBI) or neurodegeneration (Parkinson’s Disease), the link between vestibular reflex and perception is uncoupled, such that, peripheral vestibular activation elicits a vestibular ocular reflex nystagmus but without vertigo. Using structural brain imaging in acute traumatic brain injury, we recently linked vestibular agnosia to postural imbalance via disrupted right temporal white-matter circuits (inferior longitudinal fasciculus), however no white-matter tracts were specifically linked to vestibular agnosia. Given the relative difficulty in localizing the neuroanatomical correlates of vestibular-motion perception, and compatible with current theories of human consciousness (viz. the Global Neuronal Workspace Theory), we postulate that vestibular-motion perception (vertigo) is mediated by the coordinated interplay between fronto-parietal circuits linked to whole-brain broadcasting of the vestibular signal of self-motion. We thus used resting state functional MRI (rsfMRI) to map functional brain networks and hence test our postulate of an anterior-posterior cortical network mediating vestibular agnosia. Whole-brain rsfMRI was acquired from 39 prospectively recruited acute TBI patients (and 37 matched controls) with preserved peripheral and reflex vestibular function, along with self-motion perceptual thresholds during passive yaw rotations in the dark, and posturography. Following quality control of the brain imaging, 25 TBI patients’ images were analyzed. We classified 11 TBI patients with vestibular agnosia and 14 without vestibular agnosia based on laboratory testing of self-motion perception. Using independent component analysis, we found altered functional connectivity within posterior (right superior longitudinal fasciculus) and anterior networks (left rostral prefrontal cortex) in vestibular agnosia. Regions of interest analyses showed both inter-hemispheric and intra-hemispheric (left anterior-posterior) network disruption in vestibular agnosia. Assessing the brain regions linked via right inferior longitudinal fasciculus, a tract linked to vestibular agnosia in unbalanced patients (but now controlled for postural imbalance), seed-based analyses showed altered connectivity between higher order visual cortices involved in motion perception and mid-temporal regions. In conclusion, vestibular agnosia in our patient group is mediated by multiple brain network dysfunction, involving primarily left frontal and bilateral posterior networks. Understanding the brain mechanisms of vestibular agnosia provide both an insight into the physiological mechanisms of vestibular perception as well as an opportunity to diagnose and monitor vestibular cognitive deficits in brain disease such as TBI and neurodegeneration linked to imbalance and spatial disorientation.

Download Full-text