Properties Of Functional Networks Research Articles

Different patterns of functional network reorganization across the variants of primary progressive aphasia: a graph-theoretic analysis

Primary progressive aphasia (PPA) is a neurodegenerative syndrome with three main variants (nonfluent, logopenic, semantic) that are identified primarily based on language deficit profiles and are associated with neurotopographically distinct atrophic patterns. We used a graph-theoretic analytic approach to examine changes in functional network properties measured with resting-state fMRI in all three PPA variants compared with age-matched healthy controls. All three variants showed a more segregated network organization than controls. To better understand the changes underlying the increased segregation, we examined the distribution of functional “hubs”. We found that while all variants lost hubs in the left superior frontal and parietal regions, new hubs were recruited in different areas across the variants. In particular, both logopenic and semantic variants recruited significant numbers of hubs in the right hemisphere. Importantly, these functional characteristics could not be fully explained by local volume changes. These findings indicate that patterns of functional connectivity can serve as further evidence to distinguish the PPA variants, and provide a basis for longitudinal studies and for investigating treatment effects. This study also highlights the utility of graph-theoretic approaches in understanding the brain's functional reorganization in response to neurodegenerative disease.

The Prospective Sense of Agency is Rooted in Local and Global Properties of Intrinsic Functional Brain Networks.

The sense of agency (SoA) refers to a constitutional aspect of the self describing the extent to which individuals feel in control over their actions and consequences thereof. Although the SoA has been associated with mental health and well-being, it is still unknown how interindividual variability in the SoA is embedded in the intrinsic brain organization. We hypothesized that the prospective component of an implicit SoA is associated with brain networks related to SoA and sensorimotor predictions on multiple spatial scales. We replicated previous findings by showing a significant prospective SoA as indicated by intentional binding effects. Then, using task-free fMRI and graph analysis, we analyzed associations between intentional binding effects and the intrinsic brain organization at regional, modular, and whole-brain scales. The results showed that intermodular connections of a frontoparietal module including the premotor cortex, supramarginal gyrus, and dorsal precuneus are associated with individual differences in prospective intentional binding. Notably, prospective intentional binding effects were also related to global brain modularity within a specific structural resolution range. These findings suggest that an implicit SoA generated through sensorimotor predictions relies on the intrinsic organization of the brain connectome on both local and global scales.

A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction.

Increasing evidence has suggested that the dynamic properties of functional brain networks are related to individual behaviors and cognition traits. However, current fMRI-based approaches mostly focus on statistical characteristics of the windowed correlation time course, potentially overlooking subtle time-varying patterns in dynamic functional connectivity (dFC). Here, we proposed the use of an end-to-end deep learning model that combines the convolutional neural network (CNN) and long short-term memory (LSTM) network to capture temporal and spatial features of functional connectivity sequences simultaneously. The results on a large cohort (Human Connectome Project, n = 1,050) demonstrated that our model could achieve a high classification accuracy of about 93% in a gender classification task and prediction accuracies of 0.31 and 0.49 (Pearson’s correlation coefficient) in fluid and crystallized intelligence prediction tasks, significantly outperforming previously reported models. Furthermore, we demonstrated that our model could effectively learn spatiotemporal dynamics underlying dFC with high statistical significance based on the null hypothesis estimated using surrogate data. Overall, this study suggests the advantages of a deep learning model in making full use of dynamic information in resting-state functional connectivity, and highlights the potential of time-varying connectivity patterns in improving the prediction of individualized characterization of demographics and cognition traits.

Open eyes and closed eyes elicit different temporal properties of brain functional networks

The eyes are our windows to the brain. There are differences in brain activity between people who have their eyes closed (EC) and eyes open (EO). Previous studies focused on differences in brain functional properties between these eyes conditions based on an assumption that brain activity is a static phenomenon. However, the dynamic nature of the brain activity in different eyes conditions is still unclear. In this study, we collected resting-state fMRI data from 21 healthy subjects in the EC and EO conditions. Using a sliding time window approach and a k-means clustering algorithm, we calculated the temporal properties of dynamic functional connectivity (dFC) states in the eyes conditions. We also used graph theory to estimate the dynamic topological properties of functional networks in the two conditions. We detected two dFC states, a hyper-connected State 1 and a hypo-connected State 2. We showed the following results: (i) subjects in the EC condition stayed longer in the hyper-connected State 1 than those in the EO; (ii) subjects in the EO condition stayed longer in the hypo-connected State 2 than those in the EC; and (iii) the dFC state transformed into the other state more frequently during EC than during EO. We also found the variance of the characteristic path length was higher during EC than during EO in the hyper-connected State 1. These results indicate that brain activity may be more active and unstable during EC than during EO. Our findings may provide insights into the dynamic nature of the resting-state brain and could be a useful reference for future rs-fMRI studies.

Functional Characterization of Three-Dimensional Cortical Cultures for InVitro Modeling of Brain Networks.

SummaryThree-dimensional (3D) in vitro cultures recapitulate key features of the brain including morphology, cell-cell and cell-extracellular matrix interactions, gradients of factors, and mechanical properties. However, there remains a need for experimental and computational tools to investigate network functions in these 3D models. To address this need, we present an experimental system based on 3D scaffold-based cortical neuron cultures in which we expressed the genetically encoded calcium indicator GCaMP6f to record neuronal activity at the millimeter-scale. Functional neural network descriptors were computed with graph-theory-based network analysis methods, showing the formation of functional networks at 3 weeks of culture. Changes to the functional network properties upon perturbations to glutamatergic neurotransmission or GABAergic neurotransmission were quantitatively characterized. The results illustrate the applicability of our 3D experimental system for the study of brain network development, function, and disruption in a biomimetic microenvironment.

Data-Driven Parcellation Approaches Based on Functional Connectivity of Visual Cortices in Primary Open-Angle Glaucoma.

PurposeFunctional changes have been observed between diseased and healthy subjects, and functional brain atlases derived from healthy populations may fail to reflect functional characteristic of the diseased brain. Therefore the aim of this study was to generate a visual atlas based on functional connectivity from primary open-angle glaucoma (POAG) patients and to prove the applicability of the visual atlas in functional connectivity and network analysis.MethodsFunctional magnetic resonance images were acquired from 36 POAG patients and 20 healthy controls. Two data-driven approaches, K-means and Ward clustering algorithms, were adopted for visual cortices parcellation. Dice coefficient and adjusted Rand index were used to assess reproducibility of the two approaches. Homogeneity index, silhouette coefficient, and network properties were adopted to assess functional validity for the data-driven approaches and frequently used brain atlas. Graph theoretical analysis was adopted to investigate altered network patterns in POAG patients based on data-driven visual atlas.ResultsParcellation results demonstrated asymmetric patterns between left and right hemispheres in POAG patients compared with healthy controls. In terms of evaluating metrics, K-means performed better than Ward clustering in reproducibility. Data-driven parcellations outperformed frequently used brain atlases in terms of functional homogeneity and network properties. Graph theoretical analysis revealed that atlases generated by data-driven approaches were more conducive in detecting network alterations between POAG patients and healthy controls.ConclusionsOur findings suggested that POAG patients experienced functional alterations in the visual cortices. Results also highlighted the necessity of data-driven atlases for functional connectivity and functional network analysis of POAG brain.

Differences in topological properties of functional brain networks between menstrually-related and non-menstrual migraine without aura

Menstrually-related migraine without aura refers to a specific type of migraine that is associated with the female ovarian cycle. Compared with non-menstrual migraine without aura, in menstrually-related migraine without aura, there are additional attacks of migraine outside of the menstrual period. Menstrually-related migraine without aura tends to be less responsive to acute treatment and more prone to relapse than non-menstrual migraine without aura. Currently menstrually-related migraine without aura is treated no differently from any other migraine but, the differences in the central mechanisms underlying menstrually-related migraine without aura and non-menstrual migraine without aura remain poorly understood. Here, using resting-state functional magnetic resonance imaging and graph theory approaches, we aimed to explore the differences in topological properties of functional networks in 51 menstrually-related migraine without aura patients and 47 non-menstrual migraine without aura patients. The major finding of our study was that significant differences in topological properties between the two groups were mainly evident in the nodal centrality of the inferior frontal gyrus and the thalamus. Nodal centrality in inferior frontal gyrus was negatively correlated with Headache Impact Test questionnaire scores in the menstrually-related migraine without aura patients. Partial least squares correlation analysis revealed enhanced correlations of inferior frontal gyrus to pain-related behavior in the non-menstrual migraine without aura group, while within the menstrually-related migraine without aura group these effects were non-significant. These results indicate that the regulatory mechanisms in the central nervous system may differ between the two subtypes of migraine. The results provide novel insights into the pathophysiology of different subtypes of migraine, and could help us to enhance their clinical diagnosis and treatment.

Disrupted brain functional network topology in unilateral acute brainstem ischemic stroke.

This study aimed to investigate the topological properties of brain functional connectome in unilateral acute brainstem ischemic stroke using graph theory. Fifty-three acute brainstem ischemic stroke patients, consisted of 27 left-sided and 26 right-sided brainstem stroke patients, and 20 age, gender, and education-matched healthy controls (HCs) were recruited to undergo a resting-state functional magnetic resonance imaging (rs-fMRI) scan in this study. Graph theory analyses were then used to examine the group-specific topological properties of the functional connectomes seperately. The unilateral acute brainstem stroke patients and HCs all exhibited "small-world" brain network topology. The functional connectome of the left brainstem stroke patients showed significant differences in all topological properties while the right brainstem stroke patients showed a significant increase in clustering coefficient Cp (p < 0.001) and local efficiency Elocal (p < 0.001), and a significantly decrease in normalized clustering coefficient γ (p < 0.001) and global efficiency Eglobal (p< 0.001), suggesting both a shift toward regular networks. At the nodal level, abnormal nodal centralities were mainly observed in the defaut mode network, subcortical network, frontal and occipital lobe. The findings of disrupted topological properties of functional brain networks may help better understanding the disease characterization and innovation in management for acute brainstem ischemic stroke patients.

Random matrix theory for analysing the brain functional network in lower limb motor imagery.

We use random matrix theory (RMT) to investigate the statistical properties of brain functional networks in lower limb motor imagery. Functional connectivity was calculated by Pearson correlation coefficient (PCC), mutual information (MTI) and phase locking value (PLV) extracted from EEG signals. We found that when the measured subjects imagined the movements of their lower limbs the spectral density as well as the level spacings displayed deviations from the random matrix prediction. In particular, a significant difference between the left and right foot imaginary movements was observed in the maximum eigenvalue from the PCC, which can provide a theoretical basis for further study on the classification of unilateral movement of lower limbs.

Association of rs1059004 polymorphism in the OLIG2 locus with functional brain network in first-episode negative schizophrenia

Schizophrenia has often been viewed as a disorder of connectivity. The single nucleotide polymorphism rs1059004 in the oligodendrocyte lineage transcription factor 2 gene locus has been reported to be associated with schizophrenia. We measured the functional connectivity and functional brain network topology properties in 49 schizophrenic patients and 47 healthy controls. We compared the strength and diversity of the functional connectivity and topological properties of functional networks between different genotypes. The correlations among functional connectivity, topological properties and behavioral performances were also investigated in this study. We found that the connectivity strength of schizophrenic patients carrying the risk A allele was generally decreased whereas connectivity diversity was increased. Regarding topological properties, all groups showed small-world properties, the nodal efficiency showed significant differences in the right precuneus and left middle temporal pole between different genotypes in schizophrenic patients. Moreover, the nodal efficiency in the left middle temporal pole was positively correlated with the neuropsychological assessment battery results of the schizophrenic patients who were homozygous for the C allele. Our results elucidate the contribution of rs1059004 to the functional brain network, and may help enhance the present understanding of the role of risk gene in the functional dysconnectivity of schizophrenia.

Pre-treatment graph measures of a functional semantic network are associated with naming therapy outcomes in chronic aphasia

Naming treatment outcomes in post-stroke aphasia are variable and the factors underlying this variability are incompletely understood. In this study, 26 patients with chronic aphasia completed a semantic judgment fMRI task before receiving up to 12 weeks of naming treatment. Global (i.e., network-wide) and local (i.e., regional) graph theoretic measures of pre-treatment functional connectivity were analyzed to identify differences between patients who responded most and least favorably to treatment (i.e., responders and nonresponders) and determine if network measures predicted naming improvements. Responders had higher levels of global integration (i.e., average network strength and global efficiency) than nonresponders, and these measures predicted treatment effects after controlling for lesion volume and age. Group differences in local measures were identified in several regions associated with a variety of cognitive functions. These results suggest there is a meaningful and possibly prognostically-informative relationship between patients’ functional network properties and their response to naming therapy.

Frontoparietal structural properties mediate adult life span differences in executive function

Executive function (EF) refers to a set of cognitive functions that support goal-directed behaviors. Recent findings have suggested that the frontoparietal network (FPN) subserves neural processes that are related to EF. However, the FPN structural and functional network properties that mediate age-related differences in EF components remain unclear. To this end, we used three experimental tasks to test the component processes of EF based on Miyake and Friedman’s model: one common EF component process (incorporating inhibition, shifting, and updating) and two specific EF component processes (shifting and updating). We recruited 126 healthy participants (65 females; 20 to 78 years old) who underwent both structural and functional MRI scanning. We tested a mediation path model of three structural and functional properties of the FPN (i.e., gray matter volume, white matter fractional anisotropy, and intra/internetwork functional connectivity) as mediators of age-related differences in the three EF components. The results indicated that age-related common EF component differences are mediated by regional gray matter volume changes in both hemispheres of the frontal lobe, which suggests that structural changes in the frontal lobe may have an indirect influence on age-related general elements of EF. These findings suggest that the FPN mediates age-related differences in specific components of EF.

Brain Functional and Structural Signatures in Parkinson's Disease.

The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson’s disease (PD). 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal–occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.

Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised

Current explanatory concepts suggest seizures emerge from ongoing dynamics of brain networks. It is unclear how brain network properties determine focal or generalised seizure onset, or how network properties can be described in a clinically-useful manner. Understanding network properties would cast light on seizure-generating mechanisms and allow to quantify to which extent a seizure is focal or generalised. Functional brain networks were estimated in segments of scalp-EEG without interictal discharges (68 people with epilepsy, 38 controls). Simplified brain dynamics were simulated using a computer model. We introduce: Critical Coupling (Cc), the ability of a network to generate seizures; Onset Index (OI), the tendency of a region to generate seizures; and Participation Index (PI), the tendency of a region to become involved in seizures. Cc was lower in both patient groups compared with controls. OI and PI were more variable in focal-onset than generalised-onset cases. In focal cases, the regions with highest OI and PI corresponded to the side of seizure onset. Properties of interictal functional networks from scalp EEG can be estimated using a computer model and used to predict seizure likelihood and onset patterns. This may offer potential to enhance diagnosis through quantification of seizure type using inter-ictal recordings.

Functional connectivity changes in patients with brain tumours—A systematic review on resting state-fMRI

AimResting-state functional magnetic resonance imaging (rs-fMRI) has been utilised to explore the brain's functional changes in patients with brain tumours. However, it remains unclear how such alterations affect functional connectivity (FC) and thus lead to changes in behaviour. To better understand the present state of knowledge, we conducted a systematic review. Material and methodsA systematic literature search of patients with brain tumours with rs-fMRI methods was conducted using the PubMed/MEDLINE and Cochrane Library electronic databases following PRISMA guidelines. References and citations were checked in Scopus database. ResultsTen independent studies were identified, comprising of 361 adult participants (218 had brain tumours; and the remainder were healthy controls). In comparison to healthy controls, patients with brain tumours demonstrated network distortions, alterations, and associated changes in the resting state networks (RSNs). Location and types of tumour affected RSNs integrity, particularly in the Default Mode Network (DMN). The left hemisphere was reported to have the largest effect on RSNs regardless of size or type of tumour. Result also demonstrate significant alterations to the local networks (decreases in the intra-hemispheric and inter-hemispheric FC). Studies also demonstrated that alterations in rs-fMRI were related to poorer neuropsychological task performance. ConclusionHowever, more studies utilising rs-fMRI to investigate effects of brain tumours on different types of RSNs are needed to draw clear conclusions about effects of tumour growth on properties of brain functional networks. Notably, not all studies have used the same nodes or regions for the networks, even though tumour locations often overlap.

ALE meta-analysis, its role in node identification and the effects on estimates of local network organization.

Functional connectivity analyses for task-based fMRI data are generally preceded by methods for identification of network nodes. As there is no general canonical approach to identifying network nodes, different identification techniques may exert different effects on inferences drawn regarding functional network properties. Here, we compared the impact of two different node identification techniques on estimates of local node importance (based on Degree Centrality, DC) in two working memory domains: verbal and visual. The two techniques compared were the commonly used Activation Likelihood Estimate (ALE) technique (with node locations based on data aggregation), against a hybrid technique, Experimentally Derived Estimation (EDE). In the latter, ALE was first used to isolate regions of interest; then participant-specific nodes were identified based on individual-participant local maxima. Time series were extracted at each node for each dataset and subsequently used in functional connectivity analysis to: (1) assess the impact of choice of technique on estimates of DC, and (2) assess the difference between the techniques in the ranking of nodes (based on DC) in the networks they produced. In both domains, we found a significant Technique by Node interaction, signifying that the two techniques yielded networks with different DC estimates. Moreover, for the majority of participants, node rankings were uncorrelated between the two techniques (85% for the verbal working memory task and 92% for the visual working memory task). The latter effect is direct evidence that the identification techniques produced different rankings at the level of individual participants. These results indicate that node choice in task-based fMRI data exerts downstream effects that will impact interpretation and reverse inference regarding brain function.

Impaired brain network architecture in Cushing's disease based on graph theoretical analysis.

To investigate the whole functional brain networks of active Cushing disease (CD) patients about topological parameters (small world and rich club et al.) and compared with healthy control (NC). Nineteen active CD patients and twenty-two healthy control subjects, matched in age, gender, and education, underwent resting-state fMRI. Graph theoretical analysis was used to calculate the functional brain network organizations for all participants, and those for active CD patients were compared for and NCs. Active CD patients revealed higher global efficiency, shortest path length and reduced cluster efficiency compared with healthy control. Additionally, small world organization was present in active CD patients but higher than healthy control. Moreover, rich club connections, feeder connections and local connections were significantly decreased in active CD patients. Functional network properties appeared to be disrupted in active CD patients compared with healthy control. Analyzing the changes that lead to abnormal network metrics will improve our understanding of the pathophysiological mechanisms underlying CD.

Evaluating forest resilience to global threats using functional response traits and network properties.

Ecosystem functions provided by forests are threatened by direct and indirect effects of global change drivers such as climate warming land-use change, biological invasions, and shifting natural disturbance regimes. To develop resilience-based forest management, new tools and methods are needed to quantitatively estimate forest resilience to management and future natural disturbances. We propose a multidimensional evaluation of ecological resilience based on species functional response traits (e.g., functional response diversity and functional redundancy) and network properties of forested patches (e.g., connectivity, modularity, and centrality). Using a fragmented rural landscape in temperate south-eastern Canada as a reference landscape, we apply our multidimensional approach to evaluate two alternative management strategies at three levels of intensity: (1) functional enrichment of current forest patches and (2) multi-species plantations in previously non-forested patches. Within each management strategy, planted species are selected to maximize functional diversity, drought tolerance, or pest resistance. We further compare how ecological resilience under these alternative management strategies responds to three simulated disturbances: drought, pest outbreak, and timber harvesting. We found that both management strategies enhance resilience at the landscape scale by increasing functional response diversity and connectivity. Specifically, when the less functionally diverse patches are prioritized for management, functional enrichment is more effective than the establishment of new multi-species plantations in increasing resilience. In addition, randomly allocated multi-species plantations increased connectivity more than those allocated in riparian areas. Our results show that across various management strategies, planting species to enhance biodiversity led to the highest increase in functional response diversity while planting pest-resistant species led to the highest increase in landscape connectivity. Planting biodiversity-enhancing species (i.e., species that maximize functional diversity) mitigated drought effects equally well as planting with drought-tolerant species. Our multidimensional approach facilitates the characterization at the landscape scale of forest resilience to disturbances using both functional diversity and network properties while accounting for the importance of response traits to future disturbances. The simulation approach we used can be applied to forest landscapes across different biomes for the evaluation and comparison of forest management initiatives to enhance resilience.

Topological Properties of Neuromorphic Nanowire Networks.

Graph theory has been extensively applied to the topological mapping of complex networks, ranging from social networks to biological systems. Graph theory has increasingly been applied to neuroscience as a method to explore the fundamental structural and functional properties of human neural networks. Here, we apply graph theory to a model of a novel neuromorphic system constructed from self-assembled nanowires, whose structure and function may mimic that of human neural networks. Simulations of neuromorphic nanowire networks allow us to directly examine their topology at the individual nanowire–node scale. This type of investigation is currently extremely difficult experimentally. We then apply network cartographic approaches to compare neuromorphic nanowire networks with: random networks (including an untrained artificial neural network); grid-like networks and the structural network of C. elegans. Our results demonstrate that neuromorphic nanowire networks exhibit a small–world architecture similar to the biological system of C. elegans, and significantly different from random and grid-like networks. Furthermore, neuromorphic nanowire networks appear more segregated and modular than random, grid-like and simple biological networks and more clustered than artificial neural networks. Given the inextricable link between structure and function in neural networks, these results may have important implications for mimicking cognitive functions in neuromorphic nanowire networks.

Global and regional connectivity analysis of resting-state function MRI brain images using graph theory in Parkinson’s disease

Objectives Parkinson’s disease (PD) is the second most common neurodegenerative disorder which resists around 10 million people worldwide. It develops when nerve cells in a region of the brain that regulates movement become damaged; the symptoms usually begin gradually and become critical over time. In this study, we proposed to investigate the topological properties of functional brain networks within healthy controls (HCs) and PD patients. Also, we evaluated the gender difference among PD patients through graph theoretical approach. Materials and Methods The rs-fMRI (resting-state functional magnetic resonance imaging) data of fifty-one PD patients and healthy controls was applied to generate the brain functional connectome. The functional whole-brain connectome was constructed by thresholding partial correlation matrices of 160 regions from Dosenbach brain atlas. From the graph theory approach, global and nodal metrics were analysed, and we observed considerable changes in PD patients in comparison with healthy controls. Results Findings suggest that there is a significant difference in the topological characteristics of PD patients, and this was found to be evident in the default mode network (DMN) and occipital regions. Conclusion This study provides essential insights from network changes to the clinically relevant information for the PD progression.