Anatomical Brain Networks Research Articles

Brain Connectivity Dynamics and Mittag–Leffler Synchronization in Asymmetric Complex Networks for a Class of Coupled Nonlinear Fractional-Order Memristive Neural Network System with Coupling Boundary Conditions

This paper investigates the long-time behavior of fractional-order complex memristive neural networks in order to analyze the synchronization of both anatomical and functional brain networks, for predicting therapy response, and ensuring safe diagnostic and treatments of neurological disorder (such as epilepsy, Alzheimer’s disease, or Parkinson’s disease). A new mathematical brain connectivity model, taking into account the memory characteristics of neurons and their past history, the heterogeneity of brain tissue, and the local anisotropy of cell diffusion, is proposed. This developed model, which depends on topology, interactions, and local dynamics, is a set of coupled nonlinear Caputo fractional reaction–diffusion equations, in the shape of a fractional-order ODE coupled with a set of time fractional-order PDEs, interacting via an asymmetric complex network. In order to introduce into the model the connection structure between neurons (or brain regions), the graph theory, in which the discrete Laplacian matrix of the communication graph plays a fundamental role, is considered. The existence of an absorbing set in state spaces for system is discussed, and then the dissipative dynamics result, with absorbing sets, is proved. Finally, some Mittag–Leffler synchronization results are established for this complex memristive neural network under certain threshold values of coupling forces, memristive weight coefficients, and diffusion coefficients.

Age Trajectories of the Structural Connectome in Child and Adolescent Offspring of Individuals With Bipolar Disorder or Schizophrenia

BackgroundOffspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at elevated risk of developing psychiatric illness owing to both genetic predisposition and increased burden of environmental stress. Emerging evidence indicates a disruption of brain network connectivity in young offspring of patients with bipolar disorder and schizophrenia, but the age trajectories of these brain networks in this high-familial-risk population remain to be elucidated. MethodsA total of 271 T1-weighted and diffusion-weighted scans were obtained from 174 offspring of at least 1 parent diagnosed with bipolar disorder (n = 74) or schizophrenia (n = 51) and offspring of parents without severe mental illness (n = 49). The age range was 8 to 23 years; 97 offspring underwent 2 scans. Anatomical brain networks were reconstructed into structural connectivity matrices. Network analysis was performed to investigate anatomical brain connectivity. ResultsOffspring of parents with schizophrenia had differential trajectories of connectivity strength and clustering compared with offspring of parents with bipolar disorder and parents without severe mental illness, of global efficiency compared with offspring of parents without severe mental illness, and of local connectivity compared with offspring of parents with bipolar disorder. ConclusionsThe findings of this study suggest that familial high risk of schizophrenia is related to deviations in age trajectories of global structural connectome properties and local connectivity strength.

Fractal basins as a mechanism for the nimble brain

An interesting feature of the brain is its ability to respond to disparate sensory signals from the environment in unique ways depending on the environmental context or current brain state. In dynamical systems, this is an example of multi-stability, the ability to switch between multiple stable states corresponding to specific patterns of brain activity/connectivity. In this article, we describe chimera states, which are patterns consisting of mixed synchrony and incoherence, in a brain-inspired dynamical systems model composed of a network with weak individual interactions and chaotic/periodic local dynamics. We illustrate the mechanism using synthetic time series interacting on a realistic anatomical brain network derived from human diffusion tensor imaging. We introduce the so-called vector pattern state (VPS) as an efficient way of identifying chimera states and mapping basin structures. Clustering similar VPSs for different initial conditions, we show that coexisting attractors of such states reveal intricately “mingled” fractal basin boundaries that are immediately reachable. This could explain the nimble brain’s ability to rapidly switch patterns between coexisting attractors.

The triple psychological and neural bases underlying procrastination: Evidence based on a two-year longitudinal study

The triple brain anatomical network model of procrastination theorized procrastination as the result of psychological and neural dysfunction implicated in self-control, emotion regulation and episodic prospection. However, no studies have provided empirical evidence for such model. To address this issue, we designed a two-wave longitudinal study where participants underwent the resting-state scanning and completed the questionnaires at two time-points that spanned 2-year apart (T1, n = 457; T2, n = 457). Using the cross-lagged panel network modeling (CLPN), we found that triple psychological components at T1, including self-control, emotion regulation (i.e., reappraisal) and episodic prospection, negatively predicted procrastination at T2 in the temporal network. Moreover, the CLPN modeling found that functional connectivity between networks accounting for episodic prospection (EP) and emotion regulation (ER) positively predicted future procrastination in the temporal network. The centrality analyzes further showed that procrastination was greatly affected by other nodes, whilst the psychological component (i.e., episodic prospection), and the functional network connectivity (FNC) of EP- ER exerted strongest impacts on other nodes in the networks, which indicated that treatments targeting episodic prospection might largely help reduce procrastination. Collectively, these findings firstly provide evidence for testifying the triple brain anatomical network model of procrastination, and highlights the contribution of triple psychological and neural components implicated in self-control, emotion regulation and episodic prospection to procrastination that enhances our understanding of causes of procrastination.

Cortical structural differences following repeated ayahuasca use hold molecular signatures.

Serotonergic psychedelics such as ayahuasca are reported to promote both structural and functional neural plasticity via partial 5-HT2A agonism. However, little is known about how these molecular mechanisms may extend to repeated psychedelic administration in humans, let alone neuroanatomy. While early evidence suggests localised changes to cortical thickness in long-term ayahuasca users, it is unknown how such findings may be reflected by large-scale anatomical brain networks comprising cytoarchitecturally complex regions. Here, we examined the relationship between cortical gene expression markers of psychedelic action and brain morphometric change following repeated ayahuasca usage, using high-field 7 Tesla neuroimaging data derived from 24 members of an ayahuasca-using church (Santo Daime) and case-matched controls. Using a morphometric similarity network (MSN) analysis, repeated ayahuasca use was associated with a spatially distributed cortical patterning of both structural differentiation in sensorimotor areas and de-differentiation in transmodal areas. Cortical MSN remodelling was found to be spatially correlated with dysregulation of 5-HT2A gene expression as well as a broader set of genes encoding target receptors pertinent to ayahuasca's effects. Furthermore, these associations were similarly interrelated with altered gene expression of specific transcriptional factors and immediate early genes previously identified in preclinical assays as relevant to psychedelic-induced neuroplasticity. Taken together, these findings provide preliminary evidence that the molecular mechanisms of psychedelic action may scale up to a macroscale level of brain organisation in vivo. Closer attention to the role of cortical transcriptomics in structural-functional coupling may help account for the behavioural differences observed in experienced psychedelic users.

Co-evolving dynamics and topology in a coupled oscillator model of resting brain function

Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that, with the addition of only one parameter, it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel time series of active edges it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function.

Identifying network hubs from brain anatomical similarity networks

AbstractBackgroundBrain hubs are highly connected regions important for integrating cognitive processing in the healthy brain. Brain hubs also play role in neurodegeneration causing dementia. However, it is not clear what brain morphometry is most useful in identifying brain regions critical to their involvement in neurodegeneration.MethodWe examined brain network hubs from four measures of cortical morphometry, derived using anatomical T1‐weigher MRI. From a large cohort of healthy participants (N = 260) from 20 to 85 years of age, we generated 200 networks for each measure of cortical morphometry using bootstrapping. We then tested how two measures of brain network hubness – nodal strength and clustering coefficient – were mapped onto brain network hubs across all 200 networks for each morphometric feature. Regions were identified as brain hubs if ranked two standard deviations away from the corresponding group mean.ResultWe mapped out brain hubs for each four measures (features) of brain morphometry. Nodal strength and clustering coefficient showed similarity in the behaviour across the features: gray volume, regional surface area and its thickness and thickness standard deviation. However, the differences were found in what brain region qualify as a network hub for each feature.ConclusionCortical morphometry extracted from anatomical brain scans can be used as a potential imaging marker for identification of brain hubs potentially involved in early differentiation of dementia subtypes.

Examining the Effects of Normal Ageing on Cortical Connectivity of Older Adults.

With the recent advancement in computer technology, we can extract the picture of the brain as a network. The aim of this study is to constructs large scale individual anatomical brain networks using regional gray matter cortical thickness from individual subject's magnetic resonance imaging (MRI) data, as well as to investigate changes with normal aging in global network organization. The dataset includes 183 healthy subjects sMRI data with an age range from 50 to 80 plus. For all brain networks, we calculated the global network measures and nodal network measures by using network analysis toolkit GRETNA. From global network measurements we calculated small-world measurements and network efficiency measurements, from nodal measurements we calculated node clustering coefficient (CC) and node efficiency at a wide-range of threshold values. All small world measurements showed more clustering at all the given threshold values than random networks and a alike least path length, indicative of that they were "small world". To analyze the effect normal ageing on networks organization, the networks of subjects were categorized into three age groups (50s, 60s, and 70 over). The global and nodal network measurements of each group were statistically analyzed to investigate the significant difference in network organization with in age groups. Results shows that the age has no significance effect in global measurements of brain network. However, by analysis the nodal measures of brain network between age group, network nodes from brain frontal lobe and temporal lobe showed age related significant difference. The results obtained from the proposed study suggest that this network method can deliver a concise network-level picture of brain organization and be used from the outlook of composite networks to investigate inter-individual variability in brain morphology.

MEG source imaging detects optogenetically-induced activity in cortical and subcortical networks

Magnetoencephalography measures neuromagnetic activity with high temporal, and theoretically, high spatial resolution. We developed an experimental platform combining MEG-compatible optogenetic techniques in nonhuman primates for use as a functional brain-mapping platform. Here we show localization of optogenetically evoked signals to known sources in the superficial arcuate sulcus of cortex and in CA3 of hippocampus at a resolution of 750 µm3. We detect activation in subcortical, thalamic, and extended temporal structures, conforming to known anatomical and functional brain networks associated with the respective sites of stimulation. This demonstrates that high-resolution localization of experimentally produced deep sources is possible within an intact brain. This approach is suitable for exploring causal relationships between discrete brain regions through precise optogenetic control and simultaneous whole brain MEG recording with high-resolution magnetic source imaging (MSI).

Connectome analysis of male world-class gymnasts using probabilistic multishell, multitissue constrained spherical deconvolution tracking.

In athletes, long‐term intensive training has been shown to increase unparalleled athletic ability and might induce brain plasticity. We evaluated the structural connectome of world‐class gymnasts (WCGs), as mapped by diffusion‐weighted magnetic resonance imaging probabilistic tractography and a multishell, multitissue constrained spherical deconvolution method to increase the precision of tractography at the tissue interfaces. The connectome was mapped in 10 Japanese male WCGs and in 10 age‐matched male controls. Network‐based statistic identified subnetworks with increased connectivity density in WCGs, involving the sensorimotor, default mode, attentional, visual, and limbic areas. It also revealed a significant association between the structural connectivity of some brain structures with functions closely related to the gymnastic skills and the D‐score, which is used as an index of the gymnasts' specific physical abilities for each apparatus. Furthermore, graph theory analysis demonstrated the characteristics of brain anatomical topology in the WCGs. They displayed significantly increased global connectivity strength with decreased characteristic path length at the global level and higher nodal strength and degree in the sensorimotor, default mode, attention, and limbic/subcortical areas at the local level as compared with controls. Together, these findings extend the current understanding of neural mechanisms that distinguish WCGs from controls and suggest brain anatomical network plasticity in WCGs resulting from long‐term intensive training. Future studies should assess the contribution of genetic or early‐life environmental factors in the brain network organization of WCGs. Furthermore, the indices of brain topology (i.e., connection density and graph theory indices) could become markers for the objective evaluation of gymnastic performance.

Large-Scale Replication of Bipolar Disorder Dysconnectivity: A Diffusion MRI Analysis of 959 Individuals From the ENIGMA Bipolar Disorder Working Group

Bipolar disorder (BD) involves disrupted mood regulation and cognition potentially associated with altered brain connectivity patterns, including altered integration and segregation of anatomical brain networks. Establishing this as a robust finding has been hindered by limited sample sizes, and by variation in methodological pipelines. We present the largest graph-theoretical analysis to date of structural brain networks in BD.

Partial Phase Cohesiveness in Networks of Networks of Kuramoto Oscillators

Partial, instead of complete, synchronization has been widely observed in various networks, including, in particular, brain networks. Motivated by data from human brain functional networks, in this article, we analytically show that partial synchronization can be induced by strong regional connections in coupled subnetworks of Kuramoto oscillators. To quantify the required strength of regional connections, we first obtain a critical value for the algebraic connectivity of the corresponding subnetwork using the incremental two-norm. We then introduce the concept of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generalized complement graph</i> , and obtain another condition on the node strength by using the incremental <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> -norm. Under these two conditions, regions of attraction for partial phase cohesiveness are estimated in the forms of the incremental two- and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> -norms, respectively. Our result based on the incremental <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> -norm is the first known criterion that applies to noncomplete graphs. Numerical simulations are performed on a two-level network to illustrate our theoretical results; more importantly, we use real anatomical brain network data to show how our results may contribute to a better understanding of the interplay between anatomical structure and empirical patterns of synchrony.

Analyzing brain structural differences among undergraduates with different grades of self-esteem using multiple anatomical brain network

BackgroundSelf-esteem is the individual evaluation of oneself. People with high self-esteem grade have mental health and can bravely cope with the threats from the environment. With the development of neuroimaging techniques, researches on cognitive neural mechanisms of self-esteem are increased. Existing methods based on brain morphometry and single-layer brain network cannot characterize the subtle structural differences related to self-esteem.MethodTo solve this issue, we proposed a multiple anatomical brain network based on multi-resolution region of interest (ROI) template to study the brain structural connections of self-esteem. The multiple anatomical brain network consists of ROI features and hierarchal brain network features that are extracted from structural MRI. For each layer, we calculated the correlation relationship between pairs of ROIs. In order to solve the high-dimensional problem caused by the large amount of network features, feature selection methods (t-test, mRMR, and SVM-RFE) are adopted to reduce the number of features while retaining discriminative information to the maximum extent. Multi-kernel SVM is employed to integrate the various types of features by appropriate weight coefficient.ResultThe experimental results show that the proposed method can improve classification accuracy to 97.26% compared with single-layer brain network.ConclusionsThe proposed method provides a new perspective for the analysis of brain structural differences of self-esteem, which also has potential guiding significance in other researches involved brain cognitive activity and brain disease diagnosis.

Retrospective analysis of hemispheric structural network change as a function of location and size of glioma.

Gliomas are neoplasms that arise from glial cell origin and represent the largest fraction of primary malignant brain tumours (77%). These highly infiltrative malignant cell clusters modify brain structure and function through expansion, invasion and intratumoral modification. Depending on the growth rate of the tumour, location and degree of expansion, functional reorganization may not lead to overt changes in behaviour despite significant cerebral adaptation. Studies in simulated lesion models and in patients with stroke reveal both local and distal functional disturbances, using measures of anatomical brain networks. Investigations over the last two decades have sought to use diffusion tensor imaging tractography data in the context of intracranial tumours to improve surgical planning, intraoperative functional localization, and post-operative interpretation of functional change. In this study, we used diffusion tensor imaging tractography to assess the impact of tumour location on the white matter structural network. To better understand how various lobe localized gliomas impact the topology underlying efficiency of information transfer between brain regions, we identified the major alterations in brain network connectivity patterns between the ipsilesional versus contralesional hemispheres in patients with gliomas localized to the frontal, parietal or temporal lobe. Results were indicative of altered network efficiency and the role of specific brain regions unique to different lobe localized gliomas. This work draws attention to connections and brain regions which have shared structural susceptibility in frontal, parietal and temporal lobe glioma cases. This study also provides a preliminary anatomical basis for understanding which affected white matter pathways may contribute to preoperative patient symptomology.

Spatiotemporal imaging phenotypes of tau pathology in Alzheimer’s disease

AbstractBackgroundThe Braak staging scheme describes a stereotypical spread of tau pathology in Alzheimer’s disease (AD). However, apparent subtypes described at autopsy and clinical variants of AD both suggest variability in the pattern of tau spreading across the population. To address this discrepancy, we applied a validated spatiotemporal clustering algorithm to a large, multisite dataset of tau‐PET images. We verified these findings both longitudinally and on a separate tau‐PET dataset using a different radiotracer, and we characterized the phenotype and potential biological determinants of each subtype.MethodSubtype and Stage Inference (SuStaIn, Young et al., 2018 ) is a data‐driven, probabilistic algorithm that combines disease progression modeling with clustering to extract distinct spatiotemporal patterns (subtypes) from large cross‐sectional datasets. We applied SuStaIn to a multisite dataset (n=1143) of [18F]‐flortaucipir tau‐PET baseline scans to identify spatiotemporal subtypes of tau spreading. We investigated differences in demographic, cognitive and genetic measures. In a subsample of individuals with multiple tau‐PET scans over time (n=536), we tested longitudinal subtype stability and change in disease stage over time. We verified these subtypes in a second single‐site dataset (n=509) of [18F]‐RO948 tau‐PET scans. Finally we used the Epidemic Spreading Model (ESM, Iturria‐Medina et al., 2014) and imaging or transcriptomic analysis to determine if anatomical brain networks or specific cell‐types, respectively, could explain the differing spatial patterns across subtypes.ResultIn those subjects with significant tau‐PET signal in the brain, SuStaIn identified four spatiotemporal subtypes of tau‐PET spreading: Typical (worse memory, more APOE4 carriers), limbic‐sparing (younger, less APOE4 carriers), posterior (worse visuospatial cognition), and lateral‐temporal (worse language and executive function scores; Fig 1A, Fig 2). These same four subtypes were observed in a separate cohort using a different radiotracer (similarity 0.7–0.9; Fig 1B). 86% of subjects exhibited the same subtype at multiple visits (Fig 1C), and of that group, 83% advanced in disease stage or remained stable within model uncertainty. The ESM suggested different epicenters for each subtype, and each subtype pattern exhibited a different profile of cell‐type enrichment (Fig 3).ConclusionFour stable tau‐spreading phenotypes were observed with distinct cognitive profiles, which may be characterized by vulnerability of different corticolimbic networks involving distinct cell classes.

The gene expression network regulating queen brain remodeling after insemination and its parallel use in ants with reproductive workers.

Caste differentiation happens early in development to produce gynes as future colony germlines and workers as present colony soma. However, gynes need insemination to become functional queens, a transition that initiates reproductive role differentiation relative to unmated gynes. Here, we analyze the anatomy and transcriptomes of brains during this differentiation process within the reproductive caste of Monomorium pharaonis Insemination terminated brain growth, whereas unmated control gynes continued to increase brain volume. Transcriptomes revealed a specific gene regulatory network (GRN) mediating both brain anatomy changes and behavioral modifications. This reproductive role differentiation GRN hardly overlapped with the gyne-worker caste differentiation GRN, but appears to be also used by distantly related ants where workers became germline individuals after the queen caste was entirely or partially lost. The genes corazonin and neuroparsin A in the anterior neurosecretory cells were overexpressed in individuals with reduced or nonreproductive roles across all four ant species investigated.

Aberrant Whole-Brain Transitions and Dynamics of Spontaneous Network Microstates in Mild Traumatic Brain Injury.

Dynamic Functional Connectivity (DFC) analysis is a promising approach for the characterization of brain electrophysiological activity. In this study, we investigated abnormal alterations due to mild Traumatic Brain Injury (mTBI) using DFC of the source reconstructed magnetoencephalographic (MEG) resting-state recordings. Brain activity in several well-known frequency bands was first reconstructed using beamforming of the MEG data to determine ninety anatomical brain regions of interest. A DFC graph was formulated using the imaginary part of phase-locking values, which were obtained from 30 mTBI patients and 50 healthy controls (HC). Subsequently, we estimated normalized Laplacian transformations of individual, statistically and topologically filtered quasi-static graphs. The corresponding eigenvalues of each node synchronization were then computed and through the neural-gas algorithm, we quantized the evolution of the eigenvalues resulting in distinct network microstates (NMstates). The discrimination level between the two groups was assessed using an iterative cross-validation classification scheme with features either the NMstates in each frequency band, or the combination of the so-called chronnectomics (flexibility index, occupancy time of NMstate, and Dwell time) with the complexity index over the evolution of the NMstates across all frequency bands. Classification performance based on chronnectomics showed 80% accuracy, 99% sensitivity, and 49% specificity. However, performance was much higher (accuracy: 91–97%, sensitivity: 100%, and specificity: 77–93%) when focusing on the microstates. Exploring the mean node degree within and between brain anatomical networks (default mode network, frontoparietal, occipital, cingulo-opercular, and sensorimotor), a reduced pattern occurred from lower to higher frequency bands, with statistically significant stronger degrees for the HC than the mTBI group. A higher entropic profile on the temporal evolution of the modularity index was observed for both NMstates for the mTBI group across frequencies. A significant difference in the flexibility index was observed between the two groups for the β frequency band. The latter finding may support a central role of the thalamus impairment in mTBI. The current study considers a complete set of frequency-dependent connectomic markers of mTBI-caused alterations in brain connectivity that potentially could serve as markers to assess the return of an injured subject back to normality.

Modeling functional resting-state brain networks through neural message passing on the human connectome

In this work, we propose a natural model for information flow in the brain through a neural message-passing dynamics on a structural network of macroscopic regions, such as the human connectome (HC). In our model, each brain region is assumed to have a binary behavior (active or not), the strengths of interactions among them are encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by the Belief Propagation (BP) algorithm, working near the critical point of the network. We show that in the absence of direct external stimuli the BP algorithm converges to a spatial map of activations that is similar to the Default Mode Network (DMN) of the brain, which has been defined from the analysis of functional MRI data. Moreover, we use Susceptibility Propagation (SP) to compute the matrix of long-range correlations between the different regions and show that the modules defined by a clustering of this matrix resemble several Resting State Networks (RSN) determined experimentally. Both results suggest that the functional DMN and RSNs can be seen as simple consequences of the anatomical structure of the brain and a neural message-passing dynamics between macroscopic regions. With the new model, we explore predictions on how functional maps change when the anatomical brain network suffers structural alterations, like in Alzheimer’s disease and in lesions of the Corpus Callosum. The implications and novel interpretations suggested by the model, as well as the role of criticality, are discussed.

Neuroanatomical Dysconnectivity Underlying Cognitive Deficits in Bipolar Disorder

BackgroundGraph theory applied to brain networks is an emerging approach to understanding the brain’s topological associations with human cognitive ability. Despite well-documented cognitive impairments in bipolar disorder (BD) and recent reports of altered anatomical network organization, the association between connectivity and cognitive impairments in BD remains unclear. MethodsWe examined the role of anatomical network connectivity derived from T1- and diffusion-weighted magnetic resonance imaging in impaired cognitive performance in individuals with BD (n = 32) compared with healthy control individuals (n = 38). Fractional anisotropy- and number of streamlines-weighted anatomical brain networks were generated by mapping constrained spherical deconvolution-reconstructed white matter among 86 cortical/subcortical bilateral brain regions delineated in the individual’s own coordinate space. Intelligence and executive function were investigated as distributed functions using measures of global, rich-club, and interhemispheric connectivity, while memory and social cognition were examined in relation to subnetwork connectivity. ResultsLower executive functioning related to higher global clustering coefficient in participants with BD, and lower IQ performance may present with a differential relationship between global and interhemispheric efficiency in individuals with BD relative to control individuals. Spatial recognition memory accuracy and response times were similar between diagnostic groups and associated with basal ganglia and thalamus interconnectivity and connectivity within extended anatomical subnetworks in all participants. No anatomical subnetworks related to episodic memory, short-term memory, or social cognition generally or differently in BD. ConclusionsResults demonstrate selective influence of subnetwork patterns of connectivity in underlying cognitive performance generally and abnormal global topology underlying discrete cognitive impairments in BD.

Brain synchronizability, a false friend

Synchronization plays a fundamental role in healthy cognitive and motor function. However, how synchronization depends on the interplay between local dynamics, coupling and topology and how prone to synchronization a network is, given its topological organization, are still poorly understood issues. To investigate the synchronizability of both anatomical and functional brain networks various studies resorted to the Master Stability Function (MSF) formalism, an elegant tool which allows analysing the stability of synchronous states in a dynamical system consisting of many coupled oscillators. Here, we argue that brain dynamics does not fulfil the formal criteria under which synchronizability is usually quantified and, perhaps more importantly, this measure refers to a global dynamical condition that never holds in the brain (not even in the most pathological conditions), and therefore no neurophysiological conclusions should be drawn based on it. We discuss the meaning of synchronizability and its applicability to neuroscience and propose alternative ways to quantify brain networks synchronization.