The spatial chronnectome reveals a dynamic interplay between functional segregation and integration.

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.

The cost-effectiveness of alternative vaccination strategies for polyvalent meningococcal vaccines in Burkina Faso: A transmission dynamic modeling study.

BACKGROUND: The introduction of a conjugate vaccine for serogroup A Neisseria meningitidis has dramatically reduced disease in the African meningitis belt. In this context, important questions remain about the performance of different vaccine policies that target remaining serogroups. Here, we estimate the health impact and cost associated with several alternative vaccination policies in Burkina Faso. METHODS AND FINDINGS: We developed and calibrated a mathematical model of meningococcal transmission to project the disability-adjusted life years (DALYs) averted and costs associated with the current Base policy (serogroup A conjugate vaccination at 9 months, as part of the Expanded Program on Immunization [EPI], plus district-specific reactive vaccination campaigns using polyvalent meningococcal polysaccharide [PMP] vaccine in response to outbreaks) and three alternative policies: (1) Base Prime: novel polyvalent meningococcal conjugate (PMC) vaccine replaces the serogroup A conjugate in EPI and is also used in reactive campaigns; (2) Prevention 1: PMC used in EPI and in a nationwide catch-up campaign for 1-18-year-olds; and (3) Prevention 2: Prevention 1, except the nationwide campaign includes individuals up to 29 years old. Over a 30-year simulation period, Prevention 2 would avert 78% of the meningococcal cases (95% prediction interval: 63%-90%) expected under the Base policy if serogroup A is not replaced by remaining serogroups after elimination, and would avert 87% (77%-93%) of meningococcal cases if complete strain replacement occurs. Compared to the Base policy and at the PMC vaccine price of US$4 per dose, strategies that use PMC vaccine (i.e., Base Prime and Preventions 1 and 2) are expected to be cost saving if strain replacement occurs, and would cost US$51 (-US$236, US$490), US$188 (-US$97, US$626), and US$246 (-US$53, US$703) per DALY averted, respectively, if strain replacement does not occur. An important potential limitation of our study is the simplifying assumption that all circulating meningococcal serogroups can be aggregated into a single group; while this assumption is critical for model tractability, it would compromise the insights derived from our model if the effectiveness of the vaccine differs markedly between serogroups or if there are complex between-serogroup interactions that influence the frequency and magnitude of future meningitis epidemics. CONCLUSIONS: Our results suggest that a vaccination strategy that includes a catch-up nationwide immunization campaign in young adults with a PMC vaccine and the addition of this new vaccine into EPI is cost-effective and would avert a substantial portion of meningococcal cases expected under the current World Health Organization-recommended strategy of reactive vaccination. This analysis is limited to Burkina Faso and assumes that polyvalent vaccines offer equal protection against all meningococcal serogroups; further studies are needed to evaluate the robustness of this assumption and applicability for other countries in the meningitis belt.

A window-less approach for capturing time-varying connectivity in fMRI data reveals the presence of states with variable rates of change.

Functional connectivity during the resting state has been shown to change over time (i.e., has a dynamic connectivity). However, resting-state fluctuations, in contrast to task-based experiments, are not initiated by an external stimulus. Consequently, a more complicated method needs to be designed to measure the dynamic connectivity. Previous approaches have been based on assumptions regarding the nature of the underlying dynamic connectivity to compensate for this knowledge gap. The most common assumption is what we refer to as locality assumption. Under a locality assumption, a single connectivity state can be estimated from data that are close in time. This assumption is so natural that it has been either explicitly or implicitly embedded in many current approaches to capture dynamic connectivity. However, an important drawback of methods using this assumption is they are unable to capture dynamic changes in connectivity beyond the embedded rate while, there has been no evidence that the rate of change in brain connectivity matches the rates enforced by this assumption. In this study, we propose an approach that enables us to capture functional connectivity with arbitrary rates of change, varying from very slow to the theoretically maximum possible rate of change, which is only imposed by the sampling rate of the imaging device. This method allows us to observe unique patterns of connectivity that were not observable with previous approaches. As we explain further, these patterns are also significantly correlated to the age and gender of study subjects, which suggests they are also neurobiologically related.

Cross-Frequency rs-fMRI Network Connectivity Patterns Manifest Differently for Schizophrenia Patients and Healthy Controls.

Patterns of resting state fMRI functional network connectivity in schizophrenia patients have been shown to differ markedly from that of healthy controls. While some studies have explored connectivity within fixed frequency bands, the question of network phase synchrony across disparate frequency bands, or cross-frequency connectivity, has remained surprisingly underexplored. Computational modeling at the neuronal scale however has long acknowledged the existence of coupled fast and slow subsystems. Here we present preliminary evidence that cross-frequency coupling exists at the network level, that it patterns in meaningful ways over functional domains, and that this patterning differs between the healthy population and individuals with diagnosed schizophrenia.

Mutually temporally independent connectivity patterns: a new framework to study the dynamics of brain connectivity at rest with application to explain group difference based on gender.

Functional connectivity analysis of the human brain is an active area in fMRI research. It focuses on identifying meaningful brain networks that have coherent activity either during a task or in the resting state. These networks are generally identified either as collections of voxels whose time series correlate strongly with a pre-selected region or voxel, or using data-driven methodologies such as independent component analysis (ICA) that compute sets of maximally spatially independent voxel weightings (component spatial maps (SMs)), each associated with a single time course (TC). Studies have shown that regardless of the way these networks are defined, the activity coherence among them has a dynamic nature which is hard to estimate with global coherence analysis such as correlation or mutual information. Sliding window analyses in which functional network connectivity (FNC) is estimated separately at each time window is one of the more widely employed approaches to studying the dynamic nature of functional network connectivity (dFNC). Observed FNC patterns are summarized and replaced with a smaller set of prototype connectivity patterns ("states" or "components"), and then a dynamical analysis is applied to the resulting sequences of prototype states. In this work we are looking for a small set of connectivity patterns whose weighted contributions to the dynamically changing dFNCs are independent of each other in time. We discuss our motivation for this work and how it differs from existing approaches. Also, in a group analysis based on gender we show that males significantly differ from females by occupying significantly more combinations of these connectivity patterns over the course of the scan.

Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information.

Many approaches for estimating functional connectivity among brain regions or networks in fMRI have been considered in the literature. More recently, studies have shown that connectivity which is usually estimated by calculating correlation between time series or by estimating coherence as a function of frequency has a dynamic nature, during both task and resting conditions. Sliding-window methods have been commonly used to study these dynamic properties although other approaches such as instantaneous phase synchronization have also been used for similar purposes. Some studies have also suggested that spectral analysis can be used to separate the distinct contributions of motion, respiration and neurophysiological activity from the observed correlation. Several recent studies have merged analysis of coherence with study of temporal dynamics of functional connectivity though these have mostly been limited to a few selected brain regions and frequency bands. Here we propose a novel data-driven framework to estimate time-varying patterns of whole-brain functional network connectivity of resting state fMRI combined with the different frequencies and phase lags at which these patterns are observed. We show that this analysis identifies both broad-band cluster centroids that summarize connectivity patterns observed in many frequency bands, as well as clusters consisting only of functional network connectivity (FNC) from a narrow range of frequencies along with associated phase profiles. The value of this approach is demonstrated by its ability to reveal significant group differences in males versus females regarding occupancy rates of cluster that would not be separable without considering the frequencies and phase lags. The method we introduce provides a novel and informative framework for analyzing time-varying and frequency specific connectivity which can be broadly applied to the study of the healthy and diseased human brain.

Frequency-Aware Summarization of Resting-State fMRI Data.

Many brain imaging modalities reveal interpretable patterns after the data dimensionality is reduced and summarized via data-driven approaches. In functional magnetic resonance imaging (fMRI) studies, such summarization is often achieved through independent component analysis (ICA). ICA transforms the original data into a relatively small number of interpretable bases in voxel space (referred to as ICA spatial components, or spatial maps) and corresponding bases in the time domain (referred to as time-courses of corresponding spatial maps) In this work, we use the word "basis" to broadly refer to either of the two factors resulting from the transformation. A precise summarization for fMRI requires accurately detecting co-activation of voxels by measuring temporal dependence. Accurate measurement of dependence requires a proper understanding of the underlying temporal characteristics of the data. One way to understand such characteristics is to study the frequency spectrum of fMRI data. Researchers have argued that information regarding the underlying neuronal activity might be spread over a range of frequencies as a result of the heterogeneous temporal nature of the neuronal activity, which is reflected in its frequency spectrum. Many studies have accounted for heterogeneous characteristics of the frequency of the signal by either directly inspecting the contents of frequency domain-transformed data or augmenting their analyses with such information. For example, studies on fMRI data have investigated brain functional connectivity by leveraging frequency-adjusted measures of dependence (e.g., when a correlation is measured as a function of frequency, as with "coherence"). Although these studies measure dependence as a function of frequency, the formulation does not capture all characteristics of the frequency-based dependence. Incorporating frequency information into a summarization approach would enable the retention of important frequency-related information that exists in the original space but might be lost after performing a frequency-independent summarization. We propose a novel data-driven approach built upon ICA, which is based on measuring dependence as a generalized function of frequency. Applying this approach to fMRI data provides evidence of existing cross-frequency functional connectivity between different areas of the brain.

Time-varying spectral power of resting-state fMRI networks reveal cross-frequency dependence in dynamic connectivity.

Brain oscillations and synchronicity among brain regions (brain connectivity) have been studied in resting-state (RS) and task-induced settings. RS-connectivity which captures brain functional integration during an unconstrained state is shown to vary with the frequency of oscillations. Indeed, high temporal resolution modalities have demonstrated both between and cross-frequency connectivity spanning across frequency bands such as theta and gamma. Despite high spatial resolution, functional magnetic resonance imaging (fMRI) suffers from low temporal resolution due to modulation with slow-varying hemodynamic response function (HRF) and also relatively low sampling rate. This limits the range of detectable frequency bands in fMRI and consequently there has been no evidence of cross-frequency dependence in fMRI data. In the present work we uncover recurring patterns of spectral power in network timecourses which provides new insight on the actual nature of frequency variation in fMRI network activations. Moreover, we introduce a new measure of dependence between pairs of rs-fMRI networks which reveals significant cross-frequency dependence between functional brain networks specifically default-mode, cerebellar and visual networks. This is the first strong evidence of cross-frequency dependence between functional networks in fMRI and our subject group analysis based on age and gender supports usefulness of this observation for future clinical applications.

A joint time-frequency analysis of resting-state functional connectivity reveals novel patterns of connectivity shared between or unique to schizophrenia patients and healthy controls.

Functional connectivity of the resting-state (RS) brain is a vehicle to study brain dysconnectivity aspects of diseases such as schizophrenia and bipolar. Methods that are developed to measure functional connectivity are based on the underlying hypotheses regarding the actual nature of RS-connectivity including evidence of temporally dynamic versus static RS-connectivity and evidence of frequency-specific versus hemodynamically-driven connectivity over a wide frequency range. This study is derived by these observations of variation of RS-connectivity in temporal and frequency domains and evaluates such characteristics of RS-connectivity in clinical population and jointly in temporal and frequency domains (the spectro-temporal domain). We base this study on the hypothesis that by studying functional connectivity of schizophrenia patients and comparing it to the one of healthy controls in the spectro-temporal domain we might be able to make new observations regarding the differences and similarities between diseased and healthy brain connectivity and such observations could be obscured by studies which investigate such characteristics separately. Interestingly, our results include, but are not limited to, a spectrally localized (mostly mid-range frequencies) modular dynamic connectivity pattern in which sensory motor networks are anti-correlated with visual, auditory and sub-cortical networks in schizophrenia, as well as evidence of lagged dependence between default-mode and sensory networks in schizophrenia. These observations are unique to the proposed augmented domain of connectivity analysis. We conclude this study by arguing not only resting-state connectivity has structured spectro-temporal variability, but also that studying properties of connectivity in this joint domain reveals distinctive group-based differences and similarities between clinical and healthy populations.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients.

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject's trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (Nsz = 151) and healthy controls (Nhc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.

Higher dimensional analysis shows reduced dynamism of time-varying network connectivity in schizophrenia patients.

Assessments of functional connectivity between brain networks is a fixture of resting state fMRI research. Until very recently most of this work proceeded from an assumption of stationarity in resting state network connectivity. In the last few years however, interest in moving beyond this simplifying assumption has grown considerably. Applying group temporal independent component analysis (tICA) to a set of time-varying functional network connectivity (FNC) matrices derived from a large multi-site fMRI dataset (N=314; 163 healthy, 151 schizophrenia patients), we obtain a set of five basic correlation patterns (component spatial maps (SMs)) from which observed FNCs can be expressed as mutually independent linear combinations, i.e., the coefficient on each SM in the linear combination is maximally independent of the others. We study dynamic properties of network connectivity as they are reflected in this five-dimensional space, and report stark differences in connectivity dynamics between schizophrenia patients and healthy controls. We also find that the most important global differences in FNC dynamism between patient and control groups are replicated when the same dynamical analysis is performed on sets of correlation patterns obtained from either PCA or spatial ICA, giving us additional confidence in the results.