Erik B. Erhardt

Tracking Whole-Brain Connectivity Dynamics in the Resting State

Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.

A Baseline for the Multivariate Comparison of Resting-State Networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

Comparison of multi-subject ICA methods for analysis of fMRI data.

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi‐subject ICA approaches estimating subject‐specific time courses (TCs) and spatial maps (SMs) have been developed, however, there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi‐subject ICA approaches in combination with data reduction methods for simulated and fMRI task data. For multi‐subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject‐specific, spatial concatenation, and group data mean subject‐level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject‐specific and group data mean subject‐level PCA are preferred because of well‐estimated TCs and SMs. Second, aggregate independent components are estimated using either noise‐free ICA or probabilistic ICA (PICA). Third, subject‐specific SMs and TCs are estimated using back‐reconstruction. We compare several direct group ICA (GICA) back‐reconstruction approaches (GICA1‐GICA3) and an indirect back‐reconstruction approach, spatio‐temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed‐component artifacts in estimated SMs. Our evidence‐based recommendation is to use GICA3, introduced here, with subject‐specific PCA and noise‐free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation. Hum Brain Mapp, 2011.

Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study.

A key challenge in functional neuroimaging is the meaningful combination of results across subjects. Even in a sample of healthy participants, brain morphology and functional organization exhibit considerable variability, such that no two individuals have the same neural activation at the same location in response to the same stimulus. This inter-subject variability limits inferences at the group-level as average activation patterns may fail to represent the patterns seen in individuals. A promising approach to multi-subject analysis is group independent component analysis (GICA), which identifies group components and reconstructs activations at the individual level. GICA has gained considerable popularity, particularly in studies where temporal response models cannot be specified. However, a comprehensive understanding of the performance of GICA under realistic conditions of inter-subject variability is lacking. In this study we use simulated functional magnetic resonance imaging (fMRI) data to determine the capabilities and limitations of GICA under conditions of spatial, temporal, and amplitude variability. Simulations, generated with the SimTB toolbox, address questions that commonly arise in GICA studies, such as: (1) How well can individual subject activations be estimated and when will spatial variability preclude estimation? (2) Why does component splitting occur and how is it affected by model order? (3) How should we analyze component features to maximize sensitivity to intersubject differences? Overall, our results indicate an excellent capability of GICA to capture between-subject differences and we make a number of recommendations regarding analytic choices for application to functional imaging data.

SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability

We introduce SimTB, a MATLAB toolbox designed to simulate functional magnetic resonance imaging (fMRI) datasets under a model of spatiotemporal separability. The toolbox meets the increasing need of the fMRI community to more comprehensively understand the effects of complex processing strategies by providing a ground truth that estimation methods may be compared against. SimTB captures the fundamental structure of real data, but data generation is fully parameterized and fully controlled by the user, allowing for accurate and precise comparisons. The toolbox offers a wealth of options regarding the number and configuration of spatial sources, implementation of experimental paradigms, inclusion of tissue-specific properties, addition of noise and head movement, and much more. A straightforward data generation method and short computation time (3-10 seconds for each dataset) allow a practitioner to simulate and analyze many datasets to potentially understand a problem from many angles. Beginning MATLAB users can use the SimTB graphical user interface (GUI) to design and execute simulations while experienced users can write batch scripts to automate and customize this process. The toolbox is freely available at http://mialab.mrn.org/software together with sample scripts and tutorials.

Correspondence between structure and function in the human brain at rest.

To further understanding of basic and complex cognitive functions, previous connectome research has identified functional and structural connections of the human brain. Functional connectivity is often measured by using resting-state functional magnetic resonance imaging (rs-fMRI) and is generally interpreted as an indirect measure of neuronal activity. Gray matter (GM) primarily consists of neuronal and glia cell bodies; therefore, it is surprising that the majority of connectome research has excluded GM measures. Therefore, we propose that by exploring where GM corresponds to function would aid in the understanding of both structural and functional connectivity and in turn the human connectome. A cohort of 603 healthy participants underwent structural and functional scanning on the same 3 T scanner at the Mind Research Network. To investigate the spatial correspondence between structure and function, spatial independent component analysis (ICA) was applied separately to both GM density (GMD) maps and to rs-fMRI data. ICA of GM delineates structural components based on the covariation of GMD regions among subjects. For the rs-fMRI data, ICA identified spatial patterns with common temporal features. These decomposed structural and functional components were then compared by spatial correlation. Basal ganglia components exhibited the highest structural to resting-state functional spatial correlation (r = 0.59). Cortical components generally show correspondence between a single structural component and several resting-state functional components. We also studied relationships between the weights of different structural components and identified the precuneus as a hub in GMD structural network correlations. In addition, we analyzed relationships between component weights, age, and gender; concluding that age has a significant effect on structural components.

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia, which may underscore the abnormal brain performance in this mental illness.

Nasal immunity is an ancient arm of the mucosal immune system of vertebrates

The mucosal surfaces of all vertebrates have been exposed to similar evolutionary pressures for millions of years. In terrestrial vertebrates such as birds and mammals, the nasopharynx-associated lymphoid tissue (NALT) represents a first line of immune defence. Here we propose that NALT is an ancient arm of the mucosal immune system not restricted to terrestrial vertebrates. We find that NALT is present in rainbow trout and that it resembles other teleost mucosa-associated lymphoid tissues. Trout NALT consists of diffuse lymphoid cells and lacks tonsils and adenoids. The predominant B-cell subset found in trout NALT are IgT(+) B cells, similar to skin and gut. The trout olfactory organ is colonized by abundant symbiotic bacteria, which are coated by trout secretory immunoglobulin. Trout NALT is capable of mounting strong anti-viral immune responses following nasal delivery of a live attenuated viral vaccine. Our results open up a new tool for the control of aquatic infectious diseases via nasal vaccination.

In Vivo Inhibition of miR-155 Promotes Recovery after Experimental Mouse Stroke

A multifunctional microRNA, miR-155, has been recently recognized as an important modulator of numerous biological processes. In our previous in vitro studies, miR-155 was identified as a potential regulator of the endothelial morphogenesis. The present study demonstrates that in vivo inhibition of miR-155 supports cerebral vasculature after experimental stroke. Intravenous injections of a specific miR-155 inhibitor were initiated at 48 h after mouse distal middle cerebral artery occlusion (dMCAO). Microvasculature in peri-infarct area, infarct size, and animal functional recovery were assessed at 1, 2, and 3 weeks after dMCAO. Using in vivo two-photon microscopy, we detected improved blood flow and microvascular integrity in the peri-infarct area of miR-155 inhibitor-injected mice. Electron microscopy revealed that, in contrast to the control group, these animals demonstrated well preserved capillary tight junctions (TJs). Western blot analysis data indicate that improved TJ integrity in the inhibitor-injected animals could be associated with stabilization of the TJ protein ZO-1 and mediated by the miR-155 target protein Rheb. MRI analysis showed significant (34%) reduction of infarct size in miR-155 inhibitor-injected animals at 21 d after dMCAO. Reduced brain injury was confirmed by electron microscopy demonstrating decreased neuronal damage in the peri-infarct area of stroke. Preservation of brain tissue was reflected in efficient functional recovery of inhibitor-injected animals. Based on our findings, we propose that in vivo miR-155 inhibition after ischemia supports brain microvasculature, reduces brain tissue damage, and improves the animal functional recovery. SIGNIFICANCE STATEMENT In the present study, we investigated an effect of the in vivo inhibition of a microRNA, miR-155, on brain recovery after experimental cerebral ischemia. To our knowledge, this is the first report describing the efficiency of intravenous anti-miRNA injections in a mouse model of ischemic stroke. The role of miRNAs in poststroke revascularization has been unexplored and in vivo regulation of miRNAs during the subacute phase of stroke has not yet been proposed. Our investigation introduces a new and unexplored approach to cerebral regeneration: regulation of poststroke angiogenesis and recovery through direct modulation of specific miRNA activity. We expect that our findings will lead to the development of novel strategies for regulating neurorestorative processes in the postischemic brain.

Data Visualization in the Neurosciences: Overcoming the Curse of Dimensionality

In publications, presentations, and popular media, scientific results are predominantly communicated through graphs. But are these figures clear and honest or misleading? We examine current practices in data visualization and discuss improvements, advocating design choices which reveal data rather than hide it.