Martijn P. van den Heuvel

ENIGMA and the Individual: Predicting Factors that Affect the Brain in 35 Countries Worldwide

In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) – a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date – of schizophrenia and major depression – ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMAs genomic screens – now numbering over 30,000 MRI scans – have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants – and genetic variants in general – may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures – from tens of thousands of people – that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMAs efforts so far.

A multisample study of longitudinal changes in brain network architecture in 4–13-year-old children

Recent advances in human neuroimaging research have revealed that white‐matter connectivity can be described in terms of an integrated network, which is the basis of the human connectome. However, the developmental changes of this connectome in childhood are not well understood. This study made use of two independent longitudinal diffusion‐weighted imaging data sets to characterize developmental changes in the connectome by estimating age‐related changes in fractional anisotropy (FA) for reconstructed fibers (edges) between 68 cortical regions. The first sample included 237 diffusion‐weighted scans of 146 typically developing children (4–13 years old, 74 females) derived from the Pediatric Longitudinal Imaging, Neurocognition, and Genetics (PLING) study. The second sample included 141 scans of 97 individuals (8–13 years old, 62 females) derived from the BrainTime project. In both data sets, we compared edges that had the most substantial age‐related change in FA to edges that showed little change in FA. This allowed us to investigate if developmental changes in white matter reorganize network topology. We observed substantial increases in edges connecting peripheral and a set of highly connected hub regions, referred to as the rich club. Together with the observed topological differences between regions connecting to edges showing the smallest and largest changes in FA, this indicates that changes in white matter affect network organization, such that highly connected regions become even more strongly imbedded in the network. These findings suggest that an important process in brain development involves organizing patterns of inter‐regional interactions. Hum Brain Mapp 39:157–170, 2018.

Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication

Higher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs—referred to as the rich club—has been hypothesized to support task‐specific functional integration. In the present paper, we use a whole‐cortex model to estimate directed interactions between 68 cortical regions from functional magnetic resonance imaging activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state‐dependent input and output effective connectivity (EC) of the structural rich club and relate these to whole‐cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing EC during task performance as compared with rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task‐specific. Furthermore, we observe that whole‐cortex dynamics are faster during task as compared with rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing EC. Together our findings speak to a gating mechanism of the rich club that supports fast‐paced information exchange among relevant peripheral regions in a task‐specific and goal‐directed fashion, while constantly listening to the whole network.

Fluctuations between high- and low-modularity topology in time-resolved functional connectivity

ABSTRACT Modularity is an important topological attribute for functional brain networks. Recent human fMRI studies have reported that modularity of functional networks varies not only across individuals being related to demographics and cognitive performance, but also within individuals co‐occurring with fluctuations in network properties of functional connectivity, estimated over short time intervals. However, characteristics of these time‐resolved functional networks during periods of high and low modularity have remained largely unexplored. In this study we investigate basic spatiotemporal properties of time‐resolved networks in the high and low modularity periods during rest, with a particular focus on their spatial connectivity patterns, temporal homogeneity and test‐retest reliability. We show that spatial connectivity patterns of time‐resolved networks in the high and low modularity periods are represented by increased and decreased dissociation of the default mode network module from task‐positive network modules, respectively. We also find that the instances of time‐resolved functional connectivity sampled from within the high (respectively, low) modularity period are relatively homogeneous (respectively, heterogeneous) over time, indicating that during the low modularity period the default mode network interacts with other networks in a variable manner. We confirmed that the occurrence of the high and low modularity periods varies across individuals with moderate inter‐session test‐retest reliability and that it is correlated with previously‐reported individual differences in the modularity of functional connectivity estimated over longer timescales. Our findings illustrate how time‐resolved functional networks are spatiotemporally organized during periods of high and low modularity, allowing one to trace individual differences in long‐timescale modularity to the variable occurrence of network configurations at shorter timescales. HighlightsTime‐resolved functional connectivity in high/low modularity periods is investigated.High modularity is tied to increased dissociation of task‐positive/negative networks.Low modularity is associated with heterogeneous connectivity patterns over time.Frequency of high/low modularity periods has moderate inter‐session reproducibility.An interpretation of individual differences in long‐timescale modularity is provided.Functional connectivity (FC) measured over extended time periods of resting-state functional magnetic resonance imaging (static FC) has proven useful for characterizing individual differences in human brain function and dysfunction. Recent studies have shown that resting-state FC varies over time scale of tens of seconds (dynamic FC), exhibiting characteristic patterns of temporal variability in FC and transition dynamics of short-lived FC configurations, known as FC states. However, fundamental properties of FC states, such as their network topology and the dynamics of state transitions, or dynamic FC flow between states, as well as their relations to static FC, are relatively unexplored. Here we investigated these basic properties of FC states in humans and assessed how our characterization helps in uncovering individual age-related differences in dynamic FC across the lifespan. We found that dynamic FC was broadly classified into two characteristic FC states with a large proportion of weak FC (flat state) and strong FC exhibiting modular connectivity patterns (modular state), and other states representing their mixtures. These flat and modular FC states were largely constrained by the level of modularity present in static FC. Age-related differences in dynamic FC became evident when we focused on the dynamic flow between the flat and modular FC states in sets of subjects that were expressing lower levels of static FC modularity. These results contribute to our basic understanding of FC dynamics and suggest that classification of FC states can contribute to the detection of individual differences in dynamic brain organization.

Structure–function relationships during segregated and integrated network states of human brain functional connectivity

Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.

Detailed T1-Weighted Profiles from the Human Cortex Measured in Vivo at 3 Tesla MRI

Studies into cortical thickness in psychiatric diseases based on T1-weighted MRI frequently report on aberrations in the cerebral cortex. Due to limitations in image resolution for studies conducted at conventional MRI field strengths (e.g. 3xa0Tesla (T)) this information cannot be used to establish which of the cortical layers may be implicated. Here we propose a new analysis method that computes one high-resolution average cortical profile per brain region extracting myeloarchitectural information from T1-weighted MRI scans that are routinely acquired at a conventional field strength. To assess this new method, we acquired standard T1-weighted scans at 3xa0T and compared them with state-of-the-art ultra-high resolution T1-weighted scans optimised for intracortical myelin contrast acquired at 7xa0T. Average cortical profiles were computed for seven different brain regions. Besides a qualitative comparison between the 3xa0T scans, 7xa0T scans, and results from literature, we tested if the results from dynamic time warping-based clustering are similar for the cortical profiles computed from 7xa0T and 3xa0T data. In addition, we quantitatively compared cortical profiles computed for V1, V2 and V7 for both 7xa0T and 3xa0T data using a priori information on their relative myelin concentration. Although qualitative comparisons show that at an individual level average profiles computed for 7xa0T have more pronounced features than 3xa0T profiles the results from the quantitative analyses suggest that average cortical profiles computed from T1-weighted scans acquired at 3xa0T indeed contain myeloarchitectural information similar to profiles computed from the scans acquired at 7xa0T. The proposed method therefore provides a step forward to study cortical myeloarchitecture in vivo at conventional magnetic field strength both in health and disease.

Hubs in the human fetal brain network

Highlights • Network analysis has identified highly connected regions, or hubs, in the human brain.• Whether network hubs emerge in utero has yet to be examined.• We found that fetal hubs were located in both primary and association cortices.• Interestingly, hubs were identified close to fusiform facial and Wernicke’s areas.• These putative hubs may be points of vulnerability in fetal brain development.

Network-Based Asymmetry of the Human Auditory System

Abstract Converging evidence from activation, connectivity, and stimulation studies suggests that auditory brain networks are lateralized. Here we show that these findings can be at least partly explained by the asymmetric network embedding of the primary auditory cortices. Using diffusion-weighted imaging in 3 independent datasets, we investigate the propensity for left and right auditory cortex to communicate with other brain areas by quantifying the centrality of the auditory network across a spectrum of communication mechanisms, from shortest path communication to diffusive spreading. Across all datasets, we find that the right auditory cortex is better integrated in the connectome, facilitating more efficient communication with other areas, with much of the asymmetry driven by differences in communication pathways to the opposite hemisphere. Critically, the primacy of the right auditory cortex emerges only when communication is conceptualized as a diffusive process, taking advantage of more than just the topologically shortest paths in the network. Altogether, these results highlight how the network configuration and embedding of a particular region may contribute to its functional lateralization.

Cortical magnetization transfer abnormalities and connectome dysconnectivity in schizophrenia

Macroscale dysconnectivity in schizophrenia is associated with neuropathological abnormalities. The extent to which alterations in cortical myelination as revealed in vivo by magnetization transfer ratio (MTR) are related to macroscale dysconnectivity remains unknown. We acquired magnetization transfer imaging (MTI) data and diffusion weighted imaging (DWI) data from 78 schizophrenia patients and 93 healthy controls for MTR extraction and connectome reconstruction to examine the possible link between cortical myelination and macroscale dysconnectivity. Our findings showed significant cortical MTR disruptions in several prefrontal areas in schizophrenia patients, including bilateral rostral middle frontal areas, right pars orbitalis, and right frontal pole. Furthermore, cortical MTR alterations between patients and controls were significantly correlated with the level of regional disconnectivity. Together, our findings provide evidence that microstructural neuropathological abnormalities in schizophrenia are predominately present in prefrontal areas of the cortex and are associated with alterations in structural connectome architecture at the whole brain network level.

An MRI Von Economo - Koskinas atlas.

ABSTRACT The cerebral cortex displays substantial variation in cellular architecture, a regional patterning that has been of great interest to anatomists for centuries. In 1925, Constantin von Economo and George Koskinas published a detailed atlas of the human cerebral cortex, describing a cytoarchitectonic division of the cortical mantle into over 40 distinct areas. Von Economo and Koskinas accompanied their seminal work with large photomicrographic plates of their histological slides, together with tables containing for each described region detailed morphological layer‐specific information on neuronal count, neuron size and thickness of the cortical mantle. Here, we aimed to make this legacy data accessible and relatable to in vivo neuroimaging data by constructing a digital Von Economo – Koskinas atlas compatible with the widely used FreeSurfer software suite. In this technical note we describe the procedures used for manual segmentation of the Von Economo – Koskinas atlas onto individual T1 scans and the subsequent construction of the digital atlas. We provide the files needed to run the atlas on new FreeSurfer data, together with some simple code of how to apply the atlas to T1 scans within the FreeSurfer software suite. The digital Von Economo – Koskinas atlas is easily applicable to modern day anatomical MRI data and is made publicly available online. HIGHLIGHTSIn 1925 Von Economo and Koskinas published their atlas of the human brain.We digitized the 1925 atlas, making the data accessible to the neuroimaging community.The resulting FreeSurfer compatible atlas is made publicly available online.