Imaging human connectomes at the macroscale

Lagged and instantaneous dynamical influences related to brain structural connectivity

Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Indeed, different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2^* signal, providing functional connectivity (FC). Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by three different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; in particular, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C, nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.

Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures

The goal of the Brain Genomics Superstruct Project (GSP) is to enable large-scale exploration of the links between brain function, behavior, and ultimately genetic variation. To provide the broader scientific community data to probe these associations, a repository of structural and functional magnetic resonance imaging (MRI) scans linked to genetic information was constructed from a sample of healthy individuals. The initial release, detailed in the present manuscript, encompasses quality screened cross-sectional data from 1,570 participants ages 18 to 35 years who were scanned with MRI and completed demographic and health questionnaires. Personality and cognitive measures were obtained on a subset of participants. Each dataset contains a T1-weighted structural MRI scan and either one (n=1,570) or two (n=1,139) resting state functional MRI scans. Test-retest reliability datasets are included from 69 participants scanned within six months of their initial visit. For the majority of participants self-report behavioral and cognitive measures are included (n=926 and n=892 respectively). Analyses of data quality, structure, function, personality, and cognition are presented to demonstrate the dataset’s utility.

The influence of construction methodology on structural brain network measures: A review

Structural brain networks based on diffusion MRI and tractography show robust attributes such as small-worldness, hierarchical modularity, and rich-club organization. However, there are large discrepancies in the reports about specific network measures. It is hypothesized that these discrepancies result from the influence of construction methodology. We surveyed the methodological options and their influences on network measures. It is found that most network measures are sensitive to the scale of brain parcellation, MRI gradient schemes and orientation model, and the tractography algorithm, which is in accordance with the theoretical analysis of the small-world network model. Different network weighting schemes represent different attributes of brain networks, which makes these schemes incomparable between studies. Methodology choice depends on the specific study objectives and a clear understanding of the pros and cons of a particular methodology. Because there is no way to eliminate these influences, it seems more practical to quantify them, optimize the methodologies, and construct structural brain networks with multiple spatial resolutions, multiple edge densities, and multiple weighting schemes.

A Sensitive and Specific Neural Signature for Picture-Induced Negative Affect

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes.

By using images to induce negative emotions in human participants, this study uses neuroimaging to develop and validate a distributed brain signature of emotion that can predict the magnitude and type of negative affective experience in new individuals.

Emotions are an important aspect of human experience and behavior; yet, we do not have a clear understanding of how they are processed in the brain. We have identified a neural signature of negative emotion—a neural activation pattern distributed across the brain that accurately predicts how negative a person will feel after viewing an aversive image. This pattern encompasses multiple brain subnetworks in the cortex and subcortex. This neural activation pattern dramatically outperforms other brain indicators of emotion based on activation in individual regions (e.g., amygdala, insula, and anterior cingulate) as well as networks of regions (e.g., limbic and “salience” networks). In addition, no single subnetwork is necessary or sufficient for accurately determining the intensity and type of affective response. Finally, this pattern appears to be specific to picture-induced negative affect, as it did not respond to at least one other aversive experience: painful heat. Together, these results provide a neurophysiological marker for feelings induced by a widely used probe of negative affect and suggest that brain imaging has the potential to accurately uncover how someone is feeling based purely on measures of brain activity.

Unraveling the multiscale structural organization and connectivity of the human brain: the role of diffusion MRI

The structural architecture and the anatomical connectivity of the human brain show different organizational principles at distinct spatial scales. Histological staining and light microscopy techniques have been widely used in classical neuroanatomical studies to unravel brain organization. Using such techniques is a laborious task performed on 2-dimensional histological sections by skilled anatomists possibly aided by semi-automated algorithms. With the recent advent of modern magnetic resonance imaging (MRI) contrast mechanisms, cortical layers and columns can now be reliably identified and their structural properties quantified post-mortem. These developments are allowing the investigation of neuroanatomical features of the brain at a spatial resolution that could be interfaced with that of histology. Diffusion MRI and tractography techniques, in particular, have been used to probe the architecture of both white and gray matter in three dimensions. Combined with mathematical network analysis, these techniques are increasingly influential in the investigation of the macro-, meso-, and microscopic organization of brain connectivity and anatomy, both in vivo and ex vivo. Diffusion MRI-based techniques in combination with histology approaches can therefore support the endeavor of creating multimodal atlases that take into account the different spatial scales or levels on which the brain is organized. The aim of this review is to illustrate and discuss the structural architecture and the anatomical connectivity of the human brain at different spatial scales and how recently developed diffusion MRI techniques can help investigate these.

A novel brain partition highlights the modular skeleton shared by structure and function

Elucidating the intricate relationship between brain structure and function, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Recent progress in neuroimaging has helped advance our understanding of this important issue, with diffusion images providing information about structural connectivity (SC) and functional magnetic resonance imaging shedding light on resting state functional connectivity (rsFC). Here, we adopt a systems approach, relying on modular hierarchical clustering, to study together SC and rsFC datasets gathered independently from healthy human subjects. Our novel approach allows us to find a common skeleton shared by structure and function from which a new, optimal, brain partition can be extracted. We describe the emerging common structure-function modules (SFMs) in detail and compare them with commonly employed anatomical or functional parcellations. Our results underline the strong correspondence between brain structure and resting-state dynamics as well as the emerging coherent organization of the human brain.

Diffusion weighted magnetic resonance imaging and its recent trend-a survey

Since its inception in 1985, diffusion weighted magnetic resonance imaging has been evolving and is becoming instrumental in diagnosis and investigation of tissue functions in various organs including brain, cartilage, and liver. Even though brain related pathology and/or investigation remains as the main application, diffusion weighted magnetic resonance imaging (DWI) is becoming a standard in oncology and in several other applications. This review article provides a brief introduction of diffusion weighted magnetic resonance imaging, challenges involved and recent advancements.

Randomized structural sparsity via constrained block subsampling for improved sensitivity of discriminative voxel identification

In this paper, we consider voxel selection for functional Magnetic Resonance Imaging (fMRI) brain data with the aim of finding a more complete set of probably correlated discriminative voxels, thus improving interpretation of the discovered potential biomarkers. The main difficulty in doing this is an extremely high dimensional voxel space and few training samples, resulting in unreliable feature selection. In order to deal with the difficulty, stability selection has received a great deal of attention lately, especially due to its finite sample control of false discoveries and transparent principle for choosing a proper amount of regularization. However, it fails to make explicit use of the correlation property or structural information of these discriminative features and leads to large false negative rates. In other words, many relevant but probably correlated discriminative voxels are missed. Thus, we propose a new variant on stability selection "randomized structural sparsity", which incorporates the idea of structural sparsity. Numerical experiments demonstrate that our method can be superior in controlling for false negatives while also keeping the control of false positives inherited from stability selection.

Predictive structural dynamic network analysis

BACKGROUND

Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data.

NEW METHOD

Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models.

RESULTS

We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity.

COMPARISON WITH EXISTING METHODS

For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002).

CONCLUSIONS

We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders.

Ontology-based approach for in vivo human connectomics: the medial Brodmann area 6 case study

Different non-invasive neuroimaging modalities and multi-level analysis of human connectomics datasets yield a great amount of heterogeneous data which are hard to integrate into an unified representation. Biomedical ontologies can provide a suitable integrative framework for domain knowledge as well as a tool to facilitate information retrieval, data sharing and data comparisons across scales, modalities and species. Especially, it is urgently needed to fill the gap between neurobiology and in vivo human connectomics in order to better take into account the reality highlighted in Magnetic Resonance Imaging (MRI) and relate it to existing brain knowledge. The aim of this study was to create a neuroanatomical ontology, called “Human Connectomics Ontology” (HCO), in order to represent macroscopic gray matter regions connected with fiber bundles assessed by diffusion tractography and to annotate MRI connectomics datasets acquired in the living human brain. First a neuroanatomical “view” called NEURO-DL-FMA was extracted from the reference ontology Foundational Model of Anatomy (FMA) in order to construct a gross anatomy ontology of the brain. HCO extends NEURO-DL-FMA by introducing entities (such as “MR_Node” and “MR_Route”) and object properties (such as “tracto_connects”) pertaining to MR connectivity. The Web Ontology Language Description Logics (OWL DL) formalism was used in order to enable reasoning with common reasoning engines. Moreover, an experimental work was achieved in order to demonstrate how the HCO could be effectively used to address complex queries concerning in vivo MRI connectomics datasets. Indeed, neuroimaging datasets of five healthy subjects were annotated with terms of the HCO and a multi-level analysis of the connectivity patterns assessed by diffusion tractography of the right medial Brodmann Area 6 was achieved using a set of queries. This approach can facilitate comparison of data across scales, modalities and species.

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data

Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface.

Connectomics and new approaches for analyzing human brain functional connectivity

Estimating the functional interactions between brain regions and mapping those connections to corresponding inter-individual differences in cognitive, behavioral and psychiatric domains are central pursuits for understanding the human connectome. The number and complexity of functional interactions within the connectome and the large amounts of data required to study them position functional connectivity research as a “big data” problem. Maximizing the degree to which knowledge about human brain function can be extracted from the connectome will require developing a new generation of neuroimaging analysis algorithms and tools. This review describes several outstanding problems in brain functional connectomics with the goal of engaging researchers from a broad spectrum of data sciences to help solve these problems. Additionally it provides information about open science resources consisting of raw and preprocessed data to help interested researchers get started.

Age-related changes in intrinsic function of the superior temporal sulcus in autism spectrum disorders

Currently, the developmental trajectories of neural circuits implicated in autism spectrum disorders (ASD) are largely unknown. Here, we specifically focused on age-related changes in the functional circuitry of the posterior superior temporal sulcus (pSTS), a key hub underlying social-cognitive processes known to be impaired in ASD. Using a cross-sectional approach, we analysed resting-state functional magnetic resonance imaging (fMRI) data collected from children, adolescents and adults available through the autism brain imaging data exchange repository [n = 106 with ASD and n = 109 typical controls (TC), ages 7-30 years]. The observed age-related changes of pSTS intrinsic functional connectivity (iFC) suggest that no single developmental pattern characterizes ASD. Instead, pSTS circuitry displayed a complex developmental picture, with some functional circuits showing patterns consistent with atypical development in ASD relative to TC (pSTS-iFC with fusiform gyrus and angular gyrus) and others showing delayed maturation (pSTS-iFC with regions of the action perception network). Distinct developmental trajectories in different functional circuits in ASD likely reflect differential age-related changes in the socio-cognitive processes they underlie. Increasing insight on these mechanisms is a critical step in the development of age-specific interventions in ASD.

Cognitive network neuroscience

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

A review of structural and functional brain networks: small world and atlas

Brain networks can be divided into two categories: structural and functional networks. Many studies of neuroscience have reported that the complex brain networks are characterized by small-world or scale-free properties. The identification of nodes is the key factor in studying the properties of networks on the macro-, micro- or mesoscale in both structural and functional networks. In the study of brain networks, nodes are always determined by atlases. Therefore, the selection of atlases is critical, and appropriate atlases are helpful to combine the analyses of structural and functional networks. Currently, some problems still exist in the establishment or usage of atlases, which are often caused by the segmentation or the parcellation of the brain. We suggest that quantification of brain networks might be affected by the selection of atlases to a large extent. In the process of building atlases, the influences of single subjects and groups should be balanced. In this article, we focused on the effects of atlases on the analysis of brain networks and the improved divisions based on the tractography or connectivity in the parcellation of atlases.

Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders

The study of human brain networks with in vivo neuroimaging has given rise to the field of connectomics, furthered by advances in network science and graph theory informing our understanding of the topology and function of the healthy brain. Here our focus is on the disruption in neuropsychiatric disorders (pathoconnectomics) and how whole-brain computational models can help generate and predict the dynamical interactions and consequences of brain networks over many timescales. We review methods and emerging results that exhibit remarkable accuracy in mapping and predicting both spontaneous and task-based healthy network dynamics. This raises great expectations that whole-brain modeling and computational connectomics may provide an entry point for understanding brain disorders at a causal mechanistic level, and that computational neuropsychiatry can ultimately be leveraged to provide novel, more effective therapeutic interventions, e.g., through drug discovery and new targets for deep brain stimulation.

3D in vitro modeling of the central nervous system

There are currently more than 600 diseases characterized as affecting the central nervous system (CNS) which inflict neural damage. Unfortunately, few of these conditions have effective treatments available. Although significant efforts have been put into developing new therapeutics, drugs which were promising in the developmental phase have high attrition rates in late stage clinical trials. These failures could be circumvented if current 2D in vitro and in vivo models were improved. 3D, tissue-engineered in vitro systems can address this need and enhance clinical translation through two approaches: (1) bottom-up, and (2) top-down (developmental/regenerative) strategies to reproduce the structure and function of human tissues. Critical challenges remain including biomaterials capable of matching the mechanical properties and extracellular matrix (ECM) composition of neural tissues, compartmentalized scaffolds that support heterogeneous tissue architectures reflective of brain organization and structure, and robust functional assays for in vitro tissue validation. The unique design parameters defined by the complex physiology of the CNS for construction and validation of 3D in vitro neural systems are reviewed here.

Neuroanatomic and cognitive abnormalities in attention-deficit/hyperactivity disorder in the era of 'high definition' neuroimaging

The ongoing release of the Human Connectome Project (HCP) data is a watershed event in clinical neuroscience. By attaining a quantum leap in spatial and temporal resolution within the framework of a twin/sibling design, this open science resource provides the basis for delineating brain-behavior relationships across the neuropsychiatric landscape. Here we focus on attention-deficit/hyperactivity disorder (ADHD), which is at least partly continuous across the population, highlighting constructs that have been proposed for ADHD and which are included in the HCP phenotypic battery. We review constructs implicated in ADHD (reward-related processing, inhibition, vigilant attention, reaction time variability, timing and emotional lability) which can be examined in the HCP data and in future 'high definition' clinical datasets.

Probabilistic clustering of the human connectome identifies communities and hubs

A fundamental assumption in neuroscience is that brain function is constrained by its structural properties. This motivates the idea that the brain can be parcellated into functionally coherent regions based on anatomical connectivity patterns that capture how different areas are interconnected. Several studies have successfully implemented this idea in humans using diffusion weighted MRI, allowing parcellation to be conducted in vivo. Two distinct approaches to connectivity-based parcellation can be identified. The first uses the connection profiles of brain regions as a feature vector, and groups brain regions with similar connection profiles together. Alternatively, one may adopt a network perspective that aims to identify clusters of brain regions that show dense within-cluster and sparse between-cluster connectivity. In this paper, we introduce a probabilistic model for connectivity-based parcellation that unifies both approaches. Using the model we are able to obtain a parcellation of the human brain whose clusters may adhere to either interpretation. We find that parts of the connectome consistently cluster as densely connected components, while other parts consistently result in clusters with similar connections. Interestingly, the densely connected components consist predominantly of major cortical areas, while the clusters with similar connection profiles consist of regions that have previously been identified as the 'rich club'; regions known for their integrative role in connectivity. Furthermore, the probabilistic model allows quantification of the uncertainty in cluster assignments. We show that, while most clusters are clearly delineated, some regions are more difficult to assign. These results indicate that care should be taken when interpreting connectivity-based parcellations obtained using alternative deterministic procedures.

A spectrum of sharing: maximization of information content for brain imaging data

Efforts to expand sharing of neuroimaging data have been growing exponentially in recent years. There are several different types of data sharing which can be considered to fall along a spectrum, ranging from simpler and less informative to more complex and more informative. In this paper we consider this spectrum for three domains: data capture, data density, and data analysis. Here the focus is on the right end of the spectrum, that is, how to maximize the information content while addressing the challenges. A summary of associated challenges of and possible solutions is presented in this review and includes: 1) a discussion of tools to monitor quality of data as it is collected and encourage adoption of data mapping standards; 2) sharing of time-series data (not just summary maps or regions); and 3) the use of analytic approaches which maximize sharing potential as much as possible. Examples of existing solutions for each of these points, which we developed in our lab, are also discussed including the use of a comprehensive beginning-to-end neuroinformatics platform and the use of flexible analytic approaches, such as independent component analysis and multivariate classification approaches, such as deep learning.

Unraveling the miswired connectome: a developmental perspective

The vast majority of mental illnesses can be conceptualized as developmental disorders of neural interactions within the connectome, or developmental miswiring. The recent maturation of pediatric in vivo brain imaging is bringing the identification of clinically meaningful brain-based biomarkers of developmental disorders within reach. Even more auspicious is the ability to study the evolving connectome throughout life, beginning in utero, which promises to move the field from topological phenomenology to etiological nosology. Here, we scope advances in pediatric imaging of the brain connectome as the field faces the challenge of unraveling developmental miswiring. We highlight promises while also providing a pragmatic review of the many obstacles ahead that must be overcome to significantly impact public health.

Connectomics in psychiatric research: advances and applications

Psychiatric disorders disturb higher cognitive functions and severely compromise human health. However, the pathophysiological mechanisms underlying psychiatric disorders are very complex, and understanding these mechanisms remains a great challenge. Currently, many psychiatric disorders are hypothesized to reflect "faulty wiring" or aberrant connectivity in the brains. Imaging connectomics is arising as a promising methodological framework for describing the structural and functional connectivity patterns of the human brain. Recently, alterations of brain networks in the connectome have been reported in various psychiatric disorders, and these alterations may provide biomarkers for disease diagnosis and prognosis for the evaluation of treatment efficacy. Here, we summarize the current achievements in both the structural and functional connectomes in several major psychiatric disorders (eg, schizophrenia, attention-deficit/hyperactivity disorder, and autism) based on multi-modal neuroimaging data. We highlight the current progress in the identification of these alterations and the hypotheses concerning the aberrant brain networks in individuals with psychiatric disorders and discuss the research questions that might contribute to a further mechanistic understanding of these disorders from a connectomic perspective.

Neural signatures of autism spectrum disorders: insights into brain network dynamics

Neuroimaging investigations of autism spectrum disorders (ASDs) have advanced our understanding of atypical brain function and structure, and have recently converged on a model of altered network-level connectivity. Traditional task-based functional magnetic resonance imaging (MRI) and volume-based structural MRI studies have identified widespread atypicalities in brain regions involved in social behavior and other core ASD-related behavioral deficits. More recent advances in MR-neuroimaging methods allow for quantification of brain connectivity using diffusion tensor imaging, functional connectivity, and graph theoretic methods. These newer techniques have moved the field toward a systems-level understanding of ASD etiology, integrating functional and structural measures across distal brain regions. Neuroimaging findings in ASD as a whole have been mixed and at times contradictory, likely due to the vast genetic and phenotypic heterogeneity characteristic of the disorder. Future longitudinal studies of brain development will be crucial to yield insights into mechanisms of disease etiology in ASD sub-populations. Advances in neuroimaging methods and large-scale collaborations will also allow for an integrated approach linking neuroimaging, genetics, and phenotypic data.

Structurally robust control of complex networks

Robust control theory has been successfully applied to numerous real-world problems using a small set of devices called controllers. However, the real systems represented by networks contain unreliable components and modern robust control engineering has not addressed the problem of structural changes on complex networks including scale-free topologies. Here, we introduce the concept of structurally robust control of complex networks and provide a concrete example using an algorithmic framework that is widely applied in engineering. The developed analytical tools, computer simulations, and real network analyses lead herein to the discovery that robust control can be achieved in scale-free networks with exactly the same order of controllers required in a standard nonrobust configuration by adjusting only the minimum degree. The presented methodology also addresses the probabilistic failure of links in real systems, such as neural synaptic unreliability in Caenorhabditis elegans, and suggests a new direction to pursue in studies of complex networks in which control theory has a role.

Individual diversity of functional brain network economy

On average, brain network economy represents a trade-off between communication efficiency, robustness, and connection cost, although an analogous understanding on an individual level is largely missing. Evaluating resting-state networks of 42 healthy participants with seven Tesla functional magnetic resonance imaging and graph theory revealed that not even half of all possible connections were common across subjects. The strongest similarities among individuals were observed for interhemispheric and/or short-range connections, which may relate to the essential feature of the human brain to develop specialized systems within each hemisphere. Despite this marked variability in individual network architecture, all subjects exhibited equal small-world properties. Furthermore, interdependency between four major network economy metrics was observed across healthy individuals. The characteristic path length was associated with the clustering coefficient (peak correlation r=0.93), the response to network attacks (r=-0.97), and the physical connection cost in three-dimensional space (r=-0.62). On the other hand, clustering was negatively related to attack response (r=-0.75) and connection cost (r=-0.59). Finally, increased connection cost was associated with better response to attacks (r=0.65). This indicates that functional brain networks with high global information transfer also exhibit strong network resilience. However, it seems that these advantages come at the cost of decreased local communication efficiency and increased physical connection cost. Except for wiring length, the results were replicated on a subsample at three Tesla (n=20). These findings highlight the finely tuned interrelationships between different parameters of brain network economy. Moreover, the understanding of the individual diversity of functional brain network economy may provide further insights in the vulnerability to mental and neurological disorders.

The smarter, the stronger: intelligence level correlates with brain resilience to systematic insults

Neuroimaging evidences posit human intelligence as tightly coupled with several structural and functional brain properties, also suggesting its potential protective role against aging and neurodegenerative conditions. However, whether higher order cognition might in fact lead to a more resilient brain has not been quantitatively demonstrated yet. Here we document a relationship between individual intelligence quotient (IQ) and brain resilience to targeted and random attacks, as measured through resting-state fMRI graph-theoretical analysis in 102 healthy individuals. In this modeling context, enhanced brain robustness to targeted attacks (TA) in individuals with higher IQ is supported by an increased distributed processing capacity despite the systematic loss of the most important node(s) of the system. Moreover, brain resilience in individuals with higher IQ is supported by a set of neocortical regions mainly belonging to language and memory processing network(s), whereas regions related to emotional processing are mostly responsible for lower IQ individuals. Results suggest intelligence level among the predictors of post-lesional or neurodegenerative recovery, also promoting the evolutionary role of higher order cognition, and simultaneously suggesting a new framework for brain stimulation interventions aimed at counteract brain deterioration over time.

Neuroimaging and biomarkers in addiction treatment

Neuroimaging studies have made a significant contribution to the efforts to identify measurable indices, or biomarkers, of addictions and their treatments. Biomarkers in addiction treatment are needed to provide targets for treatment, detect treatment subgroups, predict treatment response, and broadly improve outcomes. Neuroimaging is important to biomarkers research as it relates neural circuits to both molecular mechanisms and behavior. A focus of recent efforts in neuroimaging in addiction has been to elucidate the neural correlates associated with dimensions of functioning in substance-use and related disorders, such as cue-reactivity, impulsivity, and cognitive control, among others. These dimensions of functioning have been related to addiction treatment outcomes and relapse, and therefore, a better understanding of these dimensions and their neural correlates may help to identify brain-behavior biomarkers of treatment response. This paper reviews recent neuroimaging studies that report potential biomarkers in addiction treatment related to cue-reactivity, impulsivity, and cognitive control, as well as recent advances in neuroimaging that may facilitate efforts to determine reliable biomarkers. This important initial work has begun to identify possible mediators and moderators of treatment response, and multiple promising indices are being tested.

Studying brain organization via spontaneous fMRI signal

In recent years, some substantial advances in understanding human (and nonhuman) brain organization have emerged from a relatively unusual approach: the observation of spontaneous activity, and correlated patterns in spontaneous activity, in the "resting" brain. Most commonly, spontaneous neural activity is measured indirectly via fMRI signal in subjects who are lying quietly in the scanner, the so-called "resting state." This Primer introduces the fMRI-based study of spontaneous brain activity, some of the methodological issues active in the field, and some ways in which resting-state fMRI has been used to delineate aspects of area-level and supra-areal brain organization.

Technology-aided assessment of sensorimotor function in early infancy

There is a pressing need for new techniques capable of providing accurate information about sensorimotor function during the first 2 years of childhood. Here, we review current clinical methods and challenges for assessing motor function in early infancy, and discuss the potential benefits of applying technology-assisted methods. We also describe how the use of these tools with neuroimaging, and in particular functional magnetic resonance imaging (fMRI), can shed new light on the intra-cerebral processes underlying neurodevelopmental impairment. This knowledge is of particular relevance in the early infant brain, which has an increased capacity for compensatory neural plasticity. Such tools could bring a wealth of knowledge about the underlying pathophysiological processes of diseases such as cerebral palsy; act as biomarkers to monitor the effects of possible therapeutic interventions; and provide clinicians with much needed early diagnostic information.

Technology-aided assessment of sensorimotor function in early infancy

There is a pressing need for new techniques capable of providing accurate information about sensorimotor function during the first 2 years of childhood. Here, we review current clinical methods and challenges for assessing motor function in early infancy, and discuss the potential benefits of applying technology-assisted methods. We also describe how the use of these tools with neuroimaging, and in particular functional magnetic resonance imaging (fMRI), can shed new light on the intra-cerebral processes underlying neurodevelopmental impairment. This knowledge is of particular relevance in the early infant brain, which has an increased capacity for compensatory neural plasticity. Such tools could bring a wealth of knowledge about the underlying pathophysiological processes of diseases such as cerebral palsy; act as biomarkers to monitor the effects of possible therapeutic interventions; and provide clinicians with much needed early diagnostic information.

Evaluation and statistical inference for human connectomes

Diffusion-weighted imaging coupled with tractography is the only method for in vivo mapping of human white-matter fascicles. Tractography takes diffusion measurements as input and produces a large collection of white-matter fascicles as output; the connectome. We introduce a method to evaluate the evidence supporting connectomes. Linear Fascicle Evaluation (LiFE) takes any connectome as input and predicts diffusion measurements as output, using the difference between the measured and predicted diffusion signals to measure prediction error. Finally, we introduce two metrics that use the prediction error to evaluate the evidence supporting properties of the connectome. One metric compares the mean prediction error between alternative hypotheses, and the second metric compares full distributions of prediction error. We use these metrics to (1) compare tractography algorithms, and (2) test hypotheses about tracts and connections.

A review of structural neuroimaging in schizophrenia: from connectivity to connectomics

In patients with schizophrenia neuroimaging studies have revealed global differences with some brain regions showing focal abnormalities. Examining neurocircuitry, diffusion-weighted imaging studies have identified altered structural integrity of white matter in frontal and temporal brain regions and tracts such as the cingulum bundles, uncinate fasciculi, internal capsules and corpus callosum associated with the illness. Furthermore, structural co-variance analyses have revealed altered structural relationships among regional morphology in the thalamus, frontal, temporal and parietal cortices in schizophrenia patients. The distributed nature of these abnormalities in schizophrenia suggests that multiple brain circuits are impaired, a neural feature that may be better addressed with network level analyses. However, even with the advent of these newer analyses, a large amount of variability in findings remains, likely partially due to the considerable heterogeneity present in this disorder.

Advanced diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical studies: A review

Diffusion MRI enabled in vivo microstructural imaging of the fiber tracts in the brain resulting in its application in a wide range of settings, including in neurological and neurosurgical disorders. Conventional approaches such as diffusion tensor imaging (DTI) have been shown to have limited applications due to the crossing fiber problem and the susceptibility of their quantitative indices to partial volume effects. To overcome these limitations, the recent focus has shifted to the advanced acquisition methods and their related analytical approaches. Advanced white matter imaging techniques provide superior qualitative data in terms of demonstration of multiple crossing fibers in their spatial orientation in a three dimensional manner in the brain. In this review paper, we discuss the advancements in diffusion MRI and introduce their roles. Using examples, we demonstrate the role of advanced diffusion MRI-based fiber tracking in neuroanatomical studies. Results from its preliminary application in the evaluation of intracranial space occupying lesions, including with respect to future directions for prognostication, are also presented. Building upon the previous DTI studies assessing white matter disease in Huntington's disease and Amyotrophic lateral sclerosis; we also discuss approaches which have led to encouraging preliminary results towards developing an imaging biomarker for these conditions.

The hubs of the human connectome are generally implicated in the anatomy of brain disorders

Brain networks or 'connectomes' include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly. Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers (n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome (P < 10(-4), permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs (P < 0.05, permutation test), including schizophrenia and Alzheimer's disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer's disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders.

Sex differences in the relationship between white matter connectivity and creativity

Creative cognition emerges from a complex network of interacting brain regions. This study investigated the relationship between the structural organization of the human brain and aspects of creative cognition tapped by divergent thinking tasks. Diffusion weighted imaging (DWI) was used to obtain fiber tracts from 83 segmented cortical regions. This information was represented as a network and metrics of connectivity organization, including connectivity strength, clustering and communication efficiency were computed, and their relationship to individual levels of creativity was examined. Permutation testing identified significant sex differences in the relationship between global connectivity and creativity as measured by divergent thinking tests. Females demonstrated significant inverse relationships between global connectivity and creative cognition, whereas there were no significant relationships observed in males. Node specific analyses revealed inverse relationships across measures of connectivity, efficiency, clustering and creative cognition in widespread regions in females. Our findings suggest that females involve more regions of the brain in processing to produce novel ideas to solutions, perhaps at the expense of efficiency (greater path lengths). Males, in contrast, exhibited few, relatively weak positive relationships across these measures. Extending recent observations of sex differences in connectome structure, our findings of sexually dimorphic relationships suggest a unique topological organization of connectivity underlying the generation of novel ideas in males and females.

Understanding Structural-Functional Relationships in the Human Brain

Relating the brain’s structural connectivity (SC) to its functional connectivity (FC) is a fundamental goal in neuroscience because it is capable of aiding our understanding of how the relatively fixed SC architecture underlies human cognition and diverse behaviors. With the aid of current noninvasive imaging technologies (e.g., structural MRI, diffusion MRI, and functional MRI) and graph theory methods, researchers have modeled the human brain as a complex network of interacting neuronal elements and characterized the underlying structural and functional connectivity patterns that support diverse cognitive functions. Specifically, research has demonstrated a tight SC-FC coupling, not only in interregional connectivity strength but also in network topologic organizations, such as community, rich-club, and motifs. Moreover, this SC-FC coupling exhibits significant changes in normal development and neuropsychiatric disorders, such as schizophrenia and epilepsy. This review summarizes recent progress regarding the SC-FC relationship of the human brain and emphasizes the important role of large-scale brain networks in the understanding of structural-functional associations. Future research directions related to this topic are also proposed.

Brain network analysis

Following the recent advances in neuroimaging technology, the research on brain network analysis becomes an emerging area in data mining community. Brain network data pose many unique challenges for data mining research. For example, in brain networks, the nodes (i.e., the brain regions) and edges (i.e., relationships between brain regions) are usually not given, but should be derived from the neuroimaging data. The network structure can be very noisy and uncertain. Therefore, innovative methods are required for brain network analysis. Many research efforts have been devoted to this area. They have achieved great success in various applications, such as brain network extraction, graph mining, neuroimaging data analysis. In this paper, we review some recent data mining methods which are used in the literature for mining brain network data.

Psychiatric disorders: diagnosis to therapy

Recent findings in a range of scientific disciplines are challenging the conventional wisdom regarding the etiology, classification, and treatment of psychiatric disorders. This Review focuses on the current state of the psychiatric diagnostic nosology and recent progress in three areas: genomics, neuroimaging, and therapeutics development. The accelerating pace of novel and unexpected findings is transforming the understanding of mental illness and represents a hopeful sign that the approaches and models that have sustained the field for the past 40 years are yielding to a flood of new data and presaging the emergence of a new and more powerful scientific paradigm.

Common intrinsic connectivity states among posteromedial cortex subdivisions: Insights from analysis of temporal dynamics

Perspectives of human brain functional connectivity continue to evolve. Static representations of functional interactions between brain regions are rapidly giving way to dynamic perspectives, which emphasize non-random temporal variations in intrinsic functional connectivity (iFC) patterns. Here, we bring this dynamic perspective to our understanding of iFC patterns for posteromedial cortex (PMC), a cortical hub known for its functional diversity. Previous work has consistently differentiated iFC patterns among PMC subregions, though assumed static iFC over time. Here, we assessed iFC as a function of time utilizing a sliding-window correlation approach, and applied hierarchical clustering to detect representative iFC states from the windowed iFC. Across subregions, five iFC states were detected over time. Although with differing frequencies, each subregion was associated with each of the states, suggesting that these iFC states are "common" to PMC subregions. Importantly, each subregion possessed a unique preferred state(s) and distinct transition patterns, explaining previously observed iFC differentiations. These results resonate with task-based fMRI studies suggesting that large-scale functional networks can be flexibly reconfigured in response to changing task-demands. Additionally, we used retest scans (~1week later) to demonstrate the reproducibility of the iFC states identified, and establish moderate to high test-retest reliability for various metrics used to quantify switching behaviors. We also demonstrate the ability of dynamic properties in the visual PMC subregion to index inter-individual differences in a measure of concept formation and mental flexibility. These findings suggest functional relevance of dynamic iFC and its potential utility in biomarker identification over time, as d-iFC methodologies are refined and mature.

A multivariate distance-based analytic framework for connectome-wide association studies

The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-DOPA pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome.

The connectomics challenge

One of the most fascinating challenges in neuroscience is the reconstruction of the connectivity map of the brain. Recent years have seen a rapid expansion in the field of connectomics, whose aim is to trace this map and understand its relationship with neural computation. Many different approaches, ranging from electron and optical microscopy to magnetic resonance imaging, have been proposed to address the connectomics challenge on various spatial scales and in different species. Here, we review the main technological advances in the microscopy techniques applied to connectomics, highlighting the potential and limitations of the different methods. Finally, we briefly discuss the role of connectomics in the Human Brain Project, the Future and Emerging Technologies (FET) Flagship recently approved by the European Commission.

Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective

Resting-state functional magnetic resonance imaging (RFMRI) enables researchers to monitor fluctuations in the spontaneous brain activities of thousands of regions in the human brain simultaneously, representing a popular tool for macro-scale functional connectomics to characterize normal brain function, mind-brain associations, and the various disorders. However, the test-retest reliability of RFMRI remains largely unknown. We review previously published papers on the test-retest reliability of voxel-wise metrics and conduct a meta-summary reliability analysis of seven common brain networks. This analysis revealed that the heteromodal associative (default, control, and attention) networks were mostly reliable across the seven networks. Regarding examined metrics, independent component analysis with dual regression, local functional homogeneity and functional homotopic connectivity were the three mostly reliable RFMRI metrics. These observations can guide the use of reliable metrics and further improvement of test-retest reliability for other metics in functional connectomics. We discuss the main issues with low reliability related to sub-optimal design and the choice of data processing options. Future research should use large-sample test-retest data to rectify both the within-subject and between-subject variability of RFMRI measurements and accelerate the application of functional connectomics.

Network dysfunction in Alzheimer's disease and frontotemporal dementia: implications for psychiatry

Structural and functional connectivity methods are changing how researchers conceptualize and explore neuropsychiatric disease. Here, we summarize emerging evidence of large-scale network dysfunction in Alzheimer's disease and behavioral variant frontotemporal dementia, focusing on the divergent impact these disorders have on the default mode network and the salience network. We update a working model for understanding the functions of these networks within a broader anatomical context and highlight the relevance of this model for understanding psychiatric illness. Finally, we look ahead to persistent challenges in the application of network-based imaging methods to patients with Alzheimer's disease, behavioral variant frontotemporal dementia, and other neuropsychiatric conditions. Recent advances and persistent needs are discussed, with an eye toward anticipating the hurdles that must be overcome for a network-based framework to clarify the biology of psychiatric illness and aid in the drug discovery process.