Groupwise whole-brain parcellation from resting-state fMRI data for network node identification

Group-wise parcellation of the cortex through multi-scale spectral clustering

The delineation of functionally and structurally distinct regions as well as their connectivity can provide key knowledge towards understanding the brain's behaviour and function. Cytoarchitecture has long been the gold standard for such parcellation tasks, but has poor scalability and cannot be mapped in vivo. Functional and diffusion magnetic resonance imaging allow in vivo mapping of brain's connectivity and the parcellation of the brain based on local connectivity information. Several methods have been developed for single subject connectivity driven parcellation, but very few have tackled the task of group-wise parcellation, which is essential for uncovering group specific behaviours. In this paper, we propose a group-wise connectivity-driven parcellation method based on spectral clustering that captures local connectivity information at multiple scales and directly enforces correspondences between subjects. The method is applied to diffusion Magnetic Resonance Imaging driven parcellation on two independent groups of 50 subjects from the Human Connectome Project. Promising quantitative and qualitative results in terms of information loss, modality comparisons, group consistency and inter-group similarities demonstrate the potential of the method.

Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering

The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC). SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem. The dictionary is comprised of an over-complete set of time course signals, with which a sparse representation of rs-fMRI signals can be constructed. Cerebellar functional regions were then identified using k-means clustering based on the sparse representation of rs-fMRI signals. We solved SDLC using a multi-block hybrid proximal alternating method that guarantees strong convergence. We evaluated the reliability of SDLC and benchmarked its classification accuracy against other clustering techniques using simulated data. We then demonstrated that SDLC can identify biologically reasonable functional regions of the cerebellum as estimated by their cerebello-cortical functional connectivity. We further provided new insights into the cerebello-cortical functional organization in children.

Change in brain network topology as a function of treatment response in schizophrenia: a longitudinal resting-state fMRI study using graph theory

A number of neuroimaging studies have provided evidence in support of the hypothesis that faulty interactions between spatially disparate brain regions underlie the pathophysiology of schizophrenia, but it remains unclear to what degree antipsychotic medications affect these. We hypothesized that the balance between functional integration and segregation of brain networks is impaired in unmedicated patients with schizophrenia, but that it can be partially restored by antipsychotic medications. We included 32 unmedicated patients with schizophrenia (SZ) and 32 matched healthy controls (HC) in this study. We obtained resting-state scans while unmedicated, and again after 6 weeks of treatment with risperidone to assess functional integration and functional segregation of brain networks using graph theoretical measures. Compared with HC, unmedicated SZ showed reduced global efficiency and increased clustering coefficients. This pattern of aberrant functional network integration and segregation was modulated with antipsychotic medications, but only in those who responded to treatment. Our work lends support to the concept of schizophrenia as a dysconnectivity syndrome, and suggests that faulty brain network topology in schizophrenia is modulated by antipsychotic medication as a function of treatment response.

Group-wise consistent cortical parcellation based on connectional profiles

For decades, seeking common, consistent and corresponding anatomical/functional regions across individual brains via cortical parcellation has been a longstanding challenging problem. In our opinion, two major barriers to solve this problem are determining meaningful cortical boundaries that segregate homogeneous regions and establishing correspondences among parcellated regions of multiple brains. To establish a corresponding system across subjects, we recently developed the Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system which possesses group-wise consistent white matter fiber connection patterns across individuals and thus provides a dense map of corresponding cortical landmarks. Despite this useful property, however, the DICCCOL landmarks are still far from covering the whole cerebral cortex and do not provide clear structural/functional cortical boundaries. To address the above limitation while leveraging the advantage of DICCCOL, in this paper, we present a novel approach for group-wise consistent parcellation of the cerebral cortex via a hierarchical scheme. In each hierarchical level, DICCCOLs are used as corresponding samples to automatically determine the cluster number so that other cortical surface vertices are iteratively classified into corresponding clusters across subjects within a group-wise classification framework. Experimental results showed that this approach can achieve consistent fine-granularity cortical parcellation with intrinsically-established structural correspondences across individual brains. Besides, comparisons with resting-state and task-based fMRI datasets demonstrated that the group-wise parcellation boundaries segregate functionally homogeneous areas.

Spin-Echo Resting-State Functional Connectivity in High-Susceptibility Regions: Accuracy, Reliability, and the Impact of Physiological Noise

Gradient-echo (GE) echo-planar imaging (EPI) is the method of choice in blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) studies, as it demonstrates substantially higher BOLD sensitivity than its spin-echo (SE) counterpart. However, it is also well known that the GE-EPI signal is prone to signal dropouts and shifts due to susceptibility effects near air-tissue interfaces. SE-EPI, in contrast, is minimally affected by these artifacts. In this study, we quantify, for the first time, the sensitivity and specificity of SE and GE EPI for resting-state fMRI functional connectivity (fcMRI) mapping, using the 1000-brain fcMRI atlas (Yeo et al., 2011 ) as the pseudoground truth. Moreover, we assess the influence of physiological processes on resting-state BOLD measured using both regular and ultrafast GE and SE acquisitions. Our work demonstrates that SE-EPI and GE-EPI are associated with similar sensitivities, specificities, and intersubject reproducibility in fcMRI for most brain networks, generated using both seed-based analysis and independent component analysis. More importantly, SE-based fcMRI measurements demonstrated significantly higher sensitivity, specificity, and intersubject reproducibility in high-susceptibility regions, spanning the limbic and frontal networks in the 1000-brain atlas. In addition, SE-EPI is significantly less sensitive to prominent sources of physiological noise, including low-frequency respiratory volume and heart rate variations. Our work suggests that SE-EPI should be increasingly adopted in the study of networks spanning susceptibility-affected brain regions, including those that are important to memory, language, and emotion.

Groupwise connectivity-based parcellation of the whole human cortical surface using watershed-driven dimension reduction

Segregating the human cortex into distinct areas based on structural connectivity criteria is of widespread interest in neuroscience. This paper presents a groupwise connectivity-based parcellation framework for the whole cortical surface using a new high quality diffusion dataset of 79 healthy subjects. Our approach performs gyrus by gyrus to parcellate the whole human cortex. The main originality of the method is to compress for each gyrus the connectivity profiles used for the clustering without any anatomical prior information. This step takes into account the interindividual cortical and connectivity variability. To this end, we consider intersubject high density connectivity areas extracted using a surface-based watershed algorithm. A wide validation study has led to a fully automatic pipeline which is robust to variations in data preprocessing (tracking type, cortical mesh characteristics and boundaries of initial gyri), data characteristics (including number of subjects), and the main algorithmic parameters. A remarkable reproducibility is achieved in parcellation results for the whole cortex, leading to clear and stable cortical patterns. This reproducibility has been tested across non-overlapping subgroups and the validation is presented mainly on the pre- and postcentral gyri.

The modular and integrative functional architecture of the human brain

Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules' processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an author-topic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network's modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules' functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in between-module communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain's modular yet integrated implementation of cognitive functions.

Parcellating cortical functional networks in individuals

The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.

A neuromarker of sustained attention from whole-brain functional connectivity

Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention--symptoms of attention deficit hyperactivity disorder--from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity

Functional magnetic resonance imaging (fMRI) studies typically collapse data from many subjects, but brain functional organization varies between individuals. Here we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a 'fingerprint' that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual's connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence: the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects on the basis of functional connectivity fMRI.

A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data

The growth of functional MRI has led to development of human brain atlases derived by parcellating resting-state connectivity patterns into functionally independent regions of interest (ROIs). All functional atlases to date have been derived from resting-state fMRI data. But given that functional connectivity between regions varies with task, we hypothesized that an atlas incorporating both resting-state and task-based fMRI data would produce an atlas with finer characterization of task-relevant regions than an atlas derived from resting-state alone. To test this hypothesis, we derived parcellation atlases from twenty-nine healthy adult participants enrolled in the Cognitive Connectome project, an initiative to improve functional MRI's translation into clinical decision-making by mapping normative variance in brain-behavior relationships. Participants underwent resting-state and task-based fMRI spanning nine cognitive domains: motor, visuospatial, attention, language, memory, affective processing, decision-making, working memory, and executive function. Spatially constrained n-cut parcellation derived brain atlases using (1) all participants' functional data (Task) or (2) a single resting-state scan (Rest). An atlas was also derived from random parcellation for comparison purposes (Random). Two methods were compared: (1) a parcellation applied to the group's mean edge weights (mean), and (2) a two-stage approach with parcellation of individual edge weights followed by parcellation of mean binarized edges (two-stage). The resulting Task and Rest atlases had significantly greater similarity with each other (mean Jaccard indices JI=0.72-0.85) than with the Random atlases (JI=0.59-0.63; all p<0.001 after Bonferroni correction). Task and Rest atlas similarity was greatest for the two-stage method (JI=0.85), which has been shown as more robust than the mean method; these atlases also better reproduced voxelwise seed maps of the left dorsolateral prefrontal cortex during rest and performing the n-back working memory task (r=0.75-0.80) than the Random atlases (r=0.64-0.72), further validating their utility. We expected regions governing higher-order cognition (such as frontal and anterior temporal lobes) to show greatest difference between Task and Rest atlases; contrary to expectations, these areas had greatest similarity between atlases. Our findings indicate that atlases derived from parcellation of task-based and resting-state fMRI data are highly comparable, and existing resting-state atlases are suitable for task-based analyses. We introduce an anatomically labeled fMRI-derived whole-brain human atlas for future Cognitive Connectome analyses.

Multimodal description of whole brain connectivity: A comparison of resting state MEG, fMRI, and DWI

Structural and functional connectivity (SC and FC) have received much attention over the last decade, as they offer unique insight into the coordination of brain functioning. They are often assessed independently with three imaging modalities: SC using diffusion‐weighted imaging (DWI), FC using functional magnetic resonance imaging (fMRI), and magnetoencephalography/electroencephalography (MEG/EEG). DWI provides information about white matter organization, allowing the reconstruction of fiber bundles. fMRI uses blood‐oxygenation level‐dependent (BOLD) contrast to indirectly map neuronal activation. MEG and EEG are direct measures of neuronal activity, as they are sensitive to the synchronous inputs in pyramidal neurons. Seminal studies have targeted either the electrophysiological substrate of BOLD or the anatomical basis of FC. However, multimodal comparisons have been scarcely performed, and the relation between SC, fMRI‐FC, and MEG‐FC is still unclear. Here we present a systematic comparison of SC, resting state fMRI‐FC, and MEG‐FC between cortical regions, by evaluating their similarities at three different scales: global network, node, and hub distribution. We obtained strong similarities between the three modalities, especially for the following pairwise combinations: SC and fMRI‐FC; SC and MEG‐FC at theta, alpha, beta and gamma bands; and fMRI‐FC and MEG‐FC in alpha and beta. Furthermore, highest node similarity was found for regions of the default mode network and primary motor cortex, which also presented the highest hubness score. Distance was partially responsible for these similarities since it biased all three connectivity estimates, but not the unique contributor, since similarities remained after controlling for distance. Hum Brain Mapp 37:20–34, 2016. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Nodal centrality of functional network in the differentiation of schizophrenia

A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy.

Connectivity-based parcellation: Critique and implications

Regional specialization and functional integration are often viewed as two fundamental principles of human brain organization. They are closely intertwined because each functionally specialized brain region is probably characterized by a distinct set of long-range connections. This notion has prompted the quickly developing family of connectivity-based parcellation (CBP) methods in neuroimaging research. CBP assumes that there is a latent structure of parcels in a region of interest (ROI). First, connectivity strengths are computed to other parts of the brain for each voxel/vertex within the ROI. These features are then used to identify functionally distinct groups of ROI voxels/vertices. CBP enjoys increasing popularity for the in-vivo mapping of regional specialization in the human brain. Due to the requirements of different applications and datasets, CBP has diverged into a heterogeneous family of methods. This broad overview critically discusses the current state as well as the commonalities and idiosyncrasies of the main CBP methods. We target frequent concerns faced by novices and veterans to provide a reference for the investigation and review of CBP studies.

Preterm birth alters neonatal, functional rich club organization

Alterations in neural networks are associated with the cognitive difficulties of the prematurely born. Using functional magnetic resonance imaging, we analyzed functional connectivity for preterm (PT) and term neonates at term equivalent age. Specifically, we constructed whole-brain networks and examined rich club (RC) organization, a common construct among complex systems where important (or "rich") nodes connect preferentially to other important nodes. Both PT and term neonates showed RC organization with PT neonates exhibiting significantly reduced connections between these RC nodes. Additionally, PT neonates showed evidence of weaker functional segregation. Our results suggest that PT birth is associated with fundamental changes of functional organization in the developing brain.

Integrated Analysis and Visualization of Group Differences in Structural and Functional Brain Connectivity: Applications in Typical Ageing and Schizophrenia

Structural and functional brain connectivity are increasingly used to identify and analyze group differences in studies of brain disease. This study presents methods to analyze uni- and bi-modal brain connectivity and evaluate their ability to identify differences. Novel visualizations of significantly different connections comparing multiple metrics are presented. On the global level, “bi-modal comparison plots” show the distribution of uni- and bi-modal group differences and the relationship between structure and function. Differences between brain lobes are visualized using “worm plots”. Group differences in connections are examined with an existing visualization, the “connectogram”. These visualizations were evaluated in two proof-of-concept studies: (1) middle-aged versus elderly subjects; and (2) patients with schizophrenia versus controls. Each included two measures derived from diffusion weighted images and two from functional magnetic resonance images. The structural measures were minimum cost path between two anatomical regions according to the “Statistical Analysis of Minimum cost path based Structural Connectivity” method and the average fractional anisotropy along the fiber. The functional measures were Pearson’s correlation and partial correlation of mean regional time series. The relationship between structure and function was similar in both studies. Uni-modal group differences varied greatly between connectivity types. Group differences were identified in both studies globally, within brain lobes and between regions. In the aging study, minimum cost path was highly effective in identifying group differences on all levels; fractional anisotropy and mean correlation showed smaller differences on the brain lobe and regional levels. In the schizophrenia study, minimum cost path and fractional anisotropy showed differences on the global level and within brain lobes; mean correlation showed small differences on the lobe level. Only fractional anisotropy and mean correlation showed regional differences. The presented visualizations were helpful in comparing and evaluating connectivity measures on multiple levels in both studies.

Influence of ROI selection on resting state functional connectivity: an individualized approach for resting state fMRI analysis

The differences in how our brain is connected are often thought to reflect the differences in our individual personalities and cognitive abilities. Individual differences in brain connectivity has long been recognized in the neuroscience community however it has yet to manifest itself in the methodology of resting state analysis. This is evident as previous studies use the same region of interest (ROIs) for all subjects. In this paper we demonstrate that the use of ROIs which are standardized across individuals leads to inaccurate calculations of functional connectivity. We also show that this problem can be addressed by taking an individualized approach by using subject-specific ROIs. Finally we show that ROI selection can affect the way we interpret our data by showing different changes in functional connectivity with aging.

The (in)stability of functional brain network measures across thresholds

The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous measure of correlations in temporal signal between brain regions. Thresholding is a necessary step to use binary graphs derived from functional connectivity data. However, there is no current consensus on what threshold to use, and network measures and group contrasts may be unstable across thresholds. Nevertheless, whole-brain network analyses are being applied widely with findings typically reported at an arbitrary threshold or range of thresholds. This study sought to evaluate the stability of network measures across thresholds in a large resting state functional connectivity dataset. Network measures were evaluated across absolute (correlation-based) and proportional (sparsity-based) thresholds, and compared between sex and age groups. Overall, network measures were found to be unstable across absolute thresholds. For example, the direction of group differences in a given network measure may change depending on the threshold. Network measures were found to be more stable across proportional thresholds. These results demonstrate that caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.

Increased segregation of brain networks in focal epilepsy: An fMRI graph theory finding

Focal epilepsy is conceived of as activating local areas of the brain as well as engaging regional brain networks. Graph theory represents a powerful quantitative framework for investigation of brain networks. Here we investigate whether functional network changes are present in extratemporal focal epilepsy.

Task-free functional magnetic resonance imaging data from 15 subjects with extratemporal epilepsy and 26 age and gender matched healthy controls were used for analysis. Local network properties were calculated using local efficiency, clustering coefficient and modularity metrics. Global network properties were assessed with global efficiency and betweenness centrality metrics. Cost-efficiency of the networks at both local and global levels was evaluated by estimating the physical distance between functionally connected nodes, in addition to the overall numbers of connections in the network.

Clustering coefficient, local efficiency and modularity were significantly higher in individuals with focal epilepsy than healthy control subjects, while global efficiency and betweenness centrality were not significantly different between the two groups. Local network properties were also highly efficient, at low cost, in focal epilepsy subjects compared to healthy controls.

Our results show that functional networks in focal epilepsy are altered in a way that the nodes of the network are more isolated. We postulate that network regularity, or segregation of the nodes of the networks, may be an adaptation that inhibits the conversion of the interictal state to seizures. It remains possible that this may be part of the epileptogenic process or an effect of medications.

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Focal epilepsy is a neurological disorder known to affect neural networks.

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We examine fMRI network changes during rest in extratemporal epilepsy patients.

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Extratemporal epilepsy subjects show increased (isolated) local network regularity.

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Network isolation may represent a ‘seizure-protection’ mechanism in focal epilepsy.

Direct imaging of functional networks

In blood-oxygenation-level-dependent functional magnetic resonance imaging (fMRI), current methods typically acquire ∼ 500,000 imaging voxels at each time point, and then use computer algorithms to reduce this data to the coefficients of a few hundred parcels or networks. This suggests that the amount of relevant information present in the fMRI signal is relatively small, and presents an opportunity to greatly improve the speed and signal to noise ratio (SNR) of the fMRI process. In this work, a theoretical framework is presented for calculating the coefficients of functional networks directly from highly undersampled fMRI data. Using predefined functional parcellations or networks and a compact k-space trajectory that samples data at optimal spatial scales, the problem of estimating network coefficients is reformulated to allow for direct least squares estimation, without Fourier encoding. By simulation, this approach is shown to allow for acceleration of the imaging process under ideal circumstances by nearly three orders of magnitude.

Development and validation of consensus clustering-based framework for brain segmentation using resting fMRI

BACKGROUND

Clustering methods are increasingly employed to segment brain regions into functional subdivisions using resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods are highly sensitive to the (i) precise algorithms employed, (ii) their initializations, and (iii) metrics used for uncovering the optimal number of clusters from the data.

NEW METHOD

To address these issues, we develop a novel consensus clustering evidence accumulation (CC-EAC) framework, which effectively combines multiple clustering methods for segmenting brain regions using rs-fMRI data. Using extensive computer simulations, we examine the performance of widely used clustering algorithms including K-means, hierarchical, and spectral clustering as well as their combinations. We also examine the accuracy and validity of five objective criteria for determining the optimal number of clusters: mutual information, variation of information, modified silhouette, Rand index, and probabilistic Rand index.

RESULTS

A CC-EAC framework with a combination of base K-means clustering (KC) and hierarchical clustering (HC) with probabilistic Rand index as the criterion for choosing the optimal number of clusters, accurately uncovered the correct number of clusters from simulated datasets. In experimental rs-fMRI data, these methods reliably detected functional subdivisions of the supplementary motor area, insula, intraparietal sulcus, angular gyrus, and striatum.

COMPARISON WITH EXISTING METHODS

Unlike conventional approaches, CC-EAC can accurately determine the optimal number of stable clusters in rs-fMRI data, and is robust to initialization and choice of free parameters.

CONCLUSIONS

A novel CC-EAC framework is proposed for segmenting brain regions, by effectively combining multiple clustering methods and identifying optimal stable functional clusters in rs-fMRI data.

Joint Spectral Decomposition for the Parcellation of the Human Cerebral Cortex Using Resting-State fMRI

Identification of functional connections within the human brain has gained a lot of attention due to its potential to reveal neural mechanisms. In a whole-brain connectivity analysis, a critical stage is the computation of a set of network nodes that can effectively represent cortical regions. To address this problem, we present a robust cerebral cortex parcellation method based on spectral graph theory and resting-state fMRI correlations that generates reliable parcellations at the single-subject level and across multiple subjects. Our method models the cortical surface in each hemisphere as a mesh graph represented in the spectral domain with its eigenvectors. We connect cortices of different subjects with each other based on the similarity of their connectivity profiles and construct a multi-layer graph, which effectively captures the fundamental properties of the whole group as well as preserves individual subject characteristics. Spectral decomposition of this joint graph is used to cluster each cortical vertex into a subregion in order to obtain whole-brain parcellations. Using rs-fMRI data collected from 40 healthy subjects, we show that our proposed algorithm computes highly reproducible parcellations across different groups of subjects and at varying levels of detail with an average Dice score of 0.78, achieving up to 9% better reproducibility compared to existing approaches. We also report that our group-wise parcellations are functionally more consistent, thus, can be reliably used to represent the population in network analyses.

GraSP: geodesic Graph-based Segmentation with Shape Priors for the functional parcellation of the cortex

Resting-state functional MRI is a powerful technique for mapping the functional organization of the human brain. However, for many types of connectivity analysis, high-resolution voxelwise analyses are computationally infeasible and dimensionality reduction is typically used to limit the number of network nodes. Most commonly, network nodes are defined using standard anatomic atlases that do not align well with functional neuroanatomy or regions of interest covering a small portion of the cortex. Data-driven parcellation methods seek to overcome such limitations, but existing approaches are highly dependent on initialization procedures and produce spatially fragmented parcels or overly isotropic parcels that are unlikely to be biologically grounded. In this paper, we propose a novel graph-based parcellation method that relies on a discrete Markov Random Field framework. The spatial connectedness of the parcels is explicitly enforced by shape priors. The shape of the parcels is adapted to underlying data through the use of functional geodesic distances. Our method is initialization-free and rapidly segments the cortex in a single optimization. The performance of the method was assessed using a large developmental cohort of more than 850 subjects. Compared to two prevalent parcellation methods, our approach provides superior reproducibility for a similar data fit. Furthermore, compared to other methods, it avoids incoherent parcels. Finally, the method's utility is demonstrated through its ability to detect strong brain developmental effects that are only weakly observed using other methods.

Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations

The cortical surface is organized into a large number of cortical areas; however, these areas have not been comprehensively mapped in the human. Abrupt transitions in resting-state functional connectivity (RSFC) patterns can noninvasively identify locations of putative borders between cortical areas (RSFC-boundary mapping; Cohen et al. 2008). Here we describe a technique for using RSFC-boundary maps to define parcels that represent putative cortical areas. These parcels had highly homogenous RSFC patterns, indicating that they contained one unique RSFC signal; furthermore, the parcels were much more homogenous than a null model matched for parcel size when tested in two separate datasets. Several alternative parcellation schemes were tested this way, and no other parcellation was as homogenous as or had as large a difference compared with its null model. The boundary map-derived parcellation contained parcels that overlapped with architectonic mapping of areas 17, 2, 3, and 4. These parcels had a network structure similar to the known network structure of the brain, and their connectivity patterns were reliable across individual subjects. These observations suggest that RSFC-boundary map-derived parcels provide information about the location and extent of human cortical areas. A parcellation generated using this method is available at http://www.nil.wustl.edu/labs/petersen/Resources.html.

Spatial and temporal functional connectivity changes between resting and attentive states

Remote brain regions show correlated spontaneous activity at rest within well described intrinsic connectivity networks (ICNs). Meta-analytic coactivation studies have uncovered networks similar to resting ICNs, suggesting that in task states connectivity modulations may occur principally within ICNs. However, it has also been suggested that specific "hub" regions dynamically link networks under different task conditions. Here, we used functional magnetic resonance imaging at rest and a continuous visual attention task in 16 participants to investigate whether a shift from rest to attention was reflected by within-network connectivity modulation, or changes in network topography. Our analyses revealed evidence for both modulation of connectivity within the default-mode (DMN) and dorsal attention networks (DAN) between conditions, and identified a set of regions including the temporoparietal junction (TPJ) and posterior middle frontal gyrus (MFG) that switched between the DMN and DAN depending on the task. We further investigated the temporal nonstationarity of flexible (TPJ and MFG) regions during both attention and rest. This showed that moment-to-moment differences in connectivity at rest mirrored the variation in connectivity between tasks. Task-dependent changes in functional connectivity of flexible regions may, therefore, be understood as shifts in the proportion of time specific connections are engaged, rather than a switch between networks per se. This ability of specific regions to dynamically link ICNs under different task conditions may play an important role in behavioral flexibility.

Connectomic profiles for individualized resting state networks and regions of interest

Functional connectivity analysis of human brain resting state functional magnetic resonance imaging (rsfMRI) data and resultant functional networks, or RSNs, have drawn increasing interest in both research and clinical applications. A fundamental yet challenging problem is to identify distinct functional regions or regions of interest (ROIs) that have accurate functional correspondence across subjects. This article presents an algorithmic framework to identify ROIs of common RSNs at the individual level. It first employed a dual-sparsity dictionary learning algorithm to extract group connectomic profiles of ROIs and RSNs from noisy and high-dimensional fMRI data, with special attention to the well-known inter-subject variability in anatomy and then identified the ROIs of a given individual by employing both anatomic and group connectomic profile constraints using an energy minimization approach. Applications of this framework demonstrated that it can identify individualized ROIs of RSNs with superior performance over commonly used registration methods in terms of functional correspondence, and a test-retest study revealed that the framework is robust and consistent across both short-interval and long-interval repeated sessions of the same population. These results indicate that our framework can provide accurate substrates for individualized rsfMRI analysis.

Disruption of functional networks in dyslexia: a whole-brain, data-driven analysis of connectivity

BACKGROUND

Functional connectivity analyses of functional magnetic resonance imaging data are a powerful tool for characterizing brain networks and how they are disrupted in neural disorders. However, many such analyses examine only one or a small number of a priori seed regions. Studies that consider the whole brain frequently rely on anatomic atlases to define network nodes, which might result in mixing distinct activation time-courses within a single node. Here, we improve upon previous methods by using a data-driven brain parcellation to compare connectivity profiles of dyslexic (DYS) versus non-impaired (NI) readers in the first whole-brain functional connectivity analysis of dyslexia.

METHODS

Whole-brain connectivity was assessed in children (n = 75; 43 NI, 32 DYS) and adult (n = 104; 64 NI, 40 DYS) readers.

RESULTS

Compared to NI readers, DYS readers showed divergent connectivity within the visual pathway and between visual association areas and prefrontal attention areas; increased right-hemisphere connectivity; reduced connectivity in the visual word-form area (part of the left fusiform gyrus specialized for printed words); and persistent connectivity to anterior language regions around the inferior frontal gyrus.

CONCLUSIONS

Together, findings suggest that NI readers are better able to integrate visual information and modulate their attention to visual stimuli, allowing them to recognize words on the basis of their visual properties, whereas DYS readers recruit altered reading circuits and rely on laborious phonology-based "sounding out" strategies into adulthood. These results deepen our understanding of the neural basis of dyslexia and highlight the importance of synchrony between diverse brain regions for successful reading.

Default mode network as a potential biomarker of chemotherapy-related brain injury

Chronic medical conditions and/or their treatments may interact with aging to alter or even accelerate brain senescence. Adult onset cancer, for example, is a disease associated with advanced aging and emerging evidence suggests a profile of subtle but diffuse brain injury following cancer chemotherapy. Breast cancer is currently the primary model for studying these "chemobrain" effects. Given the widespread changes to brain structure and function as well as the common impairment of integrated cognitive skills observed following breast cancer chemotherapy, it is likely that large-scale brain networks are involved. Default mode network (DMN) is a strong candidate considering its preferential vulnerability to aging and sensitivity to toxicity and disease states. Additionally, chemotherapy is associated with several physiological effects including increased inflammation and oxidative stress that are believed to elevate toxicity in the DMN. Biomarkers of DMN connectivity could aid in the development of treatments for chemotherapy-related cognitive decline.

Altered brain connectivity in sagittal craniosynostosis

OBJECT

Sagittal nonsyndromic craniosynostosis (sNSC) is the most common form of NSC. The condition is associated with a high prevalence (> 50%) of deficits in executive function. The authors employed diffusion tensor imaging (DTI) and functional MRI to evaluate whether hypothesized structural and functional connectivity differences underlie the observed neurocognitive morbidity of sNSC.

METHODS

Using a 3-T Siemens Trio MRI system, the authors collected DTI and resting-state functional connectivity MRI data in 8 adolescent patients (mean age 12.3 years) with sNSC that had been previously corrected via total vault cranioplasty and 8 control children (mean age 12.3 years) without craniosynostosis. Data were analyzed using the FMRIB Software Library and BioImageSuite.

RESULTS

Analyses of the DTI data revealed white matter alterations approaching statistical significance in all supratentorial lobes. Statistically significant group differences (sNSC < control group) in mean diffusivity were localized to the right supramarginal gyrus. Analysis of the resting-state seed in relation to whole-brain data revealed significant increases in negative connectivity (anticorrelations) of Brodmann area 8 to the prefrontal cortex (Montreal Neurological Institute [MNI] center of mass coordinates [x, y, z]: -6, 53, 6) and anterior cingulate cortex (MNI coordinates 6, 43, 14) in the sNSC group relative to controls. Furthermore, in the sNSC patients versus controls, the Brodmann area 7, 39, and 40 seed had decreased connectivity to left angular gyrus (MNI coordinates -31, -61, 34), posterior cingulate cortex (MNI coordinates 13, -52, 18), precuneus (MNI coordinates 10, -55, 54), left and right parahippocampus (MNI coordinates -13, -52, 2 and MNI coordinates 11, -50, 2, respectively), lingual (MNI coordinates -11, -86, -10), and fusiform gyri (MNI coordinates -30, -79, -18). Intrinsic connectivity analysis also revealed altered connectivity between central nodes in the default mode network in sNSC relative to controls; the left and right posterior cingulate cortices (MNI coordinates -5, -35, 34 and MNI coordinates 6, -42, 39, respectively) were negatively correlated to right hemisphere precuneus (MNI coordinates 6, -71, 46), while the left ventromedial prefrontal cortex (MNI coordinates 6, 34, -8) was negatively correlated to right middle frontal gyrus (MNI coordinates 40, 4, 33). All group comparisons (sNSC vs controls) were conducted at a whole brain-corrected threshold of p < 0.05.

CONCLUSIONS

This study demonstrates altered neocortical structural and functional connectivity in sNSC that may, in part or substantially, underlie the neuropsychological deficits commonly reported in this population. Future studies combining analysis of multimodal MRI and clinical characterization data in larger samples of participants are warranted.

Graph theory findings in the pathophysiology of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE.

Coupled Intrinsic Connectivity Distribution analysis: a method for exploratory connectivity analysis of paired FMRI data

We present a novel voxel-based connectivity approach for paired functional magnetic resonance imaging (fMRI) data collected under two different conditions labeled the Coupled Intrinsic Connectivity Distribution (coupled-ICD). Our proposed method jointly models both conditions to incorporate additional paired information into the connectivity metric. Voxel-based connectivity holds promise as a clinical tool to characterize a wide range of neurological and psychiatric diseases, and monitor their treatment. As such, examining paired connectivity data such as scans acquired pre- and post-intervention is an important application for connectivity methodologically. When presented with data from paired conditions, conventional voxel-based methods analyze each condition separately. However, summarizing each connection separately can misrepresent patterns of changes in connectivity. We show that commonly used methods can underestimate functional changes and subsequently introduce and evaluate our solution to this problem, the coupled-ICD metric, using two studies: 1) healthy controls scanned awake and under anesthesia, and 2) cocaine-dependent subjects and healthy controls scanned while being presented with relaxing or drug-related imagery cues. The coupled-ICD approach detected differences between paired conditions in similar brain regions as the conventional approaches while also revealing additional changes in regions not identified using conventional voxel-based connectivity analyses. Follow-up seed-based analyses on data independent from the voxel-based results also showed connectivity differences between conditions in regions detected by coupled-ICD. This approach of jointly analyzing paired resting-state scans provides a new and important tool with many applications for clinical and basic neuroscience research.

The impact of image smoothness on intrinsic functional connectivity and head motion confounds

We present a novel method for controlling the effects of group differences in motion on functional connectivity studies. Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool that allows for the assessment of whole-brain functional organization across a wide range of clinical populations. However, as highlighted by recent studies, many measures commonly used in rs-fMRI are highly correlated with subject head movement. A source of this problem is that motion itself, and motion correction algorithms, lead to spatial smoothing, which is then variable across the brain and across subjects or groups dependent upon the amount of motion present during scanning. Studies aimed at elucidating differences between populations that have different head-motion characteristics (e.g., patients often move more in the scanner than healthy control subjects) are significantly confounded by these effects. In this work, we propose a solution to this problem, uniform smoothing, which ensures that all subject images in a study have equal effective spatial resolution. We establish that differences in the intrinsic smoothness of images across a group can confound connectivity results and link these differences in smoothness to motion. We demonstrate that eliminating these smoothness differences via our uniform smoothing solution is successful in reducing confounds related to the differences in head motion between subjects.

Potential use and challenges of functional connectivity mapping in intractable epilepsy

This review focuses on the use of resting-state functional magnetic resonance imaging data to assess functional connectivity in the human brain and its application in intractable epilepsy. This approach has the potential to predict outcomes for a given surgical procedure based on the pre-surgical functional organization of the brain. Functional connectivity can also identify cortical regions that are organized differently in epilepsy patients either as a direct function of the disease or through indirect compensatory responses. Functional connectivity mapping may help identify epileptogenic tissue, whether this is a single focal location or a network of seizure-generating tissues. This review covers the basics of connectivity analysis and discusses particular issues associated with analyzing such data. These issues include how to define nodes, as well as differences between connectivity analyses of individual nodes, groups of nodes, and whole-brain assessment at the voxel level. The need for arbitrary thresholds in some connectivity analyses is discussed and a solution to this problem is reviewed. Overall, functional connectivity analysis is becoming an important tool for assessing functional brain organization in epilepsy.