Chongwon Pae

Individuality manifests in the dynamic reconfiguration of large-scale brain networks during movie viewing.

Individuality, the uniqueness that distinguishes one person from another, may manifest as diverse rearrangements of functional connectivity during heterogeneous cognitive demands; yet, the neurobiological substrates of individuality, reflected in inter-individual variations of large-scale functional connectivity, have not been fully evidenced. Accordingly, we explored inter-individual variations of functional connectivity dynamics, subnetwork patterns and modular architecture while subjects watched identical video clips designed to induce different arousal levels. How inter-individual variations are manifested in the functional brain networks was examined with respect to four contrasting divisions: edges within the anterior versus posterior part of the brain, edges with versus without corresponding anatomically-defined structural pathways, inter- versus intra-module connections, and rich club edge types. Inter-subject variation in dynamic functional connectivity occurred to a greater degree within edges localized to anterior rather than posterior brain regions, without adhering to structural connectivity, between modules as opposed to within modules, and in weak-tie local edges rather than strong-tie rich-club edges. Arousal level significantly modulates inter-subject variability in functional connectivity, edge patterns, and modularity, and particularly enhances the synchrony of rich-club edges. These results imply that individuality resides in the dynamic reconfiguration of large-scale brain networks in response to a stream of cognitive demands.

Connectivity-based change point detection for large-size functional networks.

Recent understanding that the brain at rest does not remain in a single state but transiently visits multiple states emphasizes the importance of state changes embedded in the brain network. Due to the effectiveness of larger networks in characterizing brain states, there is an increasing need for a network-based change point detection method that is applicable to large-size networks, particularly those with longer time series. This paper presents a fast and efficient method for detecting change points in the large-size functional networks of resting-state fMRI. To detect change points, a statistic for the covariance change at each time point is tested by a local false discovery rate, estimated based on the empirical null principle using a semiparametric mixture model. We present simulations and empirical analyses of task-based and resting-state fMRI data sets with various network sizes up to 300 nodes selected from the Human Connectome Project database. We demonstrate that the proposed method is not only efficient in detecting change points in large samples of large-size networks but also is less sensitive to the window size selection and provides the consequent identification of the changed edges. The covariance-based change point detection method in this study would be very useful in exploring characteristics of dynamic states in long-term large-size resting-state brain networks.

Immediate and Longitudinal Alterations of Functional Networks after Thalamotomy in Essential Tremor

Thalamotomy at the ventralis intermedius nucleus has been an effective treatment method for essential tremor, but how the brain network changes immediately responding to this deliberate lesion and then reorganizes afterwards are not clear. Taking advantage of a non-cranium-opening MRI-guided focused ultrasound ablation technique, we investigated functional network changes due to a focal lesion. To classify the diverse time courses of those network changes with respect to symptom-related long-lasting treatment effects and symptom-unrelated transient effects, we applied graph-theoretic analyses to longitudinal resting-state functional magnetic resonance imaging data before and 1 day, 7 days, and 3 months after thalamotomy with essential tremor. We found reduced average connections among the motor-related areas, reduced connectivity between substantia nigra and external globus pallidum and reduced total connection in the thalamus after thalamotomy, which are all associated with clinical rating scales. The average connectivity among whole brain regions and inter-hemispheric network asymmetry show symptom-unrelated transient increases, indicating temporary reconfiguration of the whole brain network. In summary, thalamotomy regulates interactions over the motor network via symptom-related connectivity changes but accompanies transient, symptom-unrelated diaschisis in the global brain network. This study suggests the significance of longitudinal network analysis, combined with minimal-invasive treatment techniques, in understanding time-dependent diaschisis in the brain network due to a focal lesion.

Dynamic effective connectivity in resting state fMRI

ABSTRACT Context‐sensitive and activity‐dependent fluctuations in connectivity underlie functional integration in the brain and have been studied widely in terms of synaptic plasticity, learning and condition‐specific (e.g., attentional) modulations of synaptic efficacy. This dynamic aspect of brain connectivity has recently attracted a lot of attention in the resting state fMRI community. To explain dynamic functional connectivity in terms of directed effective connectivity among brain regions, we introduce a novel method to identify dynamic effective connectivity using spectral dynamic causal modelling (spDCM). We used parametric empirical Bayes (PEB) to model fluctuations in directed coupling over consecutive windows of resting state fMRI time series. Hierarchical PEB can model random effects on connectivity parameters at the second (between‐window) level given connectivity estimates from the first (within‐window) level. In this work, we used a discrete cosine transform basis set or eigenvariates (i.e., expression of principal components) to model fluctuations in effective connectivity over windows. We evaluated the ensuing dynamic effective connectivity in terms of the consistency of baseline connectivity within default mode network (DMN), using the resting state fMRI from Human Connectome Project (HCP). To model group‐level baseline and dynamic effective connectivity for DMN, we extended the PEB approach by conducting a multilevel PEB analysis of between‐session and between‐subject group effects. Model comparison clearly spoke to dynamic fluctuations in effective connectivity – and the dynamic functional connectivity these changes explain. Furthermore, baseline effective connectivity was consistent across independent sessions – and notably more consistent than estimates based upon conventional models. This work illustrates the advantage of hierarchical modelling with spDCM, in characterizing the dynamics of effective connectivity. HighlightsWe describe efficient estimation of dynamics in resting state effective connectivity.Spectral DCM and PEB are used to model fluctuations in neuronal coupling over time.Dynamics in responses are explained in terms of its causes (effective connectivity).Baseline and dynamic components of the default mode connectivity are identified.

Analysis of structure–function network decoupling in the brain systems of spastic diplegic cerebral palsy

Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure–function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting‐state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole‐brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural–functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure–function coupling than the control group. This reduced coupling but reverse directionality in the whole‐brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation‐based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure–function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292–5306, 2017.

Hierarchical Dynamic Causal Modeling of Resting-State fMRI Reveals Longitudinal Changes in Effective Connectivity in the Motor System after Thalamotomy for Essential Tremor

Thalamotomy at the ventralis intermedius nucleus for essential tremor is known to cause changes in motor circuitry, but how a focal lesion leads to progressive changes in connectivity is not clear. To understand the mechanisms by which thalamotomy exerts enduring effects on motor circuitry, a quantitative analysis of directed or effective connectivity among motor-related areas is required. We characterized changes in effective connectivity of the motor system following thalamotomy using (spectral) dynamic causal modeling (spDCM) for resting-state fMRI. To differentiate long-lasting treatment effects from transient effects, and to identify symptom-related changes in effective connectivity, we subject longitudinal resting-state fMRI data to spDCM, acquired 1 day prior to, and 1 day, 7 days, and 3 months after thalamotomy using a non-cranium-opening MRI-guided focused ultrasound ablation technique. For the group-level (between subject) analysis of longitudinal (between-session) effects, we introduce a multilevel parametric empirical Bayes (PEB) analysis for spDCM. We found remarkably selective and consistent changes in effective connectivity from the ventrolateral nuclei and the supplementary motor area to the contralateral dentate nucleus after thalamotomy, which may be mediated via a polysynaptic thalamic–cortical–cerebellar motor loop. Crucially, changes in effective connectivity predicted changes in clinical motor-symptom scores after thalamotomy. This study speaks to the efficacy of thalamotomy in regulating the dentate nucleus in the context of treating essential tremor. Furthermore, it illustrates the utility of PEB for group-level analysis of dynamic causal modeling in quantifying longitudinal changes in effective connectivity; i.e., measuring long-term plasticity in human subjects non-invasively.

Effective connectivity during working memory and resting states: A DCM study

ABSTRACT Although the relationship between resting‐state functional connectivity and task‐related activity has been addressed, the relationship between task and resting‐state directed or effective connectivity – and its behavioral concomitants – remains elusive. We evaluated effective connectivity under an N‐back working memory task in 24 participants using stochastic dynamic causal modelling (DCM) of 7 T fMRI data. We repeated the analysis using resting‐state data, from the same subjects, to model connectivity among the same brain regions engaged by the N‐back task. This allowed us to: (i) examine the relationship between intrinsic (task‐independent) effective connectivity during resting (Arest) and task states (Atask), (ii) cluster phenotypes of task‐related changes in effective connectivity (Btask) across participants, (iii) identify edges (Btask) showing high inter‐individual effective connectivity differences and (iv) associate reaction times with the similarity between Btask and Arest in these edges. We found a strong correlation between Arest and Atask over subjects but a marked difference between Btask and Arest. We further observed a strong clustering of individuals in terms of Btask, which was not apparent in Arest. The task‐related effective connectivity Btask varied highly in the edges from the parietal to the frontal lobes across individuals, so the three groups were clustered mainly by the effective connectivity within these networks. The similarity between Btask and Arest at the edges from the parietal to the frontal lobes was positively correlated with 2‐back reaction times. This result implies that a greater change in context‐sensitive coupling – from resting‐state connectivity – is associated with faster reaction times. In summary, task‐dependent connectivity endows resting‐state connectivity with a context sensitivity, which predicts the speed of information processing during the N‐back task.

Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data

In machine learning, one of the most popular deep learning methods is the convolutional neural network (CNN), which utilizes shared local filters and hierarchical information processing analogous to the brain’s visual system. Despite its popularity in recognizing two-dimensional (2D) images, the conventional CNN is not directly applicable to semi-regular geometric mesh surfaces, on which the cerebral cortex is often represented. In order to apply the CNN to surface-based brain research, we propose a geometric CNN (gCNN) that deals with data representation on a mesh surface and renders pattern recognition in a multi-shell mesh structure. To make it compatible with the conventional CNN toolbox, the gCNN includes data sampling over the surface, and a data reshaping method for the convolution and pooling layers. We evaluated the performance of the gCNN in sex classification using cortical thickness maps of both hemispheres from the Human Connectome Project (HCP). The classification accuracy of the gCNN was significantly higher than those of a support vector machine (SVM) and a 2D CNN for thickness maps generated by a map projection. The gCNN also demonstrated position invariance of local features, which rendered reuse of its pre-trained model for applications other than that for which the model was trained without significant distortion in the final outcome. The superior performance of the gCNN is attributable to CNN properties stemming from its brain-like architecture, and its surface-based representation of cortical information. The gCNN provides much-needed access to surface-based machine learning, which can be used in both scientific investigations and clinical applications.

Energy landscape analysis of the subcortical brain network unravels system properties beneath resting state dynamics

ABSTRACT The configuration of the human brain system at rest, which is in a transitory phase among multistable states, remains unknown. To investigate the dynamic systems properties of the human brain at rest, we constructed an energy landscape for the state dynamics of the subcortical brain network, a critical center that modulates whole brain states, using resting state fMRI. We evaluated alterations in energy landscapes following perturbation in network parameters, which revealed characteristics of the state dynamics in the subcortical brain system, such as maximal number of attractors, unequal temporal occupations, and readiness for reconfiguration of the system. Perturbation in the network parameters, even those as small as the ones in individual nodes or edges, caused a significant shift in the energy landscape of brain systems. The effect of the perturbation on the energy landscape depended on the network properties of the perturbed nodes and edges, with greater effects on hub nodes and hubs‐connecting edges in the subcortical brain system. Two simultaneously perturbed nodes produced perturbation effects showing low sensitivity in the interhemispheric homologous nodes and strong dependency on the more primary node among the two. This study demonstrated that energy landscape analysis could be an important tool to investigate alterations in brain networks that may underlie certain brain diseases, or diverse brain functions that may emerge due to the reconfiguration of the default brain network at rest.

Organization of state transitions in the resting-state human cerebral cortex

The resting-state brain is often considered a nonlinear dynamic system transitioning among multiple coexisting stable states. Despite the increasing number of studies on the multistability of the brain system, the processes of state transitions have rarely been systematically explored. Thus, we investigated the state transition processes of the human cerebral cortex system at rest by introducing a graph-theoretic analysis of the state transition network. The energy landscape analysis of brain state occurrences, estimated using the pairwise maximum entropy model for resting-state fMRI data, identified multiple local minima, some of which mediate multi-step transitions toward the global minimum. The state transition among local minima is clustered into two groups according to state transition rates and most inter-group state transitions were mediated by a hub transition state. The distance to the hub transition state determined the path length of the inter-group transition. The cortical system appeared to have redundancy in inter-group transitions when the hub transition state was removed. Such a hub-like organization of transition processes disappeared when the connectivity of the cortical system was altered from the resting-state configuration. In summary, the resting-state cerebral cortex has a well-organized architecture of state transitions among stable states, when evaluated by nonlinear systematic approach.