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.ope

Evaluation of Node-Inhomogeneity Effects on the Functional Brain Network Properties Using an Anatomy-Constrained Hierarchical Brain Parcellation

To investigate functional brain networks, many graph-theoretical studies have defined nodes in a graph using an anatomical atlas with about a hundred partitions. Although use of anatomical node definition is popular due to its convenience, functional inhomogeneity within each node may lead to bias or systematic errors in the graph analysis. The current study was aimed to show functional inhomogeneity of a node defined by an anatomical atlas and to show its effects on the graph topology. For this purpose, we compared functional connectivity defined using 138 resting state fMRI data among 90 cerebral nodes from the automated anatomical labeling (AAL), which is an anatomical atlas, and among 372 cerebral nodes defined using a functional connectivity-based atlas as a ground truth, which was obtained using anatomy-constrained hierarchical modularity optimization algorithm (AHMO) that we proposed to evaluate the graph properties for anatomically defined nodes. We found that functional inhomogeneity in the anatomical parcellation induced significant biases in estimating both functional connectivity and graph-theoretical network properties. We also found very high linearity in major global network properties and nodal strength at all brain regions between anatomical atlas and functional atlas with reasonable network-forming thresholds for graph construction. However, some nodal properties such as betweenness centrality did not show significant linearity in some regions. The current study suggests that the use of anatomical atlas may be biased due to its inhomogeneity, but may generally be used in most neuroimaging studies when a single atlas is used for analysis.ope

Graph Independent Component Analysis Reveals Repertoires of Intrinsic Network Components in the Human Brain

Does each cognitive task elicit a new cognitive network each time in the brain? Recent data suggest that pre-existing repertoires of a much smaller number of canonical network components are selectively and dynamically used to compute new cognitive tasks. To this end, we propose a novel method (graph-ICA) that seeks to extract these canonical network components from a limited number of resting state spontaneous networks. Graph-ICA decomposes a weighted mixture of source edge-sharing subnetworks with different weighted edges by applying an independent component analysis on cross-sectional brain networks represented as graphs. We evaluated the plausibility in our simulation study and identified 49 intrinsic subnetworks by applying it in the resting state fMRI data. Using the derived subnetwork repertories, we decomposed brain networks during specific tasks including motor activity, working memory exercises, and verb generation, and identified subnetworks associated with performance on these tasks. We also analyzed sex differences in utilization of subnetworks, which was useful in characterizing group networks. These results suggest that this method can effectively be utilized to identify task-specific as well as sex-specific functional subnetworks. Moreover, graph-ICA can provide more direct information on the edge weights among brain regions working together as a network, which cannot be directly obtained through voxel-level spatial ICA.ope

A network analysis of ¹⁵O-H₂O PET reveals deep brain stimulation effects on brain network of Parkinson's disease

PURPOSE: As Parkinson's disease (PD) can be considered a network abnormality, the effects of deep brain stimulation (DBS) need to be investigated in the aspect of networks. This study aimed to examine how DBS of the bilateral subthalamic nucleus (STN) affects the motor networks of patients with idiopathic PD during motor performance and to show the feasibility of the network analysis using cross-sectional positron emission tomography (PET) images in DBS studies. MATERIALS AND METHODS: We obtained [¹⁵O]H₂O PET images from ten patients with PD during a sequential finger-to-thumb opposition task and during the resting state, with DBS-On and DBS-Off at STN. To identify the alteration of motor networks in PD and their changes due to STN-DBS, we applied independent component analysis (ICA) to all the cross-sectional PET images. We analysed the strength of each component according to DBS effects, task effects and interaction effects. RESULTS: ICA blindly decomposed components of functionally associated distributed clusters, which were comparable to the results of univariate statistical parametric mapping. ICA further revealed that STN-DBS modifies usage-strengths of components corresponding to the basal ganglia-thalamo-cortical circuits in PD patients by increasing the hypoactive basal ganglia and by suppressing the hyperactive cortical motor areas, ventrolateral thalamus and cerebellum. CONCLUSION: Our results suggest that STN-DBS may affect not only the abnormal local activity, but also alter brain networks in patients with PD. This study also demonstrated the usefulness of ICA for cross-sectional PET data to reveal network modifications due to DBS, which was not observable using the subtraction method.ope

Motor pathway injury in patients with periventricular leucomalacia and spastic dipl

Periventricular leucomalacia has long been investigated as a leading cause of motor and cognitive dysfunction in patients with spastic diplegic cerebral palsy. However, patients with periventricular leucomalacia on conventional magnetic resonance imaging do not always have motor dysfunction and preterm children without neurological abnormalities may have periventricular leucomalacia. In addition, it is uncertain whether descending motor tract or overlying cortical injury is related to motor impairment. To investigate the relationship between motor pathway injury and motor impairment, we conducted voxelwise correlation analysis using tract-based spatial statistics of white matter diffusion anisotropy and voxel-based-morphometry of grey matter injury in patients with periventricular leucomalacia and spastic diplegia (n = 43, mean 12.86 ± 4.79 years, median 12 years). We also evaluated motor cortical and thalamocortical connectivity at resting state in 11 patients using functional magnetic resonance imaging. The functional connectivity results of patients with spastic diplegic cerebral palsy were compared with those of age-matched normal controls. Since γ-aminobutyric acid(A) receptors play an important role in the remodelling process, we measured neuronal γ-aminobutyric acid(A) receptor binding potential with dynamic positron emission tomography scans (n = 27) and compared the binding potential map of the patient group with controls (n = 20). In the current study, white matter volume reduction did not show significant correlation with motor dysfunction. Although fractional anisotropy within most of the major white matter tracts were significantly lower than that of age-matched healthy controls (P < 0.05, family wise error corrected), fractional anisotropy mainly within the bilateral corticospinal tracts and posterior body and isthmus of the corpus callosum showed more significant correlation with motor dysfunction (P < 0.03) than thalamocortical pathways (P < 0.05, family-wise error corrected). Cortical volume of the pre- and post-central gyri and the paracentral lobule tended to be negatively correlated with motor function. The motor cortical connectivity was diminished mainly within the bilateral somatosensory cortex, paracentral lobule, cingulate motor area and visual cortex in the patient group. Thalamovisual connectivity was not diminished despite severe optic radiation injury. γ-Aminobutyric acid(A) receptor binding potential was focally increased within the lower extremity homunculus, cingulate cortex, visual cortex and cerebellum in the patient group (P < 0.05, false discovery rate corrected). In conclusion, descending motor tract injury along with overlying cortical volume reduction and reduced functional connectivity appears to be a leading pathophysiological mechanism of motor dysfunction in patients with periventricular leucomalacia. Increased regional γ-aminobutyric acid(A) receptor binding potential appears to result from a compensatory plasticity response after prenatal brain injury.ope

기능 자기공명영상을 이용한 인간 뇌연결체학의 통계적 모델 연구

Dept. of Medical Science/박사Recent studies on human brain connectome have shown that a cortical area is not responsible for a single cognitive function but is repeatedly involved in various cognitive functions by forming functional subnetworks through various types of connectivity with other brain areas. Each subnetwork thereby includes overlaps in brain areas, and execution of various brain functions can be understood in terms of context-dependent recruitment and release of functional subnetworks. To implement the modeling of brain function, we newly propose a novel method for independent component analysis of brain graph that can identify subnetworks sharing brain regions and model brain function in units of subnetworks. The method overcomes disadvantage of existing methods that searches for subnetworks by assigning each brain region to a single functional subnetwork, hence are not appropriate for spatially overlapping functional subnetworks. We, firstly, showed the validity of the method through a simulation study reflecting overlapping subnetworks. Independent component analysis of resting-state brain graphs of 104 subjects led to discovery of 49 overlapping functional subnetworks, some of which were similar to functional subnetworks previously identified. Using the subnetworks, we introduced modeling of task performance in fMRI data of working memory, motor, and verb generation. Unlike previous methods, our technique also enables group-level comparison that was demonstrated by measuring the usage extent of each functional subnetwork between men and women.prohibitio

Decoding Brain States Using Functional Magnetic Resonance Imaging

Most leading research in basic and clinical neuroscience has been carried out by functional magnetic resonance imaging (fMRI), which detects the blood oxygenation level dependent signals associated with neural activities. Among new fMRI applications, brain decoding is an emerging research area, which infers mental states from fMRI signals. Brain decoding using fMRI includes classification, identification, and reconstruction of brain states. It is generally conducted using multi-voxel pattern analysis based on neuroscientific evidence that brain functions are mediated by distributed activation patterns. Brain decoding techniques have been successful in diverse applications such as the brain computer interface, patient monitoring, and neurofeedback. These techniques have expanded our understanding of how the brain encodes distinct information. In the current paper, we reviewed recent fMRI-based brain decoding techniques and applications. We also discussed the potential implications of brain decoding in neuroscience.ope

Functional connectivity-based identification of subdivisions of the basal ganglia and thalamus using multilevel independent component analysis of resting state fMRI

This study aimed to identify subunits of the basal ganglia and thalamus and to investigate the functional connectivity among these anatomically segregated subdivisions and the cerebral cortex in healthy subjects. For this purpose, we introduced multilevel independent component analysis (ICA) of the resting-state functional magnetic resonance imaging (fMRI). After applying ICA to the whole brain gray matter, we applied second-level ICA restrictively to the basal ganglia and the thalamus area to identify discrete functional subunits of those regions. As a result, the basal ganglia and the thalamus were parcelled into 31 functional subdivisions according to their temporal activity patterns. The extracted parcels showed functional network connectivity between hemispheres, between subdivisions of the basal ganglia and thalamus, and between the extracted subdivisions and cerebral functional components. Grossly, these findings correspond to cortico-striato-thalamo-cortical circuits in the brain. This study also showed the utility of multilevel ICA of resting state fMRI in brain network research.ope

Are brain networks stable during a 24-hour period?

Despite the widespread view of the brain as a large complex network, the dynamicity of the brain network over the course of a day has yet to be explored. To investigate whether the spontaneous human brain network maintains long-term stability throughout a day, we evaluated the intra-class correlation coefficient (ICC) of results from an independent component analysis (ICA), seed correlation analysis, and graph-theoretical analysis of resting state functional MRI, acquired from 12 young adults at three-hour intervals over 24 consecutive hours. According to the ICC of the usage strength of the independent network component defined by the root mean square of the temporal weights of the network components, the default mode network centered at the posterior cingulate cortex and precuneus, the superior parietal, and secondary motor networks showed a high temporal stability throughout the day (ICC>0.5). However, high intra-individual dynamicity was observed in the default mode network, including the anterior cingulate cortex and medial prefrontal cortex or posterior-anterior cingulate cortex, the hippocampal network, and the parietal and temporal networks. Seed correlation analysis showed a highly stable (ICC>0.5) extent of functionally connected regions from the posterior cingulate cortex, but poor stability from the hippocampus throughout the day. Graph-theoretical analysis using local and global network efficiency suggested that local brain networks are temporally stable but that long-range integration behaves dynamically in the course of a day. These results imply that dynamic network properties are a nature of the resting state brain network, which remains to be further researched.ope

Neuroanatomical correlates of trait anhedonia in patients with schizophrenia: a voxel-based morphometric study

The aim of this study was to characterize the association between trait anhedonia and regional gray matter volume in patients with schizophrenia. Forty-six patients with schizophrenia and 56 healthy controls underwent magnetic resonance imaging (MRI) to acquire high-resolution T1-weighted images. Trait anhedonia was measured using the Chapman Revised Physical Anhedonia Scale (PAS). Voxel-based morphometry was performed to investigate brain volume correlates of trait anhedonia. Several brain regions in the patient group, including the left precuneus and right posterior cingulate (PCC), were found to show significantly less correlation with PAS scores than those of the control group. Post-hoc analysis revealed that negative correlations between the regional gray matter volume and the PAS scores in the patient group were found at a trend level in the left precuneus and the right PCC. In conclusion, these findings suggest that trait anhedonia in patients with schizophrenia could possibly be associated with a volume deficit in brain regions related to default-mode, which reflects the impairment of self-referential processing and reward anticipationope