Neuropharmacological computational analysis of longitudinal electroencephalograms in clozapine-treated patients with schizophrenia using hierarchical dynamic causal modeling.

The hierarchical characteristics of the brain are prominent in the pharmacological treatment of psychiatric diseases, primarily targeting cellular receptors that extend upward to intrinsic connectivity within a region, interregional connectivity, and, consequently, clinical observations such as an electroencephalogram (EEG). To understand the long-term effects of neuropharmacological intervention on neurobiological properties at different hierarchical levels, we explored long-term changes in neurobiological parameters of an N-methyl-D-aspartate canonical microcircuit model (CMM-NMDA) in the default mode network (DMN) and auditory hallucination network (AHN) using dynamic causal modeling of longitudinal EEG in clozapine-treated patients with schizophrenia. The neurobiological properties of the CMM-NMDA model associated with symptom improvement in schizophrenia were found across hierarchical levels, from a reduced membrane capacity of the deep pyramidal cell and intrinsic connectivity with the inhibitory population in DMN and intrinsic and extrinsic connectivity in AHN. The medication duration mainly affects the intrinsic connectivity and NMDA time constant in DMN. Virtual perturbation analysis specified the contribution of each parameter to the cross-spectral density (CSD) of the EEG, particularly intrinsic connectivity and membrane capacitances for CSD frequency shifts and progression. It further reveals that excitatory and inhibitory connectivity complements frequency-specific CSD changes, notably the alpha frequency band in DMN. Positive and negative synergistic interactions exist between neurobiological properties primarily within the same region in patients treated with clozapine. The current study shows how computational neuropharmacology helps explore the multiscale link between neurobiological properties and clinical observations and understand the long-term mechanism of neuropharmacological intervention reflected in clinical EEG.

Bayesian adaptive method for estimating speed-accuracy tradeoff functions of multiple task conditions.

The speed-accuracy tradeoff (SAT) often makes psychophysical data difficult to interpret. Accordingly, the SAT experimental procedure and model were proposed for an integrated account of the speed and accuracy of responses. However, the extensive data collection for a SAT experiment has blocked its popularity. For a quick estimation of SAT function (SATf), we previously developed a Bayesian adaptive SAT method, including an online stimulus selection strategy. By simulations, the method was proved efficient with high accuracy and precision with minimal trials, adequate for practically applying a single condition task. However, it calls for extensions to more general designs with multiple conditions and should be revised to achieve improved estimation performance. It also demands real experimental validation with human participants. In the current study, we suggested an improved method to measure SATfs for multiple task conditions concurrently and to enhance robustness in general designs. The performance was evaluated with simulation studies and a psychophysical experiment using a flanker task. Simulation results revealed that the proposed method with the adaptive stimulus selection strategy efficiently estimated multiple SATfs and improved performance even for cases with an extreme parameter value. In the psychophysical experiment, SATfs estimated by minimal adaptive trials (1/8 of conventional trials) showed high agreement with those by conventional trials required for reliably estimating multiple SATfs. These results indicate that the Bayesian adaptive SAT method is reliable and efficient in estimating SATfs in most experimental settings and may apply to SATf estimation in general behavioral research designs.

A populational connection distribution map for the whole brain white matter reveals ordered cortical wiring in the space of white matter.

The white matter in the brain is composed of neural fibers that wire the cortical and subcortical brain systems. Considering the complexity in terms of interconnections of many neural populations within the narrow space surrounded by the folding walls of the gray matter, the brain requires a certain way of structured wiring. To explore the three-dimensional organization of wiring in an accessible manner, we focused on voxel-level wiring patterns in the white matter according to cortical distributions in which each voxel mediates the wiring. We constructed a voxel-wise connection distribution map from the whole white matter voxels to 360 cortical regions using probabilistic tractography of the 100 diffusion imaging data in the Human Connectome Project. We then explored the spatial organization of the fiber bundles at the white matter voxels in terms of the maximal and second maximal cortical connection labels and the local gradient and entropy of cortical connection density using the population connection distribution map. We presented dominant cortical connection labels, local gradient, and connection entropy for the most representative white matter regions, including the internal capsule, external capsule, corpus callosum, cingulum bundle, and uncinate fascicles, most of which were introduced in the current study. Those major tracts showed a gradient organization of connection distributions for individual voxels. This organized pattern, as reflected in the whole brain connection distribution map, could be established to optimize wiring in the entire brain within the physical space of the white matter.

Genetic effect of single nucleotide polymorphisms in growth hormone receptor gene on the risk of non-syndromic mandibular prognathism in the Korean population.

OBJECTIVES: To evaluate the association of three single-nucleotide polymorphisms (SNPs) of growth hormone receptor (GHR) gene with mandibular prognathism (MP) and relationships between mandibular morphology and GHR gene SNPs in the Korean population. MATERIALS AND METHODS: A total of 325 subjects were divided into two groups based on sagittal maxillomandibular relationship by the lateral cephalography: the MP and control groups. From the SNPs in the GHR gene, three SNPs (rs6180, rs6182 and rs6184) were selected. SNP genotyping was performed using direct sequencing. The craniofacial measurements of lateral cephalography were analysed. RESULTS: We found a lack of association between GHR and MP. However, in the analysis according to the values of cephalometric measurements, rs6180 was significantly associated with ANB, SNB, effective mandibular length and SNMP in females. Additionally, rs6182 and rs6184 were significantly associated with ramal height in males. CONCLUSION: Growth hormone receptor SNPs may affect not only the sagittal development of mandible but also the vertical development of ramal height, and GHR SNPs may gender-differently influence mandibular morphology. This finding supports that the GHR might be susceptible on mandibular morphogenesis in the Korean population.

Re-visiting Riemannian geometry of symmetric positive definite matrices for the analysis of functional connectivity.

Common representations of functional networks of resting state fMRI time series, including covariance, precision, and cross-correlation matrices, belong to the family of symmetric positive definite (SPD) matrices forming a special mathematical structure called Riemannian manifold. Due to its geometric properties, the analysis and operation of functional connectivity matrices may well be performed on the Riemannian manifold of the SPD space. Analysis of functional networks on the SPD space takes account of all the pairwise interactions (edges) as a whole, which differs from the conventional rationale of considering edges as independent from each other. Despite its geometric characteristics, only a few studies have been conducted for functional network analysis on the SPD manifold and inference methods specialized for connectivity analysis on the SPD manifold are rarely found. The current study aims to show the significance of connectivity analysis on the SPD space and introduce inference algorithms on the SPD manifold, such as regression analysis of functional networks in association with behaviors, principal geodesic analysis, clustering, state transition analysis of dynamic functional networks and statistical tests for network equality on the SPD manifold. We applied the proposed methods to both simulated data and experimental resting state fMRI data from the human connectome project and argue the importance of analyzing functional networks under the SPD geometry. All the algorithms for numerical operations and inferences on the SPD manifold are implemented as a MATLAB library, called SPDtoolbox, for public use to expediate functional network analysis on the right geometry.

A computational framework for optimal control of a self-adjustive neural system with activity-dependent and homeostatic plasticity.

The control of the brain system has received increasing attention in the domain of brain science. Most brain control studies have been conducted to explore the brain network's graph-theoretic properties or to produce the desired state based on neural state dynamics, regarding the brain as a passively responding system. However, the self-adjusting nature of neural system after treatment has not been fully considered in the brain control. In the present study, we propose a computational framework for optimal control of the brain with a self-adjustment process in the effective connectivity after treatment. The neural system is modeled to adjust its outgoing effective connectivity as activity-dependent plasticity after treatment, followed by synaptic rescaling of incoming effective connectivity. To control this neural system to induce the desired function, the system's self-adjustment parameter is first estimated, based on which the treatment is optimized. Utilizing this framework, we conducted simulations of optimal control over a functional hippocampal circuitry, estimated using dynamic causal modeling of voltage-sensitive dye imaging from the wild type and mutant mice, responding to consecutive electrical stimuli. Simulation results for optimal control of the abnormal circuit toward a healthy circuit using a single node treatment, neural-type specific treatment as an analogy of medication, and combined treatments of medication and nodal treatment suggest the plausibility of the current framework in controlling the self-adjusting neural system within a restricted treatment setting. We believe the proposed computational framework of the self-adjustment system would help optimal control of the dynamic brain after treatment.

Empirical Bayes estimation of pairwise maximum entropy model for nonlinear brain state dynamics.

The pairwise maximum entropy model (pMEM) has recently gained widespread attention to exploring the nonlinear characteristics of brain state dynamics observed in resting-state functional magnetic resonance imaging (rsfMRI). Despite its unique advantageous features, the practical application of pMEM for individuals is limited as it requires a much larger sample than conventional rsfMRI scans. Thus, this study proposes an empirical Bayes estimation of individual pMEM using the variational expectation-maximization algorithm (VEM-MEM). The performance of the VEM-MEM is evaluated for several simulation setups with various sample sizes and network sizes. Unlike conventional maximum likelihood estimation procedures, the VEM-MEM can reliably estimate the individual model parameters, even with small samples, by effectively incorporating the group information as the prior. As a test case, the individual rsfMRI of children with attention deficit hyperactivity disorder (ADHD) is analyzed compared to that of typically developed children using the default mode network, executive control network, and salient network, obtained from the Healthy Brain Network database. We found that the nonlinear dynamic properties uniquely established on the pMEM differ for each group. Furthermore, pMEM parameters are more sensitive to group differences and are better associated with the behavior scores of ADHD compared to the Pearson correlation-based functional connectivity. The simulation and experimental results suggest that the proposed method can reliably estimate the individual pMEM and characterize the dynamic properties of individuals by utilizing empirical information of the group brain state dynamics.

Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics.

The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed-effect group-level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter-individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states.

Decreased Brain pH and Pathophysiology in Schizophrenia.

Postmortem studies reveal that the brain pH in schizophrenia patients is lower than normal. The exact cause of this low pH is unclear, but increased lactate levels due to abnormal energy metabolism appear to be involved. Schizophrenia patients display distinct changes in mitochondria number, morphology, and function, and such changes promote anaerobic glycolysis, elevating lactate levels. pH can affect neuronal activity as H+ binds to numerous proteins in the nervous system and alters the structure and function of the bound proteins. There is growing evidence of pH change associated with cognition, emotion, and psychotic behaviors. Brain has delicate pH regulatory mechanisms to maintain normal pH in neurons/glia and extracellular fluid, and a change in these mechanisms can affect, or be affected by, neuronal activities associated with schizophrenia. In this review, we discuss the current understanding of the cause and effect of decreased brain pH in schizophrenia based on postmortem human brains, animal models, and cellular studies. The topic includes the factors causing decreased brain pH in schizophrenia, mitochondria dysfunction leading to altered energy metabolism, and pH effects on the pathophysiology of schizophrenia. We also review the acid/base transporters regulating pH in the nervous system and discuss the potential contribution of the major transporters, sodium hydrogen exchangers (NHEs), and sodium-coupled bicarbonate transporters (NCBTs), to schizophrenia.

Early-Life Stress Modulates Gut Microbiota and Peripheral and Central Inflammation in a Sex-Dependent Manner.

Recent studies have reported that changes in gut microbiota composition could induce neuropsychiatric problems. In this study, we investigated alterations in gut microbiota induced by early-life stress (ELS) in rats subjected to maternal separation (MS; 6 h a day, postnatal days (PNDs) 1-21), along with changes in inflammatory cytokines and tryptophan-kynurenine (TRP-KYN) metabolism, and assessed the differences between sexes. High-throughput sequencing of the bacterial 16S rRNA gene showed that the relative abundance of the Bacteroides genus was increased and that of the Lachnospiraceae family was decreased in the feces of MS rats of both sexes (PND 56). By comparison, MS increased the relative abundance of the Streptococcus genus and decreased that of the Staphylococcus genus only in males, whereas the abundance of the Sporobacter genus was enhanced and that of the Mucispirillum genus was reduced by MS only in females. In addition, the levels of proinflammatory cytokines were increased in the colons (IFN-γ and IL-6) and sera (IL-1ß) of the male MS rats, together with the elevation of the KYN/TRP ratio in the sera, but not in females. In the hippocampus, MS elevated the level of IL-1ß and the KYN/TRP ratio in both male and female rats. These results indicate that MS induces peripheral and central inflammation and TRP-KYN metabolism in a sex-dependent manner, together with sex-specific changes in gut microbes.

Dynamic causal modeling of hippocampal activity measured via mesoscopic voltage-sensitive dye imaging.

The aim of this paper is to present a dynamic causal modeling (DCM) framework for hippocampal activity measured via voltage-sensitive dye imaging (VSDI). We propose a DCM model of the hippocampus that summarizes interactions between the hilus, CA3 and CA1 regions. The activity of each region is governed via a neuronal mass model with two inhibitory and one/two excitatory neuronal populations, which can be linked to measurement VSDI by scaling neuronal activity. To optimize the model structure for the hippocampus, we propose two Bayesian schemes: Bayesian hyperparameter optimization to estimate the unknown electrophysiological properties necessary for constructing a mesoscopic hippocampus model; and Bayesian model reduction to determine the parameterization of neural properties, and to test and include potential connections (morphologically inferred without direct evidence yet) in the model by evaluating group-level model evidence. The proposed method was applied to model spatiotemporal patterns of accumulative responses to consecutive stimuli in separate groups of wild-type mice and epileptic aristaless-related homeobox gene (Arx) conditional knock-out mutant mice (Arx-/+;Dlx5/6CRE-IRES-GFP) in order to identify group differences in the effective connectivity within the hippocampus. The causal role of each group-differing connectivity in generating mutant-like responses was further tested. The group-level analysis identified altered intra- and inter-regional effective connectivity, some of which are crucial for explaining mutant-like responses. The modelling results for the hippocampal activity suggest the plausibility of the proposed mesoscopic hippocampus model and the usefulness of utilizing the Bayesian framework for model construction in the mesoscale modeling of neural interactions using DCM.

Neurofeedback learning for mental practice rather than repetitive practice improves neural pattern consistency and functional network efficiency in the subsequent mental motor execution.

During brain modulation, repeated mental practice may not always result in efficient learning. Particularly, the effectiveness of mental motor practice depends on how well one induces neural activity in a desired state consistently across mental trials, which calls for feedbacks to adjust one's performance. We hypothesized that even a brief experience of neurofeedback learning enhances trial-by-trial neural pattern consistency during subsequent mental motor execution and that this experience would change recruitment of functional connectivity in the motor imagery and default mode networks. To test this hypothesis, we conducted an experiment with two sessions of mental motor practice before and after a neurofeedback training session, in which participants conducted four types of first-person mental motor execution tasks (walking forward, turning left, turning right, and touching a tree). During the neurofeedback training session, in which participants conducted a virtual navigation game, 10 experimental participants received real-time fMRI neuro-feedbacks, while 10 control participants simply repeated the same mental task according to given cues without feedbacks. The experimental group showed significantly higher effects of neuro-feedback training on trial-by-trial consistencies and classification accuracies of activated neural patterns than the control group. Task-performing global node strength and network efficiency were increased in the motor imagery network but decreased in the default mode network only in the experimental group. These results demonstrate that even a brief experience of feedback learning is more effective than simple practice repetitions without evaluation, which was reflected in increased neural pattern consistency and task-dependent functional connectivity during a mental motor execution task.

Dynamic causal modeling for calcium imaging: Exploration of differential effective connectivity for sensory processing in a barrel cortical column.

Multi-photon calcium imaging (CaI) is an important tool to assess activities of neural populations within a column in the sensory cortex. However, the complex asymmetrical interactions among neural populations, termed effective connectivity, cannot be directly assessed by measuring the activity of each neuron or neural population using CaI but calls for computational modeling. To estimate effective connectivity among neural populations, we proposed a dynamic causal model (DCM) for CaI by combining a convolution-based dynamic neural state model and a dynamic calcium ion concentration model for CaI signals. After conducting a simulation study to evaluate DCM for CaI, we applied it to an experimental CaI signals measured at the layer 2/3 of a barrel cortical column that differentially responds to hit and error whisking trials in mice. We first identified neural populations and constructed computational models with intrinsic connectivity of neural populations within the layer 2/3 of the barrel cortex and extrinsic connectivity with latent external modes. Bayesian model inversion and comparison shows that interactions with latent inhibitory and excitatory external modes explain the observed CaI signals within the barrel cortical column better than any other tested models, with a single external mode or without any latent modes. The best model also showed differential intrinsic and extrinsic effective connectivity between hit and error trials in the functional hierarchy. Both simulation and experimental results suggest the usefulness of DCM for CaI in terms of exploration of hierarchical interactions among neural populations observed in CaI.

Dynamic effective connectivity in resting state fMRI.

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.

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

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.

Interregional metabolic connectivity of 2-deoxy-2[18 F]fluoro-D-glucose positron emission tomography in vagus nerve stimulation for pediatric patients with epilepsy: A retrospective cross-sectional study.

OBJECTIVE: With the recognition of epilepsy as a network disease that disrupts the organizing ability of resting-state brain networks, vagus nerve stimulation (VNS) may control epileptic seizures through modulation of functional connectivity. We evaluated preoperative 2-deoxy-2[18 F]fluoro-D-glucose (FDG) positron emission tomography (PET) in VNS-implanted pediatric patients with refractory epilepsy to analyze the metabolic connectivity of patients and its prognostic role in seizure control. METHODS: Preoperative PET data of 66 VNS pediatric patients who were followed up for a minimum of 1 year after the procedure were collected for the study. Retrospective review of the patients' charts was performed, and five patients with inappropriate PET data or major health issues were excluded. We conducted an independent component analysis of FDG-PET to extract spatial metabolic components and their activities, which were used to perform cross-sectional metabolic network analysis. We divided the patients into VNS-effective and VNS-ineffective groups (VNS-effective group, ≥50% seizure reduction; VNS-ineffective group, <50% reduction) and compared metabolic connectivity differences between groups using a permutation test. RESULTS: Thirty-four (55.7%) patients showed >50% seizure reduction from baseline frequency 1 year after VNS. A significant difference in metabolic connectivity evaluated by preoperative FDG-PET was noted between groups. Relative changes in glucose metabolism were strongly connected among the areas of brainstem, cingulate gyrus, cerebellum, bilateral insula, and putamen in patients with <50% seizure control after VNS. SIGNIFICANCE: This study shows that seizure outcome of VNS may be influenced by metabolic connectivity, which can be obtained from preoperative PET imaging. This study of metabolic connectivity analysis may contribute in further understanding of the mechanism of VNS in intractable seizures.

Alternative transcription of sodium/bicarbonate transporter SLC4A7 gene enhanced by single nucleotide polymorphisms.

Genome-wide association studies have identified the single nucleotide polymorphism (SNP) rs3278 in the human SLC4A7 gene as one of the marker loci for addiction vulnerability. This marker is located in an intron of the gene, and its genomic role has been unknown. In this study, we examined rs3278 and three adjacent SNPs prevalent in alcoholics for their effects on an alternative promoter that would lead to the production of the NH2-terminally truncated protein NBCn1ΔN450, missing the first 450 amino acids. Analysis of the transcription start site database and a promoter prediction algorithm identified a cluster of three promoters in intron 7 and two short CpG-rich sites in intron 6. The promoter closest to rs3278 showed strong transcription activity in luciferase reporter gene assays. Major-to-minor allele substitution at rs3278 resulted in increased transcription activity. Equivalent substitutions at adjacent rs3772723 (intron 7) and rs13077400 (exon 8) had negligible effect; however, the substitution at nonsynonymous rs3755652 (exon 8) increased the activity by more than twofold. The concomitant substitution at rs3278/rs3755652 produced an additive effect. The rs3755652 had more profound effects on the promoter than the upstream regulatory CpG sites. The amino acid change E326K caused by rs3755652 had negligible effect on transporter function. In HEK 293 cells, NBCn1ΔN450 was expressed in plasma membranes, but at significantly lower levels than the nontruncated NBCn1-E. The pH change mediated by NBCn1ΔN450 was also low. We conclude that rs3278 and rs3755652 stimulate an alternative transcription of the SLC4A7 gene, increasing the production of a defective transporter.

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

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.

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. © 2017 Wiley Periodicals, Inc.

Association Between Secretoglobin Family 3A Member 2 (SCGB3A2) Gene Polymorphisms and Asthma in a Korean Population.

BACKGROUND Secretoglobin family 3A member 2 (SCGB3A2) plays an important role in secreting lung surfactant protein, which is a downstream target of thyroid transcription factor. MATERIAL AND METHODS We investigated whether single-nucleotide polymorphisms (SNPs) of SCGB3A2 gene contribute to susceptibility to asthma. To explore this possible association, 2 promoter SNPs (rs6882292, 659 G/A and rs1368408, -112 G/A) and missense SNP (rs151333009, stop codon) were tested in SCGB3A2 gene in 101 asthma patients and 377 healthy control subjects. SNPStats was used to obtain odds ratio (OR), 95% confidence intervals (CI), and P value adjusted for age and sex as covariables. Logistic regression method in each model (dominant, recessive, and log-additive) was applied to analyze genetic data. RESULTS rs151333009 SNP showed a monomorphic genotype. Two promoter SNPs (rs6882292, -659 G/A and rs1368408, -112 G/A) showed significant association with asthma (rs6882292, OR=2.66, 95% CI=1.42-5.01, p=0.0033 in dominant model, OR=2.45, 95% CI=1.33-4.54, p=0.0055 in log-additive model; rs1368408, OR=1.59, 95% CI=1.02-2.49, p=0.041 in dominant model, OR=3.02, 95% CI=1.15-7.90, p=0.03 in recessive model, OR=1.63, 95% CI=1.63, 95% CI=1.12-2.37, p=0.012 in log-additive model). CONCLUSIONS These results suggest that the promoter SNPs (rs6882292 and rs1368408) of SCGB3A2 gene may contribute to susceptibility to asthma in a Korean population.