Involvement of the anterior insula and frontal operculum during wh-question comprehension of wh-in-situ Korean language.

In this research, we employed functional magnetic resonance imaging (fMRI) to examine the neurological basis for understanding wh-questions in wh-in-situ languages such as Korean, where wh-elements maintain their original positions instead of moving explicitly within the sentence. Our hypothesis centered on the role of the salience and attention network in comprehending wh-questions in wh-in-situ languages, such as the discernment of wh-elements, the demarcation between interrogative types, and the allocation of cognitive resources towards essential constituents vis-à-vis subordinate elements in order to capture the speaker's communicative intent. We explored subject and object wh-questions and scrambled wh-questions, contrasting them with yes/no questions in Korean. Increased activation was observed in the left anterior insula and bilateral frontal operculum, irrespective of the wh-position or scrambling of wh-element. These results suggest the interaction between the salience and attentional system and the syntactic linguistic system, particularly the left anterior insula and bilateral frontal operculum, in comprehending wh-questions in wh-in-situ languages.

Neural processing of prototypicality and simplicity of product design in forming design preferences.

The current study investigates the neural correlates when processing prototypicality and simplicity-affecting the preference of product design. Despite its significance, not much is known about how our brain processes these visual qualities of design when forming design preferences. We posit that, although fluency is the perceptual judgment accounting for the positive effects of both prototypicality and simplicity on design preference, the neural substrates for the fluency judgment associated with prototypicality would differ from those associated with simplicity. To investigate these issues, we conducted an fMRI study of preference decisions for actual product designs with different levels of prototypicality and simplicity. The results show a significant functional gradient between the preference processing of simplicity and prototypicality-i.e., involvement of the early ventral stream of visual information processing for simplicity evaluation but recruitment of the late ventral stream and parietal-frontal brain regions for prototypicality evaluation. The interaction between the simplicity and prototypicality evaluations was found in the extrastriate cortex in the right hemisphere. The segregated brain involvements suggest that the fluency judgment for prototypicality and simplicity contribute to preference choice in different levels of cognitive hierarchy in the perceptual mechanism of the design preference.

Human-machine cooperation meta-model for clinical diagnosis by adaptation to human expert's diagnostic characteristics.

Artificial intelligence (AI) using deep learning approaches the capabilities of human experts in medical image diagnosis. However, due to liability issues in medical decisions, AI is often relegated to an assistant role. Based on this responsibility constraint, the effective use of AI to assist human intelligence in real-world clinics remains a challenge. Given the significant inter-individual variations in clinical decisions among physicians based on their expertise, AI needs to adapt to individual experts, complementing weaknesses and enhancing strengths. For this adaptation, AI should not only acquire domain knowledge but also understand the specific human experts it assists. This study introduces a meta-model for human-machine cooperation that first evaluates each expert's class-specific diagnostic tendencies using conditional probability, based on which the meta-model adjusts the AI's predictions. This meta-model was applied to ear disease diagnosis using otoendoscopy, highlighting improved performance when incorporating individual diagnostic characteristics, even with limited evaluation data. The highest accuracy was achieved by combining each expert's conditional probabilities with machine classification probability, using optimal weights specific to each individual's overall classification accuracy. This tailored model aims to mitigate potential misjudgments due to psychological effects caused by machine suggestions and to capitalize on the unique expertise of individual clinicians.

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.

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.

Differences in intrinsic functional networks in patients with essential tremor who had good and poor long-term responses after thalamotomy performed using MR-guided ultrasound.

OBJECTIVE: Thalamotomy at the nucleus ventralis intermedius using MR-guided focused ultrasound has been an effective treatment method for essential tremor (ET). However, this is not true for all cases, even for successful ablation. How the brain differs in patients with ET between those with long-term good and poor outcomes is not clear. To analyze the functional connectivity difference between patients in whom thalamotomy was effective and those in whom thalamotomy was ineffective and its prognostic role in ET treatment, the authors evaluated preoperative resting-state functional MRI in thalamotomy-treated patients. METHODS: Preoperative resting-state functional MRI data in 85 patients with ET, who were experiencing tremor relief at the time of treatment and were followed up for a minimum of 6 months after the procedure, were collected for the study. The authors conducted a graph independent component analysis of the functional connectivity matrices of tremor-related networks. The patients were divided into thalamotomy-effective and thalamotomy-ineffective groups (thalamotomy-effective group, ≥ 50% motor symptom reduction; thalamotomy-ineffective group, < 50% motor symptom reduction at 6 months after treatment) and the authors compared network components between groups. RESULTS: Seventy-two (84.7%) of the 85 patients showed ≥ 50% tremor reduction from baseline at 6 months after thalamotomy. The network analysis shows significant suppression of functional network components with connections between the areas of the cerebellum and the basal ganglia and thalamus, but enhancement of those between the premotor cortex and supplementary motor area in the noneffective group compared to the effective group. CONCLUSIONS: The present study demonstrates that patients in the noneffective group have suppressed functional subnetworks in the cerebellum and subcortex regions and have enhanced functional subnetworks among motor-sensory cortical networks compared to the thalamotomy-effective group. Therefore, the authors suggest that the functional connectivity pattern might be a possible predictive factor for outcomes of MR-guided focused ultrasound thalamotomy.

Geometric learning of functional brain network on the correlation manifold.

The correlation matrix is a typical representation of node interactions in functional brain network analysis. The analysis of the correlation matrix to characterize brain networks observed in several neuroimaging modalities has been conducted predominantly in the Euclidean space by assuming that pairwise interactions are mutually independent. One way to take account of all interactions in the network as a whole is to analyze the correlation matrix under some geometric structure. Recent studies have focused on the space of correlation matrices as a strict subset of symmetric positive definite (SPD) matrices, which form a unique mathematical structure known as the Riemannian manifold. However, mathematical operations of the correlation matrix under the SPD geometry may not necessarily be coherent (i.e., the structure of the correlation matrix may not be preserved), necessitating a post-hoc normalization. The contribution of the current paper is twofold: (1) to devise a set of inferential methods on the correlation manifold and (2) to demonstrate its applicability in functional network analysis. We present several algorithms on the correlation manifold, including measures of central tendency, cluster analysis, hypothesis testing, and low-dimensional embedding. Simulation and real data analysis support the application of the proposed framework for brain network analysis.

The increased analgesic efficacy of cold therapy after an unsuccessful analgesic experience is associated with inferior parietal lobule activation.

Prior experiences of successful and failed treatments are known to influence the efficacy of a newly applied treatment. However, whether that carry-over effect applies to non-pharmacological treatments is unknown. This study investigated how a failed treatment history with placebo analgesic cream affected the therapeutic outcomes of cold-pack treatment. The neural correlates underlying those effects were also explored using functional magnetic resonance imaging. The effect of the placebo analgesic cream was induced using placebo conditioning with small (44.5 °C to 43.7 °C, negative experience) and large (44.5 °C to 40.0 °C, positive experience) thermal stimuli changes. After the placebo conditioning, brain responses and self-reported evaluations of the effect of subsequent treatment with a cold-pack were contrasted between the two groups. The negative experience group reported less pain and lower anxiety scores in the cold-pack condition than the positive experience group and exhibited significantly greater activation in the right inferior parietal lobule (IPL), which is known to be involved in pain relief. These findings suggest that an unsatisfying experience with an initial pain-relief treatment could increase the expectations for the complementary treatment outcome and improve the analgesic effect of the subsequent treatment. The IPL could be associated with this expectation-induced pain relief process.

Analyzing differences between parent- and self-report measures with a latent space approach.

We explore potential cross-informant discrepancies between child- and parent-report measures with an example of the Child Behavior Checklist (CBCL) and the Youth Self Report (YSR), parent- and self-report measures on children's behavioral and emotional problems. We propose a new way of examining the parent- and child-report differences with an interaction map estimated using a Latent Space Item Response Model (LSIRM). The interaction map enables the investigation of the dependency between items, between respondents, and between items and respondents, which is not possible with the conventional approach. The LSIRM captures the differential positions of items and respondents in the latent spaces for CBCL and YSR and identifies the relationships between each respondent and item according to their dependent structures. The results suggest that the analysis of item response in the latent space using the LSIRM is beneficial in uncovering the differential structures embedded in the response data obtained from different perspectives in children and their parents. This study also argues that the differential hidden structures of children and parents' responses should be taken together to evaluate children's behavioral problems.

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.

Differential structure-function network coupling in the inattentive and combined types of attention deficit hyperactivity disorder.

The heterogeneous presentation of inattentive and hyperactive-impulsive core symptoms in attention deficit hyperactivity disorder (ADHD) warrants further investigation into brain network connectivity as a basis for subtype divisions in this prevalent disorder. With diffusion and resting-state functional magnetic resonance imaging data from the Healthy Brain Network database, we analyzed both structural and functional network efficiency and structure-functional network (SC-FC) coupling at the default mode (DMN), executive control (ECN), and salience (SAN) intrinsic networks in 201 children diagnosed with the inattentive subtype (ADHD-I), the combined subtype (ADHD-C), and typically developing children (TDC) to characterize ADHD symptoms relative to TDC and to test differences between ADHD subtypes. Relative to TDC, children with ADHD had lower structural connectivity and network efficiency in the DMN, without significant group differences in functional networks. Children with ADHD-C had higher SC-FC coupling, a finding consistent with diminished cognitive flexibility, for all subnetworks compared to TDC. The ADHD-C group also demonstrated increased SC-FC coupling in the DMN compared to the ADHD-I group. The correlation between SC-FC coupling and hyperactivity scores was negative in the ADHD-I, but not in the ADHD-C group. The current study suggests that ADHD-C and ADHD-I may differ with respect to their underlying neuronal connectivity and that the added dimensionality of hyperactivity may not explain this distinction.

Differential Biases and Variabilities of Deep Learning-Based Artificial Intelligence and Human Experts in Clinical Diagnosis: Retrospective Cohort and Survey Study.

BACKGROUND: Deep learning (DL)-based artificial intelligence may have different diagnostic characteristics than human experts in medical diagnosis. As a data-driven knowledge system, heterogeneous population incidence in the clinical world is considered to cause more bias to DL than clinicians. Conversely, by experiencing limited numbers of cases, human experts may exhibit large interindividual variability. Thus, understanding how the 2 groups classify given data differently is an essential step for the cooperative usage of DL in clinical application. OBJECTIVE: This study aimed to evaluate and compare the differential effects of clinical experience in otoendoscopic image diagnosis in both computers and physicians exemplified by the class imbalance problem and guide clinicians when utilizing decision support systems. METHODS: We used digital otoendoscopic images of patients who visited the outpatient clinic in the Department of Otorhinolaryngology at Severance Hospital, Seoul, South Korea, from January 2013 to June 2019, for a total of 22,707 otoendoscopic images. We excluded similar images, and 7500 otoendoscopic images were selected for labeling. We built a DL-based image classification model to classify the given image into 6 disease categories. Two test sets of 300 images were populated: balanced and imbalanced test sets. We included 14 clinicians (otolaryngologists and nonotolaryngology specialists including general practitioners) and 13 DL-based models. We used accuracy (overall and per-class) and kappa statistics to compare the results of individual physicians and the ML models. RESULTS: Our ML models had consistently high accuracies (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%), equivalent to those of otolaryngologists (balanced: mean 71.17%, SD 3.37%; imbalanced: mean 72.84%, SD 6.41%) and far better than those of nonotolaryngologists (balanced: mean 45.63%, SD 7.89%; imbalanced: mean 44.08%, SD 15.83%). However, ML models suffered from class imbalance problems (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%). This was mitigated by data augmentation, particularly for low incidence classes, but rare disease classes still had low per-class accuracies. Human physicians, despite being less affected by prevalence, showed high interphysician variability (ML models: kappa=0.83, SD 0.02; otolaryngologists: kappa=0.60, SD 0.07). CONCLUSIONS: Even though ML models deliver excellent performance in classifying ear disease, physicians and ML models have their own strengths. ML models have consistent and high accuracy while considering only the given image and show bias toward prevalence, whereas human physicians have varying performance but do not show bias toward prevalence and may also consider extra information that is not images. To deliver the best patient care in the shortage of otolaryngologists, our ML model can serve a cooperative role for clinicians with diverse expertise, as long as it is kept in mind that models consider only images and could be biased toward prevalent diseases even after data augmentation.

State-Dependent Effective Connectivity in Resting-State fMRI.

The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.

Bee Venom Prevents Mucin 5AC Production through Inhibition of AKT and SPDEF Activation in Airway Epithelia Cells.

IL-13 induces mucus metaplasia, which causes airway obstruction in asthma. Bee venom (BV) and its components have shown anti-inflammatory effects in allergic diseases such as atopic dermatitis and asthma. In this study, we investigated the effect of BV on IL-13-induced mucus metaplasia through activation of the signal transducer and activator of transcription (STAT6), and regulation of SAM-pointed domain containing Ets-like factor (SPDEF) and forkhead box A2 (FOXA2) in the airway epithelia cell line A549. In A549 cells, BV (1.0 µg/mL) inhibited IL-13 (10 ng/mL)-induced AKT phosphorylation, increase in SPDEF protein expression, and decrease in FOXA2 protein expression-but not STAT6 phosphorylation. BV also prevented the IL-13-induced increase in mucin 5AC (MUC5AC) mRNA and protein expression. Moreover, we observed that inhibition of phosphoinositide 3 kinase (PI3K)/AKT using LY294002 (50 µM) could reverse the alterations in FOXA2 and MUC5AC expression -by IL-13 and BV. However, LY294002 did not affect IL-13- and BV-induced changes in SPDEF expression. These findings indicate that BV inhibits MUC5AC production through the regulation of SPDEF and FOXA2. The inhibition of MUC5AC production through FOXA2 is mediated via the suppression of PI3K/AKT activation by BV. BV may be helpful in the prevention of mucus metaplasia in asthma.

Inter-species cortical registration between macaques and humans using a functional network property under a spherical demons framework.

Systematic evaluation of cortical differences between humans and macaques calls for inter-species registration of the cortex that matches homologous regions across species. For establishing homology across brains, structural landmarks and biological features have been used without paying sufficient attention to functional homology. The present study aimed to determine functional homology between the human and macaque cortices, defined in terms of functional network properties, by proposing an iterative functional network-based registration scheme using surface-based spherical demons. The functional connectivity matrix of resting-state functional magnetic resonance imaging (rs-fMRI) among cortical parcellations was iteratively calculated for humans and macaques. From the functional connectivity matrix, the functional network properties such as principal network components were derived to estimate a deformation field between the human and macaque cortices. The iterative registration procedure updates the parcellation map of macaques, corresponding to the human connectome project's multimodal parcellation atlas, which was used to derive the macaque's functional connectivity matrix. To test the plausibility of the functional network-based registration, we compared cortical registration using structural versus functional features in terms of cortical regional areal change. We also evaluated the interhemispheric asymmetry of regional area and its inter-subject variability in humans and macaques as an indirect validation of the proposed method. Higher inter-subject variability and interhemispheric asymmetry were found in functional homology than in structural homology, and the assessed asymmetry and variations were higher in humans than in macaques. The results emphasize the significance of functional network-based cortical registration across individuals within a species and across species.

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 adaptive model estimation to solve the speed accuracy tradeoff problem in psychophysical experiments.

Most psychological experiments measure human cognitive function through the response time and accuracy of the response to a set of stimuli. Since response time and accuracy complement each other, it is often difficult to interpret cognitive performance based on only one dependent measurement and raises a speed-accuracy tradeoff (SAT) problem. In overcoming this problem, SAT experimental paradigms and models that integrate response time and accuracy have been proposed to understand information processing in human cognitive function. However, due to a lengthy SAT experiment for reliable model estimation, SAT experiments' practical limitations have been pointed out. Thus, these limitations call for an efficient technique to shorten the number of trials required to estimate the SAT function reliably. Instead of using a block's stimulus-onset asynchrony (SOA) accuracy with long block-based task trials, we introduced a Bayesian SAT function estimation using trial-by-trial response time and correctness, which makes SAT tasks flexible and easily extendable to multiple trials. We then proposed a Bayesian adaptive method to select optimal SOA by maximizing information gain to estimate model parameters. Simulation results showed that the proposed Bayesian adaptive estimation was highly efficient and robust for accuracy and precision of estimating SAT function by enabling "multiple-step ahead search."

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.

A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles.

The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.

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.