Multimodal neural correlates of dispositional resilience among healthy individuals.

Resilient individuals are less likely to develop psychiatric disorders despite extreme psychological distress. This study investigated the multimodal structural neural correlates of dispositional resilience among healthy individuals. Participants included 92 healthy individuals. The Korean version of the Connor-Davidson Resilience Scale and other psychological measures were used. Gray matter volumes (GMVs), cortical thickness, local gyrification index (LGI), and white matter (WM) microstructures were analyzed using voxel-based morphometry, FreeSurfer, and tract-based spatial statistics, respectively. Higher resilient individuals showed significantly higher GMVs in the inferior frontal gyrus (IFG), increased LGI in the insula, and lower fractional anisotropy values in the superior longitudinal fasciculus II (SLF II). These resilience's neural correlates were associated with good quality of life in physical functioning or general health and low levels of depression. Therefore, the GMVs in the IFG, LGI in the insula, and WM microstructures in the SLF II can be associated with resilience that contributes to emotional regulation, empathy, and social cognition.

Relationship between Mediterranean diet and depression in South Korea: the Korea National Health and Nutrition Examination Survey.

Background: Several studies have shown that adherence to the Mediterranean diet is associated with a lower risk of depression; however, little is known about the Asian population. This study investigated the relationship between adherence to the Mediterranean diet and depression in a sample of the South Korean population. Methods: In total, 5,849 adults from the 2014 and 2016 Korea National Health and Nutrition Examination Surveys were included in the study. The Mediterranean diet adherence was measured using a modified alternate Mediterranean diet score (mMED) developed to adjust for Korean dietary patterns. The mMED scores using the Food Frequency Questionnaire were divided into four categories (0-2, 3-4, 5-6, and 7-9 points). Subjects with depression were defined as having moderate-to-severe depressive symptoms using the Patient Health Questionnaire-9, with a cutoff value of 10. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). A subgroup analysis was performed based on sex. Results: The results of logistic regression analysis indicated that individuals with higher mMED were 42-73% less likely to report depression compared to individuals with the lowest mMED [ORs (95% CIs) =0.58 (0.37-0.90), 0.50 (0.31-0.80), 0.27 (0.15-0.47)] after adjusting for socio-demographic and health-related variables. In women, individuals with mMED of 7-9 had 71% lower odds of depression [ORs (95% CIs): 0.29 (0.13-0.64)]. In men, individuals with mMED of 5-9 had 55% [ORs (95% CIs): 0.45 (0.23-0.91)] to 79% [ORs (95% CIs): 0.21 (0.08-0.57)] lower odds of depression. Conclusion: This study suggests that adherence to the Mediterranean diet is inversely associated with depression in both men and women among Korean adults. This study provides evidence that a Mediterranean diet is crucial in preventing depressive symptoms in Asian populations.

Self-compassion is associated with the superior longitudinal fasciculus in the mirroring network in healthy individuals.

Self-compassion (SC) involves taking an emotionally positive attitude towards oneself when suffering. Although SC has positive effects on mental well-being as well as a protective role in preventing symptoms in healthy individuals, few studies on white matter (WM) microstructures in neuroimaging studies of SC has been studied. Brain imaging data were acquired from 71 healthy participants. WM regions of mirroring network were analyzed using tract-based spatial statistics. After the WM regions associated with SC were extracted, exploratory correlation analysis with the self-forgiveness scale, the coping scale, and the world health organization quality of life scale abbreviated version was performed. We found that self-compassion scale total scores were negatively correlated with the fractional anisotropy (FA) values of the superior longitudinal fasciculus (SLF) in healthy individuals. The self-kindness and mindfulness subscale scores were also negatively correlated with FA values of the same regions. These FA values were negatively correlated with the total scores of self-forgiveness scale, and self-control coping strategy and confrontation coping strategy. Our findings suggest levels of SC may be associated with WM microstructural changes of SLF in healthy individuals. These lower WM microstructures may be associated with positive personal attitudes, such as self-forgiveness, self-control and active confrontational strategies.

Gender differences in anxiety and depressive symptomatology determined by network analysis in panic disorder.

BACKGROUND: It has been suggested that gender differences in anxiety and depressive symptoms characterize panic disorder (PD) in terms of vulnerability to stressful life events, anxiety, depressive symptom patterns, and brain structure. However, few studies have investigated the gender differences in PD using a network approach. METHODS: This study included 619 participants with PD (313 men). The Panic Disorder Severity Scale, Albany Panic and Phobia Questionnaire, and Beck Depression Inventory-II were used to evaluate symptomatology. To investigate the PD-related white matter (WM) neural correlates, tract-based spatial statistics were used. The PD-related clinical scales and WM neural correlates were included in the network analysis to identify associations between variables. To evaluate network differences between genders, network comparison tests were conducted. RESULTS: Our findings revealed that agoraphobia in men was the strongest central symptom. In addition, loss of pleasure, and not anxiety or panic symptoms, was the strongest central symptom in women with PD. The network comparison test revealed that the bridge strength score was higher in agoraphobia and tiredness in men and in self-criticalness in women. Furthermore, in the network that includes neural correlates of WM, the bridge strength score was higher in the cingulate gyrus WM in men and the cingulum hippocampus in women. LIMITATIONS: Since this is a cross-sectional network study of PD patients, the causal relationship between interactions in this network analysis for both genders may not be accurately determined. CONCLUSION: Network structures of anxiety and depressive symptomatology and related WM neural correlates can differ according to gender in PD patients.

Differences in Functional Level and Central Symptom of Network Structures in the Patients Seeking Treatment for Panic Disorder Before and During the COVID-19 Pandemic.

OBJECTIVE: Mental health problems such as anxiety, panic, and depression have been exacerbated by the coronavirus disease-2019 (COVID-19). This study aimed to compare the symptom severities and overall function before and during the COVID-19 pandemic among patients with panic disorder (PD) seeking treatment compared to healthy controls (HCs). METHODS: Baseline data were collected from the two groups (patients with PD and HCs) in two separate periods: before COVID-19 (Jan 2016-Dec 2019) and during COVID-19 (Mar 2020-Jul 2022). A total 453 participants (before COVID-19: 246 [139 patients with PD and 107 HCs], during COVID-19: 207 [86 patients with PD and 121 HCs]) was included. Scales for panic and depressive symptoms and overall function were administered. Additionally, network analyses were performed to compare the two groups within the patients with PD. RESULTS: The results of two-way analysis of variance analyses showed that patients with PD enrolled during COVID-19 showed higher levels of interoceptive fear and lower overall functioning. In addition, a network comparison test revealed that a significantly high strength and expected influence for agoraphobia and avoidance in patients with PD during COVID-19. CONCLUSION: This study suggested that the overall function could have worsened, and the importance of agoraphobia and avoidance as a central symptom may have increased in patients with PD seeking treatment during COVID-19.

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.

A multimodal study regarding neural correlates of the subjective well-being in healthy individuals.

Although happiness or subjective well-being (SWB) has drawn much attention from researchers, the precise neural structural correlates of SWB are generally unknown. In the present study, we aimed to investigate the associations between gray matter (GM) volumes, white matter (WM) microstructures, and SWB in healthy individuals, mainly young adults using multimodal T1 and diffusion tensor imaging studies. We enrolled 70 healthy individuals using magnetic resonance imaging. We measured their SWB using the Concise Measure of Subjective Well-Being. Voxel-wise statistical analysis of GM volumes was performed using voxel-based morphometry, while fractional anisotropy (FA) values were analyzed using tract-based spatial statistics. In healthy individuals, higher levels of SWB were significantly correlated with increased GM volumes of the anterior insula and decreased FA values in clusters of the body of the corpus callosum, precuneus WM, and fornix cres/stria terminalis. A correlational analysis revealed that GM volumes and FA values in these significant regions were significantly correlated with severity of psychological symptoms such as depression, anxiety, and quality of life. Our findings indicate that GM volumes and WM microstructures in these regions may contribute to SWB, and could be the neural basis for psychological symptom severity as well as quality of life in healthy individuals.

Prediction of prognosis in patients with panic disorder using pre-treatment brain white matter features.

BACKGROUND: The early identification of patients with panic disorder (PD) with a poor prognosis is important for improving treatment outcomes; however, it is challenging due to a lack of objective biomarkers. We investigated the reliability of characterizing structural white matter (WM) connectivity and its ability to predict PD prognosis after pharmacotherapy. METHODS: A total of 138 patients (59 men) with PD and 153 healthy controls (HCs; 73 men) participated in this study. PD symptom severity was measured using the Panic Disorder Severity Scale (PDSS) at baseline and follow-up periods of 8 weeks, 6 months, and 1 year. The least absolute shrinkage and selection operator (Lasso) was utilized to identify prognosis-related WM regions on diffusion imaging features. RESULTS: Lasso identified seven prognosis-related WM regions: the bilateral posterior corona radiata, bilateral posterior limb of the internal capsule, the left retrolenticular part of the internal capsule, the left sagittal stratum, and the right fornix/stria terminalis. Some of these regions showed lower mean fractional anisotropy (FA) values in patients with PD than in HCs. The predicted PDSS scores using FA from these regions consistently correlated with the actual prognosis in all periods. LIMITATIONS: This study had limited ability to evaluate individual longitudinal changes in detail owing to the data acquisition time and brain atlas resolution. CONCLUSIONS: Our findings suggest the possibility of using structural WM connectivity as a biomarker for the clinical characterization of PD. Our findings will expand our understanding of the neurobiology of PD and improve biomarker-based prognosis prediction in clinical practice.

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.

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 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.

Graph-theoretical analysis for energy landscape reveals the 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-theoretical 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 the state transition, the default mode network acts as a transition hub, while coactivation of the prefrontal cortex and default mode network is captured as the global minimum. In summary, the resting-state cerebral cortex has a well-organized architecture of state transitions among stable states, when evaluated by a graph-theoretical analysis of the nonlinear state transition network of the brain.

Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database.

BACKGROUND: Ear and mastoid disease can easily be treated by early detection and appropriate medical care. However, short of specialists and relatively low diagnostic accuracy calls for a new way of diagnostic strategy, in which deep learning may play a significant role. The current study presents a machine learning model to automatically diagnose ear disease using a large database of otoendoscopic images acquired in the clinical environment. METHODS: Total 10,544 otoendoscopic images were used to train nine public convolution-based deep neural networks to classify eardrum and external auditory canal features into six categories of ear diseases, covering most ear diseases (Normal, Attic retraction, Tympanic perforation, Otitis externa±myringitis, Tumor). After evaluating several optimization schemes, two best-performing models were selected to compose an ensemble classifier, by combining classification scores of each classifier. FINDINGS: According to accuracy and training time, transfer learning models based on Inception-V3 and ResNet101 were chosen and the ensemble classifier using the two models yielded a significant improvement over each model, the accuracy of which is in average 93·67% for the 5-folds cross-validation. Considering substantial data-size dependency of classifier performance in the transfer learning, evaluated in this study, the high accuracy in the current model is attributable to the large database. INTERPRETATION: The current study is unprecedented in terms of both disease diversity and diagnostic accuracy, which is compatible or even better than an average otolaryngologist. The classifier was trained with data in a various acquisition condition, which is suitable for the practical environment. This study shows the usefulness of utilizing a deep learning model in the early detection and treatment of ear disease in the clinical situation. FUND: This research was supported by Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT(NRF-2017M3C7A1049051).

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.

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.

Aberrant cerebro-cerebellar functional connectivity and minimal self-disturbance in individuals at ultra-high risk for psychosis and with first-episode schizophrenia.

BACKGROUND: In the tradition of phenomenology, minimal selfdisturbance has been suggested as a manifestation of the core pathogenesis of schizophrenia; however, the underlying neural mechanism remains unclear. Here, in line with the concept of "cognitive dysmetria," we investigated the cerebro-cerebellar default mode network (DMN) connectivity and its association with pre-reflective minimal selfdisturbance in individuals at ultra-high risk (UHR) for psychosis and patients with first-episode schizophrenia (FES). METHODS: Thirty-three UHR individuals, 18 FES patients, and 56 healthy controls (HCs) underwent functional magnetic resonance imaging during rest at baseline. Seed-based functional connectivity (FC) analysis was performed using the cerebellar DMN seeds from the bilateral Crus I, followed by between-group comparisons. Correlation analysis was conducted to examine the relationship between the cerebro-cerebellar FC and the self-reported severity of minimal self-disturbance in the UHR and FES groups, respectively. RESULTS: FES participants showed significantly reduced cerebellar FC with the left presupplementary motor area (preSMA), right anterior prefrontal cortex (aPFC), and precuneus compared to HCs, while UHR participants showed an intermediate decrease between the other two groups, particularly in the left preSMA and right aPFC. Minimal self-disturbance, which appeared at similar levels in both UHR and FES groups, was significantly associated with cerebro-cerebellar FC, although each group presented different patterns of associations. CONCLUSIONS: Aberrant cerebro-cerebellar FC, which may be closely related to minimal self-disturbance, may be able to provide meaningful insights into the real gestalt of schizophrenia and contribute to further research to predict future psychosis in UHR individuals.

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.