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Interoception plays an important role in emotion processing. However, the neurobiological substrates of the relationship between visceral responses and emotional experiences remain unclear. In the present study, we measured interoceptive sensitivity using the heartbeat discrimination task and investigated the effects of individual differences in interoceptive sensitivity on changes in pulse rate and insula activity in response to subjective emotional intensity. We found a positive correlation between heart rate and valence level when listening to music only in the high interoceptive sensitivity group. The valence level was also positively correlated with music-elicited anterior insula activity. Furthermore, a region of interest analysis of insula subregions revealed significant activity in the left dorsal dysgranular insula for individuals with high interoceptive sensitivity relative to individuals with low interoceptive sensitivity while listening to the high-valence music pieces. Our results suggest that individuals with high interoceptive sensitivity use their physiological responses to assess their emotional level when listening to music. In addition, insula activity may reflect the use of interoceptive signals to estimate emotions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Maekawa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Interoception, the neural sensing of visceral signals, and interoceptive awareness (IA), the conscious perception of interoception, are crucial for life survival functions and mental health. Resilience, the capacity to overcome adversity, has been associated with reduced interoceptive disturbances. Here, we sought evidence for our Insula Modular Active Control (IMAC) model that suggest that the insula, a brain region specialized in the processing of interoceptive information, realizes IA and contributes to resilience and mental health via cortico-subcortical connections., Methods: 64 healthy participants (32 females; ages 18-34 years) answered questionnaires that assess IA and resilience. Mental health was evaluated with the Beck Depression Inventory II that assesses depressive mood. Participants also underwent a 15 minute resting-state functional resonance imaging session. Pearson correlations and mediation analyses were used to investigate the relationship between IA and resilience and their contributions to depressive mood. We then performed insula seed-based functional connectivity analyzes to identify insula networks involved in IA, resilience and depressive mood., Results: We first demonstrated that resilience mediates the relationship between IA and depressive mood. Second, shared and distinct intra-insula, insula-cortical and insula-subcortical networks were associated with IA, resilience and also predicted the degree of experienced depressive mood. Third, while resilience was associated with stronger insula-precuneus, insula-cerebellum and insula-prefrontal networks, IA was linked with stronger intra-insula, insula-striatum and insula-motor networks., Conclusions: Our findings help understand the roles of insula-cortico-subcortical networks in IA and resilience. These results also highlight the potential use of insula networks as biomarkers for depression prediction., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML models. Here, we introduce an efficient and high-performance decoding package incorporating a forward variable selection (FVS) algorithm and hyper-parameter optimization that automatically identifies the best feature pairs for both classification and regression models, where a total of 18 ML models are implemented by default. First, the FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross-validation step that identifies the best subset of features based on a predefined criterion for each model. Next, the hyperparameters of each ML model are optimized at each forward iteration. Final outputs highlight an optimized number of selected features (brain regions of interest) for each model with its accuracy. Furthermore, the toolbox can be executed in a parallel environment for efficient computation on a typical personal computer. With the optimized forward variable selection decoder (oFVSD) pipeline, we verified the effectiveness of decoding sex classification and age range regression on 1,113 structural magnetic resonance imaging (MRI) datasets. Compared to ML models without the FVS algorithm and with the Boruta algorithm as a variable selection counterpart, we demonstrate that the oFVSD significantly outperformed across all of the ML models over the counterpart models without FVS (approximately 0.20 increase in correlation coefficient, r , with regression models and 8% increase in classification models on average) and with Boruta variable selection algorithm (approximately 0.07 improvement in regression and 4% in classification models). Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. Altogether, the oFVSD toolbox efficiently and effectively improves the performance of both classification and regression ML models, providing a use case example on MRI datasets. With its flexibility, oFVSD has the potential for many other modalities in neuroimaging. This open-source and freely available Python package makes it a valuable toolbox for research communities seeking improved decoding accuracy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Dang, Fermin and Machizawa.)

Interoceptive awareness (IA), the subjective and conscious perception of visceral and physiological signals from the body, has been associated with functions of cortical and subcortical neural systems involved in emotion control, mood and anxiety disorders. We recently hypothesized that IA and its contributions to mental health are realized by a brain interoception network (BIN) linking brain regions that receive ascending interoceptive information from the brainstem, such as the amygdala, insula and anterior cingulate cortex (ACC). However, little evidence exists to support this hypothesis. In order to test this hypothesis, we used a publicly available dataset that contained both anatomical neuroimaging data and an objective measure of IA assessed with a heartbeat detection task. Whole-brain Voxel-Based Morphometry (VBM) was used to investigate the association of IA with gray matter volume (GMV) and the structural covariance network (SCN) of the amygdala, insula and ACC. The relationship between IA and mental health was investigated with questionnaires that assessed depressive symptoms and anxiety. We found a positive correlation between IA and state anxiety, but not with depressive symptoms. In the VBM analysis, only the GMV of the left anterior insula showed a positive association with IA. A similar association was observed between the parcellated GMV of the left dorsal agranular insula, located in the anterior insula, and IA. The SCN linking the right dorsal agranular insula with the left dorsal agranular insula and left hyper-granular insula were positively correlated with IA. No association between GMV or SCN and depressive symptoms or anxiety were observed. These findings revealed a previously unknown association between IA, insula volume and intra-insula SCNs. These results may support development of non-invasive neuroimaging interventions, e.g., neurofeedback, seeking to improve IA and to prevent development of mental health problems, such anxiety disorders., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Trust attitude is a social personality trait linked with the estimation of others' trustworthiness. Trusting others, however, can have substantial negative effects on mental health, such as the development of depression. Despite significant progress in understanding the neurobiology of trust, whether the neuroanatomy of trust is linked with depression vulnerability remains unknown. To investigate a link between the neuroanatomy of trust and depression vulnerability, we assessed trust and depressive symptoms and employed neuroimaging to acquire brain structure data of healthy participants. A high depressive symptom score was used as an indicator of depression vulnerability. The neuroanatomical results observed with the healthy sample were validated in a sample of clinically diagnosed depressive patients. We found significantly higher depressive symptoms among low trusters than among high trusters. Neuroanatomically, low trusters and depressive patients showed similar volume reduction in brain regions implicated in social cognition, including the dorsolateral prefrontal cortex (DLPFC), dorsomedial PFC, posterior cingulate, precuneus, and angular gyrus. Furthermore, the reduced volume of the DLPFC and precuneus mediated the relationship between trust and depressive symptoms. These findings contribute to understanding social- and neural-markers of depression vulnerability and may inform the development of social interventions to prevent pathological depression., (© 2022. The Author(s).)

In the brain, the insular cortex receives a vast amount of interoceptive information, ascending through deep brain structures, from multiple visceral organs. The unique hierarchical and modular architecture of the insula suggests specialization for processing interoceptive afferents. Yet, the biological significance of the insula's neuroanatomical architecture, in relation to deep brain structures, remains obscure. In this opinion piece, we propose the Insula Hierarchical Modular Adaptive Interoception Control (IMAC) model to suggest that insula modules (granular, dysgranular and agranular), forming parallel networks with the prefrontal cortex and striatum, are specialized to form higher order interoceptive representations. These interoceptive representations are recruited in a context-dependent manner to support habitual, model-based and exploratory control of visceral organs and physiological processes. We discuss how insula interoceptive representations may give rise to conscious feelings that best explain lower order deep brain interoceptive representations, and how the insula may serve to defend the body and mind against pathological depression., (© 2022 The Authors.)

Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B 0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites., Method: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC)., Results: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI., Discussion: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed., Competing Interests: Declaration of Competing Interest Jack J. Miller would like to acknowledge the support of a Novo Nordisk Research Fellowship run in conjunction with the University of Oxford. Francisco Reyes-Madrigal has served as a speaker for Janssen (Johnson & Johnson) and AstraZeneca. Marc Thioux and Pim van Dijk were supported by The Netherlands Organization for Health Research and Development (ZonMW) and the Dorhout Mees Foundation. All other authors have no conflict of interest to declare., (Copyright © 2021. Published by Elsevier Inc.)

Previous studies have shown that genetic variations in rs53576, a common variant of the oxytocin receptor gene (OXTR) resulting from a single nucleotide polymorphism involving an adenine (A)/guanine (G) transition, are associated with attitudinal trust in men. However, the pathway from gene to behaviour has not been elucidated. We conducted the present study to determine whether amygdala volume mediates the association between OXTR rs53576 genotypes and attitudinal trust. Our results revealed that the left amygdala volume was significantly smaller in GG men than in AA and AG men, whereas it was significantly smaller in AA and AG women than in GG women. In addition, the left amygdala volume was negatively associated with attitudinal trust in men, whereas there was no such association in women. We also found a significant mediation effect of the left amygdala volume on the association between OXTR rs53576 genotypes and attitudinal trust in men. The results of our study suggest that the left amygdala volume plays a pivotal role in the association between OXTR rs53576 genotypes and attitudinal trust in men.

Altruistic punishment following social norm violations promotes human cooperation. However, experimental evidence indicates that some forms of punishment are spiteful rather than altruistic. Using two types of punishment games and seven non-strategic games, we identified strong behavioural differences between altruistic and spiteful punishers. Altruistic punishers who rejected unfair offers in the ultimatum game and punished norm violators in the third-party punishment game behaved pro-socially in various non-strategic games. Spiteful punishers who rejected unfair offers in the ultimatum game but did not punish norm violators in the third-party punishment game behaved selfishly in non-strategic games. In addition, the left caudate nucleus was larger in spiteful punishers than in altruistic punishers. These findings are in contrast to the previous assumption that altruistic punishers derive pleasure from enforcement of fairness norms, and suggest that spiteful punishers derive pleasure from seeing the target experience negative consequences.