Heartbeat-related spectral perturbation of electroencephalogram reflects dynamic interoceptive attention states in the trial-by-trial classification analysis.

Attending to heartbeats for interoceptive awareness initiates distinct electrophysiological responses synchronized with the R-peaks of an electrocardiogram (ECG), such as the heartbeat-evoked potential (HEP). Beyond HEP, this study proposes heartbeat-related spectral perturbation (HRSP), a time-frequency map of the R-peak locked electroencephalogram (EEG), and explores its characteristics in identifying interoceptive attention states using a classification approach. HRSPs of EEG brain components specified by independent component analysis (ICA) were used for the offline and online classification of interoceptive states. A convolutional neural network (CNN) designed specifically for HRSP was applied to publicly available data from a binary-state experiment (attending to self-heartbeats and white noise) and data from our four-state classification experiment (attending to self-heartbeats, white noise, time passage, and toe) with diverse input feature conditions of HRSP. From the dynamic state perspective, we evaluated the primary frequency bands of HRSP and the minimal number of averaging epochs required to reflect changing interoceptive attention states without compromising accuracy. We also assessed the utility of group ICA and models for classifying HRSP in new participants. The CNN for trial-by-trial HRSP with actual R-peaks demonstrated significantly higher classification accuracy than HRSP with sham, i.e., randomly positioned, R-peaks. Gradient-weighted class activation mapping highlighted the prominent role of theta and alpha bands between 200-600 ms post-R-peak-features absent in classifications using sham HRSPs. Online classification benefits from employing a group ICA and classification model, ensuring reliable accuracy without individual EEG precollection. These results suggest HRSP's potential to reflect interoceptive attention states, proposing transformative implications for clinical applications.

The prognostic impact of psychiatric intervention on alcohol-associated liver disease : the UK Biobank cohort study.

Background/Aims: Alcohol-associated liver disease (ALD) is a public health concern. ALD patients often have psychiatric comorbidities, but the effects of psychiatric interventions on ALD are not well-established. This study explores the prognostic impact of psychiatric intervention on ALD within UK Biobank cohort. Methods: This population-based study included 2,417 ALD patients from the UK Biobank cohort. Psychiatric intervention was defined by a consultation with psychiatrists during hospitalization or a history of medication related to alcohol use disorder and psychiatric comorbidities. Survival analysis was conducted, incorporating propensity score matching (PSM), to precisely assess the impact of psychiatric intervention. Results: Among 2,417 ALD patients, those with F10 (mental disorders due to alcohol) codes had poorer survival outcomes. Psychiatric intervention significantly improved the outcomes of both all-cause and liver-related mortality and reduced the incidence of liver cirrhosis. In subgroup or 2-year landmark analyses, psychiatric intervention consistently showed a survival benefit in ALD patients. In the multivariate analysis, psychiatric intervention was identified as a favorable prognostic factor (hazard ratio, 0.780; P = 0.002 after PSM). Conclusions: This study demonstrates the favorable effect of psychiatric intervention in ALD patients with psychiatric comorbidities. These findings emphasize the importance of integrated management for ALD patients to address both their medical and psychiatric aspects. Therefore, we suggest the potential benefits of early psychiatric interventions in improving survival outcomes in ALD.

Prosocial emotions predict individual differences in economic decision-making during ultimatum game with dynamic reciprocal contexts.

Social decision-making is known to be influenced by predictive emotions or the perceived reciprocity of partners. However, the connection between emotion, decision-making, and contextual reciprocity remains less understood. Moreover, arguments suggest that emotional experiences within a social context can be better conceptualised as prosocial rather than basic emotions, necessitating the inclusion of two social dimensions: focus, the degree of an emotion's relevance to oneself or others, and dominance, the degree to which one feels in control of an emotion. For better representation, these dimensions should be considered alongside the interoceptive dimensions of valence and arousal. In an ultimatum game involving fair, moderate, and unfair offers, this online study measured the emotions of 476 participants using a multidimensional affective rating scale. Using unsupervised classification algorithms, we identified individual differences in decisions and emotional experiences. Certain individuals exhibited consistent levels of acceptance behaviours and emotions, while reciprocal individuals' acceptance behaviours and emotions followed external reward value structures. Furthermore, individuals with distinct emotional responses to partners exhibited unique economic responses to their emotions, with only the reciprocal group exhibiting sensitivity to dominance prediction errors. The study illustrates a context-specific model capable of subtyping populations engaged in social interaction and exhibiting heterogeneous mental states.

Effects of Parental Verbal Abuse Experience on the Glutamate Response to Swear Words in the Ventromedial Prefrontal Cortex: A Functional 1H-magnetic Resonance Spectroscopy Study.

Objective: Several lines of evidence indicate verbal abuse (VA) critically impacts the developing brain; however, whether VA results in changes in brain neurochemistry has not been established. Here, we hypothesized that exposure to recurrent parental VA elicits heightened glutamate (Glu) responses during the presentation of swear words, which can be measured with functional magnetic resonance spectroscopy (fMRS). Methods: During an emotional Stroop task consisting of blocks of color and swear words, metabolite concentration changes were measured in the ventromedial prefrontal cortex (vmPFC) and the left amygdalohippocampal region (AMHC) of healthy adults (14 F/27 M, 23 ± 4 years old) using fMRS. The dynamic changes in Glu and their associations with the emotional state of the participants were finally evaluated based on 36 datasets from the vmPFC and 30 from the AMHC. Results: A repeated-measures analysis of covariance revealed a modest effect of parental VA severity on Glu changes in the vmPFC. The total score on the Verbal Abuse Questionnaire by parents (pVAQ) was associated with the Glu response to swear words (ΔGluSwe). The interaction term of ΔGluSwe and baseline N-acetyl aspartate (NAA) level in the vmPFC could be used to predict state-trait anxiety level and depressive mood. We could not find any significant associations between ΔGluSwe in the AMHC and either pVAQ or emotional states. Conclusion: Parental VA exposure in individuals is associated with a greater Glu response towards VA-related stimuli in the vmPFC and that the accompanying low NAA level may be associated with anxiety level or depressive mood.

Impaired Oxygenation of the Prefrontal Cortex During Verbal Fluency Task in Young Adults With Major Depressive Disorder and Suicidality: A Functional Near-Infrared Spectroscopy Study.

Background: Few previous studies have focused on prefrontal activation in young adults diagnosed with major depressive disorder (MDD) and suicidality via functional near-infrared spectroscopy (fNIRS). Materials and Methods: A total of 59 healthy controls (HCs), 35 patients with MDD but without suicidality, and 25 patients with MDD and suicidality, between the ages of 18-34 years, were enrolled. Changes in oxygenated hemoglobin (oxy-Hb) levels of the prefrontal cortex at baseline, 4 weeks, and 8 weeks, were evaluated using a protocol consisting of three consecutively repeated trials of rest, speech, and verbal fluency test (VFT) via fNIRS. MDD was diagnosed and suicidality was evaluated based on Mini International Neuropsychiatric Interview (MINI). Results: Oxy-Hb levels were impaired in patients with MDD compared with HCs (p = 0.018 for left prefrontal cortex; p = 0.021 for right ventromedial prefrontal cortex; p = 0.002 for left frontopolar cortex). Among the three groups including HCs, MDD without suicidality, and MDD with suicidality, prefrontal oxygenation was most decreased in MDD patients with suicidality. A significantly impaired prefrontal oxygenation in the right ventrolateral prefrontal cortex (VLPFC) was detected after adjusting for covariates in MDD patients with suicidality, compared to those without suicidality. Conclusion: Impaired prefrontal oxygenation during cognitive execution may serve as a diagnostic biomarker for suicidality in young adult patients with MDD.

Neurocomputational mechanism of controllability inference under a multi-agent setting.

Controllability perception significantly influences motivated behavior and emotion and requires an estimation of one's influence on an environment. Previous studies have shown that an agent can infer controllability by observing contingency between one's own action and outcome if there are no other outcome-relevant agents in an environment. However, if there are multiple agents who can influence the outcome, estimation of one's genuine controllability requires exclusion of other agents' possible influence. Here, we first investigated a computational and neural mechanism of controllability inference in a multi-agent setting. Our novel multi-agent Bayesian controllability inference model showed that other people's action-outcome contingency information is integrated with one's own action-outcome contingency to infer controllability, which can be explained as a Bayesian inference. Model-based functional MRI analyses showed that multi-agent Bayesian controllability inference recruits the temporoparietal junction (TPJ) and striatum. Then, this inferred controllability information was leveraged to increase motivated behavior in the vmPFC. These results generalize the previously known role of the striatum and vmPFC in single-agent controllability to multi-agent controllability, and this generalized role requires the TPJ in addition to the striatum of single-agent controllability to integrate both self- and other-related information. Finally, we identified an innate positive bias toward the self during the multi-agent controllability inference, which facilitated behavioral adaptation under volatile controllability. Furthermore, low positive bias and high negative bias were associated with increased daily feelings of guilt. Our results provide a mechanism of how our sense of controllability fluctuates due to other people in our lives, which might be related to social learned helplessness and depression.

Feasibility study on transcutaneous auricular vagus nerve stimulation using millimeter waves.

Objective. Electrical stimulation of the auricular vagus nerve is a non-invasive neuromodulation technique that has been used for various conditions, including depression, epilepsy, headaches, and cerebral ischemia. However, unwanted non-vagal nerve stimulations can occur because of diffused stimulations. The objective of this study is to develop a region-specific non-invasive vagus nerve stimulation (VNS) technique using the millimeter wave (MMW) as a stimulus for the auricular branch of the vagus nerve (ABVN).Approach. A numerical simulation was conducted to ascertain whether the MMW could excite the ABVN in the human outer-ear with a millimeter-scale spatial resolution. Additionally, MMW-induced neuronal responses in seven mice were evaluated. Transcutaneous auricular VNS (ta-VNS) was applied to the cymba conchae innervated by the AVBN using a 60-GHz continuous wave (CW). As a control, the auricle's exterior margin was stimulated and referred to as transcutaneous auricular non-vagus nerve stimulation (ta-nonVNS). During stimulation, the local field potential (LFP) in the nucleus tractus solitarii (NTS), an afferent vagal projection site, was recorded simultaneously.Main results. The ta-VNS with a stimulus level of 13 dBm showed a significant increase in the LFP power in the NTS. The mean increases in power (n = 7) in the gamma high and gamma very high bands were 8.6 ± 2.0% and 18.2 ± 5.9%, respectively. However, the ta-nonVNS with a stimulus level of 13 dBm showed a significant decrease in the LFP power in the NTS. The mean decreases in power in the beta and gamma low bands were 11.0 ± 4.4% and 10.8 ± 2.8%, respectively. These findings suggested that MMW stimulation clearly induced a different response according to the presence of ABVN.Significance. Selective auricular VNS is feasible using the MMW. This study provides the basis for the development of a new clinical treatment option using the stimulation of the ta-VNS with a square millimeter spatial resolution.

Deep graph neural network-based prediction of acute suicidal ideation in young adults.

Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffered from low sensitivity. We developed and validated a deep graph neural network model that increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training; n = 14,238 for testing) using multi-dimensional questionnaires and suicidal ideation within 2 weeks as the prediction target. The best model achieved a sensitivity of 76.3%, specificity of 83.4%, and an area under curve of 0.878 (95% confidence interval, 0.855-0.899). We demonstrated that multi-dimensional deep features covering depression, anxiety, resilience, self-esteem, and clinico-demographic information contribute to the prediction of suicidal ideation. Our model might be useful for the remote evaluation of suicide risk in the general population of young adults for specific situations such as the COVID-19 pandemic.

Aberrant EEG signal variability at a specific temporal scale in major depressive disorder.

OBJECTIVE: Signal variability is linked to irregularities in time series caused by intrinsic nonlinearities of the neural system and can be measured on variable temporal scales over short time series. By measuring refined complex multiscale permutation entropy (RCMPE) from resting-state electroencephalography (EEG) data, we investigated the presence of a specific range of time scales characterizing major depressive disorder (MDD). METHOD: We used an EEG dataset acquired from 22 MDD patients and 22 healthy controls in the eyes-closed (EC) and eyes-open (EO) states available on the PRED + CT website. Signal variability in both the EC and EO states was compared between the two groups, and their relationship to depressive symptom severity was examined. RESULTS: In the EC state, the RCMPE was higher in the MDD group than in the control group on a coarse temporal scale, approximately 20-32 ms, at almost all sensors. It also showed a negative correlation with depressive symptom severity on a fine temporal scale, approximately 2-26 ms, in the frontal, right temporal, and left parietal sensor areas in MDD. The EO state revealed a group difference but no relationship with depressive symptom severity. CONCLUSION: Our results suggested that the diagnosis of MDD as a trait and the severity of depressive symptoms as a state are linked to EEG signal variability on the coarse temporal scale and the fine scale in the resting state, respectively. SIGNIFICANCE: Signal variability reflects different characteristics of depression depending on the temporal scale.

Diffusion model-based understanding of subliminal affective priming in continuous flash suppression.

Affective states influence our decisions even when processed unconsciously. Continuous flash suppression (CFS) is a new variant of binocular rivalry that can be used to render the prime subliminal. Nonetheless, how prior information from emotional faces suppressed by CFS influences subsequent decision-making remains unclear. Here, we employed a CFS priming task to examine the effect of the two main types of information conveyed by faces, i.e., facial identity and emotion, on the evaluation of target words as positive or negative. The hierarchical diffusion model was used to investigate the underlying mechanisms. A significant interaction effect on response time was observed following the angry face prime but not the happy or neutral face primes. The results of the diffusion model analyses revealed that the priming effects of facial identity were mapped onto the drift rate and erased the 'positive bias' (the processing advantage of positive over negative stimuli). Meanwhile, the positive emotional faces increased the nondecision time in response to negative target words. The model-based analysis implies that both facial identity and emotion are processed under CFS.

Effects of emotional maltreatment on semantic network activity during cognitive reappraisal.

Maltreatment experiences alter brain development associated with emotion processing, and dysregulation of emotion may trigger mental health problems in maltreated people. However, studies revealing alterations in brain networks during cognitive reappraisal in victims of maltreatment are strikingly insufficient. In this study, 27 healthy subjects were recruited. The maltreatment experiences and positive reappraisal abilities were measured using the Childhood Trauma Questionnaire-Short Form (CTQ-SF) and Cognitive Emotion Regulation Questionnaire (CERQ), respectively. A cognitive reappraisal task using the International Affective Picture System (IAPS) was designed for functional magnetic resonance imaging (fMRI) experiments. Cognitive reappraisal induced more activities in the bilateral inferior parietal lobes and bilateral middle temporal gyri compared to the condition of "look" (false discovery rate (FDR) corrected p < 0.05). Furthermore, the left inferior parietal lobe and right middle temporal gyrus functionally interacted with components of the default mode network, including the precuneus and the posterior cingulate cortex. In residual analyses after controlling for age and depressive symptoms, the bilateral inferior parietal and middle temporal activities exhibited positive correlations with cognitive reappraisal abilities (all ps < 0.05), and emotional maltreatment experiences were negatively correlated with the left inferior parietal cortex, bilateral middle temporal cortex activities, and left inferior parietal lobe-posterior cingulate cortex connectivity (all ps < 0.05). We found that semantic networks were significant to cognitive reappraisal, especially reinterpretation, and negative effects of emotional maltreatment experiences on semantic network activities.

Expecting social punishment facilitates control over a decision under uncertainty by recruiting medial prefrontal cortex.

In many decision-making situations, sub-optimal choices are increased by uncertainty. However, when wrong choices could lead to social punishment, such as blame, people might try to improve their performance by minimizing sub-optimal choices, which could be achieved by increasing the subjective cost of errors, thereby globally reducing decision noise or reducing an uncertainty-induced component of decision noise. In this functional magnetic resonance imaging (fMRI) study, 46 participants performed a choice task in which the probability of a correct choice with a given cue and the conditional probability of blame feedback (by making an incorrect choice) changed continuously. By comparing computational models of behaviour, we found that participants optimized their performance by preferentially reducing a component of decision noise associated with uncertainty. Simultaneously, expecting blame significantly deteriorated participants' mood. Model-based fMRI analyses and dynamic causal modelling indicate that the optimization mechanism based on the expectation of being blamed would be controlled by a neural circuit centred on the right medial prefrontal cortex. These results show novel behavioural and neural mechanisms regarding how humans optimize uncertain decisions under the expectation of being blamed.

Validation of the Traumatic Antecedents Questionnaire using item response theory.

BACKGROUND: Traumatic Antecedents Questionnaire (TAQ) is a traumatic experience scale that measures exposure to traumatic events across four age periods. Although the TAQ has good convergent validity with other traumatic scales, the classification of the domains and the psychometric properties of the scale has not been verified. METHODS: A total of 290 young adults completed the TAQ, and 156 participated in the retest. The number of trauma domains was determined using principal component analysis. Rasch model was used for verifying the items that each domain might represent in one common measurement. RESULTS: When scores were transformed as binary a code 0 and 1 from the original 4 categories, 8 domains were established consisting of Domestic violence, Sexual/other rare trauma, Incompetence, Caring family, Accidents to close person, Unstable caring environment, Safe environment, and Lack of sexual/extreme trauma. Most domains had acceptable psychometric properties with a mean-square fit value within the range of 0.7-1.3. The Bland and Altman analysis suggest 98.7% of difference scores between test and retest were within ±2 standard deviations from the mean. TAQ severity showed a significant relationship with the multiplicity score of the Maltreatment and Abuse Chronology of Exposure scale (r = 0.677). CONCLUSIONS: The newly proposed scoring system and 8 domains for the TAQ demonstrated excellent internal consistency, test-retest reliability, and validity. Further studies are needed to develop new items in domains with less than 5 items to improve the psychometric properties of the scale and to create a maltreatment domain that includes bullying items.

Improving the Reliability of Pharmacokinetic Parameters at Dynamic Contrast-enhanced MRI in Astrocytomas: A Deep Learning Approach.

Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astrocytomas, but the unreliability from arterial input function (AIF) remains a challenge. Purpose To develop a deep learning model that improves the reliability of AIF for DCE MRI and to validate the reliability and diagnostic performance of PK parameters by using improved AIF in grading astrocytomas. Materials and Methods This retrospective study included 386 patients (mean age, 52 years ± 16 [standard deviation]; 226 men) with astrocytomas diagnosed with histopathologic analysis who underwent dynamic susceptibility contrast (DSC)-enhanced and DCE MRI preoperatively from April 2010 to January 2018. The AIF was obtained from each sequence: AIF obtained from DSC-enhanced MRI (AIFDSC) and AIF measured at DCE MRI (AIFDCE). The model was trained to translate AIFDCE into AIFDSC, and after training, outputted neural-network-generated AIF (AIFgenerated DSC) with input AIFDCE. By using the three different AIFs, volume transfer constant (Ktrans), fractional volume of extravascular extracellular space (Ve), and vascular plasma space (Vp) were averaged from the tumor areas in the DCE MRI. To validate the model, intraclass correlation coefficients and areas under the receiver operating characteristic curve (AUCs) of the PK parameters in grading astrocytomas were compared by using different AIFs. Results The AIF-generated, DSC-derived PK parameters showed higher AUCs in grading astrocytomas than those derived from AIFDCE (mean Ktrans, 0.88 [95% confidence interval {CI}: 0.81, 0.93] vs 0.72 [95% CI: 0.63, 0.79], P = .04; mean Ve, 0.87 [95% CI: 0.79, 0.92] vs 0.70 [95% CI: 0.61, 0.77], P = .049, respectively). Ktrans and Ve showed higher intraclass correlation coefficients for AIFgenerated DSC than for AIFDCE (0.91 vs 0.38, P < .001; and 0.86 vs 0.60, P < .001, respectively). In AIF analysis, baseline signal intensity (SI), maximal SI, and wash-in slope showed higher intraclass correlation coefficients with AIFgenerated DSC than AIFDCE (0.77 vs 0.29, P < .001; 0.68 vs 0.42, P = .003; and 0.66 vs 0.45, P = .01, respectively. Conclusion A deep learning algorithm improved both reliability and diagnostic performance of MRI pharmacokinetic parameters for differentiating astrocytoma grades. © RSNA, 2020 Online supplemental material is available for this article.

Data-driven analysis using multiple self-report questionnaires to identify college students at high risk of depressive disorder.

Depression diagnosis is one of the most important issues in psychiatry. Depression is a complicated mental illness that varies in symptoms and requires patient cooperation. In the present study, we demonstrated a novel data-driven attempt to diagnose depressive disorder based on clinical questionnaires. It includes deep learning, multi-modal representation, and interpretability to overcome the limitations of the data-driven approach in clinical application. We implemented a shared representation model between three different questionnaire forms to represent questionnaire responses in the same latent space. Based on this, we proposed two data-driven diagnostic methods; unsupervised and semi-supervised. We compared them with a cut-off screening method, which is a traditional diagnostic method for depression. The unsupervised method considered more items, relative to the screening method, but showed lower performance because it maximized the difference between groups. In contrast, the semi-supervised method adjusted for bias using information from the screening method and showed higher performance. In addition, we provided the interpretation of diagnosis and statistical analysis of information using local interpretable model-agnostic explanations and ordinal logistic regression. The proposed data-driven framework demonstrated the feasibility of analyzing depressed patients with items directly or indirectly related to depression.

Aberrant structural network of comorbid attention deficit/hyperactivity disorder is associated with addiction severity in internet gaming disorder.

BACKGROUND: Internet gaming disorder (IGD) is commonly comorbid with attention-deficit/hyperactivity disorder (ADHD). Although the addiction is more severe when comorbid with ADHD, little is known about the neural correlates of the association. This study aimed to identify whether an ADHD-related structural brain network exists in IGD patients with comorbid ADHD (IGDADHD+) by comparing them with those without comorbid ADHD (IGDADHD-) and elucidating how the sub-network is associated with addiction severity. METHODS: Brain structural networks were constructed based on streamline tractography with diffusion tensor imaging in a cohort of 46 male IGDADHD+ patients, 48 male IGDADHD- patients, and 34 healthy controls (HC). We used network-based statistics (NBS) to identify the sub-network differences between the two IGD groups. Furthermore, the edges in the sub-network that significantly contributed to explaining the Young Internet Addiction Scale (YIAS) score were delineated using partial least square (PLS) regression analyses in IGD patients. RESULTS: The YIAS score was higher in the IGDADHD+ group than in the IGDADHD- group and was correlated with the Korean Dupaul's ADHD scale score (r = 0.42, p <0.01). The NBS detected a sub-network with stronger connectivity in the IGDADHD+ group than in the IGDADHD-group. The PLS regression model showed that the sub-network is associated with the YIAS score in the IGDADHD+ group (q2 = 0.019). Edges connecting the left pre- and postcentral gyri, bilateral superior frontal gyri, medial orbital parts, and left fusiform to the inferior temporal gyrus were most important predictors in the regression model. CONCLUSION: Our results suggest that an aberrant increase in some structural connections within circuits related to inhibitory function or sensory integration can indicate how comorbid ADHD is associated with addiction severity in IGD.

Pessimistically biased perception in panic disorder during risk learning.

BACKGROUND: Despite the well-known association between anxiety and risk-avoidant decision making, it is unclear how pathological anxiety biases risk learning. We propose a Bayesian inference model with bias parameters of prior, learning, and perception during risk learning in individuals with pathological anxiety. METHODS: Patients with panic disorder (PD, n = 40) and healthy control subjects (n = 84) completed the balloon analog risk task (BART). By fitting our computational model of three bias parameters (prior belief, learning rate, and perceptual bias) to the participants' behavior, we estimated the degree of bias in risk learning and its relationship with anxiety symptoms. RESULTS: Relative to the healthy control subjects, the pathologically anxious participants exhibited a biased underestimation of perceptual evidence rather than differences in priors and learning rates. The degree of perceptual bias was correlated with the anxiety and depression symptom severity in the patients with PD. Furthermore, our proposed model was the winning model for BART data in an external data set from different patient groups. CONCLUSIONS: Our results showed that individuals with pathological anxiety demonstrate perceptual bias in evidence accumulation, which may explain why patients with anxiety overestimate risk in their daily lives. This clarification highlights the importance of interventions focusing on perceptual bias, such as enhancing the clarity of favorable outcome probabilities.

Exploring characteristic features of attention-deficit/hyperactivity disorder: findings from multi-modal MRI and candidate genetic data.

The current study examined whether machine learning features best distinguishing attention-deficit/hyperactivity disorder (ADHD) from typically developing children (TDC) can explain clinical phenotypes using multi-modal neuroimaging and genetic data. Cortical morphology, diffusivity scalars, resting-state functional connectivity and polygenic risk score (PS) from norepinephrine, dopamine and glutamate genes were extracted from 47 ADHD and 47 matched TDC. Using random forests, classification accuracy was measured for each uni- and multi-modal model. The optimal model was used to explain symptom severity or task performance and its robustness was validated in the independent dataset including 18 ADHD and 18 TDC. The model consisting of cortical thickness and volume features achieved the best accuracy of 85.1%. Morphological changes across insula, sensory/motor, and inferior frontal cortex were also found as key predictors. Those explained 18.0% of ADHD rating scale, while dynamic regional homogeneity within default network explained 6.4% of the omission errors in continuous performance test. Ensemble of PS to optimal model showed minor effect on accuracy. Validation analysis achieved accuracy of 69.4%. Current findings suggest that structural deformities relevant to salience detection, sensory processing, and response inhibition may be robust classifiers and symptom predictors of ADHD. Altered local functional connectivity across default network predicted attentional lapse. However, further investigation is needed to clarify roles of genetic predisposition.

Heartbeat Induces a Cortical Theta-Synchronized Network in the Resting State.

In the resting state, heartbeats evoke cortical responses called heartbeat-evoked responses (HERs), which reflect cortical cardiac interoceptive processing. While previous studies have reported that the heartbeat evokes cortical responses at a regional level, whether the heartbeat induces synchronization between regions to form a network structure remains unknown. Using resting-state MEG data from 85 human subjects of both genders, we first showed that heartbeat increases the phase synchronization between cortical regions in the theta frequency but not in other frequency bands. This increase in synchronization between cortical regions formed a network structure called the heartbeat-induced network (HIN), which did not reflect artificial phase synchronization. In the HIN, the left inferior temporal gyrus and parahippocampal gyrus played a central role as hubs. Furthermore, the HIN was modularized, containing five subnetworks called modules. In particular, module 1 played a central role in between-module interactions in the HIN. Furthermore, synchronization within module 1 had a positive association with the mood of an individual. In this study, we show the existence of the HIN and its network properties, advancing the current understanding of cortical heartbeat processing and its relationship with mood, which was previously confined to region level.