Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.© 2022 Copyright held by the owner/author(s).

What influence do face masks have on reading emotions in faces?

In the past years, no event has affected people around the globe more than the SARS-COVID-2 pandemic. Besides the health system and the economy, it has affected social life. A grave sequela is the social distancing due to the ubiquitous use of medical face masks. Since these face masks cover approximately two thirds of the face including the mouth and nose, we hypothesized that they may impair affect reading of emotional face expressions. We used functional magnetic resonance imaging in 16 healthy volunteers to investigate brain activity changes related to the recognition of evolving emotional face expressions in short video-clips. We found that the face masks delayed emotion recognition, but at a normal nearly 100% success rate. This effect was related to a decreased activation in the cortical network mediating face recognition. Our data support the notion that face masks can have an adverse impact of social interactions. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Attention impairment in depression and anxiety

Our experience of the world is defined not only by what surrounds us, but also by what we pay attention to. Because goal-directed attention is essential for so many aspects of cognition, from perception to learning to decision-making, impairments of attention in the context of mental illness can be severely debilitating. Despite this impact, we know relatively little from human neuroscience about the specific attention impairments that comprise "concentration difficulties," a symptom and diagnostic criterion of mood and anxiety disorders that is often not alleviated with current first-line treatments. In this dissertation, I aim to better understand mechanisms of goal-directed attention in healthy adults and characterize various forms of attention impairment in individuals with depression and anxiety using multimodal human neuroscience methods.First, I review the state of the field regarding attention impairments in depression and anxiety (Chapter 1). I highlight both the key advances in cognitive neuroscience regarding the neural correlates of subtypes of attention and the ways in which these findings might inform precision psychiatry. Next, I investigate a potential neural correlate of selective attention in a sample of healthy adults using functional magnetic resonance imaging (fMRI) (Chapter 2). Using statistical analysis tools to disentangle ongoing neural activity from stimulus-driven activity, I demonstrate that stimulus-independent neural signals are associated with the sharing of attended visual information across the cortex. Leveraging these findings, I then characterize selective attention impairments in adults with Major Depressive Disorder using fMRI and electro-encephalography (EEG) (Chapter 3). I find that feature-based selective attention impairments are severe in a subset of depressed individuals and are specifically associated with fronto-parietal hypo-connectivity and decreased posterior alpha oscillations, consistent with my prior observations of selective attention correlates in healthy adults.I then develop a machine-learning algorithm that can successfully predict changes in selective attention with antidepressant pharmacotherapy and show that stressors occurring in childhood are associated with poorer selective attention in depressed adults (Chapter 4). In a study of individuals with a range of mood and anxiety symptoms, I develop novel behavioral paradigms to assess transdiagnostic sub-domains of attention impairment (Chapter 5). These data reveal that spatial attention impairments partially mediate the association between early life stress and anxiety and are associated with increased anxiety and concentration problems during the COVID-19 pandemic. Finally, I put forward a theoretical model for how attention may become impaired in depression and anxiety and detail important directions for future research (Chapter 6).Together, these findings provide insight into the neural mechanisms underlying different subdomains of attention, clarify our understanding of attention impairments as a trans-diagnostic symptom dimension, and identify neural targets for the development of more personalized treatment, setting the stage for future studies in both basic and clinical neuroscience. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

The modulatory effects of visual speech on auditory speech perception: A multi-modal investigation of how vision alters the temporal, spatial and spectral components of speech

Visual speech information, especially that provided by the mouth and lips, is important during face-to-face communication. This has been made more evident by the increased difficulty of speech perception because mask usage has become commonplace in response to the COVID-19 pandemic. Masking obscures the mouth and lips, thus eliminating meaningful information from visual cues that are used to perceive speech correctly. To fully understand the perceptual benefits afforded by visual information during audiovisual speech perception, it is necessary to explore the underlying neural mechanisms involved. While several studies have shown neural activation of auditory regions in response to visual speech, the information represented by these activations remain poorly understood. The objective of this dissertation is to investigate the neural bases for how visual speech modulates the temporal, spatial, and spectral components of audiovisual speech perception, and the type of information encoded by these signals.Most studies approach this question by using techniques sensitive to one or two important dimensions (temporal, spatial, or spectral). Even in studies that have used intracranial electroencephalography (iEEG), which is sensitive to all three dimensions, research conventionally quantifies effects using single-subject statistics, leaving group-level variance unexplained. In Study 1, I overcome these shortcomings by investigating how vision modulates auditory speech processes across spatial, temporal and spectral dimensions in a large group of epilepsy patients with intracranial electrodes implanted (n = 21). The results of this study demonstrate that visual speech produced multiple spatiotemporally distinct patterns of theta, beta, and high-gamma power changes in auditory regions in the superior temporal gyrus (STG).While study 1 showed that visual speech evoked activity in auditory areas, it is not clear what, if any, information is encoded by these activations. In Study 2, I investigated whether these distinct patterns of activity in the STG, produced by visual speech, contain information about what word is being said. To address this question, I utilized a support-vector machine classifier to decode the identities of four word types (consonants beginning with 'b', 'd', 'g', and 'f') from activity in the STG recorded during spoken (phonemes: basic units of speech) or silent visual speech (visemes: basic units of lipreading information). Results from this study indicated that visual speech indeed encodes lipreading information in auditory regions.Studies 1 and 2 provided evidence from iEEG data obtained from patients with epilepsy. In order to replicate these results in a normative population and to leverage improved spatial resolution, in Study 3 I acquired data from a large cohort of normative subjects (n = 64) during a randomized event-related functional magnetic resonance imaging (fMRI) experiment. Similar to that of Study 2, I used machine learning to test for classification of phonemes and visemes (/fafa/, /kaka/, /mama/) from auditory, auditory-visual, and visual regions in the brain. Results conceptually replicated the results of Study 2, such that phoneme and viseme identities could both be classified from the STG, revealing that this information is encoded through distributed representations. Further analyses revealed similar spatial patterns in the STG between phonemes and visemes, consistent with the model that viseme information is used to target corresponding phoneme populations in auditory regions. Taken together, the findings from this dissertation advance our understanding of the neural mechanisms that underlie the multiple ways in which vision alters the temporal, spatial and spectral components of audiovisual speech perception. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Feedback Artificial Tree-Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder

Autism spectrum disorder (ASD) is an umbrella term for a number of neurodevelopmental conditions with many heterogeneous behavioural indications. Recent medical imaging approaches use functional Magnetic Resonance Imaging (fMRI) for human recognition of the various neurological syndromes. However, these traditional techniques are time consuming and expensive. Thus, in this research, an optimization assisted deep learning technique, named Feedback Artificial Virus Optimization (FAVO)-based deep residual network (DRN), is developed. FAVO-based DRN is designed to incorporate the Feedback Artificial Tree (FAT) algorithm with Anti Corona Virus Optimization (ACVO). First, Region-Of-Interest extraction is carried out using thresholding techniques with nub region extraction completed using the proposed FAVO algorithm. ASD classification is then carried out using a DRN classifier. Evaluation of the proposal uses the ABIDE-1 and ABIDE-2 datasets. The developed FAVO algorithm attains better accuracy, sensitivity, and specificity of 0.9214, 0.9365, and 0.9142, respectively, by considering ABIDE-2 dataset. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Application of photobiomodulation therapy in acupuncture

Photobiomodulation (PBM) therapy is a therapeutic method that can produce a range of physiological effects in cells and tissues using certain wavelengths. The reparative benefits of PBM therapy include wound healing, bone regeneration, pain reduction, and the mitigation of inflammation. Advances in the development of laser instruments, including the use of high-intensity lasers in physiotherapy, have recently led to controllable photothermal and photomechanical treatments that enable therapeutic effects to be obtained without damaging tissue. The combination of PBM therapy with acupuncture may provide new perspectives for investigating the underlying therapeutic mechanisms of acupuncture and promote its widespread application.

Telehealth cognitive-behavioral therapy for cancer-related cognitive impairment: A model for remote clinical trial participation

Background: Cancer-related cognitive impairment (CRCI) can include persistent memory symptoms, and affects many cancer survivors. Memory and Attention Adaptation Training (MAAT) is an evidencebased cognitive behavioral therapy (CBT) that improves CRCI with demonstrated efficacy in telehealth delivery. MAAT consists of 8 weekly (45-minute) video visits. The aims of this study are to confirm MAAT telehealth efficacy in a phase III RCT (MAAT versus Supportive Therapy;ST) across large catchment areas of two comprehensive cancer centers. A secondary aim is to evaluate treatment-induced brain activation as assessed by functional MRI (fMRI) in a subset of participants. We present remote treatment and data capture methods of this open NCI-sponsored (R01CA244673) randomized clinical trial (NCT 04586530). These methods have high success in participant accrual despite COVID-19 pandemic conditions, and can be readily adopted to other clinical trials to enhance rural/underserved enrollment. Methods: We are enrolling 200 adult, stage I-III breast cancer survivors 1-5 years post-chemotherapy with cognitive complaints. Individuals with CNS disease, previous brain injury, dementia or psychiatric disorder are excluded. All study procedures are completed from the participant's home (except fMRI). Eligibility screening is a semi-structured phone interview followed by detailed informed consent online (Research Electronic Data Capture: REDCap) with staff phone guidance. Consented participants complete baseline brief phone-based neurocognitive assessment and validated patient-reported outcome measures (PROs) of cognition and quality of life via REDCap. Participants are randomized to MAAT or ST and assigned treating clinicians at respective cancer centers. All 8 visits are completed through secure telehealth platforms, followed by repeat phone/online assessment posttreatment and again at 6 months. Enrollment began in 3/2021. As of 1/2022 (9 months), 56 participants are enrolled (28% of the planned sample), 47 randomized (MAAT 24;ST 23), with 24 completing post-treatment assessments. If all assessments and treatment visits were in person, travel burden per participant is 968 miles/20.5 hours driven, and $542 (US 2021 Federal rate). Thus, study travel savings to date are $30,352. Participant feedback indicates telehealth makes participation possible, similar to previous MAAT research. The current RCT demonstrates utility, efficiency and cost-savings of telehealth and remote data capture technology in the conduct of cancer control research. Elements of methods described can also be adopted for cancer therapeutic trials. Comprehensive cancer centers, where most clinical trials are based, can enhance participation of remote and/or underserved populations that have higher rates of cancer, more disease burden and less opportunity for trial participation.

Transfer Learning from Healthy to Unhealthy Patients for the Automated Classification of Functional Brain Networks in fMRI

Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.

NEURAL CORRELATES OF EMOTIONAL DYSREGULATION IN ADOLESCENTS WITH GAMING DISORDER - A LONGITUDINAL FMRI STUDY

Background: The addictive use of digital games is a rising phenomenon, especially in adolescents and under the COVID-19 pandemic (Paschke et al., 2021). A better understanding of the new International Classification of Diseases, 11th Revision (ICD-11) diagnosis gaming disorder (GD) is urgently needed. Imaging studies report alterations in cognitive control, affective and motor regions in affected adolescents (Schettler et al., 2022). At the same time, they show deficits in self-rated emotion regulation. Objectives: The study aimed to investigate the neural correlates of emotional dysregulation in adolescents with GD and neural alterations under therapy at the very first time. Methods: In a functional magnetic resonance imaging study, 20 inpatients and outpatients with GD and 20 healthy peers were examined (aged 12-18 years) at two measurement points with a 12-week interval. Within the interval, patients received therapy as described by Wendt et al. (2021). The cognitive-reappraisal paradigm of Ochsner et al. (2004) was applied to measure emotion regulation abilities. Findings: First interim analyses revealed a positive linear relationship between the severity of initial addiction symptoms and activity changes within the dorsolateral prefrontal cortex - a brain region related to top-down emotion regulation. This relationship decreased over the 12 weeks but only in the patients group. Conclusion: Adolescents with GD seem to show higher cognitive-control effort when dealing with negative emotions which could be addressed by therapy. Data collection will be completed in December 2021. Therefore, results of high novelty and clinical relevance will be presented to the scientific community for the very first time.

Memorize COVID-19 Advertisement: Customer Neuroscience Data Collection Techniques by Using EEG and fMRI

Coronavirus Disease (COVID-19) confirmed cases in the world still occurred more than 1.5 years after the first cases outbreak in Wuhan, China. Education is a main key to deal with this pandemic. The information on how to prevent COVID-19 continues to be informed by direct approach and by using advertisements on television, radio, printed media, and on the internet are being provided to gain the awareness to the people. Consumer neuroscience is necessarily needed and important for understanding consumer behavior. This research paper proposed the techniques to collect the visual data of COVID-19 advertisements by using electroencephalogram (EEG) and Functional Magnetic Resonance Imaging (fMRI) to understand the brain activity. The results of this research can be useful to create a better COVID-19 advertisement that can attract people to memorize the health protocol. © 2021 IEEE.

NEUROIMAGING FINDINGS IN INTERNET GAMING ADDICTION IN ADOLESCENCE: A SYSTEMATIC REVIEW

Internet gaming addiction (IGA) is a growing concern among adolescents, exacerbated by recent COVID-19 restrictions. The World Health Organization has recently included IGA in the 11th International Classification of Diseases. However, the validity and reliability of the proposed criteria are subjected to controversy 1. Despite growing neurobiological evidence in IGA, most systematic reviews have focused on adults or mixed adult/adolescent populations. Therefore, this systematic review explored the neuroimaging literature in adolescents with IGA. Altogether, 2263 primary studies were identified from PubMed, CINAHL Plus, PsycINFO, and Web of Science. After applying inclusion and exclusion criteria (appropriate title, , comparison group used within study, English-language, neuroimaging, and mean age under 18), 25 articles were included in this review. Functional and structural brain alterations in IGA were noted across several imaging modalities, including electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), and structural magnetic resonance imaging (MRI). Compared with healthy controls, adolescents with IGA demonstrated functional impairment in emotional regulation, reward-seeking, inattention and, inhibition control circuits, leading to increased risky decision making. Structural changes in gray and white matter were noted due to repetitive brain stimulation associated with visual, auditory, and spatial working memory. With regards to brain region processing self-concept, adolescents utilize the medial prefrontal region while having game character thoughts, compared to adults who utilize the left angular gyrus 2. In conclusion, adolescents with IGA showed common neurological findings consistent with other behavioral addictions and psychiatric disorders. Future studies are needed for potential neuroimaging markers that apply to diagnosis and informing treatment.

Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.

Neuroimaging Recovery in Human Drug Addiction: An Eye Towards Intervention Development

Drug addiction is on the rise during these COVID-19 times that intensify the factors contributing to relapse and overdose across drugs of abuse and continents. Using a multimodal approach (neuropsychology, fMRI, ERP), human neuroimaging studies in my lab have elucidated core mechanisms underlying drug addiction, with a focus on the role of the dopaminergic mesocorticolimbic circuit, especially the prefrontal cortex, in higher-order cognitive and emotional dysfunction in this population. Our theoretical model is called iRISA (Impaired Response Inhibition and Salience Attribution), postulating that abnormalities in the orbitofrontal cortex and anterior cingulate cortex (and other cortical regions underlying higher order executive function), contribute to the core clinical symptoms in addiction. Specifically, our program of research is guided by the working hypothesis that drug addicted individuals disproportionately attribute salience and value to their drug of choice at the expense of other reinforcing stimuli, with a concomitant decrease in the ability to inhibit maladaptive drug use. Our complex and multifaceted dataset has allowed us to study the impact of abstinence on recovery in these brain-behavior compromises in treatment-seeking addicted individuals, where non-linear relationships exemplify incubation of craving while other trajectories of change, including in white matter tracks and for small subcortical regions (such as the habenula), are also explored. Novel paradigm shifts in the lab include the use of naturalistic and dynamic stimuli for enhanced generalizability and validity, in addition to development of effective neurorehabilitation strategies (including cognitive reappraisal, mindfulness, and transcranial direct current stimulation) in drug addiction. Keywords: Neuroimaging, drug addiction

International Research Progress and Evolution Trend of Interpersonal Trust—Prospects under COVID-19 Pandemic

Retrospecting articles on interpersonal trust is of great importance for understanding its current status and future development in the context of the COVID-19 pandemic, especially, with the widespread use of Big Data and Blockchain. In total, 1532 articles related to interpersonal trust were collected as research database to draw keyword co-occurrence mapping and timeline mapping by VOSviewer and CiteSpace. On this basis, the research content and evolution trend of interpersonal trust were systematically analyzed. The results show that: (1) Data cleaning by code was first integrated with Knowledge Mapping and then used to review the research of interpersonal trust;(2) Developed countries have contributed the most to the research of interpersonal trust;(3) Social capital, knowledge sharing, job and organizational performance, Chinese Guanxi are the research hotspots of interpersonal trust;(4) The research hotspots on interpersonal trust evolve from the level of individual psychology and behavior to the level of social stability and development and then to the level of organization operation and management;(5) At present, the research on interpersonal trust is in the outbreak period;fMRI technology and Big Data and Blockchain technology gradually become vital research tools of interpersonal trust, which provides significant prospects for the following research of interpersonal trust under the COVID-19 pandemic.

P.0595 Burnout level associates with reduced strength of resting-state prefrontal brain activity and a sense of meaning in work

Introduction: Burnout has become a major concern in health care systems and has also been a critical issue during the COVID-19 pandemic. As many as 76% of medical professionals report burnout symptoms that may lead to medical errors, substance abuse, and even suicide [1,2]. Meanwhile, previous studies report on the importance of peoples’ sense of coherence (SOC) or control over work for dealing with burnout experience which implicates a stress-coping capacity involving comprehensibility, manageability, and meaningfulness. However, little is known on how SOC cognitively modulates burnout experiences, as neural substrates for SOC and burnout are insufficiently explored [2,3]. Aim: We aimed to investigate neurocognitive mechanisms of SOC and burnout in medical professionals. Methods: We recruited early-career registered nurses and forty-one were enrolled in this study. This study was approved by the institutional review board of Kyoto University and was conducted in accordance with the Code of Ethics of the World Medical Association. Participants were recruited by advertisements in the hospitals. Participants’ SOC and burnout levels were investigated using the sense of coherence scale and Maslach Burnout Inventory (MBI) [3]. Higher scores of SOC and MBI represented a greater sense of coherence and more burnout experience, respectively. We used functional magnetic resonance imaging and measured resting-state brain activity. We identified brain regions associated with SOC and burnout levels by correlating these trait scores to the regional fractional amplitude of low frequency fluctuations (fALFF). Subsequently, we investigated whether participants’ levels of SOC impacted their fALFF-burnout association by mediation analysis. Results: SOC and the depersonalization dimension of burnout were negatively correlated. The fALFF in the mid dorsolateral prefrontal cortex (DLPFC) correlated positively with SOC scores, and negatively with the depersonalization dimension of burnout. Based on the above correlations, we conducted a mediation analysis and observed that SOC mediates the negative relationship between DLPFC activity and burnout severity (p < 0.05). That is, the participants’ depersonalization level was better explained by their SOC level, together with their resting-state brain activity (fALFF of the DLPFC), rather than the brain activity level alone. Conclusions: The results suggest that our participants’ burnout severity associates with decreased SOC and prefrontal activity those of which may support cognitive control. It is possible that they may facilitate flexible shifting of perspective and optimistic reappraisal of work-stress. In effect, workplace stressors may be acknowledged as being more meaningful than distressing. Meanwhile, without sufficient SOC, frequent exposures to stressors can lead to maladaptive coping to exhibit emotional numbing or depersonalization. The results also suggest that the sense of coherence/control and burnout effect can be generalized and carried over into no-task, spontaneous brain activity to a certain extent. The further approach in this line may pave the ways to illuminate which intervention and training effectively impact the subjective experience of burnout in medical education. No conflict of interest

P.0334 Brain moderator buffering the relationship between anxiety and distress

Introduction: Social anxiety disorder is one of the most common psychiatric illnesses, and is characterized by avoidance of social interactions due to fear of negative evaluation, such as embarrassing oneself in the presence of others.%26nbsp;Taijin-kyofusho%26nbsp;(TKS), which is a subtype common in East Asia, additionally includes a fear of embarrassing other people [1]. Individuals with TKS exaggerate imaginations about their appearance from other's perspective and worry that their physical defects or socially inappropriate behaviours would humiliate or discomfort other people. In this regard, recent studies have indicated that TKS-related experience could be more global and has increased during the Covid-19 pandemic such as fear of infecting or distressing others [2]. While the neural basis of social anxiety has been studied extensively in the past decades, the neuro-cognitive understanding of TKS is still limited. Aim: We aimed to better understand the neuro-cognitive mechanisms of TKS experience. Methods: After all participants received a complete study description, written informed consent was obtained from each person. Data from the functional magnetic resonance imaging (fMRI) and behavioral measures were collected from twenty-three individuals. Participants completed the TKS and empathy trait questionnaires.%26nbsp;During fMRI, they watched video clips of badly singing people who expressed either authentic embarrassment (AE) or hubristic pride (HP). We expected the AE singers to embarrass the viewers via emotion sharing involving affective empathy, and the HP singers to embarrass the viewers via perspective taking involving cognitive empathy.%26nbsp;Subsequently, moderation%26nbsp;analyses were conducted to explore the neural mechanisms that may affect the relationship between empathy and TKS trait levels. We hypothesized that brain activity strength would interact with participants’ empathy disposition to predict TKS levels. Results: TKS levels correlated positively with personal distress (PD) dimension of the empathy trait. During the cognitive empathy contrast (AE %26lt;HP), TKS levels correlated negatively with the activity of the right temporoparietal junction (TPJ). Moderation analysis showed a main effect for the TPJ strength. The results also showed a significant two-way interaction between the TPJ strength and PD, and the regression model was statistically significant (p %26lt;0.05). Specifically, the TKS level was associated with higher PD for participants with lower TPJ strength, but not for those with higher TPJ strength. Conclusions: This preliminary data suggests that TPJ functioning can play a buffering role in the association between empathic distress and social anxiety involving fear of distressing other people. That is, the result links TKS to individual variation in spontaneous empathic distress susceptibility and also variation in the strength of brain activity involved in shifting perspective/attention and self-other distinction to prompt cognitive empathy [3]. It is possible that under insufficient TPJ functioning, empathic distress might boost TKS experience. Whether this hypothesis holds true in actual practice must be examined in future studies. No conflict of interest

P.0513 Midcingulate activation to inhibiting prepotent response in Emotion-Face Stroop Task is associated with Covid-related ruminative thoughts

Background: Based on fMRI studies midcingulate cortex (MCC) has been proposed to have a role in conflict monitoring and response inhibition, or more general in cognitive control [1]. However, it is not known whether this activation predicts real world psychological functioning. Actual stressors like the COVID-19 pandemic and its concomitants can easily generate negative emotions and trigger ruminative thoughts [2]. Frequent or increased ruminative thoughts to an actual naturalistic stressor might reflect deficits in cognitive control function. Thus, the aim of our study was to explore whether the recruitment of MCC when cognitive control is needed, measured before the pandemic, was associated with state and content specific ruminative thoughts during the first wave of pandemic. Methods: In our study 30 healthy subjects (16 female;mean age: 27) participated. They were enrolled in the fMRI study before the Covid-19 pandemic. In the scanner, we used the Emotion-Face Stroop Task [3] in which happy or fear word is overlaid on happy or fearful facial expression resulting congruent or incongruent conditions. In this task participants are instructed to identify the facial expression while ignoring the emotional label on the face, thus participants have to inhibit the prepotent response. Incongruent trials – when there is a mismatch between the facial expression and the label on it – generate emotional interference and requires control to successfully inhibit the prepotent response. We contacted our participants in June 2020 and asked them to fill out questionnaires on trait level rumination and Covid-related ruminative thoughts [2]. Results: In our analysis, we focused on the contrast of incongruent versus congruent trials and used a mask for MCC to test our a-priori hypothesis. We performed a small volume correction (SVC) analysis in Statistical Parameter Mapping using voxel-wise threshold p<0.05, FWE corrected. We found that Covid-related rumination was associated positively with the recruitment of MCC (peak MNI coordinates x/y/z = -6/-1/32;t(1,28) = 5.20, SVC, pFWE = 0.008), implying that those who scored higher on Covid-related ruminative thoughts showed higher involvement of MCC when more effort was needed to exert control over the prepotent response. The results did not change when gender and trait level brooding – the maladaptive component of rumination – were controlled for. Conclusion: Our result is in line with a recent study [4] showing that ruminative thoughts after a stressful task was predicted by cognitive control. Our results might suggest that healthy subjects who have to recruit MCC more to incongruent trials versus congruent one are more vulnerable to stress related (state) ruminative thoughts. Thus, those who need more inhibitory control to inhibit a prepotent response or more responsive to emotional interference might have difficulties in regulating their thoughts or emotions in an enduring stressful situation. Cognitive control trainings targeting inhibitory control over negative emotional information might help those who react to an actual naturalistic stressors with increased rumination, pointing out the need for identifying people who are at risk in times of stress. Conflict of interest Disclosure statement: This study was supported by the Hungarian Brain Research Program (Grants: 2017-1.2.1-NKP-2017-00002 KTIA_NAP_13-2- 2015-0001) and by the Thematic Excellence Programme (Tématerületi Kiválósági Program, 2020-4.1.1.-TKP2020) of the Ministry for Innovation and Technology in Hungary, within the framework of the Neurology and Translational Biotechnology thematic programmes of the Semmelweis University. The preparation of this poster for GK was supported by the Hungarian National Research, Development and Innovation Office (FK128614).

Survivors of COVID-19 exhibit altered amplitudes of low frequency fluctuation in the brain: a resting-state functional magnetic resonance imaging study at 1-year follow-up.

Although some short-term follow-up studies have found that individuals recovering from coronavirus disease 2019 (COVID-19) exhibit anxiety, depression, and altered brain microstructure, their long-term physical problems, neuropsychiatric sequelae, and changes in brain function remain unknown. This observational cohort study collected 1-year follow-up data from 22 patients who had been hospitalized with COVID-19 (8 males and 11 females, aged 54.2 ± 8.7 years). Fatigue and myalgia were persistent symptoms at the 1-year follow-up. The resting state functional magnetic resonance imaging revealed that compared with 29 healthy controls (7 males and 18 females, aged 50.5 ± 11.6 years), COVID-19 survivors had greatly increased amplitude of low-frequency fluctuation (ALFF) values in the left precentral gyrus, middle frontal gyrus, inferior frontal gyrus of operculum, inferior frontal gyrus of triangle, insula, hippocampus, parahippocampal gyrus, fusiform gyrus, postcentral gyrus, inferior parietal angular gyrus, supramarginal gyrus, angular gyrus, thalamus, middle temporal gyrus, inferior temporal gyrus, caudate, and putamen. ALFF values in the left caudate of the COVID-19 survivors were positively correlated with their Athens Insomnia Scale scores, and those in the left precentral gyrus were positively correlated with neutrophil count during hospitalization. The long-term follow-up results suggest that the ALFF in brain regions related to mood and sleep regulation were altered in COVID-19 survivors. This can help us understand the neurobiological mechanisms of COVID-19-related neuropsychiatric sequelae. This study was approved by the Ethics Committee of the Second Xiangya Hospital of Central South University (approval No. 2020S004) on March 19, 2020.