The impact of wearing a KN95 face mask on human brain function: evidence from resting state functional magnetic resonance imaging.

Background: Face masks are widely used in daily life because of the COVID-19 pandemic. The objective of this study was to explore the impact of wearing face masks on brain functions by using resting-state functional MRI (RS-fMRI). Methods: Scanning data from 15 healthy subjects (46.20 ± 6.67 years) were collected in this study. Each subject underwent RS-fMRI scans under two comparative conditions, wearing a KN95 mask and natural breathing (no mask). The amplitude of low frequency fluctuation (ALFF) and functional connectivity under the two conditions were analyzed and then compared using the paired t-test. Results: Compared with those of the no-mask condition, the ALFF activities when wearing masks were increased significantly in the right middle frontal gyrus, bilateral precuneus, right superior marginal gyrus, left inferior parietal gyrus, and left supplementary motor area and decreased significantly in the anterior cingulate gyrus, right fusiform gyrus, left superior temporal gyrus, bilateral lingual gyrus, and bilateral calcarine cortex (p < 0.05). Taking the posterior cingulate cortex area as a seed point, the correlations with the occipital cortex, prefrontal lobe, and motor sensory cortex were sensitive to wearing masks compared with not wearing masks (p < 0.05). Taking the medial prefrontal cortex region as a seed point, the functional connectivity with the bilateral temporal lobe, bilateral motor sensory cortex, and occipital lobe was influenced by wearing a KN95 mask (p < 0.05). Conclusion: This study demonstrated that wearing a KN95 face mask can cause short-term changes in human resting brain function. Both local neural activities and functional connectivity in brain regions were sensitive to mask wearing. However, the neural mechanism causing these changes and its impact on cognitive function still need further investigation.

Associations between mother's depressive symptoms during pregnancy and newborn's brain functional connectivity.

Depression during pregnancy is common and the prevalence further increased during the COVID pandemic. Recent findings have shown potential impact of antenatal depression on children's neurodevelopment and behavior, but the underlying mechanisms are unclear. Nor is it clear whether mild depressive symptoms among pregnant women would impact the developing brain. In this study, 40 healthy pregnant women had their depressive symptoms evaluated by the Beck Depression Inventory-II at ~12, ~24, and ~36 weeks of pregnancy, and their healthy full-term newborns underwent a brain MRI without sedation including resting-state fMRI for evaluation of functional connectivity development. The relationships between functional connectivities and maternal Beck Depression Inventory-II scores were evaluated by Spearman's rank partial correlation tests using appropriate multiple comparison correction with newborn's gender and gestational age at birth controlled. Significant negative correlations were identified between neonatal brain functional connectivity and mother's Beck Depression Inventory-II scores in the third trimester, but not in the first or second trimester. Higher depressive symptoms during the third trimester of pregnancy were associated with lower neonatal brain functional connectivity in the frontal lobe and between frontal/temporal lobe and occipital lobe, indicating a potential impact of maternal depressive symptoms on offspring brain development, even in the absence of clinical depression.

CROI 2021: Neurologic Complications of HIV-1 Infection or Covid-19

The 2021 Conference on Retroviruses and Opportunistic Infections (CROI) featured a timely review of the neurologic complications of COVID-19 as well as new research findings on mechanisms by which SARS-CoV-2 may affect the brain. CROI included new and important findings about the neurologic complications of HIV-1, human polyomavirus 2 (also known as JC Virus), and cryptococcus. New long-term analyses of cognition in people with HIV-1 identified that cognitive decline over time is associated with multimorbidity, particularly diabetes, chronic lung disease, and vascular disease risk conditions. These conditions are associated with aging, and the question of whether people with HIV are at risk for premature aging was addressed by several reports. New findings from large analyses of resting state networks also provided valuable information on the structural and functional networks that are affected by HIV-1 infection and cognitive impairment. Several reports addressed changes after initiating or switching antiretroviral therapy (ART). Findings that will improve understanding of the biologic mechanisms of brain injury in people with HIV were also presented and included evidence that host (eg, myeloid activation, inflammation, and endothelial activation) and viral (eg, transcriptional activity and compartmentalization) factors adversely affect brain health. Other research focused on adjunctive therapies to treat HIV-1 and its complications in the central nervous system. This summary will review these and other findings in greater detail and identify key gaps and opportunities for researchers and clinicians.Copyright © 2021, IAS-USA. All rights reserved.

Amygdala connectivity related to subsequent stress responses during the COVID-19 outbreak.

Introduction: The amygdala plays an important role in stress responses and stress-related psychiatric disorders. It is possible that amygdala connectivity may be a neurobiological vulnerability marker for stress responses or stress-related psychiatric disorders and will be useful to precisely identify the vulnerable individuals before stress happens. However, little is known about the relationship between amygdala connectivity and subsequent stress responses. The current study investigated whether amygdala connectivity measured before experiencing stress is a predisposing neural feature of subsequent stress responses while individuals face an emergent and unexpected event like the COVID-19 outbreak. Methods: Data collected before the COVID-19 pandemic from an established fMRI cohort who lived in the pandemic center in China (Hubei) during the COVID-19 outbreak were used to investigate the relationship between amygdala connectivity and stress responses during and after the pandemic in 2020. The amygdala connectivity was measured with resting-state functional connectivity (rsFC) and effective connectivity. Results: We found the rsFC of the right amygdala with the dorsomedial prefrontal cortex (dmPFC) was negatively correlated with the stress responses at the first survey during the COVID-19 outbreak, and the rsFC between the right amygdala and bilateral superior frontal gyri (partially overlapped with the dmPFC) was correlated with SBSC at the second survey. Dynamic causal modeling suggested that the self-connection of the right amygdala was negatively correlated with stress responses during the pandemic. Discussion: Our findings expand our understanding about the role of amygdala in stress responses and stress-related psychiatric disorders and suggest that amygdala connectivity is a predisposing neural feature of subsequent stress responses.

Aberrant olfactory network functional connectivity in people with olfactory dysfunction following COVID-19 infection: an exploratory, observational study.

Background: Olfactory impairments and anosmia from COVID-19 infection typically resolve within 2-4 weeks, although in some cases, symptoms persist longer. COVID-19-related anosmia is associated with olfactory bulb atrophy, however, the impact on cortical structures is relatively unknown, particularly in those with long-term symptoms. Methods: In this exploratory, observational study, we studied individuals who experienced COVID-19-related anosmia, with or without recovered sense of smell, and compared against individuals with no prior COVID-19 infection (confirmed by antibody testing, all vaccine naïve). MRI Imaging was carried out between the 15th July and 17th November 2020 at the Queen Square House Clinical Scanning Facility, UCL, United Kingdom. Using functional magnetic resonance imaging (fMRI) and structural imaging, we assessed differences in functional connectivity (FC) between olfactory regions, whole brain grey matter (GM) cerebral blood flow (CBF) and GM density. Findings: Individuals with anosmia showed increased FC between the left orbitofrontal cortex (OFC), visual association cortex and cerebellum and FC reductions between the right OFC and dorsal anterior cingulate cortex compared to those with no prior COVID-19 infection (p < 0.05, from whole brain statistical parametric map analysis). Individuals with anosmia also showed greater CBF in the left insula, hippocampus and ventral posterior cingulate when compared to those with resolved anosmia (p < 0.05, from whole brain statistical parametric map analysis). Interpretation: This work describes, for the first time to our knowledge, functional differences within olfactory areas and regions involved in sensory processing and cognitive functioning. This work identifies key areas for further research and potential target sites for therapeutic strategies. Funding: This study was funded by the National Institute for Health and Care Research and supported by the Queen Square Scanner business case.

Can NeuroIS improve executive employee recruitment? Classifying levels of executive functions using resting state EEG and data science methods

Based on the assumption that the success of an organization is largely determined by the knowledge and skills of its employees, human resource (HR) departments invest considerable resources in the employee recruitment process with the aim of selecting the best, most suitable employees. Due to the high cost of the recruitment process along with its high rate of uncertainty, HR recruiters utilize a variety of methods and instruments to improve the efficiency and effectiveness of this process. Thus far, however, neurological methods, in which neurobiological signals from an examined person are analyzed, have not been utilized for this purpose. This study is the first to propose a neuro-based decision support system to classify cognitive functions into levels, whose target is to enrich the information and indications regarding the candidate along the employee recruitment processes. We first measured relevant functional and cognitive abilities of 142 adult participants using traditional computer-based assessment, which included a battery of four tests regarding executive functions and intelligence score, consistent with actual recruitment processes. Second, using electroencephalogram (EEG) technology, which is one of the dominant measurement tools in NeuroIS research, we collected the participants' brain signals by administering a resting state EEG (rsEEG) on each participant. Finally, using advanced machine and deep learning algorithms, we leveraged the collected rsEEG to classify participants' levels of executive functions and intelligence score. Our empirical analyses show encouraging results of up to 72.6% accuracy for the executive functions and up to 71.2% accuracy for the intelligence score. Therefore, this study lays the groundwork for a novel, generic (non-stimuli based) system that supports the current employee recruitment processes, that is based on psychological theories of assessing executive functions. The proposed decision support system could contribute to the development of additional medium of assessing employees remotely which is especially relevant in the current Covid-19 pandemic. While our method aims at classification rather than at explanation, our intriguing findings have the potential to push forward NeuroIS research and practice. © 2023 Elsevier B.V.

Brain correlates of subjective cognitive complaints in COVID-19 survivors: A multimodal magnetic resonance imaging study.

Cognitive impairment represents a leading residual symptom of COVID-19 infection, which lasts for months after the virus clearance. Up-to-date scientific reports documented a wide spectrum of brain changes in COVID-19 survivors following the illness's resolution, mainly related to neurological and neuropsychiatric consequences. Preliminary insights suggest abnormal brain metabolism, microstructure, and functionality as neural under-layer of post-acute cognitive dysfunction. While previous works focused on brain correlates of impaired cognition as objectively assessed, herein we investigated long-term neural correlates of subjective cognitive decline in a sample of 58 COVID-19 survivors with a multimodal imaging approach. Diffusion Tensor Imaging (DTI) analyses revealed widespread white matter disruption in the sub-group of cognitive complainers compared to the non-complainer one, as indexed by increased axial, radial, and mean diffusivity in several commissural, projection and associative fibres. Likewise, the Multivoxel Pattern Connectivity analysis (MVPA) revealed highly discriminant patterns of functional connectivity in resting-state among the two groups in the right frontal pole and in the middle temporal gyrus, suggestive of inefficient dynamic modulation of frontal brain activity and possible metacognitive dysfunction at rest. Beyond COVID-19 actual pathophysiological brain processes, our findings point toward brain connectome disruption conceivably translating into clinical post-COVID cognitive symptomatology. Our results could pave the way for a potential brain signature of cognitive complaints experienced by COVID-19 survivors, possibly leading to identify early therapeutic targets and thus mitigating its detrimental long-term impact on quality of life in the post-COVID-19 stages.

Risk of insomnia during COVID-19: effects of depression and brain functional connectivity.

Normal sleepers may be at risk for insomnia during COVID-19. Identifying psychological factors and neural markers that predict their insomnia risk, as well as investigating possible courses of insomnia development, could lead to more precise targeted interventions for insomnia during similar public health emergencies. Insomnia severity index of 306 participants before and during COVID-19 were employed to determine the development of insomnia, while pre-COVID-19 psychometric and resting-state fMRI data were used to explore corresponding psychological and neural markers of insomnia development. Normal sleepers as a group reported a significant increase in insomnia symptoms after COVID-19 outbreak (F = 4.618, P = 0.0102, df = 2, 609.9). Depression was found to significantly contribute to worse insomnia (ß = 0.066, P = 0.024). Subsequent analysis found that functional connectivity between the precentral gyrus and middle/inferior temporal gyrus mediated the association between pre-COVID-19 depression and insomnia symptoms during COVID-19. Cluster analysis identified that postoutbreak insomnia symptoms followed 3 courses (lessened, slightly worsened, and developed into mild insomnia), and pre-COVID-19 depression symptoms and functional connectivities predicted these courses. Timely identification and treatment of at-risk individuals may help avoid the development of insomnia in the face of future health-care emergencies, such as those arising from COVID-19 variants.

Can NeuroIS improve executive employee recruitment? Classifying levels of executive functions using resting state EEG and data science methods

Based on the assumption that the success of an organization is largely determined by the knowledge and skills of its employees, human resource (HR) departments invest considerable resources in the employee recruitment process with the aim of selecting the best, most suitable employees. Due to the high cost of the recruitment process along with its high rate of uncertainty, HR recruiters utilize a variety of methods and instruments to improve the efficiency and effectiveness of this process. Thus far, however, neurological methods, in which neurobiological signals from an examined person are analyzed, have not been utilized for this purpose. This study is the first to propose a neuro-based decision support system to classify cognitive functions into levels, whose target is to enrich the information and indications regarding the candidate along the employee recruitment processes. We first measured relevant functional and cognitive abilities of 142 adult participants using traditional computer-based assessment, which included a battery of four tests regarding executive functions and intelligence score, consistent with actual recruitment processes. Second, using electroencephalogram (EEG) technology, which is one of the dominant measurement tools in NeuroIS research, we collected the participants' brain signals by administering a resting state EEG (rsEEG) on each participant. Finally, using advanced machine and deep learning algorithms, we leveraged the collected rsEEG to classify participants' levels of executive functions and intelligence score. Our empirical analyses show encouraging results of up to 72.6% accuracy for the executive functions and up to 71.2% accuracy for the intelligence score. Therefore, this study lays the groundwork for a novel, generic (non-stimuli based) system that supports the current employee recruitment processes, that is based on psychological theories of assessing executive functions. The proposed decision support system could contribute to the development of additional medium of assessing employees remotely which is especially relevant in the current Covid-19 pandemic. While our method aims at classification rather than at explanation, our intriguing findings have the potential to push forward NeuroIS research and practice.

True grit and brain: Trait grit mediates the connection of DLPFC functional connectivity density to posttraumatic growth following COVID-19.

BACKGROUND: There is increasing interest in identifying factors to predict posttraumatic growth (PTG), a positive psychological response following traumatic events (e.g., the COVID-19 pandemic). Grit, a psychological trait of perseverance and passion to pursue long-term goals, has emerged as a promising predictor for PTG. This study aimed to examine the functional connectivity markers of grit and the potential brain-grit mechanism in predicting PTG. METHODS: Baseline brain imaging scans and grit scale and other controlling measures were administered in 100 normal young adults before the COVID-19 pandemic, and follow-up PTG measurement was obtained during the period of community-level outbreak. Whole-brain correlation analysis and prediction analysis were used to identify the brain regions whose functional connectivity density (FCD) related to individuals' grit scores. Mediation analyses were performed to explore the mediation relation between FCD, grit and PTG. RESULTS: Grit was positively related to FCD in the right dorsolateral prefrontal cortex (DLPFC), a core hub implicated in self-regulation and reward-motivation processes. Furthermore, grit mediated the effect of right DLPFC FCD on COVID-related PTG. These results survived controlling for self-control and family socioeconomic status. LIMITATIONS: Our study is limited by only one-session neuroimaging data and self-reported behavioral measures in a sample of normal adults. CONCLUSIONS: This study indicates grit and right DLPFC FCD as neuropsychological contributors for the development of PTG. It deepens our understanding of the neural bases of grit, and may have clinical potential to develop targeted brain interventions aimed at improving grit to raise PTG and mental health during the pandemic.

Effects of hyperbaric oxygen therapy on functional and structural connectivity in post-COVID-19 condition patients: A randomized, sham-controlled trial.

INTRODUCTION: Post-COVID-19 condition refers to a range of persisting physical, neurocognitive, and neuropsychological symptoms after SARS-CoV-2 infection. Abnormalities in brain connectivity were found in recovered patients compared to non-infected controls. This study aims to evaluate the effect of hyperbaric oxygen therapy (HBOT) on brain connectivity in post-COVID-19 patients. METHODS: In this randomized, sham-controlled, double-blind trial, 73 patients were randomized to receive 40 daily sessions of HBOT (n = 37) or sham treatment (n = 36). We examined pre- and post-treatment resting-state brain functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) scans to evaluate functional and structural connectivity changes, which were correlated to cognitive and psychological distress measures. RESULTS: The ROI-to-ROI analysis revealed decreased internetwork connectivity in the HBOT group which was negatively correlated to improvements in attention and executive function scores (p < 0.001). Significant group-by-time interactions were demonstrated in the right hippocampal resting state function connectivity (rsFC) in the medial prefrontal cortex (PFWE = 0.002). Seed-to-voxel analysis also revealed a negative correlation in the brief symptom inventory (BSI-18) score and in the rsFC between the amygdala seed, the angular gyrus, and the primary sensory motor area (PFWE = 0.012, 0.002). Positive correlations were found between the BSI-18 score and the left insular cortex seed and FPN (angular gyrus) (PFWE < 0.0001). Tractography based structural connectivity analysis showed a significant group-by-time interaction in the fractional anisotropy (FA) of left amygdala tracts (F = 7.81, P = 0.007). The efficacy measure had significant group-by-time interactions (F = 5.98, p = 0.017) in the amygdala circuit. CONCLUSIONS: This study indicates that HBOT improves disruptions in white matter tracts and alters the functional connectivity organization of neural pathways attributed to cognitive and emotional recovery in post-COVID-19 patients. This study also highlights the potential of structural and functional connectivity analysis as a promising treatment response monitoring tool.

Reliability and similarity of resting state functional connectivity networks imaged using wearable, high-density diffuse optical tomography in the home setting.

BACKGROUND: When characterizing the brain's resting state functional connectivity (RSFC) networks, demonstrating networks' similarity across sessions and reliability across different scan durations is essential for validating results and possibly minimizing the scanning time needed to obtain stable measures of RSFC. Recent advances in optical functional neuroimaging technologies have resulted in fully wearable devices that may serve as a complimentary tool to functional magnetic resonance imaging (fMRI) and allow for investigations of RSFC networks repeatedly and easily in non-traditional scanning environments. METHODS: Resting-state cortical hemodynamic activity was repeatedly measured in a single individual in the home environment during COVID-19 lockdown conditions using the first ever application of a 24-module (72 sources, 96 detectors) wearable high-density diffuse optical tomography (HD-DOT) system. Twelve-minute recordings of resting-state data were acquired over the pre-frontal and occipital regions in fourteen experimental sessions over three weeks. As an initial validation of the data, spatial independent component analysis was used to identify RSFC networks. Reliability and similarity scores were computed using metrics adapted from the fMRI literature. RESULTS: We observed RSFC networks over visual regions (visual peripheral, visual central networks) and higher-order association regions (control, salience and default mode network), consistent with previous fMRI literature. High similarity was observed across testing sessions and across chromophores (oxygenated and deoxygenated haemoglobin, HbO and HbR) for all functional networks, and for each network considered separately. Stable reliability values (described here as a <10% change between time windows) were obtained for HbO and HbR with differences in required scanning time observed on a network-by-network basis. DISCUSSION: Using RSFC data from a highly sampled individual, the present work demonstrates that wearable HD-DOT can be used to obtain RSFC measurements with high similarity across imaging sessions and reliability across recording durations in the home environment. Wearable HD-DOT may serve as a complimentary tool to fMRI for studying RSFC networks outside of the traditional scanning environment and in vulnerable populations for whom fMRI is not feasible.

Associations between early-life stress exposure and internalizing symptomatology during the COVID-19 pandemic: Assessing the role of neurobehavioral mediators.

Background: The ongoing COVID-19 pandemic is a major stressor that has been associated with increased risk for psychiatric illness in the general population. Recent work has highlighted that experiences of early-life stress (ELS) may impact individuals' psychological functioning and vulnerability for developing internalizing psychopathology in response to pandemic-related stress. However, little is known about the neurobehavioral factors that may mediate the association between ELS exposure and COVID-related internalizing symptomatology. The current study sought to examine the mediating roles of pre-pandemic resting-state frontoamygdala connectivity and concurrent emotion regulation (ER) in the association between ELS and pandemic-related internalizing symptomatology. Methods: Retrospective life-stress histories, concurrent self-reported ER strategies (i.e., reappraisal and suppression), concurrent self-reported internalizing symptomatology (i.e., depression- and anxiety-related symptomatology), and resting-state functional connectivity data from a sample of adults (N = 64, M age = 22.12, female = 68.75%) were utilized. Results: There were no significant direct associations between ELS and COVID-related internalizing symptomatology. Neither frontoamygdala functional connectivity nor ER strategy use mediated an association between ELS and COVID-related internalizing symptomatology (ps > 0.05). Exploratory analyses identified a significant moderating effect of reappraisal use on the association between ELS and internalizing symptomatology (ß = -0.818, p = 0.047), such that increased reappraisal use buffered the impact of ELS on psychopathology. Conclusions: While frontoamygdala connectivity and ER do not appear to mediate the association between ELS and COVID-related internalizing symptomatology, our findings suggest that the use of reappraisal may buffer against the effect of ELS on mental health during the pandemic.

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.

A Neural Predictive Model of Negative Emotions for COVID-19

The long-lasting global pandemic of Coronavirus disease 2019 (COVID-19) has changed our daily life in many ways and put heavy burden on our mental health. Having a predictive model of negative emotions during COVID-19 is of great importance for identifying potential risky population. To establish a neural predictive model achieving both good interpretability and predictivity, we have utilized a large-scale (n =542) longitudinal dataset, alongside two independent samples for external validation. We built a predictive model based on psychologically meaningful resting state neural activities. The whole-brain resting-state neural activity and social-psychological profile of the subjects were obtained from Sept. to Dec. 2019 (Time 1). Their negative emotions were tracked and re-assessed twice, on Feb 22 (Time 2) and Apr 24 (Time 3), 2020, respectively. We first applied canonical correlation analysis on both the neural profiles and psychological profiles collected on Time 1, this step selects only the psychological meaningful neural patterns for later model construction. We then trained the neural predictive model using those identified features on data obtained on Time 2. It achieved a good prediction performance (r =0.44, p =8.13 ×10-27). The two most important neural predictors are associated with self-control and social interaction. This study established an effective neural prediction model of negative emotions, achieving good interpretability and predictivity. It will be useful for identifying potential risky population of emotional disorders related to COVID-19. IEEE

Altered brain regional homogeneity is associated with depressive symptoms in COVID-19.

BACKGROUND: COVID-19 is an infectious disease that has spread worldwide in 2020, causing a severe pandemic. In addition to respiratory symptoms, neuropsychiatric manifestations are commonly observed, including chronic fatigue, depression, and anxiety. The neural correlates of neuropsychiatric symptoms in COVID-19 are still largely unknown. METHODS: A total of 79 patients with COVID-19 (COV) and 17 healthy controls (HC) underwent 3 T functional magnetic resonance imaging at rest, as well as structural imaging. Regional homogeneity (ReHo) was calculated. We also measured depressive symptoms with the Patient Health Questionnaire (PHQ-9), anxiety using the General Anxiety Disorder 7-item scale, and fatigue with the Multidimension Fatigue Inventory. RESULTS: In comparison with HC, COV showed significantly higher depressive scores. Moreover, COV presented reduced ReHo in the left angular gyrus, the right superior/middle temporal gyrus and the left inferior temporal gyrus, and higher ReHo in the right hippocampus. No differences in gray matter were detected in these areas. Furthermore, we observed a negative correlation between ReHo in the left angular gyrus and PHQ-9 scores and a trend toward a positive correlation between ReHo in the right hippocampus and PHQ-9 scores. LIMITATIONS: Heterogeneity in the clinical presentation in COV, the different timing from the first positive molecular swab test to the MRI, and the cross-sectional design of the study limit the generalizability of our findings. CONCLUSIONS: Our results suggest that COVID-19 infection may contribute to depressive symptoms via a modulation of local functional connectivity in cortico-limbic circuits.

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.

Abnormality Pattern of F-18 FDG PET Whole Body with Functional MRI Brain in Post-Acute COVID-19.

Purpose: The study aimed to investigate imaging abnormalities associated with post-acute COVID-19 using F-18 FDG PET/CT and PET/ rsfMRI brain. Methods: We retrospectively recruited 13 patients with post-acute COVID-19. The post-acute COVID-19 symptoms and neuropsychiatric tests were performed before F-18 FDG PET/CT whole body with PET/rsfMRI brain. Qualitative and semiquantitative analyses were also conducted in both whole body and brain images. Results: Among the 13 patients, 8 (61.5%) had myositis, followed by 8 (61.5%) with vasculitis (mainly in the thoracic aorta), and 7 (53.8%) with lung abnormalities.. Interestingly, one patient with a very high serum RBD IgG antibody demonstrated diffuse myositis throughout the body which potentially associated with immune-mediated myositis. One patient experienced psoriasis exacerbation with autoimmune-mediated after COVID-19. Most patients had multiple areas of abnormal brain connectivity involving the frontal and parieto-temporo-occipital lobes, as well as the thalamus. Conclusion: The whole body F-18 FDG PET can be a potential tool to assess inflammatory process and support the hyperinflammatory etiology, mainly for lesions in skeletal muscle, vascular wall, and lung, as well as, multiple brain abnormalities in post-acute COVID-19. Nonetheless, further studies are recommended to confirm the results.

Changes in the Intranetwork and Internetwork Connectivity of the Default Mode Network and Olfactory Network in Patients with COVID-19 and Olfactory Dysfunction.

Olfactory dysfunction (OD) is a common symptom in coronavirus disease 2019 (COVID-19) patients. Moreover, many neurological manifestations have been reported in these patients, suggesting central nervous system involvement. The default mode network (DMN) is closely associated with olfactory processing. In this study, we investigated the internetwork and intranetwork connectivity of the DMN and the olfactory network (ON) in 13 healthy controls and 22 patients presenting with COVID-19-related OD using independent component analysis and region of interest functional magnetic resonance imaging (fMRI) analysis. There was a significant correlation between the butanol threshold test (BTT) and the intranetwork connectivity in ON. Meanwhile, the COVID-19 patients with OD showed significantly higher intranetwork connectivity in the DMN, as well as higher internetwork connectivity between ON and DMN. However, no significant difference was found between groups in the intranetwork connectivity within ON. We postulate that higher intranetwork functional connectivities compensate for the deficits in olfactory processing and general well-being in COVID-19 patients. Nevertheless, the compensation process in the ON may not be obvious at this stage. Our results suggest that resting-state fMRI is a potentially valuable tool to evaluate neurosensory dysfunction in COVID-19 patients.

No Alteration Between Intrinsic Connectivity Networks by a Pilot Study on Localized Exposure to the Fourth-Generation Wireless Communication Signals.

Neurophysiological effect of human exposure to radiofrequency signals has attracted considerable attention, which was claimed to have an association with a series of clinical symptoms. A few investigations have been conducted on alteration of brain functions, yet no known research focused on intrinsic connectivity networks, an attribute that may relate to some behavioral functions. To investigate the exposure effect on functional connectivity between intrinsic connectivity networks, we conducted experiments with seventeen participants experiencing localized head exposure to real and sham time-division long-term evolution signal for 30 min. The resting-state functional magnetic resonance imaging data were collected before and after exposure, respectively. Group-level independent component analysis was used to decompose networks of interest. Three states were clustered, which can reflect different cognitive conditions. Dynamic connectivity as well as conventional connectivity between networks per state were computed and followed by paired sample t-tests. Results showed that there was no statistical difference in static or dynamic functional network connectivity in both real and sham exposure conditions, and pointed out that the impact of short-term electromagnetic exposure was undetected at the ICNs level. The specific brain parcellations and metrics used in the study may lead to different results on brain modulation.