Activation and Connectivity within the Default Mode Network Contribute Independently to Future-Oriented Thought

Superiority illusion in older adults: Volume and functional connectivity of the precuneus

Aim

Superiority illusion (SI), a cognitive bias where individuals perceive themselves as better than others, may serve as a psychological mechanism that contributes to well-being and resilience in older adults. However, the specific neural basis of SI in elderly populations remains underexplored. This study aims to identify brain regions partially associated with SI, exploring its potential role in adaptive psychological processes.

Methods

This study combined a behavioral task, voxel-based morphometry (VBM), and resting-state functional connectivity (rsFC) analyses to investigate the neural substrates of the SI in a cohort of 145 participants, including young (n = 84), middle-aged (n = 37), and older adults (n = 24).

Results

Our findings indicated that higher SI scores in older adults were correlated with greater gray matter volume in the right precuneus and stronger rsFC between the right precuneus and the left lateral occipital cortex. However, these correlations were not evident in younger and middle-aged groups.

Conclusion

Our findings underscore the importance of the right precuneus and its connectivity in the manifestation of the SI, particularly in older adults, highlighting its potential role in adaptive aging processes.

Abnormal resting-state functional connectivity in adolescent depressive episodes

BACKGROUND

Depression is linked to abnormalities in brain networks. Resting-state functional connectivity (FC), as measured using resting-state fMRI (rs-fMRI), is a crucial tool for exploring the brain network abnormalities associated with depressive symptoms, as it reveals how disruptions in brain region interactions occur. However, research focusing on adolescents with depression is limited and inconsistent, highlighting the need for further studies in this area.

METHODS

Fifty-five adolescents with Depressive episodes (DE) and 26 healthy controls (HCs) underwent resting-state fMRI. Depressive symptoms were assessed using the 17-item Hamilton Rating Scale for Depression (HAMD-17). Seed regions were defined based on Yeo's seven-network scheme, including the sensorimotor network (SMN), ventral attention network (VAN), dorsal attention network (DAN), visual network (VN), frontoparietal network (FPN), default mode network (DMN), and limbic network (LN). These seed regions were derived from analysis of large-scale FC in healthy individuals, and were selected for their relevance to cognition, emotion, and depression research. Network-based statistical analyses were used to compare the adolescents with DE to the HCs, and correlation analyses were employed to examine the relationships between FC changes and cognitive performance.

RESULTS

The results showed significant differences in FC between the DE and HCs groups, involving 17 nodes and 17 edges across seven networks. Decreased FC was observed within the FPN, as well as between the FPN and VAN, the FPN and DMN, and the SMN and both the DAN and VN. Increased FC was observed between the FPN and VN, between the DAN and other networks (i.e., the DMN and FPN), and between the SMN and multiple networks. Notably, FC between the right superior parietal (SMN) and right precuneus (DMN) showed a negative correlation with HAMD-17 scores.

CONCLUSION

These results suggest that adolescents with DE experience widespread brain network abnormalities characterized by hypoactivity in external networks such as the SMN and VN, as well as hyperactivity in associative regions, including the DMN, FPN, SMN, and LN. Although these changes in FC are evident, the specific mechanisms linking them to clinical symptoms remain unclear and warrant further investigation.

Alteration in neural oscillatory activity and phase-amplitude coupling after sleep deprivation: Evidence for impairment and compensation effects

Insufficient sleep can significantly affect vigilance and increase slow-wave electroencephalographic power as homeostatic sleep pressure accumulates. Phase-amplitude coupling is involved in regulating the spatiotemporal integration of physiological processes. This study aimed to examine the functional associations of resting-state electroencephalographic power and delta/theta-gamma phase-amplitude coupling from the prefrontal cortex (PFC) to posterior regions with vigilance performance after sleep deprivation. Forty-six healthy adults underwent 24-hr sleep deprivation with resting-state electroencephalographic recordings, and vigilant attention was measured using the Psychomotor Vigilance Task. Power spectral and phase-amplitude coupling analyses were conducted, and correlation analysis was utilized to reveal the relationship between electroencephalographic patterns and changes in vigilance resulting from sleep deprivation. Sleep deprivation significantly declined vigilance performance, accompanied by increased resting-state electroencephalographic power in all bands and delta/theta-gamma phase-amplitude coupling. The increased theta activity in centro-parieto-occipital areas significantly correlated with decreased mean and slowest response speed. Conversely, the increased delta-low gamma and theta-high gamma phase-amplitude couplings negatively correlated with the deceleration of the fastest Psychomotor Vigilance Task reaction times. These findings suggest that sleep deprivation affects vigilance by altering electroencephalographic spectral power and information communication across frequency bands in different brain regions. The distinct effects of increased theta power and delta/theta-gamma phase-amplitude coupling might reflect the impairment and compensation of sleep deprivation on vigilance performance, respectively.

Striatal-hippocampal functional connectivity contributes to real-life positive anticipatory experiences and subjective well-being

Positive anticipatory experiences are key to daily well-being. However, the brain's functional architecture underlying real-world positive anticipatory experiences and well-being remains unexplored. In the present study, we combined an ecological momentary assessment and resting-state functional neuroimaging to identify the neural predictors of real-world positive anticipatory experiences and explore their relationships with subjective well-being (SWB). With a model-based approach, we quantified participants' accuracy in predicting positive events and the degree to which participants' affective states were influenced by the positive anticipation. We found that individuals with higher accuracy in predicting upcoming positive events showed greater SWB, and this relationship was mediated by greater positive anticipatory feelings. Importantly, functional connectivity of the dorsal and ventral striatal-hippocampal networks significantly predicted the accuracy and positive anticipatory feelings, respectively. These functional networks were further predictive of SWB. Our findings provide novel and ecologically valid evidence that the interaction between neural systems for reward-processing and memory plays an important role in real-life positive anticipatory experiences and everyday SWB.

The Limbic System in Co-Occurring Substance Use and Anxiety Disorders: A Narrative Review Using the RDoC Framework

Substance use disorders (SUDs) and anxiety disorders (ADs) are highly comorbid, a co-occurrence linked to worse clinical outcomes than either condition alone. While the neurobiological mechanisms involved in SUDs and anxiety disorders are intensively studied separately, the mechanisms underlying their comorbidity remain an emerging area of interest. This narrative review explores the neurobiological processes underlying this comorbidity, using the Research Domain Criteria (RDoC) framework to map disruptions in positive valence, negative valence, and cognitive systems across the three stages of the addiction cycle: binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation. Anxiety and substance use play a reciprocal role at each stage of addiction, marked by significant psychosocial impairment and dysregulation in the brain. A more thorough understanding of the neural underpinnings involved in comorbid SUDs and anxiety disorders will contribute to more tailored and effective therapeutic interventions and assessments.

Exploring Cognitive Deficits and Neuromodulation in Schizophrenia: A Narrative Review

Cognitive deficits are emerging as critical targets for managing schizophrenia and enhancing clinical and functional outcomes. These deficits are pervasive among individuals with schizophrenia, affecting various cognitive domains. Traditional pharmacotherapy and cognitive behavioral therapy (CBT) have limitations in effectively addressing cognitive impairments in this population. Neuromodulation techniques show promise in improving certain cognitive domains among patients with schizophrenia spectrum disorders. Understanding the mechanisms of neural circuits that underlie cognitive enhancement is essential for elucidating the pathophysiological processes of the disorder, and these insights could significantly optimize strategies for managing schizophrenia. Meanwhile, although there is an increasing body of evidence demonstrating the therapeutic effects of neuromodulation in this area, further research is still needed, particularly regarding topics such as different treatment protocols and the long-term effects of treatment.

Reverse phase protein array-based investigation of mitochondrial genes reveals alteration of glutaminolysis in the parahippocampal cortex of people who died by suicide

A moderating hub between resting state networks (RSNs) and the medial temporal lobe (MTL) is the parahippocampal cortex (PHC). Abnormal activity has been reported in depressed patients and suicide attempters in this region. Alterations in neuronal mitochondrial function may contribute to depression and suicidal behavior. However, little is known about the underlying molecular level changes in relevant structures. Specifically, expressional changes related to suicide have not been reported in the PHC. In this study, we compared the protein expression levels of genes encoding tricarboxylic acid (TCA) cycle enzymes in the PHC of adult individuals who died by suicide by reverse phase protein array (RPPA), which was corroborated by qRT-PCR at the mRNA level. Postmortem human brain samples were collected from 12 control and 10 suicidal individuals. The entorhinal cortex, which is topographically anterior to the PHC in the parahippocampal gyrus, and some other cortical brain regions were utilized for comparison. The results of the RPPA analysis revealed that the protein levels of DLD, OGDH, SDHB, SUCLA2, and SUCLG2 subunits were significantly elevated in the PHC but not in other cortical brain regions. In accordance with these findings, the mRNA levels of the respective subunits were also increased in the PHC. The subunits with altered levels are implicated in enzyme complexes involved in the oxidative decarboxylation branch of glutamine catabolism. These data suggest a potential role of glutaminolysis in the pathophysiology of suicidal behavior in the PHC.

Relationship between punishment sensitivity and risk-taking propensity

Previous research has shown that, in both laboratory and real-world contexts, punishment sensitivity is associated with lower risk-taking propensity. The neural underpinnings of the association between punishment sensitivity and risk-taking, however, remain largely unknown. To address this issue, we implemented resting-state functional connectivity (RSFC) and voxel-based morphometry (VBM) methodologies to investigate the neural basis of their relationship in the current study (N=594). The behavioral results confirmed a negative association between punishment sensitivity and risk-taking propensity, which supports the hypothesis. The VBM results demonstrated a positive correlation between punishment sensitivity and gray matter volume in the right orbitofrontal cortex (ROFC). Furthermore, the results of the RSFC analysis revealed that the functional connectivity between ROFC and the right medial temporal gyrus (RMTG) was positively associated with punishment sensitivity. Notably, mediation analysis demonstrated that punishment sensitivity acted as a complete mediator in the influence of ROFC-RMTG functional connectivity on risk-taking. These findings suggest that ROFC-RMTG functional connectivity may be the neural basis underlying the effect of punishment sensitivity on risk-taking propensity, which provides a new perspective for understanding the relationship between punishment sensitivity and risk-taking propensity.

Tai Chi Practice Buffers Aging Effects in Functional Brain Connectivity

Tai Chi (TC) practice has been shown to improve both cognitive and physical function in older adults. However, the neural mechanisms underlying the benefits of TC remain unclear. Our primary aims are to explore whether distinct age-related and TC-practice-related relationships can be identified with respect to either temporal or spatial (within/between-network connectivity) differences. This cross-sectional study examined recurrent neural network dynamics, employing an adaptive, data-driven thresholding approach to source-localized resting-state EEG data in order to identify meaningful connections across time-varying graphs, using both temporal and spatial features derived from a hidden Markov model (HMM). Mann-Whitney U tests assessed between-group differences in temporal and spatial features by age and TC practice using either healthy younger adult controls (YACs, n = 15), healthy older adult controls (OACs, n = 15), or Tai Chi older adult practitioners (TCOAs, n = 15). Our results showed that aging is associated with decreased within-network and between-network functional connectivity (FC) across most brain networks. Conversely, TC practice appears to mitigate these age-related declines, showing increased FC within and between networks in older adults who practice TC compared to non-practicing older adults. These findings suggest that TC practice may abate age-related declines in neural network efficiency and stability, highlighting its potential as a non-pharmacological intervention for promoting healthy brain aging. This study furthers the triple-network model, showing that a balancing and reorientation of attention might be engaged not only through higher-order and top-down mechanisms (i.e., FPN/DAN) but also via the coupling of bottom-up, sensory-motor (i.e., SMN/VIN) networks.

Deep learning based diagnosis of PTSD using 3D-CNN and resting-state fMRI data

BACKGROUND

The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpose, the present study aims to classify individuals with PTSD versus healthy control.

METHODS

The resting-state functional MRI (rs-fMRI) scans of 19 PTSD and 24 healthy control male subjects have been used to identify the activation pattern in most affected brain regions using group-level independent component analysis (ICA) and t-test. To classify PTSD-affected subjects from healthy control six machine learning techniques including random forest, Naive Bayes, support vector machine, decision tree, K-nearest neighbor, linear discriminant analysis, and deep learning three-dimensional 3D-CNN have been performed on the data and compared.

RESULTS

The rs-fMRI scans of the most commonly investigated 11 regions of trauma-exposed and healthy brains are analyzed to observe their level of activation. Amygdala and insula regions are determined as the most activated regions from the regions-of-interest in the brain of PTSD subjects. In addition, machine learning techniques have been applied to the components extracted from ICA but the models provided low classification accuracy. The ICA components are also fed into the 3D-CNN model, which is trained with a 5-fold cross-validation method. The 3D-CNN model demonstrated high accuracies, such as 98.12%, 98.25 %, and 98.00 % on average with training, validation, and testing datasets, respectively.

CONCLUSION

The findings indicate that 3D-CNN is a surpassing method than the other six considered techniques and it helps to recognize PTSD patients accurately.

Altered brain connectivity in mild cognitive impairment is linked to elevated tau and phosphorylated tau, but not to GAP-43 and Amyloid-β measurements: a resting-state fMRI study

Mild Cognitive Impairment (MCI) is a neurological condition characterized by a noticeable decline in cognitive abilities that falls between normal aging and dementia. Along with some biomarkers like GAP-43, Aβ, tau, and P-tau, brain activity and connectivity are ascribed to MCI; however, the link between brain connectivity changes and such biomarkers in MCI is still being investigated. This study explores the relationship between biomarkers like GAP-43, Aβ, tau, and P-tau, and brain connectivity. We enrolled 25 Participants with normal cognitive function and 23 patients with MCI. Levels of GAP-43, Aβ1-42, t-tau, and p-tau181p in the CSF were measured, and functional connectivity measures including ROI-to-voxel (RV) correlations and the DMN RV-ratio were extracted from the resting-state fMRI data. P-values below 0.05 were considered significant. The results showed that in CN individuals, higher connectivity within the both anterior default mode network (aDMN) and posterior DMN (pDMN) was associated with higher levels of the biomarker GAP-43. In contrast, MCI individuals showed significant negative correlations between DMN connectivity and levels of tau and P-tau. Notably, no significant correlations were found between Aβ levels and connectivity measures in either group. These findings suggest that elevated levels of GAP-43 indicate increased functional connectivity in aDMN and pDMN. Conversely, elevated levels of tau and p-tau can disrupt connectivity through various mechanisms. Thus, the accumulation of tau and p-tau can lead to impaired neuronal connectivity, contributing to cognitive decline.

Abnormal large-scale brain functional network dynamics in social anxiety disorder

AIMS

Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD.

METHODS

We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored.

RESULTS

Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing "widely weaker" FNC, but lower in States 2 and 4, representing "locally stronger" and "widely stronger" FNC, respectively. In State 1, representing "widely moderate" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration.

CONCLUSION

These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.

Wang P, Verhoye M, Esser KH, Van der Linden A, Keliris GA. Auditory cortical regions show resting-state functional connectivity with the default mode-like network in echolocating bats

Echolocating bats are among the most social and vocal of all mammals. These animals are ideal subjects for functional MRI (fMRI) studies of auditory social communication given their relatively hypertrophic limbic and auditory neural structures and their reduced ability to hear MRI gradient noise. Yet, no resting-state networks relevant to social cognition (e.g., default mode-like networks or DMLNs) have been identified in bats since there are few, if any, fMRI studies in the chiropteran order. Here, we acquired fMRI data at 7 Tesla from nine lightly anesthetized pale spear-nosed bats (Phyllostomus discolor). We applied independent components analysis (ICA) to reveal resting-state networks and measured neural activity elicited by noise ripples (on: 10 ms; off: 10 ms) that span this species' ultrasonic hearing range (20 to 130 kHz). Resting-state networks pervaded auditory, parietal, and occipital cortices, along with the hippocampus, cerebellum, basal ganglia, and auditory brainstem. Two midline networks formed an apparent DMLN. Additionally, we found four predominantly auditory/parietal cortical networks, of which two were left-lateralized and two right-lateralized. Regions within four auditory/parietal cortical networks are known to respond to social calls. Along with the auditory brainstem, regions within these four cortical networks responded to ultrasonic noise ripples. Iterative analyses revealed consistent, significant functional connectivity between the left, but not right, auditory/parietal cortical networks and DMLN nodes, especially the anterior-most cingulate cortex. Thus, a resting-state network implicated in social cognition displays more distributed functional connectivity across left, relative to right, hemispheric cortical substrates of audition and communication in this highly social and vocal species.

Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning

Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.

Electroencephalography microstates highlight specific mindfulness traits

The present study aimed to investigate the spontaneous dynamics of large-scale brain networks underlying mindfulness as a dispositional trait, through resting-state electroencephalography (EEG) microstates analysis. Eighteen participants had attended a standardized mindfulness-based stress reduction training (MBSR), and 18 matched waitlist individuals (CTRL) were recorded at rest while they were passively exposed to auditory stimuli. Participants' mindfulness traits were assessed with the Five Facet Mindfulness Questionnaire (FFMQ). To further explore the relationship between microstate dynamics at rest and mindfulness traits, participants were also asked to rate their experience according to five phenomenal dimensions. After training, MBSR participants showed a highly significant increase in FFMQ score, as well as higher observing and non-reactivity FFMQ sub-scores than CTRL participants. Microstate analysis revealed four classes of microstates (A-D) in global clustering across all subjects. The MBSR group showed lower duration, occurrence and coverage of microstate C than the control group. Moreover, these microstate C parameters were negatively correlated to non-reactivity sub-scores of FFMQ across participants, whereas the microstate A occurrence was negatively correlated to FFMQ total score. Further analysis of participants' self-reports suggested that MBSR participants showed a better sensory-affective integration of auditory interferences. In line with previous studies, our results suggest that temporal dynamics of microstate C underlie specifically the non-reactivity trait of mindfulness. These findings encourage further research into microstates in the evaluation and monitoring of the impact of mindfulness-based interventions on the mental health and well-being of individuals.

Monitoring the after-effects of ischemic stroke through EEG microstates

BACKGROUND AND PURPOSE

Stroke may cause extensive after-effects such as motor function impairments and disorder of consciousness (DoC). Detecting these after-effects of stroke and monitoring their changes are challenging jobs currently undertaken via traditional clinical examinations. These behavioural examinations often take a great deal of manpower and time, thus consuming significant resources. Computer-aided examinations of the electroencephalogram (EEG) microstates derived from bedside EEG monitoring may provide an alternative way to assist medical practitioners in a quick assessment of the after-effects of stroke.

METHODS

In this study, we designed a framework to extract microstate maps and calculate their statistical parameters to input to classifiers to identify DoC in ischemic stroke patients automatically. As the dataset is imbalanced with the minority of patients being DoC, an ensemble of support vector machines (EOSVM) is designed to solve the problem that classifiers always tend to be the majority classes in the classification on an imbalanced dataset.

RESULTS

The experimental results show EOSVM get better performance (with accuracy and F1-Score both higher than 89%), improving sensitivity the most, from lower than 60% (SVM and AdaBoost) to higher than 80%. This highlighted the usefulness of the EOSVM-aided DoC detection based on microstates parameters.

CONCLUSION

Therefore, the classifier EOSVM classification based on features of EEG microstates is helpful to medical practitioners in DoC detection with saved resources that would otherwise be consumed in traditional clinic checks.

EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training

Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (22 participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Behavioral tests, EEG spectral analysis (covering delta, theta, alpha, beta, gamma frequency bands) and microstate analysis (involving four microstate classes, A to D) were employed to evaluate RI effect of TMNMT. The results of the study showed that TMNMT had a stronger inhibition ability and longer inhibition time according to the behavioral tests compared to Placebo. Spectral analysis showed that RI effect of TMNMT increased significantly the power spectral density (PSD) of delta, theta bands and decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of TMNMT had shorter duration (microstate B, microstate C), higher Occurrence (microstate A, microstate C, microstate D), Coverage (microstate A) and transition probabilities (microstate A to microstate B, microstate A to microstate D and microstate D to microstate A). Meanwhile, RI effect of Placebo decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of Placebo had shorter duration (microstate C, microstate D), higher occurrence (microstate B, microstate C), lower coverage (microstate C, microstate D), higher transition probabilities (microstate A to microstate B, microstate B to microstate A). It was also found that the intensity of tinnitus symptoms was significant positively correlated with the duration of microstate B in five subgroups (ECnm, NMnm, RInm, ECpb, PBpb). Our study provided valuable experimental evidence and practical applications for the effectiveness of TMNMT as a novel music therapy for tinnitus. The observed stronger residual inhibition (RI) ability of TMNMT supported its potential applications in tinnitus treatment. Furthermore, the temporal dynamics of EEG microstates serve as novel functional and trait markers of synchronous brain activity that contribute to a deep understanding of the neural mechanism underlying TMNMT treatment for tinnitus.

Dynamic network characteristics of adolescents with major depressive disorder: Attention network mediates the association between anhedonia and attentional deficit

Attention deficit is a critical symptom that impairs social functioning in adolescents with major depressive disorder (MDD). In this study, we aimed to explore the dynamic neural network activity associated with attention deficits and its relationship with clinical outcomes in adolescents with MDD. We included 188 adolescents with MDD and 94 healthy controls. By combining psychophysics, resting-state electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) techniques, we aimed to identify dynamic network features through the investigation of EEG microstate characteristics and related temporal network features in adolescents with MDD. At baseline, microstate analysis revealed that the occurrence of Microstate C in the patient group was lower than that in healthy controls, whereas the duration and coverage of Microstate D increased in the MDD group. Mediation analysis revealed that the probability of transition from Microstate C to D mediated anhedonia and attention deficits in the MDD group. fMRI results showed that the temporal variability of the dorsal attention network (DAN) was significantly weaker in patients with MDD than in healthy controls. Importantly, the temporal variability of DAN mediated the relationship between anhedonia and attention deficits in the patient group. After acute-stage treatment, the response prediction group (RP) showed improvement in Microstates C and D compared to the nonresponse prediction group (NRP). For resting-state fMRI data, the temporal variability of DAN was significantly higher in the RP group than in the NRP group. Overall, this study enriches our understanding of the neural mechanisms underlying attention deficits in patients with MDD and provides novel clinical biomarkers.

Visuo-vestibular and cognitive connections of the vestibular neuromatrix are conserved across age and injury populations

BACKGROUND AND PURPOSE

Given the prevalence of vestibular dysfunction in pediatric concussion, there is a need to better understand pathophysiological disruptions within vestibular and associated cognitive, affective, and sensory-integrative networks. Although current research leverages established intrinsic connectivity networks, these are nonspecific for vestibular function, suggesting that a pathologically guided approach is warranted. The purpose of this study was to evaluate the generalizability of the previously identified "vestibular neuromatrix" in adults with and without postconcussive vestibular dysfunction to young athletes aged 14-17.

METHODS

This retrospective study leveraged resting-state functional MRI data from two sites. Site A included adults with diagnosed postconcussive vestibular impairment and healthy adult controls and Site B consisted of young athletes with preseason, postconcussion, and postseason time points (prospective longitudinal data). Adjacency matrices were generated from preprocessed resting-state data from each sample and assessed for overlap and network structure in MATLAB.

RESULTS

Analyses indicated the presence of a conserved "core" network of vestibular regions as well as areas subserving visual, spatial, and attentional processing. Other vestibular connections were also conserved across samples but were not linked to the "core" subnetwork by regions of interest included in this study.

CONCLUSIONS

Our results suggest that connections between central vestibular, visuospatial, and known intrinsic connectivity networks are conserved across adult and pediatric participants with and without concussion, evincing the significance of this expanded, vestibular-associated network. Our findings thus support this network as a workable model for investigation in future studies of dysfunction in young athlete populations.

Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic

Background

Social anxiety (SA) is a negative emotional response that can lead to mental health issues, which some have experienced during the coronavirus disease 2019 (COVID-19) pandemic. Little attention has been given to the neurobiological mechanisms underlying inter-individual differences in SA alterations related to COVID-19. This study aims to identify neurofunctional markers of COVID-specific SA development.

Methods

110 healthy participants underwent resting-state magnetic resonance imaging and behavioral tests before the pandemic (T1, October 2019 to January 2020) and completed follow-up behavioral measurements during the pandemic (T2, February to May 2020). We constructed individual functional networks and used graph theoretical analysis to estimate their global and nodal topological properties, then used Pearson correlation and partial least squares correlations examine their associations with COVID-specific SA alterations.

Results

In terms of global network parameters, SA alterations (T2-T1) were negatively related to pre-pandemic brain small-worldness and normalized clustering coefficient. In terms of nodal network parameters, SA alterations were positively linked to a pronounced degree centrality pattern, encompassing both the high-level cognitive networks (dorsal attention network, cingulo-opercular task control network, default mode network, memory retrieval network, fronto-parietal task control network, and subcortical network) and low-level perceptual networks (sensory/somatomotor network, auditory network, and visual network). These findings were robust after controlling for pre-pandemic general anxiety, other stressful life events, and family socioeconomic status, as well as by treating SA alterations as categorical variables.

Conclusions

The individual functional network associated with SA alterations showed a disrupted topological organization with a more random state, which may shed light on the neurobiological basis of COVID-related SA changes at the network level.

A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling

We present a general, trainable oscillatory neural network as a large-scale model of brain dynamics. The model has a cascade of two stages - an oscillatory stage and a complex-valued feedforward stage - for modelling the relationship between structural connectivity and functional connectivity from neuroimaging data under resting brain conditions. Earlier works of large-scale brain dynamics that used Hopf oscillators used linear coupling of oscillators. A distinctive feature of the proposed model employs a novel form of coupling known as power coupling. Oscillatory networks based on power coupling can accurately model arbitrary multi-dimensional signals. Training the lateral connections in the oscillator layer is done by a modified form of Hebbian learning, whereas a variation of the complex backpropagation algorithm does training in the second stage. The proposed model can not only model the empirical functional connectivity with remarkable accuracy (correlation coefficient between simulated and empirical functional connectivity- 0.99) but also identify default mode network regions. In addition, we also inspected how structural loss in the brain can cause significant aberration in simulated functional connectivity and functional connectivity dynamics; and how it can be restored with optimized model parameters by an in silico perturbational study.

Large-scale brain functional network abnormalities in social anxiety disorder

BACKGROUND

Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value.

METHODS

Forty-six SAD patients and 52 demographically-matched healthy controls (HC) were recruited to undergo clinical evaluation and resting-state functional MRI scanning. We used group independent component analysis to characterize the functional architecture of brain resting-state networks (RSNs) and investigate between-group differences in intra-/inter-network functional network connectivity (FNC). Furtherly, we explored the associations of FNC abnormalities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses.

RESULTS

SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC abnormalities among those high-order and primary RSNs. Some aberrant FNC signatures were correlated to disease severity and duration, suggesting pathophysiological relevance. Furthermore, intrinsic FNC anomalies allowed individual classification of SAD v. HC with significant accuracy, indicating potential diagnostic efficacy.

CONCLUSIONS

SAD patients show distinct patterns of functional synchronization abnormalities both within and across large-scale RSNs, reflecting or causing a network imbalance of bottom-up response and top-down regulation in cognitive, emotional and sensory domains. Therefore, this could offer insights into the neurofunctional substrates of SAD.

Designing for Hope: Biophilic Color Associations and Their Relevance to Clinical Settings

AIM

To consider one common aspect of biophilic design (BD; i.e., color) and its relationship to an important element of well-being (i.e., hope).

BACKGROUND

BD is multifaceted making the identification of critical design elements difficult. Further complexity is introduced given that practice assumptions stemming from the biophilia hypothesis may be questioned. Consistent with the biophilia hypothesis, the author considers the study's findings from the perspectives of evolutionary psychology and psychobiology.

METHODS

One hundred and fifty four adult participants engaged in one of the three experiments. Using colored test cards, Experiment #1 sought to determine which of four biophilic colors (i.e., red, yellow, green, or blue) evoked the strongest experience of hope. Considering this color alone, Experiment #2 sought to manipulate "color depth." Participants were asked to identify what color depth evoked the strongest experience of hope. Experiment #3 sought to determine if the outcomes of Experiments #1 and #2 were due to a priming effect. All participants were asked about color associations they held.

RESULTS

Experiments #1 and #2 demonstrated that yellow at maximal color depth evoked the strongest experience of hope (p < .001). Experiment #3 demonstrated that no priming effect was evident (p < .05). No participant had a strong personal preference for/against yellow. Natural world color associations existed for yellow, green, and blue. Red held emotive associations.

CONCLUSIONS

These findings clearly associate yellow with hope. From the perspectives of evolutionary psychology and psychobiology this suggests color cues can evoke time-dependent motive states. Implications for practitioners designing spaces of hope within healthcare facilities are considered.

EEG microstate changes according to mental fatigue induced by aircraft piloting simulation: An exploratory study

BACKGROUND

A continuous flight task load can induce fatigue and lead to changes in electroencephalography (EEG). EEG microstates can reflect the activities of large-scale neural networks during mental fatigue. This exploratory experiment explored the effects of mental fatigue induced by continuous simulated flight multitasking on EEG microstate indices.

METHODS

Twenty-four participants performed continuous 2-hour aircraft piloting simulation while EEG were recorded. The Stanford sleepiness scale (SSS) and critical flicker fusion frequency (CFF) were measured before and after the task. Microstate analysis was applied to EEG. Four microstate classes (A-D) were identified during the pre-task, post-task, beginning, and end phases. The effects of mental fatigue were analyzed.

RESULTS

Compared with the pre-task, the post-task had a higher global explained variance (GEV) and time parameters of class C but lower occurrence and coverage of class D. The end had a higher GEV but lower duration and coverage of class D than at the beginning. After 2 h of multitasking, the transition probability between A and D, and between B and D decreased but between A and C increased. Subjective fatigue scores were negatively correlated with occurrence and coverage of class D. Task performance was negatively correlated with duration and coverage of class C but positively correlated with duration and occurrence of class B.

CONCLUSION

Time parameters and transition probability of EEG microstates can detect mental fatigue induced by continuous aircraft piloting simulation. The global brain network activation of mental fatigue can be detected by EEG microstates that can evaluate flight fatigue.

Dysfunctional mitochondrial processes contribute to energy perturbations in the brain and neuropsychiatric symptoms

Mitochondria are complex endosymbionts that evolved from primordial purple nonsulfur bacteria. The incorporation of bacteria-derived mitochondria facilitates a more efficient and effective production of energy than what could be achieved based on previous processes alone. In this case, endosymbiosis has resulted in the seamless coupling of cytochrome c oxidase and F-ATPase to maximize energy production. However, this mechanism also results in the generation of reactive oxygen species (ROS), a phenomenon that can have both positive and negative ramifications on the host. Recent studies have revealed that neuropsychiatric disorders have a pro-inflammatory component in which ROS is capable of initiating damage and cognitive malfunction. Our current understanding of cognition suggests that it is the product of a neuronal network that consumes a substantial amount of energy. Thus, alterations or perturbations of mitochondrial function may alter not only brain energy supply and metabolite generation, but also thought processes and behavior. Mitochondrial abnormalities and oxidative stress have been implicated in several well-known psychiatric disorders, including schizophrenia (SCZ) and bipolar disorder (BPD). As cognition is highly energy-dependent, we propose that the neuronal pathways underlying maladaptive cognitive processing and psychiatric symptoms are most likely dependent on mitochondrial function, and thus involve brain energy translocation and the accumulation of the byproducts of oxidative stress. We also hypothesize that neuropsychiatric symptoms (e.g., disrupted emotional processing) may represent the vestiges of an ancient masked evolutionary response that can be used by both hosts and pathogens to promote self-repair and proliferation via parasitic and/or symbiotic pathways.

Structural brain abnormalities in schizophrenia patients with a history and presence of auditory verbal hallucination

Although many studies have demonstrated structural brain abnormalities associated with auditory verbal hallucinations (AVH) in schizophrenia, the results remain inconsistent because of the small sample sizes and the reliability of clinical interviews. We compared brain morphometries in 204 participants, including 58 schizophrenia patients with a history of AVH (AVH + ), 29 without a history of AVH (AVH-), and 117 healthy controls (HCs) based on a detailed inspection of medical records. We further divided the AVH+ group into 37 patients with and 21 patients without hallucinations at the time of the MRI scans (AVH++ and AVH+-, respectively) via clinical interviews to explore the morphological differences according to the persistence of AVH. The AVH + group had a smaller surface area in the left caudal middle frontal gyrus (F = 7.28, FDR-corrected p = 0.0008) and precentral gyrus (F = 7.68, FDR-corrected p = 0.0006) compared to the AVH- group. The AVH+ patients had a smaller surface area in the left insula (F = 7.06, FDR-corrected p = 0.001) and a smaller subcortical volume in the bilateral hippocampus (right: F = 13.34, FDR-corrected p = 0.00003; left: F = 6.80, FDR-corrected p = 0.001) compared to the HC group. Of these significantly altered areas, the AVH++ group showed significantly smaller bilateral hippocampal volumes compared to the AVH+- group, and a smaller surface area in the left precentral gyrus and caudal middle frontal gyrus compared to the AVH- group. Our findings highlighted the distinct pattern of structural alteration between the history and presence of AVH in schizophrenia, and the importance of integrating multiple criteria to elucidate the neuroanatomical mechanisms.

Early life stress is associated with the default mode and fronto-limbic network connectivity among young adults

Exposure to early life stress (ELS) is associated with a variety of detrimental psychological and neurodevelopmental effects. Importantly, ELS has been associated with regional alterations and aberrant connectivity in the structure and functioning of brain regions involved in emotion processing and self-regulation, creating vulnerability to mental health problems. However, longitudinal research regarding the impact of ELS on functional connectivity between brain regions in the default mode network (DMN) and fronto-limbic network (FLN), both implicated in emotion-related processes, is relatively scarce. Neuroimaging research on ELS has mostly focused on single nodes or bi-nodal connectivity instead of functional networks. We examined how ELS is associated with connectivity patterns within the DMN and FLN during rest in early adulthood. The participants (n = 86; 47 females) in the current functional magnetic resonance imaging (fMRI) study were young adults (18-21 years old) whose families had participated in a longitudinal study since pregnancy. ELS was assessed both prospectively (parental reports of family relationship problems and mental health problems during pregnancy and infancy) and retrospectively (self-reported adverse childhood experiences). Inter-subject representational similarity analysis (IS-RSA) and multivariate distance matrix regression (MDMR) were used to analyze the association between ELS and the chosen networks. The IS-RSA results suggested that prospective ELS was associated with complex alterations within the DMN, and that retrospective ELS was associated with alterations in the FLN. MDMR results, in turn, suggested that that retrospective ELS was associated with DMN connectivity. Mean connectivity of the DMN was also associated with retrospective ELS. Analyses further showed that ELS-related alterations in the FLN were associated with increased connectivity between the prefrontal and limbic regions, and between different prefrontal regions. These results suggest that exposure to ELS in infancy might have long-lasting influences on functional brain connectivity that persist until early adulthood. Our results also speak for the importance of differentiating prospective and retrospective assessment methods to understand the specific neurodevelopmental effects of ELS.

EEG microstates as markers of major depressive disorder and predictors of response to SSRIs therapy

BACKGROUND

Major depressive disorder (MDD) is associated with abnormal neural activities and brain connectivity. EEG microstate is a voltage topology map that reflects transient activations of the brain network. A limited number of studies on EEG microstate in MDD have focused on differences between patients and healthy controls. However, EEG microstate changes in MDD patients before and after drug treatment have not been evaluated. We assessed EEG microstate characteristics and evaluated changes in brain network dynamics in MDD patients before and after drug treatment. Moreover, we evaluated the neuro-electrophysiological mechanisms of antidepressant therapies.

METHODS

64-channel resting EEG was obtained from 101 patients with first-episode untreated depression (0 week) and 45 healthy controls (HC) from January to December 2020. MDD patients were treated with selective serotonin reuptake inhibitors (SSRI). EEG data for 51 MDD patients who had completed an 8-week follow-up was collected. After pre-processing, EEG data from different groups were subjected to microstate analysis, and the atomize and agglomerate hierarchical clustering (AAHC) was into 4 microstates. Next, EEG signals from each patient were fitted using templates of 4 microstates. Finally, microstate indices were collected and analyzed.

RESULTS

Global clustering generated 4 microstates (A, B, C, D) in all subjects, which explained 65-84% of the global variance. Compared to HC, the duration of microstate D reduced while those of microstates A and B increased in MDD patients. After the 8-week treatment period, the duration and coverage of microstate D increased, the frequency of microstate A and transition probability of microstate D to A reduced, while transition probability of microstate B to D and D to B increased in MDD patients. There were no differences in microstate features between HC and MDD at 8 weeks. In patients with first-episode untreated depression, lower average durations of microstate D, relatively higher frequencies of microstate C and lower transition probabilities of microstate D to B correlated with better effects after 8 weeks. The higher occurrence and proportion of microstate C at 8 weeks was positively correlated with the HAMD score and reduction rate. The same observation was reached for the transition probability of microstate A to C. However, the transition probability of microstate D to B showed a negative correlation with the HAMD score at 8 weeks.

CONCLUSION

Microstate D is a potential electrophysiological trait of MDD and can predict treatment outcomes of SSRIs. Therefore, EEG microstate analysis may not only be an objective method for evaluating treatment outcomes of depression, but is also a potential new approach for exploring the neuro-electrophysiological mechanisms of antidepressant therapy. Public title: Multidimensional diagnosis, individualized treatment and management techniques based on clinic-pathological characteristics of depressive disorder; Registration number: ChiCTR1900026600; Date of registration: 2019-10-15; URL: http://www.chictr.org.cn/index.aspx.

Increased gray matter following mindfulness-based stress reduction in breast cancer survivors with chronic neuropathic pain: preliminary evidence using voxel-based morphometry

PURPOSE

To investigate the impact of Mindfulness-Based Stress Reduction (MBSR) on gray matter volume (GMV) in female breast cancer survivors who suffer from chronic neuropathic pain (CNP).

METHODS

Voxel-based morphometry (VBM) was used to explore differences in GMV in 13 MBSR trainees and 10 waitlisted controls, with MRI scans and self-report measures completed pre- and post-8 weeks of training.

RESULTS

Compared to controls, the MBSR group had greater GMV in the angular gyrus and middle frontal gyrus post-training. The MBSR group's right parahippocampal gyrus GMV increased from pre- to post-training, whereas the control group's left parahippocampal gyrus, precuneus, middle temporal gyrus, and right cuneus GMV decreased over the same time period. Pain interference was significantly reduced and mindfulness was significantly increased following MBSR for the intervention group only.

CONCLUSIONS

MBSR was associated with increased GMV in regions where GMV is known to (1) increase with mindfulness and reorientation of attention and (2) decrease with the experience of chronic neuropathic pain. By contrast, the control group's decreases in GMV may be due to the negative effects of CNP which potentially may be reduced with MBSR, though further research is needed.

IMPLICATIONS FOR CANCER SURVIVORS

Given the poor efficiency of pharmacotherapies in a high percentage of women with neuropathic pain following breast cancer treatment, adjunct methods are required. MBSR may affect the brain to help alter attention and perception of pain, thus playing a potentially important role in the path to wellness for breast cancer survivors.

Neural network involvement for religious experiences in worship measured by EEG microstate analysis

To date, not much is known about large-scale brain activation patterns in religious states of mind and previous studies have not set an emphasis on experience. The present study investigated the phenomenon of religious experiences through microstate analysis, and it was the first neurocognitive research to tackle the dimension of experience directly. Hence, a total of 60 evangelical Christians participated in an experiment where they were asked to engage in worship and try to connect with God. With a bar slider, people were able to continuously rate how strongly they sensed God's presence at any given moment. A selection of songs was used to help in the induction of the desired experience. With 64 electroencephalography (EEG) electrodes, the brain activity was assessed and analyzed with five clusters of microstate classes. First, we hypothesized that the neural network for multisensory integration was involved in the religious experience. Second, we hypothesized that the same was true for the Default Mode Network (DMN). Our results suggested an association between the auditory network and the religious experience, and an association with the salience network as well as with the DMN. No associations with the network thought to be involved with multisensory integration was detected.

The No-Report Paradigm: A Revolution in Consciousness Research?

In the cognitive neuroscience of consciousness, participants have commonly been instructed to report their conscious content. This, it was claimed, risks confounding the neural correlates of consciousness (NCC) with their preconditions, i.e., allocation of attention, and consequences, i.e., metacognitive reflection. Recently, the field has therefore been shifting towards no-report paradigms. No-report paradigms draw their validity from a direct comparison with no-report conditions. We analyze several examples of such comparisons and identify alternative interpretations of their results and/or methodological issues in all cases. These go beyond the previous criticism that just removing the report is insufficient, because it does not prevent metacognitive reflection. The conscious mind is fickle. Without having much to do, it will turn inward and switch, or timeshare, between the stimuli on display and daydreaming or mind-wandering. Thus, rather than the NCC, no-report paradigms might be addressing the neural correlates of conscious disengagement. This observation reaffirms the conclusion that no-report paradigms are no less problematic than report paradigms.

Neural reactivation and judgements of vividness reveal separable contributions to mnemonic representation

Mnemonic representations vary in fidelity, sharpness, and strength-qualities that can be examined using both introspective judgements of mental states and objective measures of brain activity. Subjective and objective measures are both valid ways of "reading out" the content of someone's internal mnemonic states, each with different strengths and weaknesses. St-Laurent and colleagues (2015) compared the neural correlates of memory vividness ratings with patterns of neural reactivation evoked during memory recall and found considerable overlap between the two, suggesting a common neural basis underlying these different markers of representational quality. Here we extended this work with meta-analytic methods by pooling together four neuroimaging datasets in order to contrast the neural substrates of neural reactivation and those of vividness judgements. While reactivation and vividness judgements correlated positively with one another and were associated with common univariate activity in the dorsal attention network and anterior hippocampus, some notable differences were also observed. Vividness judgments were tied to stronger activation in the striatum and dorsal attention network, together with activity suppression in default mode network nodes. We also observed a trend for reactivation to be more closely associated with early visual cortex activity. A mediation analysis found support for the hypothesis that neural reactivation is necessary for memory vividness, with activity in the anterior hippocampus associated with greater reactivation. Our results suggest that neural reactivation and vividness judgements reflect common mnemonic processes but differ in the extent to which they engage effortful, attentional processes. Additionally, the similarity between reactivation and vividness appears to arise, partly, through hippocampal engagement during memory retrieval.

Whole-brain white matter correlates of personality profiles predictive of subjective well-being

We investigated the white matter correlates of personality profiles predictive of subjective well-being. Using principal component analysis to first determine the possible personality profiles onto which core personality measures would load, we subsequently searched for whole-brain white matter correlations with these profiles. We found three personality profiles that correlated with the integrity of white matter tracts. The correlates of an “optimistic” personality profile suggest (a) an intricate network for self-referential processing that helps regulate negative affect and maintain a positive outlook on life, (b) a sustained capacity for visually tracking rewards in the environment and (c) a motor readiness to act upon the conviction that desired rewards are imminent. The correlates of a “short-term approach behavior” profile was indicative of minimal loss of integrity in white matter tracts supportive of lifting certain behavioral barriers, possibly allowing individuals to act more outgoing and carefree in approaching people and rewards. Lastly, a “long-term approach behavior” profile’s association with white matter tracts suggests lowered sensitivity to transient updates of stimulus-based associations of rewards and setbacks, thus facilitating the successful long-term pursuit of goals. Together, our findings yield convincing evidence that subjective well-being has its manifestations in the brain.

Disrupted Cerebellar-Default Mode Network Functional Connectivity in Major Depressive Disorder With Gastrointestinal Symptoms

Gastrointestinal (GI) symptoms are one of the common somatic symptoms presented in patients with major depressive disorder (MDD). Higher frequency of GI symptoms and higher GI symptom burden were linked to greater depression severity and increased risk of suicide ideation. However, few studies have explored the underlying mechanisms of GI symptoms in MDD. Based on previous studies, the cerebellar-DMN circuits may play a potentially critical role in GI symptoms comorbid with depression. Fifty-two first-episode drug-naive patients with MDD (35 with GI symptoms and 17 without GI symptoms) and 28 matched healthy controls were recruited in the current study and underwent resting-state functional magnetic resonance imaging scan. Cerebellar seed-based functional connectivity maps were established. Relative to depressed patients without GI symptoms, significantly increased cerebellar-anterior default mode network (DMN) connectivities were found in those with GI symptoms. Both increased and decreased functional connectivities were found between cerebellum and posterior DMN in patients with GI symptoms compared with those without GI symptoms and healthy controls. Moreover, the right Crus I - right superior temporal gyrus connectivity value was related to severity of GI symptoms and depression in all patients with MDD. The support vector machine analysis demonstrated a satisfactory classification accuracy (89%) of the disrupted cerebellar-DMN connectivities for correctly identifying MDD patients with GI symptoms. These results revealed the possible neural mechanisms for the involvement of cerebellar-DMN circuits in GI symptoms co-occurred with MDD.

The Relationship Between Default Mode and Dorsal Attention Networks Is Associated With Depressive Disorder Diagnosis and the Strength of Memory Representations Acquired Prior to the Resting State Scan

Previous research indicates that individuals with depressive disorders (DD) have aberrant resting state functional connectivity and may experience memory dysfunction. While resting state functional connectivity may be affected by experiences preceding the resting state scan, little is known about this relationship in individuals with DD. Our study examined this question in the context of object memory. 52 individuals with DD and 45 healthy controls (HC) completed clinical interviews, and a memory encoding task followed by a forced-choice recognition test. A 5-min resting state fMRI scan was administered immediately after the forced-choice task. Resting state networks were identified using group Independent Component Analysis across all participants. A network modeling analysis conducted on 22 networks using FSLNets examined the interaction effect of diagnostic status and memory accuracy on the between-network connectivity. We found that this interaction significantly affected the relationship between the network comprised of the medial prefrontal cortex, posterior cingulate cortex, and hippocampal formation and the network comprised of the inferior temporal, parietal, and prefrontal cortices. A stronger positive correlation between these two networks was observed in individuals with DD who showed higher memory accuracy, while a stronger negative correlation (i.e., anticorrelation) was observed in individuals with DD who showed lower memory accuracy prior to resting state. No such effect was observed for HC. The former network cross-correlated with the default mode network (DMN), and the latter cross-correlated with the dorsal attention network (DAN). Considering that the DMN and DAN typically anticorrelate, we hypothesize that our findings indicate aberrant reactivation and consolidation processes that occur after the task is completed. Such aberrant processes may lead to continuous "replay" of previously learned, but currently irrelevant, information and underlie rumination in depression.

Prospective Memory and Default Mode Network Functional Connectivity in Normal and Pathological Aging

BACKGROUND

Prospective memory (PM), the ability to execute a previously formed intention given the proper circumstance, has been proven to be vulnerable to Alzheimer's disease. Previous studies have indicated the involvement of the frontoparietal networks; however, it is proposed that PM may also be associated with other neural substrates that support stimulus-dependent spontaneous cognition.

OBJECTIVE

The present study aimed to examine the hypothesis that PM deficit in Alzheimer's disease is related to altered functional connectivity (FC) within the default mode network (DMN).

METHODS

Thirty-four patients with very mild or mild dementia (17 with Alzheimer's disease and 17 with subcortical ischemic vascular disease) and 22 cognitively-normal participants aged above 60 received a computerized PM task and resting-state functional magnetic resonance imaging study. Seed-based functional connectivity analysis was performed at group level within the DMN.

RESULTS

We found that the dementia groups showed worse PM performance and altered FC within the DMN as compared to the normal aging individuals. The FC between the medial prefrontal cortices and precuneus/posterior cingulate cortex was significantly correlated with PM in normal aging, while the FC between the right precuneus and bilateral inferior parietal lobules was correlated with PM in patients with Alzheimer's disease.

CONCLUSION

These findings support a potential role for the DMN in PM, and corroborate that PM deficit in Alzheimer's disease was associated with altered FC within the posterior hubs of the DMN, with spatial patterning different from normal aging.

Error-Related Brain Activity in Patients With Obsessive-Compulsive Disorder and Unaffected First-Degree Relatives: Evidence for Protective Patterns

Background

Indicators of increased error monitoring are associated with obsessive-compulsive disorder (OCD), as shown in electroencephalography and functional magnetic resonance imaging studies. As most studies used strictly controlled samples (excluding comorbidity and medication), it remains open whether these findings extend to naturalistic settings. Thus, we assessed error-related brain activity in a large, naturalistic OCD sample. We also explored which activity patterns might qualify as vulnerability endophenotypes or protective factors for the disorder. To this aim, a sample of unaffected first-degree relatives of patients with OCD was also included.

Methods

Participants (84 patients with OCD, 99 healthy control participants, and 37 unaffected first-degree relatives of patients with OCD) completed a flanker task while blood oxygen level–dependent responses were measured with functional magnetic resonance imaging. Aberrant error-related brain activity in patients and relatives was identified.

Results

Patients with OCD showed increased error-related activity in the supplementary motor area and within the default mode network, specifically in the precuneus and postcentral gyrus. Unaffected first-degree relatives showed increased error-related activity in the bilateral inferior frontal gyrus.

Conclusions

Increased supplementary motor area and default mode network activity in patients with OCD replicates previous studies and might indicate excessive error signals and increased self-referential error processing. Increased activity of the inferior frontal gyrus in relatives may reflect increased inhibition. Impaired response inhibition in OCD has been demonstrated in several studies and might contribute to impairments in suppressing compulsive actions. Thus, increased inferior frontal gyrus activity in the unaffected relatives of patients with OCD may have contributed to protection from symptom development.

Abnormalities of Electroencephalography Microstates in Drug-Naïve, First-Episode Schizophrenia

OBJECTIVE

Microstate analysis is a powerful tool to probe the brain functions, and changes in microstates under electroencephalography (EEG) have been repeatedly reported in patients with schizophrenia. This study aimed to investigate the dynamics of EEG microstates in drug-naïve, first-episode schizophrenia (FE-SCH) and to test the relationship between EEG microstates and clinical symptoms.

METHODS

Resting-state EEG were recorded for 23 patients with FE-SCH and 23 healthy controls using a 64-channel cap. Three parameters, i.e., contribution, duration, and occurrence, of the four microstate classes were calculated. Group differences in EEG microstates and their clinical symptoms [assessed using the Positive and Negative Syndrome Scale (PANSS)] were analyzed.

RESULTS

Compared with healthy controls, patients with FE-SCH showed increased duration, occurrence and contribution of microstate class C and decreased contribution and occurrence of microstate class D. In addition, the score of positive symptoms in PANSS was negatively correlated with the occurrence of microstate D.

CONCLUSION

Our findings showed abnormal patterns of EEG microstates in drug-naïve, first-episode schizophrenia, which might help distinguish individuals with schizophrenia in the early stage and develop early intervention strategies.

Selective Impairment of Long-Range Default Mode Network Functional Connectivity as a Biomarker for Preclinical Alzheimer's Disease in People with Down Syndrome

BACKGROUND

Down syndrome (DS) is associated with increased risk for Alzheimer's disease (AD). In neurotypical individuals, clinical AD is preceded by reduced resting state functional connectivity in the default mode network (DMN), but it is unknown whether changes in DMN connectivity predict clinical onset of AD in DS.

OBJECTIVE

Does lower DMN functional connectivity predict clinical onset of AD and cognitive decline in people with DS?

METHODS

Resting state functional MRI (rsfMRI), longitudinal neuropsychological, and clinical assessment data were collected on 15 nondemented people with DS (mean age = 51.66 years, SD = 5.34 years, range = 42-59 years) over four years, during which 4 transitioned to dementia. Amyloid-β (Aβ) PET data were acquired on 13 of the 15 participants. Resting state fMRI, neuropsychological, and clinical assessment data were also acquired on an independent, slightly younger unimpaired sample of 14 nondemented people with DS (mean age = 44.63 years, SD = 7.99 years, range = 38-61 years).

RESULTS

Lower functional connectivity between long-range but not short-range DMN regions predicts AD diagnosis and cognitive decline in people with DS. Aβ accumulation in the inferior parietal cortex is associated with lower regional DMN functional connectivity.

CONCLUSION

Reduction of long-range DMN connectivity is a potential biomarker for AD in people with DS that precedes and predicts clinical conversion.

Novel Cognitive Functions Arise at the Convergence of Macroscale Gradients

Functions in higher-order brain regions are the source of extensive debate. Although past trends have been to describe the brain-especially posterior cortical areas-in terms of a set of functional modules, a new emerging paradigm focuses on the integration of proximal functions. In this review, we synthesize emerging evidence that a variety of novel functions in the higher-order brain regions are due to convergence: convergence of macroscale gradients brings feature-rich representations into close proximity, presenting an opportunity for novel functions to arise. Using the TPJ as an example, we demonstrate that convergence is enabled via three properties of the brain: (1) hierarchical organization, (2) abstraction, and (3) equidistance. As gradients travel from primary sensory cortices to higher-order brain regions, information becomes abstracted and hierarchical, and eventually, gradients meet at a point maximally and equally distant from their sensory origins. This convergence, which produces multifaceted combinations, such as mentalizing another person's thought or projecting into a future space, parallels evolutionary and developmental characteristics in such regions, resulting in new cognitive and affective faculties.

Event-based control of autonomic and emotional states by the anterior cingulate cortex

Despite being an intensive area of research, the function of the anterior cingulate cortex (ACC) remains somewhat of a mystery. Human imaging studies implicate the ACC in various cognitive functions, yet surgical ACC lesions used to treat emotional disorders have minimal lasting effects on cognition. An alternative view is that ACC regulates autonomic states, consistent with its interconnectivity with autonomic control regions and that stimulation evokes changes in autonomic/emotional states. At the cellular level, ACC neurons are highly multi-modal and promiscuous, and can represent a staggering array of task events. These neurons nevertheless combine to produce highly event-specific ensemble patterns that likely alter activity in downstream regions controlling emotional and autonomic tone. Since neuromodulators regulate the strength of the ensemble activity patterns, they would regulate the impact these patterns have on downstream targets. Through these mechanisms, the ACC may determine how strongly to react to the very events its ensembles represent. Pathologies arise when specific event-related representations gain excessive control over autonomic/emotional states.

Age differences in intuitive moral decision-making: Associations with inter-network neural connectivity

Positions of power involving moral decision-making are often held by older adults (OAs). However, little is known about age differences in moral decision-making and the intrinsic organization of the aging brain. In this study, younger adults (YAs; n = 117, Mage = 22.11) and OAs (n = 82, Mage = 67.54) made decisions in hypothetical moral dilemmas and completed resting-state multi-echo functional magnetic resonance imaging (fMRI) scans. Relative to YAs, OAs were more likely to endorse deontological decisions (i.e., decisions based on adherence to a moral principle or duty), but only when the choice was immediately compelling or intuitive. By contrast, there was no difference between YAs and OAs in utilitarian decisions (i.e., decisions aimed at maximizing collective well-being) when the utilitarian choice was intuitive. Enhanced connections between the posterior medial core of the default network (pmDN) and the dorsal attention network, and overall reduced segregation of pmDN from the rest of the brain, were associated with this increased deontological-intuitive moral decision-making style in OAs. The present study contributes to our understanding of age differences in decision-making styles by taking into account the intuitiveness of the moral choice, and it offers further insights as to how age differences in intrinsic brain connectivity relate to these distinct moral decision-making styles in YAs and OAs. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Mechanisms of Network Changes in Cognitive Impairment in Multiple Sclerosis

Background and Objectives

Cognitive impairment in multiple sclerosis (MS) is associated with functional connectivity abnormalities. While there have been calls to use functional connectivity measures as biomarkers, there remains to be a full understanding of why they are affected in MS. In this cross-sectional study, we tested the hypothesis that functional network regions may be susceptible to disease-related “wear and tear” and that this can be observable on co-occurring abnormalities on other magnetic resonance metrics. We tested whether functional connectivity abnormalities in cognitively impaired patients with MS co-occur with (1) overlapping, (2) local, or (3) distal changes in anatomic connectivity and cerebral blood flow abnormalities.

Methods

Multimodal 3T MRI and assessment with the Brief Repeatable Battery of Neuropsychological tests were performed in 102 patients with relapsing-remitting MS and 27 healthy controls. Patients with MS were classified as cognitively impaired if they scored ≥1.5 SDs below the control mean on ≥2 tests (n = 55) or as cognitively preserved (n = 47). Functional connectivity was assessed with Independent Component Analysis and dual regression of resting-state fMRI images. Cerebral blood flow maps were estimated, and anatomic connectivity was assessed with anatomic connectivity mapping and fractional anisotropy of diffusion-weighted MRI. Changes in cerebral blood flow and anatomic connectivity were assessed within resting-state networks that showed functional connectivity abnormalities in cognitively impaired patients with MS.

Results

Functional connectivity was significantly decreased in the anterior and posterior default mode networks and significantly increased in the right and left frontoparietal networks in cognitively impaired relative to cognitively preserved patients with MS (threshold-free cluster enhancement corrected at p ≤ 0.05, 2 sided). Networks showing functional abnormalities showed altered cerebral blood flow and anatomic connectivity locally and distally but not in overlapping locations.

Discussion

We provide the first evidence that functional connectivity abnormalities are accompanied by local cerebral blood flow and structural connectivity abnormalities but also demonstrate that these effects do not occur in exactly the same location. Our findings suggest a possibly shared pathologic mechanism for altered functional connectivity in brain networks in MS.

Pediatric Moderate-Severe Traumatic Brain Injury and Gray Matter Structural Covariance Networks: A Preliminary Longitudinal Investigation

Pediatric traumatic brain injury (TBI) is prevalent and can disrupt ongoing brain maturation. However, the long-term consequences of pediatric TBI on the brain's network architecture are poorly understood. Structural covariance networks (SCN), based on anatomical correlations between brain regions, may provide important insights into brain topology following TBI. Changes in global SCN (default-mode network [DMN], central executive network [CEN], and salience network [SN]) were compared sub-acutely (<90 days) and in the long-term (approximately 12-24 months) after pediatric moderate-severe TBI (n = 16), and compared to typically developing children assessed concurrently (n = 15). Gray matter (GM) volumes from selected seeds (DMN: right angular gyrus [rAG], CEN: right dorsolateral prefrontal cortex [rDLPFC], SN: right anterior insula) were extracted from T1-weighted images at both timepoints. No group differences were found sub-acutely; at the second timepoint, the TBI group showed significantly reduced structural covariance within the DMN seeded from the rAG and the (1) right middle frontal gyrus, (2) left superior frontal gyrus, and (3) left fusiform gyrus. Reduced structural covariance was also found within the CEN, that is, between the rDLPFC and the (1) calcarine sulcus, and (2) right occipital gyrus. In addition, injury severity was positively associated with GM volumes in the identified CEN regions. Over time, there were no significant changes in SCN in either group. The findings, albeit preliminary, suggest for the first time a long-term effect of pediatric TBI on SCN. SCN may be a complementary approach to characterize the global effect of TBI on the developing brain. Future work needs to further examine how disruptions of these networks relate to behavioral and cognitive difficulties.

Anatomy and white-matter connections of the precuneus

Purpose Advances in neuroimaging have provided an understanding of the precuneus'(PCu) involvement in functions such as visuospatial processing and cognition. While the PCu has been previously determined to be apart of a higher-order default mode network (DMN), recent studies suggest the presence of possible dissociations from this model in order to explain the diverse functions the PCu facilitates, such as in episodic memory. An improved structural model of the white-matter anatomy of the PCu can demonstrate its unique cerebral connections with adjacent regions which can provide additional clarity on its role in integrating information across higher-order cerebral networks like the DMN. Furthermore, this information can provide clinically actionable anatomic information that can support clinical decision making to improve neurologic outcomes such as during cerebral surgery. Here, we sought to derive the relationship between the precuneus and underlying major white-mater bundles by characterizing its macroscopic connectivity. Methods Structural tractography was performed on twenty healthy adult controls from the Human Connectome Project (HCP) utilizing previously demonstrated methodology. All precuneus connections were mapped in both cerebral hemispheres and inter-hemispheric differences in resultant tract volumes were compared with an unpaired, corrected Mann-Whitney U test and a laterality index (LI) was completed. Ten postmortem dissections were then performed to serve as ground truth by using a modified Klingler technique with careful preservation of relevant white matter bundles. Results The precuneus is a heterogenous cortical region with five major types of connections that were present bilaterally. (1) Short association fibers connect the gyri of the precuneus and connect the precuneus to the superior parietal lobule and the occipital cortex. (2) Four distinct parts of the cingulum bundle connect the precuneus to the frontal lobe and the temporal lobe. (3) The middle longitudinal fasciculus from the precuneus connects to the superior temporal gyrus and the dorsolateral temporal pole. (4) Parietopontine fibers travel as part of the corticopontine fibers to connect the precuneus to pontine regions. (5) An extensive commissural bundle connects the precuneus bilaterally. Conclusion We present a summary of the anatomic connections of the precuneus as part of an effort to understand the function of the precuneus and highlight key white-matter pathways to inform surgical decision-making. Our findings support recent models suggesting unique fiber connections integrating at the precuneus which may suggest finer subsystems of the DMN or unique networks, but further study is necessary to refine our model in greater quantitative detail.

Individualized video recommendation modulates functional connectivity between large scale networks

With the emergence of AI‐powered recommender systems and their extensive use in the video streaming service, questions and concerns also arise. Why can recommended video content continuously capture users' attention? What is the impact of long‐term exposure to personalized video content on one's behaviors and brain functions? To address these questions, we designed an fMRI experiment presenting participants with personally recommended videos and generally recommended ones. To examine how large‐scale networks were modulated by personalized video content, graph theory analysis was applied to investigate the interaction between seven networks, including the ventral and dorsal attention networks (VAN, DAN), frontal–parietal network (FPN), salience network (SN), and three subnetworks of default mode network (dorsal medial prefrontal (dMPFC), Core, and medial temporal lobe (MTL)). Our results showed that viewing nonpersonalized video content mainly enhanced the connectivity in the DAN‐FPN‐Core pathway, whereas viewing personalized ones increased not only the connectivity in this pathway but also the DAN‐VAN‐dMPFC pathway. In addition, both personalized and nonpersonalized short videos decreased the couplings between SN and VAN as well as between two DMN subsystems, Core and MTL. Collectively, these findings uncovered distinct patterns of network interactions in response to short videos and provided insights into potential neural mechanisms by which human behaviors are biased by personally recommended content.

The neurobiology of human fear generalization: meta-analysis and working neural model

Fear generalization to stimuli resembling a conditioned danger-cue (CS+) is a fundamental dynamic of classical fear-conditioning. Despite the ubiquity of fear generalization in human experience and its known pathogenic contribution to clinical anxiety, neural investigations of human generalization have only recently begun. The present work provides the first meta-analysis of this growing literature to delineate brain substrates of conditioned fear-generalization and formulate a working neural model. Included studies (K = 6, N = 176) reported whole-brain fMRI results and applied generalization-gradient methodology to identify brain activations that gradually strengthen (positive generalization) or weaken (negative generalization) as presented stimuli increase in CS+ resemblance. Positive generalization was instantiated in cingulo-opercular, frontoparietal, striatal-thalamic, and midbrain regions (locus coeruleus, periaqueductal grey, ventral tegmental area), while negative generalization was implemented in default-mode network nodes (ventromedial prefrontal cortex, hippocampus, middle temporal gyrus, angular gyrus) and amygdala. Findings are integrated within an updated neural account of generalization centering on the hippocampus, its modulation by locus coeruleus and basolateral amygdala, and the excitation of threat- or safety-related loci by the hippocampus.

Agonist-antagonist myoneural interface amputation preserves proprioceptive sensorimotor neurophysiology in lower limbs

The brain undergoes marked changes in function and functional connectivity after limb amputation. The agonist-antagonist myoneural interface (AMI) amputation is a procedure that restores physiological agonist-antagonist muscle relationships responsible for proprioceptive sensory feedback to enable greater motor control. We compared results from the functional neuroimaging of individuals (n = 29) with AMI amputation, traditional amputation, and no amputation. Individuals with traditional amputation demonstrated a significant decrease in proprioceptive activity, measured by activation of Brodmann area 3a, whereas functional activation in individuals with AMIs was not significantly different from controls with no amputation (P < 0.05). The degree of proprioceptive activity in the brain strongly correlated with fascicle activity in the peripheral muscles and performance on motor tasks (P < 0.05), supporting the mechanistic basis of the AMI procedure. These results suggest that surgical techniques designed to restore proprioceptive peripheral neuromuscular constructs result in desirable central sensorimotor plasticity.