Fronto-parietal Task Control Network Research Articles

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

BackgroundSocial 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. Methods110 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. ResultsIn 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. ConclusionsThe 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.

Identifying patients with cognitive motor dissociation using resting-state temporal stability

Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.

Knowing what you feel: Inferior frontal gyrus-based structural and functional neural patterns underpinning adaptive body awareness

BackgroundHeightened body awareness (BA) is conducive for increasing understanding of bodily state and improves individuals' health and well-being. Although there has been cumulative research concentrating on the self-perceived tendency to focus on negatively valenced interoceptive sensations, the specific structural and functional neural patterns underlying BA and their role in the relationship between BA and individual well-being remain unclear. MethodsVoxel-based morphometry and whole brain functional connectivity analyses were conducted to examine the structural and functional neural patterns, respectively, in 686 healthy subjects. BA and subjective well-being were assessed using questionnaires. ResultsBA was inversely related to gray matter volume of the right inferior frontal gyrus, opercular part (IFGoperc). Higher BA was correlated with enhanced IFGoperc-precuneus and IFGoperc-anterior supramarginal gyrus connectivities, and with decreased IFGoperc-lateral occipital cortex and IFGoperc-medial frontal cortex connectivities. The inferior frontal gyrus, triangular part (in the fronto-parietal task control network) acted as the hub that linked the sensory/somatomotor network, the default mode network, and the dorsal and ventral attention network. The IFGoperc-precuneus connectivity moderated the association between BA and subjective well-being. LimitationsWe were unable to rank all the networks by their relative importance, because the absolute weighted value in each module was not calculated. ConclusionOur findings demonstrated that BA was reflected by specific neural patterns mainly involved in cognitive-affective control, attentional and self-referential processing, as well as multisensory integration, which could offer some references for current therapies (e.g., mindfulness, yoga training) that are dedicated to solving health problems and improving individual well-being.

Effective and Accurate Diagnosis of Subjective Cognitive Decline Based on Functional Connection and Graph Theory View.

Subjective cognitive decline (SCD) is considered the earliest preclinical stage of Alzheimer’s disease (AD) that precedes mild cognitive impairment (MCI). Effective and accurate diagnosis of SCD is crucial for early detection of and timely intervention in AD. In this study, brain functional connectome (i.e., functional connections and graph theory metrics) based on the resting-state functional magnetic resonance imaging (rs-fMRI) provided multiple information about brain networks and has been used to distinguish individuals with SCD from normal controls (NCs). The consensus connections and the discriminative nodal graph metrics selected by group least absolute shrinkage and selection operator (LASSO) mainly distributed in the prefrontal and frontal cortices and the subcortical regions corresponded to default mode network (DMN) and frontoparietal task control network. Nodal efficiency and nodal shortest path showed the most significant discriminative ability among the selected nodal graph metrics. Furthermore, the comparison results of topological attributes suggested that the brain network integration function was weakened and network segregation function was enhanced in SCD patients. Moreover, the combination of brain connectome information based on multiple kernel-support vector machine (MK-SVM) achieved the best classification performance with 83.33% accuracy, 90.00% sensitivity, and an area under the curve (AUC) of 0.927. The findings of this study provided a new perspective to combine machine learning methods with exploration of brain pathophysiological mechanisms in SCD and offered potential neuroimaging biomarkers for diagnosis of early-stage AD.

Psilocybin acutely alters the functional connectivity of the claustrum with brain networks that support perception, memory, and attention

Psychedelic drugs, including the serotonin 2a (5-HT2A) receptor partial agonist psilocybin, are receiving renewed attention for their possible efficacy in treating a variety of neuropsychiatric disorders. Psilocybin induces widespread dysregulation of cortical activity, but circuit-level mechanisms underlying this effect are unclear. The claustrum is a subcortical nucleus that highly expresses 5-HT2A receptors and provides glutamatergic inputs to arguably all areas of the cerebral cortex. We therefore tested the hypothesis that psilocybin modulates claustrum function in humans. Fifteen healthy participants (10M, 5F) completed this within-subjects study in which whole-brain resting-state blood-oxygenation level-dependent (BOLD) signal was measured 100 ​min after blinded oral administration of placebo and 10 mg/70 ​kg psilocybin. Left and right claustrum signal was isolated using small region confound correction. Psilocybin significantly decreased both the amplitude of low frequency fluctuations as well as the variance of BOLD signal in the left and right claustrum. Psilocybin also significantly decreased functional connectivity of the right claustrum with auditory and default mode networks (DMN), increased right claustrum connectivity with the fronto-parietal task control network (FPTC), and decreased left claustrum connectivity with the FPTC. DMN integrity was associated with right-claustrum connectivity with the DMN, while FPTC integrity and modularity were associated with right claustrum and left claustrum connectivity with the FPTC, respectively. Subjective effects of psilocybin predicted changes in the amplitude of low frequency fluctuations and the variance of BOLD signal in the left and right claustrum. Observed effects were specific to claustrum, compared to flanking regions of interest (the left and right insula and putamen). This study used a pharmacological intervention to provide the first empirical evidence in any species for a significant role of 5-HT2A receptor signaling in claustrum functioning, and supports a possible role of the claustrum in the subjective and therapeutic effects of psilocybin.

Context-dependent dynamic functional connectivity alteration of lateral occipital cortex in schizophrenia

Schizophrenia has long been associated with dysfunction in visual perception. One important region underlying this is lateral occipital cortex (LOC), a mid-level visual region critical for object recognition. Although LOC of patients has exhibited structural and functional abnormalities in MR brain imaging studies, how it interacts with other networks over time under rest and with task demands remains to be clarified. The present study investigated the spatial-temporal interaction of LOC with other brain networks by examining functional connectivity communities of the brain over time. We found increased temporal instability of LOC connectivity over time under both resting and task-switching conditions in patients. In the resting state, LOC of patients had increased interaction with the frontoparietal task-control network (FPTC) and thalamus compared with controls, while during task switching, LOC showed increased interaction with the default mode network (DMN). Temporal instability of LOC connectivity was positively correlated with patients' switching cost during task performance and with hallucination severity. These results indicate that reduced stability of LOC connectivity may be an important factor underlying neurocognitive dysfunctions and symptom severity in schizophrenia.

Two-Step Feature Selection for Identifying Developmental Differences in Resting fMRI Intrinsic Connectivity Networks

Functional connectivity derived from functional magnetic resonance imaging (fMRI) is used as an effective way to assess brain architecture. There has been a growing interest in its application to the study of intrinsic connectivity networks (ICNs) during different brain development stages. fMRI data are of high dimension but small sample size, and it is crucial to perform dimension reduction before pattern analysis of ICNs. Feature selection is thus used to reduce redundancy, lower the complexity of learning, and enhance the interpretability. To study the varying patterns of ICNs in different brain development stages, we propose a two-step feature selection method. First, an improved support vector machine based recursive feature elimination method is utilized to study the differences of connectivity during development. To further reduce the highly correlated features, a combination of F-score and correlation score is applied. This method was then applied to analysis of the Philadelphia Neurodevelopmental Cohort (PNC) data. The two-step feature selection was randomly performed 20 times, and those features that showed up consistently in the experiments were chosen as the essential ICN differences between different brain ages. Our results indicate that ICN differences exist in brain development, and they are related to task control, cognition, information processing, attention, and other brain functions. In particular, compared with children, young adults exhibit increasing functional connectivity in the sensory/somatomotor network, cingulo-opercular task control network, visual network, and some other subnetworks. In addition, the connectivity in young adults decreases between the default mode network and other subnetworks such as the fronto-parietal task control network. The results are coincident with the fact that the connectivity within the brain alters from segregation to integration as an individual grows.

Functional connectivity mediates the relationship between self-efficacy and curiosity

Curiosity is an important driving force for human development. This study employed resting-state functional magnetic resonance imaging (fMRI) data (n = 400) and a cross-validation-based predictive framework based on the functional connectivity between and within the default mode network (DMN), fronto-parietal task control network (FPN), and salience network (SN) to explore the relationship between these and curiosity. We found that the curiosity network consisting of functional connections in DMN, FPN, and SN can successfully predict curiosity, and the functional connections within the prefrontal cortex (PFC) and between its nodes and the angular gyrus (AG) made a greater contribution to prediction power. More important, although curiosity has long been considered the main intrinsic motivation of learning and self-efficacy (individual's confidence in his or her own ability) is the primary factor affecting learning motivation, few studies have explored the relationship between the two. Therefore, we speculated that the relationship between the curiosity brain network and curiosity might also be related to self-efficacy. In this study, the strength of the curiosity network was calculated as a mediation variable to explore the relationship between self-efficacy and curiosity. The results revealed that the strength of the curiosity brain network mediates the association between self-efficacy and curiosity score. Our findings suggest that self-efficacy plays an important role in shaping individuals’ trait curiosity and extends understanding of the neural mechanism of curiosity.

Verbal Creativity Is Correlated With the Dynamic Reconfiguration of Brain Networks in the Resting State.

Creativity is the foundation of human culture. All inventions and innovations in history rely upon us to break with the traditional thinking and create something novel. A number of neuroimaging studies have explored the neural mechanism of creativity. However, a majority of researches have focused only on the stationary functional connectivity in resting-state fMRI and task-related fMRI, neglecting the dynamic variation of brain networks. Here, we used dynamic network analysis to investigate the relation between the dynamic reorganization of brain networks and verbal creativity in 370 healthy subjects. We found that the integration of the left lingual gyrus and left middle temporal gyrus (MTG) in default mode network (DMN) and the integration of the DMN and cerebellum, frontoparietal task control network (FPTC) and auditory network (Aud) showed positive correlation with verbal creativity performance. In addition, the recruitment of the bilateral postcentral gyrus from the sensory/somatomotor network (SMN) and the recruitment of the SMN in general displayed a significant correlation with verbal creativity scores. Taken together, these results suggested that the dynamic reorganization among the brain networks involved multiple cognitive processes, such as memory retrieval, imaginative process, cognitive control – these are all important for verbal creativity. These findings provided direct evidence that verbal creativity was related to the dynamic variation of brain mechanism during resting-state, extending past research on the neural mechanism of creativity. Meanwhile, these results bought about new perspectives for verbal creative training and rehabilitation training of depression.

Search and recovery of autobiographical and laboratory memories: Shared and distinct neural components

Functional neuroimaging evidence suggests that there are differences in the neural correlates of episodic memory for laboratory stimuli (laboratory memory) and for events from one’s own life (autobiographical memory). However, this evidence is scarce and often confounded with differences in memory testing procedures. Here, we directly compared the neural mechanisms underlying the search and recovery of autobiographical and laboratory memories while minimizing testing differences. Before scanning, participants completed a laboratory memory encoding task in which they studied four-word “chains” spread across three word pairs. During scanning, participants completed a laboratory memory retrieval task, in which they recalled the word chains, and an autobiographical memory retrieval task, in which they recalled specific personal events associated with word cues. Importantly, response times were similar in the two tasks, allowing for a direct comparison of the activation time courses. We found that during memory search (searching for the memory target), similar brain regions were activated during both the autobiographical and laboratory tasks, whereas during memory recovery (accessing the memory traces; i.e., ecphory), clear differences emerged: regions of the default mode network (DMN) were activated greater during autobiographical than laboratory memory, whereas the bilateral superior parietal lobules were activated greater during laboratory than autobiographical memory. Also, multivariate functional connectivity analyses revealed that regardless of memory stage, the DMN and ventral attention network exhibited a more integrated topology in the functional network underlying autobiographical (vs. laboratory) memory retrieval, whereas the fronto-parietal task control network exhibited a more integrated topology in the functional network underlying laboratory (vs. autobiographical) memory retrieval. These findings further characterize the shared and distinct neural components underlying autobiographical and laboratory memories, and suggest that differences in autobiographical vs. laboratory memory brain activation previously reported in the literature reflect memory recovery rather than search differences.

Group comparison of spatiotemporal dynamics of intrinsic networks in Parkinson's disease.

Recent advances with functional connectivity magnetic resonance imaging have demonstrated that at rest the brain exhibits coherent activity within a number of spatially independent maps, normally called 'intrinsic' or 'resting state' networks. These networks support cognition and behaviour, and are altered in neurodegenerative disease. However, there is a longstanding perspective, and ample functional magnetic resonance imaging evidence, demonstrating that intrinsic networks may be fractionated and that cortical elements may participate in multiple intrinsic networks at different times, dynamically changing alliances to adapt to cognitive demands. A method to probe the fine-grained spatiotemporal structure of networks may be more sensitive to subtle network changes that accompany heterogeneous cognitive deficits caused by a neurodegenerative disease such as Parkinson's disease. Here we tested the hypothesis that alterations to the latent (hidden) structure of intrinsic networks may reveal the impact of underlying pathophysiologic processes as assessed with cerebrospinal fluid biomarkers. Using a novel modelling approach that we call 'network kernel analysis', we compared fine-grained network ensembles (network kernels) that include overlapping cortical elements in 24 patients with Parkinson's disease (ages 45-86, 17 male) and normal cognition or mild cognitive impairment (n = 13), and 21 cognitively normal control subjects (ages 41-76, nine male). An omnibus measure of network disruption, calculated from correlations among network kernels, was correlated with cerebrospinal fluid biomarkers of pathophysiological processes in Parkinson's disease: concentrations of α-synuclein and amyloid-β42. Correlations among network kernels more accurately classified Parkinson's disease from controls than other functional neuroimaging measures. Inspection of the spatial maps related to the default mode network and a frontoparietal task control network kernel showed that the right insula, an area implicated in network shifting and associated with cognitive impairment in Parkinson's disease, was more highly correlated with both these networks in Parkinson's disease than in controls. In Parkinson's disease, increased correlation of the insula with the default mode network was related to lower attentional accuracy. We demonstrated that in an omnibus sense, correlations among network kernels describe biological impact of pathophysiological processes (through correlation with cerebrospinal fluid biomarkers) and clinical status (by classification of patient group). At a greater level of detail, we demonstrate aberrant involvement of the insula in the default mode network and the frontal frontoparietal task control network kernel. Network kernel analysis holds promise as a sensitive method for detecting biologically and clinical relevant changes to specific networks that support cognition and are impaired in Parkinson's disease.

Dynamic connectivity at rest predicts attention task performance.

Consistent spatial patterns of coherent activity, representing large-scale networks, have been reliably identified in multiple populations. Most often, these studies have examined "stationary" connectivity. However, there is a growing recognition that there is a wealth of information in the time-varying dynamics of networks which has neural underpinnings, which changes with age and disease and that supports behavior. Using factor analysis of overlapping sliding windows across 25 participants with Parkinson disease (PD) and 21 controls (ages 41-86), we identify factors describing the covarying correlations of regions (dynamic connectivity) within attention networks and the default mode network, during two baseline resting-state and task runs. Cortical regions that support attention networks are affected early in PD, motivating the potential utility of dynamic connectivity as a sensitive way to characterize physiological disruption to these networks. We show that measures of dynamic connectivity are more reliable than comparable measures of stationary connectivity. Factors in the dorsal attention network (DAN) and fronto-parietal task control network, obtained at rest, are consistently related to the alerting and orienting reaction time effects in the subsequent Attention Network Task. In addition, the same relationship between the same DAN factor and the alerting effect was present during tasks. Although reliable, dynamic connectivity was not invariant, and changes between factor scores across sessions were related to changes in accuracy. In summary, patterns of time-varying correlations among nodes in an intrinsic network have a stability that has functional relevance.

Age-Related Differences in the Dynamic Architecture of Intrinsic Networks

Correlations among low-frequency spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal reflect the connectivity of intrinsic large-scale networks in the brain. These correlations have typically been characterized over the entire timecourse (mean connectivity), but the mean correlations between regions vary dynamically. By focusing on the linear relationship between activity in network nodes within the default mode network (DMN), dorsal attention network (DAN), and fronto-parietal task control network (FPTC) captured by their inter-correlations, we demonstrate that this dynamic pattern of fluctuations reveals a detailed substructure, that this substructure is robust across individuals, and that the expression of specific factors is correlated with age. To do this, we conducted a chained P-technique factor analysis of the correlations in nonoverlapping temporal windows across N=145 normal aging subjects (age 56-89). The expression of factors within the DMN, FPTC, and DAN was highly correlated with age: Decreased intercorrelations within nodes in each factor were correlated with advanced age. Although these findings converge with those from stationary analysis, the ability to quantify age-related changes in the coherence of fluctuating connectivity may yield more insights into age-related cognitive decline.