Increased Global Interaction Across Functional Brain Modules During Cognitive Emotion Regulation

The multiverse of data preprocessing and analysis in graph-based fMRI: A systematic literature review of analytical choices fed into a decision support tool for informed analysis

The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.

Dynamical changes of interaction across functional brain communities during propofol-induced sedation

It is crucial to understand how anesthetics disrupt information transmission within the whole-brain network and its hub structure to gain insight into the network-level mechanisms underlying propofol-induced sedation. However, the influence of propofol on functional integration, segregation, and community structure of whole-brain networks were still unclear. We recruited 12 healthy subjects and acquired resting-state functional magnetic resonance imaging data during 5 different propofol-induced effect-site concentrations (CEs): 0, 0.5, 1.0, 1.5, and 2.0 μg/ml. We constructed whole-brain functional networks for each subject under different conditions and identify community structures. Subsequently, we calculated the global and local topological properties of whole-brain network to investigate the alterations in functional integration and segregation with deepening propofol sedation. Additionally, we assessed the alteration of key nodes within the whole-brain community structure at each effect-site concentrations level. We found that global participation was significantly increased at high effect-site concentrations, which was mediated by bilateral postcentral gyrus. Meanwhile, connector hubs appeared and were located in posterior cingulate cortex and precentral gyrus at high effect-site concentrations. Finally, nodal participation coefficients of connector hubs were closely associated to the level of sedation. These findings provide valuable insights into the relationship between increasing propofol dosage and enhanced functional interaction within the whole-brain networks.

Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states

Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.

Disentangling age- and disease-related alterations in schizophrenia brain network using structural equation modeling: A graph theoretical study based on minimum spanning tree

Functional brain networks have been shown to undergo fundamental changes associated with aging or schizophrenia. However, the mechanism of how these factors exert influences jointly or interactively on brain networks remains elusive. A unified recognition of connectomic alteration patterns was also hampered by heterogeneities in network construction and thresholding methods. Recently, an unbiased network representation method regardless of network thresholding, so called minimal spanning tree algorithm, has been applied to study the critical skeleton of the brain network. In this study, we aimed to use minimum spanning tree (MST) as an unbiased network reconstruction and employed structural equation modeling (SEM) to unravel intertwined relationships among multiple phenotypic and connectomic variables in schizophrenia. First, we examined global and local brain network properties in 40 healthy subjects and 40 schizophrenic patients aged 21–55 using resting‐state functional magnetic resonance imaging (rs‐fMRI). Global network alterations are measured by graph theoretical metrics of MSTs and a connectivity‐transitivity two‐dimensional approach was proposed to characterize nodal roles. We found that networks of schizophrenic patients exhibited a more star‐like global structure compared to controls, indicating excessive integration, and a loss of regional transitivity in the dorsal frontal cortex (corrected p <.05). Regional analysis of MST network topology revealed that schizophrenia patients had more network hubs in frontal regions, which may be linked to the “overloading” hypothesis. Furthermore, using SEM, we found that the level of MST integration mediated the influence of age on negative symptom severity (indirect effect 95% CI [0.026, 0.449]). These findings highlighted an altered network skeleton in schizophrenia and suggested that aging‐related enhancement of network integration may undermine functional specialization of distinct neural systems and result in aggravated schizophrenic symptoms.

Your presence soothes me: a neural process model of aversive emotion regulation via social buffering

The reduction of aversive emotions by a conspecific's presence-called social buffering-is a universal phenomenon in the mammalian world and a powerful form of human social emotion regulation. Animal and human studies on neural pathways underlying social buffering typically examined physiological reactions or regional brain activations. However, direct links between emotional and social stimuli, distinct neural processes and behavioural outcomes are still missing. Using data of 27 female participants, the current study delineated a large-scale process model of social buffering's neural underpinnings, connecting changes in neural activity to emotional behaviour by means of voxel-wise multilevel mediation analysis. Our results confirmed that three processes underlie human social buffering: (i) social support-related reduction of activity in the orbitofrontal cortex, ventromedial and dorsolateral prefrontal cortices, anterior and mid-cingulate; (ii) downregulation of aversive emotion-induced brain activity in the superficial cortex-like amygdala and mediodorsal thalamus; and (iii) downregulation of reported aversive feelings. Results of the current study provide evidence for a distinct neural process model of aversive emotion regulation in humans by social buffering.

Revealing Changes in Brain Functional Networks Caused by Focused-Attention Meditation Using Tucker3 Clustering

This study examines the effects of focused-attention meditation on functional brain states in novice meditators. There are a number of feature metrics for functional brain states, such as functional connectivity, graph theoretical metrics, and amplitude of low frequency fluctuation (ALFF). It is necessary to choose appropriate metrics and also to specify the region of interests (ROIs) from a number of brain regions. Here, we use a Tucker3 clustering method, which simultaneously selects the feature vectors (graph theoretical metrics and fractional ALFF) and the ROIs that can discriminate between resting and meditative states based on the characteristics of the given data. In this study, breath-counting meditation, one of the most popular forms of focused-attention meditation, was used and brain activities during resting and meditation states were measured by functional magnetic resonance imaging. The results indicated that the clustering coefficients of the eight brain regions, Frontal Inf Oper L, Occipital Inf R, ParaHippocampal R, Cerebellum 10 R, Cingulum Mid R, Cerebellum Crus1 L, Occipital Inf L, and Paracentral Lobule R increased through the meditation. Our study also provided the framework of data-driven brain functional analysis and confirmed its effectiveness on analyzing neural basis of focused-attention meditation.