Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions

Population-level task-evoked functional connectivity via Fourier analysis

Functional magnetic resonance imaging (fMRI) is a noninvasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform tasks or during periods of rest. In this paper, we propose a rigorous definition of task-evoked functional connectivity at the population level (ptFC). Importantly, our proposed ptFC is interpretable in the context of task-fMRI studies. An algorithm for estimating the ptFC is provided. We present the performance of the proposed algorithm compared to existing functional connectivity frameworks using simulations. Lastly, we apply the proposed algorithm to estimate the ptFC in a motor-task study from the Human Connectome Project.

Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states

Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Differential spatial working memory-related functional network reconfiguration in young and older adults

Functional brain networks have preserved architectures in rest and task; nevertheless, previous work consistently demonstrated task-related brain functional reorganization. Efficient rest-to-task functional network reconfiguration is associated with better cognition in young adults. However, aging and cognitive load effects, as well as contributions of intra- and internetwork reconfiguration, remain unclear. We assessed age-related and load-dependent effects on global and network-specific functional reconfiguration between rest and a spatial working memory (SWM) task in young and older adults, then investigated associations between functional reconfiguration and SWM across loads and age groups. Overall, global and network-level functional reconfiguration between rest and task increased with age and load. Importantly, more efficient functional reconfiguration associated with better performance across age groups. However, older adults relied more on internetwork reconfiguration of higher cognitive and task-relevant networks. These reflect the consistent importance of efficient network updating despite recruitment of additional functional networks to offset reduction in neural resources and a change in brain functional topology in older adults. Our findings generalize the association between efficient functional reconfiguration and cognition to aging and demonstrate distinct brain functional reconfiguration patterns associated with SWM in aging, highlighting the importance of combining rest and task measures to study aging cognition.

Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage

Decoding of cognitive states aims to identify individuals' brain states and brain fingerprints to predict behavior. Deep learning provides an important platform for analyzing brain signals at different developmental stages to understand brain dynamics. Due to their internal architecture and feature extraction techniques, existing machine-learning and deep-learning approaches are suffering from low classification performance and explainability issues that must be improved. In the current study, we hypothesized that even at the early childhood stage (as early as 3-years), connectivity between brain regions could decode brain states and predict behavioral performance in false-belief tasks. To this end, we proposed an explainable deep learning framework to decode brain states (Theory of Mind and Pain states) and predict individual performance on ToM-related false-belief tasks in a developmental dataset. We proposed an explainable spatiotemporal connectivity-based Graph Convolutional Neural Network (Ex-stGCNN) model for decoding brain states. Here, we consider a developmental dataset, N = 155 (122 children; 3-12 yrs and 33 adults; 18-39 yrs), in which participants watched a short, soundless animated movie, shown to activate Theory-of-Mind (ToM) and pain networs. After scanning, the participants underwent a ToM-related false-belief task, leading to categorization into the pass, fail, and inconsistent groups based on performance. We trained our proposed model using Functional Connectivity (FC) and Inter-Subject Functional Correlations (ISFC) matrices separately. We observed that the stimulus-driven feature set (ISFC) could capture ToM and Pain brain states more accurately with an average accuracy of 94%, whereas it achieved 85% accuracy using FC matrices. We also validated our results using five-fold cross-validation and achieved an average accuracy of 92%. Besides this study, we applied the SHapley Additive exPlanations (SHAP) approach to identify brain fingerprints that contributed the most to predictions. We hypothesized that ToM network brain connectivity could predict individual performance on false-belief tasks. We proposed an Explainable Convolutional Variational Auto-Encoder (Ex-Convolutional VAE) model to predict individual performance on false-belief tasks and trained the model using FC and ISFC matrices separately. ISFC matrices again outperformed the FC matrices in prediction of individual performance. We achieved 93.5% accuracy with an F1-score of 0.94 using ISFC matrices and achieved 90% accuracy with an F1-score of 0.91 using FC matrices.

Mapping brain state-dependent sensory responses across the mouse cortex

Sensory information must be integrated across a distributed brain network for stimulus processing and perception. Recent studies have revealed specific spatiotemporal patterns of cortical activation for the early and late components of sensory-evoked responses, which are associated with stimulus features and perception, respectively. Here, we investigated how the brain state influences the sensory-evoked activation across the mouse cortex. We utilized isoflurane to modulate the brain state and conducted wide-field calcium imaging of Thy1-GCaMP6f mice to monitor distributed activation evoked by multi-whisker stimulation. Our findings reveal that the level of anesthesia strongly shapes the spatiotemporal features and the functional connectivity of the sensory-activated network. As anesthesia levels decrease, we observe increasingly complex responses, accompanied by the emergence of the late component within the sensory-evoked response. The persistence of the late component under anesthesia raises new questions regarding the potential existence of perception during unconscious states.

Free-viewing gaze patterns reveal a mood-congruency bias in MDD during an affective fMRI/eye-tracking task

Major depressive disorder (MDD) has been related to abnormal amygdala activity during emotional face processing. However, a recent large-scale study (n = 28,638) found no such correlation, which is probably due to the low precision of fMRI measurements. To address this issue, we used simultaneous fMRI and eye-tracking measurements during a commonly employed emotional face recognition task. Eye-tracking provide high-precision data, which can be used to enrich and potentially stabilize fMRI readouts. With the behavioral response, we additionally divided the active task period into a task-related and a free-viewing phase to explore the gaze patterns of MDD patients and healthy controls (HC) and compare their respective neural correlates. Our analysis showed that a mood-congruency attentional bias could be detected in MDD compared to healthy controls during the free-viewing phase but without parallel amygdala disruption. Moreover, the neural correlates of gaze patterns reflected more prefrontal fMRI activity in the free-viewing than the task-related phase. Taken together, spontaneous emotional processing in free viewing might lead to a more pronounced mood-congruency bias in MDD, which indicates that combined fMRI with eye-tracking measurement could be beneficial for our understanding of the underlying psychopathology of MDD in different emotional processing phases.Trial Registration: The BeCOME study is registered on ClinicalTrials (gov: NCT03984084) by the Max Planck Institute of Psychiatry in Munich, Germany.

Mapping the functional and structural connectivity of the scene network

The recognition and perception of places has been linked to a network of scene-selective regions in the human brain. While previous studies have focussed on functional connectivity between scene-selective regions themselves, less is known about their connectivity with other cortical and subcortical regions in the brain. Here, we determine the functional and structural connectivity profile of the scene network. We used fMRI to examine functional connectivity between scene regions and across the whole brain during rest and movie-watching. Connectivity within the scene network revealed a bias between posterior and anterior scene regions implicated in perceptual and mnemonic aspects of scene perception respectively. Differences between posterior and anterior scene regions were also evident in the connectivity with cortical and subcortical regions across the brain. For example, the Occipital Place Area (OPA) and posterior Parahippocampal Place Area (PPA) showed greater connectivity with visual and dorsal attention networks, while anterior PPA and Retrosplenial Complex showed preferential connectivity with default mode and frontoparietal control networks and the hippocampus. We further measured the structural connectivity of the scene network using diffusion tractography. This indicated both similarities and differences with the functional connectivity, highlighting biases between posterior and anterior regions, but also between ventral and dorsal scene regions. Finally, we quantified the structural connectivity between the scene network and major white matter tracts throughout the brain. These findings provide a map of the functional and structural connectivity of scene-selective regions to each other and the rest of the brain.

Characterizing the spatiotemporal features of functional connectivity across the white matter and gray matter during the naturalistic condition

Introduction

The naturalistic stimuli due to its ease of operability has attracted many researchers in recent years. However, the influence of the naturalistic stimuli for whole-brain functions compared with the resting state is still unclear.

Methods

In this study, we clustered gray matter (GM) and white matter (WM) masks both at the ROI- and network-levels. Functional connectivity (FC) and inter-subject functional connectivity (ISFC) were calculated in GM, WM, and between GM and WM under the movie-watching and the resting-state conditions. Furthermore, intra-class correlation coefficients (ICC) of FC and ISFC were estimated on different runs of fMRI data to denote the reliability of them during the two conditions. In addition, static and dynamic connectivity indices were calculated with Pearson correlation coefficient to demonstrate the associations between the movie-watching and the resting-state.

Results

As the results, we found that the movie-watching significantly affected FC in whole-brain compared with the resting-state, but ISFC did not show significant connectivity induced by the naturalistic condition. ICC of FC and ISFC was generally higher during movie-watching compared with the resting-state, demonstrating that naturalistic stimuli could promote the reliability of connectivity. The associations between static and dynamic ISFC were weakly negative correlations in the naturalistic stimuli while there is no correlation between them under resting-state condition.

Discussion

Our findings confirmed that compared to resting-state condition, the connectivity indices under the naturalistic stimuli were more reliable and stable to investigate the normal functional activities of the human brain, and might promote the applications of FC in the cerebral dysfunction in various mental disorders.

Functional networks in the infant brain during sleep and wake states

Functional brain networks are assessed differently earlier versus later in development: infants are almost universally scanned asleep, whereas adults are typically scanned awake. Observed differences between infant and adult functional networks may thus reflect differing states of consciousness rather than or in addition to developmental changes. We explore this question by comparing functional networks in functional magnetic resonance imaging (fMRI) scans of infants during natural sleep and awake movie-watching. As a reference, we also scanned adults during awake rest and movie-watching. Whole-brain functional connectivity was more similar within the same state (sleep and movie in infants; rest and movie in adults) compared with across states. Indeed, a classifier trained on patterns of functional connectivity robustly decoded infant state and even generalized to adults; interestingly, a classifier trained on adult state did not generalize as well to infants. Moreover, overall similarity between infant and adult functional connectivity was modulated by adult state (stronger for movie than rest) but not infant state (same for sleep and movie). Nevertheless, the connections that drove this similarity, particularly in the frontoparietal control network, were modulated by infant state. In sum, infant functional connectivity differs between sleep and movie states, highlighting the value of awake fMRI for studying functional networks over development.

Stimulus-dependent functional network topology in mouse visual cortex

Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Brain mechanisms of mental processing: from evoked and spontaneous brain activities to enactive brain activity

Within the context of the computer metaphor, evoked brain activity acts as a primary carrier for the brain mechanisms of mental processing. However, many studies have found that evoked brain activity is not the major part of brain activity. Instead, spontaneous brain activity exhibits greater intensity and coevolves with evoked brain activity through continuous interaction. Spontaneous and evoked brain activities are similar but not identical. They are not separate parts, but always dynamically interact with each other. Therefore, the enactive cognition theory further states that the brain is characterized by unified and active patterns of activity. The brain adjusts its activity pattern by minimizing the error between expectation and stimulation, adapting to the ever-changing environment. Therefore, the dynamic regulation of brain activity in response to task situations is the core brain mechanism of mental processing. Beyond the evoked brain activity and spontaneous brain activity, the enactive brain activity provides a novel framework to completely describe brain activities during mental processing. It is necessary for upcoming researchers to introduce innovative indicators and paradigms for investigating enactive brain activity during mental processing.

Functional connectome stability and optimality are markers of cognitive performance

Patterns of whole-brain fMRI functional connectivity, or connectomes, are unique to individuals. Previous work has identified subsets of functional connections within these patterns whose strength predicts aspects of attention and cognition. However, overall features of these connectomes, such as how stable they are over time and how similar they are to a group-average (typical) or high-performance (optimal) connectivity pattern, may also reflect cognitive and attentional abilities. Here, we test whether individuals who express more stable, typical, optimal, and distinctive patterns of functional connectivity perform better on cognitive tasks using data from three independent samples. We find that individuals with more stable task-based functional connectivity patterns perform better on attention and working memory tasks, even when controlling for behavioral performance stability. Additionally, we find initial evidence that individuals with more typical and optimal patterns of functional connectivity also perform better on these tasks. These results demonstrate that functional connectome stability within individuals and similarity across individuals predicts individual differences in cognition.

Vascular feature as a modulator of the aging brain

The cerebral functional reorganization and declined cognitive function of aging might associate with altered vascular features. Here, we explored the altered cerebral hierarchical functional network of 2 conditions (task-free and naturalistic stimuli) in older adults and its relationship with vascular features (systemic microvascular and perfusion features, measured by magnetic resonance imaging) and behavior. Using cerebral gradient analysis, we found that compressive gradient of resting-state mainly located on the primary sensory-motor system and transmodal regions in aging, and further compress in these regions under the continuous naturalistic stimuli. Combining cerebral functional gradient, vascular features, and cognitive performance, the more compressive gradient in the resting-state, the worse vascular state, the lower cognitive function in older adults. Further modulation analysis demonstrated that both vascular features can regulate the relationship between gradient scores in the insula and behavior. Interestingly, systemic microvascular oxygenation also can modulate the relationship between cerebral gradient and cerebral perfusion. Furthermore, the less alteration of the compressive gradient with naturalistic stimuli came with lower cognitive function. Our findings demonstrated that the altered cerebral hierarchical functional structure in aging was linked with changed vascular features and behavior, offering a new framework for studying the physiological mechanism of functional connectivity in aging.

An evaluation of how connectopic mapping reveals visual field maps in V1

Abstract

Functional gradients, in which response properties change gradually across the cortical surface, have been proposed as a key organising principle of the brain. However, the presence of these gradients remains undetermined in many brain regions. Resting-state neuroimaging studies have suggested these gradients can be reconstructed from patterns of functional connectivity. Here we investigate the accuracy of these reconstructions and establish whether it is connectivity or the functional properties within a region that determine these “connectopic maps”. Different manifold learning techniques were used to recover visual field maps while participants were at rest or engaged in natural viewing. We benchmarked these reconstructions against maps measured by traditional visual field mapping. We report an initial exploratory experiment of a publicly available naturalistic imaging dataset, followed by a preregistered replication using larger resting-state and naturalistic imaging datasets from the Human Connectome Project. Connectopic mapping accurately predicted visual field maps in primary visual cortex, with better predictions for eccentricity than polar angle maps. Non-linear manifold learning methods outperformed simpler linear embeddings. We also found more accurate predictions during natural viewing compared to resting-state. Varying the source of the connectivity estimates had minimal impact on the connectopic maps, suggesting the key factor is the functional topography within a brain region. The application of these standardised methods for connectopic mapping will allow the discovery of functional gradients across the brain.

Protocol registration

The stage 1 protocol for this Registered Report was accepted in principle on 19 April 2022. The protocol, as accepted by the journal, can be found at 10.6084/m9.figshare.19771717.

The effect of hunger state on hypothalamic functional connectivity in response to food cues

The neural underpinnings of the integration of internal and external cues that reflect nutritional status are poorly understood in humans. The hypothalamus is a key integrative area involved in short- and long-term energy intake regulation. Hence, we examined the effect of hunger state on the hypothalamus network using functional magnetic resonance imaging. In a multicenter study, participants performed a food cue viewing task either fasted or sated on two separate days. We evaluated hypothalamic functional connectivity (FC) using psychophysiological interactions during high versus low caloric food cue viewing in 107 adults (divided into four groups based on age and body mass index [BMI]; age range 24-76 years; BMI range 19.5-41.5 kg/m² ). In the sated compared to the fasted condition, the hypothalamus showed significantly higher FC with the bilateral caudate, the left insula and parts of the left inferior frontal cortex. Interestingly, we observed a significant interaction between hunger state and BMI group in the dorsolateral prefrontal cortex (DLPFC). Participants with normal weight compared to overweight and obesity showed higher FC between the hypothalamus and DLPFC in the fasted condition. The current study showed that task-based FC of the hypothalamus can be modulated by internal (hunger state) and external cues (i.e., food cues with varying caloric content) with a general enhanced communication in the sated state and obesity-associated differences in hypothalamus to DLPFC communication. This could potentially promote overeating in persons with obesity.

A tale of two connectivities: intra- and inter-subject functional connectivity jointly enable better prediction of social abilities

Naturalistic functional magnetic resonance imaging (fMRI) paradigms, such as movie viewing, are attracting increased attention, given their ability to mimic the real-world cognitive demands on attention and multimodal sensory integration. Moreover, naturalistic paradigms allow for characterizing brain network responses associated with dynamic social cognition in a model-free manner using inter-subject functional connectivity (ISFC). While intra-subject functional connectivity (FC) characterizes the individual’s brain functional architecture, ISFC characterizes the neural coupling driven by time-locked extrinsic dynamic stimuli across individuals. Here, we hypothesized that ISFC and FC provide distinct and complementary information about individual differences in social cognition. To test this hypothesis, we examined a public movie-viewing fMRI dataset with 32 healthy adults and 90 typically developing children. Building three partial least squares regression (PLS) models to predict social abilities using FC and/or ISFC, we compared predictive performance to determine whether combining two connectivity measures could improve the prediction accuracy of individuals’ social-cognitive abilities measured by a Theory of Mind (ToM) assessment. Our results indicated that the joint model (ISFC + FC) yielded the highest predictive accuracy and significantly predicted individuals’ social cognitive abilities (rho = 0.34, p < 0.001). We also confirmed that the improved accuracy was not due to the increased feature dimensionality. In conclusion, we demonstrated that intra-/inter-subject connectivity encodes unique information about social abilities, and a joint investigation could help us gain a more complete understanding of the complex processes supporting social cognition.

Effectiveness of robot-assisted virtual reality mirror therapy for upper limb motor dysfunction after stroke: study protocol for a single-center randomized controlled clinical trial

Background

Upper limb motor dysfunction is a common sequela of stroke, and its clinical efficacy needs to be improved. This protocol describes a trial to verify the clinical efficacy of robot-assisted virtual reality mirror therapy (RAVRMT) in improving upper limb motor dysfunction in stroke patients, and to explore the central mechanism by using functional magnetic resonance imaging (fMRI).

Methods

This trial will be a single-center, assessor-blinded, randomized controlled clinical study. Thirty-two eligible patients will be randomly divided into 2 groups according to the ratio of 1:1, namely virtual reality mirror therapy (VRMT) group and robot-assisted virtual reality mirror therapy (RAVRMT) group. The interventions will be performed once a day for 4 weeks. Primary outcome is Fugl–Meyer motor function assessment-Upper Extremity (FMA-UE), secondary outcomes are the Montreal Cognitive Assessment (MoCA), activities of daily living (ADL), quality of life (QOL), the pain visual analogue scale (VAS-pain) and fMRI. Adverse events will be recorded, and severe adverse events will be used as criteria to discontinue the intervention.

Discussion

Combined application of robot-assisted therapy and virtual reality mirror therapy could theoretically activate mirror neuron system and reward circuits to a greater extent, but further high-quality research is needed. The results of this trial will determine whether RAVRMT could better improve upper limb motor dysfunction after stroke and explore its central mechanism using fMRI.

Trial registration

This trial was prospectively registered at ClinicalTrials.gov (ChiCTR2200061721; 01 July 2022).

Anesthetic modulations dissociate neuroelectric characteristics between sensory-evoked and spontaneous activities across bilateral rat somatosensory cortical laminae

Spontaneous neural activity has been widely adopted to construct functional connectivity (FC) amongst distant brain regions. Although informative, the functional role and signaling mechanism of the resting state FC are not intuitive as those in stimulus/task-evoked activity. In order to bridge the gap, we investigated anesthetic modulation of both resting-state and sensory-evoked activities. We used two well-studied GABAergic anesthetics of varying dose (isoflurane: 0.5–2.0% and α-chloralose: 30 and 60 mg/kg∙h) and recorded changes in electrophysiology using a pair of laminar electrode arrays that encompass the entire depth of the bilateral somatosensory cortices (S1fl) in rats. Specifically, the study focused to describe how varying anesthesia conditions affect the resting state activities and resultant FC between bilateral hemispheres in comparison to those obtained by evoked responses. As results, isoflurane decreased the amplitude of evoked responses in a dose-dependent manner mostly due to the habituation of repetitive responses. However, α-chloralose rather intensified the amplitude without exhibiting habituation. No such diverging trend was observed for the spontaneous activity, in which both anesthetics increased the signal power. For α-chloralose, overall FC was similar to that obtained with the lowest dose of isoflurane at 0.5% while higher doses of isoflurane displayed increased FC. Interestingly, only α-chloralose elicited relatively much greater increases in the ipsi-stimulus evoked response (i.e., in S1fl ipsilateral to the stimulated forelimb) than those associated with the contra-stimulus response, suggesting enhanced neuronal excitability. Taken together, the findings demonstrate modulation of the FC profiles by anesthesia is highly non-linear, possibly with a distinct underlying mechanism that affects either resting state or evoked activities differently. Further, the current study warrants thorough investigation of the basal neuronal states prior to the interpretation of resting state FC and evoked activities for accurate understanding of neural signal processing and circuitry.

Resting-state fMRI functional connectivity and mindfulness in clinical and non-clinical contexts: A review and synthesis

This review synthesizes relations between mindfulness and resting-state fMRI functional connectivity of brain networks. Mindfulness is characterized by present-moment awareness and experiential acceptance, and relies on attention control, self-awareness, and emotion regulation. We integrate studies of functional connectivity and (1) trait mindfulness and (2) mindfulness meditation interventions. Mindfulness is related to functional connectivity in the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Specifically, mindfulness-mediated functional connectivity changes include (1) increased connectivity between posterior cingulate cortex (DMN) and dorsolateral prefrontal cortex (FPN), which may relate to attention control; (2) decreased connectivity between cuneus and SN, which may relate to self-awareness; (3) increased connectivity between rostral anterior cingulate cortex region and dorsomedial prefrontal cortex (DMN) and decreased connectivity between rostral anterior cingulate cortex region and amygdala region, both of which may relate to emotion regulation; and lastly, (4) increased connectivity between dorsal anterior cingulate cortex (SN) and anterior insula (SN) which may relate to pain relief. While further study of mindfulness is needed, neural signatures of mindfulness are emerging.

Non-additive activity modulation during a decision making task involving tactic selection

Human brain imaging has revealed that stimulus-induced activity does generally not simply add to the pre-stimulus activity, but rather builds in a non-additive way on this activity. Here we investigate this subject at the single neuron level and address the question whether and to what extent a strong form of non-additivity where activity drops post-cue is present in different areas of monkey cortex, including prefrontal and agranular frontal areas, during a perceptual decision making task involving action and tactic selection. Specifically we analyze spike train data recorded in vivo from the posterior dorsomedial prefrontal cortex (pmPFC), the supplementary motor area (SMA) and the presupplementary motor area (pre-SMA). For each neuron, we compute the ratio of the trial-averaged pre-stimulus spike count to the trial-averaged post-stimulus count. We also perform the ratio and averaging procedures in reverse order. We find that the statistics of these quantities behave differently across areas. pmPFC involved in tactic selection shows stronger non-additivity compared to the two other areas which more generically just increase their firing rate pos-stimulus. pmPFC behaved more similarly to pre-SMA, a likely consequence of the reciprocal connections between these areas. The trial-averaged ratio statistic was reproduced by a surrogate inhomogeneous Poisson process in which the measured trial-averaged firing rate for a given neuron is used as its time-dependent rate. Principal component analysis (PCA) of the trial-averaged firing rates of neuronal ensembles further reveals area-specific time courses of response to the stimulus, including latency to peak neural response, for the typical population activity. Our work demonstrates subtle forms of area-specific non-additivity based on the fine variability structure of pre- and post-stimulus spiking activity on the single neuron level. It also reveals significant differences between areas for PCA and surrogate analysis, complementing previous observations of regional differences based solely on post-stimulus responses. Moreover, we observe regional differences in non-additivity which are related to the monkey's successful tactic selection and decision making.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09702-0.

Task effects on functional connectivity measures after stroke

Understanding the effect of task compared to rest on detecting stroke-related network abnormalities will inform efforts to optimize detection of such abnormalities. The goal of this work was to determine whether connectivity measures obtained during an overt task are more effective than connectivity obtained during a "resting" state for detecting stroke-related changes in network function of the brain. This study examined working memory, discrete pedaling, continuous pedaling and language tasks. Functional magnetic resonance imaging was used to examine regional and inter-regional brain network function in 14 stroke and 16 control participants. Independent component analysis was used to identify 149 regions of interest (ROI). Using the inter-regional connectivity measurements, the weighted sum was calculated across only regions associated with a given task. Both inter-regional connectivity and regional connectivity were greater during each of the tasks as compared to the resting state. The working memory and discrete pedaling tasks allowed for detection of stroke-related decreases in inter-regional connectivity, while the continuous pedaling and language tasks allowed for detection of stroke-related enhancements in regional connectivity. These observations illustrate that task-based functional connectivity allows for detection of stroke-related changes not seen during resting states. In addition, this work provides evidence that tasks emphasizing different cognitive domains reveal different aspects of stroke-related reorganization. We also illustrate that within the motor domain, different tasks can reveal inter-regional or regional stroke-related changes, in this case suggesting that discrete pedaling required more central drive than continuous pedaling.

Increased functional connectivity between presupplementary motor area and inferior frontal gyrus associated with the ability of motor response inhibition in obsessive-compulsive disorder

Recent evidence suggests that presupplementary motor area (pre‐SMA) and inferior frontal gyrus (IFG) play an important role in response inhibition. However, no study has investigated the relationship between these brain networks at resting‐state and response inhibition in obsessive–compulsive disorder (OCD). We performed resting‐state functional magnetic resonance imaging scans and then measured the response inhibition of 41 medication‐free OCD patients and 49 healthy control (HC) participants by using the stop‐signal task outside the scanner. We explored the differences between OCD and HC groups in the functional connectivity of pre‐SMA and IFG associated with the ability of motor response inhibition. OCD patients showed a longer stop‐signal reaction time (SSRT). Compared to HC, OCD patients exhibit different associations between the ability of motor response inhibition and the functional connectivity between pre‐SMA and IFG, inferior parietal lobule, dorsal anterior cingulate cortex, insula, and anterior prefrontal cortex. Additional analysis to investigate the functional connectivity difference from the seed ROIs to the whole brain voxels revealed that, compared to HC, OCD exhibited greater functional connectivity between pre‐SMA and IFG. Also, this functional connectivity was positively correlated with the SSRT score. These results provide additional insight into the characteristics of the resting‐state functional connectivity of the regions belonging to the cortico‐striato‐thalamo‐cortical circuit and the cingulo‐opercular salience network, underlying the impaired motor response inhibition of OCD. In particular, we emphasize the importance of altered functional connectivity between pre‐SMA and IFG for the pathophysiology of motor response inhibition in OCD.

Investigation of functional brain network reconfiguration during exposure to naturalistic stimuli using graph-theoretical analysis

Objective.One of the most significant features of the human brain is that it can dynamically reconfigure itself to adapt to a changing environment. However, dynamic interaction characteristics of the brain networks in naturalistic scenes remain unclear.Approach.We used open-source functional magnetic resonance imaging (fMRI) data from 15 participants who underwent fMRI scans while watching an audio-visual movie 'Forrest Gump'. The community detection algorithm based on inter-subject functional correlation was used to study the time-varying functional networks only induced by the movie stimuli. The whole brain reconfiguration patterns were quantified by the temporal co-occurrence matrix that describes the probability of two brain regions engage in the same community (or putative functional module) across time and the time-varying brain modularity. Four graph metrics of integration, recruitment, spatio-temporal diversity and within-community normalised centrality were further calculated to summarise the brain network dynamic roles and hub features in their spatio-temporal topology.Main results.Our results suggest that the networks that were involved in attention and audio-visual information processing, such as the visual network, auditory network, and dorsal attention network, were considered to play a role of 'stable loners'. By contrast, 'unstable loner' networks such as the default mode network (DMN) and fronto-parietal network tended to interact more flexibly with the other networks. In addition, global brain network showed significant fluctuations in modularity. The 'stable loner' networks always maintained high functional connectivity (FC) strength while 'unstable loner' networks, especially the DMN, exhibited high intra- and inter-network FC only during a low modularity period. Finally, changes in brain modularity were significantly associated with variations in emotions induced by the movie.Significance.Our findings provide new insight for understanding the dynamic interaction characteristics of functional brain networks during naturalistic stimuli.

Naturalistic Stimuli: A Paradigm for Multi-Scale Functional Characterization of the Human Brain

Movies, audio stories, and virtual reality are increasingly used as stimuli for functional brain imaging. Such naturalistic paradigms are in sharp contrast to the tradition of experimental reductionism in neuroscience research. Being complex, dynamic, and diverse, naturalistic stimuli set up a more ecologically relevant condition and induce highly reproducible brain responses across a wide range of spatiotemporal scales. Here, we review recent technical advances and scientific findings on imaging the brain under naturalistic stimuli. Then we elaborate on the premise of using naturalistic paradigms for multi-scale, multi-modal, and high-throughput functional characterization of the human brain. We further highlight the growing potential of using deep learning models to infer neural information processing from brain responses to naturalistic stimuli. Lastly, we advocate large-scale collaborations to combine brain imaging and recording data across experiments, subjects, and labs that use the same set of naturalistic stimuli.

Dynamic relationships between spontaneous and evoked electrophysiological activity

Spontaneous neural activity fluctuations have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioral outcomes. However, the complex electrophysiological mechanisms by which these fluctuations shape stimulus-evoked neural activity remain largely to be explored. Employing a large-scale magnetoencephalographic dataset and an electroencephalographic replication dataset, we investigate the relationship between spontaneous and evoked neural activity across a range of electrophysiological variables. We observe that for high-frequency activity, high pre-stimulus amplitudes lead to greater evoked desynchronization, while for low frequencies, high pre-stimulus amplitudes induce larger degrees of event-related synchronization. We further decompose electrophysiological power into oscillatory and scale-free components, demonstrating different patterns of spontaneous-evoked correlation for each component. Finally, we find correlations between spontaneous and evoked time-domain electrophysiological signals. Overall, we demonstrate that the dynamics of multiple electrophysiological variables exhibit distinct relationships between their spontaneous and evoked activity, a result which carries implications for experimental design and analysis in non-invasive electrophysiology.

The Relationship of Functional Connectivity of the Sensorimotor and Visual Cortical Networks Between Resting and Task States

The intrinsic activity of the human brain maintains its general operation at rest, and this ongoing spontaneous activity exhibits a high level of spatiotemporally correlated activity among different cortical areas, showing intrinsically organized brain functional connectivity (FC) networks. Many functional network properties of the human brain have been investigated extensively for both rest and task states, but the relationship between these two states has been rarely investigated yet and remains unclear. Comparing well-defined task-specific networks with corresponding intrinsic FC networks may reveal their relationship and improve our understanding of the brain's operations at both rest and task states. This study investigated the relationship of the sensorimotor and visual cortical FC networks between the resting and task states. The sensorimotor task was to rub right-hand fingers, and the visual task was to open and close eyes, respectively. Our study demonstrated a general relationship of the task-evoked FC network with its corresponding intrinsic FC network, regardless of the tasks. For each task type, the study showed that (1) the intrinsic and task-evoked FC networks shared a common network and the task enhanced the coactivity within that common network compared to the intrinsic activity; (2) some areas within the intrinsic FC network were not activated by the task, and therefore, the task activated only partial but not whole of the intrinsic FC network; and (3) the task activated substantial additional areas outside the intrinsic FC network and therefore recruited more intrinsic FC networks to perform the task.

Functional connectivity patterns predict naturalistic viewing versus rest across development

Cognitive states, such as rest and task engagement, share an 'intrinsic' functional network organization that is subject to minimal variation over time and yields stable signatures within an individual. Importantly, there are also transient state-specific functional connectivity (FC) patterns that vary across neural states. Here, we examine functional brain organization differences that underlie distinct states in a cross-sectional developmental sample. We compare FC fMRI data acquired during naturalistic viewing (i.e., movie-watching) and resting-state paradigms in a large cohort of 157 children and young adults aged 6-20. Naturalistic paradigms are commonly implemented in pediatric research because they maintain the child's attention and contribute to reduced head motion. It remains unknown, however, to what extent the brain-wide functional network organization is comparable during movie-watching and rest across development. Here, we identify a widespread FC pattern that predicts whether individuals are watching a movie or resting. Specifically, we develop a model for prediction of multilevel neural effects (termed PrimeNet), which can with high reliability distinguish between movie-watching and rest irrespective of age and that generalizes across movies. In turn, we characterize FC patterns in the most predictive functional networks for movie-watching versus rest and show that these patterns can indeed vary as a function of development. Collectively, these effects highlight a 'core' FC pattern that is robustly associated with naturalistic viewing, which also exhibits change across age. These results, focused here on naturalistic viewing, provide a roadmap for quantifying state-specific functional neural organization across development, which may reveal key variation in neurodevelopmental trajectories associated with behavioral phenotypes.

Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery

BACKGROUND

Considerable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint.

OBJECTIVE

We investigated the stability of functional connectivity across tasks and sessions in a cohort of individuals with chronic stroke using a supervised machine learning approach.

METHODS

Twelve individuals with chronic stroke underwent functional magnetic resonance imaging (fMRI) seven times over 18 weeks. The middle 6 weeks consisted of intensive aphasia therapy. We collected fMRI data during rest and performance of 2 tasks. We calculated functional connectivity metrics for each imaging run, then applied a support vector machine to classify data on the basis of participant, task, and time point (pre- or posttherapy). Permutation testing established statistical significance.

RESULTS

Whole brain functional connectivity matrices could be classified at levels significantly greater than chance on the basis of participant (87.1% accuracy; P < .0001), task (68.1% accuracy; P = .002), and time point (72.1% accuracy; P = .015). All significant effects were reproduced using only the contralesional right hemisphere; the left hemisphere revealed significant effects for participant and task, but not time point. Resting state data could also be used to classify task-based data according to subject (66.0%; P < .0001). While the strongest posttherapy changes occurred among regions outside putative language networks, connections with traditional language-associated regions were significantly more positively correlated with behavioral outcome measures, and other regions had more negative correlations and intrahemispheric connections.

CONCLUSIONS

Findings suggest the profound importance of considering interindividual variability when interpreting mechanisms of recovery in studies of functional connectivity in stroke.

Context Matters: Situational Stress Impedes Functional Reorganization of Intrinsic Brain Connectivity during Problem-Solving

Extensive research has established a relationship between individual differences in brain activity in a resting state and individual differences in behavior. Conversely, when individuals are engaged in various tasks, certain task-evoked reorganization occurs in brain functional connectivity, which can consequently influence individuals' performance as well. Here, we show that resting state and task-dependent state brain patterns interact as a function of contexts engendering stress. Findings revealed that when the resting state connectome was examined during performance, the relationship between connectome strength and performance only remained for participants under stress (who also performed worse than all other groups on the math task), suggesting that stress preserved brain patterns indicative of underperformance whereas non-stressed individuals spontaneously transitioned out of these patterns. Results imply that stress may impede the reorganization of a functional network in task-evoked brain states. This hypothesis was subsequently verified using graph theory measurements on a functional network, independent of behavior. For participants under stress, the functional network showed less topological alterations compared to non-stressed individuals during the transition from resting state to task-evoked state. Implications are discussed for network dynamics as a function of context.

How Tasks Change Whole-Brain Functional Organization to Reveal Brain-Phenotype Relationships

Functional connectivity (FC) calculated from task fMRI data better reveals brain-phenotype relationships than rest-based FC, but how tasks have this effect is unknown. In over 700 individuals performing seven tasks, we use psychophysiological interaction (PPI) and predictive modeling analyses to demonstrate that task-induced changes in FC successfully predict phenotype, and these changes are not simply driven by task activation. Activation, however, is useful for prediction only if the in-scanner task is related to the predicted phenotype. To further characterize these predictive FC changes, we develop and apply an inter-subject PPI analysis. We find that moderate, but not high, task-induced consistency of the blood-oxygen-level-dependent (BOLD) signal across individuals is useful for prediction. Together, these findings demonstrate that in-scanner tasks have distributed, phenotypically relevant effects on brain functional organization, and they offer a framework to leverage both task activation and FC to reveal the neural bases of complex human traits, symptoms, and behaviors.

Weaker Inter-hemispheric and Local Functional Connectivity of the Somatomotor Cortex During a Motor Skill Acquisition Is Associated With Better Learning

Recently, an increasing interest in investigating interactions between brain regions using functional connectivity (FC) methods has shifted the initial focus of cognitive neuroimaging research from localizing functional circuits based on task activation to mapping brain networks based on intrinsic FC dynamics. Leveraging the advantages of the latter approach, it has been shown that despite primarily invariant intrinsic organization of the large-scale functional networks, interactions between and within these networks significantly differ between various behavioral and cognitive states. These differences presumably indicate transient reconfiguration of functional connections-an instantaneous process that flexibly mediates and calibrates human behavior according to momentary demands of the environment. Nevertheless, the specificity of these reconfigured FC patterns to the task at hand and their relevance to adaptive processes during learning remain elusive. To address this knowledge gap, we investigated (1) to what extent FC within the somatomotor network is reconfigured during motor skill practice, and (2) how these changes are related to learning. We applied a seed-driven FC approach to data collected during a continuous task-free condition, so-called resting state, and during a motor sequence learning task using functional magnetic resonance imaging. During the task, participants repeatedly performed a short five-element sequence with their non-dominant (left) hand. As predicted, such unimanual sequence production was associated with lateralized activation of the right somatomotor cortex (SMC). Using this "active" region as a seed, here we show that unimanual performance of the motor sequence relies on functional segregation between the two SMC and selective integration between the "active" SMC and supplementary motor area. Whereas, greater segregation between the two SMC was associated with gains in performance rate, greater segregation within the "active" SMC itself was associated with more consistent performance by the end of training. Nether the resting-state FC patterns within the somatomotor network nor their relative modulation by the task state predicted these behavioral benefits of learning. Our results suggest that task-induced FC changes reflect reconfiguration of the connectivity patterns within the somatomotor network rather than a simple amplification or silencing of its intrinsic dynamics. Such reconfiguration not only supports motor behavior but may also predict learning.

Learning-driven cerebellar intrinsic functional connectivity changes in men

Learning involves distributed but coordinated activity among the widespread connected brain areas. Increase in areas connections' strength may be established offline, that is, aside from the task itself, in a resting-state. The resulting functional connectivity may hence constitute a neural trace of the learning episode. The present study examined whether a conditional visuomotor learning task previously shown to activate the cerebellum would modify cerebellar intrinsic connectivity in groups of young and older male subjects. In the group of young subjects, resting-state connectivity within several cerebellar networks (fronto-cerebellar, temporo-cerebellar, cerebello-cerebellar) was modified following the task. In most cases, modulation resulted in increased anticorrelations between cerebellar and cortical areas and the amplitude of changes was correlated with learning efficacy. The group of older subjects drastically differed, with sparser modifications of resting-state functional connectivity and no cerebellar networks involved. The findings of this exploratory study indicate that associative learning modifies the strength of intrinsic connectivity in young subjects but to a lesser degree in older subjects. They further suggest that functional connectivity within cerebellar networks may play an operative role in this kind of learning.

Linking resting-state networks and social cognition in schizophrenia and bipolar disorder

Individuals with schizophrenia and bipolar disorder show alterations in functional neural connectivity during rest. However, resting-state network (RSN) disruptions have not been systematically compared between the two disorders. Further, the impact of RSN disruptions on social cognition, a key determinant of functional outcome, has not been studied. Forty-eight individuals with schizophrenia, 46 with bipolar disorder, and 48 healthy controls completed resting-state functional magnetic resonance imaging. An atlas-based approach was used to examine functional connectivity within nine RSNs across the cortex. RSN connectivity was assessed via nonparametric permutation testing, and associations with performance on emotion perception, mentalizing, and emotion management tasks were examined. Group differences were observed in the medial and lateral visual networks and the sensorimotor network. Individuals with schizophrenia demonstrated reduced connectivity relative to healthy controls in all three networks. Individuals with bipolar disorder demonstrated reduced connectivity relative to controls in the medial visual network and connectivity within this network was significantly positively correlated with emotion management. In healthy controls, connectivity within the medial and lateral visual networks positively correlated with mentalizing. No significant correlations were found for either visual network in schizophrenia. Results highlight the role of altered early visual processing in social cognitive deficits in both schizophrenia and bipolar disorder. However, individuals with bipolar disorder appear to compensate for disrupted visual network connectivity on social cognitive tasks, whereas those with schizophrenia do not. The current study adds clarity on the neurophysiology underlying social cognitive deficits that result in impaired functioning in serious mental illness.

Functionally distinct language and Theory of Mind networks are synchronized at rest and during language comprehension

Communication requires the abilities to generate and interpret utterances and to infer the beliefs, desires, and goals of others ("Theory of Mind"; ToM). These two abilities have been shown to dissociate: individuals with aphasia retain the ability to think about others' mental states; and individuals with autism are impaired in social reasoning, but their basic language processing is often intact. In line with this evidence from brain disorders, functional MRI (fMRI) studies have shown that linguistic and ToM abilities recruit distinct sets of brain regions. And yet, language is a social tool that allows us to share thoughts with one another. Thus, the language and ToM brain networks must share information despite being implemented in distinct neural circuits. Here, we investigated potential interactions between these networks during naturalistic cognition using functional correlations in fMRI. The networks were functionally defined in individual participants, in terms of preference for sentences over nonwords for language, and for belief inference over physical-event processing for ToM, with both a verbal and a nonverbal paradigm. Although, across experiments, interregion correlations within each network were higher than between-network correlations, we also observed above-baseline synchronization of blood oxygenation level-dependent signal fluctuations between the two networks during rest and story comprehension. This synchronization was functionally specific: neither network was synchronized with the executive control network (functionally defined in terms of preference for a harder over easier version of an executive task). Thus, coordination between the language and ToM networks appears to be an inherent and specific characteristic of their functional architecture. NEW & NOTEWORTHY Humans differ from nonhuman primates in their abilities to communicate linguistically and to infer others' mental states. Although linguistic and social abilities appear to be interlinked onto- and phylogenetically, they are dissociated in the adult human brain. Yet successful communication requires language and social reasoning to work in concert. Using functional MRI, we show that language regions are synchronized with social regions during rest and language comprehension, pointing to a possible mechanism for internetwork interaction.

Task-evoked functional connectivity does not explain functional connectivity differences between rest and task conditions

During complex tasks, patterns of functional connectivity differ from those in the resting state. However, what accounts for such differences remains unclear. Brain activity during a task reflects an unknown mixture of spontaneous and task-evoked activities. The difference in functional connectivity between a task state and the resting state may reflect not only task-evoked functional connectivity, but also changes in spontaneously emerging networks. Here, we characterized the differences in apparent functional connectivity between the resting state and when human subjects were watching a naturalistic movie. Such differences were marginally explained by the task-evoked functional connectivity involved in processing the movie content. Instead, they were mostly attributable to changes in spontaneous networks driven by ongoing activity during the task. The execution of the task reduced the correlations in ongoing activity among different cortical networks, especially between the visual and non-visual sensory or motor cortices. Our results suggest that task-evoked activity is not independent from spontaneous activity, and that engaging in a task may suppress spontaneous activity and its inter-regional correlation.