An increasing number of studies using real-time fMRI neurofeedback have demonstrated that successful regulation of neural activity is possible in various brain regions. Since these studies focused on ...the regulated region(s), little is known about the target-independent mechanisms associated with neurofeedback-guided control of brain activation, i.e. the regulating network. While the specificity of the activation during self-regulation is an important factor, no study has effectively determined the network involved in self-regulation in general. In an effort to detect regions that are responsible for the act of brain regulation, we performed a post-hoc analysis of data involving different target regions based on studies from different research groups.
We included twelve suitable studies that examined nine different target regions amounting to a total of 175 subjects and 899 neurofeedback runs. Data analysis included a standard first- (single subject, extracting main paradigm) and second-level (single subject, all runs) general linear model (GLM) analysis of all participants taking into account the individual timing. Subsequently, at the third level, a random effects model GLM included all subjects of all studies, resulting in an overall mixed effects model. Since four of the twelve studies had a reduced field of view (FoV), we repeated the same analysis in a subsample of eight studies that had a well-overlapping FoV to obtain a more global picture of self-regulation.
The GLM analysis revealed that the anterior insula as well as the basal ganglia, notably the striatum, were consistently active during the regulation of brain activation across the studies. The anterior insula has been implicated in interoceptive awareness of the body and cognitive control. Basal ganglia are involved in procedural learning, visuomotor integration and other higher cognitive processes including motivation. The larger FoV analysis yielded additional activations in the anterior cingulate cortex, the dorsolateral and ventrolateral prefrontal cortex, the temporo-parietal area and the visual association areas including the temporo-occipital junction.
In conclusion, we demonstrate that several key regions, such as the anterior insula and the basal ganglia, are consistently activated during self-regulation in real-time fMRI neurofeedback independent of the targeted region-of-interest. Our results imply that if the real-time fMRI neurofeedback studies target regions of this regulation network, such as the anterior insula, care should be given whether activation changes are related to successful regulation, or related to the regulation process per se. Furthermore, future research is needed to determine how activation within this regulation network is related to neurofeedback success.
•Previous rtfMRI studies assessed regulation success in targeted brain regions.•There is a need to understand networks involved in brain regulation itself.•IDP meta-analysis of 12 rtfMRI studies to identify networks related to neurofeedback•Anterior insula and basal ganglia are key components of the regulation network.
Network modelling methods for FMRI Smith, Stephen M.; Miller, Karla L.; Salimi-Khorshidi, Gholamreza ...
NeuroImage (Orlando, Fla.),
01/2011, Letnik:
54, Številka:
2
Journal Article
Recenzirano
There is great interest in estimating brain “networks” from FMRI data. This is often attempted by identifying a set of functional “nodes” (e.g., spatial ROIs or ICA maps) and then conducting a ...connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution.
► We evaluate many methods for estimating brain networks from FMRI data ► Covariance-based methods are the most successful at finding network edges ► Finding network directionality is harder; Patel's tau performs the best ► Lag-based methods (e.g. Granger) perform badly ► The use of incorrect ROIs (e.g. from structural atlases) is dangerous
•Variability in amplitude of resting-state networks (RSNs) was assessed across 37,842 subjects.•Network amplitudes are closely linked to functional connectivity between RSNs.•Temporal synchrony ...between brain regions is a key factor determining RSN amplitudes.•Sex effects on temporal synchrony differ between sensory and cognitive RSNs.•Genetic variants associated with RSN amplitudes overlap with those associated with synchrony.
Resting-state fMRI studies have shown that multiple functional networks, which consist of distributed brain regions that share synchronised spontaneous activity, co-exist in the brain. As these resting-state networks (RSNs) have been thought to reflect the brain's intrinsic functional organization, intersubject variability in the networks’ spontaneous fluctuations may be associated with individuals’ clinical, physiological, cognitive, and genetic traits. Here, we investigated resting-state fMRI data along with extensive clinical, lifestyle, and genetic data collected from 37,842 UK Biobank participants, with the object of elucidating intersubject variability in the fluctuation amplitudes of RSNs. Functional properties of the RSN amplitudes were first examined by analyzing correlations with the well-established between-network functional connectivity. It was found that a network amplitude is highly correlated with the mean strength of the functional connectivity that the network has with the other networks. Intersubject clustering analysis showed the amplitudes are most strongly correlated with age, cardiovascular factors, body composition, blood cell counts, lung function, and sex, with some differences in the correlation strengths between sensory and cognitive RSNs. Genome-wide association studies (GWASs) of RSN amplitudes identified several significant genetic variants reported in previous GWASs for their implications in sleep duration. We provide insight into key factors determining RSN amplitudes and demonstrate that intersubject variability of the amplitudes primarily originates from differences in temporal synchrony between functionally linked brain regions, rather than differences in the magnitude of raw voxelwise BOLD signal changes. This finding additionally revealed intriguing differences between sensory and cognitive RSNs with respect to sex effects on temporal synchrony and provided evidence suggesting that synchronous coactivations of functionally linked brain regions, and magnitudes of BOLD signal changes, may be related to different genetic mechanisms. These results underscore that intersubject variability of the amplitudes in health and disease need to be interpreted largely as a measure of the sum of within-network temporal synchrony and amplitudes of BOLD signals, with a dominant contribution from the former.
The cortical visual system is composed of many areas serving various visual functions. In non-human primates, these are broadly organised into two distinct processing pathways: a ventral pathway for ...object recognition, and a dorsal pathway for action. In humans, recent theoretical proposals suggest the possible existence of additional pathways, but direct empirical evidence has yet to be presented. Here, we estimated the connectivity patterns between 22 human visual areas using resting-state functional MRI data of 470 individuals, leveraging the unprecedented data quantity and quality of the Human Connectome Project and a novel probabilistic atlas. An objective, data-driven analysis into the topological organisation of connectivity and subsequent quantitative confirmation revealed a highly significant triple dissociation between the retinotopic areas on the dorsal, ventral and lateral surfaces of the human occipital lobe. This suggests that the functional organisation of the human visual system involves not two but three cortical pathways.
In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain ...functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.
•We characterized resting-state functional connectivity gradients that underlie structural organization of primary somatosensory cortex (S1).•We demonstrated that these gradients are strongly ...associated to anatomical hierarchy, underlying microstructure, and Brodmann areas.•We provided evidence suggesting the division of the primary somatosensory cortex into two distinct functional parcels – Brodmann areas 3a & 3b and areas 1 & 2.•Seed-based connectivity analyses using these parcels demonstrated thalamocortical connectivity patterns reflect S1 Brodmann areas.
The primary somatosensory cortex (S1) plays a key role in the processing and integration of afferent somatosensory inputs along an anterior-to-posterior axis, contributing towards necessary human function. It is believed that anatomical connectivity can be used to probe hierarchical organization, however direct characterization of this principle in-vivo within humans remains elusive. Here, we use resting-state functional connectivity as a complement to anatomical connectivity to investigate topographical principles of human S1. We employ a novel approach to examine mesoscopic variations of functional connectivity, and demonstrate a topographic organisation spanning the region's hierarchical axis that strongly correlates with underlying microstructure while tracing along architectonic Brodmann areas. Our findings characterize anatomical hierarchy of S1 as a ‘continuous spectrum’ with evidence supporting a functional boundary between areas 3b and 1. The identification of this topography bridges the gap between structure and connectivity, and may be used to help further current understanding of sensorimotor deficits.
The ability to temporarily prioritize rapid and vigilant reactions over slower higher-order cognitive functions is essential for adaptive responding to threat. This reprioritization is believed to ...reflect shifts in resource allocation between large-scale brain networks that support these cognitive functions, including the salience and executive control networks. However, how changes in communication within and between such networks dynamically unfold as a function of threat-related arousal remains unknown. To address this issue, we collected functional MRI data and continuously assessed the heart rate from 120 healthy human adults as they viewed emotionally arousing and ecologically valid cinematographic material. We then developed an analysis method that tracks dynamic changes in large-scale network cohesion by quantifying the level of within-network and between-network interaction. We found a monotonically increasing relationship between heart rate, a physiological index of arousal, and within-network cohesion in the salience network, indicating that coordination of activity within the salience network dynamically tracks arousal. Strikingly, salience-executive control between-network cohesion peaked at moderate arousal. These findings indicate that at moderate arousal, which has been associated with optimal noradrenergic signaling, the salience network is optimally able to engage the executive control network to coordinate cognitive activity, but is unable to do so at tonically elevated noradrenergic levels associated with acute stress. Our findings extend neurophysiological models of the effects of stress-related neuromodulatory signaling at the cellular level to large-scale neural systems, and thereby explain shifts in cognitive functioning during acute stress, which may play an important role in the development and maintenance of stress-related mental disorders.
How does brain functioning change in arousing or stressful situations? Extant literature suggests that through global projections, arousal-related neuromodulatory changes can rapidly alter coordination of neural activity across brain-wide neural systems or large-scale networks. Since it is unknown how such processes unfold, we developed a method to dynamically track levels of within-network and between-network interaction. We applied this technique to human neuroimaging data acquired while participants watched realistic and emotionally arousing cinematographic material. Results demonstrate that cohesion within the salience network monotonically increases with arousal, while cohesion of this network with the executive control network peaks at moderate arousal. Our findings explain how cognitive performance shifts as a function of arousal, and provide new insights into vulnerability for stress-related psychopathology.
In the rodent brain the hemodynamic response to a brief external stimulus changes significantly during development. Analogous changes in human infants would complicate the determination and use of ...the hemodynamic response function (HRF) for functional magnetic resonance imaging (fMRI) in developing populations. We aimed to characterize HRF in human infants before and after the normal time of birth using rapid sampling of the Blood Oxygen Level Dependent (BOLD) signal. A somatosensory stimulus and an event related experimental design were used to collect data from 10 healthy adults, 15 sedated infants at term corrected post menstrual age (PMA) (median 41+1weeks), and 10 preterm infants (median PMA 34+4weeks). A positive amplitude HRF waveform was identified across all subject groups, with a systematic maturational trend in terms of decreasing time-to-peak and increasing positive peak amplitude associated with increasing age. Application of the age-appropriate HRF models to fMRI data significantly improved the precision of the fMRI analysis. These findings support the notion of a structured development in the brain's response to stimuli across the last trimester of gestation and beyond.
► First systematic characterization of the BOLD signal HRF in human neonates. ► A maturational trend in HRF morphology was identified. ► Application of empirical HRF models significantly improved neonatal fMRI analysis.
The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of ...which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.
•Artefact removal via single-subject ICA and automatic classification is investigated.•Different approaches are compared using temporal, network and spatial analysis.•Effective cleaning is achieved by removing the unique variance of artefacts.•Multiband gives comparable/improved sensitivity and higher spatiotemporal resolution.•Accelerated acquisition allows for more detailed network analyses.
Significance The human brain displays an enormous amount of intrinsic activity in the absence of any task or external stimulation. Here we demonstrate that the human spinal cord, the brain’s ...principal interface with the body, also shows such resting-state activity. We observed biologically plausible and spatially distinct networks that reflect the functional organisation of the spinal cord: networks in the anterior part likely relating to motor function and distinct networks in the posterior part likely reflecting sensory function. These networks were grouped along the spinal cord, consistent with motor output to, and sensory input from, the body. Together with previous brain imaging studies, our data suggest that resting-state activity constitutes a major functional signature of the entire central nervous system.
Spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals of the brain have repeatedly been observed when no task or external stimulation is present. These fluctuations likely reflect baseline neuronal activity of the brain and correspond to functionally relevant resting-state networks (RSN). It is not known however, whether intrinsically organized and spatially circumscribed RSNs also exist in the spinal cord, the brain’s principal sensorimotor interface with the body. Here, we use recent advances in spinal fMRI methodology and independent component analysis to answer this question in healthy human volunteers. We identified spatially distinct RSNs in the human spinal cord that were clearly separated into dorsal and ventral components, mirroring the functional neuroanatomy of the spinal cord and likely reflecting sensory and motor processing. Interestingly, dorsal (sensory) RSNs were separated into right and left components, presumably related to ongoing hemibody processing of somatosensory information, whereas ventral (motor) RSNs were bilateral, possibly related to commissural interneuronal networks involved in central pattern generation. Importantly, all of these RSNs showed a restricted spatial extent along the spinal cord and likely conform to the spinal cord’s functionally relevant segmental organization. Although the spatial and temporal properties of the dorsal and ventral RSNs were found to be significantly different, these networks showed significant interactions with each other at the segmental level. Together, our data demonstrate that intrinsically highly organized resting-state fluctuations exist in the human spinal cord and are thus a hallmark of the entire central nervous system.
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