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Olaf Sporns presents an overview of network approaches to neuroscience in which he explores the origins of brain complexity & the link between brain structure & function.

Most brain activity occurs in an ongoing manner not directly locked to external events or stimuli. Regional ongoing activity fluctuates in unison with some brain regions but not others, and the degree of long-range coupling is called functional connectivity, often measured with correlation. Strength and spatial distributions of functional connectivity dynamically change in an ongoing manner over seconds to minutes, even when the external environment is held constant. Direct evidence for any behavioral relevance of these continuous large-scale dynamics has been limited. Here, we investigated whether ongoing changes in baseline functional connectivity correlate with perception. In a continuous auditory detection task, participants perceived the target sound in roughly one-half of the trials. Very long (22–40 s) interstimulus intervals permitted investigation of baseline connectivity unaffected by preceding evoked responses. Using multivariate classification, we observed that functional connectivity before the target predicted whether it was heard or missed. Using graph theoretical measures, we characterized the difference in functional connectivity between states that lead to hits vs. misses. Before misses compared with hits and task-free rest, connectivity showed reduced modularity, a measure of integrity of modular network structure. This effect was strongest in the default mode and visual networks and caused by both reduced within-network connectivity and enhanced across-network connections before misses. The relation of behavior to prestimulus connectivity was dissociable from that of prestimulus activity amplitudes. In conclusion, moment to moment dynamic changes in baseline functional connectivity may shape subsequent behavioral performance. A highly modular network structure seems beneficial to perceptual efficiency.

Human brain functional networks contain a few densely connected hubs that play a vital role in transferring information across regions during resting and task states. However, the relationship of these functional hubs to measures of brain physiology, such as regional cerebral blood flow (rCBF), remains incompletely understood. Here, we used functional MRI data of blood-oxygenation-level–dependent and arterial-spin–labeling perfusion contrasts to investigate the relationship between functional connectivity strength (FCS) and rCBF during resting and an N -back working-memory task. During resting state, functional brain hubs with higher FCS were identified, primarily in the default-mode, insula, and visual regions. The FCS showed a striking spatial correlation with rCBF, and the correlation was stronger in the default-mode network (DMN; including medial frontal-parietal cortices) and executive control network (ECN; including lateral frontal-parietal cortices) compared with visual and sensorimotor networks. Moreover, the relationship was connection–distance dependent; i.e., rCBF correlated stronger with long-range hubs than short-range ones. It is notable that several DMN and ECN regions exhibited higher rCBF per unit connectivity strength (rCBF/FCS ratio); whereas, this index was lower in posterior visual areas. During the working-memory experiment, both FCS–rCBF coupling and rCBF/FCS ratio were modulated by task load in the ECN and/or DMN regions. Finally, task-induced changes of FCS and rCBF in the lateral-parietal lobe positively correlated with behavioral performance. Together, our results indicate a tight coupling between blood supply and brain functional topology during rest and its modulation in response to task demands, which may shed light on the physiological basis of human brain functional connectome.

Large-scale brain dynamics are characterized by repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood, and whether switching between these states is important for behavior has been little studied. Our aim was to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved fMRI connectivity in 1,003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data were used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching, which we define as the rate at which each brain region transits between different networks. We found (i) an inverse relationship between network switching and connectivity dynamics, where the latter was defined in terms of time-resolved fMRI connections with variance in time that significantly exceeded phase-randomized surrogate data; (ii) brain connectivity was lower during intervals of network switching; (iii) brain areas with frequent network switching had greater temporal complexity; (iv) brain areas with high network switching were located in association cortices; and (v) using cross-validated elastic net regression, network switching predicted intersubject variation in working memory performance, planning/reasoning, and amount of sleep. Our findings shed light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm. •Permutation for the GLM in the presence of nuisance or non-independence.•A generalised statistic that performs well even under heteroscedasticity.•Permutation and/or sign-flipping, exchangeability blocks and variance groups.•The “randomise” algorithm, as well as various practical examples.

Predictive coding is a key mechanism to understand the computational processes underlying brain functioning: in a hierarchical network, higher levels predict the activity of lower levels, and the unexplained residuals (i.e., prediction errors) are passed back to higher layers. Because of its recursive nature, we wondered whether predictive coding could be related to brain oscillatory dynamics. First, we show that a simple 2-level predictive coding model of visual cortex, with physiological communication delays between levels, naturally gives rise to alpha-band rhythms, similar to experimental observations. Then, we demonstrate that a multilevel version of the same model can explain the occurrence of oscillatory traveling waves across levels, both forward (during visual stimulation) and backward (during rest). Remarkably, the predictions of our model are matched by the analysis of 2 independent electroencephalography (EEG) datasets, in which we observed oscillatory traveling waves in both directions.

Mindfulness practice is beneficial for emotion regulation; however, the neural mechanisms underlying this effect are poorly understood. The current study focuses on effects of attention-to-breath (ATB) as a basic mindfulness practice on aversive emotions at behavioral and brain levels. A key finding across different emotion regulation strategies is the modulation of amygdala and prefrontal activity. It is unclear how ATB relevant brain areas in the prefrontal cortex integrate with amygdala activation during emotional stimulation. We proposed that, during emotional stimulation, ATB down-regulates activation in the amygdala and increases its integration with prefrontal regions. To address this hypothesis, 26 healthy controls were trained in mindfulness-based attention-to-breath meditation for two weeks and then stimulated with aversive pictures during both attention-to-breath and passive viewing while undergoing fMRI. Data were controlled for breathing frequency. Results indicate that (1) ATB was effective in regulating aversive emotions. (2) Left dorso-medial prefrontal cortex was associated with ATB in general. (3) A fronto-parietal network was additionally recruited during emotional stimulation. (4) ATB down regulated amygdala activation and increased amygdala–prefrontal integration, with such increased integration being associated with mindfulness ability. Results suggest amygdala–dorsal prefrontal cortex integration as a potential neural pathway of emotion regulation by mindfulness practice. •Attention to breath (ATB) reduces emotional responses in the amygdala.•Amygdala–prefrontal cortex functional connectivity increases during ATB.•Connectivity increase is linked with individual differences in mindful disposition.

The ability to learn language is a human trait. In adults and children, brain imaging studies have shown that auditory language activates a bilateral frontotemporal network with a left hemispheric dominance. It is an open question whether these activations represent the complete neural basis for language present at birth. Here we demonstrate that in 2-d-old infants, the language-related neural substrate is fully active in both hemispheres with a preponderance in the right auditory cortex. Functional and structural connectivities within this neural network, however, are immature, with strong connectivities only between the two hemispheres, contrasting with the adult pattern of prevalent intrahemispheric connectivities. Thus, although the brain responds to spoken language already at birth, thereby providing a strong biological basis to acquire language, progressive maturation of intrahemispheric functional connectivity is yet to be established with language exposure as the brain develops.

•Transcranial photobiomodulation (tPBM) has the potential to improve working memory in healthy older adults.•The mechanism of improving working memory by tPBM may be through alterations of the resting-state functional brain network properties.•The altered working memory were positively correlated with the changes of functional connectivity and nodal efficiency mainly in the left prefrontal cortex.•The tPBM may serve as an effective and non-invasive neurostimulation technique. Transcranial photobiomodulation (tPBM), as a novel non-invasive neurostimulation technique, has shown the compelling potential for improving cognitive function in aging population. However, the potential mechanism remains unclear. Neuroimaging studies have found that tPBM-induced physiological changes exist in both targeted and non-targeted brain areas, suggesting the necessity of understanding the modulation mechanism from the perspective of the whole brain level. This randomized, single-blind, sham-controlled crossover study aimed to investigate the hypothesis that tPBM improved working memory in healthy older adults through the mechanism of optimizing the properties of the resting-state functional brain networks. A total of 55 right-handed healthy older adults were randomly assigned to sham tPBM session group or active tPBM session group. After a washout interval, they were assigned to the opposite intervention session. Each session included the following: active or sham tPBM application with a 1064-nm laser to the left forehead; before and after, resting-state functional near-infrared spectroscopy (fNIRS) measurements; and the digital n-back task. Differences in accuracy and reaction time of the n-back task, and changes in functional connectivity and graph metrics of the brain networks were investigated and compared between the active and sham tPBM sessions. In addition, correlations between tPBM-induced changes in functional brain networks, and the n-back task were examined. The results showed that compared with the sham tPBM session, the accuracy and reaction time during 3-back task significantly improved in the active tPBM session. In addition, the global efficiency, local efficiency, nodal efficiency, and functional connectivity significantly increased in the active tPBM session, particularly in the frontoparietal areas. Importantly, the altered 3-back accuracy was positively correlated with the changes of functional connectivity and nodal efficiency mainly in left prefrontal cortex in those who had increased 3-back accuracy in the active tPBM session. This study suggests that tPBM may serve as an effective tool to improve working memory in older adults through the modulation of resting-state functional brain network properties. Investigations in large-scale samples are needed to further validate the findings of this study.