We present a history of the concepts and developments that have led us to focus on the resting state as an object of study. We then discuss resting state research performed in our laboratory since 2005 with an emphasis on papers of particular interest.

We present a case report on visual brain plasticity after total blindness acquired in adulthood. SH lost her sight when she was 27. Despite having been totally blind for 43 years, she reported to strongly rely on her vivid visual imagery. Three-Tesla magnetic resonance imaging (MRI) of SH and age-matched controls was performed. The MRI sequence included anatomical MRI, resting-state functional MRI, and task-related functional MRI where SH was instructed to imagine colours, faces, and motion. Compared to controls, voxel-based analysis revealed white matter loss along SH’s visual pathway as well as grey matter atrophy in the calcarine sulci. Yet we demonstrated activation in visual areas, including V1, using functional MRI. Of the four identified visual resting-state networks, none showed alterations in spatial extent; hence, SH’s preserved visual imagery seems to be mediated by intrinsic brain networks of normal extent. Time courses of two of these networks showed increased correlation with that of the inferior posterior default mode network, which may reflect adaptive changes supporting SH’s strong internal visual representations. Overall, our findings demonstrate that conscious visual experience is possible even after years of absence of extrinsic input.

Using modern brain imaging techniques, new discoveries are being made concerning the spontaneous activity of the brain when it is devoid of attention-demanding tasks. Spatially separated patches of neuronal assemblies have been found to show synchronized oscillatory activity behavior and are said to be functionally connected. One of the most robust of these is the default mode network, which is associated with intrinsic processes like mind wandering and self-projection. Furthermore, activity in this network is anticorrelated with activity in a network that is linked to attention to external stimuli. The integrity of both networks is disturbed in altered states of consciousness, like sleep, general anesthesia and hypnosis. In coma and related disorders of consciousness, encompassing the vegetative state (unresponsive wakefulness syndrome) and minimally conscious state, default mode network integrity correlates with the level of remaining consciousness, offering the possibility of using this information for diagnostic and prognostic purposes. Functional brain imaging is currently being validated as a valuable addition to the standardized behavioral assessments that are already in use.

Unravelling how the human brain structure gives rise to function is a central question in neuroscience and remains partially answered. Recent studies show that the graph Laplacian of the human brain’s structural connectivity (SC) plays a dominant role in shaping the pattern of resting-state functional connectivity (FC). The modeling of FC using the graph Laplacian of the brain’s SC is limited, owing to the sparseness of the Laplacian matrix. It is unable to model the negative functional correlations. We extended the graph Laplacian to the hypergraph p-Laplacian in order to describe better the nonlinear and high-order relations between SC and FC. First we estimated those possible links showing negative correlations between the brain areas shared across subjects by statistical analysis. Then we presented a hypergraph p-Laplacian model by embedding the two matrices referring to the sign of the correlations between the brain areas relying on the brain structural connectome. We tested the model on two experimental connectome datasets and evaluated the predicted FC by estimating its Pearson correlation with the empirical FC matrices. The results showed that the proposed diffusion model based on hypergraph p-Laplacian can predict functional correlations more accurately than the models using graph Laplacian as well as hypergraph Laplacian.

To what extent are spontaneous neural signals within striate cortex organized by vision? We examined the fine-scale pattern of striate cortex correlations within and between hemispheres in rest-state BOLD fMRI data from sighted and blind people. In the sighted, we find that corticocortico correlation is well modeled as a Gaussian point-spread function across millimeters of striate cortical surface, rather than degrees of visual angle. Blindness produces a subtle change in the pattern of fine-scale striate correlations between hemispheres. Across participants blind before the age of 18, the degree of pattern alteration covaries with the strength of long-range correlation between left striate cortex and Broca's area. This suggests that early blindness exchanges local, vision-driven pattern synchrony of the striate cortices for long-range functional correlations potentially related to cross-modal representation.

There is a fundamental gap in understanding how brain structural and functional network connectivity are interrelated, how they change with age, and how such changes contribute to older adults’ sensorimotor deficits. Recent neuroimaging approaches including resting state functional connectivity MRI (fcMRI) and diffusion tensor imaging (DTI) have been used to assess brain functional (fcMRI) and structural (DTI) network connectivity, allowing for more integrative assessments of distributed neural systems than in the past. Declines in corpus callosum size and microstructure with advancing age have been well documented, but their contributions to age deficits in unimanual and bimanual function are not well defined. Our recent work implicates age-related declines in callosal size and integrity as a key contributor to unimanual and bimanual control deficits. Moreover, our data provide evidence for a fundamental shift in the balance of excitatory and inhibitory interhemispheric processes that occurs with age, resulting in age differences in the relationship between functional and structural network connectivity. Training studies suggest that the balance of interhemispheric interactions can be shifted with experience, making this a viable target for future interventions.

The spontaneous cerebral hemodynamic fluctuations observed during the resting state have been frequently visualized using functional magnetic resonance imaging (rsfMRI). However, the neuronal populations and neuroelectric characteristics underlying the functional connectivity of cerebrohemodynamic activities are poorly understood. We investigated the characteristics of bi-hemispheric functional connectivity via electrophysiology and rsfMRI in the primary sensory cortex of rats anesthetized by α-chloralose. Unlike the evoked responses, the spontaneous electrophysiological activity was concentrated in the infragranular layers and could be classified into subtypes with distinctive current sources and sinks. Both neuroelectric and rsfMRI signals were interhemispherically correlated in a layer-specific manner, suggesting that there are independent neural inputs to infragranular and granular/supragranular layers. The majority of spontaneous electrophysiological activities were bilaterally paired with delays of up to ~50 ms between each pair. The variable interhemispheric delay implies the involvement of indirect, multi-neural pathways. Our findings demonstrated the diverse activity patterns of layer-specific electrophysiological substrates and suggest the recruitment of multiple, non-specific brain regions in construction of interhemispheric functional connectivity.

The phenomenon of cognitive resilience, that is, the dynamical preservation of normal functions despite neurological disorders, demonstrates that cognition can be highly robust to devastating brain injury. Here, cognitive resilience is considered across a range of neurological conditions. Simple computational models of structure-function relationships are used to discuss hypotheses about the neural mechanisms of resilience. Resilience expresses functional redundancies in brain networks and suggests a process of dynamic rerouting of brain signals. This process is underlined by a global renormalization of effective connectivity, capable of restoring information transfer between spared brain structures via alternate pathways. Local mechanisms of synaptic plasticity mediate the renormalization at the lowest level of implementation, but it is also driven by top-down cognition, with a key role of self-awareness in fostering resilience. The presence of abstraction layers in brain computation and networking is hypothesized to account for the renormalization process. Future research directions and challenges are discussed regarding the understanding and control of resilience based on multimodal neuroimaging and computational neuroscience. The study of resilience will illuminate ways by which the brain can overcome adversity and help inform prevention and treatment strategies. It is relevant to combating the negative neuropsychological impact of aging and fostering cognitive enhancement.

The inferior parietal lobule (IPL) of the human brain is a heterogeneous region involved in visuospatial attention, memory, and mathematical cognition. Detailed description of connectivity profiles of subdivisions within the IPL is critical for accurate interpretation of functional neuroimaging studies involving this region. We separately examined functional and structural connectivity of the angular gyrus (AG) and the intraparietal sulcus (IPS) using probabilistic cytoarchitectonic maps. Regions-of-interest (ROIs) included anterior and posterior AG subregions (PGa, PGp) and 3 IPS subregions (hIP2, hIP1, and hIP3). Resting-state functional connectivity analyses showed that PGa was more strongly linked to basal ganglia, ventral premotor areas, and ventrolateral prefrontal cortex, while PGp was more strongly connected with ventromedial prefrontal cortex, posterior cingulate, and hippocampus-regions comprising the default mode network. The anterior-most IPS ROIs, hIP2 and hIP1, were linked with ventral premotor and middle frontal gyrus, while the posterior-most IPS ROI, hIP3, showed connectivity with extrastriate visual areas. In addition, hIP1 was connected with the insula. Tractography using diffusion tensor imaging revealed structural connectivity between most of these functionally connected regions. Our findings provide evidence for functional heterogeneity of cytoarchitectonically defined subdivisions within IPL and offer a novel framework for synthesis and interpretation of the task-related activations and deactivations involving the IPL during cognition.

The human insula is a functionally complex yet poorly understood region of the cortex, implicated in a wide range of cognitive, motor, emotion and somatosensory activity. To elucidate the functional role of the insula, the current study used in vivo probabilistic tractography to map the structural connectivity of seven anatomically-defined insular subregions. The connectivity patterns identified reveal two complementary insular networks connected via a dual route architecture, and provide key insights about the neural basis of the numerous functions ascribed to this area. Specifically, anterior-most insular regions were associated with a ventrally-based network involving orbital/inferior frontal and anterior/polar temporal regions, forming part of a key emotional salience and cognitive control network associated with the implementation of goal-directed behavior. The posterior and dorsal-middle insular regions were associated with a network focused on posterior and (to a lesser extent) anterior temporal regions via both dorsal and ventral pathways. This is consistent with the involvement of the insula in sound-to-speech transformations, with an implicated role in the temporal resolution, sequencing, and feedback processes crucial for auditory and motor processing, and the monitoring and adjustment of expressive performance.

The human brain demonstrates complex yet systematic patterns of neural activity at rest. We examined whether functional connectivity among those brain regions typically active during rest depends on ongoing and recent task demands and individual differences. We probed the temporal coordination among these regions during periods of language comprehension and during the rest periods that followed comprehension. Our findings show that the topography of this “rest network” varies with exogenous processing demands. The network encompassed more highly interconnected regions during rest than during listening, but also when listening to unsurprising vs. surprising information. Furthermore, connectivity patterns during rest varied as a function of recent listening experience. Individual variability in connectivity strength was associated with cognitive function: more attentive comprehenders demonstrated weaker connectivity during language comprehension, and a greater differentiation between connectivity during comprehension and rest. The regions we examined have generally been thought to form an invariant physiological and functional network whose activity reflects spontaneous cognitive processes. Our findings suggest that their function extends beyond the mediation of unconstrained thought, and that they play an important role in higher-level cognitive function.

The corpus callosum is the largest white matter fiber bundle connecting the two cerebral hemispheres. In this work, we investigate the effect of callosal dysgenesis on functional magnetic resonance imaging (fMRI) resting-state networks and the functional connectome. Since alternate commissural routes between the cerebral hemispheres exist, we hypothesize that bilateral cortical networks can still be maintained in partial or even complete agenesis of the corpus callosum (AgCC). However, since these commissural routes are frequently indirect, requiring polysynaptic pathways, we hypothesize that quantitative measurements of interhemispheric functional connectivity in bilateral networks will be reduced in AgCC compared with matched controls, especially in the most highly interconnected cortical regions that are the hubs of the connectome. Seventeen resting-state networks were extracted from fMRI of 11 subjects with partial or complete AgCC and 11 matched controls. The results show that the qualitative organization of resting-state networks is very similar between controls and AgCC. However, interhemispheric functional connectivity of precuneus, posterior cingulate cortex, and insular-opercular regions was significantly reduced in AgCC. The preserved network organization was confirmed with a connectomic analysis of the resting-state fMRI data, showing five functional modules that are largely consistent across the control and AgCC groups. Hence, the reduction or even complete absence of callosal connectivity does not affect the qualitative organization of bilateral resting-state networks or the modular organization of the functional connectome, although quantitatively reduced functional connectivity can be demonstrated by measurements within bilateral cortical hubs, supporting the hypothesis that indirect polysynaptic pathways are utilized to preserve interhemispheric temporal synchrony.

The concept of resting state functional magnetic resonance imaging (fMRI) is built onto an original finding in 1995 that brain hemispheres present synchronous signal fluctuations with distinct patterns. fMRI measurements rely on blood oxygenation changes that indirectly mirror neural activity. Therefore, the origin of certain functional connectivity patterns, resting state networks (RSNs), has been a widely debated research question and numerous contributing factors have been identified. According to current understanding the fluctuations reflect maintenance of the system integrity in addition to spontaneous thought and action processes in the resting state. A popular method to study the functional connectivity in resting state fMRI is spatial independent component analysis (ICA) that decomposes signal sources into statistically independent components. The dichotomy of functional specialization versus functional integration has a correspondence in fMRI studies where RSNs play the integrative viewpoint of brain function. Although canonical large-scale RSNs are broadly distributed they also express modularity that can be accomplished by ICA with a high number of estimated components. The characteristics of high ICA dimensionality are broadly investigated in the thesis. An enduring issue in resting state research has been the confounding noise sources like motion and cardiorespiratory processes which may hamper the analysis. In this thesis the ability of ICA to separate these noise sources from the default mode network, a major RSN, is studied. Additionally, the suitability of ICA for full frequency spectrum analysis, a relatively rare setting in biosignal analysis, is investigated. The results of the thesis support the viewpoint of ICA as a robust analysis method for functional connectivity analysis. Cardiorespiratory and motion induced noise did not confound the functional connectivity analyses with ICA. High dimensional ICA provided better signal source separation, revealed the modular structure of the RSNs and pinpointed the specific aberrations in the autism spectrum disorder population. ICA was also found applicable for fully explorative analysis in both the spatial and temporal domains and indicated functional connectivity changes induced by transcranial bright light stimulation.

The advent of MRI has revolutionized the evaluation and management of drug-resistant epilepsy by allowing the detection of the lesion associated with the region that gives rise to seizures. Recent evidence indicates marked chronic alterations in the functional organization of lesional tissue and large-scale cortico-subcortical networks. In this review, we focus on recent methodological developments in functional MRI (fMRI) analysis techniques and their application to the two most common drug-resistant focal epilepsies, i.e., temporal lobe epilepsy related to mesial temporal sclerosis and extra-temporal lobe epilepsy related to focal cortical dysplasia. We put particular emphasis on methodological developments in the analysis of task-free or “resting-state” fMRI to probe the integrity of intrinsic networks on a regional, inter-regional, and connectome-wide level. In temporal lobe epilepsy, these techniques have revealed disrupted connectivity of the ipsilateral mesiotemporal lobe, together with contralateral compensatory reorganization and striking reconfigurations of large-scale networks. In cortical dysplasia, initial observations indicate functional alterations in lesional, peri-lesional, and remote neocortical regions. While future research is needed to critically evaluate the reliability, sensitivity, and specificity, fMRI mapping promises to lend distinct biomarkers for diagnosis, presurgical planning, and outcome prediction.

Spontaneous neural activity within visual cortex is synchronized by both monosynaptic, hierarchical connections between visual areas and indirect, network-level activity. We examined the interplay of hierarchical and network connectivity in human visual cortex by measuring the organization of spontaneous neural signals within the visual cortex in total darkness using functional magnetic resonance imaging (fMRI). Twenty-five blind (14 congenital and 11 postnatal) participants with equally severe vision loss and 22 sighted subjects were studied. An anatomical template based on cortical surface topology was used for all subjects to identify the quarter-field components of visual areas V1-V3, and assign retinotopic organization. Cortical visual areas that represent the same quadrant of the visual field were considered to have a hierarchical relationship, while the spatially separated quarters of the same visual area were considered homotopic. Blindness was found to enhance correlations between hierarchical cortical areas as compared to indirect, homotopic areas at both the level of visual areas (p = 0.000031) and fine, retinotopic scale (p = 0.0024). A specific effect of congenital, but not postnatal, blindness was to further broaden the cortico-cortico connections between hierarchical visual areas (p = 0.0029). This finding is consistent with animal studies that observe a broadening of axonal terminal arborization when the visual cortex is deprived of early input. We therefore find separable roles for vision in developing and maintaining the intrinsic neural activity of visual cortex.

Significance Individuals subjected to surgical transection of the corpus callosum (“split-brains”) fail to transfer information between the cerebral hemispheres, a condition known as “disconnection syndrome.” On the other hand, subjects born without the callosum (callosal dysgenesis, CD) typically show preserved interhemispheric communication. To clarify this paradox, which has defied neuroscientists for decades, we investigated CD subjects using functional and structural neuroimaging and neuropsychological tests. Results demonstrated the existence of anomalous interhemispheric tracts that cross through the midbrain and ventral forebrain, linking the parietal cortices bilaterally. These findings provide an explanation for the preserved cross-transfer of tactile information between hemispheres in CD. We suggest that this condition is associated with extensive brain rewiring, generating a new circuitry that provides functional compensatory interhemispheric integration. Why do humans born without the corpus callosum, the major interhemispheric commissure, lack the disconnection syndrome classically described in callosotomized patients? This paradox was discovered by Nobel laureate Roger Sperry in 1968, and has remained unsolved since then. To tackle the hypothesis that alternative neural pathways could explain this puzzle, we investigated patients with callosal dysgenesis using structural and functional neuroimaging, as well as neuropsychological assessments. We identified two anomalous white-matter tracts by deterministic and probabilistic tractography, and provide supporting resting-state functional neuroimaging and neuropsychological evidence for their functional role in preserved interhemispheric transfer of complex tactile information, such as object recognition. These compensatory pathways connect the homotopic posterior parietal cortical areas (Brodmann areas 39 and surroundings) via the posterior and anterior commissures. We propose that anomalous brain circuitry of callosal dysgenesis is determined by long-distance plasticity, a set of hardware changes occurring in the developing brain after pathological interference. So far unknown, these pathological changes somehow divert growing axons away from the dorsal midline, creating alternative tracts through the ventral forebrain and the dorsal midbrain midline, with partial compensatory effects to the interhemispheric transfer of cortical function.

SUMMARY: Resting-state fMRI measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. Application of this technique has allowed the identification of various RSNs, or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. Various methods exist for analyzing resting-state data, including seed-based approaches, independent component analysis, graph methods, clustering algorithms, neural networks, and pattern classifiers. Clinical applications of resting-state fMRI are at an early stage of development. However, its use in presurgical planning for patients with brain tumor and epilepsy demonstrates early promise, and the technique may have a future role in providing diagnostic and prognostic information for neurologic and psychiatric diseases.

Resting-state investigations based on the evaluation of intrinsic low-frequency fluctuations of the BOLD fMRI signal have been extensively utilized to map the structure and dynamics of large-scale functional network organization in humans. In addition to increasing our knowledge of normal brain connectivity, disruptions of the spontaneous hemodynamic fluctuations have been suggested as possible diagnostic indicators of neurological and psychiatric disease states. Though the non-invasive technique has been received with much acclamation, open questions remain regarding the origin, organization, phylogenesis, as well as the basis of disease-related alterations underlying the signal patterns. Experimental work utilizing animal models, including the use of neurophysiological recordings and pharmacological manipulations, therefore, represents a critical component in the understanding and successful application of resting-state analysis, as it affords a range of experimental manipulations not possible in human subjects. In this article, we review recent rodent and non-human primate studies and based on the examination of the homologous brain architecture propose the latter to be the best-suited model for exploring these unresolved resting-state concerns. Ongoing work examining the correspondence of functional and structural connectivity, state-dependency and the neuronal correlates of the hemodynamic oscillations are discussed. We then consider the potential experiments that will allow insight into different brain states and disease-related network disruptions that can extend the clinical applications of resting-state fMRI (RS-fMRI).

Resting-state functional magnetic resonance imaging (fMRI) has provided a novel approach for examining interhemispheric interaction, demonstrating a high degree of functional connectivity between homotopic regions in opposite hemispheres. However, heterotopic resting-state functional connectivity (RSFC) remains relatively uncharacterized. In the present study, we examine non-homotopic regions, characterizing heterotopic RSFC and comparing it to intrahemispheric RSFC, to examine the impact of hemispheric separation on the integration and segregation of processing in the brain. Resting-state fMRI scans were acquired from 59 healthy participants to examine inter-regional correlations in spontaneous low frequency fluctuations in BOLD signal. Using a probabilistic atlas, we correlated probability-weighted time series from 112 regions (56 per hemisphere) distributed throughout the entire cerebrum. We compared RSFC for pairings of non-homologous regions located in different hemispheres (heterotopic connectivity) to RSFC for the same pairings when located within hemisphere (intrahemispheric connectivity). For positive connections, connectivity strength was greater within each hemisphere, consistent with integrated intrahemispheric processing. However, for negative connections, RSFC strength was greater between the hemispheres, consistent with segregated interhemispheric processing. These patterns were particularly notable for connections involving frontal and heteromodal regions. The distribution of positive and negative connectivity was nearly identical within and between the hemispheres, though we demonstrated detailed regional variation in distribution. We discuss implications for leading models of interhemispheric interaction. The future application of our analyses may provide important insight into impaired interhemispheric processing in clinical and aging populations.

Relating the brain’s structural connectivity (SC) to its functional connectivity (FC) is a fundamental goal in neuroscience because it is capable of aiding our understanding of how the relatively fixed SC architecture underlies human cognition and diverse behaviors. With the aid of current noninvasive imaging technologies (e.g., structural MRI, diffusion MRI, and functional MRI) and graph theory methods, researchers have modeled the human brain as a complex network of interacting neuronal elements and characterized the underlying structural and functional connectivity patterns that support diverse cognitive functions. Specifically, research has demonstrated a tight SC-FC coupling, not only in interregional connectivity strength but also in network topologic organizations, such as community, rich-club, and motifs. Moreover, this SC-FC coupling exhibits significant changes in normal development and neuropsychiatric disorders, such as schizophrenia and epilepsy. This review summarizes recent progress regarding the SC-FC relationship of the human brain and emphasizes the important role of large-scale brain networks in the understanding of structural-functional associations. Future research directions related to this topic are also proposed.