Spontaneous Activity Patterns in Human Attention Networks Code for Hand Movements.

Recent evidence suggests that, in the absence of any task, spontaneous brain activity patterns and connectivity in the visual and motor cortex code for natural stimuli and actions, respectively. These "resting-state" activity patterns may underlie the maintenance and consolidation (replay) of information states coding for ecological stimuli and behaviors. In this study, we examine whether replay patterns occur in resting-state activity in association cortex grouped into high-order cognitive networks not directly processing sensory inputs or motor outputs. Fifteen participants (7 females) performed four hand movements during an fMRI study. Three movements were ecological. The fourth movement as control was less ecological. Before and after the task scans, we acquired resting-state fMRI scans. The analysis examined whether multivertex task activation patterns for the four movements computed at the cortical surface in different brain networks resembled spontaneous activity patterns measured at rest. For each movement, we computed a vector of r values indicating the strength of the similarity between the mean task activation pattern and frame-by-frame resting-state patterns. We computed a cumulative distribution function of r 2 values and used the 90th percentile cutoff value for comparison. In the dorsal attention network, resting-state patterns were more likely to match task patterns for the ecological movements than the control movement. In contrast, rest-task pattern correlation was more likely for less ecological movement in the ventral attention network. These findings show that spontaneous activity patterns in human attention networks code for hand movements.SIGNIFICANCE STATEMENT fMRI indirectly measures neural activity noninvasively. Resting-state (spontaneous) fMRI signals measured in the absence of any task resemble signals evoked by task performance both in topography and inter-regional (functional) connectivity. However, the function of spontaneous brain activity is unknown. We recently showed that spatial activity patterns evoked by visual and motor tasks in visual and motor cortex, respectively, occur at rest in the absence of any stimulus or response. Here we show that activity patterns related to hand movements replay at rest in frontoparietal regions of the human attention system. These findings show that spontaneous activity in the human cortex may mediate the maintenance and consolidation of information states coding for ecological stimuli and behaviors.

Inferring the dynamical effects of stroke lesions through whole-brain modeling.

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.

Spontaneous activity patterns in human motor cortex replay evoked activity patterns for hand movements.

Spontaneous brain activity, measured with resting state fMRI (R-fMRI), is correlated among regions that are co-activated by behavioral tasks. It is unclear, however, whether spatial patterns of spontaneous activity within a cortical region correspond to spatial patterns of activity evoked by specific stimuli, actions, or mental states. The current study investigated the hypothesis that spontaneous activity in motor cortex represents motor patterns commonly occurring in daily life. To test this hypothesis 15 healthy participants were scanned while performing four different hand movements. Three movements (Grip, Extend, Pinch) were ecological involving grip and grasp hand movements; one control movement involving the rotation of the wrist was not ecological and infrequent (Shake). They were also scanned at rest before and after the execution of the motor tasks (resting-state scans). Using the task data, we identified movement-specific patterns in the primary motor cortex. These task-defined patterns were compared to resting-state patterns in the same motor region. We also performed a control analysis within the primary visual cortex. We found that spontaneous activity patterns in the primary motor cortex were more like task patterns for ecological than control movements. In contrast, there was no difference between ecological and control hand movements in the primary visual area. These findings provide evidence that spontaneous activity in human motor cortex forms fine-scale, patterned representations associated with behaviors that frequently occur in daily life.

Subcortical-cortical dynamical states of the human brain and their breakdown in stroke.

The mechanisms controlling dynamical patterns in spontaneous brain activity are poorly understood. Here, we provide evidence that cortical dynamics in the ultra-slow frequency range (<0.01-0.1 Hz) requires intact cortical-subcortical communication. Using functional magnetic resonance imaging (fMRI) at rest, we identify Dynamic Functional States (DFSs), transient but recurrent clusters of cortical and subcortical regions synchronizing at ultra-slow frequencies. We observe that shifts in cortical clusters are temporally coincident with shifts in subcortical clusters, with cortical regions flexibly synchronizing with either limbic regions (hippocampus/amygdala), or subcortical nuclei (thalamus/basal ganglia). Focal lesions induced by stroke, especially those damaging white matter connections between basal ganglia/thalamus and cortex, provoke anomalies in the fraction times, dwell times, and transitions between DFSs, causing a bias toward abnormal network integration. Dynamical anomalies observed 2 weeks after stroke recover in time and contribute to explaining neurological impairment and long-term outcome.

Mapping correlated neurological deficits after stroke to distributed brain networks.

Understanding the relationships between brain organization and behavior is a central goal of neuroscience. Traditional teaching emphasizes that the human cerebrum includes many distinct areas for which damage or dysfunction would lead to a unique and specific behavioral syndrome. This teaching implies that brain areas correspond to encapsulated modules that are specialized for specific cognitive operations. However, empirically, local damage from stroke more often produces one of a small number of clusters of deficits and disrupts brain-wide connectivity in a small number of predictable ways (relative to the vast complexity of behavior and brain connectivity). Behaviors that involve shared operations show correlated deficits following a stroke, consistent with a low-dimensional behavioral space. Because of the networked organization of the brain, local damage from a stroke can result in widespread functional abnormalities, matching the low dimensionality of behavioral deficit. In alignment with this, neurological disease, psychiatric disease, and altered brain states produce behavioral changes that are highly correlated across a range of behaviors. We discuss how known structural and functional network priors in addition to graph theoretical concepts such as modularity and entropy have provided inroads to understanding this more complex relationship between brain and behavior. This model for brain disease has important implications for normal brain-behavior relationships and the treatment of neurological and psychiatric diseases.

Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions.

Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.

Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions.

Lesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain's structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions.

Spontaneously emerging patterns in human visual cortex and their functional connectivity are linked to the patterns evoked by visual stimuli.

The function of spontaneous brain activity is an important issue in neuroscience. Here we test the hypothesis that patterns of spontaneous activity code representational patterns evoked by stimuli. We compared in human visual cortex multivertex patterns of spontaneous activity to patterns evoked by ecological visual stimuli (faces, bodies, scenes) and low-level visual features (e.g., phase-scrambled faces). Specifically, we identified regions that preferred particular stimulus categories during localizer scans (e.g., extrastriate body area for bodies), measured multivertex patterns for each category during event-related task scans, and then correlated over vertices these stimulus-evoked patterns to the pattern measured on each frame of resting-state scans. The mean correlation coefficient was essentially zero for all regions/stimulus categories, indicating that resting multivertex patterns were not biased toward particular stimulus-evoked patterns. However, the spread of correlation coefficients between stimulus-evoked and resting patterns, positive and negative, was significantly greater for the preferred stimulus category of an ROI. The relationship between spontaneous and stimulus-evoked multivertex patterns also governed the temporal correlation or functional connectivity of patterns of spontaneous activity between individual regions (pattern-based functional connectivity). Resting multivertex patterns related to an object category fluctuated preferentially between ROIs preferring the same category, and fluctuations of the pattern for a category (e.g., body) within its preferred ROIs were largely uncorrelated with fluctuations of the pattern for a disparate category (e.g., scene) within its preferred ROIs. These results support the proposal that spontaneous multivertex activity patterns are linked to stimulus-evoked patterns, consistent with a representational function for spontaneous activity.NEW & NOTEWORTHY Spontaneous brain activity was once thought to reflect only noise, but evidence of strong spatiotemporal regularities has motivated a search for functional explanations. Here we show that the spatial pattern of spontaneous activity in human high-level and early visual cortex is related to the spatial patterns evoked by stimuli. Moreover, these patterns partly govern spontaneous spatiotemporal interactions between regions, so-called functional connectivity. These results support the hypothesis that spontaneous activity serves a representational function.

Damage to the shortest structural paths between brain regions is associated with disruptions of resting-state functional connectivity after stroke.

Focal brain lesions disrupt resting-state functional connectivity, but the underlying structural mechanisms are unclear. Here, we examined the direct and indirect effects of structural disconnections on resting-state functional connectivity in a large sample of sub-acute stroke patients with heterogeneous brain lesions. We estimated the impact of each patient's lesion on the structural connectome by embedding the lesion in a diffusion MRI streamline tractography atlas constructed using data from healthy individuals. We defined direct disconnections as the loss of direct structural connections between two regions, and indirect disconnections as increases in the shortest structural path length between two regions that lack direct structural connections. We then tested the hypothesis that functional connectivity disruptions would be more severe for disconnected regions than for regions with spared connections. On average, nearly 20% of all region pairs were estimated to be either directly or indirectly disconnected by the lesions in our sample, and extensive disconnections were associated primarily with damage to deep white matter locations. Importantly, both directly and indirectly disconnected region pairs showed more severe functional connectivity disruptions than region pairs with spared direct and indirect connections, respectively, although functional connectivity disruptions tended to be most severe between region pairs that sustained direct structural disconnections. Together, these results emphasize the widespread impacts of focal brain lesions on the structural connectome and show that these impacts are reflected by disruptions of the functional connectome. Further, they indicate that in addition to direct structural disconnections, lesion-induced increases in the structural shortest path lengths between indirectly structurally connected region pairs provide information about the remote functional disruptions caused by focal brain lesions.

Structural Disconnections Explain Brain Network Dysfunction after Stroke.

Stroke causes focal brain lesions that disrupt functional connectivity (FC), a measure of activity synchronization, throughout distributed brain networks. It is often assumed that FC disruptions reflect damage to specific cortical regions. However, an alternative explanation is that they reflect the structural disconnection (SDC) of white matter pathways. Here, we compare these explanations using data from 114 stroke patients. Across multiple analyses, we find that SDC measures outperform focal damage measures, including damage to putative critical cortical regions, for explaining FC disruptions associated with stroke. We also identify a core mode of structure-function covariation that links the severity of interhemispheric SDCs to widespread FC disruptions across patients and that correlates with deficits in multiple behavioral domains. We conclude that a lesion's impact on the structural connectome is what determines its impact on FC and that interhemispheric SDCs may play a particularly important role in mediating FC disruptions after stroke.

Brain networks' functional connectivity separates aphasic deficits in stroke.

OBJECTIVE: To investigate whether different language deficits are distinguished by the relative strengths of their association with the functional connectivity (FC) at rest of the language network (LN) and cingulo-opercular network (CON) after aphasic stroke. METHODS: In a group of patients with acute stroke and left-hemisphere damage, we identified 3 distinct, yet correlated, clusters of deficits including comprehension/lexical semantic, grapheme-phoneme knowledge, and verbal executive functions. We computed partial correlations in which the contributions of a behavioral cluster and network FC of no interest were statistically regressed out. RESULTS: We observed a double dissociation such that impairment of grapheme-phoneme knowledge was more associated with lower FC of the LN within the left hemisphere than lower FC of the CON, whereas verbal executive deficits were more related to lower FC of the CON than the LN in the left hemisphere. Furthermore, the specific association between language deficits and FC was independent of the amount of structural damage to the LN and CON. CONCLUSION: These findings indicate that after a left-hemisphere lesion, the type of language impairment is related to the abnormal pattern of correlated activity in different networks. Accordingly, they extend the concept of a neuropsychological double dissociation from structural damage to functional network abnormalities. Finally, current results strongly argue in favor of the behavioral specificity of intrinsic brain activity after focal structural damage.

Stronger prediction of motor recovery and outcome post-stroke by cortico-spinal tract integrity than functional connectivity.

OBJECTIVES: To examine longitudinal changes in structural and functional connectivity post-stroke in patients with motor impairment, and define their importance for recovery and outcome at 12 months. METHODS: First-time stroke patients (N = 31) were studied at 1-2 weeks, 3 months, and 12 months post-injury with a validated motor battery and resting-state fMRI to measure inter-hemispheric functional connectivity (FC). Fractional anisotropy (FA) of the cortico-spinal tract (CST) was derived from diffusion tensor imaging as a measure of white matter organization. ANOVAs were used to test for changes in FC, FA, and motor performance scores over time, and regression analysis related motor outcome to clinical and neuroimaging variables. RESULTS: FA of the ipsilesional CST improved significantly from 3 to 12 months and was strongly correlated with motor performance. FA improved even in the absence of direct damage to the CST. Inter-hemispheric FC also improved over time, but did not correlate with motor performance at 12 months. Clinical variables (early motor score, education level, and age) predicted 80.4% of the variation of motor outcome, and FA increased the predictability to 84.6%. FC did not contribute to the prediction of motor outcome. CONCLUSIONS: Stroke causes changes to the CST microstructure that can account for behavioral variability even in the absence of demonstrable lesion. Ipsilesional CST undergoes remodeling post-stroke, even past the three-month window when most of the motor recovery happens. FA of the CST, but not inter-hemispheric FC, can improve to the prediction of motor outcome based on early motor scores.

Distinct phase-amplitude couplings distinguish cognitive processes in human attention.

Spatial attention is the cognitive function that coordinates the selection of visual stimuli with appropriate behavioral responses. Recent studies have reported that phase-amplitude coupling (PAC) of low and high frequencies covaries with spatial attention, but differ on the direction of covariation and the frequency ranges involved. We hypothesized that distinct phase-amplitude frequency pairs have differentiable contributions during tasks that manipulate spatial attention. We investigated this hypothesis with electrocorticography (ECoG) recordings from participants who engaged in a cued spatial attention task. To understand the contribution of PAC to spatial attention we classified cortical sites by their relationship to spatial variables or behavioral performance. Local neural activity in spatial sites was sensitive to spatial variables in the task, while local neural activity in behavioral sites correlated with reaction time. We found two PAC frequency clusters that covaried with different aspects of the task. During a period of cued attention, delta-phase/high-gamma (DH) PAC was sensitive to cue direction in spatial sites. In contrast, theta-alpha-phase/beta-low-gamma-amplitude (TABL) PAC robustly correlated with future reaction times in behavioral sites. Finally, we investigated the origins of TABL PAC and found it corresponded to behaviorally relevant, sharp waveforms, which were also coupled to a low frequency rhythm. We conclude that TABL and DH PAC correspond to distinct mechanisms during spatial attention tasks and that sharp waveforms are elements of a coupled dynamical process.

Re-emergence of modular brain networks in stroke recovery.

Studies of stroke have identified local reorganization in perilesional tissue. However, because the brain is highly networked, strokes also broadly alter the brain's global network organization. Here, we assess brain network structure longitudinally in adult stroke patients using resting state fMRI. The topology and boundaries of cortical regions remain grossly unchanged across recovery. In contrast, the modularity of brain systems i.e. the degree of integration within and segregation between networks, was significantly reduced sub-acutely (n = 107), but partially recovered by 3 months (n = 85), and 1 year (n = 67). Importantly, network recovery correlated with recovery from language, spatial memory, and attention deficits, but not motor or visual deficits. Finally, in-depth single subject analyses were conducted using tools for visualization of changes in brain networks over time. This exploration indicated that changes in modularity during successful recovery reflect specific alterations in the relationships between different networks. For example, in a patient with left temporo-parietal stroke and severe aphasia, sub-acute loss of modularity reflected loss of association between frontal and temporo-parietal regions bi-hemispherically across multiple modules. These long-distance connections then returned over time, paralleling aphasia recovery. This work establishes the potential importance of normalization of large-scale modular brain systems in stroke recovery.

On the low dimensionality of behavioral deficits and alterations of brain network connectivity after focal injury.

Traditional neuropsychological approaches emphasize the specificity of behavioral deficits and the modular organization of the brain. At the population level, however, there is emerging evidence that deficits are correlated resulting in a low dimensional structure of post-stroke neurological impairments. Here we consider the implications of low dimensionality for the three-way mapping between structural damage, altered physiology, and behavioral deficits. Understanding this mapping will be aided by large-sample studies that apply multivariate models and focus on explained percentage of variance, as opposed to univariate lesion-symptom techniques that report statistical significance. The low dimensionality of behavioral deficits following stroke is paralleled by widespread, yet relatively consistent, changes in functional connectivity (FC), including a reduction in modularity. Both are related to the structural damage to white matter and subcortical grey commonly produced by stroke. We suggest that large-scale physiological abnormalities following a stroke reduce the variety of neural states visited during task processing and at rest, resulting in a limited repertoire of behavioral states.

A New Modular Brain Organization of the BOLD Signal during Natural Vision.

The resting blood oxygen level-dependent (BOLD) signal is synchronized in large-scale brain networks (resting-state networks, RSNs) defined by interregional temporal correlations (functional connectivity, FC). RSNs are thought to place strong constraints on task-evoked processing since they largely match the networks observed during task performance. However, this result may simply reflect the presence of spontaneous activity during both rest and task. Here, we examined the BOLD network structure of natural vision, as simulated by viewing of movies, using procedures that minimized the contribution of spontaneous activity. We found that the correlation between resting and movie-evoked FC (ρ = 0.60) was lower than previously reported. Hierarchical clustering and graph-theory analyses indicated a well-defined network structure during natural vision that differed from the resting structure, and emphasized functional groupings adaptive for natural vision. The visual network merged with a network for navigation, scene analysis, and scene memory. Conversely, the dorsal attention network was split and reintegrated into 2 groupings likely related to vision/scene and sound/action processing. Finally, higher order groupings from the clustering analysis combined internally directed and externally directed RSNs violating the large-scale distinction that governs resting-state organization. We conclude that the BOLD FC evoked by natural vision is only partly constrained by the resting network structure.

Measuring functional connectivity in stroke: Approaches and considerations.

Recent research has demonstrated the importance of global changes to the functional organization of brain network following stroke. Resting functional magnetic resonance imaging (R-fMRI) is a non-invasive tool that enables the measurement of functional connectivity (FC) across the entire brain while placing minimal demands on the subject. For these reasons, it is a uniquely appealing tool for studying the distant effects of stroke. However, R-fMRI studies rely on a number of premises that cannot be assumed without careful validation in the context of stroke. Here, we describe strategies to identify and mitigate confounds specific to R-fMRI research in cerebrovascular disease. Five main topics are discussed: (a) achieving adequate co-registration of lesioned brains, (b) identifying and removing hemodynamic lags in resting BOLD, (c) identifying other vascular disruptions that affect the resting BOLD signal, (d) selecting an appropriate control cohort, and (e) acquiring sufficient fMRI data to reliably identify FC changes. For each topic, we provide guidelines for steps to improve the interpretability and reproducibility of FC-stroke research. We include a table of confounds and approaches to identify and mitigate each. Our recommendations extend to any research using R-fMRI to study diseases that might alter cerebrovascular flow and dynamics or brain anatomy.

Top-down cortical interactions in visuospatial attention.

The voluntary allocation of visuospatial attention depends upon top-down influences from the frontal eye field (FEF) and intraparietal sulcus (IPS)-the core regions of the dorsal attention network (DAN)-to visual occipital cortex (VOC), and has been further associated with within-DAN influences, particularly from the FEF to IPS. However, the degree to which these influences manifest at rest and are then modulated during anticipatory visuospatial attention tasks remains poorly understood. Here, we measured both undirected and directed functional connectivity (UFC, DFC) between the FEF, IPS, and VOC at rest and during an anticipatory visuospatial attention task, using a slow event-related design. Whereas the comparison between rest and task indicated FC modulations that persisted throughout the task duration, the large number of task trials we collected further enabled us to measure shorter timescale modulations of FC across the trial. Relative to rest, task engagement induced enhancement of both top-down influences from the DAN to VOC, as well as bidirectional influences between the FEF and IPS. These results suggest that task performance induces enhanced interaction within the DAN and a greater top-down influence on VOC. While resting FC generally showed right hemisphere dominance, task-related enhancement favored the left hemisphere, effectively balancing a resting hemispheric asymmetry, particularly within the DAN. On a shorter (within-trial) timescale, VOC-to-DAN and bidirectional FEF-IPS influences were transiently elevated during the anticipatory period of the trial, evincing phasic modulations related to changing attentional demands. In contrast to these task-specific effects, resting and task-related influence patterns were highly correlated, suggesting a predisposing role for resting organization, which requires minimal tonic and phasic modulations for control of visuospatial attention.

The contribution of the human posterior parietal cortex to episodic memory.

The posterior parietal cortex (PPC) is traditionally associated with attention, perceptual decision making and sensorimotor transformations, but more recent human neuroimaging studies support an additional role in episodic memory retrieval. In this Opinion article, we present a functional-anatomical model of the involvement of the PPC in memory retrieval. Parietal regions involved in perceptual attention and episodic memory are largely segregated and often show a push-pull relationship, potentially mediated by prefrontal regions. Moreover, different PPC regions carry out specific functions during retrieval - for example, representing retrieved information, recoding this information based on task demands, or accumulating evidence for memory decisions.