Task-based core-periphery organization of human brain dynamics

As a person learns a new skill, distinct synapses, brain regions, and circuits are engaged and change over time. In this paper, we develop methods to examine patterns of correlated activity across a large set of brain regions. Our goal is to identify properties that enable robust learning of a motor skill. We measure brain activity during motor sequencing and characterize network properties based on coherent activity between brain regions. Using recently developed algorithms to detect time-evolving communities, we find that the complex reconfiguration patterns of the brain's putative functional modules that control learning can be described parsimoniously by the combined presence of a relatively stiff temporal core that is composed primarily of sensorimotor and visual regions whose connectivity changes little in time and a flexible temporal periphery that is composed primarily of multimodal association regions whose connectivity changes frequently. The separation between temporal core and periphery changes over the course of training and, importantly, is a good predictor of individual differences in learning success. The core of dynamically stiff regions exhibits dense connectivity, which is consistent with notions of core-periphery organization established previously in social networks. Our results demonstrate that core-periphery organization provides an insightful way to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.

When someone learns a new skill, his/her brain dynamically alters individual synapses, regional activity, and larger-scale circuits. In this paper, we capture some of these dynamics by measuring and characterizing patterns of coherent brain activity during the learning of a motor skill. We extract time-evolving communities from these patterns and find that a temporal core that is composed primarily of primary sensorimotor and visual regions reconfigures little over time, whereas a periphery that is composed primarily of multimodal association regions reconfigures frequently. The core consists of densely connected nodes, and the periphery consists of sparsely connected nodes. Individual participants with a larger separation between core and periphery learn better in subsequent training sessions than individuals with a smaller separation. Conceptually, core-periphery organization provides a framework in which to understand how putative functional modules are linked. This, in turn, enables the prediction of fundamental human capacities, including the production of complex goal-directed behavior.

Robust detection of dynamic community structure in networks

We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations ("optimization variance") and over randomizations of network structure ("randomization variance"). Because the modularity quality function typically has a large number of nearly degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.

Individual differences in neural regions functionally related to real and imagined stuttering

Recent brain imaging investigations of developmental stuttering show considerable disagreement regarding which regions are related to stuttering. These divergent findings have been mainly derived from group studies. To investigate functional neurophysiology with improved precision, an individual-participant approach (N=4) using event-related functional magnetic resonance imaging and test-retest reliability measures was performed while participants produced fluent and stuttered single words during two separate occasions. A parallel investigation required participants to imagine stuttering or not stuttering on single words. The overt and covert production tasks produced considerable within-subject agreement of activated voxels across occasions, but little within-subject agreement between overt and covert task activations. However, across-subject agreement for regions activated by the overt and covert tasks was minimal. These results suggest that reliable effects of stuttering are participant-specific, an implication that might correspond to individual differences in stuttering severity and functional compensation due to related structural abnormalities.

BICAR: a new algorithm for multiresolution spatiotemporal data fusion

We introduce a method for spatiotemporal data fusion and demonstrate its performance on three constructed data sets: one entirely simulated, one with temporal speech signals and simulated spatial images, and another with recorded music time series and astronomical images defining the spatial patterns. Each case study is constructed to present specific challenges to test the method and demonstrate its capabilities. Our algorithm, BICAR (Bidirectional Independent Component Averaged Representation), is based on independent component analysis (ICA) and extracts pairs of temporal and spatial sources from two data matrices with arbitrarily different spatiotemporal resolution. We pair the temporal and spatial sources using a physical transfer function that connects the dynamics of the two. BICAR produces a hierarchy of sources ranked according to reproducibility; we show that sources which are more reproducible are more similar to true (known) sources. BICAR is robust to added noise, even in a "worst case" scenario where all physical sources are equally noisy. BICAR is also relatively robust to misspecification of the transfer function. BICAR holds promise as a useful data-driven assimilation method in neuroscience, earth science, astronomy, and other signal processing domains.

Contributions from the left PMd and the SMA during sequence retrieval as determined by depth of training

The dorsal premotor cortex (PMd) and the supplementary motor area (SMA) are critical for the acquisition and expression of sequential behavior, but little is known regarding how these regions are recruited when we must simultaneously acquire multiple sequences under different amounts of training. We hypothesized that these regions contribute to the retrieval of sequences at different familiarity levels, with the left PMd supporting sequences of moderate familiarity and the SMA supporting sequences of greater familiarity. Double-pulse transcranial magnetic stimulation (TMS) was applied during the retrieval of six sequences previously learned under three different amounts of exposure during 30 days of training using a discrete sequence production task. TMS led to a significant interaction of sequence error between depth of training and stimulation location. Stimulation of the left PMd increased error during moderate sequence retrieval, whereas stimulation of the SMA increased error during the retrieval of both moderately and extensively trained sequences. The lack of a double dissociation fails to support a direct correspondence between brain region and putative behavioral learning stage. Instead, the interaction suggests that SMA and PMd support the expression of sequences over different, albeit overlapping, time scales. Separate analysis of sequence initiation time did not demonstrate any significant difference between moderately and extensively trained sequences. Instead, stimulation to either region quickened sequence initiation for these sequences, but not for those sequences with poor retrieval performance. This supports the general role of these premotor regions in the maintenance of specific sequence knowledge prior to movement onset.

Differential recruitment of the sensorimotor putamen and frontoparietal cortex during motor chunking in humans

Motor chunking facilitates movement production by combining motor elements into integrated units of behavior. Previous research suggests that chunking involves two processes: concatenation, aimed at the formation of motor-motor associations between elements or sets of elements, and segmentation, aimed at the parsing of multiple contiguous elements into shorter action sets. We used fMRI to measure the trial-wise recruitment of brain regions associated with these chunking processes as healthy subjects performed a cued-sequence production task. A dynamic network analysis identified chunking structure for a set of motor sequences acquired during fMRI and collected over 3 days of training. Activity in the bilateral sensorimotor putamen positively correlated with chunk concatenation, whereas a left-hemisphere frontoparietal network was correlated with chunk segmentation. Across subjects, there was an aggregate increase in chunk strength (concatenation) with training, suggesting that subcortical circuits play a direct role in the creation of fluid transitions across chunks.

Combining the finite element method with structural connectome-based analysis for modeling neurotrauma: connectome neurotrauma mechanics

This article presents the integration of brain injury biomechanics and graph theoretical analysis of neuronal connections, or connectomics, to form a neurocomputational model that captures spatiotemporal characteristics of trauma. We relate localized mechanical brain damage predicted from biofidelic finite element simulations of the human head subjected to impact with degradation in the structural connectome for a single individual. The finite element model incorporates various length scales into the full head simulations by including anisotropic constitutive laws informed by diffusion tensor imaging. Coupling between the finite element analysis and network-based tools is established through experimentally-based cellular injury thresholds for white matter regions. Once edges are degraded, graph theoretical measures are computed on the “damaged” network. For a frontal impact, the simulations predict that the temporal and occipital regions undergo the most axonal strain and strain rate at short times (less than 24 hrs), which leads to cellular death initiation, which results in damage that shows dependence on angle of impact and underlying microstructure of brain tissue. The monotonic cellular death relationships predict a spatiotemporal change of structural damage. Interestingly, at 96 hrs post-impact, computations predict no network nodes were completely disconnected from the network, despite significant damage to network edges. At early times () network measures of global and local efficiency were degraded little; however, as time increased to 96 hrs the network properties were significantly reduced. In the future, this computational framework could help inform functional networks from physics-based structural brain biomechanics to obtain not only a biomechanics-based understanding of injury, but also neurophysiological insight.

According to the Centers for Disease Control and Prevention in the United States, approximately 1.7 million people, on average, sustain a traumatic brain injury annually. During the last few decades, brain neurotrauma biomechanics has been an active area of research involving medical clinicians and a broad range of scientists and engineers. In addition, advances and fast growth of human connectomics continues to reveal new insights into the damaged brain. With recent advances in computational methods and high performance computing, we see the need and the exciting possibility to merge brain neurotrauma biomechanics and human connectomics science to form a new area of investigation - connectome neurotrauma mechanics. For neurotrauma, the idea is simple - inform human structural connectome analysis using physics-based predictions of biomechanical brain injury. If successful, this technique may be further used to inform human functional connectome analysis, thus providing a new tool to help understand the pathophysiology of mild traumatic brain injury.

Brain activity in adults who stutter: similarities across speaking tasks and correlations with stuttering frequency and speaking rate

Many differences in brain activity have been reported between persons who stutter (PWS) and typically fluent controls during oral reading tasks. An earlier meta-analysis of imaging studies identified stutter-related regions, but recent studies report less agreement with those regions. A PET study on adult dextral PWS (n=18) and matched fluent controls (CONT, n=12) is reported that used both oral reading and monologue tasks. After correcting for speech rate differences between the groups the task-activation differences were surprisingly small. For both analyses only some regions previously considered stutter-related were more activated in the PWS group than in the CONT group, and these were also activated during eyes-closed rest (ECR). In the PWS group, stuttering frequency was correlated with cortico-striatal-thalamic circuit activity in both speaking tasks. The neuroimaging findings for the PWS group, relative to the CONT group, appear consistent with neuroanatomic abnormalities being increasingly reported among PWS.

Telling the Right Hand From the Left Hand

Judging the laterality of a hand seen at unanticipated orientations evokes a robust feeling of bodily movement, even though no movement is produced. In two experiments, we tested a novel hypothesis to explain this phenomenon: A hand’s laterality is determined via a multisensory binding of the visual representation of the seen hand and a proprioceptive representation of the observer’s felt hand, and the felt “movement” is an obligatory aftereffect of intersensory recalibration. Consistent with the predictions implied by such a cross-modal mechanism, our results in Experiment 1 showed that manipulating observers’ selective attention can evoke illusory feelings of movement in the “wrong” hand (i.e., the hand whose laterality does not match that of the stimulus). In Experiment 2, these illusions were readily extinguished in conditions in which binding was predicted to fail, a result indicating that cross-modal binding was necessary to produce them. These results are not explained by imagery, a mechanism widely assumed to account for how hand laterality is identified.

Effector selection precedes reach planning in the dorsal parietofrontal cortex

Experimental evidence and computational modeling suggest that target selection for reaching is associated with the parallel encoding of multiple movement plans in the dorsomedial posterior parietal cortex (dmPPC) and the caudal part of the dorsal premotor cortex (PMdc). We tested the hypothesis that a similar mechanism also accounts for arm selection for unimanual reaching, with simultaneous and separate motor goal representations for the left and right arms existing in the right and left parietofrontal cortex, respectively. We recorded simultaneous electroencephalograms and functional MRI and studied a condition in which subjects had to select the appropriate arm for reaching based on the color of an appearing visuospatial target, contrasting it to a condition in which they had full knowledge of the arm to be used before target onset. We showed that irrespective of whether subjects had to select the arm or not, activity in dmPPC and PMdc was only observed contralateral to the reaching arm after target onset. Furthermore, the latency of activation in these regions was significantly delayed when arm selection had to be achieved during movement planning. Together, these results demonstrate that effector selection is not achieved through the simultaneous specification of motor goals tied to the two arms in bilateral parietofrontal cortex, but suggest that a motor goal is formed in these regions only after an arm is selected for action.

FMRI supports the sensorimotor theory of motor resonance

The neural mechanisms mediating the activation of the motor system during action observation, also known as motor resonance, are of major interest to the field of motor control. It has been proposed that motor resonance develops in infants through Hebbian plasticity of pathways connecting sensory and motor regions that fire simultaneously during imitation or self movement observation. A fundamental problem when testing this theory in adults is that most experimental paradigms involve actions that have been overpracticed throughout life. Here, we directly tested the sensorimotor theory of motor resonance by creating new visuomotor representations using abstract stimuli (motor symbols) and identifying the neural networks recruited through fMRI. We predicted that the network recruited during action observation and execution would overlap with that recruited during observation of new motor symbols. Our results indicate that a network consisting of premotor and posterior parietal cortex, the supplementary motor area, the inferior frontal gyrus and cerebellum was activated both by new motor symbols and by direct observation of the corresponding action. This tight spatial overlap underscores the importance of sensorimotor learning for motor resonance and further indicates that the physical characteristics of the perceived stimulus are irrelevant to the evoked response in the observer.

Attitudes trigger motor behavior through conditioned associations: neural and behavioral evidence

It has long been argued that attitudes prepare the body to act. While early evidence suggested that evaluations (positive or negative) are rigidly linked to specific motor behaviors (approach or avoidant), recent behavioral evidence suggests that this linkage is context dependent. Here, we report that the neural circuitry mediating the relationship between evaluations and motor responses promotes flexibility in our embodiment of attitudes. In a behavioral study, stimulus-response relationships between evaluations and actions were rapidly conditioned. In a neuroimaging (functional magnetic resonance imaging) study, repetition suppression demonstrated that these relationships are formed in neural systems traditionally implicated in arbitrary sensorimotor mappings (i.e. the dorsal premotor cortex and posterior superior parietal lobule). These data provide the first neurophysiological evidence for attitude embodiment and demonstrate that relationships between evaluation and action are inherently malleable.

Decoding intention: a neuroergonomic perspective

Decoding the intentions of other people based on non-linguistic cues such as their body movement is a major requirement of many jobs. Whether it is maintaining security at an airport or negotiating with locals in a foreign country, there is a need to maximize the effectiveness of training or real-time performance in this decoding process. This review considers the potential utility of neuroergonomic solutions, and in particular, of electroencephalographic (EEG) methods for augmenting action understanding. Focus is given to body movements and hand-object interactions, where there is a rapid growth in relevant science. The interpretation of EEG-based signals is reinforced by a consideration of functional magnetic resonance imaging experiments demonstrating underlying brain mechanisms that support goal oriented action. While no EEG method is currently implemented as a practical application for enhancing the understanding of unspoken intentions, there are a number of promising approaches that merit further development.

Human posterior parietal cortex flexibly determines reference frames for reaching based on sensory context

Current models of sensorimotor transformations emphasize the dominant role of gaze-centered representations for reach planning in the posterior parietal cortex (PPC). Here we exploit fMRI repetition suppression to test whether the sensory modality of a target determines the reference frame used to define the motor goal in the PPC and premotor cortex. We show that when targets are defined visually, the anterior precuneus selectively encodes the motor goal in gaze-centered coordinates, whereas the parieto-occipital junction, Brodman Area 5 (BA 5), and PMd use a mixed gaze- and body-centered representation. In contrast, when targets are defined by unseen proprioceptive cues, activity in these areas switches to represent the motor goal predominantly in body-centered coordinates. These results support computational models arguing for flexibility in reference frames for action according to sensory context. Critically, they provide neuroanatomical evidence that flexibility is achieved by exploiting a multiplicity of reference frames that can be expressed within individual areas.

Conserved and variable architecture of human white matter connectivity

Whole-brain network analysis of diffusion imaging tractography data is an important new tool for quantification of differential connectivity patterns across individuals and between groups. Here we investigate both the conservation of network architectural properties across methodological variation and the reproducibility of individual architecture across multiple scanning sessions. Diffusion spectrum imaging (DSI) and diffusion tensor imaging (DTI) data were both acquired in triplicate from a cohort of healthy young adults. Deterministic tractography was performed on each dataset and inter-regional connectivity matrices were then derived by applying each of three widely used whole-brain parcellation schemes over a range of spatial resolutions. Across acquisitions and preprocessing streams, anatomical brain networks were found to be sparsely connected, hierarchical, and assortative. They also displayed signatures of topo-physical interdependence such as Rentian scaling. Basic connectivity properties and several graph metrics consistently displayed high reproducibility and low variability in both DSI and DTI networks. The relative increased sensitivity of DSI to complex fiber configurations was evident in increased tract counts and network density compared with DTI. In combination, this pattern of results shows that network analysis of human white matter connectivity provides sensitive and temporally stable topological and physical estimates of individual cortical structure across multiple spatial scales.

Understanding actions of others: the electrodynamics of the left and right hemispheres. A high-density EEG neuroimaging study

Background

When we observe an individual performing a motor act (e.g. grasping a cup) we get two types of information on the basis of how the motor act is done and the context: what the agent is doing (i.e. grasping) and the intention underlying it (i.e. grasping for drinking). Here we examined the temporal dynamics of the brain activations that follow the observation of a motor act and underlie the observer's capacity to understand what the agent is doing and why.

Methodology/Principal Findings

Volunteers were presented with two-frame video-clips. The first frame (T0) showed an object with or without context; the second frame (T1) showed a hand interacting with the object. The volunteers were instructed to understand the intention of the observed actions while their brain activity was recorded with a high-density 128-channel EEG system. Visual event-related potentials (VEPs) were recorded time-locked with the frame showing the hand-object interaction (T1). The data were analyzed by using electrical neuroimaging, which combines a cluster analysis performed on the group-averaged VEPs with the localization of the cortical sources that give rise to different spatio-temporal states of the global electrical field. Electrical neuroimaging results revealed four major steps: 1) bilateral posterior cortical activations; 2) a strong activation of the left posterior temporal and inferior parietal cortices with almost a complete disappearance of activations in the right hemisphere; 3) a significant increase of the activations of the right temporo-parietal region with simultaneously co-active left hemispheric sources, and 4) a significant global decrease of cortical activity accompanied by the appearance of activation of the orbito-frontal cortex.

Conclusions/Significance

We conclude that the early striking left hemisphere involvement is due to the activation of a lateralized action-observation/action execution network. The activation of this lateralized network mediates the understanding of the goal of object-directed motor acts (mirror mechanism). The successive right hemisphere activation indicates that this hemisphere plays an important role in understanding the intention of others.

The cognitive neuroscience of prehension: recent developments

Prehension, the capacity to reach and grasp, is the key behavior that allows humans to change their environment. It continues to serve as a remarkable experimental test case for probing the cognitive architecture of goal-oriented action. This review focuses on recent experimental evidence that enhances or modifies how we might conceptualize the neural substrates of prehension. Emphasis is placed on studies that consider how precision grasps are selected and transformed into motor commands. Then, the mechanisms that extract action relevant information from vision and touch are considered. These include consideration of how parallel perceptual networks within parietal cortex, along with the ventral stream, are connected and share information to achieve common motor goals. On-line control of grasping action is discussed within a state estimation framework. The review ends with a consideration about how prehension fits within larger action repertoires that solve more complex goals and the possible cortical architectures needed to organize these actions.

Repetition suppression for performed hand gestures revealed by fMRI

The functional components of the human motor system that are used to retrieve and execute simple intransitive hand gestures were identified with a repetition suppression (RS) paradigm. Participants performed movements with the right hand to text instructions in a rapid event related design with a pseudo-random stimulus order. Brain areas associated with action retrieval were identified by comparing trials where an action was repeated to trials that involved a new action. Performance of a novel action, collapsed across individual actions, resulted in significantly greater activity in a left hemisphere predominant fronto-parietal circuit involving inferior frontal gyrus and inferior parietal cortex (supramarginal gyrus). This is consistent with previous action retrieval tasks using go, no-go paradigms and lesion studies of patients with apraxia that emphasize a role of these areas in action organization. In addition, RS effects were present in left primary sensorimotor cortex. These effects cannot be ascribed to kinematic differences, simple action related activity or differences of cognitive set. Significant RS effects for action retrieval could be identified with as little as 5 min of fMRI data and underscores the potential of using RS to characterize representational structure within the motor system.

Improving human brain mapping via joint inversion of brain electrodynamics and the BOLD signal

We present several methods to improve the resolution of human brain mapping by combining information obtained from surface electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) of the same participants performing the same task in separate imaging sessions. As an initial step in our methods we used independent component analysis (ICA) to obtain task-related sources for both EEG and fMRI. We then used that information in an integrated cost function that attempts to match both data sources and trades goodness of fit in one regime for another. We compared the performance and drawbacks of each method in localizing sources for a dual visual evoked response experiment, and we contrasted the results of adding fMRI information to simple EEG-only inversion methods. We found that adding fMRI information in a variety of ways gives superior results to classical minimum norm source estimation. Our findings lead us to favor a method which attempts to match EEG scalp dynamics along with voxel power obtained from ICA-processed blood oxygenation level dependent (BOLD) data; this method of joint inversion enables us to treat the two data sources as symmetrically as possible.

Neural substrates of practice structure that support future off-line learning

Off-line learning is facilitated when motor skills are acquired under a random practice schedule and retention suffers when a similar set of motor skills are practiced under a blocked schedule. The current study identified the neural correlates of a random training schedule while participants learned a set of four-element finger sequences using their nondominant hand during functional magnetic resonance imaging. A go/no go task was used to separately probe brain areas supporting sequence preparation and production. By the end of training, the random practice schedule, relative to the block schedule, recruited a broad premotor-parietal network as well as sensorimotor and subcortical regions during both preparation and production trials, despite equivalent motor performance. Longitudinal analysis demonstrated that preparation-related activity under a random schedule remained stable or increased over time. The blocked schedule showed the opposite pattern. Across individual subjects, successful skill retention was correlated with greater activity at the end of training in the ipsilateral left motor cortex, for both preparation and production. This is consistent with recent evidence that attributes off-line learning to training-related processing within primary motor cortex. These results reflect the importance of an overlooked aspect of motor skill learning. Specifically, how trials are organized during training-with a random schedule-provides an effective basis for the formation of enduring motor memories, through enhanced engagement of core regions involved in the active preparation and implementation of motor programs.

Basal ganglia contribution to the initiation of corrective submovements

We investigated the neural processes, with a focus on subcortical circuits, which govern corrective submovements in visually targeted action. During event-related fMRI, subjects moved a cursor to capture targets presented at varying movement amplitudes. Movements were performed in a rehearsed null and a novel viscous (25% random trials) torque field. Movement error feedback was provided after each trial. The viscous field invoked a significantly larger error at the end of the primary movement. Subjects compensated by producing more corrections than they had in the null condition. Corrective submovements were appropriately scaled such that terminal error was similar between the two conditions. Parametric analysis identified two regions where the BOLD signal correlated with the number of submovements per trial: a cerebellar region similar to the one noted in the task contrast and the contralateral dorsal putamen. A separate parametric analysis identified brain regions where activity correlated with movement amplitude. This identified the same cerebellar region as above, bilateral parietal cortex, and left motor and premotor cortex. Our data indicate that the basal ganglia and cerebellum play complementary roles in regulating ongoing actions when precise updating is required. The basal ganglia have a key role in contextually-based motor decision-making, i.e. for deciding if and when to correct a given movement by initiating corrective submovements, and the cerebellum is more generally involved in amplifying and refining the command signals for movements of different amplitudes.