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Children with autism spectrum disorder (ASD) have difficulties perceiving and producing skilled gestures, or praxis. The inferior parietal lobule (IPL) is crucial to praxis acquisition and expression, yet how IPL connectivity contributes to autism-associated impairments in praxis as well as social-communicative skill remains unclear. Using resting-state functional magnetic resonance imaging, we applied independent component analysis to test how IPL connectivity relates to praxis and social-communicative skills in children with and without ASD. Across all children (with/without ASD), praxis positively correlated with connectivity of left posterior-IPL with the left dorsal premotor cortex and with the bilateral posterior/medial parietal cortex. Praxis also correlated with connectivity of right central-IPL connectivity with the left intraparietal sulcus and medial parietal lobe. Further, in children with ASD, poorer praxis and social-communicative skills both correlated with weaker right central-IPL connectivity with the left cerebellum, posterior cingulate, and right dorsal premotor cortex. Our findings suggest that IPL connectivity is linked to praxis development, that contributions arise bilaterally, and that right IPL connectivity is associated with impaired praxis and social-communicative skills in autism. The findings underscore the potential impact of IPL connectivity and impaired skill acquisition on the development of a range of social-communicative and motor functions during childhood, including autism-associated impairments. more...

Human behavior is supported by flexible neurophysiological processes that enable the fine‐scale manipulation of information across distributed neural circuits. Yet, approaches for understanding the dynamics of these circuit interactions have been limited. One promising avenue for quantifying and describing these dynamics lies in multilayer network models. Here, networks are composed of nodes (which represent brain regions) and time‐dependent edges (which represent statistical similarities in activity time series). We use this approach to examine functional connectivity measured by non‐invasive neuroimaging techniques. These multilayer network models facilitate the examination of changes in the pattern of statistical interactions between large‐scale brain regions that might facilitate behavior. In this study, we define and exercise two novel measures of network reconfiguration, and demonstrate their utility in neuroimaging data acquired as healthy adult human subjects learn a new motor skill. In particular, we identify putative functional modules in multilayer networks and characterize the degree to which nodes switch between modules. Next, we define cohesive switches, in which a set of nodes moves between modules together as a group, and we define disjoint switches, in which a single node moves between modules independently from other nodes. Together, these two concepts offer complementary yet distinct insights into the changes in functional connectivity that accompany motor learning. More generally, our work offers statistical tools that other researchers can use to better understand the reconfiguration patterns of functional connectivity over time. Hum Brain Mapp 38:4744–4759, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. more...

We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization. more...

We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes the correlation matrices into a sparse set of representative subnetworks that define a network manifold. These subnetworks are modeled as rank-one outer-products which correspond to the elemental patterns of co-activation across the brain; the subnetworks are combined via patient-specific non-negative coefficients. The second term is a linear regression model that uses the patient-specific coefficients to predict a measure of clinical severity. We validate our framework on two separate datasets in a ten fold cross validation setting. The first is a cohort of fifty-eight patients diagnosed with Autism Spectrum Disorder (ASD). The second dataset consists of sixty three patients from a publicly available ASD database. Our method outperforms standard semi-supervised frameworks, which employ conventional graph theoretic and statistical representation learning techniques to relate the rs-fMRI correlations to behavior. In contrast, our joint network optimization framework exploits the structure of the rs-fMRI correlation matrices to simultaneously capture group level effects and patient heterogeneity. Finally, we demonstrate that our proposed framework robustly identifies clinically relevant networks characteristic of ASD. more...

Background Child abuse and other forms of adversity are associated with alterations in threat processing and emotion regulation brain circuits. Objective The goal of the current investigation is to determine if the availability of positive social support can ameliorate the negative impact of adversity on these brain systems. Participants and setting Subjects included 55 children ages 7–16 (X = 11.8, SD = 2.0). Approximately one-third of the cohort had no significant history of adversity, one-third had a history of moderate adversity, and one-third had a history of severe adversity. Brain imaging was conducted at the University of Vermont using a 3.0 T Philips scanner. Methods The Emotional Go-NoGo task with fearful and calm facial stimuli was used to assess the neural correlates of threat processing and emotion regulation in children during functional magnetic resonance imaging (fMRI). Dimensional measures of anxiety, social supports, and children’s adverse experiences were also obtained. Results A conjunction analysis was used to test if trauma-related brain activation in responding to fearful vs. calm targets was impacted by social support. This approach identified multiple activation foci, including a cluster extending from the left amygdala to several other key brain regions involved in emotion regulation, including the orbitofrontal cortex, anterior cingulate cortex (ACC), anterior insula, nucleus accumbens, and frontal pole (Family Wise Error (FWE) correction, p Conclusions Greater social support may reduce the effect that adversity has on neural processing of threat stimuli, consistent with the protective role of positive supports in promoting resilience and recovery demonstrated in the literature. more...

Human skill learning requires fine-scale coordination of distributed networks of brain regions linked by white matter tracts to allow for effective information transmission. Yet how individual differences in these anatomical pathways may impact individual differences in learning remains far from understood. Here, we test the hypothesis that individual differences in structural organization of networks supporting task performance predict individual differences in the rate at which humans learn a visuomotor skill. Over the course of 6 weeks, 20 healthy adult subjects practiced a discrete sequence production task, learning a sequence of finger movements based on discrete visual cues. We collected structural imaging data, and using deterministic tractography generated structural networks for each participant to identify streamlines connecting cortical and subcortical brain regions. We observed that increased white matter connectivity linking early visual regions was associated with a faster learning rate. Moreover, the strength of multiedge paths between motor and visual modules was also correlated with learning rate, supporting the potential role of extended sets of polysynaptic connections in successful skill acquisition. Our results demonstrate that estimates of anatomical connectivity from white matter microstructure can be used to predict future individual differences in the capacity to learn a new motor–visual skill, and that these predictions are supported both by direct connectivity in visual cortex and indirect connectivity between visual cortex and motor cortex. more...

The goal of the current study was to evaluate the impact of Tubulin Polymerization Promoting Protein (TPPP) methylation on structural and fractional anisotropy (FA) corpus callosum (CC) measures. TPPP is involved in the development of white matter tracts in the brain and was implicated in stress-related psychiatric disorders in an unbiased whole epigenome methylation study. The cohort included 63 participants (11.73 y/o ±1.91) from a larger study investigating risk and resilience in maltreated children. Voxel-based morphometry (VBM) was used to process the structural data, fractional anisotropy (FA) was determined using an atlas-based approach, and DNA specimens were derived from saliva in two batches using the 450 K (N = 39) and 850 K (N = 24) Illumina arrays, with the data from each batch analyzed separately. After controlling for multiple comparisons and relevant covariates (e.g., demographics, brain volume, cell composition, 3 PCs), 850 K derived TPPP methylation values, in interaction with a dimensional measure of children's trauma experiences, predicted left and right CC body volumes and genu, body and splenium FA (p .007, all comparisons). The findings in the splenium replicated in subjects with the 450 K data. The results extend prior investigations and suggest a role for TPPP in brain changes associated with stress-related psychiatric disorders. more...

Newly acquired motor skills become stabilized through consolidation [1]. However, we know from daily life that consolidated skills are modified over multiple bouts of practice and in response to newfound challenges [2]. Recent evidence has shown that memories can be modified through reconsolidation, in which previously consolidated memories can re-enter a temporary state of instability through retrieval, and in order to persist, undergo re-stabilization [3-8]. Although observed in other memory domains [5, 6], it is unknown whether reconsolidation leads to strengthened motor skills over multiple episodes of practice. Using a novel intervention after the retrieval of a consolidated skill, we found that skill can be modified and enhanced through exposure to increased sensorimotor variability. This improvement was greatest in those participants who could rapidly adjust their sensorimotor output in response to the relatively large fluctuations presented during the intervention. Importantly, strengthening required the reactivation of the consolidated skill and time for changes to reconsolidate. These results provide a key demonstration that consolidated motor skills continue to change as needed through the remapping of motor command to action goal, with strong implications for rehabilitation. more...

Through unbiased transcriptomics and multiple molecular tools, transient downregulation of the Orthodenticle homeobox 2 (OTX2) gene was recently causatively associated with the development of depressive-like behaviors in a mouse model of early life stress. The analyses presented in this manuscript test the translational applicability of these findings by examining peripheral markers of methylation of OTX2 and OTX2-regulated genes in relation to measures of depression and resting-state functional connectivity data collected as part of a larger study examining risk and resilience in maltreated children. The sample included 157 children between the ages of 8 and 15 years (χ = 11.4, SD = 1.9). DNA specimens were derived from saliva samples and processed using the Illumina 450 K beadchip. A subset of children (N = 47) with DNA specimens also had resting-state functional MRI data. After controlling for demographic factors, cell heterogeneity, and three principal components, maltreatment history and methylation in OTX2 significantly predicted depression in the children. In terms of the imaging data, increased OTX2 methylation was found to be associated with increased functional connectivity between the right vmPFC and bilateral regions of the medial frontal cortex and the cingulate, including the subcallosal gyrus, frontal pole, and paracingulate gyrus—key structures implicated in depression. Mouse models of early stress hold significant promise in helping to unravel the mechanisms by which child adversity confers risk for psychopathology, with data presented in this manuscript supporting a potential role for OTX2 and OTX2-related (e.g., WNT1, PAX6) genes in the pathophysiology of stress-related depressive disorders in children. more...

Sequence production tasks are a standard tool to analyze motor learning, consolidation, and habituation. As sequences are learned, movements are typically grouped into subsets or chunks. For example, most Americans memorize telephone numbers in two chunks of three digits, and one chunk of four. Studies generally use response times or error rates to estimate how subjects chunk, and these estimates are often related to physiological data. Here we show that chunking is simultaneously reflected in reaction times, errors, and their correlations. This multimodal structure enables us to propose a Bayesian algorithm that better estimates chunks while avoiding overfitting. Our algorithm reveals previously unknown behavioral structure, such as an increased error correlations with training, and promises a useful tool for the characterization of many forms of sequential motor behavior. more...

Motor sequence learning is associated with increasing and decreasing motor system activity. Here, we ask whether sequence-specific activity is contingent upon the time interval and absolute amount of training over which the skill is acquired. We hypothesize that within each motor region, the strength of any sequence representation is a non-linear function that can be characterized by 3 timescales. We had subjects train for 6 weeks and measured brain activity with functional magnetic resonance imaging. We used repetition suppression (RS) to isolate sequence-specific representations while controlling for effects related to kinematics and general task familiarity. Following a baseline training session, primary and secondary motor regions demonstrated rapidly increasing RS. With continued training, there was evidence for skill-specific efficiency, characterized by a dramatic decrease in motor system RS. In contrast, after performance had reached a plateau, further training led to a pattern of slowly increasing RS in the contralateral sensorimotor cortex, supplementary motor area, ventral premotor cortex, and anterior cerebellum consistent with skill-specific specialization. Importantly, many motor areas show changes involving more than 1 of these 3 timescales, underscoring the capacity of the motor system to flexibly represent a sequence based on the amount of prior experience. more...

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. more...

Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain’s baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution.HighlightsSensorimotor autonomy at rest predicts faster motor learning in the future.Connection between calcarine and superior precentral sulci form strongest predictor.Sensorimotor autonomy is a relatively stable individual trait. more...

Transcranial magnetic stimulation (TMS) was used to probe the involvement of the left primary motor cortex (M1) in the consolidation of a sequencing skill. In particular we asked: (1) if M1 is involved in consolidation of planning processes prior to response execution (2) whether movement preparation and movement execution can undergo consolidation independently and (3) whether sequence consolidation can occur in a stimulus specific manner. TMS was applied to left M1 while subjects prepared left hand sequential finger responses for three different movement sequences, presented in an interleaved fashion. Subjects also trained on three control sequences, where no TMS was applied. Disruption of subsequent consolidation was observed, but only for sequences where subjects had been exposed to TMS during training. Further, reduced consolidation was only observed for movement preparation, not movement execution. We conclude that left M1 is causally involved in the consolidation of effective response planning for left hand movements prior to response execution, and mediates consolidation in a sequence specific manner. These results provide important new insights into the role of M1 in sequential memory consolidation and sequence response planning. more...

Prospective remembering reflects the ability to realize intentions that must be delayed over some period of time. Recent evidence indicates that distinct modulations of the event-related brain potentials may be associated with the detection of a prospective memory cue (N300) and the recovery of an intention from memory (LPC, slow wave). The present experiments examined the degree to which these modulations were influenced by task manipulations that were expected to differentially influence cue detection and memory-related processes. Varying the number of intentions modulated the amplitude of a slow wave that may reflect memory-related processes. In contrast, varying the distinctiveness of the cues modulated the N300 and the early portion of the LPC indicating that this manipulation primarily influenced detection and categorization processes. These findings indicate that the N300, LPC and slow wave reflect functionally distinct processes associated with prospective memory. more...

Background Dalfampridine has the potential to be effective in patients with transverse myelitis (TM) as this rare disorder shares some clinical and pathogenic similarities with multiple sclerosis. Methods This is a randomized, double-blind, placebo-controlled crossover study of dalfampridine extended-release (D-ER, Ampyra®). Sixteen adult study participants with monophasic TM confirmed by MRI were enrolled if their baseline timed 25-foot walking speed was between 5 and 60 seconds. Participants were randomized to receive 10 mg twice-daily doses of either D-ER or placebo control for eight weeks, then crossed over to the second arm of placebo or dalfampridine for eight weeks. The primary outcome measure was the timed 25-foot walk. Results Of 16 enrolled participants, three withdrew and 13 completed the trial. Among the 13 completers, nine individuals showed an average timed walk that was faster in the D-ER arm compared to the placebo arm, but only four participants met the stricter statistical threshold to be classified as a responder. Analyses of secondary clinical outcome measures including strength, balance assessments, spasticity, and Expanded Disability Status Scale (EDSS) score showed trends toward improvement with D-ER. Conclusions D-ER may be beneficial in TM to improve walking speed and other neurological functions. more...

Distributed networks of brain areas interact with one another in a time-varying fashion to enable complex cognitive and sensorimotor functions. Here we use novel network analysis algorithms to test the recruitment and integration of large-scale functional neural circuitry during learning. Using functional magnetic resonance imaging (fMRI) data acquired from healthy human participants, from initial training through mastery of a simple motor skill, we investigate changes in the architecture of functional connectivity patterns that promote learning. Our results reveal that learning induces an autonomy of sensorimotor systems and that the release of cognitive control hubs in frontal and cingulate cortices predicts individual differences in the rate of learning on other days of practice. Our general statistical approach is applicable across other cognitive domains and provides a key to understanding time-resolved interactions between distributed neural circuits that enable task performance., Comment: 6 figures, 2 tables, and Supplement more...

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., 18 pages, 11 figures more...

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. more...

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance., Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4 figures, 3 tables more...

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. more...

Prospective memory represents our ability to realize intentions that must be delayed for some period of time. In this study, we examined modulations of the event-related brain potentials (ERPs) associated with target selection in visual working memory and prospective-cue detection in prospective memory. Targets and prospective cues elicited an N2pc, indicating that a common neural mechanism supports selection in working memory and prospective memory. Partial least squares analysis revealed that the N300 and prospective positivity were associated with a latent variable that contrasted the ERPs elicited by prospective-cue trials with those elicited by target-present and target-absent trials, in agreement with the idea that these modulations of the ERPs are uniquely related to prospective memory. more...

In this study we examined the neural correlates of conflict processing in the Stroop, counting, and digit-location tasks using event-related brain potentials (ERPs). The behavioral data revealed robust interference in response time and accuracy for all tasks. The interference effect for response time was greater in the Stroop task than the other tasks; in contrast, the interference effect for response accuracy was greater in the counting tasks than the other tasks. The N450 and sustained potential (SP) were elicited in each task. Partial least-squares (PLS) analysis was used to examine the structural relationships between the ERPs, task design, and behavior. TaskPLS analysis revealed that the N450 and SP were associated with a single latent variable leading to the suggestion that a common set of neural generators was recruited during conflict processing across the tasks and that there were differences between ERPs related to early processing across the three tasks. BehavioralPLS analysis revealed that the amplitude of the SP was positively correlated with response time and accuracy, indicating that this modulation of the ERPs may be related to response selection rather than to conflict resolution. more...

Prospective remembering reflects the ability of an individual to form and later realize intentions that must be delayed over minutes, hours, or days. A substantial body of literature, primarily based upon the findings of behavioral studies, indicates that there are robust declines in the efficiency of prospective remembering in later adulthood. However, the cognitive and neural mechanisms mediating age-related deficits in prospective remembering are not well understood. In this review we consider evidence from recent behavioral and electrophysiological studies indicating that decreases in the efficiency of prospective memory in older adults results from age-related differences in those neural mechanisms supporting the encoding of intentions into memory and the detection of prospective memory cues in the environment. more...

We study the temporal co-variation of network co-evolution via the cross-link structure of networks, for which we take advantage of the formalism of hypergraphs to map cross-link structures back to network nodes. We investigate two sets of temporal network data in detail. In a network of coupled nonlinear oscillators, hyperedges that consist of network edges with temporally co-varying weights uncover the driving co-evolution patterns of edge weight dynamics both within and between oscillator communities. In the human brain, networks that represent temporal changes in brain activity during learning exhibit early co-evolution that then settles down with practice, and subsequent decreases in hyperedge size are consistent with emergence of an autonomous subgraph whose dynamics no longer depends on other parts of the network. Our results on real and synthetic networks give a poignant demonstration of the ability of cross-link structure to uncover unexpected co-evolution attributes in both real and synthetic dynamical systems. This, in turn, illustrates the utility of analyzing cross-links for investigating the structure of temporal networks., Comment: 4.5 pages, 3 figures, supplementary materials more...