Increased mirror overflow movements in ADHD are associated with altered EEG alpha/beta band desynchronization.

Children with ADHD show developmentally abnormal levels of mirror overflow-unintentional movements occurring symmetrically opposite of intentional movements. Because mirror overflow correlates with ADHD behavioral symptoms, the study of disinhibition in motor control may shed light on physiologic mechanisms underlying impaired behavioral/cognitive control. This is a case-controlled study of EEG recording from 25 children with ADHD and 25 typically developing (TD) controls performing unilateral sequential finger tapping, with overflow movements measured using electronic goniometers. Consistent with previously published findings, children with ADHD showed increased mirror overflow as compared with TD peers. EEG findings revealed less lateralized alpha modulation (event-related desynchronization; ERD) and decreased magnitude of beta ERD in ADHD; both alpha and beta ERD reflect cortical activation. Moderation analysis revealed a significant association between beta ERD and overflow, independent of diagnosis; and an equivocal (p = .08) effect of diagnosis on the relationship between alpha ERD and overflow, with a significant effect in children with ADHD but not TD children. These results suggest two mechanisms involved with mirror overflow: one reflected in beta ipsilateral to the intentional movement and relevant to both children with ADHD and controls, and the other seemingly more specific to ADHD (alpha, contralateral to movement).

Assessing crossmodal matching of abstract auditory and visual stimuli in posterior superior temporal sulcus with MEG.

Associating crossmodal auditory and visual stimuli is an important component of perception, with the posterior superior temporal sulcus (pSTS) hypothesized to support this. However, recent evidence has argued that the pSTS serves to associate two stimuli irrespective of modality. To examine the contribution of pSTS to crossmodal recognition, participants (N=13) learned 12 abstract, non-linguistic pairs of stimuli over 3weeks. These paired associates comprised four types: auditory-visual (AV), auditory-auditory (AA), visual-auditory (VA), and visual-visual (VV). At week four, participants were scanned using magnetoencephalography (MEG) while performing a correct/incorrect judgment on pairs of items. Using an implementation of synthetic aperture magnetometry that computes real statistics across trials (SAMspm), we directly contrasted crossmodal (AV and VA) with unimodal (AA and VV) pairs from stimulus-onset to 2s in theta (4-8Hz), alpha (9-15Hz), beta (16-30Hz), and gamma (31-50Hz) frequencies. We found pSTS showed greater desynchronization in the beta frequency for crossmodal compared with unimodal trials, suggesting greater activity during the crossmodal pairs, which was not influenced by congruency of the paired stimuli. Using a sliding window SAM analysis, we found the timing of this difference began in a window from 250 to 750ms after stimulus-onset. Further, when we directly contrasted all sub-types of paired associates from stimulus-onset to 2s, we found that pSTS seemed to respond to dynamic, auditory stimuli, rather than crossmodal stimuli per se. These findings support an early role for pSTS in the processing of dynamic, auditory stimuli, and do not support claims that pSTS is responsible for associating two stimuli irrespective of their modality.

Temporal microstructure of cortical networks (TMCN) underlying task-related differences.

Neuro-electromagnetic recording techniques (EEG, MEG, iEEG) provide high temporal resolution data to study the dynamics of neurocognitive networks: large scale neural assemblies involved in task-specific information processing. How does a neurocognitive network reorganize spatiotemporally on the order of a few milliseconds to process specific aspects of the task? At what times do networks segregate for task processing, and at what time scales does integration of information occur via changes in functional connectivity? Here, we propose a data analysis framework-Temporal microstructure of cortical networks (TMCN)-that answers these questions for EEG/MEG recordings in the signal space. Method validation is established on simulated MEG data from a delayed-match to-sample (DMS) task. We then provide an example application on MEG recordings during a paired associate task (modified from the simpler DMS paradigm) designed to study modality specific long term memory recall. Our analysis identified the times at which network segregation occurs for processing the memory recall of an auditory object paired to a visual stimulus (visual-auditory) in comparison to an analogous visual-visual pair. Across all subjects, onset times for first network divergence appeared within a range of 0.08-0.47 s after initial visual stimulus onset. This indicates that visual-visual and visual auditory memory recollection involves equivalent network components without any additional recruitment during an initial period of the sensory processing stage which is then followed by recruitment of additional network components for modality specific memory recollection. Therefore, we propose TMCN as a viable computational tool for extracting network timing in various cognitive tasks.

Building neurocognitive networks with a distributed functional architecture.

In the past few decades, behavioral and cognitive science have demonstrated that many human behaviors can be captured by low-dimensional observations and models, even though the neuromuscular systems possess orders of magnitude more potential degrees of freedom than are found in a specific behavior. We suggest that this difference, due to a separation in the time scales of the dynamics guiding neural processes and the overall behavioral expression, is a key point in understanding the implementation of cognitive processes in general. In this paper we use Structured Flows on Manifolds (SFM) to understand the organization of behavioral dynamics possessing this property. Next, we discuss how this form of behavioral dynamics can be distributed across a network, such as those recruited in the brain for particular cognitive functions. Finally, we provide an example of an SFM style functional architecture of handwriting, motivated by studies in human movement sciences, that demonstrates hierarchical sequencing of behavioral processes.

Task-specific interhemispheric hypoconnectivity in writer's cramp - An EEG study.

OBJECTIVE: Writer's cramp (WC) is a focal task-specific dystonia characterized by abnormal posturing of the hand muscles during handwriting, but not during other tasks that involve the same set of muscles and objects such as sharpening a pencil. Our objective was to investigate the pathophysiology underlying the task specificity of this disorder using EEG. We hypothesized that premotor-parietal connectivity will be lower in WC patients specifically during handwriting and motor imagery of handwriting. METHODS: We recruited 15 WC patients and 15 healthy controls. EEG was recorded while participants performed 4 tasks - writing with a pencil, sharpening a pencil, imagining writing and imagining sharpening. We determined the connectivity changes between relevant brain regions during these tasks. RESULTS: We found reduced interhemispheric alpha coherence in the sensorimotor areas in WC patients exclusively during handwriting. WC patients also showed less reduction of task-related beta spectral power and a trend for reduced premotor-parietal coherence during motor tasks. CONCLUSION: We could not confirm an abnormality in premotor-parietal connectivity specific to handwriting by this method. However, there was a task-specific reduction in interhemispheric alpha connectivity in WC patients, whose behavioral correlate remains unknown. SIGNIFICANCE: Interhemispheric alpha connectivity can be a potential interventional target in WC.

Learning of skilled movements via imitation in ASD.

Autism spectrum disorder (ASD) consists of altered performance of a range of skills, including social/communicative and motor skills. It is unclear whether this altered performance results from atypical acquisition or learning of the skills or from atypical "online" performance of the skills. Atypicalities of skilled actions that require both motor and cognitive resources, such as abnormal gesturing, are highly prevalent in ASD and are easier to study in a laboratory context than are social/communicative skills. Imitation has long been known to be impaired in ASD; because learning via imitation is a prime method by which humans acquire skills, we tested the hypothesis that children with ASD show alterations in learning novel gestures via imitation. Eighteen participants with ASD and IQ > 80, ages 8-12.9 years, and 19 typically developing peers performed a task in which they watched a video of a model performing a novel, meaningless arm/hand gesture and copied the gesture. Each gesture video/copy sequence was repeated 4-6 times. Eight gestures were analyzed. Examination of learning trajectories revealed that while children with ASD made nearly as much progress in learning from repetition 1 to repetition 4, the shape of the learning curves differed. Causal modeling demonstrated the shape of the learning curve influenced both the performance of overlearned gestures and autism severity, suggesting that it is in the index of learning mechanisms relevant both to motor skills and to autism core features. Autism Res 2020, 13: 777-784.. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Imitation is a route by which humans learn a wide range of skills, naturally and in therapies. Imitation is known to be altered in autism spectrum disorder (ASD), but learning via imitation has not been rigorously examined. We found that the shape of the learning curve is altered in ASD, in a way that has a significant impact both on measures of autism severity and of other motor skills.

Altered task-related modulation of long-range connectivity in children with autism.

Functional connectivity differences between children with autism spectrum disorder (ASD) and typically developing children have been described in multiple datasets. However, few studies examine the task-related changes in connectivity in disorder-relevant behavioral paradigms. In this paper, we examined the task-related changes in functional connectivity using EEG and a movement-based paradigm that has behavioral relevance to ASD. Resting-state studies motivated our hypothesis that children with ASD would show a decreased magnitude of functional connectivity during the performance of a motor-control task. Contrary to our initial hypothesis, however, we observed that task-related modulation of functional connectivity in children with ASD was in the direction opposite to that of TDs. The task-related connectivity changes were correlated with clinical symptom scores. Our results suggest that children with ASD may have differences in cortical segregation/integration during the performance of a task, and that part of the differences in connectivity modulation may serve as a compensatory mechanism. Autism Res 2018, 11: 245-257. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Decreased connectivity between brain regions is thought to cause the symptoms of autism. Because most of our knowledge comes from data in which children are at rest, we do not know how connectivity changes directly lead to autistic behaviors, such as impaired gestures. When typically developing children produced complex movements, connectivity decreased between brain regions. In children with autism, connectivity increased. It may be that behavior-related changes in brain connectivity are more important than absolute differences in connectivity in autism.

Symmetry Breaking in Space-Time Hierarchies Shapes Brain Dynamics and Behavior.

In order to maintain brain function, neural activity needs to be tightly coordinated within the brain network. How this coordination is achieved and related to behavior is largely unknown. It has been previously argued that the study of the link between brain and behavior is impossible without a guiding vision. Here we propose behavioral-level concepts and mechanisms embodied as structured flows on manifold (SFM) that provide a formal description of behavior as a low-dimensional process emerging from a network's dynamics dependent on the symmetry and invariance properties of the network connectivity. Specifically, we demonstrate that the symmetry breaking of network connectivity constitutes a timescale hierarchy resulting in the emergence of an attractive functional subspace. We show that behavior emerges when appropriate conditions imposed upon the couplings are satisfied, justifying the conductance-based nature of synaptic couplings. Our concepts propose design principles for networks predicting how behavior and task rules are represented in real neural circuits and open new avenues for the analyses of neural data.

Dyspraxia in ASD: Impaired coordination of movement elements.

Children with autism spectrum disorders (ASD) have long been known to have deficits in the performance of praxis gestures; these motor deficits also correlate with social and communicative deficits. To date, the precise nature of the errors involved in praxis has not been clearly mapped out. Based on observations of individuals with ASD performing gestures, we hypothesized that the simultaneous execution of multiple movement elements is especially impaired in affected children. We examined 25 school-aged participants with ASD and 25 age-matched controls performing seven simultaneous gestures that required the concurrent performance of movement elements and nine serial gestures, in which all elements were performed serially. There was indeed a group × gesture-type interaction (P < 0.001). Whereas both groups had greater difficulty performing simultaneous than serial gestures, children with ASD had a 2.6-times greater performance decrement with simultaneous (vs. serial) gestures than controls. These results point to a potential deficit in the simultaneous processing of multiple inputs and outputs in ASD. Such deficits could relate to models of social interaction that highlight the parallel-processing nature of social communication. Autism Res 2016,. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 648-652. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Sequence Effect in Parkinson's Disease Is Related to Motor Energetic Cost.

Bradykinesia is the most disabling motor symptom of Parkinson's disease (PD). The sequence effect (SE), a feature of bradykinesia, refers to the rapid decrement in amplitude and speed of repetitive movements (e.g., gait, handwriting) and is a major cause of morbidity in PD. Previous research has revealed mixed results regarding the role of dopaminergic treatment in the SE. However, external cueing has been shown to improve it. In this study, we aimed to characterize the SE systematically and relate this phenomenon to the energetic cost of movement within the context of cost-benefit framework of motor control. We used a dynamic isometric motor task with auditory pacing to assess the SE in motor output during a 15-s task segment in PD patients and matched controls. All participants performed the task with both hands, and without and with visual feedback (VF). Patients were also tested in "on"- and "off"-dopaminergic states. Patients in the "off" state did not show higher SE compared to controls, partly due to large variance in their performance. However, patients in the "on" state and in the absence of VF showed significantly higher SE compared to controls. Patients expended higher total motor energy compared to controls in all conditions and regardless of their medication status. In this experimental situation, the SE in PD is associated with the cumulative energetic cost of movement. Dopaminergic treatment, critical for internal triggering of movement, fails to maintain the motor vigor across responses. The high motor cost may be related to failure to incorporate limbic/motivational cues into the motor plan. VF may facilitate performance by shifting the driving of movement from internal to external or, alternatively, by functioning as a motivational cue.

Practicing Novel, Praxis-Like Movements: Physiological Effects of Repetition.

Our primary goal was to develop and validate a task that could provide evidence about how humans learn praxis gestures, such as those involving the use of tools. To that end, we created a video-based task in which subjects view a model performing novel, meaningless one-handed actions with kinematics similar to praxis gestures. Subjects then imitated the movements with their right hand. Trials were repeated six times to examine practice effects. EEG was recorded during the task. As a control, subjects watched videos of a model performing a well-established (over learned) tool-use gesture. These gestures were also imitated six times. Demonstrating convergent validity, EEG measures of task-related cortical activation were similar in topography and frequency between the novel gesture task and the overlearned, praxis gesture task. As in studies assessing motor skill learning with simpler tasks, cortical activation during novel gesture learning decreased as the same gestures were repeated. In the control condition, repetition of overlearned tool-use gestures were also associated with reductions in activation, though to a lesser degree. Given that even overlearned, praxis gestures show constriction of EEG activity with repetition, it is possible that that attentional effects drive some of the repetition effects seen in EEG measures of activation during novel gesture repetition.

Decreased Modulation of EEG Oscillations in High-Functioning Autism during a Motor Control Task.

Autism spectrum disorders (ASD) are thought to result in part from altered cortical excitatory-inhibitory balance; this pathophysiology may impact the generation of oscillations on electroencephalogram (EEG). We investigated premotor-parietal cortical physiology associated with praxis, which has strong theoretical and empirical associations with ASD symptomatology. Twenty five children with high-functioning ASD (HFA) and 33 controls performed a praxis task involving the pantomiming of tool use, while EEG was recorded. We assessed task-related modulation of signal power in alpha and beta frequency bands. Compared with controls, subjects with HFA showed 27% less left central (motor/premotor) beta (18-22 Hz) event-related desynchronization (ERD; p = 0.030), as well as 24% less left parietal alpha (7-13 Hz) ERD (p = 0.046). Within the HFA group, blunting of central ERD attenuation was associated with impairments in clinical measures of praxis imitation (r = -0.4; p = 0.04) and increased autism severity (r = 0.48; p = 0.016). The modulation of central beta activity is associated, among other things, with motor imagery, which may be necessary for imitation. Impaired imitation has been associated with core features of ASD. Altered modulation of oscillatory activity may be mechanistically involved in those aspects of motor network function that relate to the core symptoms of ASD.

Intraoperative neurophysiology in deep brain surgery for psychogenic dystonia.

Psychogenic dystonia is a challenging entity to diagnose and treat because little is known about its pathophysiology. We describe two cases of psychogenic dystonia who underwent deep brain stimulation when thought to have organic dystonia. The intraoperative microelectrode recordings in globus pallidus internus were retrospectively compared with those of five patients with known DYT1 dystonia using spontaneous discharge parameters of rate and bursting, as well as movement-related discharges. Our data suggest that simple intraoperative neurophysiology measures in single subjects do not differentiate psychogenic dystonia from DYT1 dystonia.

Early sensory cortex is activated in the absence of explicit input during crossmodal item retrieval: evidence from MEG.

Crossmodal associations form a fundamental aspect of our daily lives. In this study we investigated the neural correlates of crossmodal association in early sensory cortices using magnetoencephalography (MEG). We used a paired associate recognition paradigm in which subjects were tested after multiple training sessions over a span of four weeks. Subjects had to learn 12 abstract, nonlinguistic, pairs of auditory and visual objects that consisted of crossmodal (visual-auditory, VA; auditory-visual, AV) and unimodal (visual-visual, VV; auditory-auditory, AA) paired items. Visual objects included abstract, non-nameable, fractal-like images, and auditory objects included abstract tone sequences. During scanning, subjects were shown the first item of a pair (S1), followed by a delay, then the simultaneous presentation of a visual and auditory stimulus (S2). Subjects were instructed to indicate whether either of the S2 stimuli contained the correct paired associate of S1. Synthetic aperture magnetometry (SAMspm), a minimum variance beamformer, was then used to assess source power differences between the crossmodal conditions and their corresponding unimodal conditions (i.e., AV-AA and VA-VV) in the beta (15-30 Hz) and low gamma frequencies (31-54 Hz) during the S1 period. We found greater power during S1 in the corresponding modality-specific association areas for crossmodal compared with unimodal stimuli. Thus, even in the absence of explicit sensory input, the retrieval of well-learned, crossmodal pairs activate sensory areas associated with the corresponding modality. These findings support theories which posit that modality-specific regions of cortex are involved in the storage and retrieval of sensory-specific items from long-term memory.

Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models.

The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define "effective connectivity" using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons.

Using large-scale neural models to interpret connectivity measures of cortico-cortical dynamics at millisecond temporal resolution.

Over the last two decades numerous functional imaging studies have shown that higher order cognitive functions are crucially dependent on the formation of distributed, large-scale neuronal assemblies (neurocognitive networks), often for very short durations. This has fueled the development of a vast number of functional connectivity measures that attempt to capture the spatiotemporal evolution of neurocognitive networks. Unfortunately, interpreting the neural basis of goal directed behavior using connectivity measures on neuroimaging data are highly dependent on the assumptions underlying the development of the measure, the nature of the task, and the modality of the neuroimaging technique that was used. This paper has two main purposes. The first is to provide an overview of some of the different measures of functional/effective connectivity that deal with high temporal resolution neuroimaging data. We will include some results that come from a recent approach that we have developed to identify the formation and extinction of task-specific, large-scale neuronal assemblies from electrophysiological recordings at a ms-by-ms temporal resolution. The second purpose of this paper is to indicate how to partially validate the interpretations drawn from this (or any other) connectivity technique by using simulated data from large-scale, neurobiologically realistic models. Specifically, we applied our recently developed method to realistic simulations of MEG data during a delayed match-to-sample (DMS) task condition and a passive viewing of stimuli condition using a large-scale neural model of the ventral visual processing pathway. Simulated MEG data using simple head models were generated from sources placed in V1, V4, IT, and prefrontal cortex (PFC) for the passive viewing condition. The results show how closely the conclusions obtained from the functional connectivity method match with what actually occurred at the neuronal network level.