Systems Biology Determinants of Motor Behavior in Humans

Motor skills are mediated by a dynamic and finely regulated interplay of the primary motor cortex (M1) with various cortical and subcortical regions engaged in movement preparation and execution. Several neuroimaging studies already demonstrated that increasing motor performance in simple motor tasks is associated with higher activation levels in the motor system. Additional to the extrinsic modulation of motor performance, neural activity is also influenced by intrinsic factors such as handedness. Handedness – defined as the preference to use one hand over the other – is associated with differences in activation levels in various motor tasks performed with the dominant or non-dominant hand. However, motor actions are implemented in a distributed network of motor regions rather than a single cortical area. For that reason, it is important to consider the neural processes underlying motor behavior from a network perspective that is offered by connectivity analyses. Models of effective connectivity allow the estimation of the influence that areas exert over each other while functional connectivity is defined as temporal coherence between remote, segregated neurophysiological events. The present thesis aimed to investigate how the dynamic modulation of motor performance and connectivity is mediated by extrinsic and intrinsic factors in the human motor system. In the first study, we used functional magnetic resonance imaging (fMRI) and dynamic causal modeling (DCM) to investigate effective connectivity of key motor areas at different movement frequencies performed by right-handed subjects (n=36) with the left or right hand. The network of interest consisted of motor regions in both hemispheres including M1, supplementary motor area (SMA), ventral premotor cortex (PMv), motor putamen, and motor cerebellum. The connectivity analysis showed that performing hand movements at higher frequencies was associated with a linear increase in neural coupling strength from premotor areas (SMA, PMv) contralateral to the moving hand and ipsilateral cerebellum towards contralateral, active M1. In addition, we found hemispheric differences in the amount by which the coupling of premotor areas and M1 was modulated, depending on which hand was moved. Other connections were not modulated by changes in motor performance. The results suggest that a stronger coupling, especially between contralateral premotor areas and M1, enables increased motor performance of simple unilateral hand movements. In the second study, we used fMRI and DCM to investigate effective connectivity between key motor areas during fist closures of the dominant or non-dominant hand performed by 18 right- and 18 left-handers. Handedness was assessed employing the Edinburgh-Handedness-Inventory (EHI). The network of interest consisted of key motor regions in both hemispheres including M1, SMA, PMv, motor putamen and motor cerebellum. The connectivity analysis revealed that in right-handed subjects movements of the dominant hand were associated with significantly stronger coupling of contralateral (left, i.e., dominant) SMA with ipsilateral SMA, ipsilateral PMv, contralateral motor putamen and contralateral M1 compared to equivalent connections in left-handers. The degree of handedness as indexed by the individual EHI scores also correlated with coupling parameters of these connections. In contrast, we found no differences between right- and left-handers when testing for the effect of movement speed on effective connectivity. In conclusion, the data show that handedness is associated with differences in effective connectivity within the human motor network with a prominent role of SMA in right-handers. Left-handers featured less asymmetry in effective connectivity implying different hemispheric mechanisms underlying hand motor control compared to right-handers. However, differences in task performance are inherent putative confounds for all task based fMRI studies. For example, performing a standard motor task might be less demanding when using the dominant hand compared to the non-dominant hand, which may also affect neural activation levels, e.g., in frontoparietal areas. Thus, resting-state fMRI seems an attractive approach to overcome these putative confounds as it allows investigating networks independent from performance. In the third study, we, therefore, scanned 18 right- and 18 left-handers with resting-state fMRI. Handedness was assessed by the EHI. We computed whole-brain functional connectivity maps of the left and right M1. To test for the effect of handedness, we computed differential contrasts and regression analyses including EHI as a covariate. We further used a multivariate linear support vector machine (SVM) classifier algorithm to reveal the individual specificity of brain regions showing differences between the resting-state maps of right- and left-handers. Using left M1 as a seed region revealed stronger interhemispheric functional connectivity between M1 and dorsolateral premotor cortex (PMd) in right-handers as compared to left-handers. Furthermore, this individual cluster in right PMd classified right- and left-handers with 86.2% accuracy. Control analyses using non-motor resting-state networks, including the (Broca) speech and the visual network, revealed no significant differences in functional connectivity related to handedness. Higher connectivity in right-handers might, therefore, reflect a systematic impact of handedness on an intrinsic functional level and might explain the observation that right-handedness is usually more lateralised than left-handedness. Furthermore, enhanced connectivity between M1 and PMd serves as an individual marker / endophenotype of handedness. In summary, the present thesis demonstrates that the dynamic modulation of the motor system during motor performance is mediated by a specific set of brain regions in both rightand left-handers. Furthermore, the results indicate that differences in coupling strength between right- and left-handers reflect the impact of handedness on both functional and effective connectivity

Exploring manual asymmetries during grasping: a dynamic causal modeling approach

Recording of neural activity during grasping actions in macaques showed that grasp-related sensorimotor transformations are accomplished in a circuit constituted by the anterior part of the intraparietal sulcus (AIP), the ventral (F5) and the dorsal (F2) region of the premotor area. In humans, neuroimaging studies have revealed the existence of a similar circuit, involving the putative homolog of macaque areas AIP, F5 and F2. These studies have mainly considered grasping movements performed with the right dominant hand and only a few studies have measured brain activity associated with a movement performed with the left non-dominant hand. As a consequence of this gap, how the brain controls for grasping movement performed with the dominant and the non-dominant hand still represents an open question. A functional resonance imaging experiment (fMRI) has been conducted, and effective connectivity (Dynamic Causal Modelling, DCM) was used to assess how connectivity among grasping-related areas is modulated by hand (i.e., left and right) during the execution of grasping movements towards a small object requiring precision grasping. Results underlined boosted inter-hemispheric couplings between dorsal premotor cortices during the execution of movements performed with the left rather than the right dominant hand. More specifically, they suggest that the dorsal premotor cortices may play a fundamental role in monitoring the configuration of fingers when grasping movements are performed by either the right and the left hand. This role becomes particularly evident when the hand less-skilled (i.e., the left hand) to perform such action is utilized. The results are discussed in light of recent theories put forward to explain how parieto-frontal connectivity is modulated by the execution of prehensile movements

Differences between left- and right-handers in approach/avoidance motivation: influence of consistency of handedness measures

Hand preference is often viewed as a troublesome variable in psychological research, with left-handers routinely excluded from studies. Contrary to this, a body of evidence has shown hand preference to be a useful variable when examining human behavior. A recent review argues that the most effective way of using handedness as a variable, is a comparison between individuals who use their dominant hand for virtually all manual activities (consistent handers) versus those who use their other hand for at least one activity (inconsistent handers). The authors contend that researchers should only focus on degree of handedness rather than direction of preference (left versus right). However, we argue that the field suffers from a number of methodological and empirical issues. These include a lack of consensus in choice of cut-off point to divide consistent and inconsistent categories and importantly a paucity of data from left-handers. Consequentially, researchers predominantly compare inconsistent versus consistent right-handers, largely linked to memory, cognition and language. Other research on response style and personality measures shows robust direction of handedness effects. The present study examines both strength and direction of handedness on self-reported behavioral inhibition system (BIS) and behavioral activation system (BAS) scores, using evidence from a large (N = 689) dataset including more than 200 left-handers. There were degree of handedness effects on BIS and BAS-Fun Seeking, but effects are largely driven by differences between consistent left-handers and other groups. Choice of cut-off point substantively influenced results, and suggests that unless a suitable sample of left-handers is included, researchers clarify that their degree of handedness effects are applicable only to right-handers. We concur that strength of hand preference is an important variable but caution that differences related to consistency may not be identical in right and left-handers

A tutorial on group effective connectivity analysis, part 2: second level analysis with PEB

This tutorial provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). This involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. We walk through this approach in detail, using data from a published fMRI experiment that characterised individual differences in hemispheric lateralization in a semantic processing task. The preliminary subject specific DCM analysis is covered in detail in a companion paper. This tutorial is accompanied by the example dataset and step-by-step instructions to reproduce the analyses

Interregional synchrony of visuomotor tracking: perturbation effects and individual differences

The present study evaluated the neural and behavioural correlates associated with a visuomotor tracking task during which a sensory perturbation was introduced that created a directional bias between moving hand and cursor position. The results revealed that trajectory error increased as a result of the perturbation in conjunction with a dynamic neural reorganization of cluster patterns that reflected distinct processing. In particular, a negatively activated cluster, characterizing the degraded information processing due to the perturbation, involved both hemispheres as well as midline area. Conversely, a positively activated cluster, indicative of compensatory processing was strongly confined to the left (dominant) hemisphere. In addition, a brain-behavioural association of good vs. poor performing participants enabled to localize a neural circuit within the left hemisphere and midline area that linked with successful performance. Overall, these data reinforce the functional significance of interregional synchrony in defining response output and behavioural success

Manual dexterity: functional lateralisation patterns and motor efficiency

Manual tasks are an important goal-directed ability. In this EEG work, we studied how handedness affects the hemispheric lateralisation patterns during performance of visually-driven movements with either hand. The neural correlates were assessed by means of EEG coherence whereas behavioural output was measured by motor error. The EEG data indicated that left- and right-handers showed distinct recruitment patterns. These involved local interactions between brain regions as well as more widespread associations between brain systems. Despite these differences, brain-behaviour correlations highlighted that motor efficiency depended on left-sided brain regions across groups. These results suggest that skilled hand motor control relies on different neural patterns as a function of handedness whereas behavioural efficiency is linked with the left hemisphere. In conclusion, the present findings add to our understanding about principles of lateralised organisation as a function of handedness

Neural activation and functional connectivity during motor imagery of bimanual everyday actions

© 2012 Szameitat et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Bimanual actions impose intermanual coordination demands not present during unimanual actions. We investigated the functional neuroanatomical correlates of these coordination demands in motor imagery (MI) of everyday actions using functional magnetic resonance imaging (fMRI). For this, 17 participants imagined unimanual actions with the left and right hand as well as bimanual actions while undergoing fMRI. A univariate fMRI analysis showed no reliable cortical activations specific to bimanual MI, indicating that intermanual coordination demands in MI are not associated with increased neural processing. A functional connectivity analysis based on psychophysiological interactions (PPI), however, revealed marked increases in connectivity between parietal and premotor areas within and between hemispheres. We conclude that in MI of everyday actions intermanual coordination demands are primarily met by changes in connectivity between areas and only moderately, if at all, by changes in the amount of neural activity. These results are the first characterization of the neuroanatomical correlates of bimanual coordination demands in MI. Our findings support the assumed equivalence of overt and imagined actions and highlight the differences between uni- and bimanual actions. The findings extent our understanding of the motor system and may aid the development of clinical neurorehabilitation approaches based on mental practice.This study was funded by the Medical Research Council, UK (CEG 61501; Dr Sterr)

Supraspinal Fatigue Impedes Recovery From a Low-Intensity Sustained Contraction in Old Adults

This study determined the contribution of supraspinal fatigue and contractile properties to the age difference in neuromuscular fatigue during and recovery from a low-intensity sustained contraction. Cortical stimulation was used to evoke measures of voluntary activation and muscle relaxation during and after a contraction sustained at 20% of maximal voluntary contraction (MVC) until task failure with elbow flexor muscles in 14 young adults (20.9 ± 3.6 yr, 7 men) and 14 old adults (71.6 ± 5.4 yr, 7 men). Old adults exhibited a longer time to task failure than the young adults (23.8 ± 9.0 vs. 11.5 ± 3.9 min, respectively, P \u3c 0.001). The time to failure was associated with initial peak rates of relaxation of muscle fibers and pressor response (P \u3c 0.05). Increments in torque (superimposed twitch; SIT) generated by transcranial magnetic stimulation (TMS) during brief MVCs, increased during the fatiguing contraction (P \u3c 0.001) and then decreased during recovery (P = 0.02). The increase in the SIT was greater for the old adults than the young adults during the fatiguing contraction and recovery (P \u3c 0.05). Recovery of MVC torque was less for old than young adults at 10 min post-fatiguing contraction (75.1 ± 8.7 vs. 83.6 ± 7.8% of control MVC, respectively, P = 0.01) and was associated with the recovery of the SIT (r = −0.59, r2 = 0.35, P \u3c 0.001). Motor evoked potential (MEP) amplitude and the silent period elicited during the fatiguing contraction increased less for old adults than young adults (P \u3c 0.05). The greater fatigue resistance with age during a low-intensity sustained contraction was attributable to mechanisms located within the muscle. Recovery of maximal strength after the low-intensity fatiguing contraction however, was impeded more for old adults than young because of greater supraspinal fatigue. Recovery of strength could be an important variable to consider in exercise prescription of old populations

FMRI resting slow fluctuations correlate with the activity of fast cortico-cortical physiological connections

Recording of slow spontaneous fluctuations at rest using functional magnetic resonance imaging (fMRI) allows distinct long-range cortical networks to be identified. The neuronal basis of connectivity as assessed by resting-state fMRI still needs to be fully clarified, considering that these signals are an indirect measure of neuronal activity, reflecting slow local variations in de-oxyhaemoglobin concentration. Here, we combined fMRI with multifocal transcranial magnetic stimulation (TMS), a technique that allows the investigation of the causal neurophysiological interactions occurring in specific cortico-cortical connections. We investigated whether the physiological properties of parieto-frontal circuits mapped with short-latency multifocal TMS at rest may have some relationship with the resting-state fMRI measures of specific resting-state functional networks (RSNs). Results showed that the activity of fast cortico-cortical physiological interactions occurring in the millisecond range correlated selectively with the coupling of fMRI slow oscillations within the same cortical areas that form part of the dorsal attention network, i.e., the attention system believed to be involved in reorientation of attention. We conclude that resting-state fMRI ongoing slow fluctuations likely reflect the interaction of underlying physiological cortico-cortical connections