Concurrent Changes of Brain Functional Connectivity and Motor Variability When Adapting to Task Constraints

Decoding force production of skeletal muscle from the female brain using functional near-infrared spectroscopy

OBJECTIVE

Noninvasive neural decoding enables predicting motor output from neural activities without physically damaging the human body. A recent study demonstrated the applicability of functional near-infrared spectroscopy (fNIRS) to decode muscle force production from hemodynamic signals measured in the male brain. However, given the sex differences in cerebral blood flow and muscle physiology, whether the fNIRS approach can also be applied to the female brain remains elusive. Therefore, this study aimed to evaluate whether fNIRS can be used to identify the optimal cortical region and hemodynamic predictor to decode muscle force output in females.

RESULTS

Statistical group analysis for eight healthy female adults showed that the cortical region for wrist control was topologically dorsal to that for finger control over the primary sensorimotor cortex. This cortical area was maximally activated while the wrist flexor muscles were contracted to hold a load on the subject's palm, as was the case for males. However, the dynamics of oxyhemoglobin concentration measured from the most activated cortical area differed between females and males. The signal intensity during 100% maximal voluntary contraction and the signal increase rate at 50% maximal voluntary contraction was lower and faster in females. Eight predictors were used to characterize hemodynamic signals' amplitude and temporal variation in the female cortex. Unlike the case for males, only the trajectory predictors for the amplitude of oxyhemoglobin concentration change were strongly correlated with the strengths of force produced by the wrist flexor muscles, showing a linear relationship. These results suggest gender-specific hemodynamics must be considered for decoding low-level motor control with fNIRS in females.

Dose-Response of Creatine Supplementation on Cognitive Function in Healthy Young Adults

To determine if creatine (Cr) supplementation could influence cognitive performance and whether any changes were related to changes in prefrontal cortex (PFC) activation during such cognitive tasks, thirty (M = 11, F = 19) participants were evenly randomized to receive supplementation with Cr (CR10:10 g/day or CR20:20 g/day) or a placebo (PLA:10 g/day) for 6 weeks. Participants completed a cognitive test battery (processing speed, episodic memory, and attention) on two separate occasions prior to and following supplementation. Functional near-infrared spectroscopy (fNIRS) was used to measure PFC oxyhemoglobin (O2Hb) during the cognitive evaluation. A two-way repeated measures ANOVA was used to determine the differences between the groups and the timepoints for the cognitive performance scores and PFC O2Hb. In addition, a one-way ANOVA of % change was used to determine pre- and post-differences between the groups. Creatine (independent of dosage) had no significant effect on the measures of cognitive performance. There was a trend for decreased relative PFC O2Hb in the CR10 group versus the PLA group in the processing speed test (p = 0.06). Overall, six weeks of Cr supplementation at a moderate or high dose does not improve cognitive performance or change PFC activation in young adults.

Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.

Tool Embodiment Is Reflected in Movement Multifractal Nonlinearity

Recent advances in neuroscience have linked dynamical systems theory to cognition. The main contention is that extended cognition relies on a unitary brain-body-tool system showing the expected signatures of interaction-dominance reflected in a multifractal behavior. This might be particularly relevant when it comes to understanding how the brain is able to embody a tool to perform a task. Here we applied the multifractal formalism to the dynamics of hand movement while one was performing a computer task (the herding task) using a mouse or its own hand as a tool to move an object on the screen. We applied a focus-based multifractal detrended fluctuation analysis to acceleration time series. Then, multifractal nonlinearity was assessed by comparing original series to a finite set of surrogates obtained after Iterated Amplitude Adjusted Fourier transformation, a method that removes nonlinear multiscale dependencies while preserving the linear structure of the time series. Both hand and mouse task execution demonstrated multifractal nonlinearity, a typical form of across-scales interactivity in cognitive control. In addition, a wider multifractal spectrum was observed in mouse condition, which might highlight a richer set of interactions when the cognitive system is extended to the embodied mouse. We conclude that the emergence of multifractal nonlinearity from a brain-body-tool system pleads for recent theories of radical tool embodiment. Multifractal nonlinearity may be a promising metric to appreciate how physical objects—but also virtual tools and potentially prosthetics—are efficiently embodied by the brain.

A network information theoretic framework to characterise muscle synergies in space and time

Objective Current approaches to muscle synergy extraction rely on linear dimensionality reduction algorithms that make specific assumptions on the underlying signals. However, to capture nonlinear time varying, large-scale but also muscle-specific interactions, a more generalised approach is required. Approach Here we developed a novel framework for muscle synergy extraction that relaxes model assumptions by using a combination of information- and network theory and dimensionality reduction. We first quantify informational dynamics between muscles, time-samples or muscle-time pairings using a novel mutual information formulation. We then model these pairwise interactions as multiplex networks and identify modules representing the network architecture. We employ this modularity criterion as the input parameter for dimensionality reduction, which verifiably extracts the identified modules, and also to characterise salient structures within each module. Main results This novel framework captures spatial, temporal and spatiotemporal interactions across two benchmark datasets of reaching movements, producing distinct spatial groupings and both tonic and phasic temporal patterns. Readily interpretable muscle synergies spanning multiple spatial and temporal scales were identified, demonstrating significant task dependence, ability to capture trial-to-trial fluctuations and concordance across participants. Furthermore, our framework identifies submodular structures that represent the distributed networks of co-occurring signal interactions across scales. Significance The capabilities of this framework are illustrated through the concomitant continuity with previous research and novelty of the insights gained. Several previous limitations are circumvented including the extraction of functionally meaningful and multiplexed pairwise muscle couplings under relaxed model assumptions. The extracted synergies provide a holistic view of the movement while important details of task performance are readily interpretable. The identified muscle groupings transcend biomechanical constraints and the temporal patterns reveal characteristics of fundamental motor control mechanisms. We conclude that this framework opens new opportunities for muscle synergy research and can constitute a bridge between existing models and recent network-theoretic endeavours.

Degeneracy in the nervous system: from neuronal excitability to neural coding

Degeneracy is ubiquitous across biological systems where structurally different elements can yield a similar outcome. Degeneracy is of particular interest in neuroscience too. On the one hand, degeneracy confers robustness to the nervous system and facilitates evolvability: Different elements provide a backup plan for the system in response to any perturbation or disturbance. On the other, a difficulty in the treatment of some neurological disorders such as chronic pain is explained in light of different elements all of which contribute to the pathological behavior of the system. Under these circumstances, targeting a specific element is ineffective because other elements can compensate for this modulation. Understanding degeneracy in the physiological context explains its beneficial role in the robustness of neural circuits. Likewise, understanding degeneracy in the pathological context opens new avenues of discovery to find more effective therapies.

Fast Hand Movements Unveil Multifractal Roots of Adaptation in the Visuomotor Cognitive System

Beyond apparent simplicity, visuomotor dexterity actually requires the coordination of multiple interactions across a complex system that links the brain, the body and the environment. Recent research suggests that a better understanding of how perceptive, cognitive and motor activities cohere to form executive control could be gained from multifractal formalisms applied to movement behavior. Rather than a central executive "talking" to encapsuled components, the multifractal intuition suggests that eye-hand coordination arises from multiplicative cascade dynamics across temporal scales of activity within the whole system, which is reflected in movement time series. Here we examined hand movements of sport students performing a visuomotor task in virtual reality (VR). The task involved hitting spatially arranged targets that lit up on a virtual board under critical time pressure. Three conditions were compared where the visual search field changed: whole board (Standard), half-board lower view field (LVF) and upper view field (UVF). Densely sampled (90 Hz) time series of hand motions captured by VR controllers were analyzed by a focus-based multifractal detrended fluctuation analysis (DFA). Multiplicative rather than additive interactions across temporal scales were evidenced by testing comparatively phase-randomized surrogates of experimental series, which confirmed nonlinear processes. As main results, it was demonstrated that: (i) the degree of multifractality in hand motion behavior was minimal in LVF, a familiar visual search field where subjects correlatively reached their best visuomotor response times (RTs); (ii) multifractality increased in the less familiar UVF, but interestingly only for the non-dominant hand; and (iii) multifractality increased further in Standard, for both hands indifferently; in Standard, the maximal expansion of the visual search field imposed the highest demand as evidenced by the worst visuomotor RTs. Our observations advocate for visuomotor dexterity best described by multiplicative cascades dynamics and a system-wide distributed control rather than a central executive. More importantly, multifractal metrics obtained from hand movements behavior, beyond the confines of the brain, offer a window on the fine organization of control architecture, with high sensitivity to hand-related control behavior under specific constraints. Appealing applications may be found in movement learning/rehabilitation, e.g., in hemineglect people, stroke patients, maturing children or athletes.

Entropy and Multifractal-Multiscale Indices of Heart Rate Time Series to Evaluate Intricate Cognitive-Autonomic Interactions

Recent research has clarified the existence of a networked system involving a cortical and subcortical circuitry regulating both cognition and cardiac autonomic control, which is dynamically organized as a function of cognitive demand. The main interactions span multiple temporal and spatial scales and are extensively governed by nonlinear processes. Hence, entropy and (multi)fractality in heart period time series are suitable to capture emergent behavior of the cognitive-autonomic network coordination. This study investigated how entropy and multifractal-multiscale analyses could depict specific cognitive-autonomic architectures reflected in the heart rate dynamics when students performed selective inhibition tasks. The participants (N=37) completed cognitive interference (Stroop color and word task), action cancellation (stop-signal) and action restraint (go/no-go) tasks, compared to watching a neutral movie as baseline. Entropy and fractal markers (respectively, the refined composite multiscale entropy and multifractal-multiscale detrended fluctuation analysis) outperformed other time-domain and frequency-domain markers of the heart rate variability in distinguishing cognitive tasks. Crucially, the entropy increased selectively during cognitive interference and the multifractality increased during action cancellation. An interpretative hypothesis is that cognitive interference elicited a greater richness in interactive processes that form the central autonomic network while action cancellation, which is achieved via biasing a sensorimotor network, could lead to a scale-specific heightening of multifractal behavior.

Non-orbital characterizations of strange attractors: Effective intervals and multifractality measures

Numerical simulations reveal statistical distributions given by power laws resulting from movements of large quantities of phase points captured by strange attractors immersed in one-dimensional or two-dimensional phase spaces, attractors linked to ten specific dynamic systems. Unlike the characterization given by classical approaches as generalized dimensions and spectrum of singularities, the aforementioned distributions do not have their origin in observations of successive orbits, as consequence properties that would otherwise remain hidden are revealed. Specifically, occupancy times and occupancy numbers associated with small hypercubes that cover attractors obey well-defined statistical distributions given by power laws. One application concerns the determination of the intervals in which the most likely values of those numbers and times are located (effective intervals). The use of the effective interval with occupancy numbers to quantify the multifractalities (multifractality measures) is another application. The statistical approaches underlying the results consist of new paradigms that join the well-known classic paradigms to expand knowledge about strange attractors. The possibility that other attractors immersed in spaces with the same dimensions as those considered here exhibit analogous distributions is not ruled out due to the arbitrariness of the set taken.

Physiological Resonance in Empathic Stress: Insights from Nonlinear Dynamics of Heart Rate Variability

Because most humans live and work in populated environments, researchers recently took into account that people may not only experience first-hand stress, but also second-hand stress related to the ability to empathically share another person’s stress response. Recently, researchers have begun to more closely examine the existence of such empathic stress and highlighted the human propensity to physiologically resonate with the stress responses of others. As in case of first-hand stress, empathic stress could be deleterious for health if people experience exacerbated activation of hypothalamic–pituitary–adrenal and autonomic nervous systems. Thus, exploring empathic stress in an observer watching someone else experiencing stress is critical to gain a better understanding of physiological resonance and conduct strategies for health prevention. In the current study, we investigated the influence of empathic stress responses on heart rate variability (HRV) with a specific focus on nonlinear dynamics. Classic and nonlinear markers of HRV time series were computed in both targets and observers during a modified Trier social stress test (TSST). We capitalized on multiscale entropy, a reliable marker of complexity for depicting neurovisceral interactions (brain-to-heart and heart-to-brain) and their role in physiological resonance. State anxiety and affect were evaluated as well. While classic markers of HRV were not impacted by empathic stress, we showed that the complexity marker reflected the existence of empathic stress in observers. More specifically, a linear model highlighted a physiological resonance phenomenon. We conclude on the relevance of entropy in HRV dynamics, as a marker of complexity in neurovisceral interactions reflecting physiological resonance in empathic stress.

Effects of Motor Tempo on Frontal Brain Activity: An fNIRS Study

People are able to modify the spontaneous pace of their actions to interact with their environment and others. This ability is underpinned by high-level cognitive functions but little is known in regard to the brain areas that underlie such temporal control. A salient practical issue is that current neuroimaging techniques (e.g., EEG, fMRI) are extremely sensitive to movement, which renders challenging any investigation of brain activity in the realm of whole-body motor paradigms. Within the last decade, the noninvasive imaging method of functional near-infrared spectroscopy (fNIRS) has become the reference tool for experimental motor paradigms due to its tolerance to motion artefacts. In the present study, we used a continuous-wave fNIRS system to record the prefrontal and motor hemodynamic responses of 16 participants, while they performed a spatial-tapping task varying in motor complexity and externally-paced tempi (i.e., 300 ms, 500 ms, 1200 ms). To discriminate between physiological noise and cerebral meaningful signals, the physiological data (i.e., heart and respiratory rates) were recorded so that frequency bands of such signals could be regressed from the fNIRS data. Particular attention was taken to control the precise position of the optodes in reference to the cranio-cerebral correlates of the NIR channels throughout the experimental session. Results indicated that fast pacing relied on greater activity of the motor areas whereas moving at close-to-spontaneous pace placed a heavier load on posterior prefrontal processes. These results provide new insight concerning the role of frontal cognitive control in modulating the pacing of voluntary motor behaviors.

Cerebral hemodynamics predicts the cortical area and coding scheme in the human brain for force generation by wrist muscles

The goal of this study is to identify the cortical area maximally active over the primary sensorimotor cortex (SM1) and characterize the cortical encoding for force production by wrist muscles in the human brain. The technique of functional near-infrared spectroscopy (fNIRS) was used to continuously monitor the changes in hemoglobin concentrations from the left hemisphere during isometric contractions of wrist flexion muscles over a broad range of load forces (0 ∼ 8 kgf) on the right hand. As previously shown in primate studies, this action produced hemodynamic activity predominantly in the wrist area localized dorsally to the finger region over SM1 and the hemodynamic response was systematically related to the level of load intensity. The coding scheme for force production in terms of hemodynamic signals was characterized defining eight trajectory parameters (four for amplitude coding and four for temporal coding) and analyzed for the area maximally activated over SM1. The trajectory parameter representing the oxygenated hemoglobin concentration change at the end of motor task (amplitude coding) and the timing of maximum change in oxygenated hemoglobin concentration (temporal coding) was most strongly correlated with the load variation in a superliner manner. All these results indicate the applicability of fNIRS to monitor and decode cortical activity that is correlated with low-level motor control such as isometric muscle contractions. This study may provide not only insights into cortical neural control of muscle force but also predictors of muscle force in clinical diagnostics and neural interfaces for the human brain.

Neurovascular Coupling by Functional Near Infra-Red Spectroscopy and Sport-Related Concussion in Retired Rugby Players: The UK Rugby Health Project

Aim: This study investigated cerebral hemodynamic responses to a neurovascular coupling (NVC) test in retired contact athletes with a history of repeated mild traumatic brain injury (mTBI) and in controls with no history of mTBI. Methods: Twenty-one retired rugby players (47.7 ± 12.9 year old; age at retirement: 38.5 ± 8.9 year; number of years playing rugby: 12.7 ± 3.7 year) with a history of three or more diagnosed concussions (8.9 ± 7.9 concussions per player) and 23 controls with no history of mTBI (46.5 ± 12.8 year old) performed a NVC test to detect task-orientated cerebral hemodynamic changes using functional near-infrared spectroscopy (fNIRS). Results: The NVC showed a statistically significant reduction in the cerebral hemodynamic response in comparison to the control group which had a greater relative increase of oxyhemoglobin (O2Hb). There were reductions in left middle frontal gyrus (MFG) O2Hb (-0.015 ± 0.258 μM) and relative increases in deoxyhemoglobin (HHb; -0.004 ± 0.159 μM) in the same region for the mTBI group in comparison to the control group (-0.160 ± 0.311 μM; -0.121 ± 0.076 μM for O2Hb and HHb, respectively). The mTBI group induced a greater rate of oxygen extraction compared to the control group. Conclusion: This was the first study to examine cerebral hemodynamic changes in retired rugby players in response to a NVC test, and we found reduced cerebral hemodynamic responses in participants with a history of mTBI compared to controls. These results suggest altered cerebral metabolic demands in participants with a history of multiple head injuries. Further research is needed to ascertain an understanding of the changes in hemodynamics from playing into retirement.

Fractal properties in sensorimotor variability unveil internal adaptations of the organism before symptomatic functional decline

If health can be defined as adaptability, then measures of adaptability are crucial. Convergent findings across clinical areas established the notion that fractal properties in bio-behavioural variability characterize the healthy condition of the organism, and its adaptive capacities in general. However, ambiguities remain as to the significance of fractal properties: the literature mainly discriminated between healthy vs. pathological states, thereby loosing perspective on the progression in between, and overlooking the distinction between adaptability and effective adaptations of the organism. Here, we design an experimental tapping paradigm involving gradual feedback deprivation in groups of healthy subjects and one deafferented man as a pathological-limit case. We show that distinct types of fractal properties in sensorimotor behaviour characterize, on the one hand impaired functional ability, and on the other hand internal adaptations for maintaining performance despite the imposed constraints. Findings may prove promising for early detection of internal adaptations preceding symptomatic functional decline.

A Newcomer's Guide to Functional Near Infrared Spectroscopy Experiments

This review presents a practical primer for functional near-infrared spectroscopy (fNIRS) with respect to technology, experimentation, and analysis software. Its purpose is to jump-start interested practitioners considering utilizing a non-invasive, versatile, nevertheless challenging window into the brain using optical methods. We briefly recapitulate relevant anatomical and optical foundations and give a short historical overview. We describe competing types of illumination (trans-illumination, reflectance, and differential reflectance) and data collection methods (continuous wave, time domain and frequency domain). Basic components (light sources, detection, and recording components) of fNIRS systems are presented. Advantages and limitations of fNIRS techniques are offered, followed by a list of very practical recommendations for its use. A variety of experimental and clinical studies with fNIRS are sampled, shedding light on many brain-related ailments. Finally, we describe and discuss a number of freely available analysis and presentation packages suited for data analysis. In conclusion, we recommend fNIRS due to its ever-growing body of clinical applications, state-of-the-art neuroimaging technique and manageable hardware requirements. It can be safely concluded that fNIRS adds a new arrow to the quiver of neuro-medical examinations due to both its great versatility and limited costs.