University of Groningen Neural control of balance in increasingly difficult standing tasks Nandi, Tulika

University of Groningen

Neural control of balance in increasingly difficult standing tasks

Nandi, Tulika

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Nandi, T. (2019). Neural control of balance in increasingly difficult standing tasks. University of Groningen.

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Chapter 1

As early as the 1880s, it was known that standing balance is altered by mechanical or sen-sory manipulations [1,2], and by the 1920s, it was regarded as a window into neuromuscu-lar control [3]. Clinicians and researchers often increase task difficulty by manipulating the base of support or visual input in order to detect age or pathology related balance deterio-ration, which is not apparent in quiet standing [4–6]. Indeed, when standing task difficulty increases, greater neural input is required to tune lower extremity muscle activation [7–13]. However, much is yet to be learnt about the brain areas and neurophysiological processes which underlie this increase in neural input, and how the input to multiple muscles is coordinated. Additionally, the neural basis for individual differences in balance control, driven by attributes like confidence, has not been examined. Greater insight into the neural control of increasingly difficult standing tasks in healthy young adults will contribute to a better understanding of the strategies used for managing balance deterioration induced by aging and/or pathology.

1. NeurAl CoNTrol of sTANdiNg bAlANCe: CorTiCAl iNPuTs

To musCles

Several brain areas contribute to the neural control of standing balance. During imagined standing, neuroimaging revealed activation in the cerebellum, basal ganglia, prefrontal cortex, premotor cortex, brainstem and thalamus [14–16]. However, the lack of actual muscle contraction is a major drawback of examining imagined standing and may explain why little or no activation is observed in the motor cortical areas, which are the focus of this thesis. While early animal studies suggested that balance is maintained using subcorti-cal reflexes [17,18], now there is ample evidence that descending inputs from the cortex are also important for maintaining balance. For example, electroencephalogram recordings during unconstrained standing revealed cortical activation in the fronto-central motor areas [10,11,19].

When task difficulty increases, it is essential to have more versatile muscle activation patterns to meet both expected and unexpected biomechanical demands. Therefore, the reliance on cortical descending commands which support flexible movement patterns, compared to stereotypical subcortical reflexes [13,20], is expected to increase when task difficulty increases. Specifically, it is hypothesized that when manipulations like base of support modification increase task difficulty, greater cortical inputs will be required to modulate or supplement the subcortical inputs to muscles. This thesis focuses on neural inputs to lower extremity muscles.

12 Chapter 1

2. effeCT of TAsk diffiCulTy oN siNgle musCle NeurAl

exCiTAbiliTy

When standing task difficulty increases, corticospinal excitability increases [7–9,12,13] and it is likely that both cortical and subcortical excitability changes contribute to this increase. Within the motor cortex, inhibition and facilitation, which are mediated by GABAa/ GABAb and glutamate respectively [21,22], must be carefully balanced to ensure optimal task specific corticospinal excitability. Indeed, the ratio between inhibition and facilitation is highly dynamic over time and is essential for determining whether cortical pyramidal neurons achieve action potential [22]. Therefore, both processes must be examined to make comprehensive inferences about cortical descending commands during standing. In standing, an increase in corticospinal excitability is accompanied by a decrease in GABAa inhibition [7,8] but GABAb inhibition has not been examined. Since GABAb is modulated independent of GABAa inhibition during upper extremity tasks [23], it is likely that they play different roles during motor control. GABAb receptor activation leads to more prolonged inhibition compared to GABAa, and GABAb activity may also influence glutamatergic neurons [22]. Therefore, it is important to examine both with respect to standing balance control. Additionally, the direction of change in glutamatergic intracortical facilitation in response to an increase in balance task difficulty remains uncertain given the equivocal findings from previous studies [8,24,25].

Also, little is known about the biomechanical correlates, and consequently the functional significance of cortical inhibition and facilitation for standing balance control. Previous studies employing anteroposterior (AP) or direction unspecific difficulty manipulations have found no association between cortical excitability and sway amplitude or velocity in young adults. As noted in section 1.1, the cortical contribution to balance control is expected to increase when task difficulty increases. Due to anatomical and biomechanical constraints, mediolateral (ML) control is inherently more complex than AP control [26], and indeed greater cortical activation is observed during ML compared to AP sway [27]. However, it is not known whether specific cortical neurophysiological processes, like inhibition and facilitation, are more closely associated with ML compared to AP sway dynamics.

3. effeCT of TAsk diffiCulTy oN CommoN NeurAl iNPuT To

mulTiPle musCles

In standing, balance is maintained by simultaneously activating multiple muscles in task specific combinations described as functional synergies [28,29]. Therefore, in addition to neural inputs to single muscles, it is important to examine the neural commands that

co-ordinate multiple muscles comprising a synergy. Such synergies may comprise agonists and antagonists which are co-activated to stiffen the limb. Alternatively, synergies may support reciprocal control where several agonists are co-activated to increase torque production but are not co-activated with antagonists. Such co-activation, whether supporting stiffness or reciprocal control, can be driven by common neural inputs to groups of muscles [30–33]. Reciprocal control allows for greater flexibility of responses to perturbations [34,35], and is indeed favored in functional synergies observed during standing [28,29]. However, it is not known whether common neural inputs favor stiffness or reciprocal control. Also, emerging evidence suggests that motor cortical areas contribute to common inputs in relatively difficult tasks [36,37], but the effect of task difficulty on such cortical common inputs has not been systematically examined. Additionally, it is suspected that the specific muscles pairs or groups, which receive common inputs in each task are determined by the biomechanical demands and configuration of each task.

4. iNdividuAl differeNCes iN NeurAl CoNTrol of sTANdiNg

bAlANCe

Even within a group of healthy young adults, there is a large variation in balance ca-pabilities[38] and likely in the underlying neural control strategies. One factor that can contribute to such individual differences is cognitive attributes like confidence and indeed, the association between confidence and balance capability is well documented [38–41]. However, to the best of our knowledge, the neural processes mediating the effect of confidence on muscle activation and postural sway, have not been examined.

Additionally, neural excitability, measured using transcranial magnetic stimulation (TMS), differs widely between individuals [42]. The recently introduced idea of intrinsic neural excitability [43,44] suggests that such differences are driven by factors like neurotransmit-ter concentration, synaptic strength etc. It is possible that such physiological differences are driven by individual attributes like previous motor experiences, which can induce neu-roplasticity in a manner similar to balance training [45–48]. Behaviorally, the reaction to in-creasing task difficulty is influenced by the amount of previous experience an individual has with motor tasks that challenge balance. Therefore, we suspect that differences in intrinsic excitability can influence the neural response to increasing task difficulty in standing. In this thesis we use excitability measured in a control task as an index of intrinsic excitability and examine how this intrinsic excitability influences the neural response to increasing task difficulty. Our findings were strengthened by reliability analyses which demonstrated that within individuals, excitability in both the control and more difficult tasks remains stable across days.

14 Chapter 1

5. ComPlemeNTAry APProAChes To sTudyiNg sTANdiNg

bAlANCe CoNTrol

In this thesis cortical inputs to individual muscles are examined using transcranial magnetic stimulation (TMS), while inputs that coordinate multiple muscles are deduced using EMG-EMG coherence. These approaches complement each other by providing different types of information about the firing of spinal alpha motor neurons which finally drive muscle activation. The motor evoked potential (MEP) measured using TMS and coherence in the 6-15 Hz frequency range cannot distinguish between cortical and subcortical inputs to alpha motor neurons. However, outcomes described in the next paragraph allow us to examine the cortical contribution to lower extremity muscle activation in standing. Descending inputs from the primary motor cortex (M1) to alpha motor neurons can be measured using TMS. Specifically, the excitability of different M1 interneurons which use the various neurotransmitters described in section 1.2 can be measured. Short interval intracortical inhibition and long interval intracortical inhibition are indices of GABAa and GABAb activity, respectively [49]. Intracortical facilitation measures glutamate activity but may also be affected by other neurotransmitters [49]. On the other hand, coherence analysis allows us to examine commonalities in the firing pattern of alpha motor neurons targeting different muscles. Specifically, cortical inputs to alpha motor neurons drive the coherence between muscles in the higher 16-40 Hz range while coherence in the 0-5 Hz is driven by subcortical circuits [50,51]. In chapter 6 (sections 6.4 and 6.5) it is further discussed how this combination of techniques allowed us to make comprehensive infer-ences which could not be drawn from either technique individually.

6. Thesis Aims ANd ouTliNe

The different aspects of standing balance control examined in this thesis are depicted in Figure 1. The main objective was to examine the effect of task difficulty on the neural control of lower extremity muscles, with a focus on cortical inputs to muscles. In chapters 2 and 3, we used transcranial magnetic stimulation (TMS) to examine the neural inputs to single muscles and in chapter 4 we used EMG-EMG coherence to examine coordination of neural inputs to groups of muscles. With regards, to single muscles, the aim was to examine how M1 inhibitory and facilitatory processes contribute to the overall increase in corticospinal excitability, and to determine whether M1 excitability contributes to ML sway control. With regards to multiple lower extremity muscles, the aim was to deter-mine whether cortical common inputs support functional synergies favoring reciprocal or stiffness control. Additionally, we discuss how common inputs are organized to meet the

demands imposed by the biomechanical configuration of each task. Finally, we probed the neural basis for individual differences in neural control of standing balance using two approaches. In chapter 5, we examined how self-reported confidence influences the neural response to increasing task difficulty. Additionally, supplementary analysis of the data in chapter 3 provides preliminary clues about how intrinsic neural excitability influences the response to increasing task difficulty.

16 Chapter 1

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