Protocol to determine the aging condition

The best aging condition that describes the older cohorts of this study was determined in two steps:

(i) The Pearson Correlation was computed between two calculated differences with increasing MVC. The first difference is the experimental age-related change in the higher order statistics: ΔSg(experiment), ΔSl(experiment). The second difference is in the higher order statistics calculated for the simulated aging conditions, A and D, at 3 different losses of motor units from the Young Models Sg and Sl values described in section 7.2.4.1 and

102

illustrated in Figure C.1 and C.2 (Appendix C). A total of 6 Pearson’s correlation coefficients were computed for each higher order statistic (detailed in Table 7.4).

Only aging conditions with significant positive correlations were selected. The percentage loss in motor units and the presence/absence of altered recruitment pattern (Condition D/A) in the older cohorts was determined by selecting the strongest correlation for Sg and Sl. If there was a disparity between the selected condition from the Sg and Sl statistics, preference was given to the condition selected by Sg as its relation to number of motor units is well established in literature (Kaplanis, Pattichis et al. 2009; Zhao and Li 2012; Siddiqi, Poosapadi et al. 2015).

Table 7.4 The Pearsons Correlation Coefficient computed between the experimental age-

related change in higher order statistics, and difference calculated for the simulated aging conditions A and D at different losses of motor units.

Experimental Age- related Difference in higher order statistics

Simulated Aging Conditions Difference from the Young Models’ estimated in the higher order statistics

Pearsons Correlation Coefficients

ΔSg(experiment) ΔSg(40%Motor Unit Loss,Condition A) ρ1,1

ΔSg(50%Motor Unit Loss,Condition A) ρ1,2 ΔSg(60%Motor Unit Loss,Condition A) ρ1,3 ΔSg(40%Motor Unit Loss,Condition D) ρ1,4 ΔSg(50%Motor Unit Loss,Condition D) ρ1,5 ΔSg(60%Motor Unit Loss,Condition D) ρ1,6

ΔSl(experiment) ΔSl(40%Motor Unit Loss,Condition A) ρ2,1

ΔSl(50%Motor Unit Loss,Condition A) ρ2,2 ΔSl(60%Motor Unit Loss,Condition A) ρ2,3 ΔSl(40%Motor Unit Loss,Condition D) ρ2,4 ΔSl(50%Motor Unit Loss,Condition D) ρ2,5 ΔSl(60%Motor Unit Loss,Condition D) ρ2,6

103

(ii) After narrowing the specific loss in motor units, a Pearson correlation was calculated between (a) the experimental age-related change in maximal power of the PSD (ΔPM(experiment)), and (b) the difference in maximal power calculated for all of the aging conditions (A-F) at that number of motor units (Table 7.5).This is shown in Figure C.3 (Appendix C). A total of 6 Pearson’s correlation coefficient was computed. The aging condition that had the strongest significant positive correlation was selected.

Table 7.5 The Pearsons Correlation Coefficient computed between the experimental age-

related change in maximal power of the PSD, and difference calculated for the simulated aging conditions A to F at estimated percentage loss of motor units.

Experimental Age- related Difference in maximal power of the PSD

Simulated Aging Conditions Difference from the Young Models’ estimated in the maximal power of the PSD at the specified motor unit loss. Pearsons Correlation Coefficients (ΔPM(experiment)) ΔPM(Condition A) ρ3,1 ΔPM(Condition B) ρ3,2 ΔPM(Condition C) ρ3,3 ΔPM(Condition D) ρ3,4 ΔPM(Condition E) ρ3,5 ΔPM(Condition F) ρ3,6

104

7.5 Results

The results are represented as bar graphs of the Pearson’s correlation coefficient computed between the simulated and experimental age-associated differences for the various aging conditions. These are detailed in Table 7.4 and Table 7.5

Figure 7.2 shows the correlation coefficients computed between the experimental and

simulated age-related difference in the higher order statistics: (a) Sg and (b) Sl for aging conditions A and D, at 3 different motor unit losses. The higher order statistics were used to distinguish between the losses of motor units and to determine if the recruitment pattern was altered. The results show a significant correlation for the aging condition A with 216 remaining motor units, corresponding to a 40% loss of motor units.

Figure 7.3 shows the significant correlation coefficient computed between the experimental

and simulated age-related difference in the maximal power of the PSD. This feature was used to distinguish any further muscular changes that may have occurred with age. The results show only condition B corresponding to decreased fast-slow fibres, with 216 remaining motor units had a significant correlation.

105

Figure 7.2 The pearsons correlation coefficients computed between experimental and

simulated sEMG differences in a) Gaussianity Test Statistic Sg and (b) Linearity Test Statistic Sl. The experimental difference was the age-associated change in the experimental sEMG’s higher order statistics with MVC. The simulated change was between the Young Models and the simulated sEMGs higher order statistics for two aging conditions, A- motor unit loss, and D- altered recruitment pattern, at three motor unit losses. (** significant correlation coefficient p < 0.05.

106

Figure 7.4 shows the change in experimental agonist dorsiflexion force with age, and the

change in simulated force due to the different age-altered properties and their combined effect. The loss in simulated force was greatest due to loss of fast-fibres, but the combined effect of loss of motor units and decreased fast-fibres is less. This is because in the absence of loss in total number of muscle fibres, the decreased number of motor units reflected increased innervation ratio. The increased size of the motor units could have moderated the effect of loss of fast fibres. The experimental age-related change in force was not significant (p = 0.1362).

Figure 7.3 The pearsons correlation coefficients computed between experimental and

simulated sEMG differences in maximal power of the PSD. The experimental difference was the age-associated change in the experimental sEMG’s maximal power with MVC. The simulated change was between the Young Models and the simulated sEMGs maximal power for six aging conditions, A-F, at a 40% loss in motor units. (** significant correlation coefficient p < 0.05.

107

Figure 7.4 The percentage change in experimental and simulated force due to the combined

and individual age-altered change in motor units and fast fibres

7.6 Discussion

The results show that the higher-order statistics were able to distinguish between differing losses of motor units simulated, while the power spectrum further discriminated changes in the musculature. A 40% loss of motor units with halved the number of fast fibres best represented the age-related change in the experimental sEMG features. This change corresponded to an 8% decrease in simulated force. The ability of the sEMG features to distinguish between aging conditions and the effect of altered neuromuscular properties on strength decline is discussed.