Neuromuscular Adaptations to Work-matched Maximal Eccentric versus Concentric Training

Abstract

It is unclear whether the superiority of eccentric over concentric training on neuromuscular improvements is due to higher torque (mechanical loading) achievable during eccentric contractions or due to resulting greater total work.

Purpose: This study aimed to examine neuromuscular adaptations after maximal eccentric versus concentric training matched for total work.

Methods: Twelve males conducted single-joint isokinetic (180°·s) maximal eccentric contractions of the knee extensors in one leg (ECC-leg) and concentric in the other (CON-leg), 6 sets per session (3-5 sets in the initial 1-3 sessions), 2 sessions per week for 10 wk. The preceding leg performed 10 repetitions per set. The following leg conducted the equivalent volume of work. In addition to peak torque during training, agonist EMG and MRI-based anatomical cross-sectional area (ACSA) and transverse relaxation time (T2) at midthigh as reflective of neural drive, hypertrophy, and edema, respectively, were assessed weekly throughout the training period and pre- and posttraining. Whole muscle volume was also measured pre- and posttraining.

Results: Torque and EMG (in trained contraction conditions) significantly increased in both legs after week 1 (W1) and week 4 (W4), respectively, with a greater degree for ECC-leg (torque +76%, EMG +73%: posttraining) than CON-leg (+28%, +20%). ACSA significantly increased after W4 in ECC-leg only (+4%: posttraining), without T2 changes throughout. Muscle volume also increased in ECC-leg only (+4%). Multiple regression analysis revealed that changes (%Δ) in EMG solely explained 53%-80% and 30%-56% of the total variance in %Δtorque through training in ECC-leg and CON-leg, respectively, with small contributions (+13%-18%) of %ΔACSA for both legs.

Conclusion: Eccentric training induces greater neuromuscular changes than concentric training even when matched for total work, whereas most of the strength gains during 10-wk training are attributable to the increased neural drive.

FIGURE 1

Experimental design. Arrows indicate measurement timing of each variable. Reps, repetitions; M-max, maximal muscle compound action potential; Nerve Stim, peripheral (femoral) nerve stimulation; T₁, longitudinal relaxation time; T₂, transverse relaxation time; W, week.

FIGURE 2

Example data of torque, joint angle, and EMG during concentric (left) and eccentric (right) contractions pretraining (top) and posttraining (bottom). CON-leg, concentrically trained leg; ECC-leg, eccentrically trained leg.

FIGURE 3

Work (A) and repetition (B) through training. Values are presented as mean ± SD. Note that the values are reported weekly (the sum of two sessions). *Significant difference between legs at P < 0.05. CON-leg, concentrically trained leg; ECC-leg, eccentrically trained leg.

FIGURE 4

Absolute changes in peak torque (A), agonist EMG amplitude (AGO-EMG, B), ACSA (C), and transverse relaxation time (T₂, D). Values are presented mean ± SD. The numbers 0, 1, 2, and 3 indicate a significant increase from pretraining (0), week 1, 2, 3, respectively. *Significant difference between legs at P < 0.05. CON-leg, concentrically trained leg; ECC-leg, eccentrically trained leg.

FIGURE 5

Absolute and percentage (%Δ) changes in agonist EMG amplitude normalized to maximal muscle compound action potential (AGO-EMG/M-max, A and B) and those of muscle volume (C and D) pre- and posttraining. Values are presented as mean ± SD. The number 0 indicates a significant increase from pretraining (0). *Significant difference between legs at P < 0.05. CON-leg, concentrically trained leg; ECC-leg, eccentrically trained leg.

FIGURE 6

Determinants of changes in torque throughout the training period for eccentrically trained leg (ECC-leg, A) and concentrically trained leg (CON-leg, B). Predictor variables that independently explained a significant proportion of the total variance assessed with stepwise multiple linear regressions are shown. No data at some time points for CON-leg indicate that multiple regression analysis did not find any significant predictor variables at these time points.