Convergent models of handedness and brain lateralization

Abstract

The pervasive nature of handedness across human history and cultures is a salient consequence of brain lateralization. This paper presents evidence that provides a structure for understanding the motor control processes that give rise to handedness. According to the Dynamic Dominance Model, the left hemisphere (in right handers) is proficient for processes that predict the effects of body and environmental dynamics, while the right hemisphere is proficient at impedance control processes that can minimize potential errors when faced with unexpected mechanical conditions, and can achieve accurate steady-state positions. This model can be viewed as a motor component for the paradigm of brain lateralization that has been proposed by Rogers et al. (MacNeilage et al., 2009) that is based upon evidence from a wide range of behaviors across many vertebrate species. Rogers proposed a left-hemisphere specialization for well-established patterns of behavior performed in familiar environmental conditions, and a right hemisphere specialization for responding to unforeseen environmental events. The dynamic dominance hypothesis provides a framework for understanding the biology of motor lateralization that is consistent with Roger's paradigm of brain lateralization.

Keywords: brain lateralization; handedness; laterality; manual asymmetry; motor activity; motor lateralization; motor neurons.

Figure 1

(A) Experimental Setup. Subjects held a robotic manipulandum while reaching to targets to the left and right of midline. (B) Perpendicular errors during the course of the session in which subjects experienced the consistent field. (C) Perpendicular errors during the course of the session in which subjects experienced the inconsistent (noisy) field (from Takahashi et al., 2001).

Figure 2

Experimental set-up: Subjects sat facing a table with their arm supported in an air-sled over the horizontal surface by an air-jet system. An LCD screen was positioned above the mirror, which reflected a 2-D virtual reality environment, in which a start position and target were presented (from Yadav and Sainburg, 2014).

Figure 3

(A) Shoulder, elbow, and hand trajectories from typical left (non-dominant) and right (dominant) hand arm movements toward a medial target. (B–D) Mean's ± SE for Deviation from Linearity (B), Maximum Velocity (C), and Distance Errors (D) for all movements across all subjects. (E) Elbow joint torque profiles include muscle torque, interaction torque, and net torque. (F) Group mean's ± SE for integrated flexor (positive) and extensor (negative) elbow and shoulder joint muscle torques (from Bagesteiro and Sainburg, 2002).

Figure 4

Experimental set-up and averaged trajectories for turn and reach movements made with the left (right side of workspace) and right (left side of workspace) hands of right-handers under both speed (slow, fast) and weight (weight, no weight) conditions. Ellipses depict 95% confidence intervals for end point distributions under each condition (from Pigeon et al., 2013).

Figure 5

Groups means for trajectories (normalized and averaged across subjects) made toward targets under baseline (black) and from displaced initial positions, for dominant (red) and non-dominant (blue) arms. Bar plots (right) show mean ± SE for direction error, measured as the difference in the direction of displaced and baseline movements, measured at final position (from Mutha et al., 2013).

Figure 6

Simulated trajectories for different switch times between predictive and impedance controller. Dashed line shows pure optimal predictive controller. Early switch times (Left) are controlled almost entirely by the impedance control algorithm, while late switch times (Right) are almost entirely controlled through optimal predictive control (form Yadav and Sainburg, 2014).

Figure 7

Force field structure is shown at right: fields were generated perpendicular to the direction of the target, and varied with either the square of velocity (predictable field) or linearly with velocity (unpredictable). Group mean ± SE for mean squared jerk (Left) and movement duration (Right) are shown across all 180 movements (18 cycles). Baseline performance is shown at the left of each plot (form Yadav and Sainburg, 2014).

Figure 8

Lesion locations were traced on 11 axial slices (see insert for slice level) from MRI or CT scans for each LHD (1–7) and RHD (1–7) patient. Slices are displayed left-to-right from inferior to superior (i–xi) for both groups of patients. Arrows in top row indicate location of central sulcus (from Schaefer et al., 2009a).

Figure 9

(A) Sample positional variation plots, with 95% confidence intervals, shown by ellipse, for an example LHD patient and RHD patient. (B) Group data shows ratio of positional variation at peak velocity divided by positional variation at the end of movement for LHD, RHD, left hemisphere control group (LHC), and right hemisphere control group (RHC). (C) Mean ± SE for peak velocity and (D) absolute final position error for all four groups (LHD, RHD, LHC, RHC) for movements to each target (from Schaefer et al., 2009a).