Sensory Input and Control of Grip

Johansson, Roland S.

doi:10.1002/9780470515563.ch4

Cited by 109 publications

(83 citation statements)

References 34 publications

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“…Johansson and colleagues initiated the examination of fingertip force coordination during object manipulation [see Johansson (1998) for review]. Their results have been used to study sensorimotor integration in healthy individuals, and their model has been found to be sensitive in detecting impairment in fine motor control in various patient populations (Fellows et al 1998;Gibbs et al 2008;Gordon et al 1997Gordon et al , 1999Schwarz et al 2001).…”

Section: Discussionmentioning

confidence: 99%

“…The feedforward control of fingertip forces during manipulation of objects of up to three different weights has been studied extensively by using a custom-made grip device instrumented with three-dimensional force sensors (Johansson 1998). Feedforward control is measured by the slope or the rate of change of the vertical (load) force (referred to as load force rate hereafter) before the object is lifted off the table.…”

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confidence: 99%

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Quantifying feedforward control: a linear scaling model for fingertip forces and object weight

Lü

Bilaloglu

Aluru

et al. 2015

Journal of Neurophysiology

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Quantifying feedforward control: a linear scaling model for fingertip forces and object weight

Lü

Bilaloglu

Aluru

et al. 2015

Journal of Neurophysiology

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“…Because of the repetitive similar force cycles during predictable force, a sensorimotor memory trace can be built up and the force output can be anticipated. The sensorimotor memory is used in advance to specify the appropriate motor commands (Johansson 1998;Macefield et al 1996;Schmitz et al 2005). In parallel, an efference copy of the motor command is generated and the sensory consequence of the motor action are predicted (Von Holst and Mittelstaedt 1950;Wolpert et al 1995).…”

Section: Anticipation During Predictable Forcesmentioning

confidence: 99%

The strength of the corticospinal coherence depends on the predictability of modulated isometric forces

Méndez-Balbuena

Naranjo

Wang

et al. 2013

Journal of Neurophysiology

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The strength of the corticospinal coherence depends on the predictability of modulated isometric forces. J Neurophysiol 109: 1579 -1588, 2013. First published December 19, 2012 doi:10.1152/jn.00187.2012.-Isometric compensation of predictably frequency-modulated low forces is associated with corticomuscular coherence (CMC) in beta and low gamma range. It remains unclear how the CMC is influenced by unpredictably modulated forces, which create a mismatch between expected and actual sensory feedback. We recorded electroencephalography from the contralateral hand motor area, electromyography (EMG), and the motor performance of 16 subjects during a visuomotor task in which they had to isometrically compensate target forces at 8% of the maximum voluntary contraction with their right index finger. The modulated forces were presented with predictable or unpredictable frequencies. We calculated the CMC, the cortical motor alpha-, beta-, and gamma-range spectral powers (SP), and the taskrelated desynchronization (TRD), as well as the EMG SP and the performance. We found that in the unpredictable condition the CMC was significantly lower and associated with lower cortical motor SP, stronger TRD, higher EMG SP, and worse performance. The findings suggest that due to the mismatch between predicted and actual sensory feedback leading to higher computational load and less stationary motor state, the unpredictable modulation of the force leads to a decrease in corticospinal synchrony, an increase in cortical and muscle activation, and a worse performance. coherence; human; predictable; oscillations; unpredictable THE BETA AND GAMMA OSCILLATIONS over the sensorimotor cortex are known to synchronize with oscillations in the contralateral motoneuronal pool that can be computed by coherence. Previous primate and human studies showed that beta-range corticomuscular coherence (CMC) is mainly associated with isometric compensation of steady-state forces (Baker and Baker 2003; Baker et al. 1997Baker et al. , 2006 Bressler 2009; Brown 2000; Cheyne et al. 2008;Conway et al. 1995; Engel and Fries 2010; Feige et al. 2000;Gross et al. 2000;Halliday et al. 1998;Houweling et al. 2010;Kristeva-Feige et al. 1993;Murthy and Fetz 1992, 1996a, 1996bPerez et al. 2006;Riddle and Baker 2006;Salenius et al. 1997;Sanes and Donoghue 1993;Tecchio et al. 2006;Witham et al. 2010). The CMC is most likely mediated by monosynaptic connections to the motoneurons (Baker et al. 2003;Conway et al. 1995). However, a large body of evidence was accumulated indicating that the CMC also represents sensory feedback from the moving part of the body (Baker et al. 2006). We have reported that the beta-range CMC is not specific for static forces only. The sensorimotor system may resort to stronger and also broader beta-range CMC to generate stable corticospinal interaction at higher force levels than 4% maximum voluntary contraction (MVC) when compensating for dynamic predictable frequency-modulated forces (from 8 to 16 and to 24% MVC) (Chakarov et al. 2009). Interesti...

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“…Tactile sensors in the glabrous skin are known to play a crucial role for grasp stability (Johansson, 1998). Moreover, they are responsible for initiating adaptive behaviors when mechanical perturbances jeopardize grasp stability in a restrain task whereas muscle receptors, despite significant joint movements, play no role in this respect .…”

Section: Introductionmentioning

confidence: 99%

Discharges in Human Muscle Receptor Afferents during Block Grasping

Dimitriou¹,

Edin²

2008

J. Neurosci.

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Human grasping relies on feedforward control that is monitored and corrected on-line by means of sensory feedback. While much of the sensory mechanisms underpinning hand-object interaction are known, information has been lacking about muscle receptor responses during the phases before and after actual object contact. We therefore let subjects use their thumb and fingers to grasp blocks presented to them while we recorded muscle afferents from the thumb and finger extensor muscles along with wrist and digit kinematics, and electromyographic activity. The kinematics of the task was indistinguishable from "normal" grasping. None of the afferents encoded either object contact or finger apposition. Both primary and secondary afferents were more phase advanced on the parent muscle lengths than expected from previous studies as well as from their responses to imposed length changes of their parent muscles. Thus, the discharges of both primary and secondary afferents were well correlated to the tendon velocity of their parent muscles and that of primary afferents also to acceleration whereas neither appeared to encode muscle length as such. Decoding the velocity of muscle length changes were significantly improved if the discharge of Golgi tendon organ afferents were taken into account along with that of the muscle spindle afferents. We propose that these findings may be explained by the biomechanical properties of contracting muscles. Moreover, we conclude that it seems unlikely that the muscle spindle afferents recorded in this task have any role in providing "proprioceptive" information pertaining to the size of an object grasped.

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Sensory Input and Control of Grip

Cited by 109 publications

References 34 publications

Quantifying feedforward control: a linear scaling model for fingertip forces and object weight

Quantifying feedforward control: a linear scaling model for fingertip forces and object weight

The strength of the corticospinal coherence depends on the predictability of modulated isometric forces

Discharges in Human Muscle Receptor Afferents during Block Grasping

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