Search

Explicit knowledge of object center of mass or CM location fails to guide anticipatory scaling of digit forces necessary for dexterous manipulation. We previously showed that allowing young adults to choose where to grasp the object entailed an ability to use arbitrary color cues about object CM location to gradually minimize object tilt across several trials. This conditional learning was achieved through accurate anticipatory modulation of digit position using the color cues. However, it remains unknown how aging affects the ability to use explicit color cues about object CM location to modulate digit placement for dexterous manipulation. We instructed healthy older and young adults to learn a manipulation task using arbitrary color cues about object CM location. Subjects were required to exert clockwise, counterclockwise, or no torque on the object according to the color cue and lift the object while minimizing its tilt. Older adults produced larger torque error during conditional learning trials, resulting in a slower rate of learning than young adults. Importantly, older adults showed impaired anticipatory modulation of digit position when information of the CM location was available via explicit color cues. The older adults also did not modulate their digit forces to compensate for this impairment. Interestingly, however, anticipatory modulation of digit position was intact in the same individuals when information of object CM location was implicitly conveyed from trial-to-trial. We discuss our findings in relation to age-dependent changes in processes and neural network essential for learning dexterous manipulation using arbitrary color cue about object property.

Dexterous object manipulation is a hallmark of human evolution and a critical skill for everyday activities. A previous work has used a grasping context that predominantly elicits memory-based control of digit forces by constraining where the object should be grasped. For this “constrained” grasping context, the primary motor cortex (M1) is involved in storage and retrieval of digit forces used in previous manipulations. In contrast, when choice of digit contact points is allowed (“unconstrained” grasping), behavioral studies revealed that forces are adjusted, on a trial-to-trial basis, as a function of digit position. This suggests a role of online feedback of digit position for force control. However, despite the ubiquitous nature of unconstrained hand–object interactions in activities of daily living, the underlying neural mechanisms are unknown. Using noninvasive brain stimulation, we found the role of primary motor cortex (M1) and somatosensory cortex (S1) to be sensitive to grasping context. In constrained grasping, M1 but not S1 is involved in storing and retrieving learned digit forces and position. In contrast, in unconstrained grasping, M1 and S1 are involved in modulating digit forces to position. Our findings suggest that the relative contribution of memory and online feedback modulates sensorimotor cortical interactions for dexterous manipulation.

Sensorimotor memory built through previous hand-object interactions allows subjects to plan grasp forces. The memory-based mechanism is particularly effective when contact points on the object do not change across multiple manipulations, thus allowing subjects to generate the same forces in a feedforward fashion. However, allowing subjects to choose where to grasp an object causes trial-to-trial variability in fingertip positioning, suggesting a decreased ability to predict where the object will be grasped. In this scenario, subjects modulate forces on a trial-to-trial basis as a function of fingertip positioning. We suggested that this fingertip force-to-position modulation could be implemented by transforming feedback of digit placement into an accurate distribution of fingertip forces. Thus, decreasing certainty of fingertip position on an object would cause a shift from predominantly memory- to feedback-based force control mechanisms. To gain further insight into these sensorimotor transformation mechanisms, we asked subjects to grasp and lift an object with an asymmetrical center of mass while preventing it from tilting. To isolate the effect of digit placement uncertainty, we designed two experimental conditions that differed in terms of predictability of fingertip position but had similar average fingertip positioning and force distribution. We measured corticospinal excitability to probe possible changes in sensorimotor processing associated with digit placement uncertainty. We found a differential effect of sensorimotor uncertainty after but not before object contact. Our results suggest that sensorimotor integration is rapidly tuned after object contact based on different processing demands for memory versus feedback mechanisms underlying the control of manipulative forces. NEW & NOTEWORTHY The relative contribution of predictive and feedback mechanisms for scaling digit forces to position during dexterous manipulation depends on the predictability of where the object will be grasped. We found that corticospinal excitability shortly after contact was sensitive to digit position predictability. This supports the proposition that distinct sensorimotor integration processes are engaged, depending on the role of feedback about digit placement versus sensorimotor memory in controlling manipulative forces.

Dexterous manipulation may be guided by explicit information about object property. Such a manipulation requires fine modulation of digit position and forces using explicit cues. Young adults can form arbitrary cue-object property associations for accurate modulation of digit position and forces. Aging, in contrast, might alter this conditional learning. Older adults are impaired in accurately modulating their digit forces using explicit cues about object property. However, it is not known whether older adults can use explicit cues about object property to modulate digit position. In this study, we instructed ten healthy older and ten young adults to learn a manipulation task using arbitrary color cues about object center of mass location. Subjects were required to exert clockwise, counterclockwise, or no torque on the object according to the color cue and lift the object while minimizing its tilt across sixty trials. Older adults produced larger torque error during the conditional learning trials than young adults. This resulted in a significantly slower rate of learning in older adults. Older, but not young adults, failed to modulate their digit position and forces using the color cues. Similar aging-related differences were not observed while learning the task using implicit knowledge about object property. Our findings suggest that aging impairs the ability to use explicit cues about object property to modulate both digit position and forces for dexterous manipulation. We discuss our findings in relation to age-related changes in the processes and the neural network for conditional learning.

BackgroundPrimary somatosensory cortex (S1) is involved in pain processing and thus its suppression using neuromodulatory techniques such as continuous theta burst stimulation (cTBS) might be a potential pain management strategy in patients with neuropathic pain. S1 cTBS is known to elevate pain threshold in young adults. However, the persistence of this effect is unknown.Objective/HypothesisWe hypothesized persistent elevation of pain threshold following cTBS over S1 in healthy, young adults.MethodsWe recruited ten subjects in a sham-controlled crossover design and recorded their electrical pain threshold (EPT) for 40 min following cTBS over S1. We assessed corticospinal excitability (CSE) to rule out the involvement of primary motor cortex due to spread of current.ResultscTBS over S1 elevated EPT without modulating CSE compared to sham stimulation. EPT was elevated for 40 min post-cTBS.ConclusionsS1 can be focally targeted using cTBS for a longer lasting pain relief in patients.

Neuronal firing rate variability during planning has been found to contribute to trial-to-trial variability in primate behavior. However, in humans, whether planning related mechanisms contribute to trial-to-trial behavioral variability remains unknown. We investigated the time-course of trial-to-trial variability in corticospinal excitability (CSE) using transcranial magnetic stimulation (TMS) while subjects planned to perform a self-paced reach-to-grasp task. We hypothesized that CSE variability will be modulated during task planning and that such a modulation would explain trial-to-trial behavioral variability. Able-bodied individuals were visually cued to plan their grip force before exertion of either 30% or 5% of maximum force on an object. TMS was delivered at different time points following a cue that instructed the force level. We first modeled the relation between CSE magnitude and its variability at rest (n=12) to study the component of CSE variability during task planning that was not related to changes in CSE magnitude (n=12). We found an increase in CSE variability during task planning at 30% but not at 5% of force. This effect was temporally dissociated from the decrease in CSE magnitude. Importantly, the increase in CSE variability during planning explained 64% of inter-individual differences in time to peak force rate trial-to-trial variability. These results were found to be repeatable across studies and robust to different analysis methods. Our findings suggest that the planning-related mechanisms underlying modulation in CSE variability and CSE magnitude are distinct. Notably, the extent of modulation in planning-related variability in corticospinal system within individuals may explain their trial-to-trial behavioral variability.

Humans are unique in their ability to perform dexterous object manipulation in a wide variety of scenarios. However, previous work has used a grasping context that predominantly elicits memory-based control of digit forces by constraining where the object should be grasped. For this ‘constrained’ grasping context, primary motor cortex (M1) is involved in storage and retrieval of digit forces used in previous manipulations. In contrast, when choice of digit contact points is allowed (‘unconstrained’ grasping), behavioral studies revealed that forces are adjusted, on a trial-to-trial basis, as a function of digit position. This suggests a role of online feedback that detects digit position, rather than memory, for force control. However, despite the ubiquitous nature of unconstrained hand-object interactions in activities of daily living, the underlying neural mechanisms are unknown. Using non-invasive brain stimulation and electroencephalography, we found the role of M1 to be sensitive to grasping condition. While confirming the role of M1 in storing and retrieving learned digit forces and position in ‘constrained’ grasping, we also found that M1 is involved in modulating digit forces to digit position in unconstrained grasping. Furthermore, we found that digit force modulation to position relies on sensorimotor integration mediated by primary sensory cortex (S1) and M1. This finding supports the notion of a greater contribution of somatosensory feedback of digit position in unconstrained grasping. We conclude that the relative contribution of memory and online feedback based on whether contact points are constrained or unconstrained modulates sensorimotor cortical interactions for dexterous manipulation.

Conditional learning is an important component of our everyday activities (e.g., handling a phone or sorting work files) and requires identification of the arbitrary stimulus, accurate selection of the motor response, monitoring of the response, and storing in memory of the stimulus-response association for future recall. Learning this type of conditional visuomotor task appears to engage the premotor dorsal region (PMd). However, the extent to which PMd might be involved in specific or all processes of conditional learning is not well understood. Using transcranial magnetic stimulation (TMS), we demonstrate the role of human PMd in specific stages of learning of a novel conditional visuomotor task that required subjects to identify object center of mass using a color cue and to apply appropriate torque on the object at lift onset to minimize tilt. TMS over PMd, but not vertex, increased error in torque exerted on the object during the learning trials. Analyses of digit position and forces further revealed that the slowing in conditional visuomotor learning resulted from impaired monitoring of the object orientation during lift, rather than stimulus identification, thus compromising the ability to accurately reduce performance error across trials. Importantly, TMS over PMd did not alter production of torque based on the recall of learned color-torque associations. We conclude that the role of PMd for conditional learning is highly sensitive to the stage of learning visuomotor associations. NEW & NOTEWORTHY Conditional learning involves stimulus identification, motor response selection, response monitoring, memory encoding, and recall of the learned association. Premotor dorsal (PMd) has been implicated for conditional learning. However, the extent to which PMd might be involved in specific or all stages of conditional learning is not well understood. The novel finding of our study is that PMd appears to be involved with monitoring motor responses, a sensorimotor integration stage essential for conditional learning.

Most older adults self-report disability for daily activities using their hands. Scientists have attempted to characterize specific behavioral markers of sensorimotor dysfunction, identify the changes in central and peripheral nervous systems, and establish a link between behavioral and neurophysiological alterations with aging. Neuroimaging studies have reported associations between changes in the brain and performance on cognitive and motor tasks in older adults (Sailer et al., 2000; Cabeza et al., 2002; Park et al., 2004; Park and Reuter-Lorenz, 2009; Carp et al., 2011; Bernard and Seidler, 2012; Langan et al.). For example, among older adults greater brain activation levels associated with better motor performance, which suggest over-activation of the brain (compared to young adults) serves to compensate for age-related deterioration of brain function (Mattay et al., 2002; Rossi et al., 2004; Wu and Hallett, 2005; Heuninckx et al., 2008). The over-activation may hinder performance due to the difficulty in selectively recruiting neural networks, which instead is interpreted to reflect reduced brain circuit differentiation (Peinemann et al., 2001; Riecker et al., 2006; Dennis and Cabeza, 2011; Seidler et al., 2011; Bernard and Seidler, 2012; Langan et al.). As certain age-related neurophysiological changes may be beneficial, it is important to understand how age-related changes in the nervous system correlate with behavioral outcomes. These studies further depend on identification of behavior and neurophysiological measures that are sensitive to aging. Studies published as a part of this research topic used innovative approaches to assess behavioral and neurophysiological deficits in older adults. These approaches ranged from the utilization of behavioral paradigms to neuroimaging tools that focused on unimanual motor tasks (Bonstrup et al.; de Bruin et al.; Kimura et al.; Ko et al.; Lawrence et al.; Tracy et al.; Yu et al.) or bimanual tasks (Bhakuni and Mutha; Boisgontier and Swinnen; Boisgontieret al. Chen et al.; Hoff et al.). Bimanual behavioral paradigms allow characterization of complex interaction between two sides of the brain during performance of a motor task. Boisgontier et al. used a bimanual joint position matching task that required reliance on proprioceptive information from both limbs in order to match the position of one limb to another. The authors found that directing visual attention to the active limb impaired performance in both young and older adults, but more so in older adults (Boisgontier et al.). This finding was suggested to arise from older adults' inability to handle additional attention load, in the presence of an underlying age-related increased cost of processing proprioceptive signals. Further, older adults were impaired when the target limb was placed farther away, compared to near targets (Boisgontier and Swinnen). For the farthest targets older adults most likely experienced an increase in proprioceptive signal processing load and/or an increase in noise in the joint proprioceptor firing rate. The authors suggested that these factors might have saturated the reserve for proprioceptive processing in those older adults who were dependent on compensatory mechanisms for task performance, thus impairing performance on the joint matching task. The interaction between age and interlimb coordination during skill learning was investigated using a bimanual serial reaction time task (Bhakuni and Mutha; Hoff et al.). Older adults were found to match their younger counterparts in the ability to learn a serial reaction time task (Bhakuni and Mutha; Hoff et al.). However, Ragert and colleagues noted that older adults demonstrated reduced sequence specific learning as compared with younger individuals. Importantly, older adults showed altered interlimb coordination, measured as the time cost related to hand switch (i.e., switch cost), when compared with young adults during performance of the bimanual task (Bhakuni and Mutha; Hoff et al.). While Bhakuni and Mutha reported no change in the hand switch cost with skill learning in both older and young individuals, Hoff et al. found that the hand switch cost in older adults declined to levels that matched younger individuals. In a study of animals, Chen et al. found that increased neonatal iron supplementation resulted in the depletion of striatal dopamine and reduction of motor performance on rotarod and open field tests in aging, but not in young, rats. Other studies in this special section quantified sensorimotor integration processes using unimanual tasks in elderly individuals. Specifically, Valero-Cuevas and colleagues used a novel behavioral paradigm, the Strength-Dexterity (SD) test, which provides information about the sensorimotor integration processes through quantification of the dynamic control of fingertip forces. They found that sensorimotor ability assessed using the SD test significantly contributes to hand function in the aging population, in addition to strength and coordinated upper extremity function (Lawrence et al.). Poor unimanual hand function in older adults has also been shown to result from increased variability in motor output, e.g., forces exerted on an object (Enoka et al., 2003; Christou, 2011). The age-related increase in force variability during isometric force production task is noted specifically when the task is performed under the influence of visual feedback of the applied force and the target force. No or minimal age-related difference was observed for tasks performed in the absence of visual feedback. However, the age-related and vision-dependent increased force variability was mainly reported for large muscle groups (Tracy, 2007; Tracy et al., 2007; Welsh et al., 2007; Paxton et al., 2015). Here, Tracy et al. showed age-related increase in force variability under high-gain visual feedback for an intrinsic muscle of the hand. Overall, the finding of increased force variability during an isometric task across small and large muscle groups in old age mainly when vision is made available suggests impaired processing of visuomotor information during isometric task with aging. In contrast, older adults with a co-morbid condition may demonstrate lower variability in the motor output suggestive of their reduced flexibility and adaptability to unpredictable environmental conditions. Valero-Cuevas and colleagues found that older adults with Parkinson's disease (PD) have lower force variability during a dynamic stability (i.e., the SD) test as compared with healthy older adults (Ko et al.). Importantly, the authors found that the reduced force variability correlated with greater severity of motor impairment assessed using UPDRS motor scores. Overall, these studies suggest that it is crucial to understand the relationship between motor output variability and hand function with aging when associated with other health-related problems. Yu et al. reported that older women as compared with older men have a higher prevalence of severe stroke, hypertension, obesity, and hyperlipidemia. De Bruin et al. developed a novel task to assess the effects of aging on visuospatial abilities, but failed to find any age-related decrement. Lastly, Kimura et al. studied the effects of aging on performing speedy corrective movements when the target jumps during the reach. The authors inferred that older adults potentially have intact reflexive, but impaired volitional, corrective responses to target jumps during the reach. In addition to behavioral metrics, metrics that quantify structural and functional changes in the brain will be useful to understand the effects of aging on the sensorimotor system. Bonstrup et al. investigated a cortical signature for sustained inhibition of finger movement, e.g., an increase in alpha electroencephalography (EEG) power, in young and older adults. The authors found a significant increase in alpha power over sensorimotor cortices during sustained inhibition of a learned complex finger movement sequence in young adults, but not in older adults. This finding suggests that older adults have impaired inhibitory mechanisms necessary for the temporal control of movement, and that the EEG alpha power can be a sensitive tool to detect any age-related deterioration of inhibitory neural mechanisms. The studies compiled within this special issue are intended to promote discussion on the sensorimotor deficits that elderly individuals face and will encourage further behavioral and neurophysiological studies to investigate the specific neural mechanisms.

Control of digit forces for grasping relies on sensorimotor memory gained from prior experience with the same or similar objects and on online sensory feedback. However, little is known about neural mechanisms underlying digit force planning. We addressed this question by quantifying the temporal evolution of corticospinal excitability (CSE) using single-pulse transcranial magnetic stimulation (TMS) during two reach-to-grasp tasks. These tasks differed in terms of the magnitude of force exerted on the same points on the object to isolate digit force planning from reach and grasp planning. We also addressed the role of intracortical circuitry within primary motor cortex (M1) by quantifying the balance between short intracortical inhibition and facilitation using paired-pulse TMS on the same tasks. Eighteen right-handed subjects were visually cued to plan digit placement at predetermined locations on the object and subsequently to exert either negligible force (“low-force” task, LF) or 10% of their maximum pinch force (“high-force” task, HF) on the object. We found that the HF task elicited significantly smaller CSE than the LF task, but only when the TMS pulse coincided with the signal to initiate the reach. This force planning-related CSE modulation was specific to the muscles involved in the performance of both tasks. Interestingly, digit force planning did not result in modulation of M1 intracortical inhibitory and facilitatory circuitry. Our findings suggest that planning of digit forces reflected by CSE modulation starts well before object contact and appears to be driven by inputs from frontoparietal areas other than M1.

We measured the external moments and digit-tip force directions acting on a freely moveable object while it was grasped and manipulated by old (OA) and young (YA) adults. Participants performed a grasp and lift task and a precision orientation (key-slot) task with a precision (thumb-finger) grip. During the grasp-lift task the OA group misaligned their thumb and finger contacts and produced greater grip force, greater external moments on the object around its roll axis, and oriented force vectors differently compared with the YA group. During the key-slot task, the OA group was more variable in digit-tip force directions and performed the key-slot task more slowly. With practice the OA group aligned their digits, reduced their grip force, and minimized external moments on the object, clearly demonstrating that the nervous system monitored and actively manipulated one or more variables related to object tilt. This was true even for the grip-lift task, a task for which no instructions regarding object orientation were given and which could tolerate modest amounts of object tilt without interfering with task goals. Although the OA group performed the key-slot task faster with experience, they remained slower than the YA group. We conclude that with old age comes a reduced ability to control the forces and moments applied to objects during precision grasp and manipulation. This may contribute to the ubiquitous slowing and deteriorating manual dexterity in healthy aging.

The contribution of poor finger force control to age-related decline in manual dexterity is above and beyond ubiquitous behavioral slowing. Altered control of the finger forces can impart unwanted torque on the object affecting its orientation, thus impairing manual performance. Anodal transcranial direct current stimulation (tDCS) over primary motor cortex (M1) has been shown to improve the performance speed on manual tasks in older adults. However, the effects of anodal tDCS over M1 on the finger force control during object manipulation in older adults remain to be fully explored. Here we determined the effects of anodal tDCS over M1 on the control of grip force in older adults while they manipulated an object with an uncertain mechanical property. Eight healthy older adults were instructed to grip and lift an object whose contact surfaces were unexpectedly made more or less slippery across trials using acetate and sandpaper surfaces, respectively. Subjects performed this task before and after receiving anodal or sham tDCS over M1 on two separate sessions using a cross-over design. We found that older adults used significantly lower grip force following anodal tDCS compared to sham tDCS. Friction measured at the finger-object interface remained invariant after anodal and sham tDCS. These findings suggest that anodal tDCS over M1 improved the control of grip force during object manipulation in healthy older adults. Although the cortical networks for representing objects and manipulative actions are complex, the reduction in grip force following anodal tDCS over M1 might be due to a cortical excitation yielding improved processing of object-specific sensory information and its integration with the motor commands for production of manipulative forces. Our findings indicate that tDCS has a potential to improve the control of finger force during dexterous manipulation in older adults.

Transcranial anodal stimulation (tDCS) over primary motor cortex (M1) improves dexterous manipulation in healthy older adults. However, the beneficial effects of anodal tDCS in combination with motor practice on natural and clinically relevant functional manual tasks, and the associated changes in the digit contact forces are not known. To this end, we studied the effects of 20 min of tDCS applied over M1 for the dominant hand combined with motor practice (MP) in a sham‐controlled crossover study. We monitored the forces applied to an object that healthy elderly individuals grasped and manipulated, and their performances on the Grooved Pegboard Test and the Key‐slot task. Practice improved performance on the Pegboard test, and anodal tDCS + MP improved retention of this performance gain when tested 35 min later, whereas similar performance gains degraded in the sham group after 35 min. Interestingly, grip force variability on an isometric precision grip task performed with visual feedback of precision force increased following anodal tDCS + MP, but not sham tDCS + MP. This finding suggests that anodal tDCS over M1 might alter the descending drive to spinal motor neurons involved in the performance of isometric precision grip task under visual feedback leading to increased fluctuations in the grip force exerted on the object. Our results demonstrate that anodal stimulation in combination with motor practice helps older adults to retain their improved performance on a functionally relevant manual task in healthy older adults.