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Background: The cerebellum and primary motor cortex (M1) are crucial to coordinated and accurate movements of the upper limbs. There is also appreciable evidence that these two structures exert somewhat divergent influences upon proximal versus distal upper limb control. Here, we aimed to differentially regulate the contribution of the cerebellum and M1 to proximal and distal effectors during motor adaptation, with transcranial direct current stimulation (tDCS). For this, we employed tasks that promote similar motor demands, but isolate whole arm from hand/finger movements, in order to functionally segregate the hierarchy of upper limb control., Methods: Both young and older adults took part in a visuomotor rotation task; where they adapted to a 60° visuomotor rotation using either a hand-held joystick (requiring finger/hand movements) or a 2D robotic manipulandum (requiring whole-arm reaching movements), while M1, cerebellar or sham tDCS was applied., Results: We found that cerebellar stimulation improved adaptation performance when arm movements were required to complete the task, while in contrast stimulation of M1 enhanced adaptation during hand and finger movements only. This double-dissociation was replicated in an independent group of older adults, demonstrating that the behaviour remains intact in ageing., Conclusions: These results suggest that stimulation of distinct motor areas can selectively improve motor adaptation in the proximal and distal upper limb. This also highlights new ways in which tDCS might be best applied to achieve reliable rehabilitation of upper limb motor deficits., Competing Interests: Declaration of competing interest The authors declare that no competing interest exist., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Chronic upper limb motor impairment is a common outcome of stroke. Therapeutic training can reduce motor impairment. Recently, a growing interest in evaluating motor training provided by robotic assistive devices has emerged. Robot-assisted therapy is attractive because it provides a means of increasing practice intensity without increasing the workload of physical therapists. However, movements practised through robotic assistive devices are commonly pre-defined and fixed across individuals. More optimal training may result from individualizing the selection of the trained movements based on the individual's impairment profile. This requires quantitative assessment of the degree of the motor impairment prior to training, in relevant movement tasks. However, standard clinical measures for profiling motor impairment after stroke are often subjective and lack precision. We have developed a novel robot-mediated method for systematic and fine-grained mapping (or profiling) of individual performance across a wide range of planar arm reaching movements. Here we describe and demonstrate this mapping method and its utilization for individualized training. We also present a novel principle for the individualized selection of training movements based on the performance maps., Methods and Results: To demonstrate the utility of our method we present examples of 2D performance maps produced from the kinetic and kinematics data of two individuals with stroke-related upper limb hemiparesis. The maps outline distinct regions of high motor impairment. The procedure of map-based selection of training movements and the change in motor performance following training is demonstrated for one participant., Conclusions: The performance mapping method is feasible to produce (online or offline). The 2D maps are easy to interpret and to be utilized for selecting individual performance-based training. Different performance maps can be easily compared within and between individuals, which potentially has diagnostic utility.