Hand motor impairment persists after stroke. Sensory inputs may facilitate recovery of motor function. This pilot study tested the effectiveness of tactile sensory noise in improving hand motor function in chronic stroke survivors with tactile sensory deficits, using a repeated measures design. Sensory noise in the form of subthreshold, white noise, mechanical vibration was applied to the wrist skin during motor tasks. Hand dexterity assessed by the Nine Hole Peg Test and the Box and Block Test and pinch strength significantly improved when the sensory noise was turned on compared with when it was turned off in chronic stroke survivors. The subthreshold sensory noise to the wrist appears to induce improvements in hand motor function possibly via neuronal connections in the sensoriomotor cortex. The approach of applying concomitant, unperceivable mechanical vibration to the wrist during hand motor tasks is easily adoptable for clinic use as well as unsupervised home use. This pilot study suggests a potential for a wristband-type assistive device to complement hand rehabilitation for stroke survivors with sensorimotor deficit.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in stroke research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires neuroanatomical expertise. We previously released an open-source dataset of stroke T1w MRIs and manually-segmented lesion masks (ATLAS v1.2, N = 304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N = 1271), a larger dataset of T1w MRIs and manually segmented lesion masks that includes training (n = 655), test (hidden masks, n = 300), and generalizability (hidden MRIs and masks, n = 316) datasets. Algorithm development using this larger sample should lead to more robust solutions; the hidden datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke research.

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. We previously released a large, open-source dataset of stroke T1w MRIs and manually segmented lesion masks (ATLAS v1.2, N=304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=955), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes both training (public) and test (hidden) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test data allows for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke rehabilitation research.