Aging effects on electrical and hemodynamic responses in the sensorimotor network during unilateral proximal upper limb functional tasks.

Healthy aging leads to poorer performance in upper limb (UL) daily living movements. Understanding the neural correlates linked with UL functional movements may help to better understand how healthy aging affects motor control. Two non-invasive neuroimaging methods allow for monitoring the movement-related brain activity: functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), respectively based on the hemodynamic response and electrical activity of brain regions. Coupled, they provide a better spatiotemporal mapping. The aim of this study was to evaluate the effect of healthy aging on the bilateral sensorimotor (SM1) activation patterns of functional proximal UL movements. Twenty-one young and 21 old healthy participants realized two unilateral proximal UL movements during: i) a paced reaching target task and ii) a circular steering task to capture the speed-accuracy trade-off. Combined fNIRS-EEG system was synchronised with movement capture system to record SM1 activation while moving. The circular steering task performance was significantly lower for the older group. The rate of increase in hemodynamic response was longer in the older group with no difference on the amplitude of fNIRS signal for the two tasks. The EEG results showed aging related reduction of the alpha-beta rhythms synchronisation but no desynchronisation modification. In conclusion, this study uncovers the age-related changes in brain electrical and hemodynamic response patterns in the bilateral sensorimotor network during two functional proximal UL movements using two complementary neuroimaging methods. This opens up the possibility to utilise combined fNIRS-EEG for monitoring the movement-related neuroplasticity in clinical practice.

Kinect-based assessment of proximal arm non-use after a stroke.

BACKGROUND: After a stroke, during seated reaching with their paretic upper limb, many patients spontaneously replace the use of their arm by trunk compensation movements, even though they are able to use their arm when forced to do so. We previously quantified this proximal arm non-use (PANU) with a motion capture system (Zebris, CMS20s). The aim of this study was to validate a low-cost Microsoft Kinect-based system against the CMS20s reference system to diagnose PANU. METHODS: In 19 hemiparetic stroke individuals, the PANU score, reach length, trunk length, and proximal arm use (PAU) were measured during seated reaching simultaneously by the Kinect (v2) and the CMS20s over two testing sessions separated by two hours. RESULTS: Intraclass correlation coefficients (ICC) and linear regression analysis showed that the PANU score (ICC = 0.96, r2 = 0.92), reach length (ICC = 0.81, r2 = 0.68), trunk length (ICC = 0.97, r2 = 0.94) and PAU (ICC = 0.97, r2 = 0.94) measured using the Kinect were strongly related to those measured using the CMS20s. The PANU scores showed good test-retest reliability for both the Kinect (ICC = 0.76) and CMS20s (ICC = 0.72). Bland and Altman plots showed slightly reduced PANU scores in the re-test session for both systems (Kinect: - 4.25 ± 6.76; CMS20s: - 4.71 ± 7.88), which suggests a practice effect. CONCLUSION: We showed that the Kinect could accurately and reliably assess PANU, reach length, trunk length and PAU during seated reaching in post stroke individuals. We conclude that the Kinect can offer a low-cost and widely available solution to clinically assess PANU for individualised rehabilitation and to monitor the progress of paretic arm recovery. TRIAL REGISTRATION: The study was approved by The Ethics Committee of Montpellier, France (N°ID-RCB: 2014-A00395-42) and registered in Clinical Trial (N° NCT02326688, Registered on 15 December 2014, https://clinicaltrials.gov/ct2/show/results/NCT02326688 ).

Proximal arm non-use when reaching after a stroke.

After a stroke, many people "cannot and do not" use their paretic upper limb. With recovery, some people "can but do not" use their paretic upper limb and this non-use should be counteracted with specific rehabilitation. The aim of the study was to quantify one aspect of the non-use: proximal arm non-use when reaching within one's arm length in 45 post-stroke and 45 age matched controls. Arm use refers to the contribution of the shoulder and elbow motion to the hand movement towards the target. Proximal arm non-use is calculated as the ratio of the difference between spontaneous arm use and maximal arm use. We found that proximal arm non-use has very good test-retest reliability, does not depend on time since stroke, increases with impairment (Fugl-Meyer) and loss of function (Box & Block), and most importantly, that 61% of patients with lower impairment (Fugl-Meyer >28/42) exhibit proximal arm non-use. We conclude that quantifying proximal arm non-use in post-stroke individuals provides novel information that complements routine clinical measures. It is likely that proximal arm non-use quantifies one aspect of the motor reserve that therapists can target in patient specific rehabilitation programs.

Innovative technologies applied to sensorimotor rehabilitation after stroke.

Innovative technologies for sensorimotor rehabilitation after stroke have dramatically increased these past 20 years. Based on a review of the literature on "Medline" and "Web of Science" between 1990 and 2013, we offer an overview of available tools and their current level of validation. Neuromuscular electric stimulation and/or functional electric stimulation are widely used and highly suspected of being effective in upper or lower limb stroke rehabilitation. Robotic rehabilitation has yielded various results in the literature. It seems to have some effect on functional capacities when used for the upper limb. Its effectiveness in gait training is more controversial. Virtual reality is widely used in the rehabilitation of cognitive and motor impairments, as well as posture, with admitted benefits. Non-invasive brain stimulation (rTMS and TDCS) are promising in this indication but clinical evidence of their effectiveness is still lacking. In the same manner, these past five years, neurofeedback techniques based on brain signal recordings have emerged with a special focus on their therapeutic relevance in rehabilitation. Technological devices applied to rehabilitation are revolutionizing our clinical practices. Most of them are based on advances in neurosciences allowing us to better understand the phenomenon of brain plasticity, which underlies the effectiveness of rehabilitation. The acceptation and "real use" of those devices is still an issue since most of them are not easily available in current practice.