Reproductive Isolation and Ecological Niche Partition among Larvae of the Morphologically Cryptic Sister Species Chironomus riparius and C. piger

Background One of the central issues in ecology is the question what allows sympatric occurrence of closely related species in the same general area? The non-biting midges Chironomus riparius and C. piger, interbreeding in the laboratory, have been shown to coexist frequently despite of their close relatedness, similar ecology and high morphological similarity. Methodology/Principal Findings In order to investigate factors shaping niche partitioning of these cryptic sister species, we explored the actual degree of reproductive isolation in the field. Congruent results from nuclear microsatellite and mitochondrial haplotype analyses indicated complete absence of interspecific gene-flow. Autocorrelation analysis showed a non-random spatial distribution of the two species. Though not dispersal limited at the scale of the study area, the sister species occurred less often than expected at the same site, indicating past or present competition. Correlation and multiple regression analyses suggested the repartition of the available habitat along water chemistry gradients (nitrite, conductivity, CaCO3), ultimately governed by differences in summer precipitation regime. Conclusions We show that these morphologically cryptic sister species partition their niches due to a certain degree of ecological distinctness and total reproductive isolation in the field. The coexistence of these species provides a suitable model system for the investigation of factors shaping the distribution of closely related, cryptic species

Resting-State Functional Connectivity in Stroke Patients After Upper Limb Robot-Assisted Therapy: A Pilot Study

Motor deficit is a prominent feature among stroke survivors. Robot-assisted therapies have been proposed as a strategy to boost rehabilitation, by allowing therapy to be provided in a more reproducible and intense manner, while quantitatively monitoring patient’s improvement. However, those approaches have so far not shown superiority over conventional treatments. One potential solution to reach better outcomes would be to personalize the treatment. In this regard, a better understanding of the mechanisms underlying motor recovery is pivotal to tailor therapy to each patient. Here, we explored the cortical changes occurring during robotic training. We recorded resting-state fMRI before and after the treatment in three sub-acute post-stroke survivors, and we investigated the functional connectivity between motor regions. We observed a cortical reorganization following training, consistent with motor improvements

Resting-State Functional Connectivity in Stroke Patients After Upper Limb Robot-Assisted Therapy: A Pilot Study

Motor deficit is a prominent feature among stroke survivors. Robot-assisted therapies have been proposed as a strategy to boost rehabilitation, byallowing therapy to be provided in a more reproducible and intense manner,while quantitatively monitoring patient’s improvement. However, thoseapproaches have so far not shown superiority over conventional treatments. Onepotential solution to reach better outcomes would be to personalize the treatment.In this regard, a better understanding of the mechanisms underlying motorrecovery is pivotal to tailor therapy to each patient. Here, we explored the corticalchanges occurring during robotic training. We recorded resting-state fMRI beforeand after the treatment in three sub-acute post-stroke survivors, and we investi-gated the functional connectivity between motor regions. We observed a corticalreorganization following training, consistent with motor improvement

On the Potential of EEG Biomarkers to Inform Robot-Assisted Rehabilitation in Stroke Patients

Stroke is a devastating neurological condition, often causing severe functional and cognitive deficits, sharply diminishing the patient’s quality of life. Among others, robot-assisted rehabilitation has been widely proposed to enhance the rehabilitation outcome. However, clinical scores and robotic parameters often used to inform rehabilitative-decision process are unfit to fully describe the neural reorganization that occur after a brain insult. The lack of reliable, simple, and sensitive neural biomarkers has potentially limited the clinical translation of these advanced rehabilitative technologies. Here, we show that EEG-topographic measures can be extracted as robust and sensitive biomarkers of stroke recovery to inform robotic therapies

Personalizing exoskeleton-based upper limb rehabilitation using a statistical model: A pilot study

Clinical studies have so far not been able to show if robotic therapy is superior to conventional methods. The personalization of robot-assisted therapy according to the individual motor deficits might contribute to reach this goal. Here we present a statistical approach to automatically personalize robotic rehabilitation. Our method uses different motor performance measures to estimate motor improvement and adapt the motor task within a treatment session. This approach was tested with a pilot sub-acute stroke patient and the outcome was compared to a similar patient who underwent conventional physical therapy. Pilot results showed better outcomes in clinical tests, kinematics and muscle activity for the subject who trained using the personalized robotic approach

Motor improvement estimation and task adaptation for personalized robot-aided therapy: A feasibility study

Background: In the past years, robotic systems have become increasingly popular in upper limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients' individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches. Methods: Here, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients' motor improvement for a series of point-to-point reaching movements in different directions. Additionally, it comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper-limb exoskeleton. The implementation was tested with 17 healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test undergoing rehabilitative motor training. Results: The results of the exploratory study with healthy subjects showed that the participants divided into fast and slow adapters. The model was able to correctly estimate distinct motor improvement progressions between the two groups of participants while proposing individual training protocols. For the two pilot patients, an analysis of the selected motor performance measures showed that both patients were able to retain the improvements gained during training when reaching movements were reintroduced at a later stage. These results suggest that the automated training adaptation was appropriately timed and specifically tailored to the abilities of each individual. Conclusions: The results of our exploratory study demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing encouraging results for the applicability in clinical settings. Trial registration This study is registered in ClinicalTrials.gov (NCT02770300, registered 30 March 2016, https://clinicaltrials.gov/ct2/show/NCT02770300)

Motor improvement estimation and task adaptation for personalized robot-aided therapy: A feasibility study

Background: In the past years, robotic systems have become increasingly popular in upper limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients' individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches. Methods: Here, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients' motor improvement for a series of point-to-point reaching movements in different directions. Additionally, it comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper-limb exoskeleton. The implementation was tested with 17 healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test undergoing rehabilitative motor training. Results: The results of the exploratory study with healthy subjects showed that the participants divided into fast and slow adapters. The model was able to correctly estimate distinct motor improvement progressions between the two groups of participants while proposing individual training protocols. For the two pilot patients, an analysis of the selected motor performance measures showed that both patients were able to retain the improvements gained during training when reaching movements were reintroduced at a later stage. These results suggest that the automated training adaptation was appropriately timed and specifically tailored to the abilities of each individual. Conclusions: The results of our exploratory study demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing encouraging results for the applicability in clinical settings. Trial registration This study is registered in ClinicalTrials.gov (NCT02770300, registered 30 March 2016, https://clinicaltrials.gov/ct2/show/NCT02770300)

Evolution of Cortical Asymmetry with Post-stroke Rehabilitation: A Pilot Study

The lesions induced by unilateral strokes perturb the complex and critical interhemispheric balance. While a high asymmetry measured in the acute phase is known to be a predictor for poor motor recovery, the evolution of this imbalance along motor recovery has not been studied. Here, we evaluated the evolution of the cortical power asymmetry during a robot-assisted motor task along a rehabilitation intervention. Preliminary results suggest that a reduction of the brain asymmetry towards values exhibited by healthy controls is associated with higher motor recovery