Spatial filters selection towards a rehabilitation BCI

Introducing BCI technology in supporting motor imagery (MI) training has revealed the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in stroke patients. To provide the most accurate and personalized feedback during the treatment, several stages of the electroencephalographic signal processing have to be optimized, including spatial filtering. This study focuses on data-independent approaches to optimize spatial filtering step. Specific aims were: i) assessment of spatial filters' performance in relation to the hand and foot scalp areas; ii) evaluation of simultaneous use of multiple spatial filters; iii) minimization of the number of electrodes needed for training. Our findings indicate that different spatial filters showed different performance related to the scalp areas considered. The simultaneous use of EEG signals conditioned with different spatial filters could either improve classification performance or, at same level of performance could lead to a reduction of the number of electrodes needed for successive training, thus improving usability of BCIs in clinical rehabilitation context

The Promotoer: a successful story of translational research in BCI for motor rehabilitation

Several groups have recently demonstrated in the context of randomized controlled trials (RCTs) how sensorimotor Brain-Computer Interface (BCI) systems can be beneficial for post-stroke motor recovery. Following a successful RCT, at Fondazione Santa Lucia (FSL) a further translational effort was made with the implementation of the Promotœr, an all in-one BCIsupported MI training station. Up to now, 25 patients underwent training with the Promotɶr during their admission for rehabilitation purposes (in add-on to standard therapy). Two illustrative cases are presented. Though currently limited to FSL, the Promotɶr represents a successful story of translational research in BCI for stroke rehabilitation. Results are promising both in terms of feasibility of a BCI training in the context of a real rehabilitation program and in terms of clinical and neurophysiological benefits observed in the patients

Implementing physiologically-based approaches to improve Brain-Computer Interfaces usability in post-stroke motor rehabilitation

Stroke is one of the leading causes of long-term motor disability and, as such, directly impacts on daily living activities. Identifying new strategies to recover motor function is a central goal of clinical research. In the last years the approach to the post-stroke function restore has moved from the physical rehabilitation to the evidence-based neurological rehabilitation. Brain-Computer Interface (BCI) technology offers the possibility to detect, monitor and eventually modulate brain activity. The potential of guiding altered brain activity back to a physiological condition through BCI and the assumption that this recovery of brain activity leads to the restoration of behaviour is the key element for the use of BCI systems for therapeutic purposes. To bridge the gap between research-oriented methodology in BCI design and the usability of a system in the clinical realm requires efforts towards BCI signal processing procedures that would optimize the balance between system accuracy and usability. The thesis focused on this issue and aimed to propose new algorithms and signal processing procedures that, by combining physiological and engineering approaches, would provide the basis for designing more usable BCI systems to support post-stroke motor recovery. Results showed that introduce new physiologically-driven approaches to the pre-processing of BCI data, methods to support professional end-users in the BCI control parameter selection according to evidence-based rehabilitation principles and algorithms for the parameter adaptation in time make the BCI technology more affordable, more efficient, and more usable and, therefore, transferable to the clinical realm

GUIDER: a GUI for semiautomatic, physiologically driven EEG feature selection for a rehabilitation BCI

GUIDER is a graphical user interface developed in MATLAB software environment to identify electroencephalography (EEG)-based brain computer interface (BCI) control features for a rehabilitation application (i.e. post-stroke motor imagery training). In this context, GUIDER aims to combine physiological and machine learning approaches. Indeed, GUIDER allows therapists to set parameters and constraints according to the rehabilitation principles (e.g. affected hemisphere, sensorimotor relevant frequencies) and foresees an automatic method to select the features among the defined subset. As a proof of concept, we compared offline performances between manual, just based on operator’s expertise and experience, and GUIDER semiautomatic features selection on BCI data collected from stroke patients during BCI-supported motor imagery training. Preliminary results suggest that this semiautomatic approach could be successfully applied to support the human selection reducing operator dependent variability in view of future multi-centric clinical trials

Neurophysiological constraints of control parameters for a brain computer interface system to support post-stroke motor rehabilitation

The Promotɶr is an all-in-one Brain Computer Interface (BCI)-system developed at Fondazione Santa Lucia (Rome, Italy) to support hand motor imagery practice after stroke. In this paper we focus on the optimization of control parameters for the BCI training. We compared two procedures for the feature selection: in the first, features were selected by means of a manual procedure (requiring “skilled users”), in the second a semiautomatic method, developed by us combining physiological and machine learning approaches, guided the feature selection. EEG-based BCI data set collected from 13 stroke patients were analyzed to the aim. No differences were found between the two procedures (paired-samples t-test, p=0.13). Results suggest that the semiautomatic procedure could be applied to support the manual feature selection, allowing no-skilled users to approach BCI technology for motor rehabilitation of stroke patients

Biological effects of Physalis peruviana L. (Solanaceae) crude extracts and its major withanolides on Ceratitis capitata Wiedemann (Diptera: Tephritidae)

Biological effects of Physalis peruviana crude extracts and its major withanolides (withanolide E and 4-ß-hydroxywithanolide E) were investigated on larvae and adults of the fruit fly Ceratitis capitata. High concentrations of crude extracts (10000 and 35000 ppm) in larval diet caused 100% mortality while low concentration (1000 ppm) caused significative differences in larval mortality, development delay and puparia length. Withanolide E and 4-ß-hydroxywithanolide E (500 ppm) also produced significative mortality on larvae. The application of crude extracts to adults drinking vessels caused significative lethal effects at 10000 and 35000 ppm. These data indicate that P. peruviana crude extracts and its two major withanolides could be used to develop baits to control C.capitata.EEA ChubutFil: Cirigliano, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Orgánica; ArgentinaFil: Colamarino, I. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Zoología Agrícola; ArgentinaFil: Mareggiani, G. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Zoología Agrícola; ArgentinaFil: Bado, Silvina Graciela. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Chubut; Argentin

A Small-Scale shRNA Screen in Primary Mouse Macrophages Identifies a Role for the Rab GTPase Rab1b in Controlling Salmonella Typhi Growth

Acknowledgments We are very grateful to Leigh Knodler for her generous gift of P22 phages from a S. Typhimurium glmS::Cm::mCherry strain. We thank the Microscopy and Histology Core Facility, the Centre for Genome-Enabled Biology and Medicine (CGEBM), the Iain Fraser Cytometry Centre and the qPCR Facility (University of Aberdeen) for their support and assistance in this work. We thank members of the Spanò/Baldassarre laboratory for their feedback throughout this project. The content of this manuscript has been posted as a preprint on bioRxiv (Solano-Collado et al., 2020). Funding This work was supported by the European Union’s Horizon 2020 research and innovation program Marie Skłodowska-Curie Fellowship (706040_KILLINGTYPHI) to VS-C, the Wellcome Trust (Seed Award 109680/Z/15/Z), the European Union’s Horizon 2020 ERC consolidator award (2016-726152-TYPHI), the BBSRC (BB/N017854/1) and Tenovus Scotland (G14/19) to SS.Peer reviewedPublisher PD

Topographically specific effects of ELF-1 on retinal axon guidance in vitro and retinal axon mapping in vivo

Peer reviewedPublisher PD

Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients

Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts