Personalized Brain-Computer Interface Models for Motor Rehabilitation

We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES). Specifically, we show that BCI technology can be used to learn personalized decoding models that relate the global configuration of brain rhythms in individual subjects (as measured by EEG) to their motor performance during 3D reaching movements. We demonstrate that our models capture substantial across-subject heterogeneity, and argue that this heterogeneity is a likely cause of limited effect sizes observed in TES for enhancing motor performance. We conclude by discussing how our personalized models can be used to derive optimal TES parameters, e.g., stimulation site and frequency, for individual patients.Comment: 6 pages, 6 figures, conference submissio

Towards neurofeedback training of associative brain areas for stroke rehabilitation

We propose to extend the current focus of BCI-based stroke rehabilitation beyond sensorimotor-rhythms to also include associative brain areas. In particular, we argue that neurofeedback training of brain rhythms that signal a state-of-mind beneficial for motorlearning is likely to enhance post-stroke motor rehabilitation. We propose an adaptive neurofeedback paradigm for this purpose and demonstrate its viability on EEG data recorded with five healthy subjects

Neural Signatures of Motor Skill in the Resting Brain

Stroke-induced disturbances of large-scale cortical networks are known to be associated with the extent of motor deficits. We argue that identifying brain networks representative of motor behavior in the resting brain would provide significant insights for current neurorehabilitation approaches. Particularly, we aim to investigate the global configuration of brain rhythms and their relation to motor skill, instead of learning performance as broadly studied. We empirically approach this problem by conducting a three-dimensional physical space visuomotor learning experiment during electroencephalographic (EEG) data recordings with thirty-seven healthy participants. We demonstrate that across-subjects variations in average movement smoothness as the quantified measure of subjects' motor skills can be predicted from the global configuration of resting-state EEG alpha-rhythms (8-14 Hz) recorded prior to the experiment. Importantly, this neural signature of motor skill was found to be orthogonal to (independent of) task -- as well as to learning-related changes in alpha-rhythms, which we interpret as an organizing principle of the brain. We argue that disturbances of such configurations in the brain may contribute to motor deficits in stroke, and that reconfiguring stroke patients' brain rhythms by neurofeedback may enhance post-stroke neurorehabilitation.Comment: 2019 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2019

Upcoming immunotherapeutic combinations for B-cell lymphoma

After initial introduction for B-cell lymphomas as adjuvant therapies to established cancer treatments, immune checkpoint inhibitors and other immunotherapies are now integrated in mainstream regimens, both in adult and pediatric patients. We here provide an overview of the current status of combination therapies for B-cell lymphoma, by in-depth analysis of combination therapy trials registered between 2015–2020. Our analysis provides new insight into the rapid evolution in lymphoma treatment, as propelled by new additions to the treatment arsenal. We conclude with prospects on upcoming clinical trials which will likely use systematic testing approaches of more combinations of established chemotherapy regimens with new agents, as well as new combinations of immunotherapy and targeted therapy. Future trials will be set up as basket or umbrella-type trials to facilitate the evaluation of new drugs targeting specific genetic changes in the tumor or associated immune microenvironment. As such, lymphoma patients will benefit by receiving more tailored treatment that is based on synergistic effects of chemotherapy combined with new agents targeting specific aspects of tumor biology and the immune system

The Prognostic Value of Eight Immunohistochemical Markers Expressed in the Tumor Microenvironment and on Hodgkin Reed-Sternberg Cells in Pediatric Patients With Classical Hodgkin Lymphoma

Immunohistochemical markers are associated with treatment outcome in adults with classical Hodgkin Lymphoma (cHL). Studies in children are scarce and inconsistent. We investigated in 67 children with cHL, whether the expression of CD15, CD30, PAX5, PD-1, PD-L1, CD68, CD163 and TARC at diagnosis is associated with disease free survival (DFS) and with interim remission status. Low CD15 and low TARC expression were associated with relapsed disease. Low expression of PD-L1 was associated with complete remission at interim PET-scan. Our data suggest a difference between pediatric and adult cHL. This underlines the importance of future research into specific prognostic factors in pediatric cHL, indispensable for improvement of treatment in this population

Machine Learning Logistic Regression Model for Early Decision Making in Referral of Children with Cervical Lymphadenopathy Suspected of Lymphoma

While cervical lymphadenopathy is common in children, a decision model for detecting high-grade lymphoma is lacking. Previously reported individual lymphoma-predicting factors and multivariate models were not sufficiently discriminative for clinical application. To develop a diagnostic scoring tool, we collected data from all children with cervical lymphadenopathy referred to our national pediatric oncology center within 30 months (n = 182). Thirty-nine putative lymphoma-predictive factors were investigated. The outcome groups were classical Hodgkin lymphoma (cHL), nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL), non-Hodgkin lymphoma (NHL), other malignancies, and a benign group. We integrated the best univariate predicting factors into a multivariate, machine learning model. Logistic regression allocated each variable a weighing factor. The model was tested in a different patient cohort (n = 60). We report a 12-factor diagnostic model with a sensitivity of 95% (95% CI 89–98%) and a specificity of 88% (95% CI 77–94%) for detecting cHL and NHL. Our 12-factor diagnostic scoring model is highly sensitive and specific in detecting high-grade lymphomas in children with cervical lymphadenopathy. It may enable fast referral to a pediatric oncologist in patients with high-grade lymphoma and may reduce the number of referrals and unnecessary invasive procedures in children with benign lymphadenopathy.</p

Adaptive neurofeedback on parieto-occipital cortex for motor learning performance (Motor öğrenme performansı için parieto-oksipital korteks üzerinde uyarlamalı nörogeribesleme)

Numerous electroencephalogram (EEG) based Brain-Computer Interface (BCI) systems are being used as alternative means of communication for locked-in patients. Beyond these, BCIs are also considered in the context of post-stroke motor rehabilitation. Such research usually focuses on exploiting information decoded from sensorimotor activity of the brain. Here, we propose to extend this current focus beyond sensorimotor to also include associative brain areas. In this pilot study, we present an adaptive neurofeedback training paradigm to up-regulate particular EEG activity that is likely to enhance post-stroke motor rehabilitation. Our experimental results support the interpretation that the neurofeedback paradigm enables subjects to up-regulate intended activity and sustain that modulation in inter-trial resting periods in a state that we believe can support motor learning performance. These results serve as a beginning on viability of our claim on integrating a neurofeedback approach to BCI-based motor rehabilitation protocols

Ewsr1-wt1 target genes and therapeutic options identified in a novel dsrct in vitro model

Desmoplastic small round cell tumor (DSRCT) is a rare and aggressive soft tissue sarcoma with a lack of effective treatment options and a poor prognosis. DSRCT is characterized by a chromosomal translocation, resulting in the EWSR1-WT1 gene fusion. The molecular mechanisms driving DSRCT are poorly understood, and a paucity of preclinical models hampers DSRCT research. Here, we establish a novel primary patient-derived DSRCT in vitro model, recapitulating the original tumor. We find that EWSR1-WT1 expression affects cell shape and cell survival, and we identify downstream target genes of the EWSR1-WT1 fusion. Additionally, this preclinical in vitro model allows for medium-throughput drug screening. We discover sensitivity to several drugs, including compounds targeting RTKs. MERTK, which has been described as a therapeutic target for several malignancies, correlates with EWSR1-WT1 expression. Inhibition of MERTK with the small-molecule inhibitor UNC2025 results in reduced proliferation of DSRCT cells in vitro, suggesting MERTK as a therapeutic target in DSRCT. This study underscores the usefulness of preclinical in vitro models for studying molecular mechanisms and potential therapeutic options