A closed loop brain-machine interface for epilepsy control using dorsal column electrical stimulation

Although electrical neurostimulation has been proposed as an alternative treatment for drug-resistant cases of epilepsy, current procedures such as deep brain stimulation, vagus, and trigeminal nerve stimulation are effective only in a fraction of the patients. Here we demonstrate a closed loop brain-machine interface that delivers electrical stimulation to the dorsal column (DCS) of the spinal cord to suppress epileptic seizures. Rats were implanted with cortical recording microelectrodes and spinal cord stimulating electrodes, and then injected with pentylenetetrazole to induce seizures. Seizures were detected in real time from cortical local field potentials, after which DCS was applied. This method decreased seizure episode frequency by 44% and seizure duration by 38%. We argue that the therapeutic effect of DCS is related to modulation of cortical theta waves, and propose that this closed-loop interface has the potential to become an effective and semi-invasive treatment for refractory epilepsy and other neurological disorders.We are grateful for the assistance from Jim Meloy for the design and production of the multielectrode arrays as well as setup development and maintenance, Laura Oliveira, Terry Jones, and Susan Halkiotis for administrative assistance and preparation of the manuscript. This work was funded by a grant from The Hartwell Foundation.info:eu-repo/semantics/publishedVersio

Overexpression of Hydroxynitrile Lyase in Cassava Roots Elevates Protein and Free Amino Acids while Reducing Residual Cyanogen Levels

Cassava is the major source of calories for more than 250 million Sub-Saharan Africans, however, it has the lowest protein-to-energy ratio of any major staple food crop in the world. A cassava-based diet provides less than 30% of the minimum daily requirement for protein. Moreover, both leaves and roots contain potentially toxic levels of cyanogenic glucosides. The major cyanogen in cassava is linamarin which is stored in the vacuole. Upon tissue disruption linamarin is deglycosylated by the apolplastic enzyme, linamarase, producing acetone cyanohydrin. Acetone cyanohydrin can spontaneously decompose at pHs >5.0 or temperatures >35°C, or is enzymatically broken down by hydroxynitrile lyase (HNL) to produce acetone and free cyanide which is then volatilized. Unlike leaves, cassava roots have little HNL activity. The lack of HNL activity in roots is associated with the accumulation of potentially toxic levels of acetone cyanohydrin in poorly processed roots. We hypothesized that the over-expression of HNL in cassava roots under the control of a root-specific, patatin promoter would not only accelerate cyanogenesis during food processing, resulting in a safer food product, but lead to increased root protein levels since HNL is sequestered in the cell wall. Transgenic lines expressing a patatin-driven HNL gene construct exhibited a 2–20 fold increase in relative HNL mRNA levels in roots when compared with wild type resulting in a threefold increase in total root protein in 7 month old plants. After food processing, HNL overexpressing lines had substantially reduced acetone cyanohydrin and cyanide levels in roots relative to wild-type roots. Furthermore, steady state linamarin levels in intact tissues were reduced by 80% in transgenic cassava roots. These results suggest that enhanced linamarin metabolism contributed to the elevated root protein levels

A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia

Several CSF and blood biomarkers for genetic frontotemporal dementia (FTD) have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH)), synapse dysfunction (neuronal pentraxin 2 (NPTX2)), astrogliosis (glial fibrillary acidic protein (GFAP)), and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage FTD, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic FTD using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. 275 presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialised DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on prior diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF NfL, blood pNfH, blood GFAP, and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve (AUC) of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The AUC to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic FTD revealed that NPTX2 and NfL are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions