Synthesizing Speech from Intracranial Depth Electrodes using an Encoder-Decoder Framework

Speech Neuroprostheses have the potential to enable communication for people with dysarthria or anarthria. Recent advances have demonstrated high-quality text decoding and speech synthesis from electrocorticographic grids placed on the cortical surface. Here, we investigate a less invasive measurement modality in three participants, namely stereotactic EEG (sEEG) that provides sparse sampling from multiple brain regions, including subcortical regions. To evaluate whether sEEG can also be used to synthesize high-quality audio from neural recordings, we employ a recurrent encoder-decoder model based on modern deep learning methods. We find that speech can indeed be reconstructed with correlations up to 0.8 from these minimally invasive recordings, despite limited amounts of training data

Decoding executed and imagined grasping movements from distributed non-motor brain areas using a Riemannian decoder

Using brain activity directly as input for assistive tool control can circumventmuscular dysfunction and increase functional independence for physically impaired people. The motor cortex is commonly targeted for recordings, while growing evidence shows that there exists decodable movement-related neural activity outside of the motor cortex. Several decoding studies demonstrated significant decoding from distributed areas separately. Here, we combine information from all recorded non-motor brain areas and decode executed and imagined movements using a Riemannian decoder. We recorded neural activity from 8 epilepsy patients implanted with stereotactic-electroencephalographic electrodes (sEEG), while they performed an executed and imagined grasping tasks. Before decoding, we excluded all contacts in or adjacent to the central sulcus. The decoder extracts a low-dimensional representation of varying number of components, and classified move/no-move using a minimum-distance-to-geometric-mean Riemannian classifier. We show that executed and imagined movements can be decoded from distributed non-motor brain areas using a Riemannian decoder, reaching an area under the receiver operator characteristic of 0.83 ± 0.11. Furthermore, we highlight the distributedness of the movement-related neural activity, as no single brain area is the main driver of performance. Our decoding results demonstrate a first application of a Riemannian decoder on sEEG data and show that it is able to decode from distributed brain-wide recordings outside of the motor cortex. This brief report highlights the perspective to explore motor-related neural activity beyond the motor cortex, as many areas contain decodable information.</p

Decoding executed and imagined grasping movements from distributed non-motor brain areas using a Riemannian decoder

Using brain activity directly as input for assistive tool control can circumventmuscular dysfunction and increase functional independence for physically impaired people. The motor cortex is commonly targeted for recordings, while growing evidence shows that there exists decodable movement-related neural activity outside of the motor cortex. Several decoding studies demonstrated significant decoding from distributed areas separately. Here, we combine information from all recorded non-motor brain areas and decode executed and imagined movements using a Riemannian decoder. We recorded neural activity from 8 epilepsy patients implanted with stereotactic-electroencephalographic electrodes (sEEG), while they performed an executed and imagined grasping tasks. Before decoding, we excluded all contacts in or adjacent to the central sulcus. The decoder extracts a low-dimensional representation of varying number of components, and classified move/no-move using a minimum-distance-to-geometric-mean Riemannian classifier. We show that executed and imagined movements can be decoded from distributed non-motor brain areas using a Riemannian decoder, reaching an area under the receiver operator characteristic of 0.83 ± 0.11. Furthermore, we highlight the distributedness of the movement-related neural activity, as no single brain area is the main driver of performance. Our decoding results demonstrate a first application of a Riemannian decoder on sEEG data and show that it is able to decode from distributed brain-wide recordings outside of the motor cortex. This brief report highlights the perspective to explore motor-related neural activity beyond the motor cortex, as many areas contain decodable information

Surgical and Hardware-Related Adverse Events of Deep Brain Stimulation:A Ten-Year Single-Center Experience

INTRODUCTION: Although deep brain stimulation (DBS) is effective for treating a number of neurological and psychiatric indications, surgical and hardware-related adverse events (AEs) can occur that affect quality of life. This study aimed to give an overview of the nature and frequency of those AEs in our center and to describe the way they were managed. Furthermore, an attempt was made at identifying possible risk factors for AEs to inform possible future preventive measures. MATERIALS AND METHODS: Patients undergoing DBS-related procedures between January 2011 and July 2020 were retrospectively analyzed to inventory AEs. The mean follow-up time was 43 ± 31 months. Univariate logistic regression analysis was used to assess the predictive value of selected demographic and clinical variables. RESULTS: From January 2011 to July 2020, 508 DBS-related procedures were performed including 201 implantations of brain electrodes in 200 patients and 307 implantable pulse generator (IPG) replacements in 142 patients. Surgical or hardware-related AEs following initial implantation affected 40 of 200 patients (20%) and resolved without permanent sequelae in all instances. The most frequent AEs were surgical site infections (SSIs) (9.95%, 20/201) and wire tethering (2.49%, 5/201), followed by hardware failure (1.99%, 4/201), skin erosion (1.0%, 2/201), pain (0.5%, 1/201), lead migration (0.52%, 2/386 electrode sites), and hematoma (0.52%, 2/386 electrode sites). The overall rate of AEs for IPG replacement was 5.6% (17/305). No surgical, ie, staged or nonstaged, electrode fixation, or patient-related risk factors were identified for SSI or wire tethering. CONCLUSIONS: Major AEs including intracranial surgery-related AEs or AEs requiring surgical removal or revision of hardware are rare. In particular, aggressive treatment is required in SSIs involving multiple sites or when Staphylococcus aureus is identified. For future benchmarking, the development of a uniform reporting system for surgical and hardware-related AEs in DBS surgery would be useful

Intraoperative magnetic resonance imaging versus standard neuronavigation for the neurosurgical treatment of glioblastoma: A randomized controlled trial.

BACKGROUND: Although the added value of increasing extent of glioblastoma resection is still debated, multiple technologies can assist neurosurgeons in attempting to achieve this goal. Intraoperative magnetic resonance imaging (iMRI) might be helpful in this context, but to date only one randomized trial exists. METHODS: We included 14 adults with a supratentorial tumor suspect for glioblastoma and an indication for gross total resection in this randomized controlled trial of which the interim analysis is presented here. Participants were assigned to either ultra-low-field strength iMRI-guided surgery (0.15 Tesla) or to conventional neuronavigation-guided surgery (cNN). Primary endpoint was residual tumor volume (RTV) percentage. Secondary endpoints were clinical performance, health-related quality of life (HRQOL) and survival. RESULTS: Median RTV in the cNN group is 6.5% with an interquartile range of 2.5-14.75%. Median RTV in the iMRI group is 13% with an interquartile range of 3.75-27.75%. A Mann-Whitney test showed no statistically significant difference between these groups (P =0.28). Median survival in the cNN group is 472 days, with an interquartile range of 244-619 days. Median survival in the iMRI group is 396 days, with an interquartile range of 191-599 days (P =0.81). Clinical performance did not differ either. For HRQOL only descriptive statistics were applied due to a limited sample size. CONCLUSION: This interim analysis of a randomized trial on iMRI-guided glioblastoma resection compared with cNN-guided glioblastoma resection does not show an advantage with respect to extent of resection, clinical performance, and survival for the iMRI group. Ultra-low-field strength iMRI does not seem to be cost-effective compared with cNN, although the lack of a valid endpoint for neurosurgical studies evaluating extent of glioblastoma resection is a limitation of our study and previous volumetry-based studies on this topic.Peer reviewe

Can predicting COVID-19 mortality in a European cohort using only demographic and comorbidity data surpass age-based prediction: An externally validated study.

peer reviewedOBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features