Detection superiority of 7 T MRI protocol in patients with epilepsy and suspected focal cortical dysplasia

In 11 adult patients with suspicion of Focal cortical dysplasia (FCD) on 1.5 T (n = 1) or 3 T (n = 10) magnetic resonance imaging (MRI), 7 T MRI was performed. Visibility, extent, morphological features and delineation were independently rated and subsequently discussed by three observers. Additionally, head-to-head comparisons with corresponding 3 T images were made in the eight patients with a previous 3 T MRI and sustained suspicion of FCD. Comparison with histopathology was done in the five patients that underwent surgery. All lesions, seen at 1.5 and 3 T, were also recognized on 7 T. At 7 T FLAIR highlighted the FCD-like lesions best, whereas T2 and T2* were deemed better suited to review structure and extent of the lesion. Image quality with the used 7 T MRI setup was higher than the quality with the used 3 T MRI setup. In 2 out of 11 patients diagnosis changed, in one after re-evaluation of the images, and in the other based on histopathology. With the used 7 T MRI setup, FCD-like lesions can be detected with more confidence and detail as compared to lower field strength. However, concordance between radiologic diagnosis and final diagnosis seems to be lower than expected

A three-layer MRI-based head phantom for experimental validation of tES simulations

Transcranial electric stimulation (tES) is being investigated for the relief of seizures in medically refractory epilepsy patients. In a quest to optimize the electrode placement and current for improvement of the outcome, we are investigating the exploitation of the pre-stimulation planning using finite element simulations based on individual anatomy from MRI [RM1] scans. A crucial step is validating the stimulation modeling accuracy, but commercial setups for validation do not exist.Hereto, we developed a three-layer head phantom, consisting of skin, skull, and brain tissue, that captures the crucial anatomical features and provides a convenient way of verifying the induced electric fields. It also enables systematic characterization of the uncertainties and variations in conductivity and anatomy. Experiments on the three-layer phantom bridge the gap between simulations and clinical practice since they also allow for using clinical hardware and electrodes.The developed phantom consists of an agar and salt brain layer, a graphite-doped polyurethane skull, and a skin layer made from agar gel with a different conductivity. In this way the solid skull separates the two gel layers, preventing possible ion drift over the layers. The anatomy is based on the ICBM 152 linear model, an average of 152 MRI scans, which enables us to intuitively link measurements and simulations. To perform the systematic characterization experiments, hardware and software were designed in-house. This allows for stimulations and measurements on the phantom in a cheap and modular way. The designed hardware consists of a PID-controlled tES stimulator, which can deliver 4 mA with a frequency up to 100 Hz, and a four-channel differential sensing board based on the OpenBCI Ganglion board.A realistic and modular phantom expands the possibilities of preclinical tES research by providing a tool to validate electric field simulations as well as experiment with clinical hardware and anatomical variations

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

Proyecto de restauración de la casa del bosque, Buitrago de Lozoya

Proyecto de restauración de la casa del bosque, Buitrago de Lozoy

European Expert Opinion on ANT-DBS therapy for patients with drug-resistant epilepsy (a Delphi consensus)

Introduction: Although deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) represents an established third-line therapy for patients with drug-resistant focal epilepsy, guiding reports on practical treatment principles remain scarce. Methods: An Expert Panel (EP) of 10 European neurologists and 4 neurosurgeons was assembled to share their experience with ANT-DBS therapy. The process included a review of the current literature, which served as a basis for an online survey completed by the EP prior to and following a face-to-face meeting (Delphi method). An agreement level of >= 71 % was considered as consensus. Results: Out of 86 reviewed studies, 46 (53 %) were selected to extract information on the most reported criteria for patient selection, management, and outcome. The Delphi process yielded EP consensus on 4 parameters for selection of good candidates and patient management as well as 7 reasons of concern for this therapy. Since it was not possible to give strict device programming advice due to low levels of evidence, the experts shared their clinical practice: all of them start with monopolar stimulation, 79 % using the cycling mode. Most (93 %) EP members set the initial stimulation frequency and pulse width according to the SANTE parameters, while there is more variability in the amplitudes used. Further agreement was achieved on a list of 7 patient outcome parameters to be monitored during the follow-up. Conclusions: Although current evidence is too low for definite practical guidelines, this EP report could support the selection and management of patients with ANT-DBS

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

Cost analysis of nondeterministic probabilistic programs

We consider the problem of expected cost analysis over nondeterministic probabilistic programs, which aims at automated methods for analyzing the resource-usage of such programs. Previous approaches for this problem could only handle nonnegative bounded costs. However, in many scenarios, such as queuing networks or analysis of cryptocurrency protocols, both positive and negative costs are necessary and the costs are unbounded as well. In this work, we present a sound and efficient approach to obtain polynomial bounds on the expected accumulated cost of nondeterministic probabilistic programs. Our approach can handle (a) general positive and negative costs with bounded updates in variables; and (b) nonnegative costs with general updates to variables. We show that several natural examples which could not be handled by previous approaches are captured in our framework. Moreover, our approach leads to an efficient polynomial-time algorithm, while no previous approach for cost analysis of probabilistic programs could guarantee polynomial runtime. Finally, we show the effectiveness of our approach using experimental results on a variety of programs for which we efficiently synthesize tight resource-usage bounds