Bispecific immunoglobulins (Igs) typically contain at least two distinct variable domains (Fv) that bind to two different target proteins. They are conceived to facilitate clinical development of biotherapeutic agents for diseases where improved clinical outcome is obtained or expected by combination therapy compared to treatment by single agents. Almost all existing formats are linear in their concept and differ widely in drug-like and manufacture-related properties. To overcome their major limitations, we designed cross-over dual variable Ig-like proteins (CODV-Ig). Their design is akin to the design of circularly closed repeat architectures. Indeed, initial results showed that the traditional approach of utilizing (G4S)_x linkers for biotherapeutics design does not identify functional CODV-Igs. Therefore, we applied an unprecedented molecular modeling strategy for linker design that consistently results in CODV-Igs with excellent biochemical and biophysical properties. CODV architecture results in a circular self-contained structure functioning as a self-supporting truss that maintains the parental antibody affinities for both antigens without positional effects. The format is universally suitable for therapeutic applications targeting both circulating and membrane-localized proteins. Due to the full functionality of the Fc domains, serum half-life extension as well as antibody- or complement-dependent cytotoxicity may support biological efficiency of CODV-Igs. We show that judicious choice in combination of epitopes and paratope orientations of bispecific biotherapeutics is anticipated to be critical for clinical outcome. Uniting the major advantages of alternative bispecific biotherapeutics, CODV-Igs are applicable in a wide range of disease areas for fast-track multi-parametric drug optimization.

Direct decoding of speech from the brain is a faster alternative to current electroencephalography (EEG) speller-based brain-computer interfaces (BCI) in providing communication assistance to locked-in patients. Magnetoencephalography (MEG) has recently shown great potential as a non-invasive neuroimaging modality for neural speech decoding, owing in part to its spatial selectivity over other high-temporal resolution devices. Standard MEG systems have a large number of cryogenically cooled channels/sensors (200 - 300) encapsulated within a fixed liquid helium dewar, precluding their use as wearable BCI devices. Fortunately, recently developed optically pumped magnetometers (OPM) do not require cryogens, and have the potential to be wearable and movable making them more suitable for BCI applications. This design is also modular allowing for customized montages to include only the sensors necessary for a particular task. As the number of sensors bears a heavy influence on the cost, size, and weight of MEG systems, minimizing the number of sensors is critical for designing practical MEG-based BCIs in the future. In this study, we sought to identify an optimal set of MEG channels to decode imagined and spoken phrases from the MEG signals. Using a forward selection algorithm with a support vector machine classifier we found that nine optimally located MEG gradiometers provided higher decoding accuracy compared to using all channels. Additionally, the forward selection algorithm achieved similar performance to dimensionality reduction using a stacked-sparse-autoencoder. Analysis of spatial dynamics of speech decoding suggested that both left and right hemisphere sensors contribute to speech decoding. Sensors approximately located near Broca's area were found to be commonly contributing among the higher-ranked sensors across all subjects.

Neural speech decoding-driven brain-computer interface (BCI) or speech-BCI is a novel paradigm for exploring communication restoration for locked-in (fully paralyzed but aware) patients. Speech-BCIs aim to map a direct transformation from neural signals to text or speech, which has the potential for a higher communication rate than the current BCIs. Although recent progress has demonstrated the potential of speech-BCIs from either invasive or non-invasive neural signals, the majority of the systems developed so far still assume knowing the onset and offset of the speech utterances within the continuous neural recordings. This lack of real-time voice/speech activity detection (VAD) is a current obstacle for future applications of neural speech decoding wherein BCI users can have a continuous conversation with other speakers. To address this issue, in this study, we attempted to automatically detect the voice/speech activity directly from the neural signals recorded using magnetoencephalography (MEG). First, we classified the whole segments of pre-speech, speech, and post-speech in the neural signals using a support vector machine (SVM). Second, for continuous prediction, we used a long short-term memory-recurrent neural network (LSTM-RNN) to efficiently decode the voice activity at each time point via its sequential pattern-learning mechanism. Experimental results demonstrated the possibility of real-time VAD directly from the non-invasive neural signals with about 88% accuracy.

Object. The aim of this study was to evaluate the spatial accuracy of interictal magnetoencephalography (MEG) in localizing the primary epileptogenic focus in comparison with alternative MEG-derived estimates such as ictal onset recording or sensory mapping of the periphery where seizures manifest. Methods. During this retrospective study of 12 patients with epilepsy who had undergone successful magnetic source (MS) imaging with the aid of a dual 37-channel biomagnetometer as well as simultaneous MEG/electroencephalography (EEG) recordings, ictal events were observed in five patients and quantitative comparisons of interictal spike and ictal seizure onset source localizations were made. In the eight patients who had presented with sensorimotor seizure, source localization of cortical sites concordant with seizure foci was determined using somatosensory functional mapping, and the results were quantitatively compared with interictal spike source localizations. Interictal spike sources demonstrated on MEG localized to the same region as the corresponding ictal event or somatosensory source localizations. The mean distance between the ictal foci and interictal spike sources was 1.1 ± 0.3 cm. Results of functional somatosensory mapping in patients with sensorimotor seizures demonstrated that seizure sources consistently colocalized with interictal MEG spike sources, with a mean distance of 1.5 ± 0.4 cm. No systematic directional bias was observed. Interictal sources tended to be tightly clustered, and the mean ellipsoid volume, defined by one standard deviation of the source spatial coordinates, was 1 cm³. Conclusions. Interictal spike localizations on MEG were concordant with ictal and, where relevant, functional somatosensory mapping localizations. These findings support the interpretation of interictal spikes on MEG as a useful and effective noninvasive method for localizing primary seizure foci.

Speech production involves the synchronization of neural activity between the speech centers of the brain and the oral-motor system, allowing for the conversion of thoughts into meaningful sounds. This hierarchical mechanism is hindered due to partial or complete paralysis of the articulators for patients suffering from locked-in-syndrome. These patients are in dire need of effective brain-communication interfaces (BCIs), which can at least provide a level of communication assistance. In this study, we tried to decode overt (loud) speech directly from the brain via non-invasive magnetoencephalography (MEG) signals to build the foundation for a faster, direct brain to text mapping BCI. A shallow Artificial Neural Network (ANN) was trained with wavelet features of the MEG signals for this objective. Experimental results show that a direct speech decoding is possible from MEG signals. Moreover, we found that the jaw motion and MEG signals may have complimentary information for speech decoding.