Neuronal synchrony and critical bistability: Mechanistic biomarkers for localizing the epileptogenic network.

OBJECTIVE: Postsurgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30% to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). We aimed to advance a novel approach to better characterize epileptogenicity and investigate whether the EZ encompasses a broader epileptogenic network (EpiNet) beyond the seizure zone (SZ) that exhibits seizure activity. METHODS: We first used computational modeling to test putative complex systems-driven and systems neuroscience-driven mechanistic biomarkers for epileptogenicity. We then used these biomarkers to extract features from resting-state stereoelectroencephalograms recorded from DRE patients and trained supervised classifiers to localize the SZ against gold standard clinical localization. To further explore the prevalence of pathological features in an extended brain network outside of the clinically identified SZ, we also used unsupervised classification. RESULTS: Supervised SZ classification trained on individual features achieved accuracies of .6-.7 area under the receiver operating characteristic curve (AUC). Combining all criticality and synchrony features further improved the AUC to .85. Unsupervised classification discovered an EpiNet-like cluster of brain regions, in which 51% of brain regions were outside of the SZ. Brain regions in the EpiNet-like cluster engaged in interareal hypersynchrony and locally exhibited high-amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure risk regime revealed by our computational modeling. SIGNIFICANCE: The finding that combining biomarkers improves SZ localization accuracy indicates that the novel mechanistic biomarkers for epileptogenicity employed here yield synergistic information. On the other hand, the discovery of SZ-like brain dynamics outside of the clinically defined SZ provides empirical evidence of an extended pathophysiological EpiNet.

NeuroVAD: Real-Time Voice Activity Detection from Non-Invasive Neuromagnetic Signals.

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.

Systemic immune-checkpoint blockade with anti-PD1 antibodies does not alter cerebral amyloid-ß burden in several amyloid transgenic mouse models.

Chronic inflammation represents a central component in the pathogenesis of Alzheimer's disease (AD). Recent work suggests that breaking immune tolerance by Programmed cell Death-1 (PD1) checkpoint inhibition produces an IFN-γ-dependent systemic immune response, with infiltration of the brain by peripheral myeloid cells and neuropathological as well as functional improvements even in mice with advanced amyloid pathology (Baruch et al., (): Nature Medicine, 22:135-137). Immune checkpoint inhibition was therefore suggested as potential treatment for neurodegenerative disorders when activation of the immune system is appropriate. Because a xenogeneic rat antibody (mAb) was used in the study, whether the effect was specific to PD1 target engagement was uncertain. In the present study we examined whether PD1 immunotherapy can lower amyloid-ß pathology in a range of different amyloid transgenic models performed at three pharmaceutical companies with the exact same anti-PD1 isotype and two mouse chimeric variants. Although PD1 immunotherapy stimulated systemic activation of the peripheral immune system, monocyte-derived macrophage infiltration into the brain was not detected, and progression of brain amyloid pathology was not altered. Similar negative results of the effect of PD1 immunotherapy on amyloid brain pathology were obtained in two additional models in two separate institutions. These results show that inhibition of PD1 checkpoint signaling by itself is not sufficient to reduce amyloid pathology and that additional factors might have contributed to previously published results (Baruch et al., (): Nature Medicine, 22:135-137). Until such factors are elucidated, animal model data do not support further evaluation of PD1 checkpoint inhibition as a therapeutic modality for Alzheimer's disease.

Complementary roles of cortical oscillations in automatic and controlled processing during rapid serial tasks.

Cognitive control may involve adjusting behaviour by inhibiting or altering habitual actions, requiring rapid communication between sensory, cognitive, and motor systems of the brain. Cognitive control may be achieved using top-down processing from frontal areas to inhibit prepared responses, likely mediated through frontal theta (4-8 Hz) oscillations. However there is conflicting evidence for mechanisms of response inhibition, where global and selective inhibition are either considered separate processes, or frontal areas maintain and execute goal-directed actions, including inhibition. In the current study we measured neuromagnetic oscillatory brain activity in twelve adults responding to rapidly presented visual cues. We used two tasks in the same subjects that required inhibition of a habitual "go" response. Presentation of infrequent "target" cues required subjects to completely inhibit responding (go/no-go task) or to perform an alternate response (go/switch task). Source analysis of oscillatory brain activity was compared for correct no-go and switch trials as well as error trials ("go" responses to targets). Frontal theta activity was similar in cortical location, amplitude and time course for correct no-go and switch responses reflecting an equivalent role in both global and selective response inhibition. Error-related frontal theta activity was also observed but was different in source location (errors vs correct, both tasks: p<0.005) and power (go/switch>go/no-go error, correct switch power, p=0.01). We additionally observed sensorimotor high gamma (60-90 Hz) activity accompanying motor responses, which was markedly stronger for correct switch and error responses compared with go responses, and was delayed for errors (p<0.01). These results suggest that gamma signals in the motor cortex may function to integrate inhibitory signals with sensorimotor processing, and may represent a mechanism for the overriding of habitual behaviours, as errors were predicted by a delay in gamma onset. This study supports a role for frontal areas in maintaining and executing goal-directed actions, and demonstrates that frontal theta activity and sensorimotor gamma oscillations have distinct yet complementary functional roles in monitoring and modifying habitual motor plans.

Automatic detection of ALS from single-trial MEG signals during speech tasks: a pilot study.

Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal, and fast-progressive neurodegenerative disease characterized by the degeneration of motor neurons. ALS patients often experience an initial misdiagnosis or a diagnostic delay due to the current unavailability of an efficient biomarker. Since impaired speech is typical in ALS, we hypothesized that functional differences between healthy and ALS participants during speech tasks can be explained by cortical pattern changes, thereby leading to the identification of a neural biomarker for ALS. In this pilot study, we collected magnetoencephalography (MEG) recordings from three early-diagnosed patients with ALS and three healthy controls during imagined (covert) and overt speech tasks. First, we computed sensor correlations, which showed greater correlations for speakers with ALS than healthy controls. Second, we compared the power of the MEG signals in canonical bands between the two groups, which showed greater dissimilarity in the beta band for ALS participants. Third, we assessed differences in functional connectivity, which showed greater beta band connectivity for ALS than healthy controls. Finally, we performed single-trial classification, which resulted in highest performance with beta band features (∼ 98%). These findings were consistent across trials, phrases, and participants for both imagined and overt speech tasks. Our preliminary results indicate that speech-evoked beta oscillations could be a potential neural biomarker for diagnosing ALS. To our knowledge, this is the first demonstration of the detection of ALS from single-trial neural signals.

Magnetometers vs Gradiometers for Neural Speech Decoding.

Neural speech decoding aims at providing natural rate communication assistance to patients with locked-in state (e.g. due to amyotrophic lateral sclerosis, ALS) in contrast to the traditional brain-computer interface (BCI) spellers which are slow. Recent studies have shown that Magnetoencephalography (MEG) is a suitable neuroimaging modality to study neural speech decoding considering its excellent temporal resolution that can characterize the fast dynamics of speech. Gradiometers have been the preferred choice for sensor space analysis with MEG, due to their efficacy in noise suppression over magnetometers. However, recent development of optically pumped magnetometers (OPM) based wearable-MEG devices have shown great potential in future BCI applications, yet, no prior study has evaluated the performance of magnetometers in neural speech decoding. In this study, we decoded imagined and spoken speech from the MEG signals of seven healthy participants and compared the performance of magnetometers and gradiometers. Experimental results indicated that magnetometers also have the potential for neural speech decoding, although the performance was significantly lower than that obtained with gradiometers. Further, we implemented a wavelet based denoising strategy that improved the performance of both magnetometers and gradiometers significantly. These findings reconfirm that gradiometers are preferable in MEG based decoding analysis but also provide the possibility towards the use of magnetometers (or OPMs) for the development of the next-generation speech-BCIs.

Decoding Imagined and Spoken Phrases From Non-invasive Neural (MEG) Signals.

Speech production is a hierarchical mechanism involving the synchronization of the brain and the oral articulators, where the intention of linguistic concepts is transformed into meaningful sounds. Individuals with locked-in syndrome (fully paralyzed but aware) lose their motor ability completely including articulation and even eyeball movement. The neural pathway may be the only option to resume a certain level of communication for these patients. Current brain-computer interfaces (BCIs) use patients' visual and attentional correlates to build communication, resulting in a slow communication rate (a few words per minute). Direct decoding of imagined speech from the neural signals (and then driving a speech synthesizer) has the potential for a higher communication rate. In this study, we investigated the decoding of five imagined and spoken phrases from single-trial, non-invasive magnetoencephalography (MEG) signals collected from eight adult subjects. Two machine learning algorithms were used. One was an artificial neural network (ANN) with statistical features as the baseline approach. The other was convolutional neural networks (CNNs) applied on the spatial, spectral and temporal features extracted from the MEG signals. Experimental results indicated the possibility to decode imagined and spoken phrases directly from neuromagnetic signals. CNNs were found to be highly effective with an average decoding accuracy of up to 93% for the imagined and 96% for the spoken phrases.

MEG Sensor Selection for Neural Speech Decoding.

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.

Preclinical Activity of SAR408701: A Novel Anti-CEACAM5-maytansinoid Antibody-drug Conjugate for the Treatment of CEACAM5-positive Epithelial Tumors.

PURPOSE: Carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) is a glycoprotein that has limited expression in normal adult tissues, but is overexpressed in carcinomas of the gastrointestinal tract, the genitourinary and respiratory systems, and breast cancer. As such, CEACAM5 is an attractive target for antibody-based therapies designed to selectively deliver cytotoxic drugs to certain epithelial tumors. Here, we describe preclinical data for a novel antibody-drug conjugate (ADC), SAR408701, which consists of an anti-CEACAM5 antibody (SAR408377) coupled to a maytansinoid agent DM4 via a cleavable linker. EXPERIMENTAL DESIGN: The specificity and binding affinity of SAR408701 to human and cynomolgus monkey CEACAM5 were tested in vitro. The cytotoxic activity of SAR408701 was assessed in CEACAM5-expressing tumor cell lines and using patient-derived xenograft mouse models of CEACAM5-positive tumors. Pharmacokinetic-pharmacodynamic and pharmacokinetic-efficacy relationships were established. SAR408701 toxicity was evaluated in cynomolgus monkey. RESULTS: SAR408701 bound selectively to human and cynomolgus monkey CEACAM5 with similar apparent Kd values (0.017 nmol/L and 0.024 nmol/L, respectively). Both in vitro and in vivo evaluations showed that SAR408701 has cytotoxic activity, leading to in vivo efficacy in single and repeated dosing. Single doses of SAR408701 induced significant increases in the tumor expression of phosphorylated histone H3, confirming the tubulin-targeting mechanism of action. The overall toxicity profile of SAR408701 in cynomolgus monkey was similar to that observed after intravenous administration of DM4 alone. CONCLUSIONS: On the basis of these preclinical data, the ADC SAR408701 is a promising candidate for development as a potential treatment for patients with CEACAM5-positive tumors.

Trispecific antibodies enhance the therapeutic efficacy of tumor-directed T cells through T cell receptor co-stimulation.

Despite the significant therapeutic advances provided by immune-checkpoint blockade and chimeric antigen receptor T cell treatments, many malignancies remain unresponsive to immunotherapy. Bispecific antibodies targeting tumor antigens and activating T cell receptor signaling have shown some clinical efficacy; however, providing co-stimulatory signals may improve T cell responses against tumors. Here, we developed a trispecific antibody that interacts with CD38, CD3 and CD28 to enhance both T cell activation and tumor targeting. The engagement of both CD3 and CD28 affords efficient T cell stimulation, whereas the anti-CD38 domain directs T cells to myeloma cells, as well as to certain lymphomas and leukemias. In vivo administration of this antibody suppressed myeloma growth in a humanized mouse model and also stimulated memory/effector T cell proliferation and reduced regulatory T cells in non-human primates at well-tolerated doses. Collectively, trispecific antibodies represent a promising platform for cancer immunotherapy.

Decoding Speech from Single Trial MEG Signals Using Convolutional Neural Networks and Transfer Learning.

Decoding speech directly from the brain has the potential for the development of the next generation, more efficient brain computer interfaces (BCIs) to assist in the communication of patients with locked-in syndrome (fully paralyzed but aware). In this study, we have explored the spectral and temporal features of the magnetoencephalography (MEG) signals and trained those features with convolutional neural networks (CNN) for the classification of neural signals corresponding to phrases. Experimental results demonstrated the effectiveness of CNNs in decoding speech during perception, imagination, and production tasks. Furthermore, to overcome the long training time issue of CNNs, we leveraged principal component analysis (PCA) for spatial dimension reduction of MEG data and transfer learning for model initialization. Both PCA and transfer learning were found to be highly beneficial for faster model training. The best configuration (50 principal coefficients + transfer learning) led to more than 10 times faster training than the original setting while the speech decoding accuracy remained at a similarly high level.

Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective.

Advancing the knowledge about neural speech mechanisms is critical for developing next-generation, faster brain computer interface to assist in speech communication for the patients with severe neurological conditions (e.g., locked-in syndrome). Among current neuroimaging techniques, Magnetoencephalography (MEG) provides direct representation for the large-scale neural dynamics of underlying cognitive processes based on its optimal spatiotemporal resolution. However, the MEG measured neural signals are smaller in magnitude compared to the background noise and hence, MEG usually suffers from a low signal-to-noise ratio (SNR) at the single-trial level. To overcome this limitation, it is common to record many trials of the same event-task and use the time-locked average signal for analysis, which can be very time consuming. In this study, we investigated the effect of the number of MEG recording trials required for speech decoding using a machine learning algorithm. We used a wavelet filter for generating the denoised neural features to train an Artificial Neural Network (ANN) for speech decoding. We found that wavelet based denoising increased the SNR of the neural signal prior to analysis and facilitated accurate speech decoding performance using as few as 40 single-trials. This study may open up the possibility of limiting MEG trials for other task evoked studies as well.

A single T cell epitope drives the neutralizing anti-drug antibody response to natalizumab in multiple sclerosis patients.

Natalizumab (NZM), a humanized monoclonal IgG4 antibody to α4 integrins, is used to treat patients with relapsing-remitting multiple sclerosis (MS)1,2, but in about 6% of the cases persistent neutralizing anti-drug antibodies (ADAs) are induced leading to therapy discontinuation3,4. To understand the basis of the ADA response and the mechanism of ADA-mediated neutralization, we performed an in-depth analysis of the B and T cell responses in two patients. By characterizing a large panel of NZM-specific monoclonal antibodies, we found that, in both patients, the response was polyclonal and targeted different epitopes of the NZM idiotype. The neutralizing activity was acquired through somatic mutations and correlated with a slow dissociation rate, a finding that was supported by structural data. Interestingly, in both patients, the analysis of the CD4+ T cell response, combined with mass spectrometry-based peptidomics, revealed a single immunodominant T cell epitope spanning the FR2-CDR2 region of the NZM light chain. Moreover, a CDR2-modified version of NZM was not recognized by T cells, while retaining binding to α4 integrins. Collectively, our integrated analysis identifies the basis of T-B collaboration that leads to ADA-mediated therapeutic resistance and delineates an approach to design novel deimmunized antibodies for autoimmune disease and cancer treatment.

Self-paced movements induce high-frequency gamma oscillations in primary motor cortex.

There has been increasing interest in the functional role of high-frequency (>30 Hz) cortical oscillations accompanying various sensorimotor and cognitive tasks in humans. Similar "high gamma" activity has been observed in the motor cortex, although the role of this activity in motor control is unknown. Using whole-head MEG recordings combined with advanced source localization methods, we identified high-frequency (65 to 80 Hz) gamma oscillations in the primary motor cortex during self-paced movements of the upper and lower limbs. Brief bursts of gamma activity were localized to the contralateral precentral gyrus (MI) during self-paced index finger abductions, elbow flexions and foot dorsiflexions. In comparison to lower frequency (10-30 Hz) sensorimotor rhythms that are bilaterally suppressed prior to and during movement (Jurkiewicz et al., 2006), high gamma activity increased only during movement, reaching maximal increase 100 to 250 ms following EMG onset, and was lateralized to contralateral MI, similar to findings from intracranial EEG studies. Peak frequency of gamma activity was significantly lower during foot dorsiflexion (67.4+/-5.2 Hz) than during finger abduction (75.3+/-4.4 Hz) and elbow flexion (73.9+/-3.7 Hz) although markedly similar for left and right movements of the same body part within subjects, suggesting activation of a common underlying network for gamma oscillations in the left and right motor cortex. These findings demonstrate that voluntary movements elicit high-frequency gamma oscillations in the primary motor cortex that are effector specific, and possibly reflect the activation of cortico-subcortical networks involved in the feedback control of discrete movements.

Neuromagnetic mismatch field (MMF) dependence on the auditory temporal integration window and the existence of categorical boundaries: comparisons between dissyllabic words and their equivalent tones.

Previous duration-related auditory mismatch response studies have tested vowels, words, and tones. Recently, the elicitation of strong neuromagnetic mismatch field (MMF) components in response to large (>32%) vowel-duration decrements was clearly observed within dissyllabic words. To date, however, the issues of whether this MMF duration-decrement effect also extends to duration increments, and to what degree these duration decrements and increments are attributed to their corresponding non-speech acoustic properties remainto be resolved. Accordingly, this magnetoencephalographic (MEG) study investigated whether prominent MMF components would be evoked by both duration decrements and increments for dissyllabic word stimuli as well as frequency-band matched tones in order to corroborate the relation between the MMF elicitation and the directions of duration changes in speech and non-speech. Further, the peak latency effectsdepending on stimulus types (words vs. tones) were examined. MEG responses were recorded with a whole-head 148-channel magnetometer, while subjects passively listened to the stimuli presented within an odd-ball paradigm for both shortened duration (180-->100%) and lengthened duration (100-->180%). Prominent MMF components were observed in the shortened and lengthened paradigms for the word stimuli, but only in the shortened paradigm for tones. The MMF peak latency results showed that the words ledtoearlier peak latencies than the tones. These findings suggest that duration lengthening as well as shortening in words produces a salient acoustic MMF response when the divergent point between the long and short durations fallswithin the temporal window ofauditory integration post sound onset (<200 ms), and that theearlier latency of the dissyllabic word stimuli over tones is due to a prominent syllable structure in words which is used to generate temporal categorical boundaries.

BrainWave: A Matlab Toolbox for Beamformer Source Analysis of MEG Data.

BrainWave is an easy-to-use Matlab toolbox for the analysis of magnetoencephalography data. It provides a graphical user interface for performing minimum-variance beamforming analysis with rapid and interactive visualization of evoked and induced brain activity. This article provides an overview of the main features of BrainWave with a step-by-step demonstration of how to proceed from raw experimental data to group source images and time series analyses. This includes data selection and pre-processing, magnetic resonance image co-registration and normalization procedures, and the generation of volumetric (whole-brain) or cortical surface based source images, and corresponding source time series as virtual sensor waveforms and their time-frequency representations. We illustrate these steps using example data from a recently published study on response inhibition (Isabella et al., 2015) using the sustained attention to response task paradigm in 12 healthy adult participants. In this task participants were required to press a button with their right index finger to a rapidly presented series of numerical digits and withhold their response to an infrequently presented target digit. This paradigm elicited movement-locked brain responses, as well as task-related modulation of brain rhythmic activity in different frequency bands (e.g., theta, beta, and gamma), and is used to illustrate two different types of source reconstruction implemented in the BrainWave toolbox: (1) event-related beamforming of averaged brain responses and (2) beamformer analysis of modulation of rhythmic brain activity using the synthetic aperture magnetometry algorithm. We also demonstrate the ability to generate group contrast images between different response types, using the example of frontal theta activation patterns during error responses (failure to withhold on target trials). BrainWave is free academic software available for download at http://cheynelab.utoronto.ca/brainwave along with supporting software and documentation. The development of the BrainWave toolbox was supported by grants from the Canadian Institutes of Health Research, the National Research and Engineering Research Council of Canada, and the Ontario Brain Institute.

A Robust Multiplex Mass Spectrometric Assay for Screening Small-Molecule Inhibitors of CD73 with Diverse Inhibition Modalities.

CD73/Ecto-5'-nucleotidase is a membrane-tethered ecto-enzyme that works in tandem with CD39 to convert extracellular adenosine triphosphate (ATP) into adenosine. CD73 is highly expressed on various types of cancer cells and on infiltrating suppressive immune cells, leading to an elevated concentration of adenosine in the tumor microenvironment, which elicits a strong immunosuppressive effect. In preclinical studies, targeting CD73 with anti-CD73 antibody results in favorable antitumor effects. Despite initial studies using antibodies, inhibition of CD73 catalytic activity using small-molecule inhibitors may be more effective in lowering extracellular adenosine due to better tumor penetration and distribution. To screen small-molecule libraries, we explored multiple approaches, including colorimetric and fluorescent biochemical assays, and due to some limitations with these assays, we developed a mass spectrometry (MS)-based assay. Only the MS-based assay offers the sensitivity and dynamic range required for screening small-molecule libraries at a substrate concentration close to the Km value of substrate and for evaluating the mode of binding of screening hits. To achieve a throughput suitable for high-throughput screening (HTS), we developed a RapidFire-tandem mass spectrometry (RF-MS/MS)-based multiplex assay. This assay allowed a large diverse compound library to be screened at a speed of 1536 reactions per 40-50 min.

An MRI-compatible force sensor for measuring differential isometric precision grip force.

Investigating neural correlates of fine motor control in a magnetically sensitive environment requires special considerations in sensor design. Our application requires measurement of forefinger and thumb forces during precision grip in a relatively low (<; 20 N) force range. This work describes the design, characterization and performance evaluation of an MR-compatible precision grip sensor that independently measures forefinger and thumb forces. We selective laser sintered Nylon 12 into a flexure, measuring deformation using optic fibers which matches our finite element model simulation. We found that the device was capable of measuring forces within the desired range, with some hysteresis at higher frequencies as expected. We conclude that the device performs well compared to specifications.

An MEG-Compatible Electromagnetic-Tracking System for Monitoring Orofacial Kinematics.

We describe a novel motion-tracking system, called MASK (magnetoarticulography for the assessment of speech kinematics) designed to track detailed orofacial movements during magnetoencephalographic (MEG) measures of human brain activity. A three-dimensional electromagnetic-tracking method was employed using lightweight coils energized with high-frequency sinusoidal currents, creating magnetic dipoles that can be continuously localized by the MEG sensors. In addition to being compatible with commercial MEG devices, this system has advantages over optical or video methods in that it can record nonline-of-sight movements (e.g., tongue movements) and advantages over surface electromyographic recordings, which are prone to movement-related artifacts and signal crosstalk. Static and dynamic tracking accuracy was evaluated using calibration devices with fixed intercoil distances. MEG data were collected in two healthy adult volunteers to test feasibility of tracking movements during tongue and facial movement, and during overt speech. The MASK system was shown to have sufficient static and dynamic accuracy to track orofacial movements within the MEG helmet. We successfully acquired spatially precise kinematic information time-locked to brain activity with high temporal resolution. We demonstrated successful tracking of oromotor and speech movements together with brain activity using the MASK system. This novel technology will provide an innovative tool in support of research and clinical applications for individuals with speech and other oromotor disorders.

CODV-Ig, a universal bispecific tetravalent and multifunctional immunoglobulin format for medical applications.

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.