Single threshold adaptive deep brain stimulation in Parkinson's disease depends on parameter selection, movement state and controllability of subthalamic beta activity.

BACKGROUND: Deep brain stimulation (DBS) is an invasive treatment option for patients with Parkinson's disease. Recently, adaptive DBS (aDBS) systems have been developed, which adjust stimulation timing and amplitude in real-time. However, it is unknown how changes in parameters, movement states and the controllability of subthalamic beta activity affect aDBS performance. OBJECTIVE: To characterize how parameter choice, movement state and controllability interactively affect the electrophysiological and behavioral response to single threshold aDBS. METHODS: We recorded subthalamic local field potentials in 12 patients with Parkinson's disease receiving single threshold aDBS in the acute post-operative state. We investigated changes in two aDBS parameters: the onset time and the smoothing of real-time beta power. Electrophysiological patterns and motor performance were assessed while patients were at rest and during a simple motor task. We further studied the impact of controllability on aDBS performance by comparing patients with and without beta power modulation during continuous stimulation. RESULTS: Our findings reveal that changes in the onset time control the extent of beta power suppression achievable with single threshold adaptive stimulation during rest. Behavioral data indicate that only specific parameter combinations yield a beneficial effect of single threshold aDBS. During movement, action induced beta power suppression reduces the responsivity of the closed loop algorithm. We further demonstrate that controllability of beta power is a prerequisite for effective parameter dependent modulation of subthalamic beta activity. CONCLUSION: Our results highlight the interaction between single threshold aDBS parameter selection, movement state and controllability in driving subthalamic beta activity and motor performance. By this means, we identify directions for the further development of closed-loop DBS algorithms.

Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants.

Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.

A practical guide to invasive neurophysiology in patients with deep brain stimulation.

Deep brain stimulation (DBS) offers the unique opportunity to record human neural population activity as multiunit activity and local field potentials (LFP) directly from the target area in the depth of the brain. This has led to important discoveries through characterization of pathological activity patterns and identification of motor and cognitive correlates of basal ganglia function in patients with movement disorders. These findings have been covered extensively in a large body of literature, but the technical aspects of microelectrode and LFP recordings in DBS patients are rarely reported. This review summarizes the experience from invasive neurophysiology experiments in over 500 DBS cases in the last 20 years in a single centre. It introduces the basics of intraoperative microelectrode recordings, discusses the neurophysiological and technical aspects of LFP signals and gives and outlook on current and next-generation developments - from sensing enabled implantable devices to combined electrocorticography and LFP recordings during adaptive DBS.

Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation.

Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.

Treatment resistance analysis reveals GLUT-1-mediated glucose uptake as a major target of synthetic rocaglates in cancer cells.

Rocaglates are natural compounds that have been extensively studied for their ability to inhibit translation initiation. Rocaglates represent promising drug candidates for tumor treatment due to their growth-inhibitory effects on neoplastic cells. In contrast to natural rocaglates, synthetic analogues of rocaglates have been less comprehensively characterized, but were also shown to have similar effects on the process of protein translation. Here, we demonstrate an enhanced growth-inhibitory effect of synthetic rocaglates when combined with glucose anti-metabolite 2-deoxy-D-glucose (2DG) in different cancer cell lines. Moreover, we unravel a new aspect in the mechanism of action of synthetic rocaglates involving reduction of glucose uptake mediated by downregulation or abrogation of glucose transporter GLUT-1 expression. Importantly, cells with genetically induced resistance to synthetic rocaglates showed substantially less pronounced treatment effect on glucose metabolism and did not demonstrate GLUT-1 downregulation, pointing at the crucial role of this mechanism for the anti-tumor activity of the synthetic rocaglates. Transcriptome profiling revealed glycolysis as one of the major pathways differentially regulated in sensitive and resistant cells. Analysis of synthetic rocaglate efficacy in a 3D tissue context with a co-culture of tumor and normal cells demonstrated a selective effect on tumor cells and substantiated the mechanistic observations obtained in cancer cell lines. Increased glucose uptake and metabolism is a universal feature across different tumor types. Therefore, targeting this feature by synthetic rocaglates could represent a promising direction for exploitation of rocaglates in novel anti-tumor therapies.

Natural Autoimmunity to the Thyroid Hormone Monocarboxylate Transporters MCT8 and MCT10.

The monocarboxylate transporters 8 (MCT8) and 10 (MCT10) are important for thyroid hormone (TH) uptake and signaling. Reduced TH activity is associated with impaired development, weight gain and discomfort. We hypothesized that autoantibodies (aAb) to MCT8 or MCT10 are prevalent in thyroid disease and obesity. Analytical tests for MCT8-aAb and MCT10-aAb were developed and characterized with commercial antiserum. Serum samples from healthy controls, thyroid patients and young overweight subjects were analyzed, and prevalence of the aAb was compared. MCT8-aAb were additionally tested for biological effects on thyroid hormone uptake in cell culture. Positive MCT8-aAb and MCT10-aAb were detected in all three clinical cohorts analyzed. MCT8-aAb were most prevalent in thyroid patients (11.9%) as compared to healthy controls (3.8%) and overweight adolescents (4.2%). MCT8-aAb positive serum reduced T4 uptake in cell culture in comparison to MCT8-aAb negative control serum. Prevalence of MCT10-aAb was highest in the group of thyroid patients as compared to healthy subjects or overweight adolescents (9.0% versus 4.5% and 6.3%, respectively). We conclude that MCT8 and MCT10 represent autoantigens in humans, and that MCT8-aAb may interfere with regular TH uptake and signaling. The increased prevalence of MCT8-aAb and MCT10-aAb in thyroid disease suggests that their presence may be of pathophysiological relevance. This hypothesis deserves an analysis in large prospective studies.

Reduction in false-positive Aspergillus serum galactomannan enzyme immunoassay results associated with use of piperacillin-tazobactam in the United States.

Piperacillin-tazobactam (PTZ) is known to cause false-positive results in the Platelia Aspergillus enzyme-linked immunoassay (EIA), due to contamination with galactomannan (GM). We tested 32 lots of PTZ and 27 serum specimens from patients receiving PTZ. GM was not detected in the lots of PTZ; one serum specimen (3.7%) was positive. PTZ formulations commonly used in the United States today appear to be a rare cause for false-positive GM results.