Medulloblastoma encompasses a collection of clinically and molecularly diverse tumor subtypes that together comprise the most common malignant childhood brain tumor1–4. These tumors are thought to arise within the cerebellum, with approximately 25% originating from granule neuron precursor cells (GNPCs) following aberrant activation of the Sonic Hedgehog pathway (hereafter, SHH-subtype)3–8. The pathological processes that drive heterogeneity among the other medulloblastoma subtypes are not known, hindering the development of much needed new therapies. Here, we provide evidence that a discrete subtype of medulloblastoma that contains activating mutations in the WNT pathway effector CTNNB1 (hereafter, WNT-subtype)1,3,4, arises outside the cerebellum from cells of the dorsal brainstem. We found that genes marking human WNT-subtype medulloblastomas are more frequently expressed in the lower rhombic lip (LRL) and embryonic dorsal brainstem than in the upper rhombic lip (URL) and developing cerebellum. Magnetic resonance imaging (MRI) and intra-operative reports showed that human WNT-subtype tumors infiltrate the dorsal brainstem, while SHH-subtype tumors are located within the cerebellar hemispheres. Activating mutations in Ctnnb1 had little impact on progenitor cell populations in the cerebellum, but caused the abnormal accumulation of cells on the embryonic dorsal brainstem that included aberrantly proliferating Zic1+ precursor cells. These lesions persisted in all mutant adult mice and in 15% of cases in which Tp53 was concurrently deleted, progressed to form medulloblastomas that recapitulated the anatomy and gene expression profiles of human WNT-subtype medulloblastoma. We provide the first evidence that subtypes of medulloblastoma have distinct cellular origins. Our data provide an explanation for the marked molecular and clinical differences between SHH and WNT-subtype medulloblastomas and have profound implications for future research and treatment of this important childhood cancer.

Genome-wide expression profiles can partition large tumor cohorts into subgroups that are enriched for specific genetic alterations. This approach may assist ultimately in the selection of patients for future clinical trials of molecular targeted therapies.

Objective Contemporary deep brain stimulation for Parkinson’s disease is delivered continuously, and adjustments based on patient’s changing symptoms must be made manually by a trained clinician. Patients may be subjected to energy intensive settings at times when they are not needed, possibly resulting in stimulation-induced adverse effects, such as dyskinesia. One solution is “adaptive” DBS, in which stimulation is modified in real time based on neural signals that co-vary with the severity of motor signs or of stimulation-induced adverse effects. Here we show the feasibility of adaptive DBS using a fully implanted neural prosthesis. Approach We demonstrate adaptive deep brain stimulation in two patients with Parkinson’s disease using a fully implanted neural prosthesis that is enabled to utilize brain sensing to control stimulation amplitude (Activa PC+S). We used a cortical narrowband gamma (60-90 Hz) oscillation related to dyskinesia to decrease stimulation voltage when gamma oscillatory activity is high (indicating dyskinesia) and increase stimulation voltage when it is low. Main Results We demonstrate the feasibility of “adaptive deep brain stimulation” in two patients with Parkinson’s disease. In short term in-clinic testing, energy savings were substantial (38-45%), and therapeutic efficacy was maintained. Significance This is the first demonstration of adaptive DBS in Parkinson’s disease using a fully implanted device and neural sensing. Our approach is distinct from other strategies utilizing basal ganglia signals for feedback control.

Deep brain stimulation (DBS) has become a widespread and valuable treatment for patients with movement disorders such as essential tremor (ET). However, current DBS treatment constantly delivers stimulation in an open loop, which can be inefficient. Closing the loop with sensors to provide feedback may increase power efficiency and reduce side effects for patients. New implantable neuromodulation platforms, such as the Medtronic Activa PC+S DBS system, offer important data sources by providing chronic neural sensing capabilities and a means of investigating dynamic stimulation based on symptom measurements. The authors implanted in a single patient with ET an Activa PC+S system, a cortical strip of electrodes on the hand sensorimotor cortex, and therapeutic electrodes in the ventral intermediate nucleus of the thalamus. In this paper they describe the effectiveness of the platform when sensing cortical movement intentions while the patient actually performed and imagined performing movements. Additionally, they demonstrate dynamic closed-loop DBS based on several wearable sensor measurements of tremor intensity.

Brain-computer interfaces (BCIs) are a form of technology that read a user's neural signals to perform a task, often with the aim of inferring user intention. They demonstrate potential in a wide range of clinical, commercial, and personal applications. But BCIs are not always simple to operate, and even with training some BCI users do not operate their systems as intended. Many researchers have described this phenomenon as "BCI illiteracy," and a body of research has emerged aiming to characterize, predict, and solve this perceived problem. However, BCI illiteracy is an inadequate concept for explaining difficulty that users face in operating BCI systems. BCI illiteracy is a methodologically weak concept; furthermore, it relies on the flawed assumption that BCI users possess physiological or functional traits that prevent proficient performance during BCI use. Alternative concepts to BCI illiteracy may offer better outcomes for prospective users and may avoid the conceptual pitfalls that BCI illiteracy brings to the BCI research process.

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Objective. Deep brain stimulation (DBS) is a well-established treatment for essential tremor, but may not be an optimal therapy, as it is always on, regardless of symptoms. A closed-loop (CL) DBS, which uses a biosignal to determine when stimulation should be given, may be better. Cortical activity is a promising biosignal for use in a closed-loop system because it contains features that are correlated with pathological and normal movements. However, neural signals are different across individuals, making it difficult to create a ‘one size fits all’ closed-loop system. Approach. We used machine learning to create a patient-specific, CL DBS system. In this system, binary classifiers are used to extract patient-specific features from cortical signals and determine when volitional, tremor-evoking movement is occurring to alter stimulation voltage in real time. Main results. This system is able to deliver stimulation up to 87%–100% of the time that subjects are moving. Additionally, we show that the therapeutic effect of the system is at least as good as that of current, continuous-stimulation paradigms. Significance. These findings demonstrate the promise of CL DBS therapy and highlight the importance of using subject-specific models in these systems.

To appear in Transactions on Software Engineering.