The National Institute on Drug Abuse (NIDA) is seeking a GS-15 Data Scientist for the Office of the Director. The incumbent will serve as a Data Scientist and Senior Advisor to the NIDA Director and other leadership positions, on matters related to health informatics and data science, data management, emerging technologies, opportunities for collaboration, data standards and policy within the scope of the NIDA mission. Responsibilities include planning and carrying out quality control programs, selecting statistical methods for quality control analysis, ensuring the reliability and consistency of data, providing technical expertise and project leadership to advance strategic initiatives in data- and knowledge-driven methods, and developing collaborations with researchers and administrators in neuroscience.

The Research, Innovation and Dissemination Center for Neuromathematics (NeuroMat), hosted by the University of São Paulo (USP), Brazil, and funded by the São Paulo Research Foundation (FAPESP), is offering two post-doctoral fellowships for recent PhDs with outstanding research potential. The fellowship will involve collaborations with research teams and laboratories associated with NeuroMat, strictly related to ongoing research lines developed by NeuroMat. The project may be developed at the laboratories of USP, campuses of São Paulo or Ribeirão Preto, or at UNICAMP, Campinas, in person. We seek candidates capable of developing independent research in one of the research lines below. 1. Stochastic modeling of neurobiological data. Candidates must have a strong background in probability theory with emphasis on stochastic processes. 2. Development of new statistical methods for neurobiological data. Candidates must have a strong background in statistics and data analysis and knowledge of computer programming. 3. Acquisition, processing, and quantitative analysis of neurobiological data. Candidates must have a strong background in neuroscience with previous experience in neurophysiological data acquisition, processing and analysis, and knowledge of computer programming. 4. Instrumentation development associated with brain stimuli. Candidates must have a strong background in electronic instrumentation, robotics, programing, and safety in medical equipment. The fellowship is competitive at an international level, and fellows benefit from extra funds for travel and research expenses plus limited support for relocation expenses.

Most brain functions involve interactions among multiple, distinct areas or nuclei. Yet our understanding of how populations of neurons in interconnected brain areas communicate is in its infancy. Using a population approach, we found that interactions between early visual cortical areas (V1 and V2) occur through a low-dimensional bottleneck, termed a communication subspace. In this talk, I will focus on the statistical methods we have developed for studying interactions between brain areas. First, I will describe Delayed Latents Across Groups (DLAG), designed to disentangle concurrent, bi-directional (i.e., feedforward and feedback) interactions between areas. Second, I will describe an extension of DLAG applicable to three or more areas, and demonstrate its utility for studying simultaneous Neuropixels recordings in areas V1, V2, and V3. Our results provide a framework for understanding how neuronal population activity is gated and selectively routed across brain areas.

The last twenty years have witnessed extraordinarily rapid progress in basic neuroscience, including breakthrough technologies such as optogenetics, and the collection of unprecedented amounts of neuroimaging, genetic and other data relevant to neuroscience and mental health. However, the translation of this progress into improved understanding of brain function and dysfunction has been comparatively slow. As a result, the development of therapeutics for mental health has stagnated too. One central challenge has been to extract meaning from these large, complex, multivariate datasets, which requires a shift towards systems-level mathematical and computational approaches. A second challenge has been reconciling different scales of investigation, from genes and molecules to cells, circuits, tissue, whole-brain, and ultimately behaviour. In this talk I will describe several strands of work using mathematical, statistical, and bioinformatic methods to bridge these gaps. Topics will include: using artificial neural networks to link the organization of large-scale brain connectivity to cognitive function; using multivariate statistical methods to link disease-related changes in brain networks to the underlying biological processes; and using network-based approaches to move from genetic insights towards drug discovey. Finally, I will discuss how simple organisms such as C. elegans can serve to inspire, test, and validate new methods and insights in networks neuroscience.

Scientists work in laboratories; comfortable spaces which we equip and configure to be ideal for our needs. The scientific paradigm has been adopted by clinicians, who run diagnostic tests and treatments in fully equipped hospital facilities. Yet advances in technology mean that that increasingly many functions of a laboratory can be compressed into miniature devices, or even into a smartphone app. This has the potential to be transformative for healthcare in developing nations, allowing complex tests and interventions to be made available in every village. In this talk, I will give two examples of this approach from my recent work. In the field of stroke rehabilitation, I will present basic research which we have conducted in animals over the last decade. This reveals new ways to intervene and strengthen surviving pathways, which can be deployed in cheap electronic devices to enhance functional recovery. In degenerative disease, we have used Bayesian statistical methods to improve an algorithm to measure how rapidly a subject can stop an action. We then implemented this on a portable device and on a smartphone app. The measurement obtained can act as a useful screen for Parkinson’s Disease. I conclude with an outlook for the future of this approach, and an invitation to those who would be interesting in collaborating in rolling it out to in African settings.

Animals are able to perceive self-motion and navigate in their environment using optic flow information. They often perform visually guided stabilization behaviors like the optokinetic (OKR) or optomotor response (OMR) in order to maintain their eye and body position relative to the moving surround. But how does the animal manage to perform appropriate behavioral response and how are processing tasks divided between the various non-cortical visual brain areas? Experiments have shown that the zebrafish pretectum, which is homologous to the mammalian accessory optic system, is involved in the OKR and OMR. The optic tectum (superior colliculus in mammals) is involved in processing of small stimuli, e.g. during prey capture. We have previously shown that many pretectal neurons respond selectively to rotational or translational motion. These neurons are likely detectors for specific optic flow patterns and mediate behavioral choices of the animal based on optic flow information. We investigate the motion feature extraction of brain structures that receive input from retinal ganglion cells to identify the visual computations that underlie behavioral decisions during prey capture, OKR, OMR and other visually mediate behaviors. Our study of receptive fields shows that receptive field sizes in pretectum (large) and tectum (small) are very different and that pretectal responses are diverse and anatomically organized. Since calcium indicators are slow and receptive fields for motion stimuli are difficult to measure, we also develop novel stimuli and statistical methods to infer the neuronal computations of visual brain areas.