Kavli Blog

Behavioral modeling and optogenetics elucidate mechanisms of subjective, history-dependent decision bias… Advanced transgenic approach improves light-based study of neuronal circuit dynamics… Current state of computational methods in single-cell functional genomics… Sleep promotes communication between association cortices and hippocampus…

Experimentally manipulating neural activity in behaving mice implicates the posterior parietal cortex in history-based decision bias

Making decisions based on past outcomes is a key adaptive behavior, but how do neural circuits track choice-outcome history and form subjective bias to inform decision-making? At the University of California, San Diego, Dr. Takaki Komiyama and colleagues explored the neural circuit mechanisms that underlie subjective, history-dependent decision bias. They used two-photon calcium imaging to record neural activity in the posterior parietal cortex (PPC), while mice performed an action selection task. The researchers sought to identify idiosyncratic relationships between choice-outcome history and subsequent action selection bias. They showed that action decisions were swayed by past outcomes, and that this behavior was modulated by a subpopulation of PPC neurons. Furthermore, to examine the temporal specificity of these effects, they used precise optogenetic application on PPC (i.e., light directed towards PPC to inactivate the region). Inactivation of PPC before but not during the trial diminished the extent to which an animal depended on past outcomes to make a current choice. This study is the first to demonstrate an essential role for PPC neural circuitry in using an animal’s past history of choices to influence subjective biases on subsequent actions.

Inactivating pre-stimulus activity in PPC alters bias. (a) Control (blue light directed away from PPC) and inactivation (light directed toward PPC) sessions alternated day-to-day. Continuous blue light was applied during the inter-trial interval (ITI) in randomly selected trials in both control and inactivation sessions. (b) An example session. The mouse tended to alternate choice and exhibited history dependency in light-off trials, but this tendency was reduced in light-on trials. (c) The effect of PPC ITI inactivation on the model fit in seven separate inactivation sessions in one mouse. Black, mean across sessions; gray, individual sessions.

Advanced transgenic strategies enable individual neurons within dense networks to be imaged

A key focus in neuroscience is to decipher neuronal circuit dynamics, for the sake of understanding how the brain drives perception, emotion, cognition, and behavior. Recent progress in genetically-encoded voltage and calcium indicators (GEVIs and GECIs, respectively) has benefited this effort by allowing in vivo monitoring of large identified neuronal populations. Indeed, advanced transgenic approaches now achieve high levels of indicator expression. However, targeting non-sparse cell populations leads to dense expression patterns, preventing researchers from assigning optical signal read-out to individual neurons. At Imperial College London, Dr. Thomas Knöpfel and colleagues have developed a genetic technique for sparse but strong cellular indicator expression, which allows for the resolution and segregation of individual cells and their processes within densely populated neuronal networks. The expression is termed “strong” because the expression level in individual cells is high, and it is termed “sparse” because only a fraction of the neuronal population of interest expresses the fluorescent indicator. The concept resembles Golgi staining, an important histological technique for imaging a low percentage of neurons—in their entirety—within a dense network. Utilizing GCaMP6f (GECI) or the voltage-sensitive fluorescent protein (VSFP) Butterfly 1.2 (GEVI), the researchers achieved strong intensity but sparsely-distributed indicator expression in cortical layer II/III pyramidal neurons within the mouse brain. Morphologies of individual neurons and subcellular structures were successfully resolved. Furthermore, using these fluorescent proteins and a modular transgenic approach, the team produced dual GEVI/GECI neuronal labelling, which enables monitoring of concurrent voltage and calcium activity in either the same neuron or in neighboring neurons. By enabling successful visualization of large neuronal populations, this methodological approach has the translational potential to inform animal models of neurological disease by providing rapid evaluation of cellular indicators of dysfunction.

Dual-controlled co-expression of GECI and GEVI in somatosensory cortical layer II/III pyramidal neurons in vivo. (i) Strong GCaMP6f fluorescence signal observed in individual pyramidal neurons. (ii) Strong VSFPB1.2 fluorescence signal observed in two pyramidal neurons. (iii) Merged GCaMP6f and VSFPB1.2 fluorescence signals on nuclei counterstaining (blue) showing sparse indicator expression. Inset: neuron (outlined by the dotted box in b-ii) showing optimal GEVI membrane targeting, and co-expressing GCaMP6f and VSFPB1.2.

Advances in single-cell genomics are revealing the diverse facets of cellular identity

Although cells of the same ‘type’ can exhibit significant heterogeneity, most genomic profiling studies have analyzed cell populations rather than single cells. However, technological advances have enabled genome-wide profiling of molecular information in single cells. While the field is still in its infancy, single-cell genomics are beginning to make it feasible to create a comprehensive atlas of human cells. At Harvard University, Dr. Aviv Regev and colleagues reviewed the current state of computational methods in single-cell functional genomics, focusing on single-cell RNA sequencing (scRNA-seq). The authors report that biological and technical aspects merge to determine the measured genomic profiles of cells. Sources of variation that affect single-cell genomics data are (1) technical (unwanted) factors that reflect variance due to the experimental process, and random biological factors that are either (2) intrinsic or (3) extrinsic to the molecular mechanisms of gene expression. Experimental, statistical, and computational strategies are used to reduce technical variation, so that biological variation can then be more confidently studied. The authors define a cell’s identity, which is reflected in its molecular profile, as the instantaneous intersection of all factors affecting it. These factors include multiple time-dependent processes that take place simultaneously, the cell’s response to local environmental signals, and the precise tissue location in which the cell resides. The more permanent aspects of a cell’s identity indicate its type (i.e., cell types are organized into taxonomies), but this approach also provides snapshots of the dynamic temporal transitions that cells undergo as well. Combining single-cell genomics with computational models that relate cells to one another in space and time could eventually yield an integrated understanding of how these cells function in health and disease. Importantly, this approach holds potential in contributing to a complete census of cell types in the complex mammalian brain.

(a) A cell (blue) in the multiple concurrent contexts that shape its identity. (b) The biological factors affecting the cell combine to create its unique, instantaneous identity. Computational methods dissect the molecular profile and tease apart facets of the cell’s identity. Examples include (counterclockwise from top): (1) division into discrete types; (2) continuous phenotypes (e.g., pro-inflammatory potential of each T cell, quantified through a gene expression signature derived from bulk T cell profiles (N.Y., A.R. and colleagues1)); (3) temporal progression (e.g., differentiation, such as hematopoiesis); (4) temporal vacillation between cellular states (e.g., cell cycle oscillation; A.R. and colleagues2); (5) physical locations: a schematic representation of an embryo (in situ hybridization data of landmark genes allows inferring of spatial bins) (A.R. and colleagues3).
1 Gaublomme JT, et al. Single-cell genomics unveils critical regulators of Th17 Cell pathogenicity. Cell. 2015; 163:1400–1412. 
2 Kowalczyk MS, et al. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res. 2015; 25:1860–1872. 
3 Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015; 33:495–502.

Hippocampal-neocortical communication increases during sleep after spatial learning

Hippocampal sharp-wave ripples, which are high-frequency oscillations seen on an electroencephalograph (EEG) during sleep, have long been thought to be involved in the conversion of short-term memories into long-term memories. Little is known about how ripples transfer hippocampal information to the neocortex, which is required for the consolidation of memories that can be consciously, intentionally recalled (i.e., declarative memories). At New York University, Dr. György Buzsáki and colleagues have developed a microelectrode system (NeuroGrid) for simultaneous electrophysiological monitoring of multiple sites in the rat neocortex. NeuroGrid is a conformable array of tiny linked electrodes that can be laid across a brain area, for large-scale and spatially-continuous monitoring of a large population of neurons. With this technology, the researchers made a novel observation in rats during non-rapid eye movement (NREM) sleep, the longest stage of sleep. Ripples in the association neocortex—a brain area responsible for processing complex sensory information—and the hippocampus occurred concurrently, suggesting communication between the two regions. Then, the team monitored NREM sleep brain activity in rats trained in a cheeseboard maze versus untrained rats that explored the maze randomly. The maze training actually increased the hippocampal-cortical ripple coupling, suggesting that such communication is important for memory consolidation. This is a fundamental discovery, with potential importance to the understanding of memory disorders.

(A) Micrograph of a NeuroGrid consisting of uniformly-distributed 15µm by 15µm electrodes (scale bar, 1.5mm). Inset: NeuroGrid conforming to the dorsal surface of rat cortex (scale bar, 1mm). (B) Anatomical map of neocortical ripple occurrence relative to somatosensory and visual cortex, in a sample rat. Regions with somatosensory and visual evoked potential amplitude >3 standard deviations above the mean amplitude are in blue and green, respectively. Regions with an occurrence rate of cortical ripples >0.05 Hz are in red. Ripple oscillations were rarely observed in primary somatosensory, visual, or motor cortices (rate <0.05 Hz), but were prevalent in the posterior parietal (association) cortex and midline structures (rate: 0.1-0.5 Hz).

Requests for Applications (RFAs) for the NIH BRAIN Initiative® continue to address critical components of the BRAIN 2025 Report, including novel tools to explore brain microconnectivity and non-neuronal cells, technology integration and dissemination, neuroethics, non-invasive human brain imaging, invasive human neuroscience, and team-based research on neural systems and circuits.

For Fiscal Year (FY) 2018, NIH announces 11 funding opportunity announcements (FOAs) for the BRAIN Initiative. Three new FOAs call for the development of tools for facilitating high-throughput microconnectivity analysis, methods to characterize non-neuronal cells in the brain, and resource grants for technology integration and dissemination. Additionally, several FY17 FOAs are being re-issued for research on ethical issues associated with advancements supported by the BRAIN Initiative, human brain imaging, various projects exploring neural circuits, invasive human neuroscience, and research fellowships for postdoctorates.

 

New FOAs for FY18:

RFA-MH-18-505 Tools to Facilitate High-Throughput Microconnectivity Analysis (R01)

  • This FOA provides resources for development and validation of novel tools to facilitate the detailed analysis of brain microconnectivity. Primarily, this FOA seeks to provide techniques and resources for examining complex circuits at the level of synaptic connections, alone or in combination with methods for identifying important cellular and circuit features. Technologies such as electron microscopy, nanoscale imaging, and newer methodologies including expansion microscopy and array tomography, as well as barcode-based tagging of synaptic connection may make it possible to map brain connectivity at the synapse resolution. The goal of this proposed effort is to produce next-generation, novel technologies for analysis of the microconnectome. Therefore, proposed methods should be transformative in scope and innovative in approach to studying molecular and cellular mechanisms of neural activity, particularly in analysis of micro- and macro-circuits. Plans to validate the tool/technology will also be essential. The application receipt date for this FOA is December 7, 2017.

 

RFA-DA-18-018 Tools to target, identify and characterize non-neuronal cells in the brain (R01)

  • This FOA is designed to stimulate the development and validation of technologies, tools, and methods for a detailed inventory and analysis of non-neuronal cells within the brain and to understand their contribution to the function of neural circuits underlying complex behaviors. The unique properties of non-neuronal cells limit the usefulness and applicability of tools developed to study neurons, and sites of neuro-glio-vascular interactions can be difficult to isolate experimentally. This FOA complements existing cell-census and tools development efforts initiated under RFA-MH-14-215 and RFA-MH-14-216 and is designed to develop new tools providing access to individual and defined groups of non-neuronal cells. Proposed tools or technologies should be transformative, high-risk, and aimed to overcome technical and analytical barriers to bridging experimental scales. Therefore, interdisciplinary collaborations such as with nanobiologists, computational and material scientists, and engineers are encouraged. The application receipt date for this FOA is February 1, 2018; clinical trials are not allowed.

 

RFA-NS-18-005 Research Resource Grants for Technology Integration and Dissemination (U24)

  • This U24 mechanism seeks to accelerate the scientific impact of the BRAIN Initiative by rapidly disseminating developed and validated technologies and resources to the broader neuroscience community. Proposed techniques, resources, or approaches should be at a well-validated stage wherein their value in research has already been demonstrated; proposals focused solely on development are not responsive to this FOA. Representative examples of projects responsive to this FOA include: a consortium for voltage sensors that detect changes in membrane potential, imaging services for large-scale recording or neural activity from multiple brain areas, and a resource that gathers and streamlines the distribution of transgenic mouse models for research. The application receipt date for this FOA is February 9, 2018; clinical trials are not allowed.

 

FOAs Re-issued for FY18:

RFA-MH-18-500 Research on the Ethical Implications of Advancements in Neurotechnology and Brain Science (Re-issue of MH-17-260; R01)

  • This R01 mechanism provides opportunities to consider the integration of ethical issues with BRAIN-supported scientific advances, specifically research involving human subjects and resulting from emerging technologies and research advancements. Examples of application topics include those focusing on: risk analyses, consent issues, privacy, ethical implications of neuromodulation and neuroimaging technologies, and issues associated with predictive/diagnostic research. Individuals interested in applying are encouraged to contact the scientific co-leads to discuss application ideas. The application receipt date for this FOA is December 7, 2017.

 

RFA-EB-17-005 Theories, Models and Methods for Analysis of Complex Data from the Brain (Re-issue of EB-15-006; R01)

  • This RFA utilizes a R01 mechanism to solicit new theories, computational models, and statistical tools to derive understanding of brain function from complex neuroscience data. A variety of approaches are applicable: the creation of new theories, ideas, and conceptual frameworks to organize/unify data and infer general principles of brain function; new computational models to develop testable hypotheses and design/drive experiments; and new mathematical and statistical methods to support or refute a stated hypothesis about brain function, and/or assist in detecting dynamical features and patterns in complex brain data. NIH expects that the tools developed under this FOA will be made widely available to the neuroscience research community. The application receipt dates for this FOA are December 15, 2017, October 17, 2018, and October 17, 2019.

 

RFA-EB-17-003 Proof of Concept Development of Early Stage Next Generation Human Brain Imaging (Re-issue of EB-17-001; R01)

  • This RFA uses a R01 mechanism to solicit unusually bold and potentially transformative approaches, including proof-of-concept development of brain imaging based on innovative and/or unconventional concepts aimed at revolutionizing the way non-invasive human neuroimaging is conducted. Tools and technologies can span a wide array of approaches including hardware, software, or imaging probes addressing any of the steps of the image acquisition and analysis process. Creative efforts to bridge scales from the micro- to meso- to macro-level in the brain are especially encouraged. The application receipt dates for this FOA are December 20, 2017 and December 11, 2018.

 

RFA-EB-17-004 Development of Next Generation Human Brain Imaging Tools and Technologies (Re-issue of EB-17-002; U01)

  • This RFA uses an U01 mechanism to support the full-scale development of novel imaging technologies beyond the proof of concept stage for noninvasive imaging of human brain processes in ways that are currently unachievable in healthy persons. NIH expects successful projects supported under the previous RFAs to be the basis for some of the applications submitted in response to this announcement, but previous support is not a requirement. This FOA supports an open competition for the best ideas for the full development of innovative and compelling new or next-gen non-invasive brain imaging technologies, with the intent of delivering working tools within the time frame of the BRAIN Initiative. The application receipt dates for this FOA are December 20, 2017 and December 11, 2018.

 

RFA-NS-18-008 Exploratory Team-Research BRAIN Circuit Programs – eTeamBCP (Re-issue of NS-15-005; U01)

  • This RFA uses an U01 mechanism and is part of a family of “Integrated Approaches” NIH BRAIN FOAs. This FOA promotes the integration of experimental, analytic, and theoretical capabilities for the large-scale analyses of neural systems and circuits, through interdisciplinary teams of experts who plan to conduct exploratory studies. These studies should incorporate information on cell-types and circuit function/connectivity, and be performed alongside analyses of complex, ethologically relevant behaviors. Successful exploratory studies should lead to subsequent competing applications for team-based research projects. The application receipt date for this FOA is December 15, 2017.

 

RFA-NS-18-009 Targeted BRAIN Circuits Projects – TargetedBCP (Re-issue of NS-17-014; R01)

  • This RFA uses a R01 mechanism and is part of a family of “Integrated Approaches” NIH BRAIN FOAs. The primary goal of this FOA is to solicit research projects using innovative, methodologically-integrated approaches to understand how circuit activity gives rise to mental experience and behavior. This RFA may support individual laboratories or small multi-PD/PI groups, and applications should offer specific, feasible research goals as endpoints to understand brain circuit function at cellular and sub-second levels of resolution in ethologically relevant behaviors within a 5-year term. The application receipt dates for this FOA are December 8, 2017 and March 15, 2018.

 

RFA-NS-18-010 Exploratory Research Opportunities Using Invasive Neural Recording and Stimulating Technologies in the Human Brain (Re-issue of NS-17-019; U01)

  • This RFA uses an U01 mechanism and addresses the BRAIN 2025 Report recommendation to “Advance Human Neuroscience.” This FOA seeks applications to assemble integrated, multi-disciplinary teams to tackle barriers inherent to human studies using invasive technologies and experimental protocols via exploratory research and planning activities to establish feasibility, proof-of-principle, and early stage development studies that might later compete for continued BRAIN Initiative funding. Successful projects will maximize opportunities to conduct innovative neuroscience research from invasive surgical procedures, and may incorporate methods of temporally-linked brain-behavior quantification. Note that awardees are expected to join a consortium work group to identify data standards and aggregate data for broad dissemination. The application receipt date for this FOA is January 19, 2018.

 

RFA-MH-18-510 BRAIN Initiative Fellows: Ruth L. Kirschstein National Research Service Award (NRSA) Individual Postdoctoral Fellowship (Re-issue of MH-17-250; F32)

  • The purpose of the BRAIN Initiative Fellows (F32) program is to enhance the research training of promising postdoctorates, early in their postdoctoral training period, who have the potential to become productive investigators in research areas that will advance the goals of the BRAIN Initiative. Applications are encouraged in any research area that is aligned with the BRAIN Initiative, including neuroethics. Applicants are expected to propose research training in an area that clearly complements their predoctoral research. Formal training in analytical tools appropriate for the proposed research is expected to be an integral component of the proposed research training plan. In order to maximize the training potential of the F32 award, this program encourages applications from individuals who have not yet completed their terminal doctoral degree and who expect to do so within 12 months of the application due date. On the application due date, candidates may not have completed more than 12 months of postdoctoral training. The application receipt dates for this FOA are: March 15, 2018; December 7, 2018; August 7, 2019; April 7, 2020.

 

Please visit our Active Funding Opportunities page for more details on these and other RFAs for the BRAIN Initiative.