What can invertebrates tell us about our brains?

With its hundred billion neurons and quadrillion synapses, the human central nervous system(CNS) can seem intractably complex. Fortunately, there is a class of animals whose nervous systems and behaviors are much more easily understood.  Invertebrates, such as sea slugs and worms, have on the order of only hundreds or thousands of neurons and their connections are extremely well stereotyped. This simplicity makes them amenable to experimentation and modeling, and has allowed scientists to understand the structure and function of their neural circuits.

In his review, Allen I. Selverston, Professor Emeritus at UCSD, asks if information gained from the study of invertebrates can be translated to our understanding of the human CNS.  He focuses on a particularly well characterized type of circuit called Central Pattern Generators (CPG).  CPGs are networks of neurons which produce rhythmic outputs in the absence of sensory feedback, and often control simple motor actions such as feeding or swimming. CPGs are not only found in invertebrates but vertebrates as well, where they control certain low level functions.  An example of a CPG is the leech heartbeat network which is shown in the diagram below.


Leech heartbeat neuronal network

The study CPGs using electrical and chemical manipulation of their constituent neurons has led to three primary types of discoveries.  First, it has revealed how a complex array of ion channels contributes to the distinct activity properties of individual neurons. Second, it has shed light on the types of synapses and how they are modulated and third, how circuits produce functional outputs.

Selverston uses these three types of analysis to explain how many different CPGs from the invertebrate world work. Unfortunately, he concludes that there are very few general principles for the design of these circuits that are transferable from model to model. Each CPG has its own evolutionary history that has crafted it into a bespoke circuit for the unique function that it serves. Moreover, the experimental methods used to study CPGs are unlikely to be effective in more complicated vertebrate systems because they cannot be probed with single cell techniques. This means that while the cellular and synapse level data may broadly applicable, the further study of invertebrate CPGs is unlikely to give us much insight into the human CNS.

Selverston’s review can be found here.

Leo Breston is a first year student in the Neuroscience Graduate Program. He is currently rotating in the Navlaka lab. 

Visual Action: Instructive Timing in the Primary Visual Cortex

By Tunmise Olayinka

Behavior in in the visual world necessitates reciprocal feedback between the environment and the observer. To act, an animal need to 1) sense the outside world, 2) compute upon this percept, and 3) generate an optimal response.

The long-held canonical view of the primary visual cortex (V1) is that its major role lay within only 1 and 2; that is, it functions as the initial computing gateway for the processing of visual sensory stimuli. This has been supported by the correlated spatial distribution of neurons in V1: they share a topographic mapping of response that in turn reflects the spatiotemporal structure of the visual data they process.

However Vijan Mohan K. Namboodiri and others in the audacious lab of Marshall G. Hussain Shuler have now posited that the V1 may play a broader, more instructive function; viz., in the direction of visually-responsive actions. They knew that, in visually-cued tasks, V1 predicated the learned interval between the stimulus and reward, in turn correlating with the action-response. The temporal consistency of this correlation led them to ask: is the learned timing they see in V1 used for solely sensory processing, or does it play a governing role in making actions and directing behavior?

Namboodiri et. al specifically ask these questions in rats, using a visually-cued interval timing task. In the task, the rats attempt to optimally time an action—when to a lick on a spout-in order to receive the maximal reward: water. The longer the rat patiently waits after the visual stimulus, the more reward it gets. However, this is up to a point: delays longer than the maximal delay, i.e. exceeding the target stimuli-lick interval, receive no reward. Thus within this task, the rats must compute an optimal timing for their licks, instead of simply waiting arbitrarily longer. With this task, Namoboodiri can now attempt to answer their focal questions 1) can we see representations of the the timing-delay between stimulus and reward across V1 neurons, and 2), does this representations instruct the action itself, by computing the prediction of the lapse of reward from the stimulus.

Thus with this design, they evaluated neurons in the V1 one at a time (i.e. via single unit recordings) finding that they had a variety of receptivities. Some neurons were entrained to this <i>mean expected interval between the stimulus and the reward, as predicted, while others instead represented the interval between the stimulus and the rat’s response itself, i.e. they were timed to the actual action (nosepoke entry), rather than the prediction of the delay-from-stimulus of the impending reward. Importantly, these visuotemporal representations correlated with the rats behavior: they only noted ‘interval-timing’ neurons in V1 when the rat successfully responded in visually-timed manner. In contrast, in non-visually timed trials, V1 neurons showed a consistent delay from nosepoke entry, independent of the visual stimulus. Only 2% (7/351) neurons in early training show significant action-timing (on the order of their false positive rate), helping to support that this ‘action-timing’ is indeed a computation on the interval itself; viz. the wait time-reward contingency, and not just timed to the action, and which <i>a priori, would not require visual feedback from V1.

If activity in V1 was entirely top-down processive, and driven by the action itself, their hypothesis is that it would present throughout V1, with no selectivity between the visually timed and non-visually timed neurons. However, if V1 played in a role in specifically generated and instructing the action, one would expect such corresponding visual task-based selectivity, with activity in V1 correlating with action on the visually-timed trials and not on the non-visually timed trials.

In addition, the intervals represented by these visually-timed, directive neurons were expected to demonstrate a trial-by-trial correlation with the neural representation of the interval and the action. The interval-representing neurons would reflect this timing in their firing profile, modulating their firing rate in correlation with the mean expected delay. So to instruct a delayed lick, for example, an interval neuron would simply increase the duration of its firing response. Similarly, the responding population—the action-timed neurons decoding the activity of these interval neurons, would in turn modulate their population firing rate with respect to the timing of the lick: the later they fire, the later the lick.

Namboodiri and his colleagues not only observed this behavior, they furthermore found that they could even predict the response of the action-timed neurons using the visually timed neurons, and only in that direction. Altogether, these results seem to demonstrate V1’s role in instructing timed action.

However, while suggestive, these results were only that—merely suggestive. The denouement of the experiment was to see if they could validate their hypothesis on the instructive nature of visually-timed neurons, by seeing if they could modulate this very instruction. Using the glorious power of optogenetics, they were able to consistently shift the firing rate, and thus the timing, of the action. To further delve more into a mechanistic explanation, they simulated their model using a reduced computational model. Therein, they demonstrated that this interval—the average delay between the predictive cues and the reward—could be locally generated and represented in V1.

So does V1 play an instructive role for stereotyping timing behavior on visual-cued tasks? This paper definitely motivates that idea. For visually-cued tasks, they could show that some neurons seemed to encode the interval, while others correlated to the action. They then were able to show that the antecedent firing response of the interval neurons could predict the population firing of the action-timed neurons. Then they not only showed they could directly perturb this effect—by modulating the visually timed interval neurons—but could validate it mechanistically within a computational model. Though one might argue that further elucidation into the nature of the interval representation is warranted (i.e., do these neurons represent the entire duration of the interval, or the endpoints / expiry?), altogether their results heavily suggest an excitingly novel and instructive role for the primary visual cortex.

Tunmise Olayinka is a third-year MD-PhD candidate at UCSD, currently in the labs of Bradley Voytek & Alysson Muotri.

Fragile X Syndrome: When translational regulation goes awry

Fragile X syndrome (FXS) is the most common hereditary form of intellectual disability affecting approximately 1 in 4,000 males and 1 in 6,000 females. The syndrome develops from a mutation in fmr1 on the q arm of the X chromosome, resulting in loss of RNA-binding protein FMRP, the fragile X mental retardation protein. The end result is a constellation of physical phenotypes, cognitive dysfunction, autistic behaviors, childhood seizures, and on a molecular leve, abnormal dendritic spines. Previous mouse models aimed to identify the deficits in neuronal plasticity have identified an increase in long, thin dendritic spines with an increased turnover rate and decreased response to input. Rapid protein synthesis is necessary for synaptic plasticity, which relies on the translation of existing mRNAs. Normal synaptic communication is dependent on spine dynamics and plasticity. Varying dysfunctions in circuits have been attributed to the loss of FMRP, however the mechanism by which FMRP affects plasticity, circuits and ultimately behavior was primarily unknown until recently.

Dr. Jennifer Darnell at the Laboratory of Molecular Neuro-Oncology, Rockefeller University is a leading expert on FXS. Her laboratory has recently used a new technique to begin to understand the pathophysiology of FXS and the molecular function of FMRP. High throughput sequencing cross-linking immunoprecipitation (HITS-CLIP) uses ultraviolet irradiation to create covalent bonds between proteins and RNA molecules that are in direct contact. By using this method, Dr. Darnell was able to identify 842 FMRP target mRNAs in mouse brain, which were increased in pre- and postsynaptic proteins: NMDA receptor subunits and metabotropic GluR5 receptor were among the several post synaptic mRNAs that interact with FMRP. This supports previous studies that show the increased turnover rate, increased vesicle recycling and increased vesicle pools in Fmr1 KO mice.

While this determined the binding of FMRP directly to mRNAs, it remained to be shown how this affects translation. As expected, Fmr1 KO mice exhibit increased rates of brain protein synthesis. However, the degree of increased synthesis is far more than can be explained simply by the FMRP target mRNAs. This led to the realization that in addition to the direct increase in protein synthesis, there is a global increase in protein synthesis possibly due to the downstream changes in elongation and initiation factors caused by the loss of FMRP.

But how does the presence of FMRP limit translation? Using a brain polyribosome-programmed in vitro translation system it was demonstrated that there is ribosome stalling that occurs at FMRP target transcripts. Thus, the loss of FMRP results in relief of the ribosome stalling and an increase in translation. Several of the proteins affected have been linked to the phenotypes seen in FXS: NMDA and mGluRs affecting synaptic plasticity, ERK and mTORC1 effecting neuronal translation, cAMP and several GTPases which have been shown to alter spine morphology, and SYNGAP1 which has been linked to non-syndromic mental retardation and autism spectrum disorder. The knowledge of the several pathways affected by the loss of FMRP give way to novel therapeutic approaches including most notable the use of antibiotics such as minocycline which repress translation, in order to alleviate some of the increased translational burden in FXS.

The translational role of FMRP both directly and globally, and the significant clinical phenotypes caused by the Fmr1 mutation, is an example of a minor genetic change causing catastrophic downstream effects. While there is still no cure for FXS, understanding the pathophysiology of the disease has allowed researchers to begin to test possible therapeutic approaches, many of which show promise. Dr. Darnell’s work has been integral to the understanding of not only FXS, but synaptic function, autism spectrum disorder, and molecular biology. To learn more about Dr. Darnell’s work and a list of publications, please visit her website at http://www.rockefeller.edu/research/faculty/researchaffiliates/JenniferDarnell.

Amy Taylor is a third year MDPhD candidate at UCSD in the Schizophrenia Research Program.

GCN2 kinase: protector from death by ribosome stalling

Dr. Susan Ackerman at UCSD focuses her research on the molecular mechanisms involved in maintaining homeostasis during development and aging of the mammalian brain. She is particularly interested in how altered translation elongation, caused by ribosome stalling, affects neuronal function and survival.

In her recent paper, “Activation of GCN2 kinase by ribosome stalling links translation elongation with translation initiation” Dr. Ackerman addresses the issue of determining the signaling pathways initiated by ribosome stalling. In order to study these signaling pathways, Dr. Ackerman made use of the mutant mouse line, C57BL/6J-Gtpbp2nmf205-/- , which have stalled neuronal elongation complexes. She first performed gene expression studies on isolated cerebella from control B6J mice and mutant B6J-Gtpbp2nmf205-/- mice at 3- and 5-weeks of age. Dr. Ackerman found a total of 910 and 325 differentially regulated genes in the 5-week old and 3-week old mutant cerebellum, respectively. Since ribosome stalling, as seen in these mutant mice, has been shown to cause neurodegeneration, she next performed Kegg pathway analysis and Ingenuity Pathway analysis on the differentially regulated genes and found enhanced inflammation/immune pathways. Interestingly, when these differentially regulated genes were compared to activated genes in microglia and astrocytes from mice models of amyotrophic lateral sclerosis (ALS) and Alzheimer’s disease, Dr. Ackerman found overlaps with 150 and 60 genes expressed in the activated microglia and astrocytes, respectively.


Further analysis of the differentially expressed genes revealed upregulation of activating transcription factor 4 (ATF4), which is an important component of the integrated stress response. Next, Dr. Ackerman studied the activation of ATF4 in the mutant mice and found ATF4 target genes were upregulated in both cerebellum and hippocampal tissue. Additional analysis revealed that the activation of ATF4 was dependent on GCN2 kinase, which is activated by amino acid deprivation.

Dr. Ackerman then proceeded to study the effects of the GCN2-ATF4 pathway on neuron survival. To do this, she compared the progression of neurodegeneration induced by ribosome stalling in two mutant mice strains. The first was the B6J-Gtpbp2nmf205-/- strain described earlier, and the second was the B6J-Gtpbp2nmf205-/-;Gcn2-/- strain. Dr. Ackerman found that the B6J-Gtpbp2nmf205-/-;Gcn2-/- strain showed increased granule cell death as compared to the B6J-Gtpbp2nmf205-/-. Additionally, the B6J-Gtpbp2nmf205-/-;Gcn2-/- strain showed extensive cell death within the CA1 region of the hippocampus which was not present in the B6J-Gtpbp2nmf205-/- strain.


Dr. Ackerman’s paper, “Activation of GCN2 kinase by ribosome stalling links translation elongation with translation initiation” provides a very detailed examination of the ribosome stalling triggered GCN2-AFT4 signaling pathway.

To hear more about Dr. Susan Ackerman’s research, please attend her talk on Tuesday, November 29, 2016 at 4pm in the CNCB Marilyn G. Farquhar Seminar Room.

Oscar Gonzalez is a first-year graduate student in the neurosciences graduate program and a member of Dr. Maxim Bazhenov’s lab. He is interested in the mechanisms leading to hypersynchronous activity in the brain, and the origin of resting state infra-slow fluctuations.

Spatial navigation strategies: where it’s at

When you wake up in the morning and head over to the lab, do you take the scenic route and relish in the San Diego sun, or do you take the shortcut through the library because your cat made you late because he craved attention? Memory experts like Dr. Véronique Bohbot at McGill University have begun using virtual and real environments to probe the distinct navigation strategies recruited by your brain when, for example, you orient yourself within a mental map through your morning commute. These navigation strategies are often conjured spontaneously to adapt to the current environment, such as a closed crosswalk, and vary in the amount of localized brain activity evoked in an individual, which in turn depends heavily on the grey matter volume of those specific areas, hormones, and genetic background. However, when someone’s healthy brain fails to employ optimal navigation strategies, they might only experience the mild inconvenience of being late by a few minutes, a sharp contrast to the underlying protracted spatial memory dysfunction found in Alzheimer’s Disease (AD) patients.

Long-term spatial memory impairments are found early in the development of AD, when diagnostic genotyping often reveals the presence of Apolipoprotein E (APOE), a prominent risk gene associated with the disease. A particular APOE allele, ε4, has considerable ties to the cognitive impairments and hippocampal atrophy associated with aging. Interestingly, a different allele, ε2, is known to be protective against AD neuropsychological symptoms, such as cognitive decline and neuritic plaque formation. However, most of these findings have come as a result of studies on older adults with AD onset or progression, and despite the contrast between the structural, protective or risk increasing qualities of the two alleles, it was Dr. Bohbot’s group who recently proposed that cognitive correlates are sensitive to the genotypes even in young adults.

Lesion studies support the idea that different strategies employed while navigating an environment rely on divergent brain networks. For example, the hippocampus-dependent spatial strategy involves creating relationships between the different landmarks in the environment to incorporate into a cognitive map. On the other hand, the caudate-dependent response strategy incorporates stimulus-response associations to orient yourself in space (“take a left after the second right”). In addition, the neuroanatomical basis for these two different spatial strategies also have a structural inverse relationship, such that greater gray matter volume in one correlates with less gray matter volume in the other, and vice versa. Konishi et al. (2016) used this converging evidence to assess whether recruitment of these strategies would relate to the structural differences found between young adult APOE allele carriers, hypothesizing that APOE ε2 carriers will utilize spatial strategies more, and in turn have greater gray matter volume in the hippocampus, in comparison to ε3/ε3 and ε4 allele carriers.

To test this hypothesis, genotyped participants underwent testing in a computer-based virtual reality navigation task that is akin to the eight-arm radial maze, except with landmarks more common to human environments. A subsequent verbal report of the navigation strategy they employed (i.e. “I used landmarks” vs “I used the patter of open pathways”) allowed the researchers to classify participants between spatial and response learners. Follow-up structural Magnetic Resonance Imaging (MRI) on the participants measured hippocampal structural differences between allele carriers and their most utilized navigation strategy.


Schematic drawings and first person views of the 4-on-8 virtual maze


Interestingly, their hypothesis was a home-run. A higher proportion of ε2 allele participants reported using the hippocampus-dependent spatial strategy throughout the task compared to the other allele carriers.



Apoliprotein E (APOE) e2 carriers used the hippocampus-dependent spatial strategy more than the other genotype groups.


In addition, ε2 allele carriers had greater grey matter in the hippocampus compared to both ε3/ ε3 and ε4 carriers. However, these measurements were conducted only in a subset of the 100+ participant pool from the behavioral task. Despite this, these results aligned with previously published studies looking at hippocampal structure in ε2 allele carriers.


Gray matter contrast of APOE e2 carriers and non-e2 carriers using voxel-based morphometry (VBM)


While the ε4 APOE allele has garnered the most attention due to its association with increased risk for AD onset, pursuing assessments on what makes young ε2 allele carriers cognitively distinct from the others can lead to the creation of early intervention strategies. For example, this particular study implies a future clinical scenario where training ε4 carriers to use spatial navigation strategies might mitigate the spatial learning impairments seen throughout AD progression.

Come check out Dr. Bohbot’s talk, titled “Early detection, sex differences, and intervention in healthy older adults at risk of Alzheimer’s disease”, on Tuesday, November 11th, at 4 P.M. in the CNCB  Marilyn Farquhar Seminar Room.

Christian Cazares is a first-year neuroscience graduate student in the Gremel Lab, where he is looking at the effects of stress on goal-directed and habitual behavior. He can be reached at @fleabrained and www.chriscaz.com

Konishi K, Bhat V, Banner H, Poirier J, Joober R, Bohbot VD. APOE2 Is Associated with Spatial Navigational Strategies and Increased Gray Matter in the Hippocampus. Frontiers in Human Neuroscience. 2016;10:349. doi:10.3389/fnhum.2016.00349.

Macroconnectomics: The Scaffolding for Global Neuroinformatic Investigation

The recent explosion in data acquisition, although certainly not unique to the neurosciences, has positioned the brain to be one of  the most probed, but least understood subjects in science.  Everyday countless data sets cataloging the neural activity recorded by microelectrode arrays, the pathways illuminated by retrograde tracing, or the pathological markers inferred from genome-wide association studies, along with new computational tools to visualize and analyze this data, are made publicly available online, yet a global account of brain functioning remains elusive.  Adhering to the  biological dogma that structure determines function, many neuroscientists set out to untangle the connectome – a complete account of all of the connections made within the nervous system.

Such a wiring diagram is believed to be necessary, but not sufficient, to understand the nervous system, by providing a static structural framework for analyzing dynamic interactions at the level of neurons, circuits, or cortical regions.  In this sense it would play the same role as the genome has in understanding complex interactions between genetic elements.  However, due to the enormity of the data set for humans (~1014 synapses), and the scale of individual connections (~20 nm across the synaptic cleft), only the connectome of a single species, the roundworm C. elegans with 302 neurons, has been completely described.  Despite these challenges, many researchers believe progress is possible by implementing a hierarchical approach to systematically characterize the structure of the mammalian nervous system.

Dr. Larry Swanson of the University of Southern California, in collaboration with fellow connectomics researchers, has proposed performing connectivity analyses at multiple spatial scales to reconstruct a connectome by interpolating across the spatial and temporal resolutions explored by various technologies.  To be feasible, the scales must form a nested hierarchy, and so a macroconnection is defined to be between two gray-matter regions, a mesoconnection between two neuron types, and a microconnection between two individual neurons. Although, most mammalian connectomics research is still focused on the macro-scale, Swanson believes such a macroconnectome will detail global organization themes that will help to map data at finer scales.  In particular Dr. Swanson has focused on the connectome of the albino, adult rat, and has had success in elucidating the macroconnection architecture of the cerebral cortex and nuclei.

The macroconnectomes of the rat cerebral cortex and cerebral nuclei were constructed by mining the primary literature for evidence of macroconnections, and network analyses were performed to identify distinctive architectural features.  Both of the macroconnectomes were found to be fairly dense, with ~40% of all possible macroconnections present, to have four distinct hubs (regions much more highly connected than in the network on average), and a rich club (group of highly connected regions that connect to other highly connected regions) containing the four hubs.  This means that both networks should be quite robust to lesion of any single member the rich club, as their high degree of connectivity implies redundant processing.  However, the cortex was shown to exhibit greater small-world attributes and reciprocity (prevalence of symmetric connections) than the cerebral nuclei network.  This supports the view that the cortex has a greater role in the efficient integration of global information, while the cerebral nuclei network mediate information flow in specific circuits between the cortex and subcortical regions.fig1fig2

Macroconnectomics, at the very least, provides neuroscientists a much more manageable way to visualize a rich and diverse set of anatomical data.  In addition to generating complicated connection graphs, identifying hubs or modules can serve to reduce the dimensionality of the data being considered (see the top figure), and directs attention towards brain regions with potentially greater functional roles.  Network analyses also provide a robust, well-defined strategy for the structural comparison of networks both within a single species (as was done here) and potentially across species.  Dr. Swanson even believes that, due to the relatively conserved structure of cortical regions across mammals, the architectural principles found from rat macroconnectome analyses could be used as proxies for human cortical networks until new experimental techniques allow us to probe the human brain more directly.

This hierarchical research strategy is perfectly positioned to take full advantage of the mountains of neural data currently available, as well as adapt to and integrate future avalanches, in order to eventually provide a robust framework to consider neural connectivity. In the mean time, it can also provide useful tools and databases for researchers concerned with connectivity, and generate testable hypotheses about human cortical connections based on other model mammalian systems.

Please come join us on Tuesday, November 1st, at 4pm in the CNCB  Marilyn Farquhar Seminar Room to hear more about this exciting avenue of research from Dr. Larry Swanson.

Ryan Golden is a first-year student in the neurosciences graduate program rotating in Dr. Bradley Voytek’s lab.  His interests currently lie in neural computation, how network architecture constrains information processing, and how neurommodulation influences plasticity.

Bench, Bytes, & Beyond: An investigation into the complexities of neural network analysis

Over the past several years, the term “big data” has frequently been mentioned, often in passing, as a vague and generalized concept that represents the accumulation of inconceivable amounts of information demanding storage, management, and analysis—often ranging from a few dozen terabytes to several petabytes (1015) in a single data set1. In essence, the “big data” movement seeks to capture overarching patterns and trends within these sets, where current software tools and strategies are incapable of handling this volume of information. Big Data is particularly relevant in the neurosciences, where a vast amount of high-dimensional neural data arising from incredibly complex networks is being continuously acquired.

As with Big Data as a whole, one of the central challenges of modern neuroscience is to integrate and model data from a variety of sources to determine similarities and recurring themes between them2. That is, the vast array of techniques used in neuroscience–from GWAS to viral tracing to electrophysiology to fMRI to artificial network simulations–generates data sets with varying characteristics, dimensions, and formats3,4. In order to meaningfully combine data from each of these sources, we need a comprehensive and universal strategy for integrating findings of all types and from all scales into a simple and cohesive story.

Drs. Peiran Gao and Surya Ganguli of Stanford University highlight the difficulty in extracting meaningful trends from big neural data sets in their review titled “On simplicity and complexity in the brave new world of large-scale neuroscience”. They emphasize the particular hurdle of extracting a coherent conceptual understanding of neural behavior and emergent cognitive functions from circuit connectivity. Moreover, they offer insight into what it might mean to truly “understand” the brain on every level of in all of its hierarchical complexity.

One of the central ideas highlighted in Gao and Ganguli’s review is the notion of Neuronal Task Complexity (NTC) that uses neuronal population autocorrelation across various parameters to place bounds on the dimensionality of neural data5. Thus, NTC seeks to parse meaningful differences in neuronal firing patterns from random fluctuations in signaling: in a broader sense, it attempts to increase the signal-to-noise ratio (SNR) to extract relevant information about circuit dynamics. In characterizing NTC, the authors demonstrate that it can be used to derive a general understanding of neuronal circuit behavior with relatively little information. That is, recording from more neurons in the brain does not necessarily result in a better encapsulation of the phenomena we seek to explain–more data is not always better—and NTC enables us to better quantify the experimental data needed to draw these broad conclusions.

Using NTC, we can better clarify the distinction between effective and excessive data collection, and hone in on the intrinsic principles that govern our cognition without gathering data that needlessly cloud our ability to distinguish these complexities. In the figure below from Gao and Ganguli’s review, the essential components of NTC are explained using neuronal modulation of behavioral states as an experimental example:


In addition to describing the essential components and uses of NTC as a means to measure the complexity of neural data given particular task parameters and assumptions, the authors also explain the broader meaning of what NTC tells us: that rather than simply focusing number of neurons we record from, we instead need to develop more intricate and clearly-defined behavioral experiments that will cause predictable and observable alterations in neural activity. This will help to ensure that any significant patterns we see can be meaningfully interpreted in-context and effectively incorporated into a broader perspective of how neuronal firing patterns give rise to behavior and cognition.

By underscoring the need for interaction between experimental work, data analysis, and theory behind the operation and dynamics of neural circuitry, Gao and Ganguli argue that gaining a comprehensive understanding of the brain will require a communal effort and inputs from all areas of neuroscience. By extension, the ability to test the validity and interaction between several models at once will be indispensable in determining which ones best align with acquired data and with one another. By comparing multiple models from all sub-fields of neuroscience, a more complete and accurate understanding of the brain can be derived. Thus, in catalyzing the generation of broader and more accurate conceptual frameworks, both artificial simulations of neural activity and adoption of a wider range of experimental techniques will enable us to gain a more complete understanding of how the brain makes us who we are.


Marley Rossa is a first-year graduate student currently rotating in Jeff Isaacson’s lab. She is, as of now, content with studying the electrophysiology of individual neurons and will leave the hardcore petabyte-level analysis to the more computationally-inclined.

1Ibrahim; Targio Hashem, Abaker; Yaqoob, Ibrar; Badrul Anuar, Nor; Mokhtar, Salimah; Gani, Abdullah; Ullah Khan, Samee (2015). “big data” on cloud computing: Review and open research issues”. Information Systems47: 98–115.
2Stevenson IH, Kording KP: How advances in neural recording affect data analysis. Nat Neurosci 2011, 14:139-142.
 3Chung K, Deisseroth K: Clarity for mapping the nervous system. 
Nat Methods 2013, 10:508-513.
 4Prevedel R, Yoon Y, Hoffmann M, Pak N: Simultaneous whole- 
animal 3D imaging of neuronal activity using light-field microscopy. Nat Methods 2014, 11:727-730.
5Gao P, Ganguli S: On simplicity and complexity in the brave new world of large-scale neuroscience. Curr Opin in Neurobiology 2015, 32: 148–155.
6Gao P, Trautmann E, Yu B, Santhanam G, Ryu S, Shenoy KV, Ganguli S: A theory of neural dimensionality and measurement. Computational and Systems Neuroscience Conference (COSYNE). 2014.
7Gao P, Ganguli S: Dimensionality, Coding and Dynamics of Single-Trial Neural Data. Computational and Systems Neuroscience Conference (COSYNE). 2015.









The (Hippocampal) Social Network

The hippocampus is a brain region that plays a central role in learning and memory. Due to its importance, most of the subregions of the hippocampus have been studied exhaustively. However, one of these subregions, the CA2, has avoided the limelight allotted to its neighbors. What are the functional properties of the CA2? What does it wire together with? What role does it play in behavior? Until recently, little has been known.

Hitti and Siegelbaum attack these questions head on in their paper “The hippocampal CA2 region is essential for social memory”. This comprehensive paper walks us through the story of the CA2, from mouse model creation, to circuit tracing, to a series of well-controlled behavioral experiments.

First, the authors start by demonstrating the validity of their Amigo2-Cre mouse line that dominantly targets CA2 pyramidal neurons with both imaging and electrophysiology. Next, they explore the inputs and outputs of the CA2 with molecular tracing. Unsurprisingly, the region is densely interconnected with the other hippocampal structures. Broadly, the CA2 coordinates a strong disynaptic circuit that links the entorhinal cortex to the CA1.


Next, the authors delve into their method for creating a CA2 knock-out. Using an AAV driven virus that expresses tetanus neurotoxin, they demonstrate the silencing of post-synaptic potentials (and preservation of fiber currents) from the CA2.

Now that all this is working, Hitti and Siegelbaum begin the most interesting portion of the paper – the behavior! A slew of social interaction tests are used, the expected result of which is that normal mice will habituate to previously encountered rodents. In comparison to controls, the paper shows that CA2 knockout mice fail to remember previously encountered mice! They fail to habituate, treating every encounter with a familiar mouse as it its first.


What’s truly remarkable is that the authors control for a multitude of confounding factors, social interest, spatial memory, novel object, locomotion, and even olfaction. They find that none of these other factors are significantly different across groups. Quite selectively, the CA2 to mediates social memory.

Taken together, this paper offers a comprehensive demonstration of the necessity for the CA2, a previously poorly defined hippocampal region, in the functioning of social memory in rodents.

Please come join us on Tuesday October 7th, at 4pm in the CNCB  Marilyn Farquhar Seminar Room to hear more about this story from Dr. Steven Siegelbaum!

Debha Amatya is a first-year neurosciences graduate student working in the Gage Lab to understand the relationship between common and rare variants in autism genomics

More than the MTL: Parietal Activity in Episodic Memory

The medial temporal lobe (MTL) has traditionally received credit as supporting episodic memory, a type of declarative memory that enables access to one’s past experiences. However, converging evidence suggests that parietal areas may also contribute to episodic retrieval–in particular, the retrosplenial (RSC) and posterior cingulate cortices (PCC) in the left medial parietal cortex (MPC) and the angular gyrus (AG) in the left lateral parietal cortex (LPC) (Cabeza et al., 2008; Wagner et al., 2005). As nodes of the default mode network (Greicius et al., 2003; Raichle et al., 2001)–a cluster of interacting areas implicated in self-referential thinking–the RSC/PCC and AG would seem prime candidates for promoting retrieval of autobiographical episodes. Indeed, functional imaging has corroborated these intuitions (Cabeza et al., 2008; Wagner et al., 2005). Nevertheless, fMRI has its limitations, and a technique with both high spatial and temporal resolution would potentially provide further insight into the dynamics  of the parietal lobe in episodic memory. Fortunately, Josef Parvizi’s Laboratory of Behavioral and Cognitive Neuroscience at Stanford University recognizes the shortcomings of functional imaging, frequently incorporating intracranial electrocorticography and/or intracranial electrical brain stimulation to investigate the neurological underpinnings of human behavior and cognition. In a recent study, Foster et al. (2015)  exploited the spatiotemporal precision of ECoG in three human subjects during conditions of task performance, rest, and sleep. Electrode coverage of the MPC and LPC in these epileptic patients (Figure 1b) offered the valuable opportunity to obtain electrophysiological recordings in these areas and to observe parietal activity and connectivity.

Foster et al. (2015) employed a task that required participants to judge a visually presented statement as true or false, with some statements involving episodic or semantic memory, self- or other-judgments, or arithmetic (Figure 1c). Analysis of the electrophysiological data recorded under these different task conditions most apparently demonstrated greater high-frequency broadband amplitude (HFB, 70-180 Hz) both in the RSC/PCC and AG for the episodic condition relative to the other task conditions. HFB has been suggested as an index of neuronal population response and thus supposedly reflects increased activity in these regions during episodic retrieval. In addition, the HFB response profiles of the RSC/PCC and AG across task conditions were remarkably similar; activity in other parietal regions failed to so strongly resemble that of the RSC/PCC and AG (see Figure 1d/e).


Figure 1. Parietal Subregions, Electrode Locations, Experimental Task, and Task Responses

Other analyses confirmed the notable similarity in responses between the RSC/PCC and AG: a strong positive correlation was found between the trial-level mean HFB responses in these areas with the strongest correlations arising in the episodic and semantic conditions. On a temporal scale, these two sites showed essentially identical response onset latencies for the episodic condition (Figure 2). Cumulatively, these findings suggest that the RSC/PCC and AG may receive simultaneous inputs–perhaps from the MTL–and work in parallel to process different aspects of these inputs and therefore different components of episodic memory.


Figure 2. Correlated HFB Trial Responses between PCC and AG

The study conducted by Foster et al. (2015) also examined parietal activity profiles and connectivity patterns during rest and sleep to delve deeper into the dynamics of MPC and LPC. Resting-state ECoG analysis extracting slow (<1 Hz) ongoing fluctuations in HFB amplitude displayed strong correlations between RSC/PCC and AG; a similar correlation pattern held for the low beta range as well. Resting-state fMRI yielded comparable correlations. Additionally, these correlation patterns persisted in ECoG data collected during stage-2 and stage-3 sleep, reinforcing the similarity of parietal connectivity and activity across three quite behaviorally distinct states (Figure 3). The notion that resting-state activity recapitulates task-driven activity is not novel, and some assert that such spontaneous activity reflects the strength and organization of new connections shaped by recent experience and neuronal activation (Harmelech and Malach, 2013).


Figure 3. Similarity of ECoG Correlation Patterns across Task, Rest, and Sleep States

Overall, the study by Foster et al. (2015) clearly demonstrates the utility of ECoG recordings in probing the spatiotemporal dynamics of the parietal lobe–not only during episodic retrieval per se, but also during rest and sleep. The current findings support and expand upon those of past fMRI experiments (Cabeza et al., 2008; Wagner et al., 2005), revealing highly similar neuronal responses in the RSC/PCC and AG, as well as simultaneity in these responses. Nonetheless, while Foster et al. (2015) have pioneered electrophysiological investigation of parietal activity related to memory, their study is far from exhaustive and demands future research to establish a more comprehensive framework for understanding the precise roles of the MPC and LPC in episodic retrieval.

To hear Dr. Josef Parvizi elaborate on this research, attend his talk on Tuesday, October 11, 2016, at 4:00 pm in the CNCB’s Marilyn G. Farquhar Seminar Room.

Cabeza, R., Ciaramelli, E., Olson, I.R., and Moscovitch, M. (2008). The parietal cortex and episodic memory: an attentional account. Nat. Rev. Neurosci. 9, 613–625.

Foster, B. L., Rangarajan, V., Shirer, W. R., & Parvizi, J. (2015). Intrinsic and Task-Dependent Coupling of Neuronal Population Activity in Human Parietal Cortex. Neuron, 86(2), 578–590.

Greicius, M.D., Krasnow, B., Reiss, A.L., and Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. USA 100, 253–258.

Harmelech, T., and Malach, R. (2013). Neurocognitive biases and the patterns of spontaneous correlations in the human cortex. Trends Cogn. Sci. 17, 606–615.

Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., and Shulman, G.L. (2001). A default mode of brain function. Proc. Natl. Acad. Sci. USA 98, 676–682.

Wagner, A.D., Shannon, B.J., Kahn, I., and Buckner, R.L. (2005). Parietal lobe contributions to episodic memory retrieval. Trends Cogn. Sci. 9, 445–453.

Gina D’Andrea-Penna is a first-year student in the neurosciences graduate program rotating in Dr. Bradley Voytek’s lab. Her strongest interests lie in the field of cognitive neuroscience, and she aspires to investigate and, one day, comprehend consciousness.

How to make a schizophrenic mouse

Dopamine is perhaps the best known neurotransmitter, almost certainly due to its association with the idea of reward. It’s often brought up to explain why we like the things we do, and how people can develop addictions to different types of rewards. However, dopamine isn’t just a reward chemical; it’s very important for a wide variety of brain processes, including voluntary movement, attention, sensory gating, evaluating the salience of a stimulus, decision making, and motivation. Given all this chemical does, it’s not too surprising that changes in dopamine signaling have been implicated in mental disorders, like schizophrenia, ADHD, and depression. But how, then, does a healthy brain regulate dopamine? And how does this system go wrong?

Larry Zweifel’s lab at the University of Washington studies these questions. Soden et al. examined the effect of a mutation in a gene called KCNN3 that was discovered in a schizophrenia patient (Bowen et al., 2001). This gene codes for an ion channel called SK3 that is activated when calcium is inside the cell and then lets potassium out of the neuron, reducing its excitability. The mutated form, however, has an early stop codon due to a frame shift, and therefore only produces a small fragment of the original protein. Interestingly, this mutation was found to be dominant in cell culture, needing only one copy to exert its full effect and suppress SK3 currents in neurons, likely because the protein fragments bind to and inactivate SK3 channels (Miller et al., 2001).

Since SK3 is expressed in dopamine neurons and was mutated in a schizophrenia patient, it seems a promising candidate for a gene regulating dopamine function. Soden et al. tested the effect of this mutation in a mouse model by adding the mutated gene into the genome of dopamine neurons using a viral vector and the Cre-lox system. Indeed, they found that dopamine neurons in the mice with the mutant gene were more excitable and fired less regularly than usual, making them more prone to firing bursts of action potentials.


These bursts are thought to be a functionally different form of dopamine signaling than the neurons’ regular spiking, causing different effects in dopamine-responsive brain regions, so this altered neuronal function should correspond to altered behavior in tasks where dopamine is important. Since dopamine is involved in sensory gating, meaning the brain’s filtering of irrelevant stimuli, the researchers tested this ability in the mutant mice. In their task, the mice were presented with two sounds, one which was was always followed by a reward (a sugary pellet), and one which was rarely followed by a reward. The mice learned to look for the pellet quickly after the more predictive sound, but not after the other. Once the mice had learned to distinguish the sounds, the researchers flashed a light at the same time the reward-predictive sound was played. The normal mice became distracted, but the mutant mice paid no attention to the novel stimulus and still proceeded quickly to the pellet, indicating that their sensory gating was altered.


The researchers also tested their mice on prepulse inhibition (PPI), a neurological process by which the startle response of an animal to a sudden, high amplitude stimulus, such as a loud sound, is reduced if the strong stimulus is preceded by a weaker one. This phenomenon occurs in both mice and humans, is affected by dopamine-modulating drugs, and is reduced in people with schizophrenia. Indeed, the control mice showed prepulse inhibition, while the mutant mice did not.


This paper is significant in that the authors were able to demonstrate a link across multiple levels of biology, from disrupted gene function to neuronal function to behavior. As the KCNN3 gene is in a chromosomal region (1q21) that is associated with schizophrenia, it’s possible that this gene, and pathological processes similar to that shown here by the author, are at play in more cases of schizophrenia. The ability understand how the brain is disrupted across different scales in psychiatric illness is crucial to developing better, targeted treatments for these conditions.

Bowen, T. et al. Mutation screening of the KCNN3 gene reveals a rare frameshift mutation. Mol. Psychiatry 6, 259–260 (2001).
Miller, M. J. Nuclear Localization and Dominant-negative Suppression by a Mutant SKCa3 N-terminal Channel Fragment Identified in a Patient with Schizorphrenia. Journal of Biological Chemistry 276, 27753–27756 (2001).
Soden, M. E. et al. Disruption of Dopamine Neuron Activity Pattern Regulation through Selective Expression of a Human KCNN3 Mutation. Neuron 80, 997–1009 (2013).
Jacob Garrett is a first-year PhD student in the neurosciences program. He has not yet narrowed his interests enough to provide any sort of useful description here.