BRICseq bridges brain-wide interregional connectivity to neural activity and gene expression in single animals, bioRxiv, 2018-09-20
SummaryComprehensive analysis of neuronal networks requires brain-wide measurement of connectivity, activity, and gene expression. Although high-throughput methods are available for mapping brain-wide activity and transcriptomes, comparable methods for mapping region-to-region connectivity remain slow and expensive because they require averaging across hundreds of brains. Here we describe BRICseq, which leverages DNA barcoding and sequencing to map connectivity from single individuals in a few weeks and at low cost. Applying BRICseq to the mouse neocortex, we find that region-to-region connectivity provides a simple bridge relating transcriptome to activity The spatial expression patterns of a few genes predict region-to-region connectivity, and connectivity predicts activity correlations. We also exploited BRICseq to map the mutant BTBR mouse brain, which lacks a corpus callosum, and recapitulated its known connectopathies. BRICseq allows individual laboratories to compare how age, sex, environment, genetics and species affect neuronal wiring, and to integrate these with functional activity and gene expression.
biorxiv neuroscience 100-200-users 2018High-throughput mapping of mesoscale connectomes in individual mice, bioRxiv, 2018-09-20
AbstractBrain function is determined by connectivity among brain areas, and disruption of this connectivity leads to neuropsychiatric disorders. Understanding connectivity is essential to modern neuroscience, but mesoscale connectivity atlases are currently slow and expensive to generate, exist for few model systems, and require pooling across many brains. Here we present a method, muMAPseq (multisource Multiplexed Analysis of Projections by sequencing), which leverages barcoding and high-throughput sequencing to generate atlases from single animals rapidly and at low cost. We apply muMAPseq to tracing the neocortical connectome of individual mice, and demonstrate high reproducibility, and accuracy. Applying muMAPseq to the mutant BTBR mouse strain, which lacks a corpus callosum, we recapitulate its known connectopathies, and also uncover novel deficits. muMAPseq allows individual laboratories to generate atlases tailored to individuals, disease models, and new model species, and will facilitate quantitative comparative connectomics, permitting examination of how age, sex, environment, genetics and species affect neuronal wiring.
biorxiv neuroscience 100-200-users 2018A non-spatial account of place and grid cells based on clustering models of concept learning, bioRxiv, 2018-09-19
ABSTRACTOne view is that conceptual knowledge is organized using the circuitry in the medial temporal lobe (MTL) that supports spatial processing and navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to spatial navigation tasks, so called place and grid cell-like representations emerge because of the relatively uniform distribution of possible inputs in these tasks. The same mechanism applied to conceptual tasks, where the overall space can be higher-dimensional and sampling sparser, leads to representations more aligned with human conceptual knowledge. Although the types of memory supported by the MTL are superficially dissimilar, the information processing steps appear shared. Our account suggests that the MTL uses a general-purpose algorithm to learn and organize context-relevant information in a useful format, rather than relying on navigation-specific neural circuitry.
biorxiv neuroscience 100-200-users 2018Probing variability in a cognitive map using manifold inference from neural dynamics, bioRxiv, 2018-09-17
Hippocampal neurons fire selectively in local behavioral contexts such as the position in an environment or phase of a task,1-3 and are thought to form a cognitive map of task-relevant variables.1,4,5 However, their activity varies over repeated behavioral conditions,6 such as different runs through the same position or repeated trials. Although widely observed across the brain,7-10 such variability is not well understood, and could reflect noise or structure, such as the encoding of additional cognitive information.6,11-13 Here, we introduce a conceptual model to explain variability in terms of underlying, population-level structure in single-trial neural activity. To test this model, we developed a novel unsupervised learning algorithm incorporating temporal dynamics, in order to characterize population activity as a trajectory on a nonlinear manifold—a space of possible network states. The manifold’s structure captures correlations between neurons and temporal relationships between states, constraints arising from underlying network architecture and inputs. Using measurements of activity over time but no information about exogenous behavioral variables, we recovered hippocampal activity manifolds during spatial and non-spatial cognitive tasks in rats. Manifolds were low-dimensional and smoothly encoded task-related variables, but contained an extra dimension reflecting information beyond the measured behavioral variables. Consistent with our model, neurons fired as a function of overall network state, and fluctuations in their activity across trials corresponded to variation in the underlying trajectory on the manifold. In particular, the extra dimension allowed the system to take different trajectories despite repeated behavioral conditions. Furthermore, the trajectory could temporarily decouple from current behavioral conditions and traverse neighboring manifold points corresponding to past, future, or nearby behavioral states. Our results suggest that trial-to-trial variability in the hippocampus is structured, and may reflect the operation of internal cognitive processes. The manifold structure of population activity is well-suited for organizing information to support memory,1,5,14 planning,12,15,16 and reinforcement learning.17,18 In general, our approach could find broader use in probing the organization and computational role of circuit dynamics in other brain regions.
biorxiv neuroscience 0-100-users 2018Transformation of Speech Sequences in Human Sensorimotor Circuits, bioRxiv, 2018-09-17
SummaryAfter we listen to a series of words, we can silently replay them in our mind. Does this mental replay involve a re-activation of our original perceptual representations? We recorded electrocorticographic (ECoG) activity across the lateral cerebral cortex as people heard and then mentally rehearsed spoken sentences. For each region, we tested whether silent rehearsal of sentences involved reactivation of sentence-specific representations established during perception or transformation to a distinct representation. In sensorimotor and premotor cortex, we observed reliable and temporally precise responses to speech; these patterns transformed to distinct sentence-specific representations during mental rehearsal. In contrast, we observed slower and less reliable responses in prefrontal and temporoparietal cortex; these higher-order representations, which were sensitive to sentence semantics, were shared across perception and rehearsal. The mental rehearsal of natural speech involves the transformation of time-resolved speech representations in sensorimotor and premotor cortex, combined with diffuse reactivation of higher-order semantic representations.Conflict of interestThe authors declare no competing financial interests.
biorxiv neuroscience 0-100-users 2018Evaluating the evidence for biotypes of depression attempted replication of Drysdale et.al. 2017, bioRxiv, 2018-09-16
AbstractBackgroundPsychiatric disorders are highly heterogeneous, defined based on symptoms with little connection to potential underlying biological mechanisms. A possible approach to dissect biological heterogeneity is to look for biologically meaningful subtypes. A recent study Drysdale et al. (2017) showed promising results along this line by simultaneously using resting state fMRI and clinical data and identified four distinct subtypes of depression with different clinical profiles and abnormal resting state fMRI connectivity. These subtypes were predictive of treatment response to transcranial magnetic stimulation therapy.ObjectiveHere, we attempted to replicate the procedure followed in the Drysdale et al. study and their findings in an independent dataset of a clinically more heterogeneous sample of 187 participants with depression and anxiety. We aimed to answer the following questions 1) Using the same procedure, can we find a statistically significant and reliable relationship between brain connectivity and clinical symptoms? 2) Is the observed relationship similar to the one found in the original study? 3) Can we identify distinct and reliable subtypes? 4) Do they have similar clinical profiles as the subtypes identified in the original study?MethodsWe followed the original procedure as closely as possible, including a canonical correlation analysis to find a low dimensional representation of clinically relevant resting state fMRI features, followed by hierarchical clustering to identify subtypes. We extended the original procedure using additional statistical tests, to test the statistical significance of the relationship between resting state fMRI and clinical data, and the existence of distinct subtypes. Furthermore, we examined the stability of the whole procedure using resampling.Results and ConclusionWe were not able to replicate the findings of the original study. Relationships between brain connectivity and clinical symptoms were not statistically significant and we also did not find clearly distinct subtypes of depression. We argue, that based on our rigorous approach and in-depth review of the original results, that the evidence for the existence of the distinct resting state connectivity based subtypes of depression is weak and should be interpreted with caution.
biorxiv neuroscience 100-200-users 2018