Joshua T. Vogelstein

Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex.

Neuronal responses during sensory processing are influenced by both the organization of intracortical connections and the statistical features of sensory stimuli. How these intrinsic and extrinsic factors govern the activity of excitatory and inhibitory populations is unclear. Using two-photon calcium imaging in vivo and intracellular recordings in vitro, we investigated the dependencies between synaptic connectivity, feature selectivity and network activity in pyramidal cells and fast-spiking parvalbumin-expressing (PV) interneurons in mouse visual cortex. In pyramidal cell populations, patterns of neuronal correlations were largely stimulus-dependent, indicating that their responses were not strongly dominated by functionally biased recurrent connectivity. By contrast, visual stimulation only weakly modified co-activation patterns of fast-spiking PV cells, consistent with the observation that these broadly tuned interneurons received very dense and strong synaptic input from nearby pyramidal cells with diverse feature selectivities. Therefore, feedforward and recurrent network influences determine the activity of excitatory and inhibitory ensembles in fundamentally different ways.Neuronal responses during sensory processing are influenced by both the organization of intracortical connections and the statistical features of sensory stimuli. How these intrinsic and extrinsic factors govern the activity of excitatory and inhibitory populations is unclear. Using two-photon calcium imaging in vivo and intracellular recordings in vitro, we investigated the dependencies between synaptic connectivity, feature selectivity and network activity in pyramidal cells and fast-spiking parvalbumin-expressing (PV) interneurons in mouse visual cortex. In pyramidal cell populations, patterns of neuronal correlations were largely stimulus-dependent, indicating that their responses were not strongly dominated by functionally biased recurrent connectivity. By contrast, visual stimulation only weakly modified co-activation patterns of fast-spiking PV cells, consistent with the observation that these broadly tuned interneurons received very dense and strong synaptic input from nearby pyramidal cells with diverse feature selectivities. Therefore, feedforward and recurrent network influences determine the activity of excitatory and inhibitory ensembles in fundamentally different ways.

Imaging human connectomes at the macroscale

At macroscopic scales, the human connectome comprises anatomically distinct brain areas, the structural pathways connecting them and their functional interactions. Annotation of phenotypic associations with variation in the connectome and cataloging of neurophenotypes promise to transform our understanding of the human brain. In this Review, we provide a survey of magnetic resonance imaging–based measurements of functional and structural connectivity. We highlight emerging areas of development and inquiry and emphasize the importance of integrating structural and functional perspectives on brain architecture.

Fast Nonnegative Deconvolution for Spike Train Inference From Population Calcium Imaging

Fluorescent calcium indicators are becoming increasingly popular as a means for observing the spiking activity of large neuronal populations. Unfortunately, extracting the spike train of each neuron from a raw fluorescence movie is a nontrivial problem. This work presents a fast nonnegative deconvolution filter to infer the approximately most likely spike train of each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm runs in linear time, and is fast enough that even when simultaneously imaging >100 neurons, inference can be performed on the set of all observed traces faster than real time. Performing optimal spatial filtering on the images further refines the inferred spike train estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.

Dynamically Reconfigurable Silicon Array of Spiking Neurons With Conductance-Based Synapses

A mixed-signal very large scale integration (VLSI) chip for large scale emulation of spiking neural networks is presented. The chip contains 2400 silicon neurons with fully programmable and reconfigurable synaptic connectivity. Each neuron implements a discrete-time model of a single-compartment cell. The model allows for analog membrane dynamics and an arbitrary number of synaptic connections, each with tunable conductance and reversal potential. The array of silicon neurons functions as an address-event (AE) transceiver, with incoming and outgoing spikes communicated over an asynchronous event-driven digital bus. Address encoding and conflict resolution of spiking events are implemented via a randomized arbitration scheme that ensures balanced servicing of event requests across the array. Routing of events is implemented externally using dynamically programmable random-access memory that stores a postsynaptic address, the conductance, and the reversal potential of each synaptic connection. Here, we describe the silicon neuron circuits, present experimental data characterizing the 3 mm times 3 mm chip fabricated in 0.5-mum complementary metal-oxide-semiconductor (CMOS) technology, and demonstrate its utility by configuring the hardware to emulate a model of attractor dynamics and waves of neural activity during sleep in rat hippocampus

The Predictive Capacity of Personal Genome Sequencing

A statistical method applied to monozygotic twin data assesses the ability of whole-genome sequencing to predict disease risk in the general population. Is It All in Your Genes? Imagine that everyone at birth could have their whole genome sequenced at negligible cost. Surely, this must be a worthwhile endeavor, given the list of luminaries that have already had this sequencing completed. But how well will such tests perform? Will we be able to predict what diseases individuals will develop, and die from, right from birth? In a study that seeks to answer these questions, Vogelstein and his colleagues present an unbiased assessment of the capacity of whole-genome sequencing to provide clinically relevant information assuming that future research will allow us to understand the significance of every genetic variant. Using previously published data on twins and a new mathematical framework, Vogelstein and his co-workers were able to estimate the maximum capacity of whole-genome sequencing to predict the risk for 24 relatively common diseases. They show that most of the tested individuals could be alerted to a predisposition to at least one disease. However, in any given individual, whole-genome sequencing will be relatively uninformative for most diseases, because the estimated risk of developing these diseases will be similar to that of the general population. Thus, for most patients, genetic testing will not be the dominant determinant of patient care and will not be a substitute for preventative medicine strategies incorporating routine checkups and risk management based on the history, physical status, and life-style of the individual. New DNA sequencing methods will soon make it possible to identify all germline variants in any individual at a reasonable cost. However, the ability of whole-genome sequencing to predict predisposition to common diseases in the general population is unknown. To estimate this predictive capacity, we use the concept of a “genometype.” A specific genometype represents the genomes in the population conferring a specific level of genetic risk for a specified disease. Using this concept, we estimated the maximum capacity of whole-genome sequencing to identify individuals at clinically significant risk for 24 different diseases. Our estimates were derived from the analysis of large numbers of monozygotic twin pairs; twins of a pair share the same genometype and therefore identical genetic risk factors. Our analyses indicate that (i) for 23 of the 24 diseases, most of the individuals will receive negative test results; (ii) these negative test results will, in general, not be very informative, because the risk of developing 19 of the 24 diseases in those who test negative will still be, at minimum, 50 to 80% of that in the general population; and (iii) on the positive side, in the best-case scenario, more than 90% of tested individuals might be alerted to a clinically significant predisposition to at least one disease. These results have important implications for the valuation of genetic testing by industry, health insurance companies, public policy-makers, and consumers.

Spike inference from calcium imaging using sequential Monte Carlo methods.

As recent advances in calcium sensing technologies facilitate simultaneously imaging action potentials in neuronal populations, complementary analytical tools must also be developed to maximize the utility of this experimental paradigm. Although the observations here are fluorescence movies, the signals of interest--spike trains and/or time varying intracellular calcium concentrations--are hidden. Inferring these hidden signals is often problematic due to noise, nonlinearities, slow imaging rate, and unknown biophysical parameters. We overcome these difficulties by developing sequential Monte Carlo methods (particle filters) based on biophysical models of spiking, calcium dynamics, and fluorescence. We show that even in simple cases, the particle filters outperform the optimal linear (i.e., Wiener) filter, both by obtaining better estimates and by providing error bars. We then relax a number of our model assumptions to incorporate nonlinear saturation of the fluorescence signal, as well external stimulus and spike history dependence (e.g., refractoriness) of the spike trains. Using both simulations and in vitro fluorescence observations, we demonstrate temporal superresolution by inferring when within a frame each spike occurs. Furthermore, the model parameters may be estimated using expectation maximization with only a very limited amount of data (e.g., approximately 5-10 s or 5-40 spikes), without the requirement of any simultaneous electrophysiology or imaging experiments.

Detection and localization of surgically resectable cancers with a multi-analyte blood test

SEEK and you may find cancer earlier Many cancers can be cured by surgery and/or systemic therapies when detected before they have metastasized. This clinical reality, coupled with the growing appreciation that cancers rapid genetic evolution limits its response to drugs, have fueled interest in methodologies for earlier detection of the disease. Cohen et al. developed a noninvasive blood test, called CancerSEEK that can detect eight common human cancer types (see the Perspective by Kalinich and Haber). The test assesses eight circulating protein biomarkers and tumor-specific mutations in circulating DNA. In a study of 1000 patients previously diagnosed with cancer and 850 healthy control individuals, CancerSEEK detected cancer with a sensitivity of 69 to 98% (depending on cancer type) and 99% specificity. Science, this issue p. 926; see also p. 866 A blood test that combines protein and DNA markers may allow earlier detection of eight common cancer types. Earlier detection is key to reducing cancer deaths. Here, we describe a blood test that can detect eight common cancer types through assessment of the levels of circulating proteins and mutations in cell-free DNA. We applied this test, called CancerSEEK, to 1005 patients with nonmetastatic, clinically detected cancers of the ovary, liver, stomach, pancreas, esophagus, colorectum, lung, or breast. CancerSEEK tests were positive in a median of 70% of the eight cancer types. The sensitivities ranged from 69 to 98% for the detection of five cancer types (ovary, liver, stomach, pancreas, and esophagus) for which there are no screening tests available for average-risk individuals. The specificity of CancerSEEK was greater than 99%: only 7 of 812 healthy controls scored positive. In addition, CancerSEEK localized the cancer to a small number of anatomic sites in a median of 83% of the patients.

A new look at state-space models for neural data

State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the approximate methods. We discuss a variety of examples, applying these direct optimization techniques to problems in spike train smoothing, stimulus decoding, parameter estimation, and inference of synaptic properties. Along the way, we point out connections to some related standard statistical methods, including spline smoothing and isotonic regression. Finally, we note that the computational methods reviewed here do not in fact depend on the state-space setting at all; instead, the key property we are exploiting involves the bandedness of certain matrices. We close by discussing some applications of this more general point of view, including Markov chain Monte Carlo methods for neural decoding and efficient estimation of spatially-varying firing rates.

A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data

Deducing the structure of neural circuits is one of the central problems of modern neuroscience. Recently-introduced calcium fluorescent imaging methods permit experimentalists to observe network activity in large populations of neurons, but these techniques provide only indirect observations of neural spike trains, with limited time resolution and signal quality. In this work we present a Bayesian approach for inferring neural circuitry given this type of imaging data. We model the network activity in terms of a collection of coupled hidden Markov chains, with each chain corresponding to a single neuron in the network and the coupling between the chains reflecting the networks connectivity matrix. We derive a Monte Carlo Expectation--Maximization algorithm for fitting the model parameters; to obtain the sufficient statistics in a computationally-efficient manner, we introduce a specialized blockwise-Gibbs algorithm for sampling from the joint activity of all observed neurons given the observed fluorescence data. We perform large-scale simulations of randomly connected neuronal networks with biophysically realistic parameters and find that the proposed methods can accurately infer the connectivity in these networks given reasonable experimental and computational constraints. In addition, the estimation accuracy may be improved significantly by incorporating prior knowledge about the sparseness of connectivity in the network, via standard L

CONSISTENT ADJACENCY-SPECTRAL PARTITIONING FOR THE STOCHASTIC BLOCK MODEL WHEN THE MODEL PARAMETERS ARE UNKNOWN ∗

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