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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Brain Modularity Mediates the Relation of Cognitive Performance to Task Complexity

Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model (Chen & Deem, 2015) suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole brain organization from network neuroscience to cognitive processing.

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bioRxiv2017

Myopic control of neural dynamics

Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment \"input\" to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely. This \"myopic\" controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a short-sighted manner that only sets a target trajectory of a single time step into the future (hence its myopic nature), which omits the need to pre-calculate a rigid and computationally costly neural feedback control solution. To demonstrate the breadth of this controls utility, two examples with distinctly different applications in neuroscience are studied. First, we show the myopic controls utility to probe the causal link between dynamics and behavior for cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence. Second, an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system, a relevant clinical example for neurological disorders.

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bioRxiv2018

Linking signal detection theory and encoding models to reveal independent neural representations from neuroimaging data

Many research questions in visual perception involve determining whether stimulus properties are represented and processed independently. In visual neuroscience, there is great interest in determining whether important object dimensions are represented independently in the brain. For example, theories of face recognition have proposed either completely or partially independent processing of identity and emotional expression. Unfortunately, most previous research has only vaguely defined what is meant by \"independence,\" which hinders its precise quantification and testing. This article develops a new quantitative framework that links signal detection theory from psychophysics and encoding models from computational neuroscience, focusing on a special form of independence defined in the psychophysics literature: perceptual separability. The new theory allowed us, for the first time, to precisely define separability of neural representations and to theoretically link behavioral and brain measures of separability. The framework formally specifies the relation between these different levels of perceptual and brain representation, providing the tools for a truly integrative research approach. In particular, the theory identifies exactly what valid inferences can be made about independent encoding of stimulus dimensions from the results of multivariate analyses of neuroimaging data and psychophysical studies. In addition, commonly used operational tests of independence are re-interpreted within this new theoretical framework, providing insights on their correct use and interpretation. Finally, we apply this new framework to the study of separability of brain representations of face identity and emotional expression (neutral/sad) in a human fMRI study with male and female participants.\n\nAuthor SummaryA common question in vision research is whether certain stimulus properties, like face identity and expression, are represented and processed independently. We develop a theoretical framework that allowed us, for the first time, to link behavioral and brain measures of independence. Unlike previous approaches, our framework formally specifies the relation between these different levels of perceptual and brain representation, providing the tools for a truly integrative research approach in the study of independence. This allows to identify what kind of inferences can be made about brain representations from multivariate analyses of neuroimaging data or psychophysical studies. We apply this framework to the study of independent processing of face identity and expression.

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bioRxiv2018

Offline coil position denoising enhances detection of TMS effects

OBJECTIVETranscranial magnetic stimulation (TMS) is extensively used in basic and clinical neuroscience. Previous work has shown substantial residual variability in TMS effects even despite use of on-line visual feedback monitoring of coil position. Here, we aimed to evaluate if off-line denoising of variability induced by neuronavigated coil position and orientation deviations can enhance detection of TMS effects.\n\nMETHODSRetrospective modeling was used to denoise the impact of common neuronavigated coil position and rotation deviations during TMS experimental sessions on motor evoked potentials (MEP) to single pulse TMS.\n\nRESULTSNeuronavigated coil deviations explained approximately 44% of total MEP amplitude variability. Offline denoising led to a 136.71% improvement in the signal to noise ratio (SNR) of corticospinal excitability measurements. CONCLUSIONS: Offline modeling enhanced detection of TMS effects by removing variability introduced by neuronavigated coil deviations.\n\nSIGNIFICANCEThis approach could allow more accurate determination of TMS effects in cognitive and interventional neuroscience.\n\nHIGHLIGHTSO_LICoil deviations impact TMS effects despite use of on-line neuronavigation feedback.\nC_LIO_LIOffline denoising of coil deviation impacts on TMS effects significantly reduced variability at trial level.\nC_LIO_LIOffline denoising also significantly improved overall SNR of TMS effects.\nC_LI

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bioRxiv2018

AutonoMouse: High throughput automated operant conditioning shows progressive behavioural impairment with graded olfactory bulb lesions.

Operant conditioning is a crucial tool in neuroscience research for probing brain function. While molecular, anatomical and even physiological techniques have seen radical increases in throughput, efficiency, and reproducibility in recent years, behavioural tools have seen much less of an improvement. Here we present a fully automated, high-throughput system for self-initiated conditioning of up to 25 group-housed, radio-frequency identification (RFID) tagged mice over periods of several months and >10^6 trials. We validate this \"AutonoMouse\" system in a series of olfactory behavioural tasks and show that acquired data is comparable to previous semi-manual approaches. Furthermore, we use AutonoMouse to systematically probe the impact of graded olfactory bulb lesions on olfactory behaviour and resolve the long-standing conundrum about the apparent lack of impact of lesions on olfactory abilities. The modular nature and open-source design of AutonoMouse should allow for similar robust and systematic assessments across neuroscience research areas.

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bioRxiv2018

The Reach Cage environment for wireless neural recordings during structured goal-directed behavior of unrestrained monkeys

System neuroscience of motor cognition regarding the space beyond immediate reach mandates free, yet experimentally controlled movements. We present an experimental environment (Reach Cage) and a versatile visuo-haptic interaction system (MaCaQuE) for investigating goal-directed whole-body movements of unrestrained monkeys. Two rhesus monkeys conducted instructed walk-and-reach movements towards targets flexibly positioned in the cage. We tracked 3D multi-joint arm and head movements using markerless motion capture. Movements show small trial-to-trial variability despite being unrestrained. We wirelessly recorded 192 broad-band neural signals from three cortical sensorimotor areas simultaneously. Single unit activity is selective for different reach and walk-and-reach movements. Walk-and-reach targets could be decoded from premotor and parietal but not motor cortical activity during movement planning. The Reach Cage allows systems-level sensorimotor neuroscience studies with full-body movements in a configurable 3D spatial setting with unrestrained monkeys. We conclude that the primate frontoparietal network encodes reach goals beyond immediate reach during movement planning.

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bioRxiv2018

Two-photon imaging induces brain heating and calcium microdomain hyper-activity in cortical astrocytes

Unraveling how neural networks process and represent sensory information and how this cellular dynamics instructs behavioral output is a main goal in current neuroscience. Two-photon activation of optogenetic actuators and fluorescence calcium (Ca2+) imaging with genetically encoded Ca2+ indicators allow, respectively, the all-optical stimulation and readout of activity from genetically identified cell populations. However, these techniques expose the brain to high near-infrared light doses raising the concern of light-induced adverse effects on the biological phenomena being studied. Combing Ca2+ imaging of GCaMP6f-expressing cortical astrocytes as a sensitive readout for photodamage and an unbiased machine-based event detection, we demonstrate the subtle build-up of aberrant microdomain Ca2+ signals in fine astroglial processes. Illumination conditions routinely being used in biological two-photon microscopy (920-nm excitation, 100-fs regime, ten mW average power) increased the frequency of microdomain Ca2+ events, but left their amplitude, area and duration rather unchanged. This increase in local Ca2+ activity was followed by Ca2+ transients in the otherwise silent soma. Ca2+ hyperactivity occurred without overt morphological damage. Surprisingly, at the same average power, continuous-wave 920-nm illumination was as damaging as fs pulses, indicating a linear, heating-mediated (rather than a highly non-linear) damage mechanism. In an astrocyte-specific IP3-receptor knock-out mouse (IP3R2-KO), Near-infrared light-induced Ca2+ microdomains signals persisted in the small processes, underpinning their resemblance to physiological IP3R2-independent Ca2+ signals, while somatic activity was abolished. Contrary to what has generally been believed in the field, shorter pulses and lower average power are advantageous to alleviate photodamage and allow for longer useful recording windows.\n\nSIGNIFICANCE STATEMENTImaging the fine structure and function of the brain has become possible with two-photon microscopy that uses ultrashort-pulsed infrared laser light for better tissue penetration. The high peak energy of these light pulses has raised concerns about photodamage resulting from multi-photon processes. Here, we show that the time-averaged rather than the peak laser power matters. At wavelengths and with laser powers now commonly used in neuroscience brain damage occurs as a consequence of direct infrared light absorption, i.e., heating. To counteract brain heating we explore a strategy that uses even shorter, more energetic pulses but a lower time-averaged laser power to produce the same image quality while making two-photon microscopy less invasive.

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bioRxiv2018

A critical assessment of data quality and venous effects in ultra-high-resolution fMRI

Advances in hardware, pulse sequences, and reconstruction techniques have made it possible to perform functional magnetic resonance imaging (fMRI) at sub-millimeter resolution while maintaining high spatial coverage and acceptable signal-to-noise ratio. Here, we examine whether ultra-high-resolution fMRI can be exploited for routine use in neuroscience research. We conducted fMRI in human visual cortex during a simple event-related visual experiment (7T, gradient-echo EPI, 0.8-mm isotropic voxels, 2.2-s sampling rate, 84 slices), and developed analysis and visualization tools to assess the quality of the data. We make three main observations. First, we find that the acquired fMRI images, combined with appropriate surface-based processing, provide reliable and accurate measurements of fine-scale blood oxygenation level dependent (BOLD) activity patterns. Second, we show that the highly folded structure of cortex causes substantial biases on spatial resolution and data visualization. Third, we examine the well-recognized issue of venous contributions to fMRI signals. In a systematic assessment of large sections of cortex measured at a fine scale, we show that time-averaged T2*-weighted EPI intensity is a simple, robust marker of venous effects. These venous effects are unevenly distributed across cortex, are more pronounced in gyri and outer cortical depths, and are, to a certain degree, in consistent locations across subjects relative to cortical folding. Furthermore, we show that these venous effects are strongly correlated with BOLD responses evoked by the experiment. We conclude that ultra-high-resolution fMRI can provide robust information about fine-scale BOLD activity patterns, but special care must be exercised in visualizing and interpreting these patterns, especially with regards to the confounding influence of the brains vasculature. To help translate these methodological findings to neuroscience research, we provide practical suggestions for both high-resolution and standard-resolution fMRI studies.

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bioRxiv2018

Calcium activation of frequency dependent temporally phasic, localized, and dense population of cortical neurons by continuous electrical stimulation

Electrical stimulation of the brain has become a mainstay of fundamental neuroscience research and an increasingly prevalent clinical therapy. Despite decades of use in basic neuroscience research and the growing prevalence of neuromodulation therapies, gaps in knowledge regarding activation or inactivation of neural elements over time have limited its ability to adequately interpret evoked downstream responses or fine-tune stimulation parameters to focus on desired responses. In this work, in vivo two-photon microscopy was used to image neuronal calcium activity in layer 2/3 neurons of somatosensory cortex (S1) in male C57BL/6J-Tg(Thy1-GCaMP6s)GP4.3Dkim/J mice during 30 s of continuous electrical stimulation at varying frequencies. We show frequency-dependent differences in spatial and temporal somatic responses during continuous stimulation. Our results elucidate conflicting results from prior studies reporting either dense spherical activation of somas biased towards those near the electrode, or sparse activation of somas at a distance via axons near the electrode. These findings indicate that the neural element specific temporal response local to the stimulating electrode changes as a function of applied charge density and frequency. These temporal responses need to be considered to properly interpret downstream circuit responses or determining mechanisms of action in basic science experiments or clinical therapeutic applications.\n\nSignificance StatementMicrostimulation of small populations of neurons has the potential to ameliorate symptoms associated with several neurological disorders. However, the specific mechanisms by which microstimulation elicits therapeutic responses are unclear. This work examines the effects of continuous microstimulation on the local population of neurons surrounding the implanted electrode. Stimulation was found to elicit spatiotemporal neuronal responses in a frequency dependent manner. These findings suggest that stimulation frequency may be an important consideration for applications in research or therapy. Further research aimed at understanding these neuronal activation properties may provide insight into the mechanistic mode of action of continuous microstimulation.

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bioRxiv2018

Detection of task-relevant and task-irrelevant motion sequences: application to motor adaptation in goal-directed and whole-body movements

Motor variability is inevitable in our body movements and is discussed from several various perspectives in motor neuroscience and biomechanics; it can originate from the variability of neural activities, it can reflect a large degree of freedom inherent in our body movements, it can decrease muscle fatigue, or it can facilitate motor learning. How to evaluate motor variability is thus a fundamental question in motor neuroscience and biomechanics. Previous methods have quantified (at least) two striking features of motor variability; the smaller variability in the task-relevant dimension than in the task-irrelevant dimension and the low-dimensional structure that is often referred to as synergy or principal component. However, those previous methods were not only unsuitable for quantifying those features simultaneously but also applicable in some limited conditions (e.g., a method cannot consider motion sequence, and another method cannot consider how each motion is relevant to performance). Here, we propose a flexible and straightforward machine learning technique that can quantify task-relevant variability, task-irrelevant variability, and the relevance of each principal component to task performance while considering the motion sequence and the relevance of each motion sequence to task performance in a data-driven manner. We validate our method by constructing a novel experimental setting to investigate goal-directed and whole-body movements. Furthermore, our setting enables the induction of motor adaptation by using perturbation and evaluating the modulation of task-relevant and task-irrelevant variabilities through motor adaptation. Our method enables the identification of a novel property of motor variability; the modulation of those variabilities differs depending on the perturbation schedule. Although a gradually imposed perturbation does not increase both task-relevant and task-irrelevant variabilities, a constant perturbation increases task-relevant variability.

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bioRxiv2018

Open source modules for tracking animal behavior and closed-loop stimulation based on Open Ephys and Bonsai

ObjectiveA major goal in systems neuroscience is to determine the causal relationship between neural activity and behavior. To this end, methods that combine monitoring neural activity, behavioral tracking, and targeted manipulation of neurons in closed-loop are powerful tools. However, commercial systems that allow these types of experiments are usually expensive and rely on non-standardized data formats and proprietary software which may hinder user-modifications for specific needs. In order to promote reproducibility and data-sharing in science, transparent software and standardized data formats are an advantage. Here, we present an open source, low-cost, adaptable, and easy to set-up system for combined behavioral tracking, electrophysiology and closed-loop stimulation.\n\nApproachBased on the Open Ephys system (www.open-ephys.org) we developed multiple modules to include real-time tracking and behavior-based closed-loop stimulation. We describe the equipment and provide a step-by-step guide to set up the system. Combining the open source software Bonsai (bonsai-rx.org) for analyzing camera images in real time with the newly developed modules in Open Ephys, we acquire position information, visualize tracking, and perform tracking-based closed-loop stimulation experiments. To analyze the acquired data we provide an open source file reading package in Python.\n\nMain resultsThe system robustly visualizes real-time tracking and reliably recovers tracking information recorded from a range of sampling frequencies (30-1000Hz). We combined electrophysiology with the newly-developed tracking modules in Open Ephys to record place cell and grid cell activity in the hippocampus and in the medial entorhinal cortex, respectively. Moreover, we present a case in which we used the system for closed-loop optogenetic stimulation of entorhinal grid cells.\n\nSignificanceExpanding the Open Ephys system to include animal tracking and behavior-based closed-loop stimulation extends the availability of high-quality, low-cost experimental setup within standardized data formats serving the neuroscience community.

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bioRxiv2018

An Implementation of Empirical Bayesian Inference and Non-Null Bootstrapping for Threshold Selection and Power Estimation in Multiple and Single Statistical Testing

The majority of conclusions and interpretations in quantitative sciences such as neuroscience are based on statistical tests. However, the statistical inferences, especially in multivariate analyses, commonly rely on the p-values, but not on more expressive measures such as posterior probabilities, false discovery rates (FDR) and statistical power (1 - {beta}). The aim of this report is to make these statistical measures further accessible in single and multiple statistical testing. For multiple testing, the Empirical Bayesian Inference (Efron et al., 2001; Efron, 2007) was implemented using non-parametric test statistics (e.g. the Area Under the Curve of the Receiving Operator Characteristics Curve or Spearmans rank correlation) and Gaussian Mixture Model estimation of the probability density function of the original and bootstrapped data. For single statistical tests, the same test statistics were used to construct and estimate the null and non-null probability density functions using bootstrapping under null and non-null grouping assumptions. Simulations were used to test the reliability of the results under a wide range of conditions. The results show conformity to the real truth in the simulated conditions, which is held under various conditions imposed on the simulated data. The open-source MATLAB codes are provided and the utility of the approach has been exemplified and discussed for real-world electroencephalographic signals. This implementation of Empirical Bayesian Inference and informed selection of statistical thresholds are expected to facilitate more realistic scientific deductions in versatile fields, especially in neuroscience, neural signal analysis and neuro-imaging.

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bioRxiv2018

Imaging Striatal Dopamine Release Using a Non-Genetically Encoded Near-Infrared Fluorescent Catecholamine Nanosensor

Neuromodulation plays a critical role in brain function in both health and disease. New optical tools, and their validation in biological tissues, are needed that can image neuromodulation with high spatial and temporal resolution, which will add an important new dimension of information to neuroscience research. Here, we demonstrate the use of a catecholamine nanosensor with fluorescent emission in the 1000-1300 nm near-infrared window to measure dopamine transmission in ex vivo brain slices. These near-infrared catecholamine nanosensors (nIRCats) represent a broader class of nanosensors that can be synthesized from non-covalent conjugation of single wall carbon nanotubes (SWNT) with single strand oligonucleotides. We show that nIRCats can be used to detect catecholamine efflux in brain tissue driven by both electrical stimulation or optogenetic stimulation. Spatial analysis of electrically-evoked signals revealed dynamic regions of interest approximately 2 microns in size in which transients scaled with simulation intensity. Optogenetic stimulation of dopaminergic terminals produced similar transients, whereas optogenetic stimulation of glutamatergic terminals showed no effect on nIRCat signal. Bath application of nomifensine prolonged nIRCat fluorescence signal, consistent with reuptake blockade of dopamine. We further show that the chemically synthetic molecular recognition elements of nIRCats permit measurement of dopamine dynamics in the presence of dopamine receptor agonists and antagonists. These nIRCat nanosensors may be advantageous for future use because i) they do not require virus delivery, gene delivery, or protein expression, ii) their near-infrared fluorescence facilitates imaging in optically scattering brain tissue and is compatible for use in conjunction with other optical neuroscience tool sets, iii) the broad availability of unique near-infrared colors have the potential for simultaneous detection of multiple neurochemical signals, and iv) they are compatible with pharmacology. Together, these data suggest nIRCats and other nanosensors of this class can serve as versatile new optical tools to report dynamics of extracellular neuromodulation in the brain.

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bioRxiv2018

Recording from the same neuron with high-density CMOS probes and patch-clamp: a ground-truth dataset and an experiment in collaboration.

We built a rig to perform patch-clamp and extracellular recordings from the same neuron in vivo. In this setup, the axes of two micromanipulators are precisely aligned and their relative position tracked in real-time, allowing us to accurately target patch-clamp recordings to neurons near an extracellular probe. We used this setup to generate a publicly-available dataset where a cortical neurons spiking activity is recorded in patch-clamp next to a dense CMOS Neuropixels probe. "Ground-truth" datasets of this kind are rare but valuable to the neuroscience community, as they power the development and improvement of spike-sorting and analysis algorithms, tethering them to empirical observations. In this article, we describe our approach and report exploratory and descriptive analysis on the resulting dataset. We study the detectability of patch-clamp spikes on the extracellular probe, within-unit reliability of spike features and spatiotemporal dynamics of the action potential waveform. We open discussion and collaboration on this dataset through an online repository, with a view to producing follow-up publications. PrologueOur efforts to record from the same neuron in vivo using patch-clamp and dense extracellular probes have resulted in three outputs: a publicly-available dataset (http://bit.ly/paired_recs), a manuscript, and a code repository (http://bit.ly/paired_git). Together, these three components form the publication arising from the experiments we have performed. The role of the dataset is to be downloaded and re-used. The role of the manuscript is to describe the experimental methods through which we acquired the dataset, explain it and showcase which types of questions it can be used to address. The repository has two roles: first, promoting reproducibility and error correction. By making our analysis and figure-generation code freely-available, we wish to make our analysis procedures clear and enable the reader to reproduce our results from the raw data, alerting us to any potential mistakes. Second, the repository will form a living, dynamic and interactive component of the publication: a forum for open collaboration on this dataset. Any interested scientists can contribute to it, joining us in detailed exploration of these recordings with a view to producing follow-up publications in which they will be credited for their input. Why did we opt to publish this way? The first reason is that the very nature of the project we here describe - recording the same neuron with patch-clamp and extracellular probes - invites an open science and open source approach. This is because the primary use of this type of "ground truth" validation data is to aid the development of new sorting and analysis algorithms, as well as to benchmark and improve existing ones. The second reason is that despite being conceptually very simple, this project generated a large and complex dataset that can be tackled in many ways and used to address different types of question. Some of these questions are beyond the reach of our analytical expertise; others lie even beyond the scope of our scientific imagination. By releasing the dataset and providing a repository for scientific discussion and collaboration, we aim to maximise its scientific return to the community. Instead of having each interested research group work in isolation, we hope that by encouraging collaboration and discussion between peers we can foster synergy between them that will lead to work of greater scientific value. Although datasets like ours are exquisitely suited for such an approach, we believe this publication strategy needs to become more widely adopted in neuroscience. We were pleased to note recent publications spontaneously and independently using similar approaches1-4, in what may well be evidence of convergent thinking. Perhaps the time has come for new publication and collaboration paradigms. We will elaborate on this subject during the Epilogue. For now, let us get back to electrophysiological recordings, before we begin an experiment on scientific collaboration.

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bioRxiv2018

Real-time experimental control using network-based parallel processing

Modern neuroscience research often requires the coordination of multiple processes such as stimulus generation, real-time experimental control, as well as behavioral and neural measurements. The technical demands required to simultaneously manage these processes with high temporal fidelity limits the number of labs capable of performing such work. Here we present an open-source network-based parallel processing framework that eliminates these barriers. The Real-Time Experimental Control with Graphical User Interface (REC-GUI) framework offers multiple advantages: (i) a modular design agnostic to coding language(s) and operating system(s) that maximizes experimental flexibility and minimizes researcher effort, (ii) simple interfacing to connect measurement and recording devices, (iii) high temporal fidelity by dividing task demands across CPUs, and (iv) real-time control using a fully customizable and intuitive GUI. Testing results demonstrate that the REC-GUI framework facilitates technically demanding, behavior-contingent neuroscience research. Sample code and hardware configurations are downloadable, and future developments will be regularly released.

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bioRxiv2018

Heart evoked brain oscillatory networks and its interruption in early stages of Alzheimer’s disease

Understanding how the heart influences brain dynamics will suppose a deep change for the neuroscience, psychology and medicine. A mainstay questions is the heart modulation of resting state brain networks and its relation with both the cardiac dynamics and the cognitive status. We evaluated the heart evoked basal networks for controls and two groups of mild cognitive impairment patients, stable and progressive to Alzheimers disease without cardiovascular alteration symptoms. Our results in controls show that a healthy cognitive performance correlates with the heart modulation of brain dynamics in areas of the default mode network, and that the heart influence on brain networks varies along the cardiac cycle and the spectral band. However, the cognitive deficit produced by dementia correlates with the lack of heart modulation on brain activity. The heart influence on brain networks is disrupted in patients by producing hypersynchronization, accompanied by decreased cardiac complexity. We designed a surrogate and predictive procedure based on machine learning to compare the heart evoked results with the neural activity no locked to heartbeats. Based on our longitudinal data, we conclude that the prediction to progression to Alzheimers disease is higher when considering the heart - brain interaction than when taking into account only the brain dynamics. We can conclude that brain networks in control subjects were more responsive to the heart cycle, allowing a wealthier, more complex pattern of oscillations. Our results highlight the role of heart in cognitive neuroscience by showing that basal brain networks are modulated by the cardiac dynamics.

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bioRxiv2018

A data-driven approach to the automated study of cross-species homologies

Behavioral neuroscience has made great strides in developing animal models of human behavior and psychiatric disorders. Animal models allow for the formulation of hypotheses regarding the mechanisms underlying psychiatric disorders, and the opportunity to test these hypotheses using procedures that are too invasive for human participants. However, recent scientific reviews have highlighted the low success rate of translating results from animal models into clinical interventions in humans. A potential roadblock is that bidirectional functional mappings between the human and rodent brain are incomplete. To narrow this gap, we created a framework, Neurobabel, for performing large-scale automated synthesis of human neuroimaging data and behavioral neuroscience data. By leveraging the semantics of how researchers within each field describe their studies, this framework enables region to region mapping of brain regions across species, as well as cross-species mapping of psychological functions. As a proof of concept, we utilize the framework to create a functional cross-species mapping between the amygdala and hippocampus for fear-related and spatial memories, respectively. We then proceed to address two open questions in the field: (1) Do rodents have a dorsolateral prefrontal cortex? (2) Which human brain region corresponds to the rodent prelimbic cortex?

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bioRxiv2018

Neural responses to natural and model-matched stimuli reveal distinct computations in primary and non-primary auditory cortex

A central goal of sensory neuroscience is to construct models that can explain neural responses to complex, natural stimuli. As a consequence, sensory models are often tested by comparing neural responses to natural stimuli with model responses to those stimuli. One challenge is that distinct model features are often correlated across natural stimuli, and thus model features can predict neural responses even if they do not in fact drive them. Here we propose a simple alternative for testing a sensory model: we synthesize stimuli that yield the same model response as a natural stimulus, and test whether the natural and \"model-matched\" stimulus elicit the same neural response. We used this approach to test whether a common model of auditory cortex - in which spectrogram-like peripheral input is processed by linear spectrotemporal filters - can explain fMRI responses in humans to natural sounds. Prior studies have that shown that this model has good predictive power throughout auditory cortex, but this finding could reflect stimulus-driven correlations. We observed that fMRI voxel responses to natural and model-matched stimuli were nearly equivalent in primary auditory cortex, but that non-primary regions showed highly divergent responses to the two sound sets, suggesting that neurons in non-primary regions extract higher-order properties not made explicit by traditional models. This dissociation between primary and non-primary regions was not clear from model predictions due to the influence of stimulus-driven response correlations. Our methodology enables stronger tests of sensory models and could be broadly applied in other domains.\n\nAuthor SummaryModeling neural responses to natural stimuli is a core goal of sensory neuroscience. Here we propose a new approach for testing sensory models: we synthesize a \"model-matched\" stimulus that yields the same model response as a natural stimulus, and test whether it produces the same neural response. We used model-matching to test whether a standard model of auditory cortex can explain human cortical responses measured with fMRI. Model-matched stimuli produced nearly equivalent voxel responses in primary auditory cortex, but highly divergent responses in non-primary regions. This dissociation was not evident using more standard approaches for model testing, and suggests that non-primary regions compute higher-order stimulus properties not captured by traditional models. The methodology could be broadly applied in other domains.

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