Biology subjects

Sanchez-Romero, R.

Publications and source records attributed to Sanchez-Romero, R..

9 recordsLinked to original sources

bioRxiv2024

EFMouse: a Matlab toolbox to model electric fields in the mouse brain

Research into the mechanisms underlying neuromodulation by tES using in-vivo animal models is key to overcoming experimental limitations in humans and essential to building a detailed understanding of the in-vivo consequences of tES. Insights from such animal models are needed to develop targeted and effective therapeutic applications of non-invasive brain stimulation in humans. The sheer difference in scale and geometry between animal models and the human brain contributes to the complexity of designing and interpreting animal studies. Here, we introduce EFMouse, a toolbox that extends previous approaches to model intracranial electric fields and generate predictions that can be tested with in-vivo recordings in mice. Novel functionality includes the ability to capture typical surgical approaches in the mouse (e.g., cranial recording windows), the placement of stimulation electrodes anywhere in or on the animal, and novel ways to report field predictions, including some refined measures of focality and direction homogeneity, and quantification based on regions defined in the Allen Mouse Brain Atlas. Although the EFMouse toolbox is generally applicable to planning and designing tES studies in mice, we illustrate its use by posing questions about transcranial direct current stimulation (tDCS) experiments with the goal of targeting the left visual cortex of the mouse. The EFMouse toolbox is publicly available at https://github.com/klabhub/EFMouse. Author summaryTranscranial electrical stimulation offers opportunities for studying brain activity and developing neuromodulation therapies. Even though this technique is used extensively in humans, understanding its neural consequences is still limited. Mouse models offer an opportunity to bridge this gap. However, major differences between human and mouse brains, such as brain size and cortical folding, pose a challenge to the design of appropriate stimulation protocols in mice. To address this, we developed EFMouse, an open-source computational toolbox that predicts intracranial electrical fields in the mouse brain during stimulation. This toolbox allows researchers to design experiments by simulating electrode arrangements and quantifying properties of the predicted electric field in specific brain regions. By doing so, EFMouse can guide the optimization of stimulation techniques to achieve targeted and reproducible effects. We illustrate its use by comparing a series of electrode arrangements, in terms of the strength, focality, and direction of their induced electric field. By making EFMouse publicly available, we hope to advance fundamental neuroscience research and the development of future clinical applications. HighlightsEFMouse is a novel, open-source, Matlab-based electric field simulator for the mouse brain. EFMouse quantifies induced field focality and homogeneity in regions of the Allen Mouse Brain Atlas. Montages with a return on the mouses back generate homogeneous fields perpendicular to the cortical surface. Montages with a small distance between stimulation and return electrodes on the mouses head can generate focal, but relatively weak fields.

neuroscience↗

bioRxiv2024

Integrity of arousal-regulating brain circuitry predicts human cognitive abilities

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which in turn modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (N=149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

neuroscience↗

bioRxiv2023

Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding

Functional connectivity (FC) has been invaluable for understanding the brains communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without confounded and indirect connections. However, partial correlation FC can also display low repeat reliability, impairing the accuracy of individual estimates. We hypothesized that reliability would be increased by adding regularization, which can reduce overfitting to noise in regression-based approaches like partial correlation. We therefore tested several regularized alternatives - graphical lasso, graphical ridge, and principal component regression - against unregularized partial and pairwise correlation, applying them to empirical resting-state fMRI and simulated data. As hypothesized, regularization vastly improved reliability, quantified using between-session similarity and intraclass correlation. This enhanced reliability then granted substantially more accurate individual FC estimates when validated against structural connectivity (empirical data) and ground truth networks (simulations). Graphical lasso showed especially high accuracy among regularized approaches, seemingly by maintaining more valid underlying network structures. We additionally found graphical lasso to be robust to noise levels, data quantity, and subject motion - common fMRI error sources. Lastly, we demonstrated that resting-state graphical lasso FC can effectively predict fMRI task activations and individual differences in behavior, further establishing its reliability, external validity, and ability to characterize task-related functionality. We recommend graphical lasso or similar regularized methods for calculating FC, as they can yield more valid estimates of unconfounded connectivity than field-standard pairwise correlation, while overcoming the poor reliability of unregularized partial correlation.

neuroscience↗

bioRxiv2022

Distributed network processes account for the majority of variance in localized visual category selectivity

A central goal of neuroscience is to understand how function-relevant brain activations are generated. Here we test the hypothesis that function-relevant brain activations are generated primarily by distributed network flows. We focused on visual processing in human cortex, given the long-standing literature supporting the functional relevance of brain activations in visual cortex regions exhibiting visual category selectivity. We began by using fMRI data from N=352 human participants to identify category-specific responses in visual cortex for images of faces, places, body parts, and tools. We then systematically tested the hypothesis that distributed network flows can generate these localized visual category selective responses. This was accomplished using a recently developed approach for simulating - in a highly empirically constrained manner - the generation of task-evoked brain activations by modeling activity flowing over intrinsic brain connections. We next tested refinements to our hypothesis, focusing on how stimulus-driven network interactions initialized in V1 generate downstream visual category selectivity. We found evidence that network flows directly from V1 were sufficient for generating visual category selectivity, but that additional, globally distributed (whole-cortex) network flows increased category selectivity further. Using null network architectures we also found that each regions unique intrinsic "connectivity fingerprint" was key to the generation of category selectivity. These results generalized across regions associated with all four visual categories tested (bodies, faces, places, and tools), and provide evidence that the human brains intrinsic network organization plays a prominent role in the generation of functionally relevant, localized responses. Author SummaryA fundamental question in neuroscience has persisted for over a century: to what extent do distributed processes drive brain function? The existence of category-selective regions within visual cortex provides long-standing evidence supporting localized computations, wherein specialized functions (e.g., selective responsiveness to face images) are thought to be primarily generated by within-region processes. This account was recently updated to include category selectivity dispersed across visual cortex, in the absence of category-selective regions. Here we provide groundwork evidence demonstrating that locally-exhibited visual-category-selective responses can be accurately generated via distributed activity flowing over globally connected systems. These processes were simulated via empirically-based computational models initialized by stimulus-evoked activity patterns and empirical connectivity matching each category-selective regions unique intrinsic functional connectivity fingerprint. Results demonstrate that activity flowing over the human brains distributed network architecture can account for the generation of category selectivity in visual cortex regions.

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bioRxiv2021

Causally informed activity flow models provide mechanistic insight into the emergence of cognitive processes from brain network interactions

Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain. Highlights- Activity flow models provide insight into how neurocognitive effects are generated from brain network interactions. - Functional connectivity methods grounded in statistical causal principles facilitate mechanistic interpretations of task activity flow models. - Mechanistic activity flow models accurately predict task-evoked neural effects across a wide variety of brain regions and cognitive tasks.

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bioRxiv2020

The functional relevance of task-state functional connectivity

Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the human brains ability to adaptively alter its functionality via rapid changes in inter-regional relationships. We utilized activity flow mapping - an approach for building empirically-derived network models - to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance. Significance StatementHuman cognition is highly dynamic, yet the human brains functional network organization is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the brains intrinsic (resting-state) network organization strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.

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bioRxiv2019

Combining multiple functional connectivity methods to improve causal inferences

Cognition and behavior emerge from brain network interactions, suggesting that causal interactions should be central to the study of brain function. Yet approaches that characterize relationships among neural time series--functional connectivity (FC) methods--are dominated by methods that assess bivariate statistical associations rather than causal interactions. Such bivariate approaches result in substantial false positives since they do not account for confounders (common causes) among neural populations. A major reason for the dominance of methods such as bivariate Pearson correlation (with functional MRI) and coherence (with electrophysiological methods) may be their simplicity. Thus, we sought to identify an FC method that was both simple and improved causal inferences relative to the most popular methods. We started with partial correlation, showing with neural network simulations that this substantially improves causal inferences relative to bivariate correlation. However, the presence of colliders (common effects) in a network resulted in false positives with partial correlation, though this was not a problem for bivariate correlations. This led us to propose a new combined functional connectivity method (combinedFC) that incorporates simple bivariate and partial correlation FC measures to make more valid causal inferences than either alone. We release a toolbox for implementing this new combinedFC method to facilitate improvement of FC-based causal inferences. CombinedFC is a general method for functional connectivity and can be applied equally to resting-state and task-based paradigms.

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bioRxiv2018

Causal Discovery of Feedback Networks with Functional Magnetic Resonance Imaging

We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback that generate noisy time series closely matching real BOLD time series. We compare: an adaptation for time series of the first correct method for recovering the structure of cyclic linear systems; multivariate Granger causal regression; the GIMME algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods proposed by Hyv{diaeresis}arinen and Smith; a method due to Patel, et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, the Fast Adjacency Skewness (FASK) and Two-Step, which exploit non-Gaussian features of the BOLD signal in different ways. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical resting state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability a directed edge is in the true generating structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the data generating model). Recovering inhibitory direct feedback loops between two regions is especially challenging.

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bioRxiv2017

Identification of Mechanisms of Functional Signaling Between Human Hippocampus Regions

BackgroundStandard BOLD connectivity analyses depend on aggregating the signals of individual voxels within regions of interest (ROIs). In certain cases, this spatial aggregation implies a loss of valuable functional and anatomical information about subsets of voxels that drive the ROI level connectivity.\n\nNew MethodWe use the FGES algorithm, a data-driven score-based graphical search method, to identify subsets of voxels that are chiefly responsible for exchanging signals between ROIs. We apply the method to high-resolution resting state functional magnetic resonance imaging (rs-fMRI) data from medial temporal lobe regions of interest of a single healthy individual measured repeated times over a year and a half.\n\nResultsThe FGES algorithm recovered subsets of voxels within larger medial temporal lobe ROIs of entorhinal cortex and hippocampus subfields that show spatially consistency across different scanning sessions, and are statistically significant under tests that validate the role of these subsets as main drivers of effective connectivity between hippocampal regions of interest.\n\nComparison with Existing MethodsIn contrast to standard functional connectivity methods, the FGES algorithm is robust against false positive connections produced by transitive closures of adjacencies (correlation methods) and common effect conditioning (Markov random field methods).\n\nConclusionsThe FGES algorithm allows for identification of communication subsets of voxels driving the connectivity between regions of interest, recovering valuable anatomical and functional information that is lost when ROIs are aggregated. The FGES algorithm is specially suited for voxelwise connectivity research, given its short running time and scalability to big data problems.

neuroscience↗