Biology subjects

Mujica-Parodi, L. R.

Publications and source records attributed to Mujica-Parodi, L. R..

5 recordsLinked to original sources

bioRxiv2021

Quantifying control circuit regulation in the human brain.

As a field, control systems engineering has developed quantitative methods to characterize the regulation of systems or processes, whose functioning is ubiquitous within synthetic systems. In this context, a control circuit is objectively "well regulated" when discrepancy between desired and achieved output trajectories is minimized, and "robust" to the degree that it is able to regulate well in response to a wide range of stimuli. Most psychiatric disorders are assumed to reflect dysregulation of brain circuits. Yet, probing circuit regulation requires fundamentally different analytic strategies than the correlations relied upon for analyses of connectivity and their resultant networks. Here, we demonstrate how well-established methods for system identification in control systems engineering may be applied to functional magnetic resonance imaging (fMRI) data to extract generative computational models of human brain circuits. These provide two quantitative measures of direct relevance for psychiatric disorders: a circuits sensitivity to external perturbation and its dysregulation.

neuroscience↗

bioRxiv2021

Parameter estimation for correlated Ornstein-Uhlenbeck time-series

In this article, we develop a Maximum likelihood (ML) approach to estimate parameters from correlated time traces that originate from coupled Ornstein-Uhlenbeck processes. The most common technique to characterize the correlation between time-series is to calculate the Pearson correlation coefficient. Here we show that for time series with memory (or a characteristic relaxation time), our method gives more reliable results, but also results in coupling coefficients and their uncertainties given the data. We investigate how these uncertainties depend on the number of samples, the relaxation times and sampling time. To validate our analytic results, we performed simulations over a wide range of correlation coefficients both using our maximum likelihood solutions and Markov-Chain Monte-Carlo (MCMC) simulations. We found that both ML and MCMC result in the same parameter estimations. We also found that when analyzing the same data, the ML and MCMC uncertainties are strongly correlated, while ML underestimates the uncertainties by a factor of 1.5 to 3 over a large range of parameters. For large datasets, we can therfore use the less computationally expensive maximum likelihood method to run over the whole dataset, and then we can use MCMC on a few samples to determine the factor by which the ML method underestimates the uncertainties. To illustrate the application of our method, we apply it to time series of brain activation using fMRI measurements of the human default mode network. We show that our method significantly improves the interpretation of multi-subject measurements of correlations between brain regions by providing parameter confidence intervals for individual measurements, which allows for distinguishing between the variance from differences between subjects from variance due to measurement error.

neuroscience↗

bioRxiv2020

A data-driven predictome for cognitive, psychiatric, medical, and lifestyle factors on the brain

Most neuroimaging studies individually provide evidence on a narrow aspect of the human brain function, on distinct data sets that often suffer from small sample sizes. More generally, the high technical and cost demands of neuroimaging studies (combined with the statistical unreliability of neuroimaging pilot studies) may lead to observational bias, discouraging discovery of less obvious associations that nonetheless have important neurological implications. To address these problems, we built a machine-learning based classification framework, NeuroPredictome, optimized for the reliability and robustness of its associations. NeuroPredictome is grounded in a large-scale dataset, UK-Biobank (N=19,831), which includes resting and task functional MRI as well as structural T1-weighted and diffusion tensor imaging. Participants were assessed with respect to a comprehensive set of 5,034 phenotypes, including the physical and lifestyle factors most relevant to general medicine. Results generated by data-driven classifiers were then cross-validated, using deep-learning textual analyses, against 14,371 peer-reviewed research articles, providing an unbiased hypothesis-generator of linkages between diverse phenotypes and the brain. Our results show that neuroimaging reveals as many neurological links to physical and lifestyle factors as to cognitive factors, supporting a more integrative approach to medicine that considers disease interactions between multiple organs and systems.

neuroscience↗

bioRxiv2020

Development of an MRI Compatible Nasal Drug Delivery Method: Probing Nicotine Addiction Dynamics

BackgroundSubstance abuse is a fundamentally dynamic disease, characterized by repeated oscillation between craving, drug self-administration, reward, and satiety. To model nicotine addiction as a control system, an MR-compatible nicotine delivery system is needed to elicit cyclical cravings. MethodUsing a concentric nebulizer, inserted into one nostril, we delivered each dose--each equivalent to a single cigarette puff--using a syringe pump by nebulizing the nicotine solution using pressurized medical air. A control mechanism permits dual modes: one delivers puffs on a fixed interval programmed by researchers; with the other, subjects press a button to self-administer each nicotine dose. Subjects were therefore able to intuitively "smoke" the equivalent of a cigarette, one "puff" at a time. We dosed each "puff" such that one cigarette would be equal, in nicotine content, to 10 puffs. ResultsWe tested the viability of this delivery method for studying the brains response to nicotine addiction in three steps. First, we established the pharmacokinetics of nicotine delivery, using a dosing scheme designed to gradually achieve saturation, as with a cigarette. Second, we lengthened the time between micro-doses to elicit craving cycles, using both fixed-interval and subject-driven behavior. Finally, we confirmed that the fixed-interval protocol reliably activates brain circuits linked to addiction. ConclusionOur MR-compatible nasal delivery method enables the measurement of neural circuit responses to drug doses on a single-subject level, allowing the development of data-driven predictive models to quantify individual dysregulations of the reward control circuit causing addiction.

neuroscience↗

bioRxiv2020

Younger, but not older, brains are poised at a critical point of functional activity

Brain aging is associated with hypometabolism and associated global changes in functional connectivity. Using fMRI, we show that network synchrony, a collective property of brain activity, decreases with age. Applying quantitative methods from statistical physics, we provide a generative (Ising) model for these changes as a function of the average communication strength between brain regions. In particular, we find healthy brains to be poised at a critical point of this communication strength, enabling a balance between segregated (to functional domains) and integrated (between domains) patterns of synchrony. However, one characteristic of criticality is a high sensitivity to small changes. Thus, minute weakening of pairwise communication between regions, as seen in the aging brain, gives rise to qualitatively abrupt changes in synchrony. Finally, by experimentally modulating metabolic activity in younger adults, we show how metabolism alone-independent of other changes associated with aging-can provide a mechanism for global changes in synchrony.

neuroscience↗