Biology subjects

Cecchi, G.

Publications and source records attributed to Cecchi, G..

7 recordsLinked to original sources

bioRxiv2018

Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis

ALS is a fatal neurodegenerative disease with no cure. Experts typically measure disease progression via the ALSFRS-R score, which includes measurements of various abilities known to decline. We propose instead the use of speech analysis as a proxy for ALS progression. This technique enables 1) frequent non-invasive, inexpensive, longitudinal analysis, 2) analysis of data recorded in the wild, and 3) creation of an extensive ALS databank for future analysis. Patients and trained medical professionals need not be co-located, enabling more frequent monitoring of more patients from the convenience of their own homes. The goals of this study are the identification of acoustic speech features in naturalistic contexts which characterize disease progression and development of machine models which can recognize the presence and severity of the disease. We evaluated subjects from the Prize4Life Israel dataset, using a variety of frequency, spectral, and voice quality features. The dataset was generated using the ALS Mobile Analyzer, a cell-phone app that collects data regarding disease progress using a self-reported ALSFRS-R questionnaire and several active tasks that measure speech and motor skills. Classification via leave-five-subjects-out cross-validation resulted in an accuracy rate of 79% (61% chance) for males and 83% (52% chance) for females.

neuroscience↗

bioRxiv2018

Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM

Many real-world data sets, especially in biology, are produced by highly multivariate and nonlinear complex dynamical systems. In this paper, we focus on brain imaging data, including both calcium imaging and functional MRI data. Standard vector-autoregressive models are limited by their linearity assumptions, while nonlinear general-purpose, large-scale temporal models, such as LSTM networks, typically require large amounts of training data, not always readily available in biological applications; furthermore, such models have limited interpretability. We introduce here a novel approach for learning a nonlinear differential equation model aimed at capturing brain dynamics. Specifically, we propose a variable-projection optimization approach to estimate the parameters of the multivariate (coupled) van der Pol oscillator, and demonstrate that such a model can accurately represent nonlinear dynamics of the brain data. Furthermore, in order to improve the predictive accuracy when forecasting future brain-activity time series, we use this analytical model as an unlimited source of simulated data for pretraining LSTM; such model-specific data augmentation approach consistently improves LSTM performance on both calcium and fMRI imaging data.

neuroscience↗

bioRxiv2018

Predicting natural language descriptions of smells

There has been recent progress in predicting whether common verbal descriptors such as \"fishy\", \"floral\" or \"fruity\" apply to the smell of odorous molecules. However, the number of descriptors for which such a prediction is possible to date is very small compared to the large number of descriptors that have been suggested for the profiling of smells. We show here that the use of natural language semantic representations on a small set of general olfactory perceptual descriptors allows for the accurate inference of perceptual ratings for mono-molecular odorants over a large and potentially arbitrary set of descriptors. This is a noteworthy approach given that the prevailing view is that humans capacity to identify or characterize odors by name is poor [1, 2, 3, 4, 5]. Our methods, when combined with a molecule-to-ratings model using chemoinformatic features, also allow for the zero-shot learning inference [6, 7] of perceptual ratings for arbitrary molecules. We successfully applied our semantics-based approach to predict perceptual ratings with an accuracy higher than 0.5 for up to 70 olfactory perceptual descriptors in a well-known dataset, a ten-fold increase in the number of descriptors from previous attempts. Moreover we accurately predict paradigm odors of four common families of molecules with an AUC of up to 0.75. Our approach solves the need for the consuming task of handcrafting domain specific sets of descriptors in olfaction and collecting ratings for large numbers of descriptors and odorants [8, 9, 10, 11] while establishing that the semantic distance between descriptors defines the equivalent of an odorwheel.

neuroscience↗

bioRxiv2018

An entropic barriers diffusion theory of decision-making in multiple alternative tasks

We present a theory of decision-making in the presence of multiple choices that departs from traditional approaches by explicitly incorporating entropic barriers in a stochastic search process. We analyze response time data from an on-line repository of 15 million blitz chess games, and show that our model fits not just the mean and variance, but the entire response time distribution (over several response-time orders of magnitude) at every stage of the game. We apply the model to show that (a) higher cognitive expertise corresponds to the exploration of more complex solution spaces, and (b) reaction times of users at an on-line buying website can be similarly explained. Our model can be seen as a synergy between diffusion models used to model simple two-choice decision-making and planning agents in complex problem solving.

animal behavior and cognition↗

bioRxiv2017

Brain and psychological determinants of placebo pill response in chronic pain patients

Placebo response is universally observed in randomized controlled trials (RCTs), yet these effects are commonly dismissed as consequences of uncontrollable confounds. In this prospective neuroimaging-based RCT performed in chronic back pain patients, we demonstrate that the intensity, but not quality, of pain is diminished with placebo pill ingestion. The response to placebo pills depended on brain: subcortical limbic volume asymmetry, sensorimotor cortical thickness, and functional coupling of the dorsolateral prefrontal cortex (DLPFC) with the periaqueductal grey (PAG), the rostral anterior cingulate cortex (rACC), and the precentral gyrus (PreCG); and psychological factors. All features were present before exposure to the pill; most remained stable across treatment and washout periods, although specific functional coupling between DLPFC and PAG dissipated with repeated exposure. These brain properties and specific psychological factors, such as interoceptive awareness and openness, were also predictive of the magnitude of response (continuous variable). We used machine learning in a fully cross-validated procedure and demonstrated that psychological factors were sufficient for classifying and predicting response magnitude; and response magnitude could also be predicted from a functional network (nodes mainly located in the limbic community, the DLPFC, the orbitofrontal cortex, and the temporo-parietal junction); the combined model explained 36% of the variance. Together, our results demonstrate that placebo pill analgesia observed in clinical trials depends on a combination of brain properties and specific psychological factors.

neuroscience↗

bioRxiv2017

Learning to read the imprints of consciousness on global brain dynamics: an application to intra-operative monitoring of anesthesia

In daily life, in the operating room and in the laboratory, the operational way to assess wakefulness and consciousness is through responsiveness. A number of studies suggest that the awake, conscious state is not the default behavior of an assembly of neurons, but rather a very special state of activity that has to be actively maintained and curated to support its functional properties. Thus responsiveness is a feature that requires active maintenance, such as a homeostatic mechanism to balance excitation and inhibition. In this work we developed a method for monitoring such maintenance processes, focusing on a specific signature of their behavior derived from the theory of dynamical systems: stability analysis of dynamical modes. When such mechanisms are at work, their modes of activity are at marginal stability, neither damped (stable) nor exponentially growing (unstable) but rather hovering in between. We have previously shown that, conversely, under induction of anesthesia those modes become more stable and thus less responsive, then reversed upon emergence to wakefulness. We take advantage of this effect to build a single-trial classifier which detects whether a subject is awake or unconscious achieving high performance. We show that our approach can be developed into a mean for intra-operative monitoring of the depth of anesthesia, an application of fundamental importance to modern clinical practice.

neuroscience↗

bioRxiv2016

Reverse-engineering human olfactory perception from chemical features of odor molecules

Despite 25 years of progress in understanding the molecular mechanisms of olfaction, it is still not possible to predict whether a given molecule will have a perceived odor, or what olfactory percept it will produce. To address this stimulus-percept problem for olfaction, we organized the crowd-sourced DREAM Olfaction Prediction Challenge. Working from a large olfactory psychophysical dataset, teams developed machine learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models predicted odor intensity and pleasantness with high accuracy, and also successfully predicted eight semantic descriptors (\"garlic\", \"fish\", \"sweet\", \"fruit\", \"burnt\", \"spices\", \"flower\", \"sour\"). Regularized linear models performed nearly as well as random-forest-based approaches, with a predictive accuracy that closely approaches a key theoretical limit. The models presented here make it possible to predict the perceptual qualities of virtually any molecule with an impressive degree of accuracy to reverse-engineer the smell of a molecule.\n\nOne Sentence SummaryResults of a crowdsourcing competition show that it is possible to accurately predict and reverse-engineer the smell of a molecule.

neuroscience↗