Biology subjects

Cecchi, G.

Publications and source records attributed to Cecchi, G..

14 recordsLinked to original sources

bioRxiv2026

Spatial distribution and habitat suitability of tsetse (Glossina spp.) in Cote dIvoire: An ensemble modeling approach to support targeted disease control

Background Tsetse are vectors of trypanosomes responsible for African animal trypanosomosis (AAT) and human African trypanosomiasis (HAT). While Cote dIvoire has successfully eliminated HAT as a public health problem and approaches elimination of transmission, AAT remains a major obstacle to agriculture and livestock production. Understanding the spatial distribution of tsetse is essential for prioritizing and sustaining disease control and elimination efforts. Methodology/Principal Findings Using 1,702 occurrence records from the national tsetse atlas we modeled the habitat suitability of the nine tsetse species present in Cote dIvoire. We identified suitable habitats in unsampled areas and quantified environmental constraints on tsetse distribution. Resampling the data to a 1km x 1km grid produced spatially explicit outputs at a resolution more relevant for operational planning. An ensemble modeling approach was employed integrating four algorithms--Random Forest, XGBoost, Maximum Entropy (MaxEnt), and Generalized Additive Models (GAM)-- with satellite-derived environmental and anthropogenic predictors--which achieved high predictive accuracy, area under the curve and True Skill Statistics 0.80 and 0.83, respectively. Distance to waterbodies, soil moisture, distance to protected areas, maximum land surface temperature, and sheep density were key drivers of habitat suitability. Importantly, the models identified suitable habitats in 11 administrative regions not covered by the atlas, providing an improved national tsetse risk profile. Conclusions/Significance These results provide a detailed assessment of the ecological suitability of tsetse across Cote dIvoire and their persistence in agroecological mosaics with high human and livestock densities. We offer a high-resolution blueprint for vector and disease control, particularly in areas where field data are currently lacking. We provide a robust framework for evidence-based decision-making within the Progressive Control Pathway (PCP) for AAT by enabling the identification of priority areas and resource allocation optimization to improve livestock productivity through more effective AAT control and reduce the risk of resurgence of HAT.

ecology↗

bioRxiv2026

Alzheimers Disease Brain Phenotypes are Age-dependent

A widespread view of neurodegenerative disorders, including Alzheimers Disease (AD), frames their effects as accelerated aging, with the brain-age gap (BAG, the deviation of predicted brain age from chronological age) as a staple biomarker. However, BAG relies on a fundamental, untested assumption: that AD can be identified via age-invariant brain phenotypes. Using invariant representation learning on brain MRI from 44,178 individuals, we created neural representations that optimally convey age information (age-aware) or conversely remove it (age-invariant) while minimizing reconstruction distortion. We provide the first causal evidence that age information is necessary in brain biomarkers for AD detection: age-aware representations achieve competitive state-of-the-art performance and significantly outperform age-invariant ones (0.84 vs. 0.77 AUC, p < 0.001, with external validation). This necessity reveals a conceptual flaw in BAG: by subtracting chronological age, it discards the very information essential for accurate detection. Using conditional decoders to simulate aging trajectories, we found that healthy aging and AD operate along multiple independent anatomical dimensions (deep gray matter, frontoparietal, temporal). AD patients diverge from rather than accelerate healthy aging, showing pathological temporal shifts alongside, remarkably, relative frontoparietal preservation. Furthermore, representational similarity analysis suggests that even models pretrained on non-age tasks (e.g., sex or BMI) implicitly converge toward age-related features when optimized for AD. Given that the AD phenotype cannot be decoupled from age, our results establish a hard limit for age-independent biomarkers and favor multidimensional models that preserve aging structure over unidimensional summaries like BAG.

neuroscience↗

bioRxiv2023

Multimodal fusion of brain signals for robust prediction of psychosis transition

Psychosis symptoms are often evident before diagnosis, suggesting the underlying biology of high-risk status may predict later disease outcomes. However, a single predictor remains unknown, indicating a need for algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n=74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC=0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.

neuroscience↗

bioRxiv2023

Speak and you shall predict: speech at initial cocaine abstinence as a biomarker of long-term drug use behavior

ImportanceValid biomarkers that can predict longitudinal clinical outcomes at low cost are a holy grail in psychiatric research, promising to ultimately be used to optimize and tailor intervention and prevention efforts. ObjectiveTo determine if baseline linguistic markers in natural speech, as compared to non-speech clinical and demographic measures, can predict drug use severity measures at future sessions in initially abstinent individuals with cocaine use disorder (iCUD). DesignA longitudinal cohort study (August 2017 - March 2020), where baseline measures were used to predict outcomes collected at three-month intervals for up to one year of follow-up. ParticipantsEighty-eight initially abstinent iCUD were studied at baseline; 57 (46 male, age 50.7+/-7.9 years) came back for at least another session. Main Outcomes and MeasuresOutcomes were self-reported symptoms of withdrawal, craving, abstinence duration and frequency of cocaine use in the past 90 days at each study session. The predictors were derived from 5-min recordings of vocal descriptions of the positive consequences of abstinence and the negative consequences of using cocaine; the baseline cocaine and other common drug use measures, demographic and neuropsychological variables were used for comparison. ResultsModels using the non-speech variables showed the best predictive performance at three(r>0.45, P<2x10-3) and six months follow-up (r>0.37, P<3x10-2). At 12 months, the natural language processing-based model showed significant correlations with withdrawal (r=0.43, P=3x10-2), craving (r=0.72, P=5x10-5), days of abstinence (r=0.76, P=1x10-5), and cocaine use in the past 90 days (r=0.61, P=2x10-3), significantly outperforming the other models for abstinence prediction. Conclusions and RelevanceAt short time intervals, maximal predictive power was obtained with models that used baseline drug use (in addition to demographic and neuropsychological) measures, potentially reflecting a slow rate of change in these measures, which could be estimated by linear functions. In contrast, short speech samples predicted longer-term changes in drug use, implying deeper penetrance by potentially capturing non-linear dynamics over longer intervals. Results suggest that, compared to the common outcome measures used in clinical trials, speech-based measures could be leveraged as better predictors of longitudinal drug use outcomes in initially abstinent iCUD, as potentially generalizable to other substance use disorders and related comorbidity. Key PointsO_ST_ABSQuestionC_ST_ABSCan natural language processing be leveraged to predict longitudinal drug use outcomes in individuals with substance use disorder? FindingsIn this prospective study that included initially abstinent individuals with cocaine use disorder (iCUD), models using demographics, neuropsychological measures and drug use patterns at baseline were compared to those using minimally structured short natural speech samples relating the positive consequences of abstinence and the negative consequences of using drugs, showing a differential prediction of outcomes measured up to one year later. At three and six months, the former outperformed speech models, including approximately 50% of the variability in craving and 40% in abstinence duration. At 12 months from baseline, speech models were superior, predicting 50% of the variability in abstinence duration. MeaningSpeech variables derived through natural language processing can predict clinically meaningful drug use outcome measures in addiction, with greater value at longer intervals. The applicability of language modeling to aid in assessing treatment response and risk in drug addiction warrants further investigation in clinical settings.

neuroscience↗

bioRxiv2022

Expansive Linguistic Representations to Predict Interpretable Odor Mixture Discriminability

Language is often thought as being poorly adapted to precisely describe or quantify smell and olfactory attributes. In this work, we show that semantic descriptors of odors can be implemented in a model to successfully predict odor mixture discriminability, an olfactory attribute. We achieved this by taking advantage of the structure-to-percept model we previously developed for monomolecular odorants, using chemical descriptors to predict pleasantness, intensity and 19 semantic descriptors such as fish, cold, burnt, garlic, grass and sweet for odor mixtures, followed by a metric learning to obtain odor mixture discriminability. Through this expansion of the representation of olfactory mixtures, our Semantic model outperforms state of the art methods by taking advantage of the intermediary semantic representations learned from human perception data to enhance and generalize the odor discriminability/similarity predictions. As 10 of the semantic descriptors were selected to predict discriminability/similarity, our approach meets the need of rapidly obtaining interpretable attributes of odor mixtures as illustrated by the difficulty of finding olfactory metamers. More fundamentally, it also shows that language can be used to establish a metric of discriminability in the everyday olfactory space.

neuroscience↗

bioRxiv2021

Predicting human decision making in psychological tasks with recurrent neural networks

Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions and theory of mind, i.e. what others are thinking. This makes predicting human decision making challenging to be treated agnostically to the underlying psychological mechanisms. We propose to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by the human subjects at each step of their decision making, the first application of such methods in this research domain. In this study, we collate the human data from 8 published literature of the Iterated Prisoners Dilemma comprising 168,386 individual decisions and postprocess them into 8,257 behavioral trajectories of 9 actions each for both players. Similarly, we collate 617 trajectories of 95 actions from 10 different published studies of Iowa Gambling Task experiments with healthy human subjects. We train our prediction networks on the behavioral data from these published psychological experiments of human decision making, and demonstrate a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as the Iowa Gambling Task and multi-agent scenarios such as the Iterated Prisoners Dilemma. In the prediction, we observe that the weights of the top performers tends to have a wider distribution, and a bigger bias in the LSTM networks, which suggests possible interpretations for the distribution of strategies adopted by each group. Author summaryThe challenge of predicting human decision is important for many application domains like economy, marketing and artificial intelligence. But the domain where the human behavior modeling is especially crucial is psychology, where the goal is to describe, explain and predict these behaviors. Within this domain, creating agents that effectively mimic human decision making is particularly important. However, despite the many behavioral tasks and analytical methods developed to simulate the process of human decision making in real situations, there have not been a predictive model that can well predict the human decision making trajectories in these psychological tasks. In this predictive modeling problem, we propose to use the long short-term memory networks (LSTM), a popular recurrent neural network architecture and effectively predict the time series of the actions taken by the human subjects at each step of their decision making in both the Iowa Gambling Task, a single-agent game with monetary reward, and the Iterated Prisoners Dilemma, a complex multi-agent social game with non-monetary capital at stake. To the best of our knowledge, this is the first application of such methods in this research domain.

neuroscience↗

bioRxiv2021

MVD-Fuse: Detection of White Matter Degeneration via Multi-View Learning of Diffusion Microstructure

Detecting neuro-degenerative disorders in early-stage and asymptomatic patients is challenging. Diffusion MRI (dMRI) has shown great success in generating biomarkers for cellular organization at the microscale level using complex biophysical models, but there has never been a consensus on a clinically usable standard model. Here, we propose a new framework (MVD-Fuse) to integrate measures of diverse diffusion models to detect alterations of white matter microstructure. The spatial maps generated by each measure are considered as a different diffusion representation (view), the fusion of these views being used to detect differences between clinically distinct groups. We investigate three different strategies for performing intermediate fusion: neural networks (NN), multiple kernel learning (MKL) and multi-view boosting (MVB). As a proof of concept, we applied MVD-Fuse to a classification of premanifest Huntingtons disease (pre-HD) individuals and healthy controls in the TRACK-ON cohort. Our results indicate that the MVD-Fuse boosts predictive power, especially with MKL (0.90 AUC vs 0.85 with the best single diffusion measure). Overall, our results suggest that an improved characterization of pathological brain microstructure can be obtained by combining various measures from multiple diffusion models.

neuroscience↗

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↗