Biology subjects

Gomez-Ramirez, J.

Publications and source records attributed to Gomez-Ramirez, J..

7 recordsLinked to original sources

bioRxiv2020

The aging human brain: A causal analysis of the effect of sex and age on brain volume

We study how brain volume loss at old age is affected by factors such as age, APOE gene, sex, and school level. The quantitative characterization of brain volume loss at old age relative to young age requires at least in principle two MRI scans performed at both young and old age. There is, however, a way to address the problem by having only one MRI scan at old age. We compute the total brain loss of elderly subjects as the ratio between the estimated brain volume and the estimated total intracranial volume. Magnetic resonance imaging (MRI) scans of 890 healthy subjects aged 70 to 85 were assessed. The causal analysis of factors affecting brain atrophy was performed using Probabilistic Bayesian Modeling and the Mathematics of Causal Inference. We find that healthy subjects get into their seventies with an average brain volume loss of 30% from their maximum brain volume at a young age. Both age and the sexes are causally related to brain atrophy, with women getting to elderly age with 1% larger brain volume relative to intracranial volume than men. How the brain ages and what are the reasons for sex differences in adult lifespan are causal questions that need to be addressed with causal inference and empirical data. The graphical causal modeling presented here can be instrumental in understanding a puzzling scientific inquiry -the biological age of the brain.

neuroscience↗

bioRxiv2020

A comparative analysis of automated MRI brain segmentation in a large longitudinal dataset: Freesurfer vs. FSL

The study of brain volumetry and morphology of the different brain structures can determine the diagnosis of an existing disease, quantify its prognosis or even help to identify an early detection of dementia. Manual segmentation is an extremely time consuming task and automated methods are thus, gaining importance as clinical tool for diagnosis. In the last few years, AI-based segmentation has delivered, in some cases, superior results than manual segmentation, in both time and accuracy. In this study we aim at performing a comparative analysis of automated brain segmentation. In order to test the performance of automated segmentation methods, the two most commonly used software libraries for brain segmentation Freesurfer and FSL, were put to work in each of the 4028 MRIs available in the study. We find a lack of linear correlation between the segmentation results obtained from Freesurfer and FSL. On the other hand. Freesurfer volume estimates of subcortical brain structures tends to be larger than FSL estimates of same areas. The study builds on an uniquely large, longitudinal dataset of over 4,000 MRIs, all performed with identical equipment to help researchers understand what to expect from fully automated segmentation procedures.

neuroscience↗

bioRxiv2019

Selecting the most important self-assessed features for predicting conversion to Mild Cognitive Impairment with Random Forest and Permutation-based methods

Alzheimers Disease (AD) is a complex, multifactorial and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. are called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study we leverage on The Vallecas Project, a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.

neuroscience↗

bioRxiv2019

Exploratory Data Analysis in The Vallecas Project: A six years longitudinal study in healthy brain aging

AO_SCPLOWBSTRACTC_SCPLOWAlzheimers Disease (AD) is a complex, multifactorial and comorbid condition. The asymptomatic behavior in early stages of the disease is a paramount obstacle to formulate a preclinical and predictive model of AD. Not surprisingly, the AD drug approval rate is one of the lowest in the industry, an exiguous 0.4%. The identification of risk factors, preferably obtained by the subject herself, is sorely needed given that the incidence of Alzheimers disease grows exponentially with age [Ferri et al., 2005], [Ganguli and Rodriguez, 2011].\n\nDuring the last 7 years, researchers at Proyecto Vallecas have collected information about the projects volunteers, aged 70 or more. The Proyecto Vallecas dataset includes information about a wide range of factors including magnetic resonance imaging, genetic, demographic, socioeconomic, cognitive performance, subjective memory complaints, neuropsychiatric disorders, cardiovascular, sleep, diet, physical exercise and self assessed quality of life. The subjects in each visit were diagnosed as healthy, mild cognitive impairment (MCI) or dementia.\n\nIn this study we perform Exploratory Data Analysis to summarize the main characteristics of this unique longitudinal dataset. The objective is to characterize the evolution of the collected features over time and most importantly, how their dynamics are related to cognitive decline. We show that the longitudinal dataset of Proyecto Vallecas, if conveniently exploited, holds promise to identifying either factors promoting healthy aging and risk factors related to cognitive decline.

neuroscience↗

bioRxiv2019

Using time causal quantifiers to characterize Sleep stages

Sleep plays substantial role in daily cognitive performance, mood and memory. The study of sleep has attracted the interest of neuroscientists, clinicans and the overall population, with increasing number of adults suffering from insufficient amounts of sleep. Sleep is an activity composed of different stages whose temporal dynamics, cycles and inter dependencies are not fully understood. Healthy body function and personal well being, however, depends on proper unfolding and continuance of the sleep cycles. The characterization of the different sleep stages can be undertaken with the development of biomarkers derived from sleep recording. For this purpose, in this work we analyzed single-channel EEG signals from 106 healthy subjects. The signals were quantified using the permutation vector approach using five different information theoretic measures: i) Shannons entropy, ii) MPR statistical complexity, iii) Fisher information, iv) Renyi Min-entropy and v) Lempel-Ziv complexity. The results show that all five information theory-based measures make possible to quantify and classify the underlying dynamics of the different sleep stages. In addition to this, we combine these measures to show that planes containing pairs of measures, such as the plane composed of Lempel-Ziv and Shannon, have a better performance for differentiating sleep states than measures used individually for the same purpose.

neuroscience↗

bioRxiv2017

Eyes closed or Eyes open? Exploring the alpha desynchronization hypothesis in resting state functional connectivity networks with intracranial EEG

This paper addresses a fundamental question, are eyes closed and eyes open resting states equivalent baseline conditions, or do they have consistently different electrophysiological signatures? We compare the functional connectivity patterns in an eyes closed resting state with an eyes open resting state, and show that functional connectivity in the alpha band decreases in the eyes open condition compared to eyes closed. This \"alpha desynchronization \" or reduction in the number of connections from eyes closed to eyes open, is here, for the first time, studied with intracranial recordings. We provide two calculations of the wiring cost, local and mesoscopic, defined in terms of the distance between the electrodes and the likelihood that they are functionally connected. We find that, in agreement with the \"alpha desynchronization\" hypothesis, the local wiring cost decreases going from eyes closed to eyes open. However, when the wiring cost calculation takes into account the connectivity pattern, the wiring cost variation from eyes closed to eyes open is not as consistent and shows regional specificity. The wiring cost measure defined here, provides a new avenue for understanding the electrophysiology of resting state.

neuroscience↗

bioRxiv2017

Boredom begets creativity: a solution to the exploitation-exploration trade-off in predictive coding

Here, we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non recurrent problems. We argue that there is a key ingredient that has been overlooked by researchers that needs to be incorporated to understand intelligent behavior in biological and technical systems. This ingredient is boredom. We propose a mathematical model based on the Black-Scholes-Merton equation which provides mechanistic insights into the interplay between boredom and prediction pleasure as the key drivers of behavior.

neuroscience↗