Network-Based Biomarkers in Alzheimer’s Disease: Review and Future Directions
- J. Gómez-Ramírez, Jinglong Wu
- BiologyFrontiers in Aging Neuroscience
- 4 February 2014
It is argued that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments.
Conciliating neuroscience and phenomenology via category theory.
- A. Ehresmann, J. Gómez-Ramírez
- BiologyProgress in Biophysics and Molecular Biology
- 1 December 2015
On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology.
- J. Gómez-Ramírez, R. Sanz
- BiologyProgress in Biophysics and Molecular Biology
- 1 September 2013
The Role of Chronic Stress as a Trigger for the Alzheimer Disease Continuum
- M. Ávila-Villanueva, J. Gómez-Ramírez, F. Maestú, C. Venero, J. Ávila, M. Fernández-Blázquez
- BiologyFrontiers in Aging Neuroscience
- 22 October 2020
This research attacked the mode of causality of Alzheimer's disease through a probabilistic process called “ ‘spatially aggregating’”, a type of “spiking” that is associated with cell death.
From Brains to Systems
- C. Hernández, R. Sanz, I. Aleksander
- Computer Science
- 2011
Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation
- J. Gómez-Ramírez, M. Fernández-Blázquez, J. Gonzalez-Rosa
- Medicine, PsychologyBrain Science
- 29 April 2022
Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes, and the cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes.
Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods
- J. Gómez-Ramírez, M. Ávila-Villanueva, M. Fernández-Blázquez
- PsychologyScientific Reports
- 30 September 2019
Using machine learning (random forest) and permutation-based methods, a set of most important self-reported variables for MCI conversion is selected which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet.
Stepping beyond the newtonian paradigm in biology towards an integrable model of life: Accelerating discovery in the biological foundations of science
- P. Simeonov, Edwin H. Brezina, P. Siregar
- Biology
- 2012
The first challenge is to build a coherent, coherent, and effective system for naturalistic and naturalistic biocomputation based on the principles of quantum mechanics and physics, and the goal is to devise a program for this purpose.
Exploring the alpha desynchronization hypothesis in resting state networks with intracranial electroencephalography and wiring cost estimates
- J. Gómez-Ramírez, Shelagh Freedman, D. Mateos, J. P. Pérez Velázquez, T. Valiante
- Computer ScienceScientific Reports
- 15 November 2017
This paper compares the functional connectivity patterns in an eyes closed resting state with an eyes open resting state to investigate the alpha desynchronization hypothesis and finds that the wiring cost variation from eyes closed to eyes open is sensitive to the eyes closed and eyes open conditions.
Integrated Information and State Differentiation
- William Marshall, J. Gómez-Ramírez, G. Tononi
- Computer ScienceFrontiers in Psychology
- 28 June 2016
The relationship between Φ as defined in Integrated Information Theory and state differentiation (D), the number of, and difference between potential system states, is investigated and the differentiation evoked by sensory inputs is investigated.
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