Jaime Gómez-Ramirez

Network-based biomarkers in Alzheimer's disease: review and future directions.

By 2050 it is estimated that the number of worldwide Alzheimer’s disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue 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. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.

On the limitations of standard statistical modeling in biological systems: a full Bayesian approach for biology.

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist.

What the Escherichia Coli Tells Neurons about Learning

The Escherichia coli is a bacterium that comfortingly lives in the human gut and one of the best known living organisms. The sensitivity of this cell to environmental changes is reflected in two kind of movements that can be observed in a swimming bacterium: “run” towards an attractant, for example food, and “tumbling”, in which a new direction is chosen randomly for the next “run”.

A new vision for biomedicine: A systems approach

While it is widely recognised that, on the one hand, reductionistic approaches are inadequate to deal with the multifactorial and complex nature of health and disease, and on the other hand, a system level understanding of the normal and pathological functioning of biological systems is sorely needed; no clear procedure have been put forth about the actual implementation of such a program. In this paper we review the tools and the mode of thinking that systems biology has set forward in the last twenty years, pinpointing its key methodological and epistemic aspects, together with the technical obstacles and conceptual limitations that it is faced with. A new approach conducive to a system level understanding of biomedical systems is thus, proposed.

On some recent insights in Integral Biomathics

This paper summarizes the results in Integral Biomathics obtained to this moment and provides an outlook for future research in the field.

A Quantitative Study of Network Robustness in Resting-State fMRI in Young and Elder Adults

Brain connectivity analysis has shown great promise in understanding how aging affects functional connectivity; however, an explanatory framework to study healthy aging in terms of network efficiency is still missing. Here, we study network robustness, i.e., resilience to perturbations, in resting-state functional connectivity networks (rs-fMRI) in young and elder subjects. We apply analytic measures of network communication efficiency in the human brain to investigate the compensatory mechanisms elicited in aging. Specifically, we quantify the effect of “lesioning” (node canceling) of either single regions of interest (ROI) or whole networks on global connectivity metrics (i.e., efficiency). We find that young individuals are more resilient than old ones to random “lesioning” of brain areas; global network efficiency is over 3 times lower in older subjects relative to younger subjects. On the other hand, the “lesioning” of central and limbic structures in young subjects yield a larger efficiency loss than in older individuals. Overall, our study shows a more idiosyncratic response to specific brain network “lesioning” in elder compared to young subjects, and that young adults are more resilient to random deletion of single nodes compared to old adults.

Hippocampal categories: a mathematical foundation for navigation and memory.

It goes without saying that in science, experiments are essential; hypothesis need to be contrasted against empirical results in order to build scientific theories. In a system of overwhelming complexity like the brain, it is very likely that hidden variables, unknown by the experimentalist, are interacting with those few elements of which the values are expected and can be validated or rejected in the laboratory. Thus, at the end of the day, the experimentalist is refuting or validating tentative models that are somehow prisoners of the lack of knowledge about the structure of the system. The global picture being missing, a key is to look for the fundamental structure which must be found not in the objects, but in the relationships between the objects-their morphisms. How components at the same level interact (the objects here being neurons) and how superior levels constrain those levels below and emerge from those above is tackled here with a mathematical tooling. The mathematical theory of categories is proposed as a valid foundational framework for theoretical modeling in brain sciences.

Stepping beyond the Newtonian Paradigm in Biology

The INBIOSA project brings together a group of experts across many disciplines who believe that science requires a revolutionary transformative step in order to address many of the vexing challenges presented by the world. It is INBIOSA’s purpose to enable the focused collaboration of an interdisciplinary community of original thinkers.

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.

Comments on Aur’s “From Neuroelectrodynamics to Thinking Machines”

In [1] Dorian Aur heatedly criticizes what he calls the ‘‘current neurophysiological doctrine,’’ which relies on the measurement of neural events on a millisecond time scale, that is, spikes or action potential. Aur’s intention is no other than to terminate one of the most fundamental ideas in neuroscience since the pioneering work of Edgard Adrian in the 20’s, the functional relevance of these nerve impulses as carriers of information. In Aur’s view, the spike timing and other related forms of neural coding expressed in terms of temporal observables are no more than epiphenomena. The principles of neural computation must be found in the spatial distribution of electrical processes that occur during the action potential. Thus, ‘‘current mainstream provides a weak understanding of computations performed in the brain,’’ because it ignores the ‘‘hidden information’’ embedded in the complex microscopic interactions inside the cell, during the millisecond time frame of a spike. Thus, ‘‘intrinsic computational processes’’ are decided at a much slower time scale and smaller space scale than is commonly assumed in neurophysiology. Neuroelectrodynamics (NED), a new theoretical framework that borrows from Hamiltonian mechanics, Thermodynamics, Quantum Physics and non-Turing computation, is surmised as the ‘‘change in paradigm required’’ to understand ‘‘brain language.’’ This review highlights the methodological pitfalls and conceptual errors introduced in the model suggested by Aur. First, it is shown that the mathematical equations proposed are not adequate for the studied system, that is, the brain, and second, a discussion on the aftermath of the dismissal of spike trains as carriers of relevant information, as stated by Aur, is sketched. First, with regard to the methodological aspects, Aur makes a claim for ‘‘adequate techniques’’ in order to understand ‘‘the neuron’s language.’’ For Aur, the diversity found in actual recording of action potential propagation in nerve cells needs to be explained in terms of the spatial distribution of electrical charges inside the neuron. The spike directivity vector is presented as the tool put on place to reveal the hidden information laying in the intracellular interactions inside the cell. Thus, while mainstream neurophysiology assumes that it is the timing of the spike that matters, Aur announces a new approach to understand neural computation, up to now unperceived by neurophysiologists, in which meaningful patterns are built upon spike directivity vectors that quantify transient charge density taking place during action potential. The methodological implications of Aur’s approach are of a devastating complexity, owing to the stratospheric dimensionality of the neuron models needed to capture the dynamics of ions, molecules and proteins inside every single cell. It is hard to imagine how one may come to grips with the dynamics of such a gargantuan system. There are millions of proteins inside each neuron! Surprisingly enough, Aur’s bet is Hamiltonian mechanics, which is mainly geometry in phase space [2]. Although Aur’s modeling choice is entirely legitimate, the actual model proposed does not apply neither aims at the physical reality for which claims to be conceived, the brain. It goes without saying that a model is always a simplified description of some features of a system, for example point neuron models are simplifications unable to simulate J. Gomez-Ramirez (&) Autonomous Systems Laboratory, Universidad Politécnica de Madrid, José Gutierrez Abascal, 2, 28006 Madrid, Spain e-mail: jd.gomez@upm.es