Dr. Arlex Marín García

SAVE OFFLINE

COORDINACIÓN DE LA INVESTIGACIÓN CIENTÍFICA -UPEID CENTRO DE CIENCIAS DE LA COMPLEJIDAD DIRECCIÓN

OFICIO/CCC/Coord/235/2020

Asunto: Renovación de beca posdoctoral

DR. WILLIAM HENRY LEE ALARDÍN COORDINACIÓN DE LA INVESTIGACIÓN CIENTÍFICA PRESENTE.

Estimado Dr. Lee

Mediante la presente me permito informarle que después de ver el desempeño Dr. Arlex Marín García. Para trabajar en el Programa Ciencias Físicas Matemáticas y las Ingenierías, coordinado por la Dra. Ana Leonor Rivera, en el proyecto “Análisis de correlaciones no-lineales entre observables fisiológicas en salud y enfermedad”, le solicito la renovación de su beca posdoc por un año más a partir del 1 de marzo del 2021.

Sin más por el momento, aprovecho la ocasión para enviarle un cordial saludo.

Atentamente “POR MI RAZA HABLARÁ EL ESPÍRITU” Ciudad Universitaria, Cd. México a 23 de octubre del 2020 El Coordinador

DR. ALEJANDRO FRANK HOEFLICH

c.c.p. Dr. José Manuel Saniger Blesa. - Secretario de investigación y desarrollo CIC c.c.p. Lic. Adriana Cruz Cortes. -Secretaria Administrativa C3 c.c.p. archivo

PROPUESTA DE TRABAJO PARA EL PERIODO 15 DICIEMBRE DE 2020 AL 15 DE DICIEMBRE DE 2021 Dr. Gabriel E García Peña Técnico Académico Centro de Ciencias de la Complejidad Universidad Nacional Autónoma de México 20 de Septiembre de 2020 1. Apoyar a la Coordinación Académica en los proyectos transversales y los programas del C3 y en apoyo del cumplimiento de la misión y visión del C3. 1.1 Integrar datos de los proyectos del C3 enfocados a la pandemia de COVID19. 1.2 Ayudar en la sinergia de los esfuerzos de la Coordinación de Investigación con los de la Unidad de Comunicación, y las Coordinaciones de Ciencia de Datos y Vinculación. 1.3 Colaborar en el desarrollo web y contenidos de la página de difusión de la aplicación UNAM COVID|UNAM. 2. Apoyar en los esfuerzos del C3 en el area de COVID-19 2.1 Proyecto de investigación “Dinámica epidémica basada en ciencia de datos y redes de contactos: estudio de la infección por SARS-Cov-2”. 2.2 Aplicación digital UNAM COVID19 (https://coronavirusapoyamexico.c3.unam.mx/) 2.3 Aplicación digital SolidaridadUNAM (https://solidaridadunam.c3.unam.mx/landing.html) 2.4 EPI-PUMA (http://species.c3.unam.mx/covid19/geoportal_v0.1.html). Colaborar en el desarrollo web y generación de contenidos de divulgación científica para la página de difusión de Epi-PUMA. 3. Analizar la movilidad humana en el contexto de la pandemia COVID 19 Se producirán dos investgaciones que servirán para la divulgación científica: 3.1 En colaboración con la Coordinación Académica del C3, a nálizar a la relaciónes entre los casos de COVID-19, la movilidad y la calidad del aire. Estudios recientes muestran que la calidad del aire puede incrementar los casos de gravedad ante el COVID-19. Se ha encontrado una correlación entre el número de casos reportados de esta enfermedad en localidades con mala calidad del aire. Sin embargo, detectar la evidencia de esta relación causal no es fácil, debido a que la contaminación del aire también está relacionada con la movilidad de las personas. Cuando las personas se mueven ocurren más contagios con el coronavirus, y entonces también esperamos encontrar más pacientes con COVID-19 en localidades con mucho movimiento. Más aún las ciudades con peor calidad del aire también son las más pobladas, lo cual lleva a una mayor probabilidad de contagio por la cantidad de gente. 1

Para estudiar estas relaciones, se propone analizar las series de tiempo de diversos parámetros asociados a la calidad del aire (ozono, NOx, CO, partículas suspendidas), el número de casos confirmados y de decesos debidos al COVID-19, y la movilidad reportada por los dispositivos móviles y plataformas como Facebook. En una relación causal, primero ocurre la causa y después el efecto. Por lo que debemos analizar la probabilidad en que los aumentos en contaminación preceden un aumento en los casos COVID-19, y la probabilidad en que los aumentos en la movilidad los preceden. La probabilidad condicional de tener COVID-19 con respecto a nuestra hipótesis H será, la relación entre el número de casos dada la hipótesis y el número de veces que la hipótesis ocurre. Así es que la probabilidades condicionales de nuestras dos hipótesis, serán: N(covid | contaminación) / N contaminación y N(covid | alta movilidad) / N alta movilidad Evidencia de los efectos conjuntos será: N(covid | contaminación y alta movilidad) / N contaminación y alta movilidad y cómo no toda la contaminación se debe a la movilidad, podemos analizar las probabilidad condicional de los efectos separados: N(covid | contaminación y baja movilidad) / N contaminación y baja movilidad

3.2 Analizar las relaciones entre la pobreza, las actividades productivas y la movilidad en el contexto de la epidemia de COVID-19. Las relaciones entre los indicadores de pobreza y la incidencia de COVID-19 deben ser análizados para entender: que sectores de la población son los más vulnerables, y las actividades productivas que serán más afectadas por el COVID-19. Actualmente, la población se encuentra en la disyuntiva entre reactivar sus actividades económicas productivas y reducir su movilidad para reducir los contagios de COVID-19. En este análisis se utilizará el marco teórico de la plataforma Epi-PUMA, naive Bayes, para comparar las probabilidades condicionales de padecer COVID-19 dada (1) la movilidad y (2) los indicadores de pobreza. Debido a que la epidemia a afectado a los distintos sectores de la población en distinto momento, el análisis considerará las variaciones temporales en las probabilidades condicionales.

2

Cronograma de actividades por horas laborales repartidas en 12 meses, del 15 de diciembre de 2020 al 15 de diciembre del 2021 mes del periodo 1

2

3

4

5

6

7

8

9

10

11

12

20

20

20

20

20

20

20

20

20

20

20

20

2.1 Proyeto de Investigación

20

20

60

60

100 100 20

20

60

60

100 100

2.2 UNAM COVID

20

20

20

20

20

20

20

20

20

20

20

20

1. Apoyo a la Coordinación Académica 2. Apoyo en los esfuerzos del COVID19

2.3 Solidaridad UNAM 2.3 Epi-PUMA

80

60

20

3. Análisis del COVID-19 y la movilidad, contaminación y pobreza.

20

40

20

Total de horas laborales

160 160 160 160 160 160 160 160 160 160 160 160

40

40

40

80

60

20

20

40

20

40

Vo. Bo.

Dr. Gabriel Ernesto García Peña

Dra. Ana Leonor Rivera López Coordinación Académica del C3

3

40

40

Arlex Oscar Marín García personal information México, April 13, 1985 e–mail

[email protected]

phone

(+52) 777 446 3060

academic experience 2009–2010

Research Assistant, Institute of Physics–UNAM Automatic Tracking of Light Sources

In collaboration with Gloria Koenigsberger Ph.D, the basic engineering framework and codebase was designed and implemented in order to construct a light–tracking robot. Gloria Koenigsberger, Ph.D · [email protected]

2016–2018

Posdoctoral Researcher, Institute of Physics–UNAM Brain Signal Analysis

In collaboration with François Leyvraz Ph.D and Thomas Seligman Ph.D, the complete MEG–fMRI dataset from the Human Connectome Project was aquired in order to apply new techniques of multivariate analysis. This includes alternative pre–processing techniques and the application of both linear and non–linear similarity measures of brain function in several cognitive and motor tasks. Within the same project, the effects of non–stationarity in random finite sequences was studied from the perspective of Random Matrix Theory and Spectral Theory in collaboration with Soham Biswas Ph.D from UAdG, México and Anirban Chakraborti Ph.D from JNU, India; both with applications to EEG/MEG/fMRI time series analysis. Thomas Seligman, Ph.D · [email protected] François Leyvraz, Ph.D · [email protected]

2018–2019

Posdoctoral Researcher, Institute of Nuclear Sciences–UNAM Multivariate Analysis of Biological Signals

In collaboration with Alejandro Frank Ph.D, Ana Leonor Rivera Ph.D, Rubén Fossión Ph.D, Wady Ríos–Herrera Ph.D, and Maia Angelova Ph.D, new techniques for the univariate, bivariate, and multivariate analysis of biological signals in humans where developed and applied to the study of peri–ictal EEG recordings from epileptic patients, EKG recordings from healthy controls in rhythmic respiration experiments, actigraphy recordings of acute insomnia patients, and its corresponding controls. Within the same project, the statistical and spectral characteristics of ensembles of Ising models where studied from the perspective of spectral matrix theory and statistical physics. Ana Leonor Rivera, Ph.D · [email protected] Alejandro Frank, Ph.D · [email protected]

2

2020–2021

Posdoctoral Researcher, Centre for Complex Sciences–UNAM Time–Series perspective to Health and Disease

In collaboration with Ana Leonor Rivera Ph.D and Rubén Fossión Ph.D, perturbations of the health condition through the monitoring of physiological variables was studied using techniques obtained from time–series analysis and graph theory. Both at group–wise as well as subject–wise level, the behaviour of the interrelations between different variables can be characterized and thus a physiological network is defined; properties of these networks, topological and statistical, provide rich information about previously undetected relationships between physiological variables and their disruption in disease. Ana Leonor Rivera, Ph.D · [email protected] Ruben Fossión, Ph.D · [email protected]

education 2003-2010

Autonomous University of the State of Morelos–UAEM Bs.c in Computer Science

Faculty of Sciences Tesis: Design and implementation of Multivariate analysis methods for EEG analysis Abstract: This research is focused in the development of time series analysis tools to assist in the detection of genuine correlations. Furthermore, numerical models and synthetic data is used to asess the performance of diverse linear interrelation measures present in the EEG analysis literature. Advisor: Markus Müller, Ph.D · [email protected]

2010-2015

Autonomous University of the State of Morelos–UAEM Ph.D in Computer Modelling and Scientific Computing

Faculty of Sciences Dissertation: Genuine Correlation & Epilepsy: Intra– and Extra–craneal EEG Abstract: This research in centered in the description of changes in genuine linear cross–correlation among the time series derived from EEG recordings of patients with farmaco–resistant temporal lobe epilepsy. The methods used range from spectral analysis, time series analysis, complex networks and spectral graph theory. Advisor: Markus Müller, Ph.D · [email protected]

published articles October 2011

“Evolution of Genuine Cross-Correlation Strength of Focal Onset Seizures” Journal of Clinical Neurophysiology

“To quantify the evolution of genuine zero-lag cross-correlations of focal onset seizures, we apply a recently introduced multivariate measure to broad band and to narrow–band EEG data. For frequency components below 12.5 Hz, the

3

strength of genuine cross-correlations decreases significantly during the seizure and the immediate postseizure period, while higher frequency bands show a tendency of elevated cross-correlations during the same period. We conclude that in terms of genuine zero-lag cross-correlations, the electrical brain activity as assessed by scalp electrodes shows a significant spatial fragmentation, which might promote seizure offset.” Authors: Markus Müller, Gerold Baier, Yurytzy López Jiménez, Arlex Marín, Christian Rummel & Kaspar Schindler

October 2013

“Genuine cross-correlations: Which surrogate based measure reproduces analytical results best?” Neural Networks

“The analysis of short segments of noise-contaminated, multivariate real world data constitutes a challenge. In this paper we compare several techniques of analysis, which are supposed to correctly extract the amount of genuine cross-correlations from a multivariate data set. In order to test for the quality of their performance we derive time series from a linear test model, which allows the analytical derivation of genuine correlations. We compare the numerical estimates of the four measures with the analytical results for different correlation pattern. In the bivariate case all but one measure performs similarly well. However, in the multivariate case measures based on the eigenvalues of the equal-time cross-correlation matrix do not extract exclusively information about the amount of genuine correlations, but they rather reflect the spatial organization of the correlation pattern. This may lead to failures when interpreting the numerical results as illustrated by an application to three electroencephalographic recordings of three patients suffering from pharmacoresistent epilepsy.” Authors: Arlex Marín, Markus Müller, Kaspar Schindler & Christian Rummel

July 2018

“Characteristic Fluctuations around Stable Attractor Dynamics extracted from Electroencephalographic Recordings during Sleep and Epileptic Seizures” Brain Connectivity

“Since the discovery of electrical activity of the brain electroencephalographic recordings (EEG) constitute one of the most popular techniques of brain research. However, EEG-signals are highly non-stationary and one should expect that averages of the cross-correlation coefficient, which may take positive and negative values with equal probability, (almost) vanish when estimated over long data segments. Instead, we found that the average zero-lag crosscorrelation matrix estimated with a running window over the whole night of sleep EEGs of healthy subjects shows a characteristic correlation pattern containing pronounced non-zero values. A similar correlation structure has already been encountered in scalp EEG-signals containing focal onset seizures. Therefore, we conclude that this structure is independent of the physiological state and because of its pronounced similarity across subjects, we believe that it depicts a generic feature of the brain dynamics. Namely, we interpret this pattern as a manifestation of a dynamical ground state of the brain activity, necessary to preserve an efficient operational mode, or, expressed in terms of dynamical system theory, as a “shadow” of the evolution on (or close to) an attractor in phase space. Non-stationary dynamical aspects of higher cerebral processes should manifest in deviations from this stable pattern. We confirm this hypothesis via a correlation analysis of EEG recordings of 10 healthy subjects during night sleep and 20 recordings of 9 epilepsy patients. In particular we show that the estimation of deviations from the stationary correlation structures provides a more significant differentiation of sleep stages and more homogeneous results across subjects. ”

4

Authors: Paola Olguín–Rodríguez, Daniel Arzate–Mena, Arlex Marín–García, Maria Corsi–Cabrera, Heidemarie Gast, Johannes Mathis, Irma Yolanda del Rio–Portilla, Christian Rummel, Kaspar Schindler & Markus Müller

October 2020

“Physiological network from anthropometric and blood test biomarkers” Frontiers in Physiology

“Currently, physiology research focuses on molecular mechanism underlying the functioning of living organisms. Reductionist strategies are used to isolate systems into their components and to measure changes in physiological variables between experimental conditions. However, how these isolated physiological variables translate into the emergence –and colapse– of biological functions of the organism as a whole is an often less tractable question. To generate a useful representation of physiology as a system, known and unknown interactions between heterogeneous physiological components must be taken into account. In this work we use a Complex Inference Networks approach to build physiological networks from biomarkers, we employ two unrelated databases to generate Spearman correlation matrices of 81 and 54 physiological variables, respectively, including endocrine, mechanic, biochemical, anthropometric, physiological, and cellular variables. From these correlation matrices we generated physiological networks by selecting a p value threshold indicating statistically significant links. We compared the networks from both samples to show which features are robust and representative for physiology in health. We found that although network topology is sensitive to the p value threshold, an optimal value may be defined by combining criteria of stability of topological features and network connectedness. Unsupervised community detection algorithms allowed to obtain functional clusters that correlate well with current medical knowledge. Finally, we describe the topology of the physiological networks obtained, which lie between random and ordered structural features, and may reflect system robustness and adaptability. Modularity of physiological networks allows to explore functional clusters that are consistent even when considering different physiological variables. Altogether complex inference networks from biomarkers provide an efficient implementation of a systems biology approach that is visually understandable and robust. We hypothesize that physiological networks allow to translate concepts such as homeostasis into quantifiable properties of biological systems useful for health and disease determination. ” Authors: Antonio Barajas Martínez, Elizabeth Ibarra Coronado, Ivette Cruz Bautista, Martha Patricia Sierra Vargas, Carlos Aguilar Salinas, Ruben Fossion, Christopher R. Stephens, Claudia Isabel Ariadna Vargas Domínguez, Rogelio García Torrentera, Octavio Gamaliel Aztatzi Aguilar, Yazmín Debray García, Karen Bobadilla Lozoya, Paloma Almeda Valdes, Maria Augusta Naranjo Meneses, Dulce Abril Mena Orozco, Cesar Ernesto Lamm Chung, Juan Claudio Toledo-Roy, Vania Garcés, Romel Calero, Juan Antonio López-Rivera, Adriana Robles Cabrera, Octavio Bureos-Lecona, Alejandro Frank & Ana Leonor Rivera

book chapters October 2017

“EEG del Sueño y su Análisis Cuantitativo: Correlaciones Aleatorias, Correlaciones Genuinas y Teoría de Gráficas” La Naturaleza de los Sueños

“In this work, the difference between random and genuine linear correlations in Sleep EEG from healthy subjects in compared. Using tools from Graph Theory and Fourier Analysis it is shown that in each of the sleep stages linear correlations are distorted in specific ways and that this is related to spectral changes in the electrical activity in the brain. After correcting for this effects,

5

Graph–Theoretical analysis can be performed and differences between sleep stages can be determined with greater statistical confidence.” Authors: Arlex Marín–García, Maria Corsi-Cabrera, Heidemarie Gast, Wady Alexander Ríos Herrera, Paola Olguín-Rodríguez, Alejandra Rosales–Lagarde, Kaspar Schindler & Markus Müller

teaching 2019–I

“Tópicos Selectos de Matemáticas computacionales” postgraduate course at the Computer Modelling and Scientific Computing programme, IICBA–UAEM.

2019–I

“Tópicos Selectos de Física de los Sistemas Complejos” postgraduate course at the Computer Modelling and Scientific Computing programme, IICBA–UAEM.

academic aptitudes Basic Intermediate Advanced

java, html python, R, Julia LATEX, fortran77/90/95, C/C++, gnuplot, OCTAVE/MATLAB

relevant information Scholarships

2010 · CONACyT: Proyect No. 156667 / Scholarship No. 25561 2010–2015 · CONACyT–PNPC: Scholarship No. 251759 2016–2018 · CONACyT: Project No. 254515 / Scholarship No. 25168 2018–2019 · DGAPA–PAPIIT: Project No. IV100116 2020–2020 · DGAPA–C3: Posdoctoral Grant

Seminars

2010 · Institute of Physics–UNAM “Análisis de la Red Funcional de EEG de pacientes con Epilépsia” ICF–Cuernavaca, México. 2011 · 1st Guadalajara-Cuernavaca meeting on open systems “Performance of Genuine Interrelation Measures” CIC–Cuernavaca, México. 2013 · XV Reunión de Neuroimagen “Coincidencias en Epilépsia y Sueño” cimat–Guanajuato, México. 2013 · LVI Congreso Nacional de Física “Correlaciones Genuinas: Resultados Analíticos” UASLP–San Luis Potosí, México. 2014 · Causality, Information Transfer and Dynamical Networks “Graph Theoretical Analysis of EEG on Focal Onset Seizures” MPIKS–Dresden, Germany.

6

2014 · Recent Developments in Brain Signal Analysis “Evolución Peri–ictal de Correlaciones Genuinas” CIC–Cuernavaca, México. 2016 · Time Series and Correlations Analysis “Genuine Linear Correlations in Intra–Cranial Epileptic EEG” CIC–Cuernavaca, México. 2017 · Time Series and Correlations Analysis “MEG in the Human Connectome Project” CIC–Cuernavaca, México. 2018 · Statistical Techniques for Time Series Analysis and Many–Body Quantum Theory “The Influence of Reference Scheme in Brain Signal Analysis” CIC–Cuernavaca, México. 2019 · Science, Art & Cognition 2018 “Inter– and Intra–Subject Stability of Linear Correlations in MEG” CIC–Cuernavaca, México. 2020 · XVI Mexican Symposium on Medical Physics “Spectral and Statistical analysis of actigraphic recordings of acute insomnia patients” C3–Online.

Languages

Research Interests

Spanish

· Native Speaker

English

· Advanced

Brain Signals Analysis · Medical Physics · Random Matrix Theory · Spectral Theory · Multivariate Analysis · Complex Systems · Statistical Physics · Computer Modelling · Complex Networks · Discrete Mathematics · Parallel and Distributed Systems · Data Mining · Artificial Intelligence · Human–Computer Interfaces

Ciudad de México, a 23 de Octubre de 2020

ASUNTO: Programa de Becas Posdoctorales DGAPA-UNAM A quién corresponda: Me comprometo a que, en caso de ausentarme de la sede de la estancia posdoctoral (Centro de Ciencias de la Complejidad) por más de un mes continuo o tenga planeado disfrutar de año o semestre sabático durante el periodo total de la beca, dejo a cargo al Dr. Ruben Fossión con quién colaboraremos directamente en el proyecto “Salud y enfermedad: un enfoque desde las Ciencias de la Complejidad en la búsqueda de Alertas Tempranas” e igualmente esta adscrito al Instituto de Ciencias Nucleares, para que de seguimiento al plan de trabajo del investigador posdoctoral. Reciba un cordial saludo.

Atentamente,

_______________________________ Dra. Ana Leonor Rivera López Inv. Tit. B de T.C. Instituto de Ciencias Nucleares, Inv. Asoc. Centro de Ciencias de la Complejidad Universidad Nacional Autónoma de México Miembro del Sistema Nacional de Investigadores Nivel II.

Physiological net work from ant hropomet ric and blood t est biomarkers Ant oni o Bar aj as Mar t í ne z 1, El i z abe t h Ibar r a Cor onado2, Mar t ha P. Sie r r a Var gas3, Ive t t e Cr uz Baut ist a4, Paloma Alme da Valde s4, Car los A. Aguilar - Salinas4, Ruben Y. Fossion5, Chr i st ophe r R. St e ph e n s5, Claudia I. Var gas Domíngue z 3, Oct avi o G. At z at z i Agui l ar 3, Yaz mín De br ay Gar cía3, Roge l i o Gar cí a T or r e nt e r a3, Kar e n Bobadilla Loz oya3, Mar í a A. Nar anj o Me ne se s4, Dulce A. Me n a Or oz co4, Ce sar E. Lamm Chung4, Vania Mar t íne z Gar cé s6, Oct avi o A. Le cona2, Ar l e x Mar í n- Gar cí a2, Al e j andr o Fr ank 2, Ana L. Rive r a7* 1

w e i v re

Facul t ad de Medi ci na, Uni ver si dad Naci onal Aut ónoma de Méxi co, Mexi co, 2Cent r o de Ci enci as de l a

Compl ej i dad, Uni ver si dad Naci onal Aut ónoma de Méxi co, Mexi co, 3Inst i t ut o Naci onal de Enf er medades Respi r at or i as-Méxi co (INER), Mexi co, 4Inst i t ut o Naci onal de Ci enci as Médi cas y Nut r i ci ón Sal vador Zubi r án (INCMNSZ), Mexi co, 5Inst i t ut o de Ci enci as Nucl ear es, Uni ver si dad Naci onal Aut ónoma de Méxi co, Mexi co, 6Facul t y of Medi ci ne, Nat i onal Aut onomous Uni ver si t y of Mexi co, Mexi co, 7Nat i onal Aut onomous

In

Uni ver si t y of Mexi co, Mexi co

Submi t t ed t o Jour na l : Fr ont i er s i n Physi ol ogy

Speci a l t y Sect i on: Fr act al and Net w or k Physi ol ogy Art icle t ype: Or i gi nal Resear ch Ar t i cl e Ma nuscr i pt ID: 612598 Recei ved on: 30 Sep 2020

Fr ont i er s websi t e li nk: w w w . f r ont i er si n. or g

A non–parametric model–free analysis of actigraphic recordings of acute insomnia patients A. Mar´ın–Garc´ıa1,* , R. Fossion1,2 , and A.L. Rivera1,2 1 Centro

´ ´ ´ de Ciencias de la Complejidad, Universidad Nacional Autonoma de Mexico, Mexico ´ ´ ´ de Ciencias Nucleares, Universidad Nacional Autonoma de Mexico, Mexico * [email protected] 2 Instituto

ABSTRACT Disruptions in circadian and ultradian rhythms between acute insomnia patients and healthy age– matched controls can be indentified by the spectral and statistical properties of continous actigraphic time series using a non–parametric model–free analysis. On the one hand, temporal shifts of the circadian cycle between controls and insomniac groups is found using a measure of distance between group–wise probability density functions of gross motor movement without the need for a parametric analysis; on the other hand, both Fourier amplitudes and phases show statistically significant differences between groups, indicating perturbations on the Fourier amplitudes and phases of circadian and ultradian components. The proposed methodology is fully data–driven and non–parametric, reproduces previously obtained results while simultaneously opens the possibility of new insights with the use of novel spectral and statistical properties of actigraphic recordings.

1 Introduction Circadian, ultradian and infradian rhythms are physical, mental and behavioral patterns of activity that exhibit stable periodicities at several time–scales1–8 . A large body of evidence has accummulated establishing a direct link between physiological systems and central and peripherical biological clocks, as well as their disturbances in disease9 . Actigraphy, the monitoring of physical activity, is a non-invasive and ecologically valid method to study behavioral patterns in humans10–13 , but not without limitations12, 14, 15 . Through the study of actigraphic recordings it has been observed that perturbations from the healthy condition of circadian rhythmicity are present in a number of illnesses, both physiological16 and cognitive17–22 in nature. However, studies using actigraphy to determine in which manner circadian and ultradian disruptions are a general feature of insomnia has offer mixed–results3 , and while a wide variety of parametric methods for the analysis of actigraphic recordings exist23–27 , non–parametric, model–free analyses have shown a greater sensitivity to subtle differences between health and disease28–35 . In this context, spontaneous fluctuations physiological time-series analysis are often not random but, according to spectral analyses, they present a fractal structure that might reflect the scale–free contribution of a wide variety of physiological processes at multiple time–scales36–39 . The loss of fractality, then, is associated with an impoverishment of this balance: an increase in the dominance of a few rhythmicities and the corresponding diminishing of others. Heart rate variability, where the fractal characteristic of spectral amplitudes relates to health, exhibit a departure from this power law behaviour with ageing40 and in chronic-degenerative disease41–43 . The relationship between physiological and cognitive disturbances