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Brain health in diverse settings: How age, demographics and cognition shape brain function

May 2024
NeuroImage 295:120636

May 2024
295:120636

DOI:10.1016/j.neuroimage.2024.120636

Authors:

Vicente Medel

Universidad Adolfo Ibáñez

Sebastian Moguilner

Harvard Medical School

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NeuroImage 295 (2024) 120636

Available online 21 May 2024

Brain health in diverse settings: How age, demographics and cognition

shape brain function

Hernan Hernandez

, Sandra Baez

, Vicente Medel

, Sebastian Moguilner

Jhosmary Cuadros

, Hernando Santamaria-Garcia

, Enzo Tagliazucchi

, Pedro A. Valdes-

Sosa

, Francisco Lopera

, John Fredy OchoaG´

omez

, Alfredis Gonz´

alez-Hern´

andez

Jasmin Bonilla-Santos

, Rodrigo A. Gonzalez-Montealegre

, Tuba Aktürk

, Ebru Yıldırım

Renato Anghinah

, Agustina Legaz

, Sol Fittipaldi

, G¨

orsev G. Yener

Javier Escudero

, Claudio Babiloni

, Susanna Lopez

, Robert Whelan

, Alberto A

Fern´

andez Lucas

, Adolfo M. García

, David Huepe

, Gaetano Di Caterina

, Marcio Soto-

A˜

nari

, Agustina Birba

, Agustin Sainz-Ballesteros

, Carlos Coronel

, Eduar Herrera

Daniel Abasolo

, Kerry Kilborn

, Nicol´

as Rubido

, Ruaridh Clark

, Ruben Herzog

Deniz Yerlikaya

, Bahar Güntekin

, Mario A. Parra

, Pavel Prado

Agustin Ibanez

Latin American Brain Health Institute, Universidad Adolfo Iba˜

nez, Santiago de Chile, Chile

Universidad de los Andes, Bogota, Colombia

Global Brain Health Institute (GBHI), University of California, San Francisco, US Trinity College Dublin, Dublin, Ireland

Harvard Medical School, Boston, MA, USA

Advanced Center for Electrical and Electronic Engineering, Universidad T´

ecnica Federico Santa María, Valparaíso 2390123, Chile

Grupo de Bioingeniería, Decanato de Investigaci´

on, Universidad Nacional Experimental del T´

achira, San Crist´

obal 5001, Venezuela

Ponticia Universidad Javeriana (PhD Program in Neuroscience) Bogot´

a, San Ignacio, Colombia

Center of Memory and Cognition Intellectus, Hospital Universitario San Ignacio Bogot´

a, San Ignacio, Colombia

University of Buenos Aires, Argentina

Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitaci´

on, Universidad San Sebasti´

an, Santiago, Chile

The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Sciences Technology of China, Chengdu, China

Cuban Neuroscience Center, La Habana, Cuba

Grupo de Neurociencias de Antioquia, University of Antioquia, Medellín, Colombia

Master programme of Clinical Neuropsychology, Universidad Surcolombiana, Neiva Huila, Colombia

Department of Psychology, Universidad Cooperativa de Colombia, Colombia

Neurocognition and Psychophysiology Laboratory, Universidad Surcolombiana, Neiva Huila, Colombia

Reference Center of Behavioural Disturbances and Dementia, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil

Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center, University of Sao Paulo, Sao Paulo, Brazil

Cognitive Neuroscience Center, Universidad de San Andr´

es, Buenos Aires, Argentina

National Scientic and Technical Research Council (CONICET), Buenos Aires, Argentina

Facultad de Psicología, Universidad Nacional de C´

ordoba, C´

ordoba, Argentina

Faculty of Medicine, Izmir University of Economics, 35330, Izmir, Turkey

Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, Izmir, Turkey

Izmir Biomedicine and Genome Center, Izmir, Turkey

School of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Scotland, UK

Department of Physiology and Pharmacology “V. Erspamer”, Sapienza University of Rome, Rome, Italy

Hospital San Raffaele Cassino, Cassino, (FR), Italy

School of Psychology, Trinity College Dublin, Dublin, Ireland

Department of Legal Medicine, Psychiatry and Pathology at the Complutense University of Madrid, Madrid, Spain

Cognitive Neuroscience Center, Universidad de San Andr´

ess, Buenos Aires, Argentina

Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile

Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ib´

a˜

nez

Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK

Universidad Cat´

olica San Pablo, Arequipa, Peru

* Corresponding author.

E-mail address: agustin.ibanez@gbhi.org (A. Ibanez).

Contents lists available at ScienceDirect

NeuroImage

journal homepage: www.elsevier.com/locate/ynimg

https://doi.org/10.1016/j.neuroimage.2024.120636

Received 8 February 2024; Received in revised form 17 April 2024; Accepted 4 May 2024

NeuroImage 295 (2024) 120636

Centro Interdisciplinario de Neurociencia de Valparaíso (CINV), Universidad de Valparaíso, Valparaíso, Chile

Departamento de Estudios Psicol´

ogicos, Universidad ICESI, Cali, Colombia

Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH,

School of Psychology, University of Glasgow, Glasgow, Scotland, UK

Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, AB24 3UE, UK

Centre for Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, UK

Sorbonne Universit´

e, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris 75013, France

Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey

Health Sciences and Technology Research Institute (SABITA), Istanbul Medipol University, Istanbul, Turkey

Department of Biophysics, School of Medicine, Istanbul Medipol University, Turkey

Department of Psychological Sciences and Health, University of Strathclyde, United Kingdom and Associate Researcher of the Latin American Brain Health Institute

(BrainLat), Universidad Adolfo Ib´

a˜

nez, Santiago, Chile

Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA

Cognitive Neuroscience Center, Universidad de San Andr´

es and Consejo Nacional de Investigaciones Cientícas y T´

ecnicas, Buenos Aires, Argentina

Trinity College Dublin, The University of Dublin, Dublin, Ireland

ARTICLE INFO

Keywords:

Age

Cognition

Education

Individual differences

Sex

Brain dynamics

ABSTRACT

Diversity in brain health is inuenced by individual differences in demographics and cognition. However, most

studies on brain health and diseases have typically controlled for these factors rather than explored their po-

tential to predict brain signals. Here, we assessed the role of individual differences in demographics (age, sex,

and education; n =1298) and cognition (n =725) as predictors of different metrics usually used in case-control

studies. These included power spectrum and aperiodic (1/f slope, knee, offset) metrics, as well as complexity

(fractal dimension estimation, permutation entropy, Wiener entropy, spectral structure variability) and con-

nectivity (graph-theoretic mutual information, conditional mutual information, organizational information) from

the source space resting-state EEG activity in a diverse sample from the global south and north populations.

Brain-phenotype models were computed using EEG metrics reecting local activity (power spectrum and

aperiodic components) and brain dynamics and interactions (complexity and graph-theoretic measures). Elec-

trophysiological brain dynamics were modulated by individual differences despite the varied methods of data

acquisition and assessments across multiple centers, indicating that results were unlikely to be accounted for by

methodological discrepancies. Variations in brain signals were mainly inuenced by age and cognition, while

education and sex exhibited less importance. Power spectrum activity and graph-theoretic measures were the

most sensitive in capturing individual differences. Older age, poorer cognition, and being male were associated

with reduced alpha power, whereas older age and less education were associated with reduced network inte-

gration and segregation. Findings suggest that basic individual differences impact core metrics of brain function

that are used in standard case-control studies. Considering individual variability and diversity in global settings

would contribute to a more tailored understanding of brain function.

1. Introduction

Individual differences encompass variations observed within a pop-

ulation in relation to a specic trait, including socio-demographic

characteristics, as well as psychological and cognitive factors (Senner

et al., 1814). Individual differences in age (Cole et al., 2018; Bethlehem

et al., 2022), sex (Dotson and Duarte, 2020; Tang et al., 2022; Allouh

et al., 2020), education (Garcia et al., 2018; Members et al., 2010), and

cognition (Valsd´

ottir et al., 2022; Heger et al., 2021) are currently

assumed to play a relevant role in brain function, driving diversity in

brain health (Santamaria-Garcia et al., 2023; Greene et al., 2022; Aranda

et al., 2021; Livingston et al., 2020; Holmes and Patrick, 2018). Di-

versity in the context of brain health refers to wide range of factors

related to populations in terms of race, ethnicity, gender, age, socio-

economic status, cognitive variability, geographical location, genetic

background, and health status, among others (Santamaria-Garcia et al.,

2023; Fittipaldi et al., 2023; Matshabane, 2021). Diversity impacts

healthy aging (Santamaria-Garcia et al., 2023), psychiatric conditions

(Holmes and Patrick, 2018; Resende et al., 2019), and neuro-

degeneration (Ibanez et al., 2023; Walsh et al., 2022). However, these

effects are not universally shared across regions (Santamaria-Garcia

et al., 2023; Greene et al., 2022; Holmes and Patrick, 2018; Alladi and

Hachinski, 2018), and may not generalize to more diverse, underrep-

resented populations in brain health research (Dotson and Duarte, 2020;

Santamaria-Garcia et al., 2023; Baez et al., 2023).

Recent ndings underscore the signicance of individual differences

in demographic predictors, showing that brain-phenotype models pri-

marily reect these variables rather than the intended cognitive do-

mains, particularly in non-stereotypical populations (Greene et al.,

2022). Notwithstanding, most studies on brain health and aging

(Jawinski et al., 2022; Yan et al., 2015), psychiatry (Wolfers et al., 2020;

Elad et al., 2021), and neurodegeneration (Pinaya et al., 2021; Verdi

et al., 2022) have typically controlled for these variables rather than

investigating their impact on brain-phenotype associations. Incorpo-

rating the role of individual differences, including demographics and

cognition, in functional brain dynamics across large, heterogeneous and

underrepresented populations may provide a direct pathway to a better

understanding of the impact of diversity on brain health.

The study of demographics and cognition as predictors of brain

signals has predominantly relied on MRI or fMRI (Bethlehem et al.,

2022; Elad et al., 2021; Di Biase et al., 2023; Rutherford et al., 2022;

Habes et al., 2021; Rosenberg et al., 2020). A limited number of reports

have incorporated MEG (Dimitriadis, 2022; Stier et al., 2023), and only

one has used EEG (Hill et al., 2022). Age has consistently emerged as the

most frequently studied demographic factor for predicting structural

and functional changes in the human brain, spanning from

post-conception to aging, both in healthy individuals (Bethlehem et al.,

2022; Di Biase et al., 2023; Rutherford et al., 2022; Dimitriadis, 2022;

Stier et al., 2023) and in patients with psychiatric (Elad et al., 2021) or

neurodegenerative conditions (Bethlehem et al., 2022). While sex is

considered a predictor of brain changes across the lifespan (Bethlehem

et al., 2022; Rutherford et al., 2022; Stier et al., 2023), the impacts of

other relevant variables, such as education and cognition (Rosenberg

Equal contribution.

H. Hernandez et al.

NeuroImage 295 (2024) 120636

et al., 2020; Greene et al., 2020; Garo-Pascual et al., 2023; Cesnaite

et al., 2023), have been less explored. Both education (Gordon et al.,

2008; Jokinen et al., 2016) and cognition (Habes et al., 2021) have been

associated with white matter changes and increased functional con-

nectivity (Franzmeier et al., 2071a, 2017b). However, while previous

studies have investigated how demographic and cognitive factors in-

uence brain signals, few have explored the effects of age, sex, educa-

tion, and cognition simultaneously. This research gap highlights the

need for studies that comprehensively explore the interplay between

demographic factors, cognitive abilities, and brain signals using various

imaging modalities, including EEG. These advancements would repre-

sent a crucial step towards robustly quantifying individual variations

and their associations with signatures of brain functional organization or

reorganization, especially in global research settings exhibiting larger

heterogeneity.

Given the limited accessibility of neuroimaging or MEG measures on

a large scale, more cost-effective and scalable techniques, such as EEG,

may be better suited for studying individual differences in global set-

tings. Neuroimaging measures often exhibit poor reliability and low

power, requiring thousands of participants to demonstrate brain-

phenotype associations (Marek et al., 2022). The non-invasiveness,

scalability, availability, temporal resolution, and low cost of EEG

make it an attractive approach to meet this need (Prado et al., 2022).

Although many studies have explored the association of EEG metrics

with demographics and cognition, only one study have used these var-

iables to predict EEG signals (Hill et al., 2022). Studies have identied

age-related EEG changes in resting and task performance (Valsd´

ottir

et al., 2022; Cesnaite et al., 2023; Al Zoubi et al., 2018; Carrier et al.,

2001; Hinault et al., 2023; Javaid et al., 2022; Merkin et al., 2023;

Meunier et al., 2009; Murty et al., 2020; Smith et al., 2023; Smits et al.,

2016; Song et al., 2014; Stacey et al., 2021; Trammell et al., 2017;

Trondle et al., 2023; Valdes-Sosa et al., 2021; Voytek et al., 2015;

Zappasodi et al., 2015), including alpha amplitude, as well as slowdown

and increases in global power (Cesnaite et al., 2023; Trondle et al., 2023;

Gaal et al., 2010), topographic reorganization in delta and theta fre-

quencies (Ishii et al., 2017; Rossini et al., 2007), and attenuated spon-

taneous gamma oscillations (Murty et al., 2020). Complexity changes

across the lifespan with increases during young adulthood and decreases

in the elderly population (Zappasodi et al., 2015) have been reported.

Age also induces changes in the aperiodic components (Hinault et al.,

2023; Trondle et al., 2023; Donoghue et al., 2020) and reductions in

global and local efciencies as well as small-worldness (Javaid et al.,

2022; Meunier et al., 2009; Achard and Bullmore, 2007; Onoda and

Yamaguchi, 2013). The association of sex and EEG metrics is less

conclusive (Carrier et al., 2001; Pravitha et al., 2005; Miraglia et al.,

2015). Conversely, the link between cognition and EEG signals is

well-established, with theta (Finnigan and Robertson, 2011) and alpha

(Lejko et al., 2020) associations, decrease in random and spontaneous

neural activity (Smith et al., 2023; Ouyang et al., 2020; Pei et al., 2023),

increased complexity (Smith et al., 2023; Ouyang et al., 2020; Pei et al.,

2023), higher network integration (Finnigan and Robertson, 2011;

Bullmore and Sporns, 2009; Iinuma et al., 2022; McBride et al., 2014),

and small-world properties (Liao et al., 2017). The association between

education and EEG metrics has been rarely reported, with limited evi-

dence (Wilkinson et al., 2023). Furthermore, although EEG studies have

improved preprocessing pipelines (Prado et al., 2022; Bigdely-Shamlo

et al., 2015; Ballesteros et al., 2023; Prado et al., 2023), several meth-

odological caveats continue to hinder their effective use in large-scale

studies. The recording heterogeneity, different layouts of electrodes

and ampliers, lack of harmonization, disparate processing pipelines,

and small sample sizes, have restricted broader application of EEG

(Prado et al., 2022; Jovicich et al., 2019). Thus, there is a critical need

for studies in large and diverse samples using demographic or cognitive

variables to predict brain signals via EEG while addressing its sources of

heterogeneity.

To address these gaps, this study explored EEG data from a large and

diverse sample of healthy participants under resting-state conditions

from regions in the global south (Argentina, Brazil, Colombia, Chile,

Cuba) and the global north (Ireland, Italy, Turkey, United Kingdom). We

aimed to validate the relationships between demographic (i.e., age, sex,

and education; n =1298) and cognitive factors (n =725) with the EEG

metrics across a diverse, heterogeneous, and global dataset. Addition-

ally, we investigated their utility in predicting signicant changes in

brain function, even in the presence of large heterogeneity. We

computed brain-phenotype models of local activity [i.e., power spec-

trum and aperiodic components (Hill et al., 2022; Varela et al., 2001)],

and brain dynamics and interactions [i.e., complexity, and

graph-theoretic measures (Bullmore and Sporns, 2009; Iinuma et al.,

2022)]. We selected these metrics based on their established relevance

to demographic factors, their potential implications for understanding

brain function, and their frequent use across different studies. These

metrics have shown associations with the features assessed in our study,

namely age (Cesnaite et al., 2023; Stacey et al., 2021; Gaal et al., 2010),

sex (Carrier et al., 2001; Pravitha et al., 2005; Miraglia et al., 2015),

education (Wilkinson et al., 2023), and cognition (Finnigan and Rob-

ertson, 2011; Bullmore and Sporns, 2009; Iinuma et al., 2022; McBride

et al., 2014). Power spectrum metrics reveal dominant rhythms linked

with cognitive processes and brain states (Klimesch, 1999), while

spectral EEG metrics interpret non-rhythmic brain activity relevant to

aging and cognition (Donoghue et al., 2020). Complexity metrics pro-

vide insights into the dynamics of different brain regions and their in-

teractions, potentially serving as biomarkers for brain health disorders

(Tononi et al., 1994; Lau et al., 2022). Graph-theoretic metrics illumi-

nate functional connectivity patterns and network organization (Bull-

more and Sporns, 2009). These metrics served as outcomes to evaluate

the predictive value of each demographic and cognitive variable under

two conditions: (a) when it served as the most robust predictor within

the model, and (b) when another variable was considered the best

predictor. Additionally, we explored the source space for each of

brain-phenotype models.

We hypothesized that despite the sample diversity and heterogene-

ity, a subset of the demographic and cognitive variables would signi-

cantly predict each EEG metric. We expected that age (Rosenberg et al.,

2020; Hill et al., 2022; Carrier et al., 2001; Javaid et al., 2022; Merkin

et al., 2023; Meunier et al., 2009; Murty et al., 2020; Smith et al., 2023;

Smits et al., 2016) and cognition (Carrier et al., 2001; Hinault et al.,

2023; Smits et al., 2016; Song et al., 2014) would emerge as the most

robust predictors of EEG metrics. We also anticipated that the combined

effects of these two factors would result in models explaining a high

proportion of variance of EEG measures. The inuence of sex (Merkin

et al., 2023; Stacey et al., 2021; Trammell et al., 2017) and education

(Trondle et al., 2023) would be weaker. Our results suggest that indi-

vidual differences in diverse settings impact core metrics of brain

function that are used in standard case-control studies. The role of in-

dividual differences and diversity in contributing to brain function needs

to be addressed more systematically in global contexts.

2. Materials and methods

2.1. Participants

This multicentric study involved 1298 healthy adult participants

(age: mean =46.60, SD =20.76, range 18–91 years;; years of formal

education: mean =13.77, SD =4.38; sex: M =597, F =701) repre-

senting diverse populations (Fig. 1A) from the global south (Argentina,

Brazil, Colombia, Chile, Cuba) and the global north (Ireland, Italy,

Turkey, United Kingdom). Demographic characteristics and sample sizes

for each country are provided in Table 1. Participants had no history of

psychiatric and/or neurological disorders, alcohol/drug abuse, signi-

cant visual and/or auditory impairments. Furthermore, participants

were not using any ferromagnetic implant. No participant reported

subjective cognitive complaints or functional impairments. Data on the

H. Hernandez et al.

NeuroImage 295 (2024) 120636

general cognitive state of 725 participants were available. The study

protocol was approved by the Institutional Ethics Committee at each

participating center, and all participants provided written informed

consent in accordance with the Declaration of Helsinki. The data and

analysis codes are freely available at the following GitHub link https

://github.com/euroladbrainlat/Brain-health-in-diverse-setting. The

data in the repository has been anonymized and pre-processed.

We used the G*Power 3.1.9.7 software (Faul et al., 2007) for power

analysis, specically employing an F-test for multiple linear regressions.

Our study’s sample size, comprising 725 subjects with cognitive mea-

surements, surpassed the required minimum for conducting multiple

linear regressions with four variables. This allows for the detection of

small effect sizes (0.02 (Cohen, 1988)) with a statistical power of 0.88.

Additionally, the larger total sample of 1298 subjects was adequate for

performing multiple linear regressions with three variables, effectively

identifying small effect sizes with a statistical power of 0.99.

2.2. Demographic and cognitive variables

2.2.1. Demographics

The demographic information includes age at the time of assessment

(in years), sex (male or female), and years of formal education. Data on

gender identity was not available.

2.2.2. Cognition

The general cognitive state was evaluated using the raw total score

from the Mini-Mental State Examination (MMSE) (Folstein et al., 1975).

This test is a widely recognized screening tool for cognitive impairment

and assessing general cognitive functioning. It evaluates various cogni-

tive domains, including orientation to time and place, short-term

memory recall, working memory, language abilities, visuoconstruc-

tional skills, and basic motor commands. This instrument consists of 30

items, and each correct answer adds one point to the total score, which

ranges from 0 to 30. A score of 24 or more indicates normal cognitive

functioning (Creavin et al., 2016; Mukaetova-Ladinska et al., 2022; Cebi

et al., 2020; Foderaro et al., 2022; Kochhann et al., 2010).

Fig. 1. Study design. (A) Sample: Participants were recruited from a multicenter study encompassing regions in the global south (Argentina, Brazil, Chile,

Colombia, Cuba), and the global north (Italy, Ireland, Turkey, United Kingdom). (B) Predictors: The dataset comprised a total of 1298 participants, with cognitive

data available for 725 individuals. Regression models tuned with cross validation and data partition were developed using years of education, age, sex, and cognitive

state as predictors. (C) EEG data preprocessing: The preprocessing steps included electrode re-referencing, noise removal through signal ltering, resampling to 512

Hz, artifact removal (e.g., blinks and eye movements), and transformation into a common source space using the Automated Anatomical Labeling (AAL) atlas. (D)

Outcomes: EEG data in the source space was used to compute four groups of metrics, categorized into power spectrum, aperiodic, complexity, and connectivity

metrics. (E) Data analysis: Linear regressions were applied, utilizing data partitioning with an 80% training sample and a 20% testing set, cross-validated (k =10

repetitions). We reported the importance of predictors and the results on the inuence of each predictor on specic brain regions.

H. Hernandez et al.

NeuroImage 295 (2024) 120636

2.3. EEG data acquisition, processing, and harmonization

The EEG acquisition parameters at each center are detailed in Sup-

plementary Table 1. Participants were situated in comfortable chairs

within dimly lit, electromagnetically quiet rooms, and were advised to

stay still and alert. Resting-state EEG (rsEEG) with closed eyes was

recorded using various systems, encompassing distinct sensor types,

calibrations, and electrode congurations (Supplementary Table 1). In

some instances, EEGs with open eyes were also captured. Due to

differing recording durations across centers, EEG analyses were limited

to the rst ve minutes of each recording.

The EEG data was processed ofine using a customized, automated,

and validated pipeline that incorporated harmonization protocols spe-

cically designed to mitigate batch effects and methodological varia-

tions in multi-center EEG studies (Ballesteros et al., 2023; Prado et al.,

2023). The processing workow involved several steps, including data

preprocessing, EEG rescaling, spatial normalizations, and EEG source

localization.

2.3.1. Pre-processing

Raw EEG data was ltered between 0.5 and 40 Hz using an 8th order

zero-phase shift Butterworth lter. Data were re-sampled to 512 Hz and

referenced using the reference electrode standardization technique

(REST) (Hu et al., 2018). Blinking, ocular artifacts and myogenic activity

were corrected using two distinct ICA techniques (Delorme and Makeig,

2004): ICLabel (a tool for the classication of EEG independent com-

ponents into signals and different categories of noise) (Pion-Tonachini

et al., 2019) and EyeCatch (a tool for identifying eye-related ICA scalp

maps) (Bigdely-Shamlo et al., 2013). ICA techniques [Infomax ICA

(G´

orecka and Walerjan, 2011)] were exclusively used to remove ocular

artifacts (blinking and eye movements). These artifacts typically fall

among the rst components of ICA. After ensuring artifact correction

through ICA techniques (specically, ICLabel and EyeCatch), we con-

ducted a visual inspection of the EEG. Malfunctioning channels were

replaced using weighted spherical interpolations (Kothe and Makeig,

2013).

2.3.2. Normalization

The normalization process adhered to multicentric studies guidelines

(Prado et al., 2022). To minimize variability across centers, Z-score

transformations of EEG time series were implemented (Ballesteros et al.,

2023; Prado et al., 2023a, 2023b). The normalization consisted of

computing the mean voltage of each EEG channel and Z-transforming

the corresponding voltage samples, considering the mean and the

standard deviation of the distribution. This normalization reduces the

electrode-by-electrode variability and was conducted independently for

each recruitment center, therefore reducing the inter-site variability.

Furthermore, a spatial normalization of EEG was performed by

combining different electrode congurations, creating virtual electrodes

calculated from topographic interpolation transforms. This method has

been effectively utilized in studies investigating the variance between

acquisition systems in contrast to between-subject and between-session

variances (Melnik et al., 2017). The technique projects electrode posi-

tions onto a mesh-head model consisting of 1082 points and interpolates

EEG activity. We adjusted the EEGLAB headplot (Prado et al., 2023)

function to map the original EEG onto a 6067-point mesh-head model

(Kothe and Makeig, 2013; Melnik et al., 2017).

2.3.3. EEG source estimation

EEG source generators were estimated using the standardized low-

resolution electromagnetic tomography (sLORETA) method (Pascual--

Marqui, 2002). This method estimates the standardized current density

at specic virtual sensors located in the cortical gray matter and the

hippocampus of an average brain (MNI 305, Brain Imaging Centre,

Montreal Neurologic Institute). This estimation is based on a linear,

weighted summation of a unique scalp voltage conguration or the EEG

cross-spectrum at the sensor level. Essentially, sLORETA serves as a

distributed EEG inverse solution technique that extends from a stan-

dardized version of minimum norm current density estimation. It

effectively addresses challenges associated with estimating deep sources

of EEG activity and ensures precise localization, even in cases with

signicant correlation among nearby generators (Asadzadeh et al.,

2020).

Electrode layouts were aligned with the MNI152 scalp coordinates

(Mazziotta et al., 2001). When computing the sLORETA transformation

matrix, a signal-to-noise ratio of 1 was selected as the regularization

method. Standardized current density maps were produced using a three

concentric spheres head model, in a predened source space of 6242

voxels (with a voxel size of 5 ×5 ×5 mm) of the MNI average brain. The

brain was segmented into 82 brain regions using the Automated

Anatomical Labeling (AAL) atlas (Rolls et al., 2015). Standarized current

densities were estimated for each of the 153,600 Vage distributions

comprising the ve-minutes of rsEEG (sampled at 512 Hz). Standarized

current density time series estimated in voxels belonging to the same

AAL regions were averaged, leading to a mean time series for each brain

area (Prado et al., 2023; Cruzat et al., 2023; Herzog et al., 2022).

Table 1

Demographic and cognitive state information.

Country Sex Age Education Cognition

Sex n Mean SD Mean SD n Mean SD

Argentina Female 29 66.76 8.53 17.03 2.03 28 29.04 0.69

Male 12 66.33 7.92 15.42 2.94 12 28.17 1.85

Chile Female 59 57.03 17.10 15.49 3.73 49 28.80 1.47

Male 21 62.62 18.45 14.33 4.35 14 28.30 2.27

Brazil Female 58 59.33 16.88 11.88 3.68 58 28.03 1.61

Male 35 62.69 15.91 13.51 2.34 35 28.29 1.51

Colombia Female 96 49.61 13.85 12.43 4.64 96 27.82 3.03

Male 37 45.49 15.49 12.68 4.59 37 28.00 3.13

Cuba Female 53 37.51 11.34 13.25 2.79 31 29.68 0.70

Male 149 29.68 7366 12.99 2.81 94 29.28 1.42

Italy Female 12 60.55 7.35 15.25 4.85 – — —

Male 9 62.90 9.24 15.44 5.46 – — —

United Kingdom Female 34 56.82 17.40 15.00 3.67 – — —

Male 19 53.89 21.13 15.11 10.27 – — —

Ireland Female 41 65.76 4.54 15.50 3.78 40 29.25 0.90

Male 44 69.41 5.85 15.01 4.17 44 28.75 1.10

Turkey Female 319 43.48 22.02 13.37 4.98 118 28.85 1.10

Male 271 39.53 22.03 14.19 4.12 69 29.10 1.36

H. Hernandez et al.

NeuroImage 295 (2024) 120636

2.4. EEG descriptors

We computed four categories of EEG descriptors: (i) power spectrum

and (ii) aperiodic spectral metrics, (iii) complexity, and (iv) graph-

theoretic measures. These categories reect neural activity (i.e., power

spectrum or aperiodic spectral metrics) (Cesnaite et al., 2023; Donoghue

et al., 2020), and brain dynamics and interactions (i.e., complexity, and

graph-theoretic measures) (Zappasodi et al., 2015; Vecchio et al., 2022).

Power spectrum metrics are relevant in EEG data analysis, offering in-

sights into brain activity by characterizing how signal power is distrib-

uted across various frequencies (Wang et al., 2015). These metrics

establish a baseline for typical spectral activity, serving as a valuable

benchmark for identifying pathological conditions (Buzs´

aki, 2006) The

aperiodic spectral components of EEG represent non-rhythmic brain

activity. These components facilitate physiological interpretations

related to aging and cognition (Donoghue et al., 2020). For example,

these aperiodic metrics has been linked to excitation/inhibition balance

(Medel et al., 2023; Martinez-Canada et al., 2023), and electrophysio-

logical noise (Voytek et al., 2015). Complexity metrics enable the

evaluation of dynamical brain complexity, which have been explored as

potential biomarkers for diagnosing mental health disorders (Tononi

et al., 1994; Lau et al., 2022). Lastly, graph- theoretic metrics shed light

on the structure of functional connectivity patterns and network orga-

nization (Bullmore and Sporns, 2009).

2.4.1. Power spectrum metrics

Power spectrum analyses were conducted in both the canonical EEG

frequency bands and the subject-specic EEG frequency bands (Babiloni

et al., 2020). Canonical bands provide standardized ranges ensuring

consistency and comparability across studies. However, these bands

may not effectively capture individual differences (Doppelmayr et al.,

1998; Bazanova and Vernon, 2014). Subject-specic bands enhance the

sensitivity to detect specic physiological, cognitive, or behavioral

states that might be masked when using generic bands (Klimesch, 1999).

For instance, the individual alpha frequency peak (IAF) increases during

childhood and slows in middle and older age (Turner et al., 2023), de-

creases with neurodegeneration (Moretti et al., 2004), and increases

with cognitive demands (Haegens et al., 2014). Analyzing power with

canonical and subject-specic frequency bands allows for a compre-

hensive assessment of EEG data.

The canonical frequency bands were dened as follows: delta (δ):

1.5–6 Hz; theta (θ): 6.5–8.0 Hz; alpha1 (

1): 8.5–10 Hz; alpha2 (

2):

10.5–12.0 Hz; beta1 (β1): 12.5–18.0 Hz; beta2 (β2): 18.5–21.0 Hz; beta3

(β3): 21.5–30.0 Hz; gamma (γ): 30.0–40.0 Hz. This EEG band denition

is used for spectral analysis in the EEG source space with LORETA-KEY

software (Grech et al., 2008). It has implemented in many studies for

conducting EEG frequency-specic analyses in dementia (e.g., (Prado

et al., 2023; Aoki et al., 2019)), and other conditions. (e.g. (Lee et al.,

2019; Aoki et al., 2015; Aoki et al., 2019; Ouyang et al., 2020; Krause

et al., 2015)).

Subject-specic frequency bands were determined using the IAF,

dened as the frequency with maximum power in the alpha-frequency

band, and the theta/alpha-frequency transition (TF), dened as the

frequency with minimum power in the second half of theta frequency

range (Martinez-Canada et al., 2023; Babiloni et al., 2020; Ince et al.,

2017). The subject-specic frequency bands are dened as: δ (TF-4 to

TF-2), θ (TF-2 to TF),

low

(TF to IAF), and

high

(IAF to IAF+2) (Babiloni

et al., 2020). The β and γ frequency bands corresponded to the canonical

division.

We computed both the power spectral density (PSD) and the

normalized PSD (nPSD) using Welch’s method with 1-second Hanning

windows with 50% overlap. Subsequently, we calculated the mean of

the nPSD in each frequency band (equivalently percent power) (Li et al.,

2007) and the percent of the nPSD of a given frequency band relative to

the total nPSD (relative power density) (Wang et al., 2015; Babiloni

et al., 2020).

2.4.2. Aperiodic spectral metrics

We computed the aperiodic components of the EEG power spectral

density (PSD): the 1/f slope (a), knee (k), and offset (b) (Martinez-Ca-

nada et al., 2023; Pathania et al., 2021; van Nifterick et al., 2023). We

applied the tting oscillations and one-over-F (FOOOF) algorithm to the

PSD, covering the frequency band from 0.5 to 40 Hz, peak width limits

from 1 to 6 Hz, maximum number of peaks of 6, minimum peak height of

0.2, and peak threshold of 2.0 (Martinez-Canada et al., 2023; Pathania

et al., 2021; van Nifterick et al., 2023). The FOOOF algorithm was

designed to model the aperiodic component of the PSD (Donoghue et al.,

2020), using a Lorentzian function as follows:

A=b−log(k+Fx)

where A is the aperiodic component, b is the offset, x is the exponent,

and x=-a.

2.4.3. Complexity metrics

Complexity descriptors of the EEG source space provide insights into

the dynamics of different brain regions and their interactions. A high

complexity value in an EEG signal indicates an increased degree of ir-

regularity, indicating that the EEG is less predictable. This is often

associated with a healthy and alert brain state (Medel et al., 2023).

Conversely, a low comlexity values suggests a more regular or repetitive

signal, could be observed in pathological states (Sun et al., 2020)

anaesthesia or deep sleep (Sarasso et al., 2021; Boncompte et al., 2021).

Fractal dimension (FD), permutation entropy (PE), Wiener Entropy

(WE), and spectral structure variability (SSV) (Sun et al., 2020; Burns

and Rajan, 2015) were computed to assess the complexity of EEG sig-

nals, with code sourced from a GitHub repository (https://github.com/tf

burns/MATLAB-functions-for-complexity-measures-of-one-dimensiona

l-signals) (Burns and Rajan, 2016). We include measures of predict-

ability (FD) as well as regularity (PE, WE, SSV), which represent the two

key aspects of brain dynamics (Lau et al., 2022). Predictability refers to

the ability to anticipate the temporal evolution of the system’s states,

while regularity measures the frequency and pattern of repetitions in the

system’s trajectory (Lau et al., 2022). Changes in FD are linked to var-

iations (Zappasodi et al., 2015) and cognition (Smits et al., 2016;

Hemmati et al., 2013). Similarly, both PE and WE have been associated

with individual differences in age (Al Zoubi et al., 2018; Shumbaya-

wonda et al., 2018) and cognition (Parbat and Chakraborty, 2021; Seker

et al., 2021). These metrics collectively capture broad conceptions of

complexity (Burns and Rajan, 2015). The equations for complexity

metrics are described below.

Fractional dimension: The FD is a statistic index of complexity

details in a patter and can be expresed as:

FD =a(NLD −NLD0)k

where a, k y NLD0, was set to the suggested parameters values: 1.9079,

0.18383 y 0.097178, respectively (Kalauzi et al., 2009). The NLD was

calculate as:

NLD =1

N

i=2

|yn(i) − yn(i−1)|

where yn(i)represents the ith signal sample after amplitude

normalization.

Permutation entropy: The PE is an ordinal-based non-parametric

metric of the temporal dependence structure in linear or non-linear time

series. Therefore, PE can be expressed as follows:

Considering a time series represented by xt, con t=1,…,T, and the

embedded vector Xt= [Xt+Xt+l,…,+Xt+(n−1)], where n is the

embedding dimension and l is the lag. Then, the vector Xt is arranged

from smallest to largest. Finally, PE was dened as:

H. Hernandez et al.

NeuroImage 295 (2024) 120636

PE = − 

n=1

)ln(p)

where p(

) = f(

)/(T− (n−1)l)y f(

)represents the frequency of the

symbols of length n derived from the ordinal relationships between Xt.

(Ouyang et al., 2013)

Wiener entropy and spectral structure variation: The WE, alter-

natively referred to as spectral atness or tonality coefcient, signies

the uniform distribution of signal energy across the frequency domain.

This metric provides a quantiable measure of how closely the signal

mirrors a sinusoidal function, as opposed to exhibiting characteristics

reminiscent of noise. The spectral atness measure (SFM) was used to

quantify spectral structure (Singh, 2011), and the equation for its

calculation is as follows:

SFM(t) = log N

i=1S(t,f)

1

NN

i=1S(t,f)

where N is the number of points in the Fourier transform, and S(t,f)is

the associated power at each frequency component. Finally, the Wiener

entropy is calculated as the average of SFM(t), and the spectral structure

variability is calculated as the variance of SFM(t).

2.4.4. Connectivity graph metrics

Calculating graph metrics required determining functional connec-

tivity across the entire space of different brain regions. Functional

connectivity was assessed in the time-domain using information theo-

retic measures, which were calculated with custom Matlab codes (Prado

et al., 2023; Herzog et al., 2022). These measures were derived using the

Gaussian copulas approximation (Ince et al., 2017), a robust computa-

tional framework that combines copulas statistical theory with an ana-

lytic solution for the entropy of multivariate Gaussian distributions. The

incorporation of Gaussian copulas-based methods strengthens the

robustness and cost-effectiveness of our methodology, resulting in a

reduction of computational time required for functional connectivity

calculations. We computed both pairwise and high-order interactions

metrics, specically mutual information (MI) (Prado et al., 2023; Herzog

et al., 2022; Santamaria-Garcia et al., 2023), conditional mutual infor-

mation (CMI) (Prado et al., 2023; Ince et al., 2017), and organizational

information (O_info) (Prado et al., 2023) metrics (Supplementary Data

S1). MI gauges the shared information between two random variables

(or time series), while CMI assesses the shared information between two

random variables (or time series) with respect to a specied third vari-

able. Finally, O_info, an extension of Shannon’s mutual information,

enhances our understanding of the crucial characteristics of multivariate

systems, especially those involving high-order interactions (Herzog

et al., 2022). Information theory metrics surpass traditional measures

like coherence or phase locking value because they can capture

non-linear interactions (Imperatori et al., 2019; Kida et al., 2016) and

provide better results(109). This is crucial for exploring brain dynamics,

where complex non-linear interactions among neural regions shape

patterns of functional connectivity (Imperatori et al., 2019; Kida et al.,

2016).

Measures of segregation, integration, and global metrics were

included to measure complementary dimensions of network topology

(Bullmore and Sporns, 2009; Rubinov and Sporns, 2010). Global Ef-

ciency provides insights into the efciency of information exchange

across the entire network, crucial for global information processing

(Rubinov and Sporns, 2010). Transitivity measures the prevalence of

clustered connectivity, reecting localized, interconnected communities

key for local processing and network segregation (Rubinov and Sporns,

2010). Small-worldness captures the balance between random and

regular networks, indicating an optimal organization of localized and

distributed processing, and integration and segregation, enhancing

network efciency and robustness (Bassett and Bullmore, 2017). Lastly,

density quanties the overall connectivity of the network, offering a

direct assessment of the complexity and connectivity level (Rubinov and

Sporns, 2010). All these measures have been shown to be sensitive to

individual differences in age (Javaid et al., 2022; Meunier et al., 2009;

Achard and Bullmore, 2007; Onoda and Yamaguchi, 2013), sex (Mir-

aglia et al., 2015) and cognition (Finnigan and Robertson, 2011; Bull-

more and Sporns, 2009; Iinuma et al., 2022; McBride et al., 2014; Liao

et al., 2017) . The equations of these metrics are described below.

Connection weight: The connection weight expresses the strength

with which nodes are linked, where wij is the connection weight between

node i and node j.

Weighted shortest path length: The shortest path length is a

fundamental metric to evaluate integration, which is dened as:

dij =

auv∈gi ↔j

f(wuv)

where f serves as a mapping from weight to length, and gi↔j represents

the shortest weighted path between i and j.

Weighted characteristic path length: The characteristic path

length is a measure employed to evaluate communication efciency in a

weighted network. The equation for its calculation is as follows:

L=1

n

ij∕=idij

n−1

where n represents the number of nodes in the network, and dij is the

length of the shortest path between node i and node j.

Number of triangles: The number of triangles is a fundamental

metric to evaluate segregation, which is dened as:

ti=1

2

j,hwijwih wjh1

where ti is the weighted geometric mean of triangles around i, j and h are

indices representing the neighboring nodes of i in the weighted matrix.

Weighted degree: The weighted degree is a measure employed to

evaluate the importance or connectivity of a node in a weighted

network. This metric can be calculated as follows:

ki=

wij

where ki is the degree of a node i.

Weighted clustering coefcient: The clustering coefcient is a

metric used to evaluate the tendency of nodes in a network to form local

groupings or clusters. The equation is as follows

C=1

n

2ti

ki(ki−1)

where ti is the number of triangles around i and ki is the weighted degree

of a node i.

Weighted global efciency: Global efciency was employed to

assess integration, crucial in quantifying the effective sharing of infor-

mation across the brain, essential for coordinated cognitive activity.

This metric evaluates the network’s ability to exchange information

globally (Yu et al., 2021; Stanley et al., 2015). The equation for its

calculation is as follows.

E=1

n(n−1)

i∕=j

dij

where n represents the number of nodes in the network, and dij is the

length of the shortest path between node i and node j.

Weighted transitivity: Transitivity was selected to measure segre-

gation due to its reection of local interconnectivity, indicating the

H. Hernandez et al.

NeuroImage 295 (2024) 120636

tendency of nodes to form closed clusters or triads. It serves as an in-

dicator of local connectivity (Rubinov and Sporns, 2010) and can be

expressed as follow.

T=i2ti

iki(ki−1)

where ti is the number of triangles around i and ki is the weighted degree

of a node i.

Weighted density: Density served as global measure, indicating the

overall connectedness of the network addressing complexity and resil-

ience of brain functioning (Rubinov and Sporns, 2010). The equation for

its calculation is as follows.

D=i∕=jwij

n(n−1)

where wij represents the weight of the connection between node i and

node j, and n is the total number of nodes in the network.

Weighted small-worldness: Small-worldness was utilized to eval-

uate both integration and segregation, capturing the optimal balance

between specialized processing within clusters and the global integra-

tion essential for comprehensive brain functionality. A network exhibits

small-world characteristics if it displays a high clustering coefcient

(reecting segregation) and a short characteristic path length (indi-

cating integration) (Rubinov and Sporns, 2010; Bassett and Bullmore,

2017; Yu et al., 2021).

The commonly used measure to quantify small-worldness in a

network includes the clustering coefcient (C) and the characteristic

path length (L). The formula for small-worldness is expressed as the ratio

of the observed clustering coefcient in the network (C) to the expected

clustering coefcient in a random graph with the same number of nodes

and edges (Cr), normalized by the observed characteristic path length in

the network (L) and the expected characteristic path length in the

random graph (Lr).

=C/Cr

L/Lr

A crucial difference between transitivity and the clustering coef-

cient is found in the normalization procedure. Clustering is normalized

individually for each node, while transitivity is normalized across the

entire node set (Rubinov and Sporns, 2010).

2.5. Simplication of the EEG analytical domain

To streamline the analysis of spectral and complexity results from

EEG data, we adopted two complementary approaches. Initially,

anatomically and functionally related AAL regions were merged to form

more consolidated regions of interest (ROIs), as detailed in Supple-

mentary Table 3. Subsequently, we rened the EEG analytical space by

applying specic statistical criteria. The methodology for these pro-

cesses is elaborated in the subsequent sections.

2.5.1. Region selection for analysis

We combined regions from the AAL atlas to generate ten cohesive

ROIs. This approach involved two critical criteria: (i) grouping together

brain regions associated with a specic cortical gyrus (e.g., superior,

middle, and inferior orbital gyri) into a single ROI, and (ii) assembling

neighboring regions with established functional coupling, such as the

Rolandic operculum and insula. The proposed method restructures EEG

analysis by concentrating on ROIs that include both structurally and

functionally related regions, rather than analyzing each small region in

isolation. We utilized a mean averaging approach, calculating the

average value of the metrics derived from the signals of the regions

within the AAL atlas that are associated with each ROI. This simpli-

cation enhances data interpretation and facilitates the identication of

patterns or signicant features in brain activity.

2.5.2. Statistical reduction of spectral and complexity features

To rene the analysis of spectral and complexity metrics, we applied

statistical criteria. Specically, ROIs demonstrating statistically signi-

cant relative power density and equivalent percent power (Wang et al.,

2015; Jeong et al., 2021; Moretti et al., 2004) were identied through

mean-vs-zero non-parametric permutation tests (

=0.05; 5000 ran-

domizations (Manly, 1997)). This renement, applied to both canonical

and individual EEG band classications (Babiloni et al., 2020), was

carried out for each frequency band. The results were then adjusted for

multiple comparisons using the Benjamini and Hochberg False Discov-

ery Rate (FDR) method. ROIs representing the Individual Alpha Fre-

quency (IAF) (Klimesch, 1999; Moretti et al., 2004) were those

displaying statistically signicant

activities. Similarly, ROIs indicative

of the θ-

transition (TF) (Klimesch, 1999; Moretti et al., 2004) were

those with statistically signicant θ activity. Moreover, the analytical

space of aperiodic (Martinez-Canada et al., 2023; Pathania et al., 2021;

van Nifterick et al., 2023) and complexity (Sun et al., 2020; Burns and

Rajan, 2015) metrics was further reduced by employing mean-vs-zero

non-parametric permutation tests, followed by correction using the

Benjamini and Hochberg FDR method.

2.6. Multiple linear regression models

We used multiple linear regression models to understand the rela-

tionship between predictors and outcome variables (Bishop, 2006). The

regression models were constructed using the four predictors outlined in

Section 2.2. We constructed an individual regression model for each

ROI. Regression models were generated and categorized based on the

highest-rated predictors. This approach allowed us to separate the re-

sults into three distinct groups according to the top-rated predictor. The

rst group prioritized age as the top-rated predictor, the second

emphasized education, and the third focused on cognition. None of the

models identied gender as the most important predictor. The mathe-

matical representation of the linear regression model is:

Y=B0+B1x+B1x1+B2x2+Bnxn+ϵ

where Y is the outcome variable, B0 is the intercept, B= [B1,B2,…,

Bn]is the coefcients vector of the independent variables matrix X=

[x1,x2,…,xn]and ϵ represents the error term. The coefcients B1,B2,

…,Bn are determined using the least squares method, which aims to

minimize the sum of the squared differences (errors) between the

observed values (actual values) and the values predicted by the model.

The formulas to calculate the coefcients are:

B=XTX−1XTY

where XT is the transpose of matrix of vector X .

The EEG parameters obtained from the chosen ROIs (see Supple-

mentary Table 3) and the connectivity outcomes estimated in whole-

brain analyses were utilized as inputs for the multiple linear regres-

sion models described above.

2.7. Data partition

Models were trained on a training sample (80%) and tested in a

testing set (20%), with k =10 folds (Muller and Guido, 2018). For each

iteration, we computed the estimation coefcients for the predictors,

R-squared, Cohen’s f

, Fisher’s F of the model, and the model’s signi-

cance. We reported the mean estimation values for each predictor along

with their standard deviation, average R-squared and Cohen’s f

(Selya

et al., 2012), and overall Fisher’s F. To determine the overall model

signicance, we combined the model’s p-values obtained in each iter-

ation using the Fisher method (Fisher, 1992).

H. Hernandez et al.

NeuroImage 295 (2024) 120636

2.8. Statistical criteria for the reduction of feature space for frequency

and complexity metrics

A further reduction of the analytical space was conducted using

statistical criteria. To this end, ROIs with statistically signicant relative

power density and equivalent percent power (Wang et al., 2015; Moretti

et al., 2004; Jeong et al., 2021) were selected by implementing

mean-vs-zero non-parametric permutation tests (

=0.05; 5000 ran-

domizations (Manly, 1997)). This analysis was conducted for each fre-

quency band, using both canonical and individual (Babiloni et al., 2020)

EEG band classications. Results were corrected for multiple compari-

sons using the Benjamini and Hochberg FDR method (Benjamini and

Hochberg, 1995). Representative ROIs for IAF (Klimesch, 1999; Moretti

et al., 2004) were those for which θ and

activities were statistically

signicant. Likewise, ROIs for TF (Klimesch, 1999; Moretti et al., 2004)

were those for which θ activity was statistically signicant. Further-

more, mean-vs-zero non-parametric permutation tests followed by

Benjamini and Hochberg FDR was the method selected for further

reducing the analytical space of the aperiodic (Martinez-Canada et al.,

2023; Pathania et al., 2021; van Nifterick et al., 2023) and complexity

(Sun et al., 2020; Burns and Rajan, 2015) metrics.

2.9. Quality of the signal

We conducted additional analyses to conrm that the number of

electrodes does not impact signal quality and does not interfere with the

observed effects. We calculated the Overall Data Quality (OQD) index in

the source space, using the methodology proposed by Zhao et al. (2023),

and conducted a linear regression to verify that the number of channels

cannot predict signal quality. We used the number of channels as a

predictor and the quality of the nal signals as the outcome. The method

for calculating OQD segmented into 1-second epochs, each labeled as 1

for low-quality epochs or 0 for high-quality epochs. The OQD represents

the percentage of EEG epochs with good quality, ranging from 0 for

signals where all epochs were classied as low quality, to 100 for signals

where all epochs were classied as high quality (Zhao et al., 2023).

3. Results

For each type of EEG descriptor (spectral, complexity, and connec-

tivity EEG outcomes), we categorized the regression models based on

their top-rated predictors, resulting in three distinct groups according to

the best-evaluated predictor. The rst group prioritized age as the best-

evaluated predictor, the second group emphasized education, and the

third group centered on cognition. None of the models identied sex as

the most important predictor. For each group of models and sets of EEG

outcomes, we reported the three models with the highest R

values.

Additionally, we reported the adjusted R

. In the Supplementary Ma-

terial (Supplementary Figures 1–3), we provided an assessment of the

model t quality using Q-Q plots and residual vs. tted values plots. In

most cases, the results for the total sample and the subsample with

available cognitive data were similar; therefore, we report the results for

the latter. Further details on the results for the total sample are provided

in Supplementary Tables 1 to 7.

Some participants had portions of the recording with their eyes open

(Supplementary Table 1). Therefore, we analyzed data only from par-

ticipants who underwent eye-close rsEEG exclusively. These results were

comparable to those reported with the entire sample, and are detailed in

Supplementary Tables 8 - 11. In addition, we checked for collinearity

among the predictors (see Supplementary S2 and Supplementary

Figure 4) and conducted supplementary analyses using Ridge re-

gressions (Supplementary Tables 12–15), which is a suitable method for

predictors exhibiting collinearity (Tsigler and Bartlett, 2024). The re-

sults obtained with Ridge were comparable to the previous results.

We conducted additional analyses to ensure that variations in the

number of channels across recruitment centers did not inuence the

results. We applied a linear regression to check if the number of channels

predicts signal quality in the source space. The results showed that the

model was not signicant (Supplementary Table 16). Finally, we con-

ducted an analysis of the correlation across the outcomes reported in the

main results (see Supplementary S3, Supplementary Figure 5, and

Supplementary Tables 17–20) to assess the effect of their correlation on

the reported effects.

3.1. Age, education and cognition as the best predictors of power spectrum

metrics

Statistical descriptors for all the regression models reported here are

shown in Table 2.

The two regression models predicting age with the highest R

values

had subject-specic

low

equivalent power as the EEG outcome measure.

The third one had cannonical γ equivalent power. The rst model

reached the highest values in the left occipital region, the second in the

left parietal region, and the third in the left orbitofrontal region

(Table 2A and Fig. 2A). For all three models, the most important and

signicant predictors were age and cognition. In the model for the left

occipital region, sex was the third most important predictor. For the

other two models, education was the third signicant predictor. When

analyzing the total sample, the results for the three models remained

signicant, although R

values decreased (Fig. 2A, and Supplementary

Table 4A).

For education as the best rated predictor, the three models with the

highest R

values had IAF as the outcome measure. The model with best

scores corresponded with the right medial frontal gyrus, and revealed

education, cognition, age, and sex as signicant predictors (Fig. 2B and

Table 2B). In the second and third models, the highest values were

reached in the left orbitofrontal cortex and the left inferior frontal gyrus,

respectively, education and age were the signicant predictors dis-

playing similar values. Sex and cognition were signicant in the best

model, but not in the other two. In the total sample, R

values were low,

but the models remained statistically signicant (Fig. 2B and Supple-

mentary Table 4B).

When cognition was the best-evaluted predictor, the two best models

estimated canon

relative power metrics more accurately. The third

one performed better in estimating subject-specic

low equivalent

power. The rst two models were in the left occipital regions and the

third one in the right parietal region (Fig. 2C and Table 2C). For the

three models, cognition and age emerged as the most important and

signicant predictors. Sex reached signicance in the three models, but

with lower estimates. Education was not statistically signicant in any

model. In the total sample, the models were also statistically signicant,

but showing a decrease in the R

values (Fig. 2C and Supplementary

Table 4C).

3.2. Age and cognition as the best predictors of aperiodic spectral metrics

Statistical values for all models reported here are shown in Table 3.

For age as the top predictor, the three best-evaluated models had

slope as the outcome metric. These models were placed in the left

temporal region, the right temporal region, and the left hippocampus,

respectively (Fig. 3A and Table 3A). In all three models, age and

cognition emerged as the most important and signicant predictors. In

the rst model, sex also reached signicance, but not in the second and

the third one. Education was not statistically signicant in any model. In

the total sample, the models were also statistically signicant, but with

lower R

values (see Fig. 3A and Supplementary Table 5A).

The best model with cognition as the best-evaluated predictor, esti-

mated slope metrics more accurately. The second and the third models

performed better in estimating offset metrics. The best model of cogni-

tion was in the left occipital region, the second in the left temporal re-

gion and the third in the left hippocampus (Fig. 3C and Table 3B). It is

worth noting that, although reaching signicance, all these models

H. Hernandez et al.

NeuroImage 295 (2024) 120636

showed R

values lower than 0.1. In the three models, cognition and age

were the most important and signicant predictors. Sex and education

did not reach signicance in any model. In the total sample, the models

were also statistically signicant, but with lower R

values (Fig. 3C and

Supplementary Table 5B).

The models in which education was the best-rated predictor were not

statistically signicant (Fig. 3B).

3.3. Cognition as the best predictor of complexity metrics

Statistical values for all models reported here are shown in Table 4.

There were no models where age, education or sex were identied as the

most highly valued predictors.

For cognition as the top predictor, the FD and the WE metrics in the

whole brain were the main outcomes in the two best models. The third

model performed better in spectral structure variation. In all the models,

cognition showed much higher estimates compared to other predictors.

For the best two models, age, and sex were also signicant predictors. In

the third model, sex was signicant. Education did not reach signi-

cance in any model (Table 4A and Fig. 4C). In the total sample, the

models were also statistically signicant but with an important decrease

in R

values (Fig. 3C and Supplementary Table 6A).

3.4. Age and education as the best predictors of graph-theoretic measures

Statistical values for all models reported here are shown in Table 5.

When age was the top predictor, the best model estimated transi-

tivity in the CMI matrix more accurately. The second and the third ones

performed better in estimating global efciency and small-worldness in

the MI and CMI matrices, respectively. For all the three models, age and

education were the most important and signicant predictors. In the rst

model, cognition was also signicant. Sex did not reach signicance in

any model (Fig. 4A and Table 5A). In the total sample, the models were

also statistically signicant with almost the same performance as in the

subsample (Fig. 4A and Supplementary Table 7A).

For education as the top predictor, the best model had transitivity as

outcome, the second one global efciency and the third one small-

worldness, all of them in the organizational information. The three

models revealed education and age as the most important and signi-

cant predictors. Cognition and sex were not statistically signicant in

any model (Table 5B and Fig. 4B). The models were also statistically

signicant in the total sample (Fig. 4B and Supplementary Table 7B).

There were no models where cognition was identied as the most

highly valued predictor.

4. Discussion

This study aimed to characterize EEG-derived endophenotypes based

on demographic and cognitive factors across a diverse sample of par-

ticipants. Despite multimodal diversity and heterogeneity (diverse

populations, data acquisition, multicentric assessments, ampliers,

number and type of electrodes), individual differences shaped electro-

physiological brain dynamics. Age emerged as the most robust and

systematic predictor of EEG signals, followed by cognition. Education

and sex were less inuential predictors. Power spectrum activity and

graph-theoretic measures were the most sensitive in capturing individ-

ual differences. Results are relevant for better understanding the indi-

vidual differences that lead to diversity. The use of more affordable and

scalable measures, such as EEG metrics, could be instrumental in

creating future brain charts. The present ndings may challenge tradi-

tional interpretations of case-control differences in brain signatures,

emphasizing the inuence of demographic and cognitive factors in

brain-phenotype associations.

Age was associated with changes power spectrum and aperiodic

spectral activity and less integrated and segregated networks. Age pre-

dicted a decrease in alpha power at parieto-occipital regions (Trondle

et al., 2023; Donoghue et al., 2020), while the opposite direction was

observed for gamma power in orbitofrontal regions (Rempe et al., 2023;

Hunt et al., 2019). This pattern of results could be explained by the

gradual loss of cholinergic function in the basal forebrain with age

Table 2

Results for the power spectrum metrics on the subsample with available

cognitive data.

A. Models with age as the best evaluated feature

Left occipital region (subj spec

low

equivalent power), F =129.36, p <1e-15, R

0.27, R

adjusted =0.26, CI =0.07, F

=0.36

Predictors Estimate t value p value

Age −0.01592 6.563429 <1e-15

Cognition 0.009309 2.016705 5.81E-08

Sex −0.00224 1.878269 6.25E-07

Education −0.00181 0.470018 0.917864

Left parietal region (subj spec

low

equivalent power), F =125.99, p <1e15, R

0.26, R

adjusted =0.26, CI =0.047, F

=0.35

Predictors Estimate t value p value

Age −0.01568 6.421747 <1e-15

Cognition 0.008753 1.8797 7.22E-07

Education −0.00388 1.005555 0.074094

Sex −0.00264 2.201016 1.19E-09

Left orbitofrontal region (canonical γ equivalent power), F =120.68, p <1e15, R

0.25, R

adjusted =0.25, CI =0.05, F

=0.34

Predictors Estimate t value p value

Age 0.014329 7.140123 <1e-15

Cognition 0.008861 2.333015 8.69E-11

Education −0.00572 1.790197 2.97E-06

Sex 0.000972 0.990132 0.07891

B. Models with education as the best evaluated feature

Right medial frontal gyrus (IAF), F =18.19, p <1e15, R

=0.05, R

adjusted =0.05,

CI =0.025, F

=0.04

Predictors Estimate t value p value

Education −1.01733 1.670948 2.23E-05

Cognition 0.944278 1.283513 0.004605

Age −0.79489 2.075072 1.58E-08

Sex −0.25695 1.36426 0.001602

Left inferior frontal gyrus (IAF), F =12.22, p <1e15, R

=0.03, R

adjusted =0.03, CI

=0.04, F

=0.024

Predictors Estimate t value p value

Education −0.96513 1.353999 0.001883

Age −0.89932 2.00498 6.22E-08

Cognition 0.265095 0.330327 0.990641

Sex −0.19967 0.906448 0.149072

Left orbitofrontal region (IAF), F =9.59, p <1e15, R

=0.026, R

adjusted =0.02, CI

=0.07, F

=0.01

Predictors Estimate t value p value

Education −0.99361 1.475303 0.000392

Age −0.96511 2.275339 2.48E-10

Cognition 0.266543 0.315654 0.996028

Sex −0.15246 0.732682 0.415125

C. Models with cognition as the best evaluated feature

Left occipital region (canonical

relative power), F =69.25, p <1e15, R

=0.16, R

adjusted =0.16, CI =0.06, F

=0.18

Predictors Estimate t value p value

Cognition 0.000834 2.464738 3.98E-12

Age −0.00071 4.037384 <1e-15

Education −0.0002 0.718862 0.436808

Sex −0.00011 1.299236 0.003882

Left occipital region (canon

equivalent power), F =69.25, p <1e15, R

=0.16, R

adjusted =0.16, CI =0.06, F

=0.18

Predictors Estimate t value p value

Cognition 0.005837 2.464738 3.98E-12

Age −0.00497 4.037384 <1e-15

Education −0.00141 0.718862 0.436808

Sex −0.00078 1.299236 0.003882

Right occipital region (subject spec

high

equivalent power), F =68.55, p <1e15, R

0.16, R

adjusted =0.16, CI =0.09, F

=0.19

Predictors Estimate t value p value

Cognition 0.007048 2.334146 9.2E-11

Age −0.00704 4.415218 <1e-15

Sex −0.00134 1.698314 1.38E-05

Education −0.00082 0.347323 0.979016

H. Hernandez et al.

NeuroImage 295 (2024) 120636

(Schreckenberger et al., 2004) which is associated with diminished

cholinergic input in the thalamus (Hindriks and van Putten, 2013;

Pfurtscheller and Lopes da Silva, 1999), leading to decreased power in

alpha oscillations (Trondle et al., 2023; Schliebs and Arendt, 2011).

Conversely, increased gamma in the orbitofrontal cortex suggests a

potential compensatory mechanism associated with age-related changes

in frontal lobes, regions susceptible to developmental and aging pro-

cesses (Rempe et al., 2023).

The decrease in the aperiodic slope with increasing age aligns with

previous results (Hill et al., 2022; Merkin et al., 2023; Trondle et al.,

2023) and supports the proposed association between older ages and

increased asynchronous background neuronal ring (Trondle et al.,

2023; Donoghue et al., 2020). As age advances, there is an increase in

random and spontaneous neural activity (Cremer and Zeef, 1987) and a

decrease in the signal-to-noise ratio (Voytek et al., 2015). This reduction

in signal-to-noise may stem from heightened spontaneous/baseline

neural spiking activity (Cremer and Zeef, 1987), disrupting neural

communication delity and potentially contributing to typical

age-related cognitive decline (Voytek et al., 2015). Furthermore, the

ndings of age-related decreases in alpha power and a attened aperi-

odic slope are highly compatible. These two phenomena may reect

aspects of the same neurobiological changes (Trondle et al., 2023).

Decreased thalamic inhibitory control over cortical areas, due to

impaired cholinergic input, is reected in diminished cortical alpha

Fig. 2. Power spectrum metrics results. (A) The three best models, where age was the best-evaluated feature, estimated low-frequency metrics more accurately.

Age and cognition emerged as the most signicant predictors with the highest evaluation. While sex was statistically signicant, its impact in the models was

minimal. When analyzing the total sample, R

values decreased, but the predictors remained signicant. (B) The three best models, where education was the best-

evaluated feature, exhibited low R

values but maintained statistical signicance. These models IAF metrics more accurately. Both education and age demonstrated

signicance and displayed similar values in the models. Sex and cognition were signicant in the best model. In the total sample, R

was very low, but the models

remained statistically signicant. (C) The three best models, where cognition was the best-evaluated feature, had a similar structure to panel A, but they estimated

high and canonical frequencies more accurately. Cognition was the best-evaluated feature, but age reached almost the same value. Education did not reach statistical

signicance. For the total sample, there was a signicant decrease in R

. The brains in the right column represent the intensity of predictors in the respective brain

regions for statistically signicant models, for the subsample with cognitive data. The models in occipital regions were the most signicant ones, with parietal regions

also demonstrating signicant models. Some additional regions in the left hemisphere emerged, including the inferior, middle and orbital frontal gyri. The statistical

values for the models with the complete sample are provided within each box.

H. Hernandez et al.

NeuroImage 295 (2024) 120636

power, leading to higher cortical excitability, an increased

excitation-to-inhibition ratio, and a higher level of neural noise (Trondle

et al., 2023). These effects, coupled with decreased network integration

(reduced global efciency and small-worldness) (Javaid et al., 2022;

Meunier et al., 2009; Achard and Bullmore, 2007; Onoda and Yama-

guchi, 2013), suggests disruptions in local and network communication

effectiveness. Age was also associated with a loss of functional special-

ization (segregation), manifesting as smaller and more local modules

across brain networks (Song et al., 2014). Thus, aging may be charac-

terized by an increased asynchronous activity and reduced network

integration and segregation.

Better cognition was associated with increased cortical activation,

and decreased asynchronous neural activity. Improved cognition pre-

dicted enhanced information processing capacity, as reected in

increased alpha power across occipital electrodes (Hindriks and van

Putten, 2013; Pfurtscheller and Lopes da Silva, 1999). Better cognition

was also associated with an increase in general spiking activity (offset)

(Pei et al., 2023; Zhang et al., 2023; Waschke et al., 2021) and a decrease

in neural noise (slope) (Smith et al., 2023; Ouyang et al., 2020; Pei et al.,

2023) in occipital, temporal, and hippocampal regions. This aligns with

previous results showing that aperiodic activity reects dynamic ad-

justments of metacognitive states crucial for successful cognitive per-

formance (Zhang et al., 2023). A positive association with whole-brain

entropy and a negative with FD and spectral structure conrmed the

role of complex dynamics in cognition (Iinuma et al., 2022; Parbat and

Chakraborty, 2021). Thus, increased cognitive performance is consis-

tently associated with both local and global dynamics, involving

increased cortical activation, enhanced alpha oscillations, and complex

brain dynamics.

Fewer years of education correlated with higher individual alpha

frequencies in frontal regions and reduced integration and segregation

of brain networks. However, these associations were weaker in com-

parison with other metrics, as reported with fMRI (Raz et al., 2005).

While direct assessments of education as a predictor of EEG metrics have

been limited in prior studies, previous research including education as a

covariate, showed no signicant effects on periodic frequency (da Cruz

et al., 2020) or graph-theoretic measures (Tan et al., 2019). Similarly,

sex did not emerge as the most inuential predictor in any model. Sex

differences have been observed (Carrier et al., 2001; Pravitha et al.,

2005) and not detected in EEG studies (Trondle et al., 2023; Rempe

et al., 2023; Gaubert et al., 2019). Our results suggest that sex is a less

inuential individual predictor, although it inuences signals when

combined with other demographic or cognitive variables. The impact of

education and gender on brain phenotypes should be further investi-

gated by exploring more specic measures capturing population

diversity.

Across all models, periodic frequency and graph-theoretic measures

demonstrated the highest sensitivity in capturing individual differences.

Older age, worse cognition, and being male predicted lower alpha

power, while older age and lower education were associated with less

integrated and segregated networks. Even when combined with age,

education was not found to be an inuential predictor, supporting a

weak relationship between this factor and brain signals (Raz et al., 2005;

da Cruz et al., 2020; Tan et al., 2019). Older age and worse cognition,

when combined, are linked to reduced alpha power and increased neural

noise, aligning with the neural noise hypothesis of aging (Voytek et al.,

2015; Cremer and Zeef, 1987), stating that with increasing age, neural

noise rises, and the reliability of neural communication diminishes,

contributing to cognitive decline. Therefore, age-related cognitive

decline might be attributed to these combined effects, where decreased

alpha oscillatory activity allows for more neural noise, which, in turn,

disrupts neural communication (Voytek et al., 2015). As complexity and

aperiodic components can emerge by intrinsical modulation of brain

dynamics, or as a consequence of metabolic or energetic impairments

(Medel et al., 2023; Kluger et al., 2023), future studies should investi-

gate the inuence of individual differences in demographics and

cognition on changes in these metrics.

It is worth noting that some models demonstrated low R

values (R

<0.1); the residuals of these models did not show an approximately

normal distribution and exhibited poor t. However, due to the intrinsic

complexity of neuroimaging data and the inherent variability in brain-

behavior associations, even models with low R

can provide valuable

insights into the effects among variables. Additionally, the variation in

values provide important information about the strongest effects.

4.1. Limitations

Several limitations of our study should be acknowledged. Cognitive

data were only available for a subsample of participants. Additionally,

cognition was assessed using a screening tool. Although the MMSE has

been widely used as a reliable measure of general cognitive state (Fol-

stein et al., 1975), it may not fully encompass the spectrum of cognitive

abilities. Contrary to prior ndings associating graph metrics with

cognition (Bullmore and Sporns, 2009; Yu et al., 2021; Stanley et al.,

2015), our results indicated that MMSE scores did not possess strong

predictive value. Limited performance variability in MMSE scores, with

all participants scoring above 24, affects the range and reduces vari-

ability. We acknowledge the limitations of the MMSE as a tool for

assessing cognition, particularly in healthy populations. However, we

Table 3

Results for aperiodic spectral metrics on the subsample with available cognitive

data.

A. Models with age as the best evaluated feature

Left temporal region (slope), F =50.12, p <1e15, R

=0.12, R

adjusted =0.12, CI =

0.06, F

=0.13

Predictors Estimate t value p value

Age −1.43333 3.930518 <1e-15

Cognition 0.856312 1.208334 0.01079

Education −0.24692 0.469671 0.900581

Sex −0.23427 1.30627 0.003451

Right temporal region (slope), F =41.16, p <1e15, R

=0.10, R

adjusted =0.10, CI

=0.06, F

=0.11

Predictors Estimate t value p value

Age −1.30515 3.67437 <1e-15

Cognition 0.877152 1.278449 0.005588

Education −0.18117 0.338246 0.986777

Sex −0.16609 0.947864 0.117285

Left hippocampus (slope), F =37.40, p <1e15, R

=0.10, R

adjusted =0.09, CI =

0.05, F

=0.10

Predictors Estimate t value p value

Age −1.27768 3.474221 <1e-15

Cognition 0.896495 1.265951 0.006104

Education 0.136882 0.272953 0.999229

Sex −0.13321 0.732632 0.425399

B. Models with cognition as the best evaluated feature

Left occipital (slope), F =26.55, p <1e15, R

=0.07, R

adjusted =0.07, CI =0.025,

=0.07

Predictors Estimate t value p value

Cognition 1.226913 1.675558 2.23E-05

Age −1.01193 2.689972 1.39E-14

Education −0.11348 0.282464 0.998198

Sex −0.04598 0.324079 0.991951

Left Temporal (offset), F =22.15, p <1e15, R

=0.06, R

adjusted =0.06, CI =0.03,

=0.05

Predictors Estimate t value p value

Cognition 1.931183 1.398192 0.001154

Age −1.76673 2.492283 1.74E-12

Education −0.61812 0.574947 0.792155

Sex −0.35667 1.023012 0.061897

Left hippocampus (offset), F =18.55, p <1e15, R

=0.05, R

adjusted =0.05, CI =

0.03, F

=0.05

Predictors Estimate t value p value

Cognition 2.300525 1.679279 2.35E-05

Age −1.58893 2.239911 5.16E-10

Education 0.167667 0.158416 0.999986

Sex −0.12838 0.36527 0.981173

H. Hernandez et al.

NeuroImage 295 (2024) 120636

employed the MMSE because it remains a valuable cognitive screening

in both clinical and research settings. Its widespread availability, ease of

use, and brevity render it a useful tool for initial cognitive assessment in

environments necessitating rapid and accessible evaluations (Creavin

et al., 2016). Further assessments should explore the role of individual

differences in cognition using more comprehensive measures across

various cognitive domains. Moreover, the measure of years of education

may not be sufciently sensitive to capture population diversity in this

factor. Future studies should consider incorporating more detailed

measures that provide information on the quality and nature of educa-

tion received by participants. As we did not inquire about gender

identities, a factor that can signicantly inuence diversity among

populations, future studies should systematically address individual

differences related to gender identities to provide a more comprehensive

understanding of the associated brain signatures. Also, although

country-level analyses are highly relevant, we included only a limited

number of nations with unbalanced sample sizes, thereby reducing the

possibilities for cross-country interpretations. Although these effects are

beyond the scope of this work, future global approaches with larger,

balanced, and more diverse samples should explore country-level effects

effectively.

It is also worth noting that the choice of parceling scheme and

methodology for dening the ROIs can inuence the outcomes. In our

study, we opted to employ ROIs for two primary reasons: (i) To mitigate

potential effects resulting from employing different electrode congu-

rations and quantities, given the EEG’s low spatial resolution and the

Fig. 3. Results on the models using aperiodic spectral metrics as outcomes. (A) The three best models when age was the best-evaluated feature identied age

and cognition as the metrics with the highest evaluation, both of which were statistically signicant. Education and sex did not reach statistical signicance. In the

total sample, R

experienced a decrease, but the models remained statistically signicant. (B) The models when education was the best-evaluated feature were

presented with transparency because none of them were statistically signicant. (C) The three best models when cognition was the best-evaluated feature had an R

lower than 0.1, but they were still statistically signicant. Education and Sex did not attain statistical signicance in any model. In the total sample, the models

experienced a signicant decrease in R

. The brains in the right column represent the intensity of predictors in the respective brain regions for statistically signicant

models, for the subsample with cognitive data. The models in hippocampus and temporal regions were the most signicant ones. The statistical values for the models

with the complete sample are provided within each box.

H. Hernandez et al.

NeuroImage 295 (2024) 120636

variance in estimation error that arises from using low and high-density

electrode congurations. This approach facilitates comparison of results

across a broad spectrum of electrode arrangements and quantities. (ii)

To reduce the number of regressions. Despite this reduction, we still had

to construct a substantial number of models. On the other hand, using

ROIs requires nding consistent effects in broad regions, and as our

results demonstrate, they displayed robust effects. Future studies using

only high-density arrays should explore the impact of spatial variation

using ner areas. In adittion, we observed variations in the number of

channels across different centers, ranging from 21 to 132 electrodes. To

address the potential effects of these variations, we employed a mesh

model approach for integrating the electrode layouts (Melnik et al.,

2017). We applied spatial normalization to ensure that the EEG inver-

sion process remains unaffected by the number of channels (Prado et al.,

2023). This was veried by conrming that the number of channels did

not predict the signal quality in the source space. Furthermore, our re-

sults were reported with an MNI average brain for source estimation,

given that individual MRI data was unavailable. Source space provides

more adequate spatial resolution with 64+channels. Although chal-

lenging, source analysis can be efciently done with low-density elec-

trodes (Nguyen-Danse et al., 2021; Soler et al., 2020; Baroumand et al.,

2018). We constructed a regression model to predict signal quality based

on the number of channels, which yielded non-signicant results, sug-

gesting that the number of channels did not impact the reported asso-

ciations. We avoided analyzing small regions and focus only on larger

effects. However, using the MNI average brain for source estimation

with low electrode density represents a limitation. Future studies should

conduct further analyses of the impact of the number of channels on

source variability and its associations with brain phenotypes.

Moreover, the correlation among the reported outcomes exhibited

expected values. Metrics that were spatially close showed high levels of

correlation, as did the connectivity metrics. However, the regressions of

predictors with the outcomes were independent of the relationships

among the outcomes. We opt to use different measures as is typical in the

literature (Cesnaite et al., 2023; Zappasodi et al., 2015; Donoghue et al.,

2020; Vecchio et al., 2022) and emphasize their individual effects.

Finally, we identied collinearity between cognition and education in

the sample with cognitive data, while there were no collinearity in the

complete sample. Collinearity can cause issues with estimated accuracy,

high coefcient variances, interpretation challenges, among others

(Snee, 1983). However, our models showed a high level of consistency

and precision in their estimates across both samples (sample with

cognitive data and the total sample). The results yielded the same effects

when employing a collinearity-robust model such as Ridge regression

(Tsigler and Bartlett, 2024).

4.2. Implications and future directions

Despite the diversity of our sample and the heterogeneity in data

acquisition across centers, our results revealed that demographic and

cognitive factors robustly predicted EEG modulations. These ndings

reect the potential of using more affordable and scalable techniques,

such as EEG, for the study of individual differences contributing to di-

versity and brain signatures. Although we did not include EEG micro-

states in this work, as it was beyond the scope of our study, previous

reports have shown sex-specic changes in microstate dynamics during

adolescence as well as at older age (Tomescu et al., 2018). Additionally,

changes in microstates have been associated with age, from childhood

(Hill et al., 2023) to adulthood (Koenig et al., 2002). As EEG microstates

can inform the temporal dynamics of large-scale brain networks across

millisecond timescales, future studies should use them to explore the

predictive value of demographic and cognitive factors across large and

diverse populations. Similarly, given that other complexity metrics like

multiscale entropy have shown sensitivity to variations in age (Wang

et al., 2016) and cognition (Maturana-Candelas et al., 2019), future

research should leverage these tools in large and diverse samples.

Future studies should employ EEG metrics in normative modeling to

create brain charts as anchor points for standardized quantication of

brain functioning over the lifespan, considering individual differences

contributing to population diversity. Normative modeling with neuro-

imaging measures (Bethlehem et al., 2022; Elad et al., 2021; Di Biase

et al., 2023; Rutherford et al., 2022; Habes et al., 2021; Rosenberg et al.,

2020) has claried individual differences in the context of brain devel-

opment or aging, brain health and disease, and mapping variations

across multiple cognitive domains. The development of has brain charts

challenged traditional interpretations of case-control differences (Mar-

quand et al., 2016), which become problematic in domains such as

neurodegeneration and psychiatry where disorders are diagnosed based

on symptoms that overlap between disorders, often yielding heteroge-

neous clinical groups. As normative modeling does not require cate-

gorical partitioning, applying this approach using EEG signals will allow

for a more global applicability in parsing heterogeneity and diversity in

brain health, psychiatry, and neurodegeneration.

Our ndings endorse EEG as a potential tool that, in the future,

should be critically incorporated into case-control studies in clinical

settings (Rossini et al., 2020). While EEG offers numerous benets for

investigating physiological changes linked to pathology, it often relies

on group-level data to identify associations or differences, which limits

its utility at the individual diagnostic level. Established methods like

MRI, PET, and cerebral spinal uid analysis offer undeniable value in

individual diagnosis; however, they are often expensive and have

limited accessibility, particularly in resource-constrained settings. EEG

offers complementary accessible tools that can aid in revealing physio-

logical changes linked to early pathology and the risk of dementia

(Rossini et al., 2020; Parra, 2022).

4.3. Conclusion

Variations in EEG metric typically used in case-control studies are

inuenced by individual differences in demographics and cognition.

Older age, poorer cognition, and being male were associated with

reduced alpha power, whereas older age and less education were linked

to less integrated and segregated networks. Moreover, older age and

worse cognition were linked to reduced alpha power and increased

neural noise. Our ndings pave the way for the future use of EEG-based

brain charts for standardized quantication of brain functioning over

the lifespan, considering individual differences contributing to

Table 4

Results for complexity metrics on the subsample with available cognitive data.

A. Models with cognition as the best evaluated feature

Whole brain (FD), F =55.40, p <1e15, R

=0.13, R

adjusted =0.13, CI =0.08, F

0.16

Predictors Estimate t value p value

Cognition −0.42149 2.832611 <1e-15

Age 0.177473 2.26391 3.4E-10

Sex 0.094606 2.451106 4.67E-12

Education −0.06247 0.497431 0.862861

Whole brain (WE), F =39.14, p <1e15, R

=0.10, R

adjusted =0.10, CI =0.042, F

=0.11

Predictors Estimate t value p value

Cognition 0.468037 3.762755 <1e-15

Sex −0.07337 2.284629 1.97E-10

Age 0.072522 1.117352 0.02743

Education 0.018191 0.226368 0.999692

Left inferior frontal gyrus (spectral structure variation), F =36.81, p <1e15, R

0.09, R

adjusted =0.09, CI =0.04, F

=0.10

Predictors Estimate t value p value

Cognition −0.15205 3.071085 <1e-15

Sex 0.030633 2.395504 1.67E-11

Education −0.00804 0.24164 0.999116

Age 9.56E-05 0.225578 0.999868

H. Hernandez et al.

NeuroImage 295 (2024) 120636

Fig. 4. Results on the models using connectivity and complexity metrics as outcomes. In terms of connectivity metrics, cognition never emerged as the best-

evaluated feature, whereas in complexity metrics, neither age nor education stood out as the top-rated predictors. (A) The three best models when age was the

primary feature exhibited signicant results with high R

values and effect sizes for measures of segregation, integration, and small-worldness. Age and education

were the best evaluated and signicant predictors in the models. In the total sample, the models maintained almost the same performance. (B) The three best models

when education was the primary feature yielded a low R

but remained statistically signicant. Education and age were the best-evaluated predictors, whereas

cognition and sex did not hold signicance. (C) Results for complexity metrics. The three best models when cognition was the primary feature for predicting

connectivity metrics emerged with several complexity metrics: fractal dimension, spectral structure variability, and Wiener entropy. Cognition had a much higher

value compared to other predictors. In the total sample, the models signicantly decreased R

values (see values inside the box). Brains in this panel indicate that the

most predominant regions were the whole brain, along with the occipital, inferior frontal gyrus, and temporal lobe. These regions prevailed in both hemispheres. The

brains to the right of panels (A) and (B) represent the utilized connectivity metrics for the subsample with cognitive data. The statistical values for the models with

the complete sample are provided within each box.

H. Hernandez et al.

NeuroImage 295 (2024) 120636

population diversity. Such an approach will allow for more tailored

global models to understanding the variations and diversity in brain

health, psychiatry, and neurology.

CRediT authorship contribution statement

Hernan Hernandez: Writing – review & editing, Writing – original

draft, Methodology, Investigation, Formal analysis, Conceptualization.

Sandra Baez: Writing – review & editing, Writing – original draft,

Investigation, Formal analysis, Conceptualization. Vicente Medel:

Writing – review & editing, Supervision, Investigation, Conceptualiza-

tion. Sebastian Moguilner: Writing – review & editing, Methodology,

Investigation, Formal analysis, Conceptualization. Jhosmary Cuadros:

Writing – review & editing, Supervision, Investigation, Data curation.

Hernando Santamaria-Garcia: Writing – review & editing, Supervi-

sion, Investigation, Conceptualization. Enzo Tagliazucchi: Writing –

review & editing, Supervision, Investigation, Conceptualization. Pedro

A. Valdes-Sosa: Writing – review & editing, Supervision, Investigation,

Conceptualization. Francisco Lopera: Writing – review & editing, Su-

pervision, Investigation, Conceptualization. John Fredy OchoaG´

omez:

Writing – review & editing, Supervision, Investigation. Alfredis

Gonz´

alez-Hern´

andez: Writing – review & editing, Supervision, Inves-

tigation. Jasmin Bonilla-Santos: Writing – review & editing,

Supervision, Investigation. Rodrigo A. Gonzalez-Montealegre:

Writing – review & editing, Supervision, Investigation. Tuba Aktürk:

Writing – review & editing, Supervision, Investigation. Ebru Yıldırım:

Writing – review & editing, Supervision, Investigation. Renato Anghi-

nah: Writing – review & editing, Supervision, Investigation, Data

curation. Agustina Legaz: Writing – review & editing, Supervision,

Investigation, Data curation. Sol Fittipaldi: Writing – review & editing,

Supervision, Investigation. G¨

orsev G. Yener: Writing – review & edit-

ing, Supervision, Resources, Investigation, Funding acquisition. Javier

Escudero: Writing – review & editing, Supervision, Investigation.

Claudio Babiloni: Writing – review & editing, Supervision, Investiga-

tion. Susanna Lopez: Writing – review & editing, Supervision, Investi-

gation. Robert Whelan: Writing – review & editing, Supervision,

Investigation, Conceptualization. Alberto A Fern´

andez Lucas: Writing

– review & editing, Supervision, Investigation. Adolfo M. García:

Writing – review & editing, Supervision, Investigation. David Huepe:

Writing – review & editing, Supervision, Investigation. Gaetano Di

Caterina: Writing – review & editing, Supervision, Investigation. Mar-

cio Soto-A˜

nari: Writing – review & editing, Validation, Investigation.

Agustina Birba: Writing – review & editing, Supervision, Investigation.

Agustin Sainz-Ballesteros: Writing – review & editing, Supervision,

Investigation. Carlos Coronel: Writing – review & editing, Validation,

Investigation. Eduar Herrera: Writing – review & editing, Supervision,

Investigation. Daniel Abasolo: Writing – review & editing, Supervision,

Investigation. Kerry Kilborn: Writing – review & editing, Supervision,

Investigation. Nicol´

as Rubido: Writing – review & editing, Supervision,

Investigation. Ruaridh Clark: Writing – review & editing, Supervision,

Investigation. Ruben Herzog: Writing – review & editing, Supervision,

Investigation. Deniz Yerlikaya: Writing – review & editing, Supervi-

sion, Investigation. Bahar Güntekin: Writing – review & editing, Su-

pervision, Investigation. Mario A. Parra: Writing – review & editing,

Supervision, Resources, Investigation, Funding acquisition. Pavel

Prado: Writing – review & editing, Supervision, Investigation. Agustin

Ibanez: Writing – review & editing, Writing – original draft, Supervi-

sion, Methodology, Investigation, Funding acquisition,

Conceptualization.

Declaration of competing interest

none.

Data availability

Both the data and analysis codes are openly accessible through the

following GitHub link https://github.com/euroladbrainlat/Brain-

health-in-diverse-setting.

Acknowledgments

This work was supported by the Latin American Brain Health Insti-

tute (BrainLat) Seed Grant BL-SRGP2020-02 awarded to MAP and AI.

AMG is an Atlantic Fellow at the Global Brain Health Institute (GBHI)

and is partially supported with funding from the National Institute On

Aging of the National Institutes of Health (R01AG075775,

2P01AG019724); ANID (FONDECYT Regular 1210176, 1210195);

GBHI, Alzheimer’s Association, and Alzheimer’s Society (Alzheimer’s

Association GBHI ALZ UK-22-865742); the Latin American Brain Health

Institute (BrainLat), Universidad Adolfo Ib´

a˜

nez, Santiago, Chile (#BL-

SRGP2021-01); Programa Interdisciplinario de Investigaci´

on Experi-

mental en Comunicaci´

on y Cognici´

on (PIIECC), Facultad de Human-

idades, USACH. AI is supported by grants from the MULTI-PARTNER

CONSORTIUM TO EXPAND DEMENTIA RESEARCH IN LATIN AMER-

ICA [ReDLat, supported by Fogarty International Center (FIC), National

Institutes of Health, National Institutes of Aging (R01 AG057234, R01

AG075775, R01 AG21051, R01 AG083799, CARDS-NIH), Alzheimer’s

Table 5

Results for graph- theoretic measures on the subsample with available cognitive

data.

A. Models with age as the best evaluated feature

Transitivity (CMI), F =90.62, p <1e15, R

=0.20, R

adjusted =0.20, CI =0.062, F

=0.26]

Predictors Estimate t value p value

Age −0.16318 5.923662 <1e-15

Education −0.06215 1.44298 0.000756

Cognition 0.059867 1.105595 0.032926

Sex −0.0124 0.921539 0.138854

Global efciency (MI), F =88.94, p <1e15, R

=0.20, R

adjusted =0.20, CI =0.045,

=0.24

Predictors Estimate t value p value

Age −0.197 6.135292 <1e-15

Education −0.0807 1.563886 0.000111

Cognition 0.037216 0.576938 0.756623

Sex −0.00805 0.509363 0.870492

Small worldness (CMI), F =81.90, p <1e15, R

=0.19, R

adjusted =0.18, CI =0.06,

=0.23

Predictors Estimate t value p value

Age −0.14729 5.520251 <1e-15

Education −0.05611 1.346702 0.00252

Cognition 0.050124 0.952187 0.115618

Sex −0.01036 0.791281 0.315885

B. Models with education as the best evaluated feature

Transitivity (O_info), F =8.40, p =0.026, R

=0.02, R

adjusted =0.02, CI =0.04, F

=0.01

Predictors Estimate t value p value

Education 0.002819 1.238426 0.008346

Age 0.00281 1.977057 1.33E-07

Cognition −0.00181 0.645702 0.717019

Sex 0.000173 0.257998 0.999564

Global efciency (O_info), F =8.04, p =0.014, R

=0.03, R

adjusted =0.02, CI =

0.04, F

=0.024

Predictors Estimate t value p value

Education 0.001219 1.355786 0.00202

Age 0.001002 1.79685 3.16E-06

Cognition −0.00065 0.571754 0.848408

Sex 4.97E-05 0.246322 0.99862

Small worldness (O_info), F =7.30, p =0.017, R

=0.02, R

adjusted =0.02, CI =

0.04, F

=0.02

Predictors Estimate t value p value

Education 0.000295 1.412363 0.001037

Age 0.00025 1.921802 3.78E-07

Cognition −0.0002 0.758745 0.468241

Sex 1.49E-05 0.267985 0.998259

H. Hernandez et al.

NeuroImage 295 (2024) 120636

Association (SG-20-725707), Rainwater Charitable Foundation – The

Blueeld project to cure FTD, and Global Brain Health Institute)], USS-

FIN-23-FAPE-09, ANID/FONDECYT Regular (1210195 and 1210176

and 1220995); ANID/FONDAP/15150012; ANID/PIA/ANILLOS

ACT210096; FONDEF ID20I10152, and ANID/FONDAP 15150012. The

contents of this publication are solely the responsibility of the authors

and do not represent the ofcial views of these institutions.

Supplementary materials

Supplementary material associated with this article can be found, in

the online version, at doi:10.1016/j.neuroimage.2024.120636.

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H. Hernandez et al.

Brain clocks capture diversity and disparity in aging and dementia

Preprint

Full-text available

Mar 2024

Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer’s disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R²=0.37, F²=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer’s disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer’s disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.

Article

Full-text available

Jun 2024
BSRCCS

Earlier research has suggested gender differences in event-related potentials/oscillations (ERPs/EROs). Yet, the alteration in event-related oscillations (EROs) in the delta and theta frequency bands have not been explored between genders across the three age groups of adulthood, i.e., 18–50, 51–65, and >65 years. Data from 155 healthy elderly participants who underwent a neurological examination, comprehensive neuropsychological assessment (including attention, memory, executive function, language, and visuospatial skills), and magnetic resonance imaging (MRI) from past studies were used. The delta and theta ERO powers across the age groups and between genders were compared and correlational analyses among the ERO power, age, and neuropsychological tests were performed. The results indicated that females displayed higher theta ERO responses than males in the frontal, central, and parietal regions but not in the occipital location between 18 and 50 years of adulthood. The declining theta power of EROs in women reached that of men after the age of 50 while the theta ERO power was more stable across the age groups in men. Our results imply that the cohorts must be recruited at specified age ranges across genders, and clinical trials using neurophysiological biomarkers as an intervention endpoint should take gender into account in the future.

Cardiometabolic state links neurovascular burden with brain structure and function across age: evidence from EEG and MRI

Preprint

Full-text available

Jun 2024

Aging affects brain structure and function alongside metabolic and vascular processes leading to energetic impairments. While local neurometabolic dysfunction in aging is well-documented, the influence of systemic cardiometabolic and vascular markers on brain structure and function remains less understood. We examine the link between cardiometabolic dysfunction (measured by an allostatic load index) and neurovascular burden (measured by white matter hyperintensities) with brain changes, including ventricular and hippocampal volume, as well as EEG activity, across age. Analyzing data from 196 healthy individuals across age (20-75 years), we found a significant positive correlation between allostatic load index and white-matter hyperintensities, irrespective of age. White matter hyperintensities are also positively linked with ventricular enlargement, but not hippocampal atrophy. The allostatic load index mediated the relationship between white-matter hyperintensities and ventricular volume. Regarding brain function, changes in the spectral aperiodic exponent but not periodic alpha power were linked to white matter hyperintensities and the allostatic load index. Such index mediated the relationship between spectral aperiodic exponent and white matter hyperintensities. Thus, findings suggest that the cardiometabolic state, as measured by an allostatic load index, plays a crucial role in brain health across age, particularly influencing ventricular enlargement and increased aperiodic activity.

Heterogeneous factors influence social cognition across diverse settings in brain health and age-related diseases

Article

Full-text available

Jan 2024

Aging diminishes social cognition, and changes in this capacity can indicate brain diseases. However, the relative contribution of age, diagnosis and brain reserve to social cognition, especially among older adults and in global settings, remains unclear when considering other factors. Here, using a computational approach, we combined predictors of social cognition from a diverse sample of 1,063 older adults across nine countries. Emotion recognition, mentalizing and overall social cognition were predicted via support vector regressions from various factors, including diagnosis (subjective cognitive complaints, mild cognitive impairment, Alzheimer’s disease and behavioral variant frontotemporal dementia), demographics, cognition/executive function, brain reserve and motion artifacts from functional magnetic resonance imaging recordings. Higher cognitive/executive functions and education ranked among the top predictors, outweighing age, diagnosis and brain reserve. Network connectivity did not show predictive values. The results challenge traditional interpretations of age-related decline, patient–control differences and brain associations of social cognition, emphasizing the importance of heterogeneous factors.

Complexity and 1/f slope jointly reflect brain states

Article

Full-text available

Dec 2023

Characterization of brain states is essential for understanding its functioning in the absence of external stimuli. Brain states differ on their balance between excitation and inhibition, and on the diversity of their activity patterns. These can be respectively indexed by 1/f slope and Lempel–Ziv complexity (LZc). However, whether and how these two brain state properties relate remain elusive. Here we analyzed the relation between 1/f slope and LZc with two in-silico approaches and in both rat EEG and monkey ECoG data. We contrasted resting state with propofol anesthesia, which directly modulates the excitation-inhibition balance. We found convergent results among simulated and empirical data, showing a strong, inverse and non trivial monotonic relation between 1/f slope and complexity, consistent at both ECoG and EEG scales. We hypothesize that differentially entropic regimes could underlie the link between the excitation-inhibition balance and the vastness of the repertoire of brain systems.

Global South research is critical for understanding brain health, ageing and dementia

Article

Full-text available

Nov 2023

EEG microstates in early‐to‐middle childhood show associations with age, biological sex, and alpha power

Article

Full-text available

Oct 2023

Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large‐scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large ( N = 139) cohort of children spanning early‐to‐middle childhood (4–12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV , duration , coverage , and occurrence , for five microstate classes (A–E) across both eyes‐closed and eyes‐open resting‐state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/ f ‐like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes‐open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large‐scale functional brain networks during early‐to‐middle childhood, extending understanding of neural dynamics across this important period for brain development.

Combining aperiodic 1/f slopes and brain simulation: An EEG/MEG proxy marker of excitation/inhibition imbalance in Alzheimer's disease

Article

Full-text available

Sep 2023

Introduction: Accumulation and interaction of amyloid-beta (Aβ) and tau proteins during progression of Alzheimer's disease (AD) are shown to tilt neuronal circuits away from balanced excitation/inhibition (E/I). Current available techniques for noninvasive interrogation of E/I in the intact human brain, for example, magnetic resonance spectroscopy (MRS), are highly restrictive (i.e., limited spatial extent), have low temporal and spatial resolution and suffer from the limited ability to distinguish accurately between different neurotransmitters complicating its interpretation. As such, these methods alone offer an incomplete explanation of E/I. Recently, the aperiodic component of neural power spectrum, often referred to in the literature as the '1/f slope', has been described as a promising and scalable biomarker that can track disruptions in E/I potentially underlying a spectrum of clinical conditions, such as autism, schizophrenia, or epilepsy, as well as developmental E/I changes as seen in aging. Methods: Using 1/f slopes from resting-state spectral data and computational modeling, we developed a new method for inferring E/I alterations in AD. Results: We tested our method on recent freely and publicly available electroencephalography (EEG) and magnetoencephalography (MEG) datasets of patients with AD or prodromal disease and demonstrated the method's potential for uncovering regional patterns of abnormal excitatory and inhibitory parameters. Discussion: Our results provide a general framework for investigating circuit-level disorders in AD and developing therapeutic interventions that aim to restore the balance between excitation and inhibition.

Factors associated with healthy aging in Latin American populations

Article

Full-text available

Aug 2023
NAT MED

Latin American populations may present patterns of sociodemographic, ethnic and cultural diversity that can defy current universal models of healthy aging. The potential combination of risk factors that influence aging across populations in Latin American and Caribbean (LAC) countries is unknown. Compared to other regions where classical factors such as age and sex drive healthy aging, higher disparity-related factors and between-country variability could influence healthy aging in LAC countries. We investigated the combined impact of social determinants of health (SDH), lifestyle factors, cardiometabolic factors, mental health symptoms and demographics (age, sex) on healthy aging (cognition and functional ability) across LAC countries with different levels of socioeconomic development using cross-sectional and longitudinal machine learning models (n = 44,394 participants). Risk factors associated with social and health disparities, including SDH (β > 0.3), mental health (β > 0.6) and cardiometabolic risks (β > 0.22), significantly influenced healthy aging more than age and sex (with null or smaller effects: β < 0.2). These heterogeneous patterns were more pronounced in low-income to middle-income LAC countries compared to high-income LAC countries (cross-sectional comparisons), and in an upper-income to middle-income LAC country, Costa Rica, compared to China, a non-upper-income to middle-income LAC country (longitudinal comparisons). These inequity-associated and region-specific patterns inform national risk assessments of healthy aging in LAC countries and regionally tailored public health interventions.

Modulatory dynamics of periodic and aperiodic activity in respiration-brain coupling

Article

Full-text available

Aug 2023

Bodily rhythms such as respiration are increasingly acknowledged to modulate neural oscillations underlying human action, perception, and cognition. Conversely, the link between respiration and aperiodic brain activity – a non-oscillatory reflection of excitation-inhibition (E:I) balance – has remained unstudied. Aiming to disentangle potential respiration-related dynamics of periodic and aperiodic activity, we applied recently developed algorithms of time-resolved parameter estimation to resting-state MEG and EEG data from two labs (total N = 78 participants). We provide evidence that fluctuations of aperiodic brain activity (1/f slope) are phase-locked to the respiratory cycle, which suggests that spontaneous state shifts of excitation-inhibition balance are at least partly influenced by peripheral bodily signals. Moreover, differential temporal dynamics in their coupling to non-oscillatory and oscillatory activity raise the possibility of a functional distinction in the way each component is related to respiration. Our findings highlight the role of respiration as a physiological influence on brain signalling.

Developmental changes in individual alpha frequency: Recording EEG data during public engagement events

Article

Full-text available

Jul 2023

Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while participants rested with their eyes closed. A total of 289 good quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: IAF increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.

Adult lifespan trajectories of neuromagnetic signals and interrelations with cortical thickness

Article

Full-text available

Jul 2023
NEUROIMAGE

Oscillatory power and phase synchronization map neuronal dynamics and are commonly studied to differentiate the healthy and diseased brain. Yet, little is known about the course and spatial variability of these features from early adulthood into old age. Leveraging magnetoencephalography (MEG) resting-state data in a cross-sectional adult sample (n = 350), we probed lifespan differences (18-88 years) in connectivity and power and interaction effects with sex. Building upon recent attempts to link brain structure and function, we tested the spatial correspondence between age effects on cortical thickness and those on functional networks. We further probed a direct structure-function relationship at the level of the study sample. We found MEG frequency-specific patterns with age and divergence between sexes in low frequencies. Connectivity and power exhibited distinct linear trajectories or turning points at midlife that might reflect different physiological processes. In the delta and beta bands, these age effects corresponded to those on cortical thickness, pointing to co-variation between the modalities across the lifespan. Structure-function coupling was frequency-dependent and observed in unimodal or multimodal regions. Altogether, we provide a comprehensive overview of the topographic functional profile of adulthood that can form a basis for neurocognitive and clinical investigations. This study further sheds new light on how the brain's structural architecture relates to fast oscillatory activity.

Brain structure and phenotypic profile of superagers compared with age-matched older adults: a longitudinal analysis from the Vallecas Project

Article

Full-text available

Jul 2023

Background: Cognitive abilities, particularly memory, normally decline with age. However, some individuals, often designated as superagers, can reach late life with the memory function of individuals 30 years younger. We aimed to characterise the brain structure of superagers and identify demographic, lifestyle, and clinical factors associated with this phenotype. Methods: We selected cognitively healthy participants from the Vallecas Project longitudinal cohort recruited between Oct 10, 2011, and Jan 14, 2014, aged 79·5 years or older, on the basis of their delayed verbal episodic memory score. Participants were assessed with the Free and Cued Selective Reminding Test and with three non-memory tests (the 15-item version of the Boston Naming Test, the Digit Symbol Substitution Test, and the Animal Fluency Test). Participants were classified as superagers if they scored at or above the mean values for a 50-56-year-old in the Free and Cued Selective Reminding Test and within one standard deviation of the mean or above for their age and education level in the three non-memory tests, or as typical older adults if they scored within one standard deviation of the mean for their age and education level in the Free and Cued Selective Reminding Test. Data acquired as per protocol from up to six yearly follow-ups were used for longitudinal analyses. Findings: We included 64 superagers (mean age 81·9 years; 38 [59%] women and 26 [41%] men) and 55 typical older adults (82·4 years; 35 [64%] women and 20 [36%] men). The median number of follow-up visits was 5·0 (IQR 5·0-6·0) for superagers and 5·0 (4·5-6·0) for typical older adults. Superagers exhibited higher grey matter volume cross-sectionally in the medial temporal lobe, cholinergic forebrain, and motor thalamus. Longitudinally, superagers also showed slower total grey matter atrophy, particularly within the medial temporal lobe, than did typical older adults. A machine learning classification including 89 demographic, lifestyle, and clinical predictors showed that faster movement speed (despite no group differences in exercise frequency) and better mental health were the most differentiating factors for superagers. Similar concentrations of dementia blood biomarkers in superager and typical older adult groups suggest that group differences reflect inherent superager resistance to typical age-related memory loss. Interpretation: Factors associated with dementia prevention are also relevant for resistance to age-related memory decline and brain atrophy, and the association between superageing and movement speed could provide potential novel insights into how to preserve memory function into the ninth decade. Funding: Queen Sofia Foundation, CIEN Foundation, Spanish Ministry of Science and Innovation, Alzheimer's Association, European Research Council, MAPFRE Foundation, Carl Zeiss Foundation, and the EU Comission for Horizon 2020. Translation: For the Spanish translation of the abstract see Supplementary Materials section.

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