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Background Processing of linguistic negation has been associated to inhibitory brain mechanisms. However, no study has tapped this link via multimodal measures in patients with core inhibitory alterations, a critical approach to reveal direct neural correlates and potential disease markers. Methods Here we examined oscillatory, neuroanatomical, and functional connectivity signatures of a recently reported Go/No-go negation task in healthy controls and behavioral variant frontotemporal dementia (bvFTD) patients, typified by primary and generalized inhibitory disruptions. To test for specificity, we also recruited persons with Alzheimer's disease (AD), a disease involving frequent but nonprimary inhibitory deficits. Results In controls, negative sentences in the No-go condition distinctly involved frontocentral delta (2–3 Hz) suppression, a canonical inhibitory marker. In bvFTD patients, this modulation was selectively abolished and significantly correlated with the volume and functional connectivity of regions supporting inhibition (e.g. precentral gyrus, caudate nucleus, and cerebellum). Such canonical delta suppression was preserved in the AD group and associated with widespread anatomo-functional patterns across non-inhibitory regions. Discussion These findings suggest that negation hinges on the integrity and interaction of spatiotemporal inhibitory mechanisms. Moreover, our results reveal potential neurocognitive markers of bvFTD, opening a new agenda at the crossing of cognitive neuroscience and behavioral neurology.

The proposal to use brain connectivity as a biomarker for dementia phenotyping can be potentiated by conducting large-scale multicentric studies using high-density electroencephalography (hd- EEG). Nevertheless, several barriers preclude the development of a systematic "ConnEEGtome" in dementia research. Here we review critical sources of variability in EEG connectivity studies, and provide general guidelines for multicentric protocol harmonization. We describe how results can be impacted by the choice for data acquisition, and signal processing workflows. The implementation of a particular processing pipeline is conditional upon assumptions made by researchers about the nature of EEG. Due to these assumptions, EEG connectivity metrics are typically applicable to restricted scenarios, e.g., to a particular neurocognitive disorder. "Ground truths" for the choice of processing workflow and connectivity analysis are impractical. Consequently, efforts should be directed to harmonizing experimental procedures, data acquisition, and the first steps of the preprocessing pipeline. Conducting multiple analyses of the same data and a proper integration of the results need to be considered in additional processing steps. Furthermore, instead of using a single connectivity measure, using a composite metric combining different connectivity measures brings a powerful strategy to scale up the replicability of multicentric EEG connectivity studies. These composite metrics can boost the predictive strength of diagnostic tools for dementia. Moreover, the implementation of multi-feature machine learning classification systems that include EEG-based connectivity analyses may help to exploit the potential of multicentric studies combining clinical-cognitive, molecular, genetics, and neuroimaging data towards a multi-dimensional characterization of the dementia.

Multicentric initiatives based on high-density electroencephalography (hd-EEG) are urgently needed for classification and characterization of diseases subtypes in diverse and low-resource settings. These initiatives are, however, not without challenges, with sources of variability arising from differing data acquisition and harmonization methods, multiple preprocessing pipelines, and different theoretical modes and methods to compute source space/scalp functional connectivity. Our team developed a novel pipeline aimed at the harmonization of hd-EEG datasets and dementia classification. This pipeline handles data from recording to machine learning classification based on multi-metric measures of source space connectivity. A user interface is provided for those with limited background in MATLAB. Here, we present our pipeline and detail a comprehensive step-by-step example for prospective analysts reviewing the five main stages of the pipeline: data pre-processing; normalization; source transformation; connectivity metrics and dementia classification. This detailed step-by-step pipeline may improve the assessment of heterogenous, multicentric, and multi-methods approaches to functional connectivity in aging and dementia.

In this chapter, we describe a step-by-step implementation of an automated anatomical MRI feature extractor based on artificial intelligence machine learning to for classification. We applied the DenseNet – a state-of-the-art convolutional neural network producing more robust results than previous deep learning network architectures – to data from male (n = 400) and female (n = 400), age-, and education- matched healthy adult subjects. Moreover, we illustrate how an occlusion sensitivity analysis provides meaningful insights about the relevant information that the neural network used to make accurate classifications. This addresses the “black-box” limitations inherent in many deep learning implementations. The use of this approach with a specific dataset demonstrates how future implementations can use raw MRI scans to study a range of outcome measures, including neurological and psychiatric disorders.

Healthy brain dynamics can be understood as the emergence of a complex system far from thermodynamic equilibrium. Brain dynamics are temporally irreversible and thus establish a preferred direction in time (i.e., arrow of time). However, little is known about how the time-reversal symmetry of spontaneous brain activity is affected by Alzheimer's disease (AD). We hypothesized that the level of irreversibility would be compromised in AD, signaling a fundamental shift in the collective properties of brain activity toward equilibrium dynamics. We investigated the irreversibility from resting-state fMRI and EEG data in male and female human patients with AD and elderly healthy control subjects (HCs). We quantified the level of irreversibility and, thus, proximity to nonequilibrium dynamics by comparing forward and backward time series through time-shifted correlations. AD was associated with a breakdown of temporal irreversibility at the global, local, and network levels, and at multiple oscillatory frequency bands. At the local level, temporoparietal and frontal regions were affected by AD. The limbic, frontoparietal, default mode, and salience networks were the most compromised at the network level. The temporal reversibility was associated with cognitive decline in AD and gray matter volume in HCs. The irreversibility of brain dynamics provided higher accuracy and more distinctive information than classical neurocognitive measures when differentiating AD from control subjects. Findings were validated using an out-of-sample cohort. Present results offer new evidence regarding pathophysiological links between the entropy generation rate of brain dynamics and the clinical presentation of AD, opening new avenues for dementia characterization at different levels. A.I. was supported by Takeda Grant CW2680521; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); Fondo para la Investigación Científica y Tecnológica (FONCYT)-Proyectos de Investigación Científica y Tecnológica (PICT) Grants 2017-1818 and 2017-1820; Agencia Nacional de Investigación y Desarrollo (ANID)/Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica (FONDECYT) Regular Grants 1210195, 1210176, and 1220995); ANID/Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias (FONDAP) Grant 15150012; ANID-PIA ANILLOS Grant ACT210096; ANID/Fondo Nacional de la Defensa Argentino (FONDEF) Grants ID20I10152 and ID22I10029; and the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat), funded by the National Institutes of Aging of the National Institutes of Health under Grant R01-AG-057234, Alzheimer's Association Grant SG-20-725707-ReDLat, the Rainwater Foundation, and the Global Brain Health Institute. J.C. was supported by BrainLat Postdoctoral Fellowship. Y.S.-P. is supported by European Union's Horizon 2020 Research And Innovation Program under Marie Sklodowska-Curie Grant 896354. E.T. is supported by Grants PICT-2018-03103 and PICT-2019-02294 funded by Agencia I+D+I (Argentina), a Mercator fellowship granted by the German Research Foundation, and by ANID/FONDECYT Regular Grant 1220995 (Chile). G.D. was supported by the Human Brain Project Specific Grant Agreement 3 Grant 945539 and by the Spanish Research Project AWAKENING: Using Whole-Brain Models Perturbational Approaches for Predicting External Stimulation to Force Transitions between Different Brain States (reference PID2019-105772GB-I00/AEI/10.13039/501100011033), financed by the Spanish Ministry of Science, Innovation, and Universities (MCIU) and State Research Agency (AEI). M.L.K. is supported by the Center for Music in the Brain, funded by the Danish National Research Foundation (Grant DNRF117); and the Center for Eudaimonia and Human Flourishing at Linacre College, funded by the Pettit and Carlsberg Foundations.

Global initiatives call for further understanding of the impact of inequity on aging across underserved populations. Previous research in low- and middle-income countries (LMICs) presents limitations in assessing combined sources of inequity and outcomes (i.e., cognition and functionality). In this study, we assessed how social determinants of health (SDH), cardiometabolic factors (CMFs), and other medical/social factors predict cognition and functionality in an aging Colombian population. We ran a cross-sectional study that combined theory- (structural equation models) and data-driven (machine learning) approaches in a population-based study (N = 23,694;M = 69.8 years) to assess the best predictors of cognition and functionality. We found that a combination of SDH and CMF accurately predicted cognition and functionality, although SDH was the stronger predictor. Cognition was predicted with the highest accuracy by SDH, followed by demographics, CMF, and other factors. A combination of SDH, age, CMF, and additional physical/psychological factors were the best predictors of functional status. Results highlight the role of inequity in predicting brain health and advancing solutions to reduce the cognitive and functional decline in LMICs.

Adverse environments cause well-established detrimental effects on subjects living there. They ultimately increase the likelihood of developing negative cognitive, emotional, and social outcomes. However, these adverse effects are not ubiquitous across all people. While some individuals develop maladaptive behavior and seem unable to cope with these difficulties, others develop strong coping mechanisms to manage the psychosocial effects of an adverse environment. Most research in this field, however, does not include multiple variables or complex models that allow them to control multivariate interactions simultaneously and as such, it is still unclear what interaction of social, emotional, and cognitive traits promote psychosocial adaptation in adverse environments. To address this, we employ an innovative combined methodological approachusing a theoretically driven person-centered approach (Latent Profile Analysis) aided by machine learning classification to model and predict different profiles of psychosocial adaptation within a sample of 254 adults. Our results showed a two-profile solution discriminated in this sample optimally into good and poor configurations for psychosocial adaptations. These results highlight the importance of distinct social, cognitive, and affective resources in developing adequate coping mechanisms in adults living in adverse environments. Importantly, the machine learning procedures allowed us to identify the most important features. High self-esteem and cognitive and behavioral self-regulation, low-stress levels, higher education, and increased social support are crucial factors for differentiating diverse pathways of psychosocial adaptation signaling risk and protective factors with important and straightforward implications for designing psychosocial interventions in these contexts.

Recent frameworks in cognitive neuroscience and behavioral neurology underscore interoceptive priors as core modulators of negative emotions. However, the field lacks experimental designs manipulating the priming of emotions via interoception and exploring their multimodal signatures in neurodegenerative models. Here, we designed a novel task that involves interoceptive and control-exteroceptive priming conditions followed by post-interoception and post-exteroception facial emotion recognition (FER). We recruited 114 participants, including healthy controls (HCs) as well as patients with behavioral variant frontotemporal dementia (bvFTD), Parkinson's disease (PD), and Alzheimer's disease (AD). We measured online EEG modulations of the heart-evoked potential (HEP), and associations with both brain structural and resting-state functional connectivity patterns. Behaviorally, post-interoception negative FER was enhanced in HCs but selectively disrupted in bvFTD and PD, with AD presenting generalized disruptions across emotion types. Only bvFTD presented impaired interoceptive accuracy. Increased HEP modulations during post-interoception negative FER was observed in HCs and AD, but not in bvFTD or PD patients. Across all groups, post-interoception negative FER correlated with the volume of the insula and the ACC. Also, negative FER was associated with functional connectivity along the (a) salience network in the post-interoception condition, and along the (b) executive network in the post-exteroception condition. These patterns were selectively disrupted in bvFTD (a) and PD (b), respectively. Our approach underscores the multidimensional impact of interoception on emotion, while revealing a specific pathophysiological marker of bvFTD. These findings inform a promising theoretical and clinical agenda in the fields of nteroception, emotion, allostasis, and neurodegeneration.SIGNIFICANCE STATEMENTWe examined whether and how emotions are primed by interoceptive states combining multimodal measures in healthy controls and neurodegenerative models. In controls, negative emotion recognition and ongoing HEP modulations were increased after interoception. These patterns were selectively disrupted in patients with atrophy across key interoceptive-emotional regions (e.g., the insula and the cingulate in frontotemporal dementia, frontostriatal networks in Parkinson's disease), whereas persons with Alzheimer's disease presented generalized emotional processing abnormalities with preserved interoceptive mechanisms. The integration of both domains was associated with the volume and connectivity (salience network) of canonical interoceptive-emotional hubs, critically involving the insula and the anterior cingulate. Our study reveals multimodal markers of interoceptive-emotional priming, laying the groundwork for new agendas in cognitive neuroscience and behavioral neurology.

Across Latin American and Caribbean countries (LACs), the fight against dementia faces pressing challenges, such as heterogeneity, diversity, political instability, and socioeconomic disparities. These can be addressed more effectively in a collaborative setting that fosters open exchange of knowledge. In this work, the Latin American and Caribbean Consortium on Dementia (LAC-CD) proposes an agenda for integration to deliver a Knowledge to Action Framework (KtAF). First, we summarize evidence-based strategies (epidemiology, genetics, biomarkers, clinical trials, nonpharmacological interventions, networking, and translational research) and align them to current global strategies to translate regional knowledge into transformative actions. Then we characterize key sources of complexity (genetic isolates, admixture in populations, environmental factors, and barriers to effective interventions), map them to the above challenges, and provide the basic mosaics of knowledge toward a KtAF. Finally, we describe strategies supporting the knowledge creation stage that underpins the translational impact of KtAF. Fil: Parra, Mario Alfredo. Universidad Autónoma del Caribe; Colombia Fil: Baez, Sandra. Universidad de los Andes; Colombia Fil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Gonzalez Campo, Cecilia. Universidad de San Andrés; Argentina Fil: Santamaría García, Hernando. Pontificia Universidad Javeriana; Colombia Fil: Aprahamian, Ivan. No especifíca; Fil: Bertolucci, Paulo. Universidade Federal de Sao Paulo; Brasil Fil: Bustin, Julian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina Fil: Camargos Bicalho, Maria Aparecida. Universidade Federal de Minas Gerais; Brasil Fil: Cano Gutierrez, Carlos. Hospital Universitario San Ignacio; Colombia Fil: Caramelli, Paulo. Universidade Federal de Minas Gerais; Brasil Fil: Chaves, Marcia L. F.. Universidade Federal do Rio Grande do Sul; Brasil Fil: Perez Cogram, Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina Fil: Beber, Bárbara Costa. Universidade Federal do Rio Grande do Sul; Brasil Fil: Court, Felipe A.. Universidad Mayor; Chile Fil: de Souza, Leonardo Cruz. Universidade Federal de Minas Gerais; Brasil Fil: Custodio, Nilton. Peruvian Institute of Neurosciences; Perú Fil: Damian, Andres. Universidad de la República; Uruguay Fil: de la Cruz, Myriam. University of California; Estados Unidos Fil: Diehl Rodriguez, Roberta. Universidade de Sao Paulo; Brasil Fil: Dozzi Brucki, Sonia Maria. Universidade de Sao Paulo; Brasil Fil: Fajersztajn, Lais. Universidade de Sao Paulo; Brasil Fil: Farías, Gonzalo A.. Universidad de Chile; Chile Fil: De Felice, Fernanda G.. Universidade Federal do Rio de Janeiro; Brasil Fil: Ferrari, Raffaele. Colegio Universitario de Londres; Reino Unido Fil: Ferreira de Oliveira, Fabricio. Universidade de Sao Paulo; Brasil Fil: Ferreira, Sergio Teixeira. Universidade Federal do Rio de Janeiro; Brasil Fil: Ferretti, Ceres. Universidade de Sao Paulo; Brasil Fil: Figueredo Balthazar, Marcio Luiz. Universidade Estadual de Campinas; Brasil Fil: Ferreira Frota, Norberto Anizio. Universidade de Fortaleza; Brasil Fil: Fuentes, Patricio. No especifíca; Fil: García, Adolfo Martín. Universidad Nacional de Cuyo; Argentina. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Garcia, Patricia J.. Cayetano Heredia University; Perú Fil: de Gobbi Porto, Fábio Henrique. Universidade de Sao Paulo; Brasil Fil: Duque Peñailillo, Lissette. Ecuadorian Dementia Association; Ecuador Fil: Engler, Henry Willy. Universidad de la República; Uruguay Fil: Maier, Irene. Universidad de Chile; Chile Fil: Mata, Ignacio F.. University of Pittsburgh; Estados Unidos Fil: González Billault, Christian. Universidad de Chile; Chile Fil: Lopez, Oscar L.. University of Pittsburgh; Estados Unidos Fil: Morelli, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina Fil: Nitrini, Ricardo. Universidade de Sao Paulo; Brasil Fil: Quiroz, Yakeel T.. Harvard Medical School; Estados Unidos Fil: Guerrero Barragan, Alejandra. Universidad de la Sabana; Colombia Fil: Huepe, David. Universidad Adolfo Ibañez; Chile Fil: Pio, Fabricio Joao. Hospital Governador Celso Ramos; Brasil Fil: Suemoto, Claudia Kimie. Universidade de Sao Paulo; Brasil Fil: Kochhann, Renata. Pontificia Universidade Católica do Rio Grande do Sul; Brasil Fil: Kochen, Sara Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; Argentina Fil: Kumfor, Fiona. University of Western Sydney; Australia Fil: Lanata, Serggio. University of California; Estados Unidos Fil: Miller, Bruce. University of California; Estados Unidos Fil: Lessa Mansur, Leticia. Universidade de Sao Paulo; Brasil Fil: Lie Hosogi, Mirna. Universidade de Sao Paulo; Brasil Fil: Lillo, Patricia. Universidad de Chile; Chile Fil: Llibre Guerra, Jorge. University of California; Estados Unidos Fil: Lira, David. Peruvian Institute of Neurosciences; Perú Fil: Lopera, Francisco. Universidad de Antioquia; Colombia Fil: Comas, Adelina. No especifíca; Fil: Avila Funes, José Alberto. Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán; México Fil: Sosa, Ana Luisa. Instituto Nacional de Neurología y Neurocirugía; México Fil: Ramos, Claudia. University of California; Estados Unidos Fil: de Paula França Resende, Elisa. University of California; Estados Unidos Fil: Snyder, Heather M.. No especifíca; Fil: Tarnanas, Ioannis. University of California; Estados Unidos Fil: Yokoyama, Jenifer. University of California; Estados Unidos Fil: Llibre, Juan. Centro de Estudios de Alzheimer; Cuba Fil: Cardona Londoño, Juan Felipe. Universidad del Valle; Colombia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Possin, Kate. University of California; Estados Unidos Fil: Kosik, Kenneth. University of California; Estados Unidos Fil: Montesinos, Rosa. Peruvian Institute of Neurosciences; Perú Fil: Moguilner, Sebastian Gabriel. University of California; Estados Unidos Fil: Solis, Patricia Cristina Lourdes. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; Argentina. Universidad Nacional Arturo Jauretche; Argentina. Universidad de Buenos Aires; Argentina Fil: de Lucena Ferretti Rebustini, Renata Eloah. Universidade de Sao Paulo; Brasil Fil: Ramirez, Jeronimo Martin. No especifíca; Fil: Matallana, Diana. Pontificia Universidad Javeriana; Colombia Fil: Mbakile Mahlanza, Lingani. University of California; Estados Unidos Fil: Mendonça Marques Ton, Alyne. Universidade Federal do Rio de Janeiro; Brasil Fil: Melo Tavares, Ronnielly. Centro Universitario de Belo Horizonte.; Brasil Fil: Miotto, Eliane C.. Universidade de Sao Paulo; Brasil Fil: Muniz-Terrera, Graciela. University of Edinburgh; Reino Unido Fil: MuñozN evárez, Luis Arnoldo. University of California; Estados Unidos Fil: Orozco, David. Universidad Nacional del Sur; Argentina Fil: Okada de Oliveira, Maira. University of California; Estados Unidos Fil: Piguet, Olivier. University of Western Sydney; Australia Fil: Pintado Caipa, Maritza. University of California; Estados Unidos Fil: Piña Escudero, Stefanie Danielle. University of California; Estados Unidos Fil: Porcello Schilling, Lucas. Pontificia Universidade Católica do Rio Grande do Sul; Brasil Fil: Rodrigues Palmeira, André Luiz. Hospital Ernesto Dornelles; Brasil Fil: Sanches Yassuda, Mônica. Universidade de Sao Paulo; Brasil Fil: Santacruz Escudero, Jose Manuel. Pontificia Universidad Javeriana; Colombia Fil: Serafim, Rodrigo Bernardo. Universidade Federal do Rio de Janeiro; Brasil Fil: Smid, Jerusa. Universidade de Sao Paulo; Brasil Fil: Slachevsky, Andrea. Universidad de Chile; Chile Fil: Serrano, Cecilia Mariela. No especifíca; Fil: Soto Añari, Marcio. Universidad Católica San Pablo; Perú Fil: Takada, Leonel Tadao. Universidade de Sao Paulo; Brasil Fil: Tenenholz Grinberg, Lea. Universidade de Sao Paulo; Brasil Fil: Teixeira, Antonio Lucio. Universidade Federal de Minas Gerais; Brasil Fil: Tonidandel Barbosa, Maira. Universidade Federal de Minas Gerais; Brasil Fil: Trépel, Dominic. Trinity College; Estados Unidos Fil: Ibañez, Agustin Mariano. Universidad Autónoma del Caribe; Colombia. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Objective. The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings. Approach. We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat). Main results. The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens). Results. Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data. Significance. The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.

Fil: Moguilner, Sebastian. Comisión Nacional de Energía Atómica; Argentina Fil: Moguilner, Sebastian. Fundación Escuela de Medicina Nuclear; Argentina Fil: Moguilner, Sebastian. University of California. Global Brain Health Institute; Irlanda Fil: Birba, Agustina. Universidad de San Andrés. Centro de Neurociencias Cognitivas; Argentina Fil: Birba, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Fino, Daniel. Comisión Nacional de Energía Atómica; Argentina Fil: Fino, Daniel. Fundación Escuela de Medicina Nuclear; Argentina Fil: Fino, Daniel. Universidad Nacional de Cuyo. Instituto Balseiro; Argentina Fil: Fino, Daniel. Fundacion Argentina para el Desarrollo en Salud; Argentina Fil: Isoardi, Roberto. Comisión Nacional de Energía Atómica; Argentina Fil: Isoardi, Roberto. Fundación Escuela de Medicina Nuclear; Argentina Fil: Isoardi, Roberto. Universidad Nacional de Cuyo. Instituto Balseiro; Argentina Fil: Huetagoyena, Celeste. Neuromed; Argentina Fil: Huetagoyena, Celeste. Pontificia Universidad Católica Argentina. Facultad de Humanidades y Ciencias Económicas; Argentina Fil: Otoya, Raúl. Universidad Nacional de Cuyo. Instituto Balseiro; Argentina Fil: Tirapu, Viviana. Comisión Nacional de Energía Atómica; Argentina Fil: Tirapu, Viviana. Fundación Escuela de Medicina Nuclear; Argentina Fil: Tirapu, Viviana. Pontificia Universidad Católica Argentina. Facultad de Humanidades y Ciencias Económicas; Argentina Fil: Cremaschi, Fabián. Comisión Nacional de Energía Atómica; Argentina Fil: Cremaschi, Fabián. Fundación Escuela de Medicina Nuclear; Argentina Fil: Cremaschi, Fabián. Universidad Nacional de Cuyo. Facultad de Medicina. Departamento de Neurociencias; Argentina Fil: Cremaschi, Fabián. Hospita Santa Isabel de Hungría; Argentina Fil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Ibáñez, Agustín. Universidad de San Andrés. Centro de Neurociencias Cognitivas; Argentina Fil: Ibáñez, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Ibáñez, Agustín. Universidad Adolfo Ibáñez; Chile Fil: Ibáñez, Agustín. University of California. Global Brain Health Institute; Irlanda Fil: García, Adolfo M. Universidad de San Andrés. Centro de Neurociencias Cognitivas; Argentina Fil: García, Adolfo M. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: García, Adolfo M. Universidad de Santiago de Chile. Facultad de Humanidades. Departamento de Lingüística y Literatura; Chile Fil: García, Adolfo M. University of California. Global Brain Health Institute; Irland Abstract: Built on neurodegenerative lesions models, the disrupted motor grounding hypothesis (DMGH) posits that motor-system alterations selectively impair action comprehension. However, major doubts remain concerning the dissociability, neural signatures, and etiological generalizability of such deficits. Few studies have compared action-concept outcomes between disorders affecting and sparing motor circuitry, and none has examined their multimodal network predictors via data-driven approaches. Here, we first assessed action- and object-concept processing in patients with frontal lobe epilepsy (FLE), patients with posterior cortex epilepsy (PCE), and healthy controls. Then, we examined structural and functional network signatures via diffusion tensor imaging and resting-state connectivity measures. Finally, we used these measures to predict behavioral performance with an XGBoost machine learning regression algorithm. Relative to controls, FLE (but not PCE) patients exhibited selective action-concept deficits together with structural and functional abnormalities along motor networks. The XGBoost model reached a significantly large effect size only for action-concept outcomes in FLE, mainly predicted by structural (cortico-spinal tract, anterior thalamic radiation, uncinate fasciculus) and functional (M1- parietal/supramarginal connectivity) motor networks. These results extend the DMGH, suggesting that action-concept deficits are dissociable markers of frontal/motor (relative to posterior) disruptions, directly related to the structural and functional integrity of motor networks, and traceable beyond canonical movement disorders.

The quantification of biological age in humans is an important scientific endeavor in the face of ageing populations. The frailty index (FI) methodology is based on the accumulation of health deficits and captures variations in health status within individuals of the same age. The aims of this study were to assess whether the addition of age to an FI improves its mortality prediction and whether the associations of the individual FI items differ in strength. We utilized data from The Irish Longitudinal Study on Ageing to conduct, by sex, machine learning analyses of the ability of a 32-item FI to predict 8-year mortality in 8174 wave 1 participants aged 50 or more years. By wave 5, 559 men and 492 women had died. In the absence of age, the FI was an acceptable predictor of mortality with AUCs of 0.7. When age was included, AUCs improved to 0.8 in men and 0.9 in women. After age, deficits related to physical function and self-rated health tended to have higher importance scores. Not all FI variables seemed equally relevant to predict mortality, and age was by far the most relevant feature. Chronological age should remain an important consideration when interpreting the prognostic significance of an FI.

Research on how the brain construes meaning during language use has prompted two conflicting accounts. According to the ‘grounded view’, word understanding involves quick reactivations of sensorimotor (embodied)experiences evoked by the stimuli, with simultaneous or later engagement of multimodal (conceptual)systems integrating information from various sensory streams. Contrariwise, for the ‘symbolic view’, this capacity depends crucially on multimodal operations, with embodied systems playing epiphenomenal roles after comprehension. To test these contradictory hypotheses, the present magnetoencephalography study assessed implicit semantic access to grammatically constrained action and non-action verbs (n = 100 per category)while measuring spatiotemporally precise signals from the primary motor cortex (M1, a core region subserving bodily movements)and the anterior temporal lobe (ATL, a putative multimodal semantic hub). Convergent evidence from sensor- and source-level analyses revealed that increased modulations for action verbs occurred earlier in M1 (∼130–190 ms)than in specific ATL hubs (∼250–410 ms). Moreover, machine-learning decoding showed that trial-by-trial classification peaks emerged faster in M1 (∼100–175 ms)than in the ATL (∼345–500 ms), with over 71% accuracy in both cases. Considering their latencies, these results challenge the ‘symbolic view’ and its implication that sensorimotor mechanisms play only secondary roles in semantic processing. Instead, our findings support the ‘grounded view’, showing that early semantic effects are critically driven by embodied reactivations and that these cannot be reduced to post-comprehension epiphenomena, even when words are individually classified. Briefly, our study offers non-trivial insights to constrain fine-grained models of language and understand how meaning unfolds in neural time. Fil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina Fil: Moguilner, Sebastian Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro | Universidad Nacional de Cuyo. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro; Argentina Fil: Torquati, Kathya. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina Fil: García Marco, Enrique. Universidad de La Laguna; España. Universidad Nacional de Educación a Distancia; España Fil: Herrera, Eduar. Universidad Icesi; Colombia Fil: Muñoz, Edison. Universidad de Santiago de Chile; Chile Fil: Castillo, Eduardo. Florida Hospital for Children; Estados Unidos Fil: Kleineschay, Tara. Florida Hospital for Children; Estados Unidos Fil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina. Universidad Adolfo Ibañez; Chile. Universidad Autónoma del Caribe; Colombia

From molecular mechanisms to global brain networks, atypical fluctuations are the hallmark of neurodegeneration. Yet, traditional fMRI research on resting-state networks (RSNs) has favored static and average connectivity methods, which by overlooking the fluctuation dynamics triggered by neurodegeneration, have yielded inconsistent results. The present multicenter study introduces a data-driven machine learning pipeline based on dynamic connectivity fluctuation analysis (DCFA) on RS-fMRI data from 300 participants belonging to three groups: behavioral variant frontotemporal dementia (bvFTD) patients, Alzheimer's disease (AD) patients, and healthy controls. We considered non-linear oscillatory patterns across combined and individual resting-state networks (RSNs), namely: the salience network (SN), mostly affected in bvFTD; the default mode network (DMN), mostly affected in AD; the executive network (EN), partially compromised in both conditions; the motor network (MN); and the visual network (VN). These RSNs were entered as features for dementia classification using a recent robust machine learning approach (a Bayesian hyperparameter tuned Gradient Boosting Machines (GBM) algorithm), across four independent datasets with different MR scanners and recording parameters. The machine learning classification accuracy analysis revealed a systematic and unique tailored architecture of RSN disruption. The classification accuracy ranking showed that the most affected networks for bvFTD were the SN + EN network pair (mean accuracy = 86.43%, AUC = 0.91, sensitivity = 86.45%, specificity = 87.54%); for AD, the DMN + EN network pair (mean accuracy = 86.63%, AUC = 0.89, sensitivity = 88.37%, specificity = 84.62%); and for the bvFTD vs. AD classification, the DMN + SN network pair (mean accuracy = 82.67%, AUC = 0.86, sensitivity = 81.27%, specificity = 83.01%). Moreover, the DFCA classification systematically outperformed canonical connectivity approaches (including both static and linear dynamic connectivity). Our findings suggest that non-linear dynamical fluctuations surpass two traditional seed-based functional connectivity approaches and provide a pathophysiological characterization of global brain networks in neurodegenerative conditions (AD and bvFTD) across multicenter data.

Fil: Moguilner, Sebastian. University of California San Francisco. Global Brain Health Institute; Estados Unidos Fil: Moguilner, Sebastian. Trinity College Dublin; Reino Unido Fil: Moguilner, Sebastian. Fundación Centro Diagnóstico Nuclear; Argentina Fil: Moguilner, Sebastian. Comisión Nacional de Energía Atómica; Argentina Fil: Birba, Agustina. Universidad de San Andrés; Argentina Fil: Birba, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Fino, Daniel. Fundación Centro Diagnóstico Nuclear; Argentina Fil: Fino, Daniel. Comisión Nacional de Energía Atómica; Argentina Fil: Fino, Daniel. Fundación Argentina para el Desarrollo en Salud;, Argentina Fil: Isoardi, Roberto. Fundación Centro Diagnóstico Nuclear; Argentina Fil: Isoardi, Roberto. Comisión Nacional de Energía Atómica; Argentina Fil: Huetagoyena, Celeste. Neurociencias Médicas; Argentina Fil: Huetagoyena, Celeste. Pontificia Universidad Católica Argentina; Argentina Fil: Otoya, Raúl. Neurociencias Médicas; Argentina Fil: Tirapu, Viviana. Fundación Centro Diagnóstico Nuclear; Argentina Fil: Tirapu, Viviana. Comisión Nacional de Energía Atómica; Argentina Fil: Tirapu, Viviana. Neurociencias Médicas; Argentina Fil: Cremaschi, Fabián. Fundación Centro Diagnóstico Nuclear; Argentina Fil: Cremaschi, Fabián. Comisión Nacional de Energía Atómica; Argentina Fil: Cremaschi, Fabián. Universidad Nacional de Cuyo. Facultad de Medicina. Departamento de Neurociencias; Argentina Fil: Cremaschi, Fabián. Hospital Santa Isabel de Hungría; Argentina Fil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Ibáñez, Agustín. University of California San Francisco. Global Brain Health Institute; Estados Unidos Fil: Ibáñez, Agustín. Trinity College Dublin; Reino Unido Fil: Ibáñez, Agustín. Universidad de San Andrés; Argentina Fil: Ibáñez, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Ibáñez, Agustín. Universidad Adolfo Ibáñez; Escuela de Psicología. Centro de Neurociencia Social y Cognitiva; Chile Fil: García, Adolfo M. University of California San Francisco. Global Brain Health Institute; Estados Unidos Fil: García, Adolfo M. Trinity College Dublin; Reino Unido Fil: García, Adolfo M. Universidad de San Andrés; Argentina Fil: García, Adolfo M. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: García, Adolfo M. Universidad Nacional de Cuyo. Facultad de Educación, Argentina Fil: García, Adolfo M. Universidad de Santiago de Chile. Facultad de Humanidades. Departamento de Lingüística y Literatura; Chile Abstract: The pressing call to detect sensitive cognitive markers of frontal lobe epilepsy (FLE) remains poorly addressed. Standard frameworks prove nosologically unspecific (as they reveal deficits that also emerge across other epilepsy subtypes), possess low ecological validity, and are rarely supported by multimodal neuroimaging assessments. To bridge these gaps, we examined naturalistic action and non-action text comprehension, combined with structural and functional connectivity measures, in 19 FLE patients, 19 healthy controls, and 20 posterior cortex epilepsy (PCE) patients. Our analyses integrated inferential statistics and data-driven machine-learning classifiers. FLE patients were selectively and specifically impaired in action comprehension, irrespective of their neuropsychological profile. These deficits selectively and specifically correlated with (a) reduced integrity of the anterior thalamic radiation, a subcortical structure underlying motoric and action-language processing as well as epileptic seizure spread in this subtype; and (b) hypoconnectivity between the primary motor cortex and the left-parietal/supramarginal regions, two putative substrates of action-language comprehension. Moreover, machine-learning classifiers based on the above neurocognitive measures yielded 75% accuracy rates in discriminating individual FLE patients from both controls and PCE patients. Briefly, action-text assessments, combined with structural and functional connectivity measures, seem to capture ecological cognitive deficits that are specific to FLE, opening new avenues for discriminatory characterizations among epilepsy types.

Biomarkers represent a critical research area in neurodegeneration disease as they can contribute to studying potential disease-modifying agents, fostering timely therapeutic interventions, and alleviating associated financial costs. Functional connectivity (FC) analysis represents a promising approach to identify early biomarkers in specific diseases. Yet, virtually no study has tested whether potential FC biomarkers prove to be reliable and reproducible across different centers. As such, their implementation remains uncertain due to multiple sources of variability across studies: the numerous international centers capable conducting FC research vary in their scanning equipment and their samples’ socio-cultural background, and, more troublingly still, no gold-standard method exists to analyze FC. In this unprecedented study, we aim to address both issues by performing the first multicenter FC research in the behavioral-variant frontotemporal dementia (bvFTD), and by assessing multiple FC approaches to propose a gold-standard method for analysis. We enrolled 52 bvFTD patients and 60 controls from three international clinics (with different fMRI recording parameters), and three additional neurological patient groups. To evaluate FC, we focused on seed analysis, inter-regional connectivity, and several graph-theory approaches. Only graph-theory analysis, based on weighted-matrices, yielded consistent differences between bvFTD and controls across centers. Also, graph metrics robustly discriminated bvFTD from the other neurological conditions. The consistency of our findings across heterogeneous contexts highlights graph-theory as a potential gold-standard approach for brain network analysis in bvFTD. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

The search for biomarkers of neurodegenerative diseases via fMRI functional connectivity (FC) research has yielded inconsistent results. Yet, most FC studies are blind to non-linear brain dynamics. To circumvent this limitation, we developed a “weighted Symbolic Dependence Metric” (wSDM) measure. Using symbolic transforms, we factor in local and global temporal features of the BOLD signal to weigh a robust copula-based dependence measure by symbolic similarity, capturing both linear and non-linear associations. We compared this measure with a linear connectivity metric (Pearson’s R) in its capacity to identify patients with behavioral variant frontotemporal dementia (bvFTD) and controls based on resting-state data. We recruited participants from two international centers with different MRI recordings to assess the consistency of our measure across heterogeneous conditions. First, a seed-analysis comparison of the salience network (a specific target of bvFTD) and the default-mode network (as a complementary control) between patients and controls showed that wSDM yields better identification of resting-state networks. Moreover, machine learning analysis revealed that wSDM yielded higher classification accuracy. These results were consistent across centers, highlighting their robustness despite heterogeneous conditions. Our findings underscore the potential of wSDM to assess fMRI-derived FC data, and to identify sensitive biomarkers in bvFTD. Fil: Moguilner, Sebastian Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina. Universidad Nacional de Cuyo; Argentina. Comisión Nacional de Energía Atómica; Argentina Fil: García, Adolfo Martín. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Mikulan, Ezequiel Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Hesse Rizzi, Eugenia Fátima. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: García Cordero, Indira Ruth. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Melloni, Margherita. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Cervetto, Sabrina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Serrano, Cecilia Mariela. Unidad Asistencial "Dr. César Milstein"; Argentina Fil: Herrera Murcia, Eduar. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Reyes, Pablo. Pontificia Universidad Javeriana; Colombia Fil: Matallana, Diana. Pontificia Universidad Javeriana; Colombia Fil: Manes, Facundo Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina. Universidad Adolfo Ibañez; Chile. Universidad Autónoma del Caribe; Colombia Fil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina

Biomarkers represent a critical research area in neurodegeneration disease as they can contribute to studying potential disease‐modifying agents, fostering timely therapeutic interventions, and alleviating associated financial costs. Functional connectivity (FC) analysis represents a promising approach to identify early biomarkers in specific diseases. Yet, virtually no study has tested whether potential FC biomarkers prove to be reliable and reproducible across different centers. As such, their implementation remains uncertain due to multiple sources of variability across studies: the numerous international centers capable conducting FC research vary in their scanning equipment and their samples’ socio‐cultural background, and, more troublingly still, no gold‐standard method exists to analyze FC. In this unprecedented study, we aim to address both issues by performing the first multicenter FC research in the behavioral‐variant frontotemporal dementia (bvFTD), and by assessing multiple FC approaches to propose a gold‐standard method for analysis. We enrolled 52 bvFTD patients and 60 controls from three international clinics (with different fMRI recording parameters), and three additional neurological patient groups. To evaluate FC, we focused on seed analysis, inter‐regional connectivity, and several graph‐theory approaches. Only graph‐theory analysis, based on weighted‐matrices, yielded consistent differences between bvFTD and controls across centers. Also, graph metrics robustly discriminated bvFTD from the other neurological conditions. The consistency of our findings across heterogeneous contexts highlights graph‐theory as a potential gold‐standard approach for brain network analysis in bvFTD. Hum Brain Mapp 38:3804–3822, 2017. © 2017 Wiley Periodicals, Inc.

Consciousness impairments have been described as a cornerstone of epilepsy. Generalized seizures are usually characterized by a complete loss of consciousness, while focal seizures have more variable degrees of responsiveness. In addition to these impairments that occur during ictal episodes, alterations of consciousness have also been repeatedly observed between seizures (i.e., during interictal periods). In this opinion paper, we review evidence supporting the novel hypothesis that epilepsy produces consciousness impairments which remain present interictally. Then, we discuss therapies aimed to reduce seizure frequency, which may modulate consciousness between epileptic seizures. We conclude with a consideration of relevant pathophysiological mechanisms. In particular, the thalamocortical network seems to be involved in both seizure generation and interictal consciousness impairments, which could inaugurate a promising translational agenda for epilepsy studies. Fil: Moguilner, Sebastian Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina. Fundación Escuela de Medicina Nuclear; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina Fil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina Fil: Mikulan, Ezequiel Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina Fil: García, Maria del Carmen. Hospital Italiano; Argentina Fil: Vaucheret, Esteban. Hospital Italiano; Argentina Fil: Amarillo Gómez, Yimy. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Bekinchstein, Tristán. University of Cambridge; Reino Unido Fil: Ibáñez Barassi, Agustín Mariano. Universidad Autonoma del Caribe; Colombia. Universidad Adolfo Ibañez; Chile. Australian Research Council; Australia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencias Cognitivas y Traslacional; Argentina