Repositorio Institucional Fleni
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Task Force Consensus on Nosology and Cut-Off Values for Axial Postural Abnormalities in Parkinsonism
No full textBackground:There is no consensus with regard to the nosology and cut-off values for posturalabnormalities in parkinsonism.ObjectiveObjective:To reach a consensus regarding the nosology and cut-off values.MethodsMethods:Using a modified Delphi panel method, multiple rounds of questionnaires were conducted bymovement disorder experts to define nosology and cut-offs of postural abnormalities.ResultsResults:After separating axial from appendicular postural deformities, a full agreement was found for thefollowing terms and cut-offs: camptocormia, with thoracic fulcrum (>45 ) or lumbar fulcrum (>30 ), Pisasyndrome (>10 ), and antecollis (>45 ).“Anterior trunkflexion,”with thoracic (≥25 to≤45 ) or lumbar fulcrum(>15 to≤30 ),“lateral trunkflexion”(≥5 to≤10 ), and“anterior neckflexion”(>35 to≤45 ) were chosen for milderpostural abnormalities.ConclusionsConclusions:For axial postural abnormalities, we recommend the use of proposed cut-offs and six unique terms,namely camptocormia, Pisa syndrome, antecollis, anterior trunkflexion, lateral trunkflexion, anterior neckflexion, to harmonize clinical practice and future research.Fil: Merello, Marcelo. Fleni. Departamento de Neurología. Servicio de Movimientos Anormales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Tinazzi, Michele. University of Verona. Department of Neurosciences Biomedicine and Movement Sciences. Movement Disorders Division. Neurology Unit; Italia.Fil: Geroin, Christian. University of Verona. Department of Neurosciences Biomedicine and Movement Sciences. Movement Disorders Division. Neurology Unit; Italia.Fil: Bhidayasiri, Roongroj. Thai Red Cross Society. Chulalongkorn University and King Chulalongkorn Memorial Hospital. Faculty of Medicine. Department of Medicine. ChulalongkornCentre of Excellence for Parkinson’s Disease and Related Disorders; Tailandia. The Royal Society of Thailand. The Academy of Science; Tailandia.Fil: Bloem, Bastiaan R. Donders Institute for Brain, Cognition, and Behavior. Radboud University Medical Centre. Department of Neurology; Países Bajos.Fil: Capato, Tamine. Donders Institute for Brain, Cognition, and Behavior. Radboud University Medical Centre. Department of Neurology; Países Bajos.University of Sao Paulo. Department of Neurology. Movement Disorders Center; Brasil.Fil: Djaldetti, Ruth. Tel Aviv Universit. Sackler Faculty of Medicine. Rabin Medical Center. Department of Neurology; Israel.Fil: Doherty, Karen. Royal Victoria Hospital. Department of Neurology; Irlanda. Queens University. Centre for Medical Education; Irlanda.Fil: Fasano, Alfonso. University of Toronto. Division of Neurology; Canadá. Krembil Brain Institute; Canadá. Toronto Western Hospital. Movement Disorders Clinic. Edmond J. Safra Program inParkinson’s Disease and Morton and Gloria Shulman; Canadá.Fil: Tibar, Houyam. University of Rabat. Medical school of Mohamed Rabat; Marruecos. Ibn Sina University hospital. Neurogénétique Hôpital des spécialités OTO-Neuro-Ophtalmologique. Service de Neurologie; Marruecos.Fil: Lopiano, Leonardo. University of Turin. Department of Neuroscience “Rita Levi Montalcini”; Italia. A.O.U. Città della Salutee della Scienza di Torino. Neurology Unit; Italia.Fil: Margraf, Nils G.Christian-Albrechts-University. UKSH. Department of Neurology; Alemania.Fil: Moreau, Caroline. Lille University Hospital. Expert center for Parkinson’s disease, Neurological department; Francia.Fil: Ugawa, Yoshikazu. Fukushima Medical University. School of Medicine. Department of Human Neurophysiology; Japón.Fil: Artusi, Carlo Alberto. University of Turin. Department of Neuroscience “Rita Levi Montalcini”; Italia. A.O.U. Città della Salutee della Scienza di Torino. Neurology Unit; Italia
Multicenter study of COVID-19 incidence and determinants in Argentinian physicians
No full textBackground: Information about COVID infection in physicians is limited. This knowledge would allow the implementation of actions to reduce its impact. The objective was determining the incidence of SARSCoV-2 infection in physicians from health institutions in Argentina, its characteristics, and associated factors.
Methods: We conducted a multicenter prospective / retrospective cohort study with nested case-control study. Physicians active at the beginning of the pandemic were included, those on leave due to risk factors were excluded. The incidence of confirmed cases was estimated. We conducted bivariate analyses with various factors and used those significant in a logistic regression.
Results: Three hundred and forty three physicians with COVID-infection from 8 centers were included. The incidence of disease was 12.1% and that of global absenteeism related to COVID, 34.1%. Almost 70% of close contacts were work-related. In the multivariate analysis living in Autonomous City of Buenos Aires (CABA) (OR 0.19, p = 0.01), working in high-risk areas (OR 0.22, p = 0.01) and individual transportation (OR 0, 34, p = 0.03) reduced the risk of COVID. The odds of infection increased 5.6 times (p = 0.02) for each close contact isolation.
Discussion: The number of close contact isolation increased considerably the risk of infection. Living in Buenos Aires City, individual transportation and working in high-risk areas reduced it. Given the high frequency of close contact in the workplace, we strongly recommend the reinforcement of prevention measures in rest areas and non-COVID-wards.Fil: Del Castillo, Marcelo Ernesto. Fleni. Departamento de Medicina Interna; Argentina. Sociedad Argentina de Infectología. Comisión de Infecciones Asociadas a los Cuidados de la Salud y Seguridad del Paciente; Argentina.Fil: Rodríguez, Viviana M. Hospital General de Agudos Enrique Tornú; Argentina. Sociedad Argentina de Infectología. Comisión de Infecciones Asociadas a los Cuidados de la Salud y Seguridad del Paciente; Argentina.Fil: Klajn, Diana S. Hospital General de Agudos Enrique Tornú; Argentina.Fil: Carbone, Edith A. Hospital Aeronáutico Central; Argentina. Sociedad Argentina de Infectología. Comisión de Infecciones Asociadas a los Cuidados de la Salud y Seguridad del Paciente; Argentina.Fil: Rodríguez Rivera, Julieta. Fleni; Argentina. Sociedad Argentina de Infectología. Comisión de Infecciones Asociadas a los Cuidados de la Salud y Seguridad del Paciente; Argentina.Fil: Colque, Ángel M. Complejo Médico Policial ChurrucaVisca; Argentina. Sociedad Argentina de Infectología. Comisión de Infecciones Asociadas a los Cuidados de la Salud y Seguridad del Paciente; Argentina.Fil: Torres, Cecilia V. Sanatorio Julio Méndez; Argentina. Sociedad Argentina de Infectología. Comisión de Infecciones Asociadas a los Cuidados de la Salud y Seguridad del Paciente; Argentina.Fil: Nuccetelli, Yanina. Instituto de Cardiología de Corrientes Juana F. Cabral; Argentina. Sociedad Argentina de Infectología. Comisión de Infecciones Asociadas a los Cuidados de la Salud y Seguridad del Paciente; Argentina.Fil: Bangher, María Del Carmen. Instituto de Cardiología de Corrientes Juana F. Cabral; Argentina. Sociedad Argentina de Infectología. Comisión de Infecciones Asociadas a los Cuidados de la Salud y Seguridad del Paciente; Argentina.Fil: Bonnal, Juan Pablo. Hospital Señor del Milagro; Argentina. Sociedad Argentina de Infectología. Comisión de Infecciones Asociadas a los Cuidados de la Salud y Seguridad del Paciente; Argentina
Diabetic patients treated with metformin during early stages of Alzheimer's disease show a better integral performance: data from ADNI study
No full textWe evaluated the effect of the antidiabetic drug metformin on patients enrolled in the ADNI study considering patients with mild cognitive impairment (MCI) due to Alzheimer's disease (AD). Employing data from this observational study, we performed a principal component analysis focusing on the cognitive sphere by evaluating data from neuropsychological tests included in a modified version of the Alzheimer's Disease Cooperative Study-Preclinical Alzheimer Cognitive Composite (ADCS-PACC). Second, we included the levels of amyloid-β, tau, and phosphorylated tau in CSF. We found that MCI metformin-treated patients were globally characterized as subjects with a better cognitive performance and CSF biomarkers profile than the mean population of MCI patients. On the other hand, control subjects and type 2 diabetes patients (T2D) were paired by age, gender, ApoE allele, and years of education, defining three groups: MCI, MCI + T2D, and MCI + T2D + metformin. We evaluated the effect of T2D and metformin treatment employing the PACC score and composites defined from standardized ADNI variables to evaluate the memory and learning function. We found that MCI + T2D patients had a worse cognitive performance than MCI patients, but this deleterious effect was not observed in MCI + T2D + metformin patients. These cognitive variations were associated with changes in cortical thickness and hippocampal volume. Finally, no differences were found in metabolic plasmatic parameters (glycemia, cholesterol, triglycerides). Our study-employing different strategies for data analysis from the global study ADNI-shows a beneficial effect of metformin treatment on cognitive performance, CSF biomarkers profile, and neuroanatomical measures in MCI due to AD patients.Fil: Sevlever, Gustavo Emilio. Fleni. Departamento de Neuropatología y Biología Molecular. Laboratorio de Biología Molecular; Argentina. Fleni. Departamento de Neuropatología y Biología Molecular. Sector Biobancos; Argentina.Fil: Pomilio, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires, Buenos Aires. Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales; Argentina.Fil: González Pérez, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires, Buenos Aires. Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales; Argentina.Fil: Calandri, Ismael Luis. Fleni. Departamento de Neurología. Servicio de Neurología Cognitiva, Neuropsicología y Neuropsiquiatría. Centro de Memoria y Envejecimiento; Argentina.Fil: Crivelli, Lucía. Fleni. Departamento de Neurología. Servicio de Neurología Cognitiva, Neuropsicología y Neuropsiquiatría. Centro de Memoria y Envejecimiento; Argentina.Fil: Allegri, Ricardo Francisco. Fleni. Departamento de Neurología. Servicio de Neurología Cognitiva, Neuropsicología y Neuropsiquiatría. Centro de Memoria y Envejecimiento; Argentina. Universidad de La Costa. Departamento de Neurociencias; Colombia.Fil: Saravia, Flavia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Departamento de Química Biológica, Facultad de Ciencias Exactas y Naturales; Argentina
Forging Connections in Latin America to Advance AI in Radiology
No full textThe first Latin American Meeting on Artificial Intelligence in Healthcare was held during the 2022 Jornada Paulista de Radiologia (JPR), the annual radiology meeting in São Paulo State. The event was created to foster the discussion among Latin American countries about the maturity, challenges, and opportunities in developing and using AI in those countries. Technological improvements in artificial intelligence (AI) have created high expectations in health care. AI is recognized increasingly as a game-changer in clinical radiology. To counter the fear that AI would “take over” radiology, the program included activities to educate radiologists. The development of AI in Latin America is on its early days, but although there are some pioneer cases, many regions still lack world-class technological infrastructure and resources. Legislation, regulation and public policies in data privacy and protection, digital health and AI are recent advances in many countries. The meeting program was developed with a broad scope strategy, with expertise from different countries, backgrounds, and specialties, with the objective to ensure all levels of maturity (from basic concepts to advanced techniques), perspectives (clinical, technical, ethical, and business/management) and specialties (both informatics/data science experts and the usual radiology clinical groups). It was an opportunity to connect with peers from other countries and share lessons learned about AI in health care in different countries and contexts.Fil: Chaves, Hernán. Fleni. Departamento de Diagnóstico por Imágenes; Argentina.Fil: Campos Kitamura, Felipe. Diagnósticos da América SA–Dasa. Department of Applied Innovation and AI; Brasil. Universidade Federal de São Paulo. Department of Diagnostic Imaging; Brasil.Fil: Nascimento, Felipe Barjud Pereira Do. Hospital Israelita Albert Einstein. Department of Radiology and Imaging Diagnosis; Brasil.Fil: Elizondo-Riojas, Guillermo. Universidad Autónoma de Nuevo León. University Hospital; México.Fil: Henríquez Leighton, Héctor. Universidad de los Andes. Department of Radiology; Chile.Fil: Salinas-Miranda, Emmanuel. University of Toronto. Joint Department of Medical Imaging. Lunenfeld Tanenbaum Research Institute; Canadá.Fil: Júlio, Thiago. Colombian Association of Radiology. Artificial Intelligence Committee. Department of Informatics; Colombia.Fil: da Rocha, Antônio José. Colombian Association of Radiology. Artificial Intelligence Committee. Department of Medical Sciences; Colombia. Sociedade Paulista de Radiologia. Department of Neuroradiology; Brasil.Fil: Nomura, César Higa. Superintendent of Diagnostic Medicine at Hospital Sírio-Libanês; Brasil
Global impact of the COVID-19 pandemic on subarachnoid haemorrhage hospitalisations, aneurysm treatment and in-hospital mortality: 1-year follow-up
No full textBackground: Prior studies indicated a decrease in the incidences of aneurysmal subarachnoid haemorrhage (aSAH) during the early stages of the COVID-19 pandemic. We evaluated differences in the incidence, severity of aSAH presentation, and ruptured aneurysm treatment modality during the first year of the COVID-19 pandemic compared with the preceding year.
Methods: We conducted a cross-sectional study including 49 countries and 187 centres. We recorded volumes for COVID-19 hospitalisations, aSAH hospitalisations, Hunt-Hess grade, coiling, clipping and aSAH in-hospital mortality. Diagnoses were identified by International Classification of Diseases, 10th Revision, codes or stroke databases from January 2019 to May 2021.
Results: Over the study period, there were 16 247 aSAH admissions, 344 491 COVID-19 admissions, 8300 ruptured aneurysm coiling and 4240 ruptured aneurysm clipping procedures. Declines were observed in aSAH admissions (-6.4% (95% CI -7.0% to -5.8%), p=0.0001) during the first year of the pandemic compared with the prior year, most pronounced in high-volume SAH and high-volume COVID-19 hospitals. There was a trend towards a decline in mild and moderate presentations of subarachnoid haemorrhage (SAH) (mild: -5% (95% CI -5.9% to -4.3%), p=0.06; moderate: -8.3% (95% CI -10.2% to -6.7%), p=0.06) but no difference in higher SAH severity. The ruptured aneurysm clipping rate remained unchanged (30.7% vs 31.2%, p=0.58), whereas ruptured aneurysm coiling increased (53.97% vs 56.5%, p=0.009). There was no difference in aSAH in-hospital mortality rate (19.1% vs 20.1%, p=0.12).
Conclusion: During the first year of the pandemic, there was a decrease in aSAH admissions volume, driven by a decrease in mild to moderate presentation of aSAH. There was an increase in the ruptured aneurysm coiling rate but neither change in the ruptured aneurysm clipping rate nor change in aSAH in-hospital mortality.Fil: Pujol Lereis, Virginia Andrea. Fleni. Departamento de Neurología. Servicio de Neurología Vascular; Argentina
Pragmatic Approach on Neuroimaging Techniques for the Differential Diagnosis of Parkinsonisms
No full textBackground: Rapid advances in neuroimaging technologies in the exploration of the living human brain also apply to movement disorders. However, the accurate diagnosis of Parkinson's disease (PD) and atypical parkinsonian disorders (APDs) still remains a challenge in daily practice.
Methods: We review the literature and our own experience as the Movement Disorder Society-Neuroimaging Study Group in Movement Disorders with the aim of providing a practical approach to the use of imaging technologies in the clinical setting.
Results: The enormous amount of articles published so far and our increasing recognition of imaging technologies contrast with a lack of imaging protocols and updated algorithms for differential diagnosis. The distinctive pathological involvement in different brain structures and the correlation with imaging findings obtained with magnetic resonance, positron emission tomography, or single-photon emission computed tomography illustrate what qualitative and quantitative measures may be useful in the clinical setting.
Conclusion: We delineate a pragmatic approach to discuss imaging technologies, updated imaging algorithms, and their implications for differential diagnoses in PD and APDs.Fil: Peralta, Cecilia. CEMIC; Argentina.Fil: Strafella, Antonio P. Toronto Western Hospital University Health Network Toronto Ontario; Canada.Fil: van Eimeren, Thilo. Department of Nuclear Medicine University of Cologne; Alemania.Fil: Ceravolo, Roberto. Department of Clinical and Experimental Medicine University of Pisa; Italia.Fil: Seppi, Klaus. Medical University Innsbruck; Austria.Fil: Kaasinen, Valtteri. Turku University Hospital; Finlandia.Fil: Arena, Julieta E. Fleni. Departamento de Neurología. Sección de Movimientos Anormales; Argentina.Fil: Lehericy, Stephane. Centre de NeuroImagerie de Recherche-CENIR; Francia
GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices
No full textNowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.Fil: Miriuka, Santiago Gabriel. FLENI-CONICET. Laboratorio de Investigación Aplicada a las Neurociencias; Argentina.Fil: Isa-Jara, Ramiro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Faculty of Informatics and Electronic; Ecuador.Fil: Pérez-Sosa, Camilo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina.Fil: Macote-Yparraguirre, Erick. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina.Fil: Revollo, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina.Fil: Lerner, Betiana. Florida International University. Department of Electrical and Computer Engineering; Estados Unidos. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Collaborative Research Institute Intelligent Oncology; Alemania.Fil: Delrieux, Claudio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; Argentina.Fil: Pérez, Maximiliano. Florida International University. Department of Electrical and Computer Engineering; Estados Unidos. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Collaborative Research Institute Intelligent Oncology; Alemania.Fil: Mertelsmann, Roland. University of Freiburg. Faculty of Medicine. Medical Center. Department of Medicine I; Alemania
Tobacco growing and tobacco use
No full textTobacco use is associated with an annual global economic cost of two trillion dollars and mortality of half of its regular users. Tobacco leaf cultivation is the starting point of the tobacco cycle. Tobacco farming employs millions of small-scale tobacco farmers around the globe, most of whom are out growers who rely on the tobacco industry. This paper aims to map the regions of greatest tobacco production globally (i.e., the US, Brazil, China, Indonesia, India, and Zambia) and tobacco use rates in these locations. Smoking rates were higher in those areas, except for India, where important population subgroups reported an upward trend for tobacco smoking. In general, there was a relationship between tobacco farming and tobacco smoking. Tobacco farming may lead to a higher risk of tobacco use and lower adherence to tobacco control policies. Therefore, promoting viable alternative livelihoods for tobacco farmers must have dual benefits. Additionally, specific health prevention policies might be necessary for those populations reporting higher tobacco use and lower perception of tobacco-related health risks.Fil: Waisman Campos, Marcela. Fleni. Departamento de Neurología. Servicio de Neurología Cognitiva, Neuropsicología y Neuropsiquiatría; Argentina.Fil: Sousa Martins-da-Silva. University of São Paulo. Departamento de Psiquiatría; Brasil.Fil: Torales, Julio. National University of Asunción. School of Medical Sciences. Department of Psychiatry; Paraguay.Fil: Vieira Becker, Ruth Francyelle. Torrens University. Occupational Therapy; Australia.Fil: Moura, Helena F. University of Brasília. Faculty of Medicine; Brasil.Fil: Fidalgo, Thiago M. Young Leaders Program from the National Academy of Medicine; Brasil. Universidade Federal de São Paulo. Escola Paulista de Medicina. Department of Psychiatry; Brasil.Fil: Ventriglio, Antonio. University of Foggia. Department of Clinical and Experimental Medicine; Italia.Fil: Castaldelli-Maia, João Mauricio. University of São Paulo. Department of Psychiatry; Brasil. FMABC University Center. Medical School. Department of Neuroscience; Brasil. Columbia University. Mailman School of Public Health. Department of Epidemiology; Estados Unidos
Natural language signatures of psilocybin microdosing
No full textRationale: Serotonergic psychedelics are being studied as novel treatments for mental health disorders and as facilitators of improved well-being, mental function, and creativity. Recent studies have found mixed results concerning the effects of low doses of psychedelics ("microdosing") on these domains. However, microdosing is generally investigated using instruments designed to assess larger doses of psychedelics, which might lack sensitivity and specificity for this purpose.
Objectives: Determine whether unconstrained speech contains signatures capable of identifying the acute effects of psilocybin microdoses.
Methods: Natural speech under psilocybin microdoses (0.5 g of psilocybin mushrooms) was acquired from thirty-four healthy adult volunteers (11 females: 32.09 ± 3.53 years; 23 males: 30.87 ± 4.64 years) following a double-blind and placebo-controlled experimental design with two measurement weeks per participant. On Wednesdays and Fridays of each week, participants consumed either the active dose (psilocybin) or the placebo (edible mushrooms). Features of interest were defined based on variables known to be affected by higher doses: verbosity, semantic variability, and sentiment scores. Machine learning models were used to discriminate between conditions. Classifiers were trained and tested using stratified cross-validation to compute the AUC and p-values.
Results: Except for semantic variability, these metrics presented significant differences between a typical active microdose and the inactive placebo condition. Machine learning classifiers were capable of distinguishing between conditions with high accuracy (AUC [Formula: see text] 0.8).
Conclusions: These results constitute first evidence that low doses of serotonergic psychedelics can be identified from unconstrained natural speech, with potential for widely applicable, affordable, and ecologically valid monitoring of microdosing schedules
Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease
No full text"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.Fil: Allegri, Ricardo Francisco. Fleni. Departamento de Neurología. Servicio de Neurología Cognitiva, Neuropsicología y Neuropsiquiatría; Argentina.Fil: Millar, Peter R. Washington University. Department of Neurology; Estados Unidos.Fil: Luckett, Patrick H. Washington University. Department of Neurology; Estados Unidos.Fil: Gordon, Brian A. Washington University. Department of Radiology; Estados Unidos.Fil: Benzinger, Tammie L.S. Washington University. Department of Radiology; Estados Unidos.Fil: Schindler, Suzanne E. Washington University. Department of Neurology; Estados Unidos.Fil: Fagan, Anne M. Washington University. Department of Neurology; Estados Unidos.Fil: Cruchaga, Carlos. Washington University. Department of Psychiatry; Estados Unidos.Fil: Bateman, Randall J. Washington University. Department of Neurology; Estados Unidos.Fil: Jucker, Mathias. German Center for Neurodegenerative Diseases (DZNE); Alemania. University of Tübingen. Hertie Institute for Clinical Brain Research; Alemania.Fil: Lee, Jae-Hong. University of Ulsan College of Medicine. Department of Neurology, Asan Medical Center; Corea.Fil: Mori, Hiroshi. Nagaoka Sutoku University; Japón. Osaka City University Medical School. Department of Clinical Neuroscience; Japón.Fil: Ances, Beau M. Washington University. Department of Neurology; Estados Unidos.Fil: Salloway, Stephen P. Brown University. Department of Neurology; Estados Unidos.Fil: Igor, Yakushev. Technical University of Munich. Department of Nuclear Medicine; Alemania.Fil: Morris, John C. Washington University. Department of Neurology; Estados Unidos