Repositorio Institucional Fleni
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Trigeminal Neuralgia Crisis - Intravenous Phenytoin as Acute Rescue Treatment
No full textObjective: The aim of this retrospective cohort study was to analyze responses to intravenous (IV) phenytoin (PHT) for trigeminal neuralgia (TN) crisis in a group of patients treated at our institution.
Background: TN is one of the most common causes of facial pain. Its treatment relies on preventive therapy with either carbamazepine or oxcarbazepine. During severe pain episodes, patients may be unable to eat, drink, or even swallow oral medication, requiring in-hospital treatment. There is scarce evidence to support IV medication use for TN, making management of this condition difficult.
Methods: We reviewed clinical records of patients with TN crisis consulting the emergency department at a tertiary neurological referral center in Buenos Aires, Argentina, treated with IV PHT as analgesic strategy, and with at least 1-month posttreatment follow-up. Demographic features, magnetic resonance imaging findings, and therapeutic management were analyzed.
Results: Thirty-nine patients with TN were included, 18 (46.2%) receiving IV PHT more than once (total number of infusions administered, 65). Immediate pain relief was observed in 89.2% (58/65) and 15.4% (10/65) presented side effects.
Conclusions: We recommend IV PHT as acute rescue treatment in TN crisis.Fil: Schnell, Susana. Fleni. Departamento de Neurología; Argentina.Fil: Marrodán, Mariano. Fleni. Departamento de Neurología. Servicio de Neuroinmunología y Enfermedades Desmielinizantes; Argentina.Fil: Goicochea, María Teresa. Fleni. Departamento de Neurología. Clínica del Dolor. Clínica de Cefaleas; Argentina.Fil: Bonamico, Lucas. Fleni. Departamento de Neurología. Clínica del Dolor. Clínica de Cefaleas; Argentina.Fil: Acosta, Julián Nicolás. Fleni. Departamento de Neurología; Argentina
COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence
No full textPurpose: To investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support.
Materials and methods: An AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system.
Results: Discrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, p < 0.0001). When working with AI support, physicians increased their diagnostic sensitivity from 47% to 61% (p < 0.001), although specificity decreased from 79% to 75% (p = 0.007).
Conclusions: Our results suggest interpreting chest radiographs (CXR) supported by AI, increases physician diagnostic sensitivity for COVID-19 detection. This approach involving a human-machine partnership may help expedite triaging efforts and improve resource allocation in the current crisis.Fil: Farez, Mauricio Franco. Fleni. Centro para la Investigación de Enfermedades Neuroinmunológicas; Argentina. Entelai; Argentina.Fil: Chaves, Hernán. Fleni. Departamento de Diagnóstico por Imágenes; Argentina. Entelai; Argentina.Fil: Serra, María Mercedes. Fleni. Departamento de Diagnóstico por Imágenes; Argentina. Entelai; Argentina.Fil: Dorr, Francisco. Entelai; Argentina.Fil: Ramirez, Andrés. Entelai; Argentina.Fil: Costa, Martín Elías. Entelai; Argentina.Fil: Seia, Joaquín. Entelai; Argentina.Fil: Castro, Marcelo. Clínica Indisa. Department of Diagnostic Imaging; Chile.Fil: Eyheremendy, Eduardo. Hospital Alemán. Department of Diagnostic Imaging; Argentina.Fil: Fernández Slezak, Diego. Entelai; Argentina.Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Tecnológicas; Argentina
What is the role of axonal ion channels in multiple sclerosis?
No full textResumen no disponibleFil: Correale, Jorge. Fleni. Departamento de Neurología. Servicio de Neuroinmunología y Enfermedades Desmielinizantes; ArgentinaFil: Marrodán, Mariano. Fleni. Departamento de Neurología. Servicio de Neuroinmunología y Enfermedades Desmielinizantes; Argentina. Mayo Clinic. Department of Neurology; Estados Unidos.Fil: Benarroch, Eduardo E. Fleni. Departamento de Neurología. Servicio de Neuroinmunología y Enfermedades Desmielinizantes; Argentina. Mayo Clinic. Department of Neurology; Estados Unidos
Biphasic cortical macro- and microstructural changes in autosomal dominant Alzheimer's disease
No full textMethods: In this study we tested this model fitting linear versus quadratic trajectories and computed the timing of the inflection points vertexwise of cortical thickness and cortical diffusivity-a novel marker of cortical microstructure-changes in 389 participants from the Dominantly Inherited Alzheimer Network.
Results: In early preclinical AD, between 20 and 15 years before estimated symptom onset, we found increases in cortical thickness and decreases in cortical diffusivity followed by cortical thinning and cortical diffusivity increases in later preclinical and symptomatic stages. The inflection points 16 to 19 years before estimated symptom onset are in agreement with the start of tau biomarker alterations.
Discussion: These findings confirm a biphasic trajectory for brain structural changes and have direct implications when interpreting magnetic resonance imaging measures in preventive AD clinical trials.Fil: Allegri, Ricardo Francisco. Fleni. Departamento de Neurología. Servicio de Neurología Cognitiva, Neuropsicología y Neuropsiquiatría; Argentina.Fil: Montal, Victor. Universitat Autònoma de Barcelona. Biomedical Research Institute Sant Pau. Hospital de la Santa Creu i Sant Pau. Sant Pau Memory Unit, Department of Neurology; España. Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); España.Fil: Vilaplana, Eduard. Universitat Autònoma de Barcelona. Biomedical Research Institute Sant Pau. Hospital de la Santa Creu i Sant Pau. Sant Pau Memory Unit, Department of Neurology; España. Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); España.Fil: Pegueroles, Jordi. Universitat Autònoma de Barcelona. Biomedical Research Institute Sant Pau. Hospital de la Santa Creu i Sant Pau. Sant Pau Memory Unit, Department of Neurology; España. Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); España.Fil: Bejanin, Alexandre. Universitat Autònoma de Barcelona. Biomedical Research Institute Sant Pau. Hospital de la Santa Creu i Sant Pau. Sant Pau Memory Unit, Department of Neurology; España. Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); España.Fil: Alcolea, Daniel. Universitat Autònoma de Barcelona. Biomedical Research Institute Sant Pau. Hospital de la Santa Creu i Sant Pau. Sant Pau Memory Unit, Department of Neurology; España. Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); España.Fil: Carmona-Iragui, María. Universitat Autònoma de Barcelona. Biomedical Research Institute Sant Pau. Hospital de la Santa Creu i Sant Pau. Sant Pau Memory Unit, Department of Neurology; España. Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); España. Fundació Catalana de Síndrome de Down. Barcelona Down Medical Center; España.Fil: Clarimón, Jordi. Universitat Autònoma de Barcelona. Biomedical Research Institute Sant Pau. Hospital de la Santa Creu i Sant Pau. Sant Pau Memory Unit, Department of Neurology; España. Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); España.Fil: Lleó, Alberto. Universitat Autònoma de Barcelona. Biomedical Research Institute Sant Pau. Hospital de la Santa Creu i Sant Pau. Sant Pau Memory Unit, Department of Neurology; España. Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); España.Fil: Fortea, Juan. Universitat Autònoma de Barcelona. Biomedical Research Institute Sant Pau. Hospital de la Santa Creu i Sant Pau. Sant Pau Memory Unit, Department of Neurology; España. Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); España. Fundació Catalana de Síndrome de Down. Barcelona Down Medical Center; España.Fil: Levin, Johannes. Ludwig-Maximilians-Universität München. Department of Neurology; Alemania. Munich Cluster for Systems Neurology (SyNergy). German Center for Neurodegenerative Diseases; Alemania.Fil: Cruchaga, Carlos. Washington University School of Medicine. Department of Neurology; Estados Unidos. The Hope Center for Neurological Disorders; Estados Unidos. Washington University School of Medicine. NeuroGenomics and Informatics; Estados Unidos. Washington University School of Medicine. Knight Alzheimer's Disease Research Center; Estados Unidos.Fil: Graff-Radford, Neill R. Mayo Clinic. Department of Neurology; Estados Unidos.Fil: Noble, James M. Columbia University Irving Medical Center. Taub Institute for Research on Alzheimer's Disease and the Aging Brain. Department of Neurology; Estados Unidos.Fil: Lee, Jae-Hong. University of Ulsan College of Medicine. Asan Medical Center. Department of Neurology; Corea.Fil: Karch, Celeste M. Washington University School of Medicine. Department of Psychiatry; Estados Unidos.Fil: Laske, Christoph. German Center for Neurodegenerative Diseases (DZNE); Alemania. University of Tübingen. Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy. Section for Dementia Research; Alemania.Fil: Schofield, Peter R. Neuroscience Research Australia; Australia. University of New South Wales. School of Medical Sciences; Australia.Fil: Salloway, Stephen. Butler Hospital. Neurology and the Memory and Aging Program; Estados Unidos.Fil: Ances, Beau. Washington University School of Medicine. Department of Neurology; Estados Unidos. The Hope Center for Neurological Disorders; Estados Unidos. Washington University School of Medicine. Knight Alzheimer's Disease Research Center; Estados Unidos. Washington University in St. Louis. Department of Radiology; Estados Unidos.Fil: Benzinger, Tammie L.S. Washington University School of Medicine. Knight Alzheimer's Disease Research Center; Estados Unidos. Washington University in St. Louis. Department of Radiology; Estados Unidos.Fil: McDale, Eric. Washington University School of Medicine. Knight Alzheimer's Disease Research Center; Estados Unidos. Washington University School of Medicine. Department of Neurology; Estados Unidos.Fil: Bateman, Randall J. Washington University School of Medicine. Department of Neurology; Estados Unidos. The Hope Center for Neurological Disorders; Estados Unidos. Washington University School of Medicine. Knight Alzheimer's Disease Research Center; Estados Unidos.Fil: Blesa, Rafael. Universitat Autònoma de Barcelona. Biomedical Research Institute Sant Pau. Hospital de la Santa Creu i Sant Pau. Sant Pau Memory Unit, Department of Neurology; España. Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED); España.Fil: Sánchez-Valle, Raquel. Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS). Fundació Clínic per a la Recerca Biomèdica. Hospital Clínic. Alzheimer's Disease and Other Cognitive Disorders Unit; España
Data augmentation based on dynamical systems for the classification of brain states
No full textThe application of machine learning algorithms to neuroimaging data shows great promise for the classification of physiological and pathological brain states. However, classifiers trained on high dimensional data are prone to overfitting, especially for a low number of training samples. We describe the use of whole-brain computational models for data augmentation in brain state classification. Our low dimensional model is based on nonlinear oscillators coupled by the empirical structural connectivity of the brain. We use this model to enhance a dataset consisting of functional magnetic resonance imaging recordings acquired during all stages of the human wake-sleep cycle. After fitting the model to the average functional connectivity of each state, we show that the synthetic data generated by the model yields classification accuracies comparable to those obtained from the empirical data. We also show that models fitted to individual subjects generate surrogates with enough information to train classifiers that present significant transfer learning accuracy to the whole sample. Whole-brain computational modeling represents a useful tool to produce large synthetic datasets for data augmentation in the classification of certain brain states, with potential applications to computer-assisted diagnosis and prognosis of neuropsychiatric disorders.Fil: Pallavicini, Carla. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Fleni; Argentina.Fil: Sanz Perl, Yonatan. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina.Fil: Perez Ipiña, Ignacio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina.Fil: Kringelbach, Morten. University of Oxford. Department of Psychiatry; Reino Unido. Aarhus University. Dept. of Clinical Medicine. Center for Music in the Brain; Dinamarca.Fil: Deco, Gustavo. Universitat Pompeu Fabra. Department of Information and Communication Technologies. Computational Neuroscience Group. Center for Brain and Cognition; España. Universitat Pompeu Fabra. Institució Catalana de la Recerca i Estudis Avançats (ICREA); España.Fil: Laufs, Helmut. University of Kiel. Department of Neurology; Alemania.Fil: Tagliazucchi, Enzo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Prevalence of stroke in Argentina: A door-to-door population-based study (EstEPA).
No full textBackground: Stroke burden is highest and is still rising in low- and middle-income countries. Epidemiologic stroke data
are lacking in many of these countries. Stroke prevalence in Argentina has been unexplored for almost three decades.
Aim: This population-based study aims to determine prevalence of stroke in a representative sample of the
Argentinean population.
Methods: We performed a door-to-door survey of randomly selected households in a city of 18,650 inhabitants.
A structured questionnaire screening for potential stroke cases was used. All subjects screened positive were then
evaluated by stroke neurologists for final adjudication. Data about stroke subtypes, neurological status, vascular risk
factors, medications, and diagnostic tests were also collected.
Results: Among 2156 surveys, 294 were screened positive for a possible stroke. After neurological evaluation, there
were 41 confirmed cases. The adjusted stroke prevalence was 1,974/100,000 inhabitants older than 40 years, and it was
higher in men than in women (26.3% vs 13.2%, p<0.01). Prevalence of ischemic stroke, intracranial hemorrhage, and
transient ischemic attack were 15.8%, 2.93%, and 2.93%, respectively. The most prevalent vascular risk factors in
stroke survivors were hypertension, obstructive sleep apnea, and dyslipidemia.
Conclusion: Approximately 2 in every 100 subjects older than 40 years in this population are stroke survivors. Stroke
prevalence in Argentina has remained stable over the last 30 years; it is higher than in most Latin American countries and
similar to western populations.
Keywords
Epidemiology, prevalence, stroke, Argentina, Latin America, population-basedFil: Ameriso, Sebastián Francisco. Fleni. Centro Integral de Neurología Vascular; Argentina.Fil: Gómez Schneider, Maia Macarena. Fleni. Centro Integral de Neurología Vascular; Argentina.Fil: Hawkes, Maximiliano Alberto. Fleni. Centro Integral de Neurología Vascular; Argentina.Fil: Pujol Lereis, Virginia Andrea. Fleni. Centro Integral de Neurología Vascular; Argentina.Fil: Dossi, Daiana Elizabeth. Fleni. Centro Integral de Neurología Vascular; Argentina.Fil: Alet, Matías Javier. Fleni. Centro Integral de Neurología Vascular; Argentina.Fil: Rodríguez Lucci, Federico. Fleni. Centro Integral de Neurología Vascular; Argentina.Fil: Povedano, Guillermo Pablo. Fleni. Centro Integral de Neurología Vascular; Argentina.Fil: González, C.D. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Química Biológica. Laboratorio de Regulación Génica en Células Madre; Argentina.Fil: Melcon, Mario O. Fundación para Investigaciones Neuroepidemiológicas; Argentina.
Interpretation of Variants of Uncertain Signicance in the Clinical Setting: A Case of Treatable Ataxia
No full textBackground: Spinocerebellar ataxia type 38 (SCA38) is an autosomal dominant cerebellar ataxia caused
by pathogenic variants in the elongation of very long chain fatty acids-like 5 gene (ELOVL5).
Improvement of ataxia with a docosahexaenoic acid (DHA) replacement therapy has been reported.
Case presentation: A 73-year-old man of Hispanic descent presented with gait and limb ataxia, dysarthria,
slow and hypometric saccades, hearing loss, mild cognitive impairment, and hypopalesthesia. The initial
scale for the assessment and rating of ataxia (SARA) score was 11. After a negative routine workout for
ataxia and testing for common forms due to expanded repeats, whole-exome sequencing (WES)
identied a heterozygous variant (c.327+1G>A) in the ELOVL5 gene that was predicted to have a negative
effect on splicing but was categorized as a Variant of Uncertain Signicance (VUS). The patient was
started on DHA 600 mg/day. Four months later, the patient showed a considerable reduction in the scale
for the assessment and rating of ataxia (SARA) score, from 11 to 5 points, with a clear improvement in
gait and limb ataxia that was sustained at 24 months of follow-up.
Conclusions: We illustrate the case of a patient presenting with a variant considered genetically and
biochemically of uncertain signicance. Despite being a VUS, its location in a gene that is known to
cause ataxia (SCA38), as well as a compatible phenotype, led to the interpretation of this variant as
probably pathogenic from a clinical practice standpoint, especially considering prior reports that showed
clinical improvement with a specic, over-the-counter, pharmacological treatment. A further satisfactory
response to treatment supported our clinical approach.Fil: Wilken, Miguel. Fleni. Departamento de Neurología. Servicio de Movimientos Anormales; Argentina. Fleni.Fil: Rossi, Malco. Fleni. Departamento de Neurología. Servicio de Movimientos Anormales; Argentina.Fil: Merello, Marcelo. Fleni. Departamento de Neurología. Servicio de Movimientos Anormales; Argentina
Cardiovascular and Renal Outcomes with Empagliflozin in Heart Failure
No full textBackground: Sodium-glucose cotransporter 2 (SGLT2) inhibitors reduce the risk of hospitalization for heart failure in patients regardless of the presence or absence of diabetes. More evidence is needed regarding the effects of these drugs in patients across the broad spectrum of heart failure, including those with a markedly reduced ejection fraction.
Methods: In this double-blind trial, we randomly assigned 3730 patients with class II, III, or IV heart failure and an ejection fraction of 40% or less to receive empagliflozin (10 mg once daily) or placebo, in addition to recommended therapy. The primary outcome was a composite of cardiovascular death or hospitalization for worsening heart failure.
Results: During a median of 16 months, a primary outcome event occurred in 361 of 1863 patients (19.4%) in the empagliflozin group and in 462 of 1867 patients (24.7%) in the placebo group (hazard ratio for cardiovascular death or hospitalization for heart failure, 0.75; 95% confidence interval [CI], 0.65 to 0.86; P<0.001). The effect of empagliflozin on the primary outcome was consistent in patients regardless of the presence or absence of diabetes. The total number of hospitalizations for heart failure was lower in the empagliflozin group than in the placebo group (hazard ratio, 0.70; 95% CI, 0.58 to 0.85; P<0.001). The annual rate of decline in the estimated glomerular filtration rate was slower in the empagliflozin group than in the placebo group (-0.55 vs. -2.28 ml per minute per 1.73 m2 of body-surface area per year, P<0.001), and empagliflozin-treated patients had a lower risk of serious renal outcomes. Uncomplicated genital tract infection was reported more frequently with empagliflozin.
Conclusions: Among patients receiving recommended therapy for heart failure, those in the empagliflozin group had a lower risk of cardiovascular death or hospitalization for heart failure than those in the placebo group, regardless of the presence or absence of diabetes. (Funded by Boehringer Ingelheim and Eli Lilly; EMPEROR-Reduced ClinicalTrials.gov number, NCT03057977.).Fil: Perrone, Sergio Victor. Fleni. Departamento de Neurología. Servicio de Cardiología; Argentina. Hospital de Alta Complejidad El Cruce Nestor Kirchner; Argentina.Fil: Packer, Milton. Baylor University Medical Center. Baylor Heart and Vascular Institute; Estados Unidos
Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL
No full textBackground and purpose: There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC).
Materials and methods: Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV).
Results: Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94-0.97)) than FreeSurfer and CAT12 (0.92 (0.88-0.96)) and FSL (0.87 (0.79-0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20-3.13% vs. mean CV 1.05, range 0.21-3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49-5.91% vs. mean CV 3.84, range 2.62-5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively.
Conclusion: Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.Fil: Chaves, Hernán. Fleni. Departamento de Diagnóstico por Imágenes; Argentina.Fil: Yáñez, Paulina. Fleni. Departamento de Diagnóstico por Imágenes; Argentina.Fil: Cejas, Claudia Patricia. Fleni. Departamento de Diagnóstico por Imágenes; Argentina.Fil: Dorr, Francisco. Entelai; Argentina.Fil: Costa, Martín Elías. Entelai; Argentina.Fil: Serra, María Mercedes. Fleni. Departamento de Diagnóstico por Imágenes; Argentina. Entelai; Argentina.Fil: Fernández Slezak, Diego. Entelai; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; Argentina. Instituto en Ciencias de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Sevlever, Gustavo Emilio. Fleni. Departamento de Neuropatología y Biología Molecular; Argentina.Fil: Farez, Mauricio Franco. Fleni. Departamento de Neurología; Argentina
A soluble phosphorylated tau signature links tau, amyloid and the evolution of stages of dominantly inherited Alzheimer’s disease.
No full textDevelopment of tau-based therapies for Alzheimer's disease requires an understanding of the timing of disease-related changes in tau. We quantified the phosphorylation state at multiple sites of the tau protein in cerebrospinal fluid markers across four decades of disease progression in dominantly inherited Alzheimer's disease. We identified a pattern of tau staging where site-specific phosphorylation changes occur at different periods of disease progression and follow distinct trajectories over time. These tau phosphorylation state changes are uniquely associated with structural, metabolic, neurodegenerative and clinical markers of disease, and some (p-tau217 and p-tau181) begin with the initial increases in aggregate amyloid-β as early as two decades before the development of aggregated tau pathology. Others (p-tau205 and t-tau) increase with atrophy and hypometabolism closer to symptom onset. These findings provide insights into the pathways linking tau, amyloid-β and neurodegeneration, and may facilitate clinical trials of tau-based treatments.Fil: Allegri, Ricardo Francisco. Fleni. Departamento de Neurología. Servicio de Neurología Cognitiva, Neuropsicología y Neuropsiquiatría; Argentina.Fil: Chrem Méndez, Patricio Alexis. Fleni. Departamento de Neurología. Servicio de Neurología Cognitiva, Neuropsicología y Neuropsiquiatría. Centro de Memoria y Envejecimiento; ArgentinaFil: Barthélemy, Nicolas R. Washington University School of Medicine. Department of Neurology; Estados Unidos.Fil: Li, Yan. Washington University School of Medicine. Department of Neurology; Estados Unidos. Washington University School of Medicine. Division of Biostatistics; Estados Unidos.Fil: Joseph-Mathurin, Nelly. Washington University School of Medicine. Department of Radiology; Estados Unidos.Fil: Gordon, Brian A. Washington University School of Medicine. Department of Radiology; Estados Unidos.Fil: Hassenstab, Jason. Washington University School of Medicine. Department of Neurology; Estados Unidos.Fil: Benzinger, Tammie L.S. Washington University School of Medicine. Department of Radiology; Estados Unidos.Fil: Buckles, Virginia. Washington University School of Medicine. Department of Neurology; Estados Unidos.Fil: Fagan, Anne M. Washington University School of Medicine. Department of Neurology; Estados Unidos.Fil: Perrin, Richard J. Washington University School of Medicine. Department of Pathology; Estados Unidos.Fil: Goate, Alison M. Icahn School of Medicine at Mount Sinai. Department of Neuroscience; Estados Unidos.Fil: Morris, John C. Washington University School of Medicine. Department of Neurology; Estados Unidos.Fil: Karch, Celeste M. Washington University School of Medicine. Department of Psychiatry; Estados Unidos.Fil: Xiong, Chengjie. Washington University School of Medicine. Division of Biostatistics; Estados Unidos.Fil: Berman, Sarah B. University of Pittsburgh School of Medicine; Estados Unidos.Fil: Ikeuchi, Takeshi. Niigata University; Japón.Fil: Mori, Hiroshi. Osaka City University; Japón.Fil: Shimada, Hiroyuki. Osaka City University; Japón.Fil: Shoji, Mikio. Hirosaki University; Japón.Fil: Suzuki, Kazushi. Tokyo University; Japón.Fil: Noble, James. Columbia University. College of Physicians and Surgeons; Estados Unidos.Fil: Farlow, Martin R. Indiana University, Indianapolis. Department of Neurology; Estados Unidos.Fil: Chhatwal, Jasmeer P. Massachusetts General Hospital; Estados Unidos. Harvard Medical School; Estados Unidos