Lifespan Changes of the Human Brain In Alzheimer's Disease.

Brain imaging studies have shown that slow and progressive cerebral atrophy characterized the development of Alzheimer's Disease (AD). Despite a large number of studies dedicated to AD, key questions about the lifespan evolution of AD biomarkers remain open. When does the AD model diverge from the normal aging model? What is the lifespan trajectory of imaging biomarkers for AD? How do the trajectories of biomarkers in AD differ from normal aging? To answer these questions, we proposed an innovative way by inferring brain structure model across the entire lifespan using a massive number of MRI (N = 4329). We compared the normal model based on 2944 control subjects with the pathological model based on 3262 patients (AD + Mild cognitive Impaired subjects) older than 55 years and controls younger than 55 years. Our study provides evidences of early divergence of the AD models from the normal aging trajectory before 40 years for the hippocampus, followed by the lateral ventricles and the amygdala around 40 years. Moreover, our lifespan model reveals the evolution of these biomarkers and suggests close abnormality evolution for the hippocampus and the amygdala, whereas trajectory of ventricular enlargement appears to follow an inverted U-shape. Finally, our models indicate that medial temporal lobe atrophy and ventricular enlargement are two mid-life physiopathological events characterizing AD brain.

Towards a unified analysis of brain maturation and aging across the entire lifespan: A MRI analysis.

There is no consensus in literature about lifespan brain maturation and senescence, mainly because previous lifespan studies have been performed on restricted age periods and/or with a limited number of scans, making results instable and their comparison very difficult. Moreover, the use of nonharmonized tools and different volumetric measurements lead to a great discrepancy in reported results. Thanks to the new paradigm of BigData sharing in neuroimaging and the last advances in image processing enabling to process baby as well as elderly scans with the same tool, new insights on brain maturation and aging can be obtained. This study presents brain volume trajectory over the entire lifespan using the largest age range to date (from few months of life to elderly) and one of the largest number of subjects (N = 2,944). First, we found that white matter trajectory based on absolute and normalized volumes follows an inverted U-shape with a maturation peak around middle life. Second, we found that from 1 to 8-10 y there is an absolute gray matter (GM) increase related to body growth followed by a GM decrease. However, when normalized volumes were considered, GM continuously decreases all along the life. Finally, we found that this observation holds for almost all the considered subcortical structures except for amygdala which is rather stable and hippocampus which exhibits an inverted U-shape with a longer maturation period. By revealing the entire brain trajectory picture, a consensus can be drawn since most of the previously discussed discrepancies can be explained. Hum Brain Mapp 38:5501-5518, 2017. © 2017 Wiley Periodicals, Inc.

Increased amygdala and parahippocampal gyrus activation in schizophrenic patients with auditory hallucinations: an fMRI study using independent component analysis.

OBJECTIVE: Hallucinations in patients with schizophrenia have strong emotional connotations. Functional neuroimaging techniques have been widely used to study brain activity in patients with schizophrenia with hallucinations or emotional impairments. However, few of these studies have investigated the association between hallucinations and emotional dysfunctions using an emotional auditory paradigm. Independent component analysis (ICA) is an analysis method that is especially useful for decomposing activation during complex cognitive tasks in which multiple operations occur simultaneously. Our aim in this study is to analyze brain activation after the presentation of emotional auditory stimuli in patients with schizophrenia with and without chronic auditory hallucinations using ICA methodology. It was hypothesized that functional connectivity differences in limbic regions responsible for emotional processing would be demonstrated. METHODS: The present functional magnetic resonance imaging (fMRI) study compared neural activity in 41 patients with schizophrenia (27 with auditory hallucinations, 14 without auditory hallucinations) with 31 controls. Neural activity data was generated while participants were presented with an auditory paradigm containing emotional words. The comparison was performed using a multivariate approach, ICA. Differences in temporo-spatial aspects of limbic network were examined in three study groups. RESULTS: Limbic networks responded differently in patients with auditory hallucinations compared to healthy controls and patients without auditory hallucinations. Unlike control subjects and non-hallucinators, the group of hallucinatory patients showed an increase of activity in the parahippocampal gyrus and the amygdala during the emotional session. CONCLUSIONS: These findings may reflect an increase in parahippocampal gyrus and amygdala activity during passive listening of emotional words in patients with schizophrenia and auditory hallucinations.

Mapas de resonancia magnética funcional obtenidos con PC / Functional magnetic resonance maps obtained by personal computer

Objetivos: El estudio de resonancia magnética funcional (RMf) tiene una especial relevancia en el análisis de diversas activaciones cerebrales. En este trabajo se describe el desarrollo de un programa informático para PC que analiza estas imágenes para obtener mapas de activación funcional de una forma sencilla. Material y Métodos: Los mapas de activación se basan en las diferencias temporales de oxihemoglobina en un plano tomográfico. Para detectar estas diferencias se comparan las intensidades registradas repetidamente durante dos estados distintos del cerebro, control y activación. Los experimentos se han realizado en un equipo de RM de 1,5 Teslas. Para comprobar el correcto funcionamiento del programa se han realizado estudios de RMf en cuatro sujetos sanos (12 cortes contiguos, 80 imágenes por corte cada 3,1 segundos, 960 imágenes totales).Todas las imágenes se transfirieron a un PC. El procesamiento de las imágenes se hizo píxel a píxel dentro de cada secuencia, obteniéndose una curva de intensidad/tiempo. El análisis mediante métodos estadísticos (t de Student y correlación cruzada) permite decidir la activación de cada píxel. En la preparación de las imágenes se usó filtrado espacial, filtrado temporal, corrección de la línea base, normalización y segmentación del parénquima. En el posprocesado del resultado se usó la eliminación de píxeles aislados, superposición de una imagen anatómica de mayor resolución espacial y el filtrado anti-aliasing. Resultados: La aplicación (Xfun 1.0, Valencia) se ha desarrollado en el entorno Microsoft Visual C++ 5.0 Developer Studio para Windows NT Workstation. En un ejemplo representativo el programa tardó 8,2 segundos para calcular y presentar los resultados de un estudio completo (12 mapas funcionales). En los experimentos de activación motora y visual se observó la activación correspondiente a regiones próximas al surco central del hemisferio contralateral a la mano que ejercía la acción y en la corteza occipital. Discusión: Si bien existen programas que calculan mapas de activación, el desarrollo de un programa para PC en entorno Windows tiene unas características clave para su utilización rutinaria: facilidad de manejo y alta capacidad de cómputo. La aplicación desarrollada es capaz de discriminar cuáles son las zonas del cerebro que se activan como respuesta a un estímulo de las que no se modifican (AU)