Crystallized and fluid intelligence are predicted by microstructure of specific white-matter tracts

Studies of the neural basis of intelligence have focused on comparing brain imaging variables with global scales instead of the cognitive domains integrating these scales or quotients. Here, the relation between mean tract‐based fractional anisotropy (mTBFA) and intelligence indices was explored. Deterministic tractography was performed using a regions of interest approach for 10 white‐matter fascicles along which the mTBFA was calculated. The study sample included 83 healthy individuals from the second wave of the Cuban Human Brain Mapping Project, whose WAIS‐III intelligence quotients and indices were obtained. Inspired by the “Watershed model” of intelligence, we employed a regularized hierarchical Multiple Indicator, Multiple Causes model (MIMIC), to assess the association of mTBFA with intelligence scores, as mediated by latent variables summarizing the indices. Regularized MIMIC, used due to the limited sample size, selected relevant mTBFA by means of an elastic net penalty and achieved good fits to the data. Two latent variables were necessary to describe the indices: Fluid intelligence (Perceptual Organization and Processing Speed indices) and Crystallized Intelligence (Verbal Comprehension and Working Memory indices). Regularized MIMIC revealed effects of the forceps minor tract on crystallized intelligence and of the superior longitudinal fasciculus on fluid intelligence. The model also detected the significant effect of age on both latent variables

Extensibility of adaptation capabilities in the CAIN content adaptation engine

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)This paper describes the extensibility mechanism that has been incorporated to the CAIN Adaptation Engine, that provides audiovisual content adaptation based on user preferences, network capabilities and terminal limitations. The integration of new adaptation modules needs no code modifications in the core system, so it does not have to be recompiled for adding or modifying adaptation modules.This work is partially supported by the European Commission 6th Framework Program under project FP6-001765 (aceMedia). This work is also supported by the Ministerio de Ciencia y Tecnología of the Spanish Government under project TIN2004-07860 (MEDUSA) and by the Comunidad de Madrid under project P-TIC-0223-0505 (PROMULTIDIS). The authors want to thank Víctor Fernández-Carbajales for successful testing of the extensibility mechanism

Texture-based Classification for the Automatic Rating of the Perivascular Spaces in Brain MRI

Los espacios perivasculares (EVP) se relacionan con una cognición deficiente, depresión en la edad avanzada, enfermedad de Parkinson, inflamación, hipertensión y enfermedad de pequeños vasos cerebrales, cuando están agrandados y son visibles en imágenes de resonancia magnética (MRI). En este artículo exploramos cómo clasificar la densidad del PVS agrandado en los ganglios basales (BG) mediante la descripción de la textura de la RM cerebral estructural. La textura de la región BG se describe mediante estadísticas de primer orden y características derivadas de la matriz de co-ocurrencia, ambas computadas a partir de la imagen original y los coeficientes producidos por la transformada de wavelet discreta (WSF y WCF, respectivamente), y patrones binarios locales (LBP). Los resultados experimentales con un clasificador de Máquina de vectores de soporte (SVM) muestran que WCF logra una precisión del 80.03%

White matter hyperintensity reduction and outcomes after minor stroke

Objective: To assess factors associated with white matter hyperintensity (WMH) change in a large cohort after observing obvious WMH shrinkage 1 year after minor stroke in several participants in a longitudinal study. Methods: We recruited participants with minor ischemic stroke and performed clinical assessments and brain MRI. At 1 year, we assessed recurrent cerebrovascular events and dependency and repeated the MRI. We assessed change in WMH volume from baseline to 1 year (normalized to percent intracranial volume [ICV]) and associations with baseline variables, clinical outcomes, and imaging parameters using multivariable analysis of covariance, model of changes, and multinomial logistic regression. Results: Among 190 participants (mean age 65.3 years, range 34.3–96.9 years, 112 [59%] male), WMH decreased in 71 participants by 1 year. At baseline, participants whose WMH decreased had similar WMH volumes but higher blood pressure (p = 0.0064) compared with participants whose WMH increased. At 1 year, participants with WMH decrease (expressed as percent ICV) had larger reductions in blood pressure (β = 0.0053, 95% confidence interval [CI] 0.00099–0.0097 fewer WMH per 1–mm Hg decrease, p = 0.017) and in mean diffusivity in normal-appearing white matter (β = 0.075, 95% CI 0.0025–0.15 fewer WMH per 1-unit mean diffusivity decrease, p = 0.043) than participants with WMH increase; those with WMH increase experienced more recurrent cerebrovascular events (32%, vs 16% with WMH decrease, β = 0.27, 95% CI 0.047–0.50 more WMH per event, p = 0.018). Conclusions: Some WMH may regress after minor stroke, with potentially better clinical and brain tissue outcomes. The role of risk factor control requires verification. Interstitial fluid alterations may account for some WMH reversibility, offering potential intervention targets

The possible causes for sulcal hyperintensities on FLAIR images on brain MRI: the dataset derived from a systematic review

This report describes the data related to the article entitiled: “Relationship between inferior frontal sulcal hyperintensities on brain MRI, ageing and cerebral small vessel disease”. This systematic review was conducted to assess possible causes for sulcal hyperintensities on fluid-attenuated inversion recovery (FLAIR) images on brain MRI

Corticotropinoma as a Component of Carney Complex.

Known germline gene abnormalities cause one-fifth of the pituitary adenomas in children and adolescents, but, in contrast with other pituitary tumor types, the genetic causes of corticotropinomas are largely unknown. In this study, we report a case of Cushing disease (CD) due to a loss-of-function mutation in PRKAR1A, providing evidence for association of this gene with a corticotropinoma. A 15-year-old male presenting with hypercortisolemia was diagnosed with CD. Remission was achieved after surgical resection of a corticotropin (ACTH)-producing pituitary microadenoma, but recurrence 3 years later prompted reoperation and radiotherapy. Five years after the original diagnosis, the patient developed ACTH-independent Cushing syndrome, and a diagnosis of primary pigmented nodular adrenocortical disease was confirmed. A PRKAR1A mutation (c.671delG, p.G225Afs*16) was detected in a germline DNA sample from the patient, which displayed loss of heterozygosity in the corticotropinoma. No other germline or somatic mutations of interest were found. As corticotropinomas are not a known component of Carney complex (CNC), we performed loss of heterozygosity and messenger RNA stability studies in the patient's tissues, and analyzed the effect of Prkar1a silencing on AtT-20/D16v-F2 mouse corticotropinoma cells. No PRKAR1A defects were found among 97 other pediatric CD patients studied. Our clinical case and experimental data support a role for PRKAR1A in the pathogenesis of a corticotroph cell tumor. This is a molecularly confirmed report of a corticotropinoma presenting in association with CNC. We conclude that germline PRKAR1A mutations are a novel, albeit apparently infrequent, cause of CD

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

pp. 1465-1481En el cerebro, los espacios perivasculares agrandados (PVS) se relacionan con la enfermedad de los vasos pequeños (SVD), mala cognición, inflamación e hipertensión. Proponemos un esquema totalmente automático que utiliza una máquina de vectores de soporte (SVM) para clasificar la carga de PVS en los ganglios basales (BG) como baja o alta. Evaluamos el rendimiento de tres tipos diferentes de descriptores extraídos de la región BG en imágenes de RMN ponderadas en T2: (I) estadísticas obtenidas de los coeficientes de la transformada de Wavelet, (II) patrones binarios locales y (III) bolsa de palabras visuales (BoW), descriptores basados en la caracterización de claves locales obtenidas de una rejilla densa con las características de transformación de la función de escala-invariante (SIFT). Cuando se utilizaron estos últimos, el SVM clasificador alcanzó la mejor precisión (81,16%). Lo obtenido del clasificador utilizando los descriptores del BoW se comparó con las calificaciones visuales realizadas por un neurorradiólogo experimentado (observador 1) y por un analista de imágenes entrenado (observador 2). El acuerdo y la correlación cruzada entre el clasificador y el observador 2 (κ = 0,67 (0,58 – 0,76)) fueron ligeramente más altos que entre el clasificador y el observador 1 (κ = 0,62 (0,53 – 0,72)) y entre ambos observadores (κ = 0,68 (0,61 – 0,75)). Por último, se construyeron tres modelos de regresión logística que utilizan variables clínicas como variable independiente y cada una de las clasificaciones de PVS como variable dependiente, para evaluar clínicamente lo significativas que resultan las predicciones del clasificador. El ajuste del modelo para el clasificador era bueno (área bajo la curva (AUC) valores: 0,93 (modelo 1), 0,90 (modelo 2) y 0,92 (modelo 3)) y un poco mejor (es decir, valores de AUC: 0,02 unidades superiores) que las del modelo para el observador 2. Estos resultados sugieren que, aunque se puede mejorar, un clasificador automático para evaluar la carga de PVS de la resonancia magnética del cerebro puede proporcionar resultados clínicamente significativos cercanos a los de un observador entrenado.S