Un modelo de atención visual para la detección de regiones de interés en imágenes radiológicas

La detección, segmentación y cuantificación de lesiones de esclerosis múltiple (MS) en imágenes de resonancia magnética (MRI) ha sido un área de estudio muy activa en las ´últimas dos décadas. Esto es debido la necesidad de correlacionar estas medidas con la efectividad de los tratamientos farmacológicos. Muchos métodos han sido desarrollados y la mayoría no son específicos para los diferentes tipos de lesiones, es decir que no pueden distinguir entre lesiones agudas y crónicas. Los médicos radiólogos por su parte son capaces de distinguir entre diferentes niveles de la enfermedad haciendo uso de las imágenes de resonancia magnética de diferentes tipos. La principal motivación de este trabajo es la de emular mediante un modelo computacional la percepción visual del radiólogo, haciendo uso de los principios fisiológicos del sistema visual. De esta manera logramos detectar satisfactoriamente las lesiones de esclerosis múltiple en imágenes de resonancia magnética del cerebro. Este tipo de análisis nos permite estudiar y mejorar el estudio de las redes neuronales al poder introducir información a priori.Abstract. The detection, segmentation and quantification of multiple sclerosis (MS) lesions on magnetic resonance images (MRI) has been a very active field for the last two decades because of the urge to correlate these measures with the e↵ectiveness of pharmacological treatment. A myriad of methods has been developed and most of these are non specific for the type of lesions, e.g. they do not di↵erentiate between acute and chronic lesions. On the other hand, radiologists are able to distinguish between several stages of the disease on di↵erent types of MRI images. The main motivation of the work presented here is to computationally emulate the visual perception of the radiologist by using modeling principles of the neuronal centers along the visual system. By using this approach we were able to successfully detect multiple sclerosis lesions in brain MRI. This type of approach allows us to study and improve the analysis of brain networks by introducing a priori informationMaestrí

Altered white matter microstructure in 22q11.2 deletion syndrome: A multisite diffusion tensor imaging study

22q11.2 deletion syndrome (22q11DS)—a neurodevelopmental condition caused by a hemizygous deletion on chromosome 22—is associated with an elevated risk of psychosis and other developmental brain disorders. Prior single-site diffusion magnetic resonance imaging (dMRI) studies have reported altered white matter (WM) microstructure in 22q11DS, but small samples and variable methods have led to contradictory results. Here we present the largest study ever conducted of dMRI-derived measures of WM microstructure in 22q11DS (334 22q11.2 deletion carriers and 260 healthy age- and sex-matched controls; age range 6–52 years). Using harmonization protocols developed by the ENIGMA-DTI working group, we identified widespread reductions in mean, axial and radial diffusivities in 22q11DS, most pronounced in regions with major cortico-cortical and cortico-thalamic fibers: the corona radiata, corpus callosum, superior longitudinal fasciculus, posterior thalamic radiations, and sagittal stratum (Cohen’s d’s ranging from −0.9 to −1.3). Only the posterior limb of the internal capsule (IC), comprised primarily of corticofugal fibers, showed higher axial diffusivity in 22q11DS. 22q11DS patients showed higher mean fractional anisotropy (FA) in callosal and projection fibers (IC and corona radiata) relative to controls, but lower FA than controls in regions with predominantly association fibers. Psychotic illness in 22q11DS was associated with more substantial diffusivity reductions in multiple regions. Overall, these findings indicate large effects of the 22q11.2 deletion on WM microstructure, especially in major cortico-cortical connections. Taken together with findings from animal models, this pattern of abnormalities may reflect disrupted neurogenesis of projection neurons in outer cortical layers.Sin financiación15.992 JCR (2020) Q1, 10/295 Biochemistry & Molecular Biology5.071 SJR (2020) Q1, 5/85 Cellular and Molecular NeuroscienceNo data IDR 2020UE

Cortical microstructural associations with CSF amyloid and pTau

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aβ1-42 than pTau181 and better distinguished Aβ+ from Aβ- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline

Pervasive alterations of intra-axonal volume and network organization in young children with a 16p11.2 deletion

Abstract Reciprocal Copy Number Variants (CNVs) at the 16p11.2 locus confer high risk for autism spectrum disorder (ASD) and other neurodevelopmental disorders (NDDs). Morphometric MRI studies have revealed large and pervasive volumetric alterations in carriers of a 16p11.2 deletion. However, the specific neuroanatomical mechanisms underlying such alterations, as well as their developmental trajectory, are still poorly understood. Here we explored differences in microstructural brain connectivity between 24 children carrying a 16p11.2 deletion and 66 typically developing (TD) children between 2 and 8 years of age. We found a large pervasive increase of intra-axonal volume widespread over a high number of white matter tracts. Such microstructural alterations in 16p11.2 deletion children were already present at an early age, and led to significant changes in the global efficiency and integration of brain networks mainly associated to language, motricity and socio-emotional behavior, although the widespread pattern made it unlikely to represent direct functional correlates. Our results shed light on the neuroanatomical basis of the previously reported increase of white matter volume, and align well with analogous evidence of altered axonal diameter and synaptic function in 16p11.2 mice models. We provide evidence of a prevalent mechanistic deviation from typical maturation of brain structural connectivity associated with a specific biological risk to develop ASD. Future work is warranted to determine how this deviation contributes to the emergence of symptoms observed in young children diagnosed with ASD and other NDDs

Multi-site Normative Modeling of Diffusion Tensor Imaging Metrics Using Hierarchical Bayesian Regression

Multi-site imaging studies can increase statistical power and improve the reproducibility and generalizability of findings, yet data often need to be harmonized. One alternative to data harmonization in the normative modeling setting is Hierarchical Bayesian Regression (HBR), which overcomes some of the weaknesses of data harmonization. Here, we test the utility of three model types, i.e., linear, polynomial and b-spline - within the normative modeling HBR framework - for multi-site normative modeling of diffusion tensor imaging (DTI) metrics of the brain’s white matter microstructure, across the lifespan. These models of age dependencies were fitted to cross-sectional data from over 1,300 healthy subjects (age range: 2–80 years), scanned at eight sites in diverse geographic locations. We found that the polynomial and b-spline fits were better suited for modeling relationships of DTI metrics to age, compared to the linear fit. To illustrate the method, we also apply it to detect microstructural brain differences in carriers of rare genetic copy number variants, noting how model complexity can impact findings

ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research.

The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features

ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research

The ENIGMA‐DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder‐oriented working groups used the ENIGMA‐DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive–compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA‐defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross‐diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large‐scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross‐diagnosis features

Alternative diffusion anisotropy measures for the investigation of white matter alterations in 22q11.2 deletion syndrome

Diffusion MRI (dMRI) is widely used to study the brain’s white matter (WM) microstructure in a range of psychiatric and neurological diseases. As the diffusion tensor model has limitations in brain regions with crossing fibers, novel diffusion MRI reconstruction models may offer more accurate measures of tissue properties, and a better understanding of the brain abnormalities in specific diseases. Here we studied a large sample of 249 participants with 22q11.2 deletion syndrome (22q11DS), a neurogenetic condition associated with high rates of developmental neuropsychiatric disorders, and 224 age-matched healthy controls (HC) (age range: 8-35 years). Participants were scanned with dMRI at eight centers worldwide. Using a meta-analytic approach, we assessed the profile of group differences in four diffusion anisotropy measures to better understand the patterns of WM microstructural abnormalities and evaluate their consistency across alternative measures. When assessed in atlas-defined regions of interest, we found statistically significant differences for all anisotropy measures, all showing a widespread but not always coinciding pattern of effects. The tensor distribution function fractional anisotropy (TDF-FA) showed largest effect sizes all in the same direction (greater anisotropy in 22q11DS than HC). Fractional anisotropy based on the tensor model (FA) showed the second largest effect sizes after TDF-FA; some regions showed higher mean values in 22q11DS, but others lower. Generalized fractional anisotropy (GFA) showed the opposite pattern to TDF-FA with most regions showing lower anisotropy in 22q11DS versus HC. Anisotropic power maps (AP) showed the lowest effect sizes also with a mixed pattern of effects across regions. These results were also consistent across skeleton projection methods, with few differences when projecting anisotropy values from voxels sampled on the FA map or projecting values from voxels sampled from each anisotropy map. This study highlights that different mathematical definitions of anisotropy may lead to different profiles of group differences, even in large, well-powered population studies. Further studies of biophysical models derived from multi-shell dMRI and histological validations may help to understand the sources of these differences. 22q11DS is a promising model to study differences among novel anisotropy/dMRI measures, as group differences are relatively large and there exist animal models suitable for histological validation