9,542 research outputs found
Inter and intra-hemispheric structural imaging markers predict depression relapse after electroconvulsive therapy: a multisite study.
Relapse of depression following treatment is high. Biomarkers predictive of an individual's relapse risk could provide earlier opportunities for prevention. Since electroconvulsive therapy (ECT) elicits robust and rapidly acting antidepressant effects, but has a >50% relapse rate, ECT presents a valuable model for determining predictors of relapse-risk. Although previous studies have associated ECT-induced changes in brain morphometry with clinical response, longer-term outcomes have not been addressed. Using structural imaging data from 42 ECT-responsive patients obtained prior to and directly following an ECT treatment index series at two independent sites (UCLA: n = 17, age = 45.41±12.34 years; UNM: n = 25; age = 65.00±8.44), here we test relapse prediction within 6-months post-ECT. Random forests were used to predict subsequent relapse using singular and ratios of intra and inter-hemispheric structural imaging measures and clinical variables from pre-, post-, and pre-to-post ECT. Relapse risk was determined as a function of feature variation. Relapse was well-predicted both within site and when cohorts were pooled where top-performing models yielded balanced accuracies of 71-78%. Top predictors included cingulate isthmus asymmetry, pallidal asymmetry, the ratio of the paracentral to precentral cortical thickness and the ratio of lateral occipital to pericalcarine cortical thickness. Pooling cohorts and predicting relapse from post-treatment measures provided the best classification performances. However, classifiers trained on each age-disparate cohort were less informative for prediction in the held-out cohort. Post-treatment structural neuroimaging measures and the ratios of connected regions commonly implicated in depression pathophysiology are informative of relapse risk. Structural imaging measures may have utility for devising more personalized preventative medicine approaches
Buckling without bending: a new paradigm in morphogenesis
A curious feature of organ and organoid morphogenesis is that in certain
cases, spatial oscillations in the thickness of the growing "film" are
out-of-phase with the deformation of the slower-growing "substrate," while in
other cases, the oscillations are in-phase. The former cannot be explained by
elastic bilayer instability, and contradict the notion that there is a
universal mechanism by which brains, intestines, teeth, and other organs
develop surface wrinkles and folds. Inspired by the microstructure of the
embryonic cerebellum, we develop a new model of 2d morphogenesis in which
system-spanning elastic fibers endow the organ with a preferred radius, while a
separate fiber network resides in the otherwise fluid-like film at the outer
edge of the organ and resists thickness gradients thereof. The tendency of the
film to uniformly thicken or thin is described via a "growth potential".
Several features of cerebellum, +blebbistatin organoid, and retinal fovea
morphogenesis, including out-of-phase behavior and a film thickness amplitude
that is comparable to the radius amplitude, are readily explained by our simple
analytical model, as may be an observed scale-invariance in the number of folds
in the cerebellum. We also study a nonlinear variant of the model, propose
further biological and bio-inspired applications, and address how our model is
and is not unique to the developing nervous system.Comment: version accepted by Physical Review
Intersubject Regularity in the Intrinsic Shape of Human V1
Previous studies have reported considerable intersubject variability in the three-dimensional geometry of the human primary visual cortex (V1). Here we demonstrate that much of this variability is due to extrinsic geometric features of the cortical folds, and that the intrinsic shape of V1 is similar across individuals. V1 was imaged in ten ex vivo human hemispheres using high-resolution (200 μm) structural magnetic resonance imaging at high field strength (7 T). Manual tracings of the stria of Gennari were used to construct a surface representation, which was computationally flattened into the plane with minimal metric distortion. The instrinsic shape of V1 was determined from the boundary of the planar representation of the stria. An ellipse provided a simple parametric shape model that was a good approximation to the boundary of flattened V1. The aspect ration of the best-fitting ellipse was found to be consistent across subject, with a mean of 1.85 and standard deviation of 0.12. Optimal rigid alignment of size-normalized V1 produced greater overlap than that achieved by previous studies using different registration methods. A shape analysis of published macaque data indicated that the intrinsic shape of macaque V1 is also stereotyped, and similar to the human V1 shape. Previoud measurements of the functional boundary of V1 in human and macaque are in close agreement with these results
Function-based Intersubject Alignment of Human Cortical Anatomy
Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis
From genes to folds: a review of cortical gyrification theory.
Cortical gyrification is not a random process. Instead, the folds that develop are synonymous with the functional organization of the cortex, and form patterns that are remarkably consistent across individuals and even some species. How this happens is not well understood. Although many developmental features and evolutionary adaptations have been proposed as the primary cause of gyrencephaly, it is not evident that gyrification is reducible in this way. In recent years, we have greatly increased our understanding of the multiple factors that influence cortical folding, from the action of genes in health and disease to evolutionary adaptations that characterize distinctions between gyrencephalic and lissencephalic cortices. Nonetheless it is unclear how these factors which influence events at a small-scale synthesize to form the consistent and biologically meaningful large-scale features of sulci and gyri. In this article, we review the empirical evidence which suggests that gyrification is the product of a generalized mechanism, namely the differential expansion of the cortex. By considering the implications of this model, we demonstrate that it is possible to link the fundamental biological components of the cortex to its large-scale pattern-specific morphology and functional organization.This work was funded by the Bernard Wolfe Health Neuroscience Fund and the Wellcome Trust.This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s00429-014-0961-
Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data
The conventional CNN, widely used for two-dimensional images, however, is not
directly applicable to non-regular geometric surface, such as a cortical
thickness. We propose Geometric CNN (gCNN) that deals with data representation
over a spherical surface and renders pattern recognition in a multi-shell mesh
structure. The classification accuracy for sex was significantly higher than
that of SVM and image based CNN. It only uses MRI thickness data to classify
gender but this method can expand to classify disease from other MRI or fMRI
dataComment: 29 page
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