1,721,049 research outputs found
Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study
Get PDFMultivariate pattern analysis (MVPA) methods have become an important tool in neuroimaging, revealing complex associations and yielding powerful prediction models. Despite methodological developments and novel application domains, there has been little effort to compile benchmark results that researchers can reference and compare against. This study takes a significant step in this direction. We employed three classes of state-of-the-art MVPA algorithms and common types of structural measurements from brain Magnetic Resonance Imaging (MRI) scans to predict an array of clinically relevant variables (diagnosis of Alzheimer’s, schizophrenia, autism, and attention deficit and hyperactivity disorder; age, cerebrospinal fluid derived amyloid-β levels and mini-mental state exam score). We analyzed data from over 2,800 subjects, compiled from six publicly available datasets. The employed data and computational tools are freely distributed (https://www.nmr.mgh.harvard.edu/lab/mripredict), making this the largest, most comprehensive, reproducible benchmark image-based prediction experiment to date in structural neuroimaging. Finally, we make several observations regarding the factors that influence prediction performance and point to future research directions. Unsurprisingly, our results suggest that the biological footprint (effect size) has a dramatic influence on prediction performance. Though the choice of image measurement and MVPA algorithm can impact the result, there was no universally optimal selection. Intriguingly, the choice of algorithm seemed to be less critical than the choice of measurement type. Finally, our results showed that cross-validation estimates of performance, while generally optimistic, correlate well with generalization accuracy on a new dataset.BrightFocus Foundation (Alzheimer’s Disease pilot grant (AHAF A2012333))National Institutes of Health (U.S.) (K25 grant (NIBIB 1K25EB013649-01))National Center for Research Resources (U.S.) (U24 RR021382)National Institutes of Health. National Institute for Biomedical Imaging and Bioengineering (R01EB006758)National Institute of Neurological Disorders and Stroke (U.S.) (R01 NS052585-01, 1R21NS072652-01, 1R01NS070963, R01NS083534)National Institutes of Health (U.S.) (Blueprint for Neuroscience Research (5U01-MH093765)
Massively expedited genome-wide heritability analysis (MEGHA)
Get PDFThe discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide Complex Trait Analysis (GCTA) is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that is increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a novel, fast and accurate statistical method for high-dimensional heritability analysis using genome-wide single nucleotide polymorphism (SNP) data from unrelated individuals, termed Massively Expedited Genome-wide Heritability Analysis (MEGHA), and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural magnetic resonance imaging (MRI) scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale heritability-based screening and high-dimensional heritability profile construction
Stepwise Connectivity of the Modal Cortex Reveals the Multimodal Organization of the Human Brain
Get PDFHow human beings integrate information from external sources and internal cognition to produce a coherent experience is still not well understood. During the past decades, anatomical, neurophysiological and neuroimaging research in multimodal integration have stood out in the effort to understand the perceptual binding properties of the brain. Areas in the human lateral occipitotemporal, prefrontal, and posterior parietal cortices have been associated with sensory multimodal processing. Even though this, rather patchy, organization of brain regions gives us a glimpse of the perceptual convergence, the articulation of the flow of information from modality-related to the more parallel cognitive processing systems remains elusive. Using a method called stepwise functional connectivity analysis, the present study analyzes the functional connectome and transitions from primary sensory cortices to higher-order brain systems. We identify the large-scale multimodal integration network and essential connectivity axes for perceptual integration in the human brain.Alzheimer's Foundation of America (Grant NIRG-11-205690)Alzheimer's Foundation of America (Grant ZEN-10-174210)National Institutes of Health (U.S.) (NIH Grant K25NS069805)National Institutes of Health (U.S.) (NIH Grant K25EB013649-01)National Institutes of Health (U.S.) (NIH Grant R01AG037497)National Institutes of Health (U.S.) (NIH Grant R01AG036694
Multidimensional heritability analysis of neuroanatomical shape
Get PDFIn the dawning era of large-scale biomedical data, multidimensional phenotype vectors will play an increasing role in examining the genetic underpinnings of brain features, behaviour and disease. For example, shape measurements derived from brain MRI scans are multidimensional geometric descriptions of brain structure and provide an alternate class of phenotypes that remains largely unexplored in genetic studies. Here we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide SNP and MRI data from 1,320 unrelated, young and healthy individuals. We replicate our findings in an extended twin sample from the Human Connectome Project (HCP). Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and can serve as a complementary phenotype to study the genetic determinants and clinical relevance of brain structure.National Institute for Biomedical Imaging and Bioengineering (U.S.) (P41EB015896)United States. National Institutes of Health (S10RR023043)United States. National Institutes of Health (S10RR023401)United States. National Institutes of Health (K25CA181632)United States. National Institutes of Health (K01MH099232)United States. National Institutes of Health (K99MH101367)United States. National Institutes of Health (R21AG050122-01A1)United States. National Institutes of Health (R41AG052246-01)United States. National Institutes of Health (1K25EB013649-01)United States. National Institutes of Health (K24MH094614)United States. National Institutes of Health (R01MH101486
Morphometricity as a measure of the neuroanatomical signature of a trait
Get PDFComplex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute for Biomedical Imaging and Bioengineering (U.S.) (P41EB015896)National Institute for Biomedical Imaging and Bioengineering (U.S.) (R21EB018907)National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01EB019956)National Institute on Aging (5R01AG008122)National Institute on Aging (R01AG016495)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS0525851)National Institute of Neurological Diseases and Stroke (U.S.) (R21NS072652)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institute of Neurological Diseases and Stroke (U.S.) (5U01NS086625)United States. National Institutes of Health (5U01-MH093765)United States. National Institutes of Health (R01NS083534)United States. National Institutes of Health (R01NS070963)United States. National Institutes of Health (R41AG052246)United States. National Institutes of Health (1K25EB013649-01
Image-Driven Population Analysis Through Mixture Modeling
No full textWe present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. The output of the algorithm is a small number of template images that represent different modes in a population. This is in contrast with traditional, hypothesis-driven computational anatomy approaches that assume a single template to construct an atlas. We derive the algorithm based on a generative model of an image population as a mixture of deformable template images. We validate and explore our method in four experiments. In the first experiment, we use synthetic data to explore the behavior of the algorithm and inform a design choice on parameter settings. In the second experiment, we demonstrate the utility of having multiple atlases for the application of localizing temporal lobe brain structures in a pool of subjects that contains healthy controls and schizophrenia patients. Next, we employ iCluster to partition a data set of 415 whole brain MR volumes of subjects aged 18 through 96 years into three anatomical subgroups. Our analysis suggests that these subgroups mainly correspond to age groups. The templates reveal significant structural differences across these age groups that confirm previous findings in aging research. In the final experiment, we run iCluster on a group of 15 patients with dementia and 15 age-matched healthy controls. The algorithm produces two modes, one of which contains dementia patients only. These results suggest that the algorithm can be used to discover subpopulations that correspond to interesting structural or functional “modes.”National Science Foundation (CAREER Grant 0642971)National Institute of Mental Health Grant (5R01-MH050740-13)Morphometry Biomedical Informatics Research Network (NIH NCRR mBIRN U24-RR021382) and (NIH NINDS R01-NS051826 grant)Neuroimaging Analysis Center (NIH CRR NAC P41-RR13218)National Alliance for Medical Image Analysis (NIH NIBIB NAMIC U54-ED005149)Dept. of Veterans Affairs Merit Award
A Surface-based Analysis of Language Lateralization and Cortical Asymmetry
No full textAmong brain functions, language is one of the most lateralized. Cortical language areas are also some of the most asymmetrical in the brain. An open question is whether the asymmetry in function is linked to the asymmetry in anatomy. To address this question, we measured anatomical asymmetry in 34 participants shown with fMRI to have language dominance of the left hemisphere (LLD) and 21 participants shown to have atypical right hemisphere dominance (RLD). All participants were healthy and left-handed, and most (80%) were female. Gray matter (GM) volume asymmetry was measured using an automated surface-based technique in both ROIs and exploratory analyses. In the ROI analysis, a significant difference between LLD and RLD was found in the insula. No differences were found in planum temporale (PT), pars opercularis (POp), pars triangularis (PTr), or Heschl's gyrus (HG). The PT, POp, insula, and HG were all significantly left lateralized in both LLD and RLD participants. Both the positive and negative ROI findings replicate a previous study using manually labeled ROIs in a different cohort [Keller, S. S., Roberts, N., Garcia-Finana, M., Mohammadi, S., Ringelstein, E. B., Knecht, S., et al. Can the language-dominant hemisphere be predicted by brain anatomy? Journal of Cognitive Neuroscience, 23, 2013–2029, 2011]. The exploratory analysis was accomplished using a new surface-based registration that aligns cortical folding patterns across both subject and hemisphere. A small but significant cluster was found in the superior temporal gyrus that overlapped with the PT. A cluster was also found in the ventral occipitotemporal cortex corresponding to the visual word recognition area. The surface-based analysis also makes it possible to disentangle the effects of GM volume, thickness, and surface area while removing the effects of curvature. For both the ROI and exploratory analyses, the difference between LLD and RLD volume laterality was most strongly driven by differences in surface area and not cortical thickness. Overall, there were surprisingly few differences in GM volume asymmetry between LLD and RLD indicating that gross morphometric asymmetry is only subtly related to functional language laterality.National Institutes of Health (U.S.) (R01NS052585-03)National Institute on Aging (5R01AG029411)National Institute on Aging (5R01AG022381-08)National Institute of Neurological Disorders and Stroke (U.S.) (1R01NS069696-01A1)National Center for Research Resources (U.S.) (P41-RR14075)National Center for Research Resources (U.S.) (R01 RR16594-01A1)National Center for Research Resources (U.S.) (BIRN Morphometric Project BIRN002)National Center for Research Resources (U.S.) (Functional Imaging Biomedical Informatics Research Network [FBIRN] U24 RR021382)National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01 EB001550)National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01 EB006758)National Institute for Biomedical Imaging and Bioengineering (U.S.) (1K25 EB013649-01
Function-based Intersubject Alignment of Human Cortical Anatomy
Get PDFMaking 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.National Institute of Mental Health (U.S.) (Grant 5R01MH075706
Supervised Nonparametric Image Parcellation
Get PDFAuthor Manuscript 2010 August 25. 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part IISegmentation of medical images is commonly formulated as a supervised learning problem, where manually labeled training data are summarized using a parametric atlas. Summarizing the data alleviates the computational burden at the expense of possibly losing valuable information on inter-subject variability. This paper presents a novel framework for Supervised Nonparametric Image Parcellation (SNIP). SNIP models the intensity and label images as samples of a joint distribution estimated from the training data in a non-parametric fashion. By capitalizing on recently developed fast and robust pairwise image alignment tools, SNIP employs the entire training data to segment a new image via Expectation Maximization. The use of multiple registrations increases robustness to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with manual labels for the white matter, cortex and subcortical structures. SNIP yields better segmentation than state-of-the-art algorithms in multiple regions of interest.NAMIC (NIHNIBIBNAMICU54-EB005149)NAC (NIHNCRRNACP41-RR13218)mBIRN (NIHNCRRmBIRNU24-RR021382)NIH NINDS (Grant R01-NS051826)National Science Foundation (U.S.) (CAREER Grant 0642971)NCRR (P41-RR14075)NCRR (R01 RR16594-01A1)NIBIB (R01 EB001550)NIBIB (R01EB006758)NINDS (R01 NS052585-01)Mind Research InstituteEllison Medical FoundationSingapore. Agency for Science, Technology and Researc
Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease
Get PDFWe used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.United States. National Institutes of Health (1K25EB013649-01)United States. National Institutes of Health (1R21AG050122-01A1)United States. National Institutes of Health (P01AG036694)United States. National Institutes of Health (F32AG044054
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