A very simple, re-executable neuroimaging publication.
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ABSTRACT:
SUBMITTER: Ghosh SS
PROVIDER: S-EPMC5516225 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeIn dual modality positron emission tomography (PET)/magnetic resonance imaging (MRI), attenuation correction (AC) methods are continually improving. Although a new AC can sometimes be generated from existing MR data, its application requires a new reconstruction. We evaluate an approximate 2D projection method that allows offline image-based reprocessing.Procedure2-Deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) brain scans were acquired (Siemens HR+) for six subjects. Attenuation data were obtained using the scanner's transmission source (SAC). Additional scanning was performed on a Siemens mMR including production of a Dixon-based MR AC (MRAC). The MRAC was imported to the HR+ and the PET data were reconstructed twice: once using native SAC (ground truth); once using the imported MRAC (imperfect AC). The re-projection method was implemented as follows. The MRAC PET was forward projected to approximately reproduce attenuation-corrected sinograms. The SAC and MRAC images were forward projected and converted to attenuation-correction factors (ACFs). The MRAC ACFs were removed from the MRAC PET sinograms by division; the SAC ACFs were applied by multiplication. The regenerated sinograms were reconstructed by filtered back projection to produce images (SUBAC PET) in which SAC has been substituted for MRAC. Ideally SUBAC PET should match SAC PET. Via coregistered T1 images, FreeSurfer (FS; MGH, Boston) was used to define a set of cortical gray matter regions of interest. Regional activity concentrations were extracted for SAC PET, MRAC PET, and SUBAC PET.ResultsSUBAC PET showed substantially smaller root mean square error than MRAC PET with averaged values of 1.5 % versus 8.1 %.ConclusionsRe-projection is a viable image-based method for the application of an alternate attenuation correction in neuroimaging.
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Project description:Different mutations in the gene encoding humans IGF-I cause intrauterine growth retardation, postnatal growth failure, microcephaly, mental retardation, bilateral sensorineural deafness and multiple dysmorphic features. Insight into the role of IGFs in inner ear cochlear ganglion neurogenesis has come from the study of genetically modified mice. Postnatal cochlear development is severely impaired in mice Igf1-/-, which develop smaller cochlea and cochlear ganglia, an immature tectorial membrane and they display a significant decrease in the number and size of auditory neurons. We used microarrays to define the genetic signatures of Igf-1 +/+ and Igf-1-/- mouse cochea and identify the differentially expressed genes. Keywords: comparison between WT and KO
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Project description:Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.
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Project description:Objective: This study evaluated the bibliometric data of the most common publication types of the neuroimaging literature. Methods: PubMed was searched to identify all published papers with "neuroimaging" as their MeSH Major Topics, and they were further searched by the following publication types: case report, clinical trial, comparative study, editorial, evaluation study, guideline, meta-analysis, multicenter study, randomized controlled trial, review, technical report, and validation study. The proportion of papers belonging to each publication type published in neuroimaging journals was calculated. Year-adjusted mean citation counts for each publication type were computed using data from Web of Science. Publication trend and its correlation with citation performance were assessed. Results: Review and comparative study were the most common publication types. Publication types with the highest proportion in neuroimaging journals were guideline, validation study, and technical reports. Since the year 2000, multicenter study, review, and meta-analysis showed the strongest linear increase in annual publication count. These publication types also had the highest year-adjusted citation counts (4.7-10.0). Publication types with the lowest year-adjusted citation counts were editorial and case report (0.5-1.0). It was estimated that 12.5% of the publications labeled as case reports were incorrectly labeled. Conclusions: Neuroimaging literature has been expanding with papers of higher levels of evidence, such as meta-analyses, multicenter studies, and randomized controlled trials.
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Project description:Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources of information. Their simple combination usually does not provide an improvement if compared with using the best source alone. In this paper, we proposed a framework based on a recent multiple kernel learning algorithm called EasyMKL and we investigated the benefits of this approach for diagnosing two different mental health diseases. The well known Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset tackling the Alzheimer Disease (AD) patients versus healthy controls classification task, and a second dataset tackling the task of classifying an heterogeneous group of depressed patients versus healthy controls. We used EasyMKL to combine a huge amount of basic kernels alongside a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our results show that the proposed approach, called EasyMKLFS, outperforms baselines (e.g. SVM and SimpleMKL), state-of-the-art random forests (RF) and feature selection (FS) methods.
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