scholarly journals Multiclass Sparse Bayesian Regression for fMRI-Based Prediction

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Vincent Michel ◽  
Evelyn Eger ◽  
Christine Keribin ◽  
Bertrand Thirion
Keyword(s):  
Brain Regions ◽  
Bayesian Regression ◽  
Data Sets ◽  
Brain Images ◽  
Popular Approach ◽  
Prediction Function ◽  
Neuroimaging Data ◽  
Behavioral Parameters ◽  
The Brain ◽  
Better Than

Inverse inferencehas recently become a popular approach for analyzing neuroimaging data, by quantifying the amount of information contained in brain images on perceptual, cognitive, and behavioral parameters. As it outlines brain regions that convey information for an accurate prediction of the parameter of interest, it allows to understand how the corresponding information is encoded in the brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, as there are far more features (voxels) than samples (images), and dimension reduction is thus a mandatory step. We introduce in this paper a new model, calledMulticlass Sparse Bayesian Regression(MCBR), that, unlike classical alternatives, automatically adapts the amount of regularization to the available data. MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient regularization. We detail these framework and validate our algorithm on simulated and real neuroimaging data sets, showing that it performs better than reference methods while yielding interpretable clusters of features.

Structural magnetic resonance imaging markers of susceptibility and transition to schizophrenia: A review of familial and clinical high risk population studies

2014 ◽  
Vol 29 (2) ◽  
pp. 144-154 ◽  
Author(s):  
C Bois ◽  
HC Whalley ◽  
AM McIntosh ◽  
SM Lawrie
Keyword(s):  
High Risk ◽  
Grey Matter ◽  
Brain Regions ◽  
High Risk Population ◽  
Clinical High Risk ◽  
Cross Sectional ◽  
Structural Abnormalities ◽  
Neuroimaging Data ◽  
Imaging Markers ◽  
The Brain

There is a growing consensus that a symptomatology as complex and heterogeneous as schizophrenia is likely to be produced by widespread perturbations of brain structure, as opposed to isolated deficits in specific brain regions. Structural brain-imaging studies have shown that several features of the brain, such as grey matter, white matter integrity and the morphology of the cortex differ in individuals at high risk of the disorder compared to controls, but to a lesser extent than in patients, suggesting that structural abnormalities may form markers of vulnerability to the disorder. Research has had some success in delineating abnormalities specific to those individuals that transition to psychosis, compared to those at high risk that do not, suggesting that a general risk for the disorder can be distinguished from alterations specific to frank psychosis. In this paper, we review cross-sectional and longitudinal studies of individuals at familial or clinical high risk of the disorder. We conclude that the search for reliable markers of schizophrenia is likely to be enhanced by methods which amalgamate structural neuroimaging data into a coherent framework that takes into account the widespread distribution of brain alterations, and relates this to leading hypotheses of schizophrenia.

scholarly journals 7,8-dihydroxyflavone Improves Neuropathological Changes in the Brain of Tg26 Mice, A Model for HIV-associated Neurocognitive Disorder

2020 ◽  
Author(s):  
Joseph Bryant ◽  
Sanketh Andhavarapu ◽  
Christopher Bever ◽  
Poornachander Guda ◽  
Akhil Katuri ◽  
...  
Keyword(s):  
Therapeutic Potential ◽  
Brain Regions ◽  
Mitochondrial Damage ◽  
Data Sets ◽  
Neurocognitive Disorder ◽  
Hiv Patients ◽  
Neuroprotective Effects ◽  
Downstream Signaling ◽  
Hiv Associated Neurocognitive Disorder ◽  
The Brain

Abstract Background: The combined antiretroviral therapy (cART) era has significantly increased the lifespan of HIV patients, turning a fatal disease to a chronic one. However, this lower but persistent level of HIV infection increases the susceptibility of HIV-associated neurocognitive disorder (HAND). Therefore, research is currently seeking improved treatment for this complication of HIV. In HIV+ patients, low levels of brain derived neurotrophic factor (BDNF) has been associated with worse neurocognitive impairment. Hence, BDNF administration has been gaining relevance as a possible adjunct therapy for HAND. However, systemic administration of BDNF is impractical because of poor pharmacological profile.Methods: We investigated the neuroprotective effects of BDNF-mimicking 7,8 dihydroxyflavone (DHF), a bioactive high-affinity TrkB agonist, in the memory-involved hippocampus and brain cortex of Tg26 mice, a murine model for HAND. We immunohistochemically stained brain tissue sections from vehicle-treated wild type (WT), vehicle-treated Tg26, and DHF (5 mg/kg, i.p)-treated Tg26 mice to examine activation of TrkB and downstream signaling, expression of HIV-1 chemokine co-receptors CXCR4 and CCR5, neuroinflammation, and mitochondrial damage. A one-way ANOVA with a Bonferroni Comparison post-hoc test was performed to analyze the data sets. Results: In the brain regions of Tg26 mice, we observed astrogliosis, increased CXCR4 and CCR5 expression, neuroinflammation, and mitochondrial damage. Hippocampi and cortices of DHF treated mice exhibited a reversal of these pathological changes, suggesting the therapeutic potential of DHF in HAND. Our data indicates that DHF increases the phosphorylation of TrkB, providing new insights about the role of the TrkB-Akt-NFkB signaling pathway in mediating these pathological hallmarks.Conclusions: Our study provides an overview of how targeting BDNF-TrkB signaling in the pathophysiology of HAND may be relevant for future therapies, and sheds light on 7,8 Dihydroxyflavone as a potential adjunct therapeutic agent to current antiviral therapy.

scholarly journals Same Brain, Different Look?—The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

2021 ◽  
Vol 10 (21) ◽  
pp. 4987
Author(s):  
Ronja Thieleking ◽  
Rui Zhang ◽  
Maria Paerisch ◽  
Kerstin Wirkner ◽  
Alfred Anwander ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Diffusion Imaging ◽  
Mean Diffusivity ◽  
Brain Regions ◽  
Clinical Diagnostics ◽  
Neuroimaging Data ◽  
Age Range ◽  
The Impact ◽  
The Brain ◽  
Healthy Participants

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

Visualization of Hidden Structure and Shape in Ct Image via Non-Linear Perspective Foreground and Back Ground Projection

2021 ◽  
Author(s):  
parthee pan ◽  
Raja Paul Perinbam ◽  
Krishna Murthy ◽  
Shanker Rajendiran Nagalingam ◽  
krishna kumari s ◽  
...  
Keyword(s):  
Occipital Lobe ◽  
Brain Regions ◽  
Medical Image Segmentation ◽  
Linear Perspective ◽  
Brain Images ◽  
Non Linear ◽  
Back Ground ◽  
Percent Accuracy ◽  
The Brain ◽  
Ct And Mri

Abstract The neurologist analyse the brain images to diagnose the disease via structure and shape of the part in the scanned Medical images such as CT, MRI, and PET.The Medical image segmentation perform less in the regions where no or little contrast,artefacts over the different boundary regions. The manual process of segmentation show poor boundary differentiation dueto discernibility in shape and location, intra and inter observer reliability. In this paper, we propose a dyadic Cat optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non- linear perspective Foreground and Back Ground projection. The DCO algorithm remove the artefacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm show the region boundary such as plerygomaxillary fissure, occipital lobe, vaginal process zygomatic arch, maxilla and piriform aperture with high visibility in the regions of inadequately visible boundary and distinguish the deformable shape. The DCO algorithm show the increased SSIM and 90 percent accuracy.

Visualization of Pterygomaxillary Fissure Structure and Shape in CT Image via Non-Linear Perspective Projection

2021 ◽  
Vol 11 (6) ◽  
pp. 1580-1589
Author(s):  
R. Partheepan ◽  
J. Raja Paul Perinbam ◽  
M. Krishnamurthy ◽  
N. R. Shanker
Keyword(s):  
Similarity Index ◽  
Occipital Lobe ◽  
Structural Similarity ◽  
Brain Regions ◽  
Medical Image Segmentation ◽  
Linear Perspective ◽  
Brain Images ◽  
Non Linear ◽  
Back Ground ◽  
The Brain

The neurologist analyses the brain images to diagnose disease via structure and shape of the part in scanned Medical images such as CT, MRI, and PET. The Medical image segmentation performs less in the regions where no or little contrast, artifacts over the different boundary regions. The manual process of segmentation shows poor boundary differentiation due to discernibility in shape and location, intra and inter observer reliability. In this paper, we propose dyadic CAT optimization (DCO) algorithm to segment the regions in the brain from CT and MRI image via Non-linear perspective Foreground and Back Ground projection. The DCO algorithm removes the artifacts in the boundary regions and provide the exact structure and shape of the brain regions. The DCO algorithm shows the region boundary for pterygomaxillary fissure, occipital lobe, vaginal process zygomatic arch, maxilla and piriform aperture in brain image with high visibility in the regions of inadequately visible boundary and distinguishes the deformable shape. The DCO algorithm applies on 50 images and eight images with complex bone and muscle mass structure for performance evaluation. The DCO algorithm shows the increased Structural similarity index (SSIM) with 90% accuracy.

scholarly journals The Potential of Using Brain Images for Authentication

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Fanglin Chen ◽  
Zongtan Zhou ◽  
Hui Shen ◽  
Dewen Hu
Keyword(s):  
Gray Matter ◽  
Recognition System ◽  
Identity Authentication ◽  
Data Sets ◽  
Brain Images ◽  
Automated Recognition ◽  
Accuracy Requirement ◽  
Potential Possibility ◽  
Two Stages ◽  
The Brain

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition.

scholarly journals Communicability distance reveals hidden patterns of Alzheimer disease

2020 ◽  
Author(s):  
Eufemia Lella ◽  
Ernesto Estrada
Keyword(s):  
Alzheimer Disease ◽  
Path Length ◽  
Brain Regions ◽  
Brain Network ◽  
Efficient System ◽  
Brain Hemispheres ◽  
Disconnection Syndrome ◽  
Path Lengths ◽  
The Brain ◽  
Better Than

AbstractThe communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer disease. Using this distance we obtain the shortest communicability path lengths between different regions of brain networks from Alzheimer diseased (AD) patients and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only and 20% affects vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of Alzheimer disease. Finally, we show that in 76.9% damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions.

T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance

2017 ◽  
Author(s):  
John D Lewis ◽  
Alan C Evans ◽  
Jussi Tohka
Keyword(s):  
Brain Development ◽  
Cognitive Performance ◽  
Gray Matter ◽  
Brain Regions ◽  
Brain Maturation ◽  
Chronological Age ◽  
Brain Images ◽  
Cortical Gray Matter ◽  
The Brain ◽  
Age Prediction

The maturational schedule of human brain development appears to be narrowly confined. The chronological age of an individual can be predicted from brain images with considerable accuracy, and deviation from the typical pattern of brain maturation has been related to cognitive performance. Methods using multi-modal data, or complex measures derived from voxels throughout the brain have shown the greatest accuracy, but are difficult to interpret in terms of the biology. Measures based on the cortical surface(s) have yielded less accurate predictions, suggesting that perhaps developmental changes related to cortical gray matter are not strongly related to chronological age, and that perhaps development is more strongly related to changes in subcortical regions or in deep white matter. We show that a simple metric based on the white/gray contrast at the inner border of the cortical gray-matter is a comparably good predictor of chronological age, and our usage of an elastic net penalized linear regression model reveals the brain regions which contribute most to age-prediction. We demonstrate this in two large datasets: the NIH Pediatric Data, with 832 scans of typically developing children, adolescents, and young adults; and the Pediatric Imaging, Neurocognition, and Genetics data, with 760 scans of individuals in a similar age-range. Moreover, we show that the residuals of age-prediction based on this white/gray contrast metric are more strongly related to IQ than are those from cortical thickness, suggesting that this metric is more sensitive to aspects of brain development that reflect cognitive performance.

scholarly journals Genetic, Cellular, and Connectomic Characterization of the Adult Human Brain Regions Commonly Plagued by Glioma

2020 ◽  
Author(s):  
Ayan S. Mandal ◽  
Rafael Romero-Garcia ◽  
Michael G. Hart ◽  
John Suckling
Keyword(s):  
Brain Regions ◽  
Association Cortex ◽  
Low Grade ◽  
Expression Of Genes ◽  
Neuroimaging Data ◽  
Frequency Map ◽  
Multiple Graph ◽  
Anatomic Localization ◽  
The Brain

AbstractA better understanding of the nonrandom localization patterns of gliomas across the brain could lend clues to the origins of these types of tumors. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumor frequency map. Using this map, we demonstrated that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with stem-like cells also exhibited a high glioma frequency. Furthermore, gliomas were localized to brain regions enriched with the expression of genes associated with chromatin organization and synaptic signaling. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of oncogenesis.

scholarly journals Feasibility of FreeSurfer processing for T1-weighted brain images of 5-year-olds: semiautomated protocol of FinnBrain Neuroimaging Lab

2021 ◽  
Author(s):  
Elmo P Pulli ◽  
Eero Silver ◽  
Venla Kumpulainen ◽  
Anni Copeland ◽  
Harri Merisaari ◽  
...  
Keyword(s):  
Quality Control ◽  
Rating Scale ◽  
Region Of Interest ◽  
Birth Cohort Study ◽  
Data Sets ◽  
Similar Data ◽  
Brain Images ◽  
Neuroimaging Data ◽  
Interest Level ◽  
Pediatric Neuroimaging

Pediatric neuroimaging is a quickly developing field that still faces important methodological challenges. One key challenge is the use of many different atlases, automated segmentation tools, manual edits in semiautomated protocols, and quality control protocols, which complicates comparisons between studies. In this article, we present our semiautomated segmentation protocol using FreeSurfer v6.0, ENIGMA consortium software, and the quality control protocol that was used in FinnBrain Birth Cohort Study. We used a dichotomous quality rating scale for inclusion and exclusion of images, and then explored the quality on a region of interest level to exclude all regions with major segmentation errors. The effects of manual edits on cortical thickness values were minor: less than 2% in all regions. Supplementary materials cover registration and additional edit options in FreeSurfer and comparison to the computational anatomy toolbox (CAT12). Overall, we conclude that despite minor imperfections FreeSurfer can be reliably used to segment cortical metrics from T1-weighted images of 5-year-old children with appropriate quality assessment in place. However, custom templates may be needed to optimize the results for the subcortical areas. Our semiautomated segmentation protocol provides high quality pediatric neuroimaging data and could help investigators working with similar data sets.

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