scholarly journals Relationships between Human Brain Structural Connectomes and Traits

2018 ◽  
Author(s):  
Zhengwu Zhang ◽  
Genevera I. Allen ◽  
Hongtu Zhu ◽  
David Dunson
Keyword(s):  
Dimensionality Reduction ◽  
Fluid Intelligence ◽  
Imaging Techniques ◽  
Brain Connectivity ◽  
Image Resolution ◽  
Data Set ◽  
Human Connectome Project ◽  
Dimensionality Reduction Technique ◽  
Components Analysis

AbstractAdvanced brain imaging techniques make it possible to measure individuals’ structural connectomes in large cohort studies non-invasively. However, due to limitations in image resolution and pre-processing, questions remain about whether reconstructed connectomes are measured accurately enough to detect relationships with human traits and behaviors. Using a state-of-the-art structural connectome processing pipeline and a novel dimensionality reduction technique applied to data from the Human Connectome Project (HCP), we show strong relationships between connectome structure and various human traits. Our dimensionality reduction approach uses a tensor characterization of the connectomes and relies on a generalization of principal components analysis. We analyze over 1100 scans for 1076 subjects from the HCP and the Sherbrooke test-retest data set as well as 175 human traits that measure domains including cognition, substance use, motor, sensory and emotion. We find that brain connectomes are associated with many traits. Specifically, fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity.

scholarly journals Magnetoencephalography in the Detection and Characterization of Brain Abnormalities Associated with Traumatic Brain Injury: A Comprehensive Review

2021 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Geoffrey W. Peitz ◽  
Elisabeth A. Wilde ◽  
Ramesh Grandhi
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Imaging Techniques ◽  
Brain Connectivity ◽  
Functional Brain Imaging ◽  
Original Research ◽  
High Temporal Resolution ◽  
Post Traumatic Stress ◽  
Clinical Role

Magnetoencephalography (MEG) is a functional brain imaging technique with high temporal resolution compared with techniques that rely on metabolic coupling. MEG has an important role in traumatic brain injury (TBI) research, especially in mild TBI, which may not have detectable features in conventional, anatomical imaging techniques. This review addresses the original research articles to date that have reported on the use of MEG in TBI. Specifically, the included studies have demonstrated the utility of MEG in the detection of TBI, characterization of brain connectivity abnormalities associated with TBI, correlation of brain signals with post-concussive symptoms, differentiation of TBI from post-traumatic stress disorder, and monitoring the response to TBI treatments. Although presently the utility of MEG is mostly limited to research in TBI, a clinical role for MEG in TBI may become evident with further investigation.

scholarly journals Distributed Patterns of Functional Connectivity Predict Working Memory Performance in Novel Healthy and Memory-impaired Individuals

2020 ◽  
Vol 32 (2) ◽  
pp. 241-255 ◽  
Author(s):  
Emily W. Avery ◽  
Kwangsun Yoo ◽  
Monica D. Rosenberg ◽  
Abigail S. Greene ◽  
Siyuan Gao ◽  
...  
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Sustained Attention ◽  
Fluid Intelligence ◽  
Memory Performance ◽  
Data Set ◽  
Human Connectome Project ◽  
Connectivity Patterns ◽  
Intelligence Models

Individual differences in working memory relate to performance differences in general cognitive ability. The neural bases of such individual differences, however, remain poorly understood. Here, using a data-driven technique known as connectome-based predictive modeling, we built models to predict individual working memory performance from whole-brain functional connectivity patterns. Using n-back or rest data from the Human Connectome Project, connectome-based predictive models significantly predicted novel individuals' 2-back accuracy. Model predictions also correlated with measures of fluid intelligence and, with less strength, sustained attention. Separate fluid intelligence models predicted working memory score, as did sustained attention models, again with less strength. Anatomical feature analysis revealed significant overlap between working memory and fluid intelligence models, particularly in utilization of prefrontal and parietal regions, and less overlap in predictive features between working memory and sustained attention models. Furthermore, showing the generality of these models, the working memory model developed from Human Connectome Project data generalized to predict memory in an independent data set of 157 older adults (mean age = 69 years; 48 healthy, 54 amnestic mild cognitive impairment, 55 Alzheimer disease). The present results demonstrate that distributed functional connectivity patterns predict individual variation in working memory capability across the adult life span, correlating with constructs including fluid intelligence and sustained attention.

MORPHOMETRICAL CHARACTERIZATION OF CYTISUS AND ALLIES (GENISTEAE: LEGUMINOSAE) AS AN AID IN TAXONOMIC DISCRIMINATION

1996 ◽  
Vol 44 (2-3) ◽  
pp. 95-114 ◽  
Author(s):  
Fernando González-Andrés ◽  
Jesús-María Ortiz
Keyword(s):  
Multivariate Analyses ◽  
Intermediate Position ◽  
Adult Plant ◽  
Data Sets ◽  
Data Set ◽  
Operational Taxonomic Units ◽  
Biochemical Studies ◽  
Seed Morphometry ◽  
Components Analysis

Twenty-four accessions belonging to the genus Cytisus and allied taxa were characterized by adult plant morphometry. Twenty-six characters were measured in flowers, 9 in leaves, and 5 in fruits. Two data sets were prepared, the first including only floral parameters and the second with all the parameters. Two different multivariate analyses were carried out for every data set: cluster analysis and principal components analysis. All these studies produced a similar grouping of the operational taxonomic units. Four clear groups were defined: (i) Cytisophyllum sessilifolium; (ii) Cytisus baeticus, C. reverchonii, C. scoparius., (iii) Chamaecytisus species; (iv) Genista species. On the other hand, Cytisus villosus showed an intermediate position between Cytisus and Chamaecytisus, and Cytisus heterochrous and C. purgans an intermediate position between Cytisus and Genista. This grouping agrees with that obtained by other recent seed morphometry and biochemical studies, and supports the generic arrangement presented by Bisby (1981).

scholarly journals Dimensionality Reduction on Cloud Images Based on Various Climate Zones

2019 ◽  
Vol 8 (2) ◽  
pp. 3288-3292
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Weather Forecasting ◽  
Image Data ◽  
Test Case ◽  
Training Set ◽  
Data Set ◽  
Climate Zones ◽  
Rainfall Prediction ◽  
Components Analysis

In recent decades, cloud image classification has become a research hotspot in the field of weather forecasting. Initially, cloud images that fall on various climate zones are categorized based on their regions. Dimensionality reduction is performed in the cloud images by applying Principal Components Analysis (PCA) to enhance the classification accuracy of cloud images. The proposed system uses the training set to learn the features of cloud images and classifies the test case images into low, medium and high. The experimental results are obtained by implementing the INSAT weather image data set using MATLAB tool. The proposed methodology can be used in various applications like Rainfall Prediction, Oceanography and Cyclone Forecasting.

Principal Components Analysis Competitive Learning

2004 ◽  
Vol 16 (11) ◽  
pp. 2459-2481 ◽  
Author(s):  
Ezequiel López-Rubio ◽  
Juan Miguel Ortiz-de-Lazcano-Lobato ◽  
José Muñoz-Pérez ◽  
José Antonio Gómez-Ruiz
Keyword(s):  
Dimensionality Reduction ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Neural Model ◽  
Competitive Learning ◽  
Experimental Results ◽  
Data Set ◽  
Input Distribution ◽  
Local Pca ◽  
Components Analysis

We present a new neural model that extends the classical competitive learning by performing a principal components analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA methods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution while retaining the dimensionality-reduction properties of the PCA. Furthermore, every neuron is able to modify its behavior to adapt to the local dimensionality of the input distribution. Hence, our model has a dimensionality estimation capability. The experimental results we present show the dimensionality-reduction capabilities of the model with multisensor images.

Quantitative nano-characterization of heterogeneous catalysts

1995 ◽  
Vol 53 ◽  
pp. 398-399
Author(s):  
P.A. Crozier ◽  
M. Pan
Keyword(s):  
Heterogeneous Catalysts ◽  
Low Dose ◽  
Imaging Techniques ◽  
Structural Features ◽  
Catalyst Characterization ◽  
New Developments ◽  
Double Phase ◽  
Phase Systems ◽  
Dose Imaging

Heterogeneous catalysts can be of varying complexity ranging from single or double phase systems to complicated mixtures of metals and oxides with additives to help promote chemical reactions, extend the life of the catalysts, prevent poisoning etc. Although catalysis occurs on the surface of most systems, detailed descriptions of the microstructure and chemistry of catalysts can be helpful for developing an understanding of the mechanism by which a catalyst facilitates a reaction. Recent years have seen continued development and improvement of various TEM, STEM and AEM techniques for yielding information on the structure and chemistry of catalysts on the nanometer scale. Here we review some quantitative approaches to catalyst characterization that have resulted from new developments in instrumentation.HREM has been used to examine structural features of catalysts often by employing profile imaging techniques to study atomic details on the surface. Digital recording techniques employing slow-scan CCD cameras have facilitated the use of low-dose imaging in zeolite structure analysis and electron crystallography. Fig. la shows a low-dose image from SSZ-33 zeolite revealing the presence of a stacking fault.

Coherent electron nanodiffraction from clean silver nano particles in a UHV STEM

1993 ◽  
Vol 51 ◽  
pp. 1058-1059
Author(s):  
J. Liu ◽  
M. Pan ◽  
G. E. Spinnler
Keyword(s):  
Supported Catalysts ◽  
Imaging Techniques ◽  
Nano Particles ◽  
Metal Particles ◽  
Shape Structure ◽  
Dynamical Simulations ◽  
Small Metal Particles ◽  
Extract Information

Small metal particles have peculiar chemical and physical properties as compared to bulk materials. They are especially important in catalysis since metal particles are common constituents of supported catalysts. The structural characterization of small particles is of primary importance for the understanding of structure-catalytic activity relationships. The shape and size of metal particles larger than approximately 5 nm in diameter can be determined by several imaging techniques. It is difficult, however, to deduce the shape of smaller metal particles. Coherent electron nanodiffraction (CEND) patterns from nano particles contain information about the particle size, shape, structure and defects etc. As part of an on-going program of STEM characterization of supported catalysts we report some preliminary results of CEND study of Ag nano particles, deposited in situ in a UHV STEM instrument, and compare the experimental results with full dynamical simulations in order to extract information about the shape of Ag nano particles.

Why Are Children with Cochlear Implants at Risk for Executive Functioning Delays?

2020 ◽  
pp. 247-267
Author(s):  
William G. Kronenberger ◽  
David B. Pisoni
Keyword(s):  
Executive Functioning ◽  
Cochlear Implants ◽  
Family Environment ◽  
Fluid Intelligence ◽  
Deaf Children ◽  
Data Set ◽  
Theoretical Approaches ◽  
Model Research ◽  
Psychosocial Influences ◽  
Auditory Experience

Prelingually deaf children with cochlear implants (CIs) have about 2 to 5 times more risk for delays in specific domains of executive functioning (EF) than normal-hearing (NH) children, with about 25% to 40% of children with CIs showing delays in specific EF subdomains. This chapter reviews the rationale and evidence for two theoretical approaches to explaining this elevated risk for EF delay: language-focused approaches and biopsychosocial systems theories, such as the auditory neurocognitive model. Research supporting language-focused approaches, which attribute risk of EF delays entirely to language delays, has significant limitations. Furthermore, results from an extensive data set of EF outcomes in CI users are inconsistent with language-focused approaches. In contrast, biopsychosocial systems theories, which attribute risk for EF delay to a system of factors, including auditory experience, language, family environment/experiences, fluid intelligence, and psychosocial influences, provide the strongest evidence and potential for explaining EF delays and outcomes in children with CIs.

scholarly journals Current Trends in Non-Invasive Imaging of Interactions in the Liver Tumor Microenvironment Mediated by Tumor Metabolism

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3645
Author(s):  
Isabel Theresa Schobert ◽  
Lynn Jeanette Savic
Keyword(s):  
Tumor Microenvironment ◽  
Liver Cancer ◽  
Cancer Cells ◽  
Imaging Techniques ◽  
Treatment Strategies ◽  
Tumor Metabolism ◽  
Resistance Mechanisms ◽  
Metabolic Reprogramming ◽  
Non Invasive

With the increasing understanding of resistance mechanisms mediated by the metabolic reprogramming in cancer cells, there is a growing clinical interest in imaging technologies that allow for the non-invasive characterization of tumor metabolism and the interactions of cancer cells with the tumor microenvironment (TME) mediated through tumor metabolism. Specifically, tumor glycolysis and subsequent tissue acidosis in the realms of the Warburg effect may promote an immunosuppressive TME, causing a substantial barrier to the clinical efficacy of numerous immuno-oncologic treatments. Thus, imaging the varying individual compositions of the TME may provide a more accurate characterization of the individual tumor. This approach can help to identify the most suitable therapy for each individual patient and design new targeted treatment strategies that disable resistance mechanisms in liver cancer. This review article focuses on non-invasive positron-emission tomography (PET)- and MR-based imaging techniques that aim to visualize the crosstalk between tumor cells and their microenvironment in liver cancer mediated by tumor metabolism.

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