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Lauren A. Gross;
Eileen M. Moore;
Jeffrey R. Wozniak;
Claire Coles;
Julie A Kable;
Elizabeth R. Sowell;
Kenneth L. Jones;
Edward P. Riley;
Sarah N. Mattson
Prenatal alcohol exposure can impact both brain development and neurobehavioral function, including verbal learning and recall, although the relation between verbal recall and brain structure in this population has not been examined fully. We aimed to determine the structural neural correlates of verbal learning and recall in youth with histories of heavy prenatal alcohol exposure using a region of interest (ROI) approach. As part of an ongoing multisite project, subjects (age 10–16 years) with prenatal alcohol exposure (AE, n = 81) and controls (CON, n = 81) were tested using the CVLT-C and measures of cortical volume, surface area, and thickness as well as hippocampal volume were derived from MRI. Group differences in brain and memory indices were tested with ANOVA. Multiple regression analyses tested whether brain ROIs significantly predicted memory performance. The AE group had lower scores than the CON group on all CVLT-C variables (ps ≤ .001) and volume and surface area (ps < .025), although results varied by ROI. No group differences in cortical thickness were found. The relations between cortical structure and memory performance differed between group among some ROIs, particularly those in the frontal cortex, generally with smaller surface area and/or thinner cortex predicting better performance in CON but worse performance in AE. Cortical surface area appears to be the most sensitive index to the effects of prenatal alcohol exposure, while cortical thickness appears to be the least sensitive. These findings also indicate that the neural correlates of verbal memory are altered in youth with heavy prenatal alcohol exposure compared to controls.
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Yundi Shi;
Francois Budin;
Eva Yapuncich;
Ashley Rumple;
Jeffrey T. Young;
Christa Payne;
Xiaodong Zhang;
Xiaoping Hu;
Jodi Godfrey;
Brittany Howell;
Maria Sanchez;
Martin A. Styner
Computational anatomical atlases have shown to be of immense value in neuroimaging as they provide age appropriate reference spaces alongside ancillary anatomical information for automated analysis such as subcortical structural definitions, cortical parcellations or white fiber tract regions. Standard workflows in neuroimaging necessitate such atlases to be appropriately selected for the subject population of interest. This is especially of importance in early postnatal brain development, where rapid changes in brain shape and appearance render neuroimaging workflows sensitive to the appropriate atlas choice. We present here a set of novel computation atlases for structural MRI and Diffusion Tensor Imaging as crucial resource for the analysis of MRI data from non-human primate rhesus monkey (Macaca mulatta) data in early postnatal brain development. Forty socially-housed infant macaques were scanned longitudinally at ages 2 weeks, 3, 6, and 12 months in order to create cross-sectional structural and DTI atlases via unbiased atlas building at each of these ages. Probabilistic spatial prior definitions for the major tissue classes were trained on each atlas with expert manual segmentations. In this article we present the development and use of these atlases with publicly available tools, as well as the atlases themselves, which are publicly disseminated to the scientific community.
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Karsten Tabelow;
Evelyne Balteau;
John Ashburner;
Martina F. Callaghan;
Bogdan Draganski;
Gunther Helms;
Ferath Kherif;
Tobias Leutritz;
Antoine Lutti;
Christophe Phillips;
Enrico Reimer;
Lars Ruthotto;
Maryam Seif;
Nikolaus Weiskopf;
Gabriel Ziegler;
Siawoosh Mohammadi
Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R 1 and R 2⋆ , proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.
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Tyrone D. Cannon;
Frank Sun;
Sarah Jacobson McEwen;
Xenophon Papademetris;
George He;
Theo G. M. van Erp;
Aron Jacobson;
Carrie E. Bearden;
Elaine Walker;
Xiaoping Hu;
Lei Zhou;
Larry J. Seidman;
Heidi W. Thermenos;
Barbara Cornblatt;
Doreen M. Olvet;
Diana Perkins;
Aysenil Belger;
Kristin Cadenhead;
Ming Tsuang;
Heline Mirzakhanian;
Jean Addington;
Richard Frayne;
Scott W. Woods;
Thomas H. McGlashan;
R. Todd Constable;
Maolin Qiu;
Daniel H. Mathalon;
Paul Thompson;
Arthur W. Toga
Multisite longitudinal neuroimaging designs are used to identify differential brain structural change associated with onset or progression of disease. The reliability of neuroanatomical measurements over time and across sites is a crucial aspect of power in such studies. Prior work has found that while within-site reliabilities of neuroanatomical measurements are excellent, between-site reliability is generally more modest. Factors that may increase between-site reliability include standardization of scanner platform and sequence parameters and correction for between-scanner variations in gradient nonlinearities. Factors that may improve both between- and within-site reliability include use of registration algorithms that account for individual differences in cortical patterning and shape. In this study 8 healthy volunteers were scanned twice on successive days at 8 sites participating in the North American Prodrome Longitudinal Study (NAPLS). All sites employed 3 Tesla scanners and standardized acquisition parameters. Site accounted for 2 to 30% of the total variance in neuroanatomical measurements. However, site-related variations were trivial (<1%) among sites using the same scanner model and 12-channel coil or when correcting for between-scanner differences in gradient nonlinearity and scaling. Adjusting for individual differences in sulcal-gyral geometries yielded measurements with greater reliabilities than those obtained using an automated approach. Neuroimaging can be performed across multiple sites at the same level of reliability as at a single site, achieving within- and between-site reliabilities of 0.95 or greater for gray matter density in the majority of voxels in the prefrontal and temporal cortical surfaces as well as for the volumes of most subcortical structures.
Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders.