Author name cluster

Jody Tanabe

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

YNIMG Journal 2019 Journal Article

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

Donald J. Hagler
SeanN. Hatton
M. Daniela Cornejo
Carolina Makowski
Damien A. Fair
Anthony Steven Dick
Matthew T. Sutherland
B.J. Casey

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Details DOI

YNIMG Journal 2012 Journal Article

Nicotine increases brain functional network efficiency

Korey P. Wylie
Donald C. Rojas
Jody Tanabe
Laura F. Martin
Jason R. Tregellas

Despite the use of cholinergic therapies in Alzheimer's disease and the development of cholinergic strategies for schizophrenia, relatively little is known about how the system modulates the connectivity and structure of large-scale brain networks. To better understand how nicotinic cholinergic systems alter these networks, this study examined the effects of nicotine on measures of whole-brain network communication efficiency. Resting state fMRI was acquired from fifteen healthy subjects before and after the application of nicotine or placebo transdermal patches in a single blind, crossover design. Data, which were previously examined for default network activity, were analyzed with network topology techniques to measure changes in the communication efficiency of whole-brain networks. Nicotine significantly increased local efficiency, a parameter that estimates the network's tolerance to local errors in communication. Nicotine also significantly enhanced the regional efficiency of limbic and paralimbic areas of the brain, areas which are especially altered in diseases such as Alzheimer's disease and schizophrenia. These changes in network topology may be one mechanism by which cholinergic therapies improve brain function.

Details DOI

YNIMG Journal 2006 Journal Article

Bilateral spatial filtering: Refining methods for localizing brain activation in the presence of parenchymal abnormalities

Scott A. Walker
David Miller
Jody Tanabe

Functional MRI (fMRI) is an important tool for pre-surgical localization of eloquent cortex prior to resection of brain lesions. To increase the inherently low activation signal to noise ratio, fMRI pre-processing steps often include spatial smoothing. However, the effects of smoothing in the presence of brain lesions have not been studied. We have adapted the widely used method of Gaussian spatial filtering to include an “edge stopping” function. This method, termed bilateral filtering, minimizes blurring of apparent brain activity across anatomic boundaries and into regions of non-activation. fMRI data were acquired in a patient with a known low grade glioma during a blocked finger-tapping paradigm. Simulated activity was superimposed on baseline images of non-activated brain using the same paradigm, with additive signal equal to 1, 3, and 5% of the mean physiologic background. Comparison of Gaussian and bilateral filtering suggests that the modified technique more accurately locates brain activation and increases the significance of activation bordering sharp transitions. Thus, spatial pre-processing with a bilateral filter may be particularly useful in the pre-operative assessment of brain lesions.

Details DOI

YNIMG Journal 2002 Journal Article

Brain Activation during Smooth-Pursuit Eye Movements

Jody Tanabe
Jason Tregellas
David Miller
Randal G. Ross
Robert Freedman

A potential application of studying eye movements with functional MRI (fMRI) is to examine patient populations with known eye movement dysfunction, but the reliability with which normal subjects demonstrate activity in specific brain regions has not been established. To date, fMRI studies of smooth-pursuit eye movements have used relatively small numbers of subjects and have been restricted to fixed-effects analyses. We extend these studies to whole brain imaging at 1. 5 T, properly accounting for intersubject variation using random effects analysis. Smooth-pursuit eye movements elicited activation consistently in dorsal cortical eye fields and cerebellum. Subcortical activation was greatly attenuated, but not eliminated, with the random-effects second-level analysis. In addition, session-dependent changes in activation were greater in some regions than others and may indicate areas of brain, such as the supplementary eye fields, that are sensitive to attentional modulation of eye movements.

Details DOI

YNIMG Journal 2002 Journal Article

Comparison of Detrending Methods for Optimal fMRI Preprocessing

Jody Tanabe
David Miller
Jason Tregellas
Robert Freedman
Francois G. Meyer

Because of the inherently low signal to noise ratio (SNR) of fMRI data, removal of low frequency signal intensity drift is an important preprocessing step, particularly in those brain regions that weakly activate. Two known sources of drift are noise from the MR scanner and aliasing of physiological pulsations. However, the amount and direction of drift is difficult to predict, even between neighboring voxels. Further, there is no concensus on an optimal baseline drift removal algorithm. In this paper, five voxel-based detrending techniques were compared to each other and an auto-detrending algorithm, which automatically selected the optimal method for a given voxel time-series. For a significance level of P < 10−6, linear and quadratic detrending moderately increased the percentage of activated voxels. Cubic detrending decreased activation, while a wavelet approach increased or decreased activation, depending on the dataset. Spline detrending was the best single algorithm. However, auto-detrending (selecting the best algorithm or none, if detrending is not useful) appears to be the most judicious choice, particularly for analyzing fMRI data with weak activations in the presence of baseline drift.

Details DOI