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      The effect of estradiol add-back: a longitudinal MRI study in prostate cancer patients

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          Abstract

          We investigated the effect of estradiol add-back therapy (EAT) on brain activation related to cognitive function and affect in addition to putative changes in gray and white matter volume in testosterone depleted participants with prostate cancer. We conducted a randomized controlled, double-blinded trial in which 40 patients received 0.9 mg of transdermal estradiol per day for 6 months or matched placebo. Anatomical MRI and three functional MRI (fMRI) scans were obtained for the emotion recognition task, verbal memory task, and visuospatial memory task. Activation in corresponding cognitive and affective brain networks was demonstrated for all tasks. Longitudinally, there was no difference in brain activation, reaction time, or accuracy in response to the fMRI tasks between the EAT group and placebo group at 6 months. In addition, there was no detectable change in whole-brain gray or white matter volume or in hippocampal volume between the two groups after 6 months. This study supports earlier findings that EAT does not improve verbal memory or affect and has no immediate effect on hippocampal volume in testosterone depleted patients with prostate cancer.

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          Most cited references45

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          Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

          An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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            A fast diffeomorphic image registration algorithm.

            This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg-Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.
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              A component based noise correction method (CompCor) for BOLD and perfusion based fMRI.

              A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.

                Author and article information

                Journal
                Endocr Connect
                Endocr Connect
                EC
                Endocrine Connections
                Bioscientifica Ltd (Bristol )
                2049-3614
                07 February 2024
                16 January 2024
                01 March 2024
                : 13
                : 3
                : e230409
                Affiliations
                [1 ]Department of Psychiatry , University of Melbourne, Austin Health, Heidelberg, Victoria, Australia
                [2 ]Department of Endocrinology , Austin Health, Heidelberg, Victoria, Australia
                [3 ]Florey Institute of Neuroscience and Mental Health , Parkville, Victoria, Australia
                [4 ]Department of Florey Institute , University of Melbourne, Parkville, Victoria, Australia
                Author notes
                Correspondence should be addressed to O Dandash: orwa.dandash@ 123456unimelb.edu.au
                Author information
                http://orcid.org/0000-0001-8845-7280
                Article
                EC-23-0409
                10.1530/EC-23-0409
                10895318
                38226639
                1124bb99-95f5-4b53-9b2f-beb781cd578c
                © the author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : 01 October 2023
                : 16 January 2024
                Funding
                Funded by: Medical Research Council, doi http://dx.doi.org/10.13039/501100000265;
                Categories
                Research

                prostate cancer,mri,androgen deprivation,estradiol
                prostate cancer, mri, androgen deprivation, estradiol

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