The impact of the Siemens Tim Trio to Prisma upgrade and the addition of volumetric navigators on cortical thickness, structure volume, and 1H-MRS indices: An MRI reliability study with implications for longitudinal study designs

[1]  Rolf Gruetter,et al.  MR spectroscopy of the human brain with enhanced signal intensity at ultrashort echo times on a clinical platform at 3T and 7T , 2009, Magnetic resonance in medicine.

[2]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[3]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[4]  C. Jack,et al.  Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers , 2013, The Lancet Neurology.

[5]  R. Berman,et al.  Longitudinal four-dimensional mapping of subcortical anatomy in human development , 2014, Proceedings of the National Academy of Sciences.

[6]  D. Louis Collins,et al.  An Object-Based Method for Rician Noise Estimation in MR Images , 2009, MICCAI.

[7]  D. Collins,et al.  Performing label‐fusion‐based segmentation using multiple automatically generated templates , 2013, Human brain mapping.

[8]  R. Hoge,et al.  Measurement Variability Following MRI System Upgrade , 2019, Front. Neurol..

[9]  Peter Jezzard,et al.  Advanced processing and simulation of MRS data using the FID appliance (FID‐A)—An open source, MATLAB‐based toolkit , 2017, Magnetic resonance in medicine.

[10]  Esben Thade Petersen,et al.  Improvement in diagnostic quality of structural and angiographic MRI of the brain using motion correction with interleaved, volumetric navigators , 2019, PloS one.

[11]  M. Dylan Tisdall,et al.  Head motion during MRI acquisition reduces gray matter volume and thickness estimates , 2015, NeuroImage.

[12]  Anders M. Dale,et al.  Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.

[13]  Peter Green,et al.  SIMR: an R package for power analysis of generalized linear mixed models by simulation , 2016 .

[14]  Naoto Hayashi,et al.  Effects of the use of multiple scanners and of scanner upgrade in longitudinal voxel‐based morphometry studies , 2013, Journal of magnetic resonance imaging : JMRI.

[15]  R. Gruetter,et al.  In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time , 1999, Magnetic resonance in medicine.

[16]  D. Selkoe Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.

[17]  S. A. Wijtenburg,et al.  In vivo assessment of neurotransmitters and modulators with magnetic resonance spectroscopy: Application to schizophrenia , 2015, Neuroscience & Biobehavioral Reviews.

[18]  Alan C. Evans,et al.  Changes in cortical thickness during the course of illness in schizophrenia. , 2011, Archives of general psychiatry.

[19]  D. Cicchetti Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology. , 1994 .

[20]  Robin A. de Graaf,et al.  In Vivo NMR Spectroscopy , 2019 .

[21]  Christine L. Tardif,et al.  Hippocampal subfield volumes across the healthy lifespan and the effects of MR sequence on estimates , 2020, NeuroImage.

[22]  M. Mallar Chakravarty,et al.  Normative brain size variation and brain shape diversity in humans , 2018, Science.

[23]  Adrian Preda,et al.  Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium , 2018, Biological Psychiatry.

[24]  Hyunwoo Lee,et al.  Estimating and accounting for the effect of MRI scanner changes on longitudinal whole-brain volume change measurements , 2019, NeuroImage.

[25]  Jennifer Fedor,et al.  Cortical and subcortical brain morphometry differences between patients with autism spectrum disorders (ASD) and healthy individuals across the lifespan: results from the ENIGMA-ASD working group , 2017 .

[26]  Bruce Fischl,et al.  Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI , 2012, Magnetic resonance in medicine.

[27]  Lei Ai,et al.  Is it time to switch your T1W sequence? Assessing the impact of prospective motion correction on the reliability and quality of structural imaging , 2019, NeuroImage.

[28]  S. Provencher Automatic quantitation of localized in vivo 1H spectra with LCModel , 2001, NMR in biomedicine.

[29]  M. Mallar Chakravarty,et al.  Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: Comparing automated approaches to manual delineation , 2017, NeuroImage.

[30]  Margot J. Taylor,et al.  Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder , 2019, Molecular Psychiatry.

[31]  Tristan Glatard,et al.  Head-to-Head Comparison of Two Popular Cortical Thickness Extraction Algorithms: A Cross-Sectional and Longitudinal Study , 2015, PloS one.

[32]  Borys Shuter,et al.  Reproducibility of brain tissue volumes in longitudinal studies: Effects of changes in signal-to-noise ratio and scanner software , 2008, NeuroImage.

[33]  Anders M. Dale,et al.  The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.

[34]  Sabina Sonia Tangaro,et al.  Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease , 2016, NeuroImage.

[35]  J. Giedd,et al.  Subtle in‐scanner motion biases automated measurement of brain anatomy from in vivo MRI , 2016, Human brain mapping.

[36]  M. Mallar Chakravarty,et al.  Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates , 2014, NeuroImage.

[37]  Arno Klein,et al.  101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..

[38]  M. Dylan Tisdall,et al.  Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion , 2016, NeuroImage.

[39]  Stine K. Krogsrud,et al.  Development and aging of cortical thickness correspond to genetic organization patterns , 2015, Proceedings of the National Academy of Sciences.

[40]  Heath R. Pardoe,et al.  Motion and morphometry in clinical and nonclinical populations , 2016, NeuroImage.

[41]  A. Dale,et al.  Cortical Surface-Based Analysis II: Inflation, Flattening, and a Surface-Based Coordinate System , 1999, NeuroImage.

[42]  Alan C. Evans,et al.  Cortical thickness analysis examined through power analysis and a population simulation , 2005, NeuroImage.

[43]  S. Vos,et al.  Reliability of brain volume measurements: A test-retest dataset , 2014, Scientific Data.

[44]  Jon Pipitone,et al.  Hippocampal (subfield) volume and shape in relation to cognitive performance across the adult lifespan , 2015, Human brain mapping.

[45]  Anders M. Dale,et al.  MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths , 2009, NeuroImage.

[46]  Anders M. Dale,et al.  Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data , 2006, NeuroImage.

[47]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[48]  David N. Kennedy,et al.  Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses , 2020, bioRxiv.

[49]  Lachlan T. Strike,et al.  Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group , 2015, Molecular Psychiatry.

[50]  Christine L. Tardif,et al.  MR‐based age‐related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan , 2019, Human brain mapping.

[51]  I. Melle,et al.  Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium , 2016, Molecular Psychiatry.

[52]  J. Debbins,et al.  A Validation Study of Multicenter Diffusion Tensor Imaging: Reliability of Fractional Anisotropy and Diffusivity Values , 2012, American Journal of Neuroradiology.

[53]  R Gruetter,et al.  Field mapping without reference scan using asymmetric echo‐planar techniques , 2000, Magnetic resonance in medicine.

[54]  Tyrone D. Cannon,et al.  Progressive Reduction in Cortical Thickness as Psychosis Develops: A Multisite Longitudinal Neuroimaging Study of Youth at Elevated Clinical Risk , 2015, Biological Psychiatry.