A Multi-Modal MRI Analysis of Cortical Structure in Relation to Gender Dysphoria, Sexual Orientation, and Age in Adolescents.

Gender dysphoria (GD) is characterized by distress due to an incongruence between experienced gender and sex assigned at birth. Sex-differentiated brain regions are hypothesized to reflect the experienced gender in GD and may play a role in sexual orientation development. Magnetic resonance brain images were acquired from 16 GD adolescents assigned female at birth (AFAB) not receiving hormone therapy, 17 cisgender girls, and 14 cisgender boys (ages 12-17 years) to examine three morphological and microstructural gray matter features in 76 brain regions: surface area (SA), cortical thickness (CT), and T1 relaxation time. Sexual orientation was represented by degree of androphilia-gynephilia and sexual attraction strength. Multivariate analyses found that cisgender boys had larger SA than cisgender girls and GD AFAB. Shorter T1, reflecting denser, macromolecule-rich tissue, correlated with older age and stronger gynephilia in cisgender boys and GD AFAB, and with stronger attractions in cisgender boys. Thus, cortical morphometry (mainly SA) was related to sex assigned at birth, but not experienced gender. Effects of experienced gender were found as similarities in correlation patterns in GD AFAB and cisgender boys in age and sexual orientation (mainly T1), indicating the need to consider developmental trajectories and sexual orientation in brain studies of GD

Quantitative examination of a novel clustering method using magnetic resonance diffusion tensor tractography

MR diffusion tensor imaging (DTI) can measure and visualize organization of white matter fibre tracts in vivo. DTI is a relatively new imaging technique, and new tools developed for quantifying fibre tracts require evaluation. The purpose of this study was to compare the reliability of a novel clustering approach with a multiple region of interest (MROI) approach in both healthy and disease (schizophrenia) populations. DTI images were acquired in 20 participants (n=10 patients with schizophrenia: 56 ± 15 years; n=10 controls: 51 ± 20 years) (1.5 Tesla GE system) with diffusion gradients applied in 23 non-collinear directions, repeated three times. Whole brain seeding and creation of fibre tracts were then performed. Interrater reliability of the clustering approach, and the MROI approach, were each evaluated and the methods compared. There was high spatial (voxel-based) agreement within and between the clustering and MROI methods. Fractional anisotropy, trace, and radial and axial diffusivity values showed high intraclass correlation (p<0.001 for all tracts) for each approach. Differences in scalar indices of diffusion between the clustering and MROI approach were minimal. The excellent interrater reliability of the clustering method and high agreement with the MROI method, quantitatively and spatially, indicates that the clustering method can be used with confidence. The clustering method avoids biases of ROI drawing and placement, and, not limited by a priori predictions, may be a more robust and efficient way to identify and measure white matter tracts of interest

nnResting state fMRI scanner instabilities revealed by longitud inal phantom scans in a multi-center study

Quality assurance (QA) is crucial in longitudinal and/or multi-site studies, which involve the collection of data from a group of subjects over time and/or at different locations. It is important to regularly monitor the performance of the scanners over time and at different locations to detect and control for intrinsic differences (e.g., due to manufacturers) and changes in scanner performance (e.g., due to gradual component aging, software and/or hardware upgrades, etc.). As part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Canadian Biomarker Integration Network in Depression (CAN-BIND), QA phantom scans were conducted approximately monthly for three to four years at 13 sites across Canada with 3T research MRI scanners. QA parameters were calculated for each scan using the functional Biomarker Imaging Research Network\u27s (fBIRN) QA phantom and pipeline to capture between- and within-scanner variability. We also describe a QA protocol to measure the full-width-at-half-maximum (FWHM) of slice-wise point spread functions (PSF), used in conjunction with the fBIRN QA parameters. Variations in image resolution measured by the FWHM are a primary source of variance over time for many sites, as well as between sites and between manufacturers. We also identify an unexpected range of instabilities affecting individual slices in a number of scanners, which may amount to a substantial contribution of unexplained signal variance to their data. Finally, we identify a preliminary preprocessing approach to reduce this variance and/or alleviate the slice anomalies, and in a small human data set show that this change in preprocessing can have a significant impact on seed-based connectivity measurements for some individual subjects. We expect that other fMRI centres will find this approach to identifying and controlling scanner instabilities useful in similar studies

Impaired Structural Connectivity of Socio-Emotional Circuits in Autism Spectrum Disorders: A Diffusion Tensor Imaging Study

Abnormal white matter development may disrupt integration within neural circuits, causing particular impairments in higher-order behaviours. In autism spectrum disorders (ASDs), white matter alterations may contribute to characteristic deficits in complex socio-emotional and communication domains. Here, we used diffusion tensor imaging (DTI) and tract based spatial statistics (TBSS) to evaluate white matter microstructure in ASD.DTI scans were acquired for 19 children and adolescents with ASD (∼8-18 years; mean 12.4±3.1) and 16 age and IQ matched controls (∼8-18 years; mean 12.3±3.6) on a 3T MRI system. DTI values for fractional anisotropy, mean diffusivity, radial diffusivity and axial diffusivity, were measured. Age by group interactions for global and voxel-wise white matter indices were examined. Voxel-wise analyses comparing ASD with controls in: (i) the full cohort (ii), children only (≤12 yrs.), and (iii) adolescents only (>12 yrs.) were performed, followed by tract-specific comparisons. Significant age-by-group interactions on global DTI indices were found for all three diffusivity measures, but not for fractional anisotropy. Voxel-wise analyses revealed prominent diffusion measure differences in ASD children but not adolescents, when compared to healthy controls. Widespread increases in mean and radial diffusivity in ASD children were prominent in frontal white matter voxels. Follow-up tract-specific analyses highlighted disruption to pathways integrating frontal, temporal, and occipital structures involved in socio-emotional processing.Our findings highlight disruption of neural circuitry in ASD, particularly in those white matter tracts that integrate the complex socio-emotional processing that is impaired in this disorder

Children recruit distinct neural systems for implicit emotional face processing.

The ability to properly distinguish facial emotions has a protracted development, not maturing until well into adolescence. Emotional faces activate emotion-specific neural networks in adults; whether these networks are operational in children is not known. Using an implicit face-processing task in 10-year-old children, we determined that the emotions of fear, disgust and sadness recruited distinct neural systems. These systems included a number of regions typically associated with processing emotions in adults, namely the amygdala and parahippocampal gyrus, insula and cingulate gyrus, as well as the fusiform and superior temporal gyri. Thus, in spite of immature behavioral responses to emotional faces in explicit tasks, neural networks for emotion-specific processing are present in young children

High-resolution In Vivo manual segmentation protocol for human hippocampal subfields using 3T magnetic resonance imaging

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has been heavily studied. While many imaging studies treat the hippocampus as a single unitary neuroanatomical structure, it is, in fact, composed of several subfields that have a complex three-dimensional geometry. As such, it is known that these subfields perform specialized functions and are differentially affected through the course of different disease states. Magnetic resonance (MR) imaging can be used as a powerful tool to interrogate the morphology of the hippocampus and its subfields. Many groups use advanced imaging software and hardware (\u3e3T) to image the subfields; however this type of technology may not be readily available in most research and clinical imaging centers. To address this need, this manuscript provides a detailed step-by-step protocol for segmenting the full anterior-posterior length of the hippocampus and its subfields: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus (DG), strata radiatum/lacunosum/moleculare (SR/SL/SM), and subiculum. This protocol has been applied to five subjects (3F, 2M; age 29-57, avg. 37). Protocol reliability is assessed by resegmenting either the right or left hippocampus of each subject and computing the overlap using the Dice\u27s kappa metric. Mean Dice\u27s kappa (range) across the five subjects are: whole hippocampus, 0.91 (0.90-0.92); CA1, 0.78 (0.77-0.79); CA2/CA3, 0.64 (0.56-0.73); CA4/dentate gyrus, 0.83 (0.81-0.85); strata radiatum/lacunosum/moleculare, 0.71 (0.68-0.73); and subiculum 0.75 (0.72-0.78). The segmentation protocol presented here provides other laboratories with a reliable method to study the hippocampus and hippocampal subfields in vivo using commonly available MR tools

Diurnal oscillations of MRI metrics in the brains of male participants

Abstract Regulation of biological processes according to a 24-hr rhythm is essential for the normal functioning of an organism. Temporal variation in brain MRI data has often been attributed to circadian or diurnal oscillations; however, it is not clear if such oscillations exist. Here, we provide evidence that diurnal oscillations indeed govern multiple MRI metrics. We recorded cerebral blood flow, diffusion-tensor metrics, T1 relaxation, and cortical structural features every three hours over a 24-hr period in each of 16 adult male controls and eight adult male participants with bipolar disorder. Diurnal oscillations are detected in numerous MRI metrics at the whole-brain level, and regionally. Rhythmicity parameters in the participants with bipolar disorder are similar to the controls for most metrics, except for a larger phase variation in cerebral blood flow. The ubiquitous nature of diurnal oscillations has broad implications for neuroimaging studies and furthers our understanding of the dynamic nature of the human brain

Prediction error did not depend on ROI size, head motion or subject age.

<p><b>A, B.</b> Prediction error of ROIs was only weakly correlated with the ROI size, both in single subjects (<b>A</b>, <i>r</i> = -0.19, n = 104 cortical ROIs) and across subjects (<b>B</b>, <i>r</i> = -0.2, n = 4,784 cortical ROIs). <b>C.</b> Average prediction error was not correlated with subject head motion (<i>r</i> = -0.09, <i>p</i> = 0.56). <b>D.</b> Average prediction error was not correlated with subject age (<i>r</i> = -0.1, <i>p</i> = 0.49).</p

RFE models perform similarly to Lasso and elastic net models, but with fewer predictor ROIs.

<p><b>A.</b> The average prediction error (across ROIs and n = 46 subjects) of models generated using RFE, RFE2, Lasso or elastic net was similar (average difference < 3% of activity variance), and significantly smaller than null models (average difference ~ 90%). Statistics reflect 108/112 cortical ROIs (excluding temporal pole and entorhinal cortex that had large prediction error in all models). <b>B.</b> The average number of predictor ROIs selected by RFE models was significantly smaller than that selected by RFE2, Lasso or elastic net models.</p

Multiregional prediction gain across ROIs.

<p><b>A.</b> Observed activity (black) of right inferior parietal cortex₂ (the middle ROI of inferior parietal cortex, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005410#sec004" target="_blank">Methods</a>) in an example subject, and predicted activity of a model using 5 predictor ROIs selected by RFE (blue). <b>B.</b> Observed (black) and predicted (red) activity for the same ROI, but with a model using 1 predictor ROI, the one most correlated with the ROI in the training data. The difference in prediction error of the two models for the ROI (the “multiregional prediction gain”) was 49% (<i>p</i> < 10⁻¹⁵). <b>C.</b> Lateral and medial views of both hemispheres, showing the average prediction gain in the 49 cortical ROIs that were well-modeled (prediction error 13–23% as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005410#pcbi.1005410.g003" target="_blank">Fig 3</a>). Other ROIs with larger prediction error are colored in gray.</p