Scan-rescan reliability of subcortical brain volumes derived from automated segmentation

Comparing Automated Subcortical Volume Estimation Methods; Amygdala Volumes Estimated by FSL and FreeSurfer Have Poor Consistency

Subcortical volumes are a promising source of biomarkers and features in biosignatures, and automated methods facilitate extracting them in large, phenotypically rich datasets. However, while extensive research has verified that the automated methods produce volumes that are similar to those generated by expert annotation, the consistency of methods with each other is understudied. Using data from the UK Biobank, we compare the estimates of subcortical volumes produced by two popular software suites: FSL and FreeSurfer. Although most subcortical volumes exhibit good to excellent consistency across the methods, the tools produce diverging estimates of amygdalar volume. Through simulation, we show that this poor consistency can lead to conflicting results, where one but not the other tool suggests statistical significance, or where both tools suggest a significant relationship but in opposite directions. Considering these issues, we discuss several ways in which care should be taken when reporting on relationships involving amygdalar volume.

Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort

INTRODUCTION

The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort.

METHODS

Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures.

RESULTS

Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone.

CONCLUSION

Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.

Hippocampal subfield volumetric changes after radiotherapy for brain metastases

Background

Changes in the hippocampus after brain metastases radiotherapy can significantly impact neurocognitive functions. Numerous studies document hippocampal atrophy correlating with the radiation dose. This study aims to elucidate volumetric changes in patients undergoing whole-brain radiotherapy (WBRT) or targeted stereotactic radiotherapy (SRT) and to explore volumetric changes in the individual subregions of the hippocampus.

Method

Ten patients indicated to WBRT and 18 to SRT underwent brain magnetic resonance before radiotherapy and after 4 months. A structural T1-weighted sequence was used for volumetric analysis, and the software FreeSurfer was employed as the tool for the volumetry evaluation of 19 individual hippocampal subregions.

Results

The volume of the whole hippocampus, segmented by the software, was larger than the volume outlined by the radiation oncologist. No significant differences in volume changes were observed in the right hippocampus. In the left hippocampus, the only subregion with a smaller volume after WBRT was the granular cells and molecular layers of the dentate gyrus (GC-ML-DG) region (median change -5 mm3, median volume 137 vs. 135 mm3; P = .027), the region of the presumed location of neuronal progenitors.

Conclusions

Our study enriches the theory that the loss of neural stem cells is involved in cognitive decline after radiotherapy, contributes to the understanding of cognitive impairment, and advocates for the need for SRT whenever possible to preserve cognitive functions in patients undergoing brain radiotherapy.

Intersite brain MRI volumetric biases persist even in a harmonized multisubject study of multiple sclerosis

BACKGROUND AND PURPOSE

Multicenter study designs involving a variety of MRI scanners have become increasingly common. However, these present the issue of biases in image-based measures due to scanner or site differences. To assess these biases, we imaged 11 volunteers with multiple sclerosis (MS) with scan and rescan data at four sites.

METHODS

Images were acquired on Siemens or Philips scanners at 3 Tesla. Automated white matter lesion detection and whole-brain, gray and white matter, and thalamic volumetry were performed, as well as expert manual delineations of T1 magnetization-prepared rapid acquisition gradient echo and T2 fluid-attenuated inversion recovery lesions. Random-effect and permutation-based nonparametric modeling was performed to assess differences in estimated volumes within and across sites.

RESULTS

Random-effect modeling demonstrated model assumption violations for most comparisons of interest. Nonparametric modeling indicated that site explained >50% of the variation for most estimated volumes. This expanded to >75% when data from both Siemens and Philips scanners were included. Permutation tests revealed significant differences between average inter- and intrasite differences in most estimated brain volumes (P < .05). The automatic activation of spine coil elements during some acquisitions resulted in a shading artifact in these images. Permutation tests revealed significant differences between thalamic volume measurements from acquisitions with and without this artifact.

CONCLUSION

Differences in brain volumetry persisted across MR scanners despite protocol harmonization. These differences were not well explained by variance component modeling; however, statistical innovations for mitigating intersite differences show promise in reducing biases in multicenter studies of MS.

DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism

BACKGROUND

The hippocampus is a key area of the brain responsible for learning, memory, and other abilities. Accurately segmenting the hippocampus and precisely calculating the volume of the hippocampus is of great significance for predicting Alzheimer's disease and amnesia. Most of the segmentation algorithms currently involved are based on templates, such as the more popular FreeSufer.

METHODS

This study proposes Deephipp, a deep learning network based on a 3D dense block using an attention mechanism for accurate segmentation of the hippocampus. DeepHipp is based on the following novelties: (i) DeepHipp adopts powerful data augmentation schemes to enhance the segmentation ability. (ii) DeepHipp is designed to incorporate 3D dense-block to capture multiple-scale features of the hippocampus. (iii) DeepHipp creatively uses the attention mechanism in the field of hippocampal image segmentation, extracting useful hippocampus information in a massive feature map, and improving the accuracy and sensitivity of the model.

CONCLUSIONS

We describe the illustrative results and show extensive qualitative and quantitative comparisons with other methods. Our achievement demonstrates that the accuracy of DeepHipp can reach 83.63%, which is superior to most existing methods in terms of accuracy and efficiency of hippocampus segmentation. It is noticeable that deep learning can potentially lead to an effective segmentation of medical images.

Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer

On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here, we assessed the spatial and numerical reliability for newly deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer 7. In a sample of participants with repeated structural imaging scans (N = 928), we found numerical reliability (as assessed by intraclass correlations, ICCs) was reasonable. Approximately 95% of hippocampal subfields had "excellent" numerical reliability (ICCs ≥ 0.90), while only 67% of amygdala subnuclei met this same threshold. In terms of spatial reliability, 58% of hippocampal subfields and 44% of amygdala subnuclei had Dice coefficients ≥ 0.70. Notably, multiple regions had poor numerical and/or spatial reliability. We also examined correlations between spatial reliability and person-level factors (e.g., participant age; T1 image quality). Both sex and image scan quality were related to variations in spatial reliability metrics. Examined collectively, our work suggests caution should be exercised for a few hippocampal subfields and amygdala nuclei with more variable reliability.

Retinal layer assessments as potential biomarkers for brain atrophy in the Rhineland Study

Retinal assessments have been discussed as biomarkers for brain atrophy. However, available studies did not investigate all retinal layers due to older technology, reported inconsistent results, or were based on small sample sizes. We included 2872 eligible participants of the Rhineland Study with data on spectral domain-optical coherence tomography (SD-OCT) and brain magnetic resonance imaging (MRI). We used multiple linear regression to examine relationships between retinal measurements and volumetric brain measures as well as fractional anisotropy (FA) as measure of microstructural integrity of white matter (WM) for different brain regions. Mean (SD) age was 53.8 ± 13.2 years (range 30-94) and 57% were women. Volumes of the inner retina were associated with total brain and grey matter (GM) volume, and even stronger with WM volume and FA. In contrast, the outer retina was mainly associated with GM volume, while both, inner and outer retina, were associated with hippocampus volume. While we extend previously reported associations between the inner retina and brain measures, we found additional associations of the outer retina with parts of the brain. This indicates that easily accessible retinal SD-OCT assessments may serve as biomarkers for clinical monitoring of neurodegenerative diseases and merit further research.

Different FreeSurfer versions might generate different statistical outcomes in case-control comparison studies

PURPOSE

Neuroimaging pipelines have long been known to generate mildly differing results depending on various factors, including software version. While considered generally acceptable and within the margin of reasonable error, little is known about their effect in common research scenarios such as inter-group comparisons between healthy controls and various pathological conditions. The aim of the presented study was to explore the differences in the inferences and statistical significances in a model situation comparing volumetric parameters between healthy controls and type 1 diabetes patients using various FreeSurfer versions.

METHODS

T1- and T2-weighted structural scans of healthy controls and type 1 diabetes patients were processed with FreeSurfer 5.3, FreeSurfer 5.3 HCP, FreeSurfer 6.0 and FreeSurfer 7.1, followed by inter-group statistical comparison using outputs of individual FreeSurfer versions.

RESULTS

Worryingly, FreeSurfer 5.3 detected both cortical and subcortical volume differences out of the preselected regions of interest, but newer versions such as FreeSurfer 5.3 HCP and FreeSurfer 6.0 reported only subcortical differences of lower magnitude and FreeSurfer 7.1 failed to find any statistically significant inter-group differences.

CONCLUSION

Since group averages of individual FreeSurfer versions closely matched, in keeping with previous literature, the main origin of this disparity seemed to lie in substantially higher within-group variability in the model pathological condition. Ergo, until validation in common research scenarios as case-control comparison studies is included into the development process of new software suites, confirmatory analyses utilising a similar software based on analogous, but not fully equivalent principles, might be considered as supplement to careful quality control.

A Comparative Analysis of MRI Automated Segmentation of Subcortical Brain Volumes in a Large Dataset of Elderly Subjects

In this study, we perform a comparative analysis of automated image segmentation of subcortical structures in the elderly brain. Manual segmentation is very time-consuming and automated methods are gaining importance as a clinical tool for diagnosis. The two most commonly used software libraries for brain segmentation -FreeSurfer and FSL- are put to work in a large dataset of 4,028 magnetic resonance imaging (MRI) scans collected for this study. We find a lack of linear correlation between the segmentation volume estimates obtained from FreeSurfer and FSL. On the other hand, FreeSurfer volume estimates tend to be larger thanFSL estimates of the areas putamen, thalamus, amygdala, caudate, pallidum, hippocampus, and accumbens. The characterization of the performance of brain segmentation algorithms in large datasets as the one presented here is a necessary step towards partially or fully automated end-to-end neuroimaging workflow both in clinical and research settings.

Automated subcortical volume estimation from 2D MRI in epilepsy and implications for clinical trials

Purpose

Most techniques used for automatic segmentation of subcortical brain regions are developed for three-dimensional (3D) MR images. MRIs obtained in non-specialist hospitals may be non-isotropic and two-dimensional (2D). Automatic segmentation of 2D images may be challenging and represents a lost opportunity to perform quantitative image analysis. We determine the performance of a modified subcortical segmentation technique applied to 2D images in patients with idiopathic generalised epilepsy (IGE).

Methods

Volume estimates were derived from 2D (0.4 × 0.4 × 3 mm) and 3D (1 × 1x1mm) T1-weighted acquisitions in 31 patients with IGE and 39 healthy controls. 2D image segmentation was performed using a modified FSL FIRST (FMRIB Integrated Registration and Segmentation Tool) pipeline requiring additional image reorientation, cropping, interpolation and brain extraction prior to conventional FIRST segmentation. Consistency between segmentations was assessed using Dice coefficients and volumes across both approaches were compared between patients and controls. The influence of slice thickness on consistency was further assessed using 2D images with slice thickness increased to 6 mm.

Results

All average Dice coefficients showed excellent agreement between 2 and 3D images across subcortical structures (0.86–0.96). Most 2D volumes were consistently slightly lower compared to 3D volumes. 2D images with increased slice thickness showed lower agreement with 3D images with lower Dice coefficients (0.55–0.83). Significant volume reduction of the left and right thalamus and putamen was observed in patients relative to controls across 2D and 3D images.

Conclusion

Automated subcortical volume estimation of 2D images with a resolution of 0.4 × 0.4x3mm using a modified FIRST pipeline is consistent with volumes derived from 3D images, although this consistency decreases with an increased slice thickness. Thalamic and putamen atrophy has previously been reported in patients with IGE. Automated subcortical volume estimation from 2D images is feasible and most reliable at using in-plane acquisitions greater than 1 mm x 1 mm and provides an opportunity to perform quantitative image analysis studies in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00234-021-02811-x.

Regional radiomics similarity networks (R2SNs) in the human brain: Reproducibility, small-world properties and a biological basis

A structural covariance network (SCN) has been used successfully in structural magnetic resonance imaging (sMRI) studies. However, most SCNs have been constructed by a unitary marker that is insensitive for discriminating different disease phases. The aim of this study was to devise a novel regional radiomics similarity network (R2SN) that could provide more comprehensive information in morphological network analysis. R2SNs were constructed by computing the Pearson correlations between the radiomics features extracted from any pair of regions for each subject (AAL atlas). We further assessed the small-world property of R2SNs, and we evaluated the reproducibility in different datasets and through test-retest analysis. The relationships between the R2SNs and general intelligence/interregional coexpression of genes were also explored. R2SNs could be replicated in different datasets, regardless of the use of different feature subsets. R2SNs showed high reproducibility in the test-retest analysis (intraclass correlation coefficient > 0.7). In addition, the small-word property (σ > 2) and the high correlation between gene expression (R = 0.29, p < 0.001) and general intelligence were determined for R2SNs. Furthermore, the results have also been repeated in the Brainnetome atlas. R2SNs provide a novel, reliable, and biologically plausible method to understand human morphological covariance based on sMRI.

Whole brain and deep gray matter structure segmentation: Quantitative comparison between MPRAGE and MP2RAGE sequences

Objective

T1-weighted MRI images are commonly used for volumetric assessment of brain structures. Magnetization prepared 2 rapid gradient echo (MP2RAGE) sequence offers superior gray (GM) and white matter (WM) contrast. This study aimed to quantitatively assess the agreement of whole brain tissue and deep GM (DGM) volumes obtained from MP2RAGE compared to the widely used MP-RAGE sequence.

Methods

Twenty-nine healthy participants were included in this study. All subjects underwent a 3T MRI scan acquiring high-resolution 3D MP-RAGE and MP2RAGE images. Twelve participants were re-scanned after one year. The whole brain, as well as DGM segmentation, was performed using CAT12, volBrain, and FSL-FAST automatic segmentation tools based on the acquired images. Finally, contrast-to-noise ratio between WM and GM (CNR_WG), the agreement between the obtained tissue volumes, as well as scan-rescan variability of both sequences were explored.

Results

Significantly higher CNR_WG was detected in MP2RAGE vs. MP-RAGE (Mean ± SD = 0.97 ± 0.04 vs. 0.8 ± 0.1 respectively; p<0.0001). Significantly higher total brain GM, and lower cerebrospinal fluid‏ volumes were obtained from MP2RAGE vs. MP-RAGE based on all segmentation methods (p<0.05 in all cases). Whole-brain voxel-wise comparisons revealed higher GM tissue probability in the thalamus, putamen, caudate, lingual gyrus, and precentral gyrus based on MP2RAGE compared with MP-RAGE. Moreover, significantly higher WM probability was observed in the cerebellum, corpus callosum, and frontal-and-temporal regions in MP2RAGE vs. MP-RAGE. Finally, MP2RAGE showed a higher mean percentage of change in total brain GM compared to MP-RAGE. On the other hand, MP-RAGE demonstrated a higher overtime percentage of change in WM and DGM volumes compared to MP2RAGE.

Conclusions

Due to its higher CNR, MP2RAGE resulted in reproducible brain tissue segmentation, and thus is a recommended method for volumetric imaging biomarkers for the monitoring of neurological diseases.

Stability of hippocampal subfield volumes after trauma and relationship to development of PTSD symptoms

BACKGROUND

The hippocampus plays a central role in post-traumatic stress disorder (PTSD) pathogenesis, and the majority of neuroimaging research on PTSD has studied the hippocampus in its entirety. Although extensive literature demonstrates changes in hippocampal volume are associated with PTSD, fewer studies have probed the relationship between symptoms and the hippocampus' functionally and structurally distinct subfields. We utilized data from a longitudinal study examining post-trauma outcomes to determine whether hippocampal subfield volumes change post-trauma and whether specific subfields are significantly associated with, or prospectively related to, PTSD symptom severity. As a secondary aim, we leveraged our unique study design sample to also investigate reliability of hippocampal subfield volumes using both cross-sectional and longitudinal pipelines available in FreeSurfer v6.0.

METHODS

Two-hundred and fifteen traumatically injured individuals were recruited from an urban Emergency Department. Two-weeks post-injury, participants underwent two consecutive days of neuroimaging (time 1: T1, and time 2: T2) with magnetic resonance imaging (MRI) and completed self-report assessments. Six-months later (time 3: T3), participants underwent an additional scan and were administered a structured interview assessing PTSD symptoms. First, we calculated reliability of hippocampal measurements at T1 and T2 (automatically segmented with FreeSurfer v6.0). We then examined the prospective (T1 subfields) and cross-sectional (T3 subfields) relationship between volumes and PTSD. Finally, we tested whether change in subfield volumes between T1 and T3 explained PTSD symptom variability.

RESULTS

After controlling for sex, age, and total brain volume, none of the subfield volumes (T1) were prospectively related to T3 PTSD symptoms nor were subfield volumes (T3) associated with current PTSD symptoms (T3). Tl - T2 reliability of all hippocampal subfields ranged from good to excellent (intraclass correlation coefficient (ICC) values > 0.83), with poorer reliability in the hippocampal fissure.

CONCLUSION

Our study was a novel examination of the prospective relationship between hippocampal subfield volumes in relation to PTSD in a large trauma-exposed urban sample. There was no significant relationship between subfield volumes and PTSD symptoms, however, we confirmed FreeSurfer v6.0 hippocampal subfield segmentation is reliable when applied to a traumatically-injured sample, using both cross-sectional and longitudinal analysis pipelines. Although hippocampal subfield volumes may be an important marker of individual variability in PTSD, findings are likely conditional on the timing of the measurements (e.g. acute or chronic post-trauma periods) and analysis strategy (e.g. cross-sectional or prospective).

Neuroanatomical norms in the UK Biobank: The impact of allometric scaling, sex, and age

Few neuroimaging studies are sufficiently large to adequately describe population‐wide variations. This study's primary aim was to generate neuroanatomical norms and individual markers that consider age, sex, and brain size, from 629 cerebral measures in the UK Biobank (N = 40,028). The secondary aim was to examine the effects and interactions of sex, age, and brain allometry—the nonlinear scaling relationship between a region and brain size (e.g., total brain volume)—across cerebral measures. Allometry was a common property of brain volumes, thicknesses, and surface areas (83%) and was largely stable across age and sex. Sex differences occurred in 67% of cerebral measures (median |β| = .13): 37% of regions were larger in males and 30% in females. Brain measures (49%) generally decreased with age, although aging effects varied across regions and sexes. While models with an allometric or linear covariate adjustment for brain size yielded similar significant effects, omitting brain allometry influenced reported sex differences in variance. Finally, we contribute to the reproducibility of research on sex differences in the brain by replicating previous studies examining cerebral sex differences. This large‐scale study advances our understanding of age, sex, and brain allometry's impact on brain structure and provides data for future UK Biobank studies to identify the cerebral regions that covary with specific phenotypes, independently of sex, age, and brain size.

A Robust and Accurate Deep-learning-based Method for the Segmentation of Subcortical Brain: Cross-dataset Evaluation of Generalization Performance

Purpose:

To analyze subcortical brain volume more reliably, we propose a deep learning segmentation method of subcortical brain based on magnetic resonance imaging (MRI) having high generalization performance, accuracy, and robustness.

Methods:

First, local images of three-dimensional (3D) bounding boxes were extracted for seven subcortical structures (thalamus, putamen, caudate, pallidum, hippocampus, amygdala, and accumbens) from a whole brain MR image as inputs to the neural network. Second, dilated convolution layers, which input information of variable scope, were introduced to the blocks that make up the neural network. These blocks were connected in parallel to simultaneously process global and local information obtained by the dilated convolution layers. To evaluate generalization performance, different datasets were used for training and testing sessions (cross-dataset evaluation) because subcortical brain segmentation in clinical analysis is assumed to be applied to unknown datasets.

Results:

The proposed method showed better generalization performance that can obtain stable accuracy for all structures, whereas the state-of-the-art deep learning method obtained extremely low accuracy for some structures. The proposed method performed segmentation for all samples without failing with significantly higher accuracy (P < 0.005) than conventional methods such as 3D U-Net, FreeSurfer, and Functional Magnetic Resonance Imaging of the Brain’s (FMRIB’s) Integrated Registration and Segmentation Tool in the FMRIB Software Library (FSL-FIRST). Moreover, when applying this proposed method to larger datasets, segmentation was robustly performed for all samples without producing segmentation results on the areas that were apparently different from anatomically relevant areas. On the other hand, FSL-FIRST produced segmentation results on the area that were apparently and largely different from the anatomically relevant area for about one-third to one-fourth of the datasets.

Conclusion:

The cross-dataset evaluation showed that the proposed method is superior to existing methods in terms of generalization performance, accuracy, and robustness.

Quantitative comparison of subcortical and ventricular volumetry derived from MPRAGE and MP2RAGE images using different brain morphometry software

OBJECTIVE

In brain volume assessment with MR imaging, it is of interest to know the effects of the pulse sequence and software used, to determine whether they provide equivalent data. The aim of this study was to compare cross-sectional volumes of subcortical and ventricular structures and their repeatability derived from MP2RAGE and MPRAGE images using MorphoBox, and FIRST or ALVIN.

MATERIALS AND METHODS

MPRAGE and MP2RAGE T1-weighted images were obtained from 24 healthy volunteers. Back-to-back scans were performed in 12 of them. Volumes, coefficients of variation, concordance, and correlations were determined.

RESULTS

Significant differences were found for volumes derived from MorphoBox and FIRST. Ventricular volumes determined by MorphoBox and ALVIN were similar. Differences between volumes obtained using MPRAGE and MP2RAGE were significant for a few regions. Coefficients of variation, ranged from 0.2 to 9.1%, showed a significant inverse correlation with the mean volume. There was a correlation between volume measures, but agreement was rated as poor for most regions.

CONCLUSION

MP2RAGE sequences and MorphoBox are valid options for assessing subcortical and ventricular volumes, in the same way as MPRAGE and FIRST or ALVIN, accepted tools for clinical research. However, caution is needed when comparing volumes obtained with different tools.

Comparison of structural MRI brain measures between 1.5 and 3 T: Data from the Lothian Birth Cohort 1936

Multi‐scanner MRI studies are reliant on understanding the apparent differences in imaging measures between different scanners. We provide a comprehensive analysis of T₁‐weighted and diffusion MRI (dMRI) structural brain measures between a 1.5 T GE Signa Horizon HDx and a 3 T Siemens Magnetom Prisma using 91 community‐dwelling older participants (aged 82 years). Although we found considerable differences in absolute measurements (global tissue volumes were measured as ~6–11% higher and fractional anisotropy [FA] was 33% higher at 3 T than at 1.5 T), between‐scanner consistency was good to excellent for global volumetric and dMRI measures (intraclass correlation coefficient [ICC] range: .612–.993) and fair to good for 68 cortical regions (FreeSurfer) and cortical surface measures (mean ICC: .504–.763). Between‐scanner consistency was fair for dMRI measures of 12 major white matter tracts (mean ICC: .475–.564), and the general factors of these tracts provided excellent consistency (ICC ≥ .769). Whole‐brain structural networks provided good to excellent consistency for global metrics (ICC ≥ .612). Although consistency was poor for individual network connections (mean ICCs: .275−.280), this was driven by a large difference in network sparsity (.599 vs. .334), and consistency was improved when comparing only the connections present in every participant (mean ICCs: .533–.647). Regression‐based k‐fold cross‐validation showed that, particularly for global volumes, between‐scanner differences could be largely eliminated (R² range .615–.991). We conclude that low granularity measures of brain structure can be reliably matched between the scanners tested, but caution is warranted when combining high granularity information from different scanners.

Reliability and sensitivity of two whole-brain segmentation approaches included in FreeSurfer - ASEG and SAMSEG

Accurate and reliable whole-brain segmentation is critical to longitudinal neuroimaging studies. We undertake a comparative analysis of two subcortical segmentation methods, Automatic Segmentation (ASEG) and Sequence Adaptive Multimodal Segmentation (SAMSEG), recently provided in the open-source neuroimaging package FreeSurfer 7.1, with regard to reliability, bias, sensitivity to detect longitudinal change, and diagnostic sensitivity to Alzheimer’s disease. First, we assess intra- and inter-scanner reliability for eight bilateral subcortical structures: amygdala, caudate, hippocampus, lateral ventricles, nucleus accumbens, pallidum, putamen and thalamus. For intra-scanner analysis we use a large sample of participants (n = 1629) distributed across the lifespan (age range = 4–93 years) and acquired on a 1.5T Siemens Avanto (n = 774) and a 3T Siemens Skyra (n = 855) scanners. For inter-scanner analysis we use a sample of 24 participants scanned on the day with three models of Siemens scanners: 1.5T Avanto, 3T Skyra and 3T Prisma. Second, we test how each method detects volumetric age change using longitudinal follow up scans (n = 491 for Avanto and n = 245 for Skyra; interscan interval = 1–10 years). Finally, we test sensitivity to clinically relevant change. We compare annual rate of hippocampal atrophy in cognitively normal older adults (n = 20), patients with mild cognitive impairment (n = 20) and Alzheimer’s disease (n = 20). We find that both ASEG and SAMSEG are reliable and lead to the detection of within-person longitudinal change, although with notable differences between age-trajectories for most structures, including hippocampus and amygdala. In summary, SAMSEG yields significantly lower differences between repeated measures for intra- and inter-scanner analysis without compromising sensitivity to changes and demonstrating ability to detect clinically relevant longitudinal changes.

Understanding the role of aerobic fitness, spatial learning, and hippocampal subfields in adolescent males

Physical exercise during adolescence, a critical developmental window, can facilitate neurogenesis in the dentate gyrus and astrogliogenesis in Cornu Ammonis (CA) hippocampal subfields of rats, and which have been associated with improved hippocampal dependent memory performance. Recent translational studies in humans also suggest that aerobic fitness is associated with hippocampal volume and better spatial memory during adolescence. However, associations between fitness, hippocampal subfield morphology, and learning capabilities in human adolescents remain largely unknown. Employing a translational study design in 34 adolescent males, we explored the relationship between aerobic fitness, hippocampal subfield volumes, and both spatial and verbal memory. Aerobic fitness, assessed by peak oxygen utilization on a high-intensity exercise test (VO2 peak), was positively associated with the volumetric enlargement of the hippocampal head, and the CA1 head region specifically. Larger CA1 volumes were also associated with spatial learning on a Virtual Morris Water Maze task and verbal learning on the Rey Auditory Verbal Learning Test, but not recall memory. In line with previous animal work, the current findings lend support for the long-axis specialization of the hippocampus in the areas of exercise and learning during adolescence.

Test-retest reliability of hippocampal subfield volumes in a developmental sample: Implications for longitudinal developmental studies

The hippocampus (Hc) is composed of cytoarchitectonically distinct subfields: dentate gyrus (DG), cornu ammonis sectors 1-3 (CA1-3), and subiculum. Limited evidence suggests differential maturation rates across the Hc subfields. While longitudinal studies are essential in demonstrating differential development of Hc subfields, a prerequisite for interpreting meaningful longitudinal effects is establishing test-retest consistency of Hc subfield volumes measured in vivo over time. Here, we examined test-retest consistency of Hc subfield volumes measured from structural MR images in two independent developmental samples. Sample One (n = 28, ages 7-20 years, M = 12.64, SD = 3.35) and Sample Two (n = 28, ages 7-17 years, M = 11.72, SD = 2.88) underwent MRI twice with a 1-month and a 2-year delay, respectively. High-resolution PD-TSE-T₂ -weighted MR images (0.4 × 0.4 × 2 mm³ ) were collected and manually traced using a longitudinal manual demarcation protocol. In both samples, we found excellent consistency of Hc subfield volumes between the two visits, assessed by two-way mixed intraclass correlation (ICC (3) single measures ≥ 0.87), and no difference between children and adolescents. The results further indicated that discrepancies between repeated measures were not related to Hc subfield volumes, or visit number. In addition to high consistency, with the applied longitudinal protocol, we detected significant variability in Hc subfield volume changes over the 2-year delay, implying high sensitivity of the method in detecting individual differences. Establishing unbiased, high longitudinal consistency of Hc subfield volume measurements optimizes statistical power of a hypothesis test and reduces standard error of the estimate, together improving external validity of the measures in constructing theoretical models of memory development.

Autism classified by magnetic resonance imaging: A pilot study of a potential diagnostic tool

OBJECTIVES

Individual anatomical biomarkers have limited power for the classification of autism. The present study introduces a multivariate classification approach using structural magnetic resonance imaging data from individuals with and without autism.

METHODS

The classifier utilizes z-normalization, parameter weighting, and interindividual comparison on brain segmentation data, for estimation of an individual summed total index (TI). The TI indicates whether the gross morphological pattern of each individual's brain is in the direction of cases or controls.

RESULTS

Morphometric analysis found significant differences within subcortical gray matter structures and limbic areas. There was no significant difference in total brain volume. A case-control pilot-study of TIs in normally intelligent individuals with autism (24) and without (21) yielded a maximal accuracy of 78.9% following cross-validation. It showed a high accuracy compared with machine learning methods when tested on the same dataset. The TI correlated well with the autism quotient (R = 0.51) across groups.

CONCLUSION

These results are on par with studies on autism using machine learning. The main contributions are its transparency and simplicity. The possibility of including additional neuroimaging data further increases the potential of the classifier as a diagnostic aid for neuropsychiatric disorders, as well as a research tool for neuroscientific investigations.

Automatic multispectral MRI segmentation of human hippocampal subfields: an evaluation of multicentric test-retest reproducibility

Accurate and reproducible automated segmentation of human hippocampal subfields is of interest to study their roles in cognitive functions and disease processes. Multispectral structural MRI methods have been proposed to improve automated hippocampal subfield segmentation accuracy, but the reproducibility in a multicentric setting is, to date, not well characterized. Here, we assessed test-retest reproducibility of FreeSurfer 6.0 hippocampal subfield segmentations using multispectral MRI analysis pipelines (22 healthy subjects scanned twice, a week apart, at four 3T MRI sites). The harmonized MRI protocol included two 3D-T1, a 3D-FLAIR, and a high-resolution 2D-T2. After within-session T1 averaging, subfield volumes were segmented using three pipelines with different multispectral data: two longitudinal ("long_T1s" and "long_T1s_FLAIR") and one cross-sectional ("long_T1s_FLAIR_crossT2"). Volume reproducibility was quantified in magnitude (reproducibility error-RE) and space (DICE coefficient). RE was lower in all hippocampal subfields, except for hippocampal fissure, using the longitudinal pipelines compared to long_T1s_FLAIR_crossT2 (average RE reduction of 0.4-3.6%). Similarly, the longitudinal pipelines showed a higher spatial reproducibility (1.1-7.8% of DICE improvement) in all hippocampal structures compared to long_T1s_FLAIR_crossT2. Moreover, long_T1s_FLAIR provided a small but significant RE improvement in comparison to long_T1s (p = 0.015), whereas no significant DICE differences were found. In addition, structures with volumes larger than 200 mm³ had better RE (1-2%) and DICE (0.7-0.95) than smaller structures. In summary, our study suggests that the most reproducible hippocampal subfield FreeSurfer segmentations are derived from a longitudinal pipeline using 3D-T1s and 3D-FLAIR. Adapting a longitudinal pipeline to include high-resolution 2D-T2 may lead to further improvements.

Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting

Three-dimensional (3D) Magnetic resonance fingerprinting (MRF) permits whole-brain volumetric quantification of T1 and T2 relaxation values, potentially replacing conventional T1-weighted structural imaging for common brain imaging analysis. The aim of this study was to evaluate the repeatability and reproducibility of 3D MRF in evaluating brain cortical thickness and subcortical volumetric analysis in healthy volunteers using conventional 3D T1-weighted images as a reference standard. Scan-rescan tests of both 3D MRF and conventional 3D fast spoiled gradient recalled echo (FSPGR) were performed. For each sequence, the regional cortical thickness and volume of the subcortical structures were measured using standard automatic brain segmentation software. Repeatability and reproducibility were assessed using the within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), and mean percent difference and ICC, respectively. The wCV and ICC of cortical thickness were similar across all regions with both 3D MRF and FSPGR. The percent relative difference in cortical thickness between 3D MRF and FSPGR across all regions was 8.0 ± 3.2%. The wCV and ICC of the volume of subcortical structures across all structures were similar between 3D MRF and FSPGR. The percent relative difference in the volume of subcortical structures between 3D MRF and FSPGR across all structures was 7.1 ± 3.6%. 3D MRF measurements of human brain cortical thickness and subcortical volumes are highly repeatable, and consistent with measurements taken on conventional 3D T1-weighted images. A slight, consistent bias was evident between the two, and thus careful attention is required when combining data from MRF and conventional acquisitions.

The YOUth cohort study: MRI protocol and test-retest reliability in adults

The YOUth cohort study is a unique longitudinal study on brain development in the general population. As part of the YOUth study, 2000 children will be included at 8, 9 or 10 years of age and planned to return every three years during adolescence. Magnetic resonance imaging (MRI) brain scans are collected, including structural T1-weighted imaging, diffusion-weighted imaging (DWI), resting-state functional MRI and task-based functional MRI. Here, we provide a comprehensive report of the MR acquisition in YOUth Child & Adolescent including the test-retest reliability of brain measures derived from each type of scan. To measure test-retest reliability, 17 adults were scanned twice with a week between sessions using the full YOUth MRI protocol. Intraclass correlation coefficients were calculated to quantify reliability. Global brain measures derived from structural T1-weighted and DWI scans were reliable. Resting-state functional connectivity was moderately reliable, as well as functional brain measures for both the inhibition task (stop versus go) and the emotion task (face versus house). Our results complement previous studies by presenting reliability results of regional brain measures collected with different MRI modalities. YOUth facilitates data sharing and aims for reliable and high-quality data. Here we show that using the state-of-the art YOUth MRI protocol brain measures can be estimated reliably.

Serial MRI studies over 12 months using manual and atlas-based region of interest in patients with amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by loss of upper and lower motor neurons. There is a need for an imaging biomarker to track disease progression. Previously, magnetic resonance imaging (MRI) has shown loss of grey and white matter in the brain of patients with ALS compared to controls. We performed serial diffusion tractography imaging (DTI) study of patients with ALS looking for changes over time.

METHODS

On all subjects (n = 15), we performed three MRI studies at 6 month intervals. DTI changes were assessed with tract-based spatial statistics (TBSS) and region of interest (ROI) studies. Cortic-spinal tract (CST) was selected for our ROI at the upper level; the posterior limb of internal capsule (PLIC), and a lower level in the pons.

RESULTS

There was no significant change in DTI measures over 12 months of observation. Better correlation of manual and atlas-based ROI methods was found in the posterior limb of the internal capsule than the pons.

CONCLUSION

While previous DTI studies showed significant differences between ALS subjects and controls, within individual subjects there is little evidence of progression over 12 months. This suggests that DTI is not a suitable biomarker to assess disease progression in ALS.

Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning

Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI. Advantages are the timely availability of results while maintaining a clinically relevant accuracy. Materials and Methods: An anonymized dataset of 574 subjects (443 healthy controls and 131 patients with epilepsy) was used for the supervised training of a convolutional neural network (CNN). A silver-standard ground truth was generated with FreeSurfer 6.0. Results: The CNN predicts a total of 165 morphometric measures directly from raw MR images. Analysis of the results using intraclass correlation coefficients showed, in general, good correlation with FreeSurfer generated ground truth data, with some of the regions nearly reaching human inter-rater performance (ICC > 0.75). Cortical thicknesses predicted by the CNN showed cross-sectional annual age-related gray matter atrophy rates both globally (thickness change of -0.004 mm/year) and regionally in agreement with the literature. A statistical test to dichotomize patients with epilepsy from healthy controls revealed similar effect sizes for structures affecting all subtypes as reported in a large-scale epilepsy study. Conclusions: We demonstrate the general feasibility of using deep learning to estimate human brain morphometry directly from T1-weighted MRI within seconds. A comparison of the results to other publications shows accuracies of comparable magnitudes for the subcortical volumes and cortical thicknesses.

MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice

Early evaluation of treatment response and prediction of disease evolution are key issues in the management of people with multiple sclerosis (MS). In the past 20 years, MRI has become the most useful paraclinical tool in both situations and is used clinically to assess the inflammatory component of the disease, particularly the presence and evolution of focal lesions - the pathological hallmark of MS. However, diffuse neurodegenerative processes that are at least partly independent of inflammatory mechanisms can develop early in people with MS and are closely related to disability. The effects of these neurodegenerative processes at a macroscopic level can be quantified by estimation of brain and spinal cord atrophy with MRI. MRI measurements of atrophy in MS have also been proposed as a complementary approach to lesion assessment to facilitate the prediction of clinical outcomes and to assess treatment responses. In this Consensus statement, the Magnetic Resonance Imaging in MS (MAGNIMS) study group critically review the application of brain and spinal cord atrophy in clinical practice in the management of MS, considering the role of atrophy measures in prognosis and treatment monitoring and the barriers to clinical use of these measures. On the basis of this review, the group makes consensus statements and recommendations for future research.

Test-retest of automated segmentation with different motion correction strategies: A comparison of prospective versus retrospective methods

Test-retest of automated image segmentation algorithms (FSL FAST, FSL FIRST, and FREESURFER) are computed on magnetic resonance images from 12 unsedated children aged 9.4±2.6 years ([min,max] ​= ​[6.5 years, 13.8 years]) using different approaches to motion correction (prospective versus retrospective). The prospective technique, PROMO MPRAGE, dynamically estimates motion using specially acquired navigator images and adjusts the remaining acquisition accordingly, whereas the retrospective technique, MPnRAGE, uses a self-navigation property to retrospectively estimate and account for motion during image reconstruction. To increase the likelihood and range of motions, participants heads were not stabilized with padding during repeated scans. When motion was negligible both techniques had similar performance. When motion was not negligible, the automated image segmentation and anatomical labeling software tools showed the most consistent performance with the retrospectively corrected MPnRAGE technique (≥80% volume overlaps for 15 of 16 regions for FIRST and FREESURFER, with greater than 90% volume overlaps for 12 regions with FIRST and 11 regions with FREESURFER). Prospectively corrected MPRAGE with linear view-ordering also demonstrated lower performance than MPnRAGE without retrospective motion correction.

Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change

Little is still known about the neuroanatomical substrates related to changes in specific cognitive abilities in the course of healthy aging, and the existing evidence is predominantly based on cross-sectional studies. However, to understand the intricate dynamics between developmental changes in brain structure and changes in cognitive ability, longitudinal studies are needed. In the present article, we review the current longitudinal evidence on correlated changes between magnetic resonance imaging-derived measures of brain structure (e.g. gray matter/white matter volume, cortical thickness), and laboratory-based measures of fluid cognitive ability (e.g. intelligence, memory, processing speed) in healthy older adults. To theoretically embed the discussion, we refer to the revised Scaffolding Theory of Aging and Cognition. We found 31 eligible articles, with sample sizes ranging from n = 25 to n = 731 (median n = 104), and participant age ranging from 19 to 103. Several of these studies report positive correlated changes for specific regions and specific cognitive abilities (e.g. between structures of the medial temporal lobe and episodic memory). However, the number of studies presenting converging evidence is small, and the large methodological variability between studies precludes general conclusions. Methodological and theoretical limitations are discussed. Clearly, more empirical evidence is needed to advance the field. Therefore, we provide guidance for future researchers by presenting ideas to stimulate theory and methods for development.

Amygdala Changes in Chronic Insomnia and Their Association with Sleep and Anxiety Symptoms: Insight from Shape Analysis

Affective disorders, such as anxiety and depression, are common comorbidities associated with chronic insomnia disorder (CID). However, the underlying neural mechanisms of these comorbidities are still not clear. The present study is aimed at investigating structural changes in the amygdala of CID patients using surface-based shape analysis. A total of 65 medication-naive patients with CID and 55 healthy controls (HCs) matched for age, sex, and years of education were enrolled in this study and were subjected to structural magnetic resonance imaging (MRI). The Oxford Centre for Functional MRI of the Brain (FMRIB) created an Integrated Registration and Segmentation Tool (FIRST) that was employed in this study to assess the volumetric and surface alterations in patients with CID. Shape correlations between the amygdala and clinical features were also analyzed. Atrophic changes in the amygdala were observed at the local level, not for the entire amygdala volume. The left atrophic changes in the amygdala were in the superficial and basolateral nuclei while right atrophic changes were in the basolateral nuclei in CID patients. Insomnia severity was associated with the centromedial right amygdala while anxiety was linked with the basolateral nuclei. These findings indicate localized amygdala atrophy in CID. Separate amygdala regions are associated with insomnia and anxiety in CID. This evidence helps elucidate the neural mechanisms underlying the bidirectional relationship between insomnia and anxiety.

Network Communities of Dynamical Influence

Fuelled by a desire for greater connectivity, networked systems now pervade our society at an unprecedented level that will affect it in ways we do not yet understand. In contrast, nature has already developed efficient networks that can instigate rapid response and consensus when key elements are stimulated. We present a technique for identifying these key elements by investigating the relationships between a system's most dominant eigenvectors. This approach reveals the most effective vertices for leading a network to rapid consensus when stimulated, as well as the communities that form under their dynamical influence. In applying this technique, the effectiveness of starling flocks was found to be due, in part, to the low outdegree of every bird, where increasing the number of outgoing connections can produce a less responsive flock. A larger outdegree also affects the location of the birds with the most influence, where these influentially connected birds become more centrally located and in a poorer position to observe a predator and, hence, instigate an evasion manoeuvre. Finally, the technique was found to be effective in large voxel-wise brain connectomes where subjects can be identified from their influential communities.

Multivariate models of brain volume for identification of children and adolescents with fetal alcohol spectrum disorder

Magnetic resonance imaging (MRI) studies of fetal alcohol spectrum disorder (FASD) have shown reductions of brain volume associated with prenatal exposure to alcohol. Previous studies consider regional brain volumes independently but ignore potential relationships across numerous structures. This study aims to (a) identify a multivariate model based on regional brain volume that discriminates children/adolescents with FASD versus healthy controls, and (b) determine if FASD classification performance can be increased by building classification models separately for each sex. Three‐dimensional T1‐weighted MRI from two independent childhood/adolescent datasets were used for training (79 FASD, aged 5.7–18.9 years, 35 males; 81 controls, aged 5.8–18.5 years, 32 males) and testing (67 FASD, aged 6.0–19.6 years, 38 males; 74 controls, aged 5.2–19.5 years, 42 males) a classification model. Using FreeSurfer, 87 regional brain volumes were extracted for each subject and were used as input into a support vector machine generating a classification model from the training data. The model performed moderately well on the test data with accuracy 77%, sensitivity 64%, and specificity 88%. Regions that contributed heavily to prediction in this model included temporal lobe and subcortical gray matter. Further investigation of two separate models for males and females showed slightly decreased accuracy compared to the model including all subjects (male accuracy 70%; female accuracy 67%), but had different regional contributions suggesting sex differences. This work demonstrates the potential of multivariate analysis of brain volumes for discriminating children/adolescents with FASD and provides indication of the most affected regions.

Structural correlates of commission errors in prospective memory

Prospective memory refers to remembering to perform an intended future action, such as remembering to take medication with breakfast. Historically, the field has focused on failures to initially remember a prospective memory task (omission errors), but interestingly, individuals will occasionally repeat a prospective memory action after it has been completed (e.g., double dosing). These failures in prospective memory deactivation/forgetting are termed commission errors. The current registered study investigated structural neuroimaging correlates of a laboratory measure of commission errors in 47 healthy older adults. Extant theories differed in their predicted outcomes: commission error risk was predicted to be highest in individuals with smaller medial temporal lobe volume (output monitoring theory), larger lateral prefrontal cortex volume (residual activation theory), or a combination of larger medial temporal lobe volume and smaller lateral prefrontal cortex volume (dual mechanisms theory). In registered analyses, we found that a higher number of commission errors was associated with larger medial temporal lobe/hippocampal grey matter volume (supporting dual mechanisms theory), but not with grey or white matter volume in the lateral parietal lobe, frontal pole, or a composite of ventrolateral/dorsolateral prefrontal cortex (not supporting dual mechanisms theory). In post hoc analyses, smaller volume in the lateral orbitofrontal cortex was associated with a higher number of commission errors, possibly indicating that the dual mechanisms theory of PFC control was conceptually correct, but that a different PFC subregion than anticipated exerts control over commission errors. Collectively, the registered and post hoc analysis findings showed a functional dissociation across MTL/PFC regions that was more consistent with the dual mechanisms theory than the alternative theories.

Test-retest reproducibility of a multi-atlas automated segmentation tool on multimodality brain MRI

INTRODUCTION

The increasing use of large sample sizes for population and personalized medicine requires high-throughput tools for imaging processing that can handle large amounts of data with diverse image modalities, perform a biologically meaningful information reduction, and result in comprehensive quantification. Exploring the reproducibility of these tools reveals the specific strengths and weaknesses that heavily influence the interpretation of results, contributing to transparence in science.

METHODS

We tested-retested the reproducibility of MRICloud, a free automated method for whole-brain, multimodal MRI segmentation and quantification, on two public, independent datasets of healthy adults.

RESULTS

The reproducibility was extremely high for T1-volumetric analysis, high for diffusion tensor images (DTI) (however, regionally variable), and low for resting-state fMRI.

CONCLUSION

In general, the reproducibility of the different modalities was slightly superior to that of widely used software. This analysis serves as a normative reference for planning samples and for the interpretation of structure-based MRI studies.

Callous and uncaring traits are associated with reductions in amygdala volume among youths with varying levels of conduct problems

BACKGROUND

The emergence of callous unemotional (CU) traits, and associated externalizing behaviors, is believed to reflect underlying dysfunction in the amygdala. Studies of adults with CU traits or psychopathy have linked characteristic patterns of amygdala dysfunction to reduced amygdala volume, but studies in youths have not thus far found evidence of similar amygdala volume reductions. The current study examined the association between CU traits and amygdala volume by modeling CU traits and externalizing behavior as independent continuous variables, and explored the relative contributions of callous, uncaring, and unemotional traits.

METHODS

CU traits and externalizing behavior problems were assessed in 148 youths using the Inventory of Callous Unemotional Traits (ICU) and the Child Behavior Checklist (CBCL). For a subset of participants (n = 93), high-resolution T1-weighted images were collected and volume estimates for the amygdala were extracted.

RESULTS

Analyses revealed that CU traits were associated with increased externalizing behaviors and decreased bilateral amygdala volume. These results were driven by the callous and uncaring sub-factors of CU traits, with unemotional traits unrelated to either externalizing behaviors or amygdala volume. Results persisted after accounting for covariation between CU traits and externalizing behaviors. Bootstrap mediation analyses indicated that CU traits mediated the relationship between reduced amygdala volume and externalizing severity.

CONCLUSIONS

These findings provide evidence that callous-uncaring traits account for reduced amygdala volume among youths with conduct problems. These findings provide a framework for further investigation of abnormal amygdala development as a key causal pathway for the development of callous-uncaring traits and conduct problems.

Parsing neurodevelopmental features of irritability and anxiety: Replication and validation of a latent variable approach

Irritability and anxiety are two common clinical phenotypes that involve high-arousal negative affect states (anger and fear), and that frequently co-occur. Elucidating how these two forms of emotion dysregulation relate to perturbed neurodevelopment may benefit from alternate phenotyping strategies. One such strategy applies a bifactor latent variable approach that can parse shared versus unique mechanisms of these two phenotypes. Here, we aim to replicate and extend this approach and examine associations with neural structure in a large transdiagnostic sample of youth (N = 331; M = 13.57, SD = 2.69 years old; 45.92% male). FreeSurfer was used to extract cortical thickness, cortical surface area, and subcortical volume. The current findings replicated the bifactor model and demonstrate measurement invariance as a function of youth age and sex. There were no associations of youth's factor scores with cortical thickness, surface area, or subcortical volume. However, we found strong convergent and divergent validity between parent-reported irritability and anxiety factors with clinician-rated symptoms and impairment. A general negative affectivity factor was robustly associated with overall functional impairment across symptom domains. Together, these results support the utility of the bifactor model as an alternative phenotyping strategy for irritability and anxiety, which may aid in the development of targeted treatments.

3D quantitative synthetic MRI-derived cortical thickness and subcortical brain volumes: Scan-rescan repeatability and comparison with conventional T1 -weighted images

Background

Previous quantitative synthetic MRI of the brain has been solely performed in 2D.

Purpose

To evaluate the feasibility of the recently developed sequence 3D‐QALAS for brain cortical thickness and volumetric analysis.

Study Type

Reproducibility/repeatability study.

Subjects

Twenty‐one healthy volunteers (35.6 ± 13.8 years).

Field Strength/Sequence

3D T₁‐weighted fast spoiled gradient recalled echo (FSPGR) sequence was performed once, and 3D‐QALAS sequence was performed twice with a 3T scanner.

Assessment

FreeSurfer and FIRST were used to measure cortical thickness and volume of subcortical structures, respectively. Agreement with FSPGR and scan–rescan repeatability were evaluated for 3D‐QALAS.

Statistical Tests

Percent relative difference and intraclass correlation coefficient (ICC) were used to assess reproducibility and scan–rescan repeatability of the 3D‐QALAS sequence‐derived measurements.

Results

Percent relative difference compared with FSPGR in cortical thickness of the whole cortex was 3.1%, and 89% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.65, and 74% of the structures showed substantial to almost perfect agreement. For volumes of subcortical structures, the median percent relative differences were lower than 10% across all subcortical structures, except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement. For the scan–rescan test, percent relative difference in cortical thickness of the whole cortex was 2.3%, and 97% of the regional areas showed less than 10% relative difference in cortical thickness. The mean ICC across all regions was 0.73, and 80% showed substantial to almost perfect agreement. For volumes of subcortical structures, relative differences were less than 10% across all subcortical structures except for the accumbens area, and all structures showed ICCs of substantial to almost perfect agreement.

Data Conclusion

3D‐QALAS could be reliably used for measuring cortical thickness and subcortical volumes in most brain regions.

Level of Evidence: 3

Technical Efficacy: Stage 1

J. Magn. Reson. Imaging 2019;50:1834–1842.

Neural responses to children's faces: Test-retest reliability of structural and functional MRI

INTRODUCTION

Functional MRI (fMRI) is commonly used to investigate the neural mechanisms underlying psychological processes and behavioral responses. However, to draw well-founded conclusions from fMRI studies, more research on the reliability of fMRI is needed.

METHODS

We invited a sample of 41 female students to participate in two identical fMRI sessions, separated by 5 weeks on average. To investigate the potential effect of left-handedness on the stability of neural activity, we oversampled left-handed participants (N = 20). Inside the scanner, we presented photographs of familiar and unfamiliar children's faces preceded by neutral and threatening primes to the participants. We calculated intraclass correlations (ICCs) to investigate the test-retest reliability of peak activity in areas that showed significant activity during the first session (primary visual cortex, fusiform face area, inferior frontal gyrus, and superior temporal gyrus). In addition, we examined how many trials were needed to reliably measure the effects.

RESULTS

Across all participants, only fusiform face area activity in response to faces showed good test-retest reliability (ICC = 0.71). All other test-retest reliabilities were low (0.01 ≤ ICC ≤ 0.35). Reliabilities varied only slightly with increasing numbers of trials, with no consistent increase in ICCs. Test-retest reliabilities for left-handed participants (0.28 ≤ ICC ≤0.66) were generally somewhat higher than for right-handed participants (-0.13 ≤ ICC ≤0.75), but not statistically significant.

CONCLUSION

Our study shows good test-retest reliability for fusiform facer area activity in response to faces, but low test-retest reliability for other contrasts and areas.

FAst Segmentation Through SURface Fairing (FASTSURF): A novel semi-automatic hippocampus segmentation method

Objective

The objective is to present a proof-of-concept of a semi-automatic method to reduce hippocampus segmentation time on magnetic resonance images (MRI).

Materials and methods

FAst Segmentation Through SURface Fairing (FASTSURF) is based on a surface fairing technique which reconstructs the hippocampus from sparse delineations. To validate FASTSURF, simulations were performed in which sparse delineations extracted from full manual segmentations served as input. On three different datasets with different diagnostic groups, FASTSURF hippocampi were compared to the original segmentations using Jaccard overlap indices and percentage volume differences (PVD). In one data set for which back-to-back scans were available, unbiased estimates of overlap and PVD were obtained. Using longitudinal scans, we compared hippocampal atrophy rates measured by manual, FASTSURF and two automatic segmentations (FreeSurfer and FSL-FIRST).

Results

With only seven input contours, FASTSURF yielded mean Jaccard indices ranging from 72(±4.3)% to 83(±2.6)% and PVDs ranging from 0.02(±2.40)% to 3.2(±3.40)% across the three datasets. Slightly poorer results were obtained for the unbiased analysis, but the performance was still considerably better than both tested automatic methods with only five contours.

Conclusions

FASTSURF segmentations have high accuracy and require only a fraction of the delineation effort of fully manual segmentation. Atrophy rate quantification based on completely manual segmentation is well reproduced by FASTSURF. Therefore, FASTSURF is a promising tool to be implemented in clinical workflow, provided a future prospective validation confirms our findings.

A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology

The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the serial histology of 12 whole thalami from six autopsy samples, combined with manual segmentations of the whole thalamus and surrounding structures (caudate, putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects. The 3D structure of the histological data and corresponding manual segmentations was recovered using the ex vivo MRI as reference frame, and stacks of blockface photographs acquired during the sectioning as intermediate target. The atlas, which was encoded as an adaptive tetrahedral mesh, shows a good agreement with previous histological studies of the thalamus in terms of volumes of representative nuclei. When applied to segmentation of in vivo scans using Bayesian inference, the atlas shows excellent test-retest reliability, robustness to changes in input MRI contrast, and ability to detect differential thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas and companion segmentation tool are publicly available as part of the neuroimaging package FreeSurfer.

Relevance of neuroimaging for neurocognitive and behavioral outcome after pediatric traumatic brain injury

This study aims to (1) investigate the neuropathology of mild to severe pediatric TBI and (2) elucidate the predictive value of conventional and innovative neuroimaging for functional outcome. Children aged 8–14 years with trauma control (TC) injury (n = 27) were compared to children with mild TBI and risk factors for complicated TBI (mild^RF+, n = 20) or moderate/severe TBI (n = 17) at 2.8 years post-injury. Neuroimaging measures included: acute computed tomography (CT), volumetric analysis on post-acute conventional T1-weighted magnetic resonance imaging (MRI) and post-acute diffusion tensor imaging (DTI, analyzed using tract-based spatial statistics and voxel-wise regression). Functional outcome was measured using Common Data Elements for neurocognitive and behavioral functioning. The results show that intracranial pathology on acute CT-scans was more prevalent after moderate/severe TBI (65%) than after mild^RF+ TBI (35%; p = .035), while both groups had decreased white matter volume on conventional MRI (ps ≤ .029, ds ≥ −0.74). The moderate/severe TBI group further showed decreased fractional anisotropy (FA) in a widespread cluster affecting all white matter tracts, in which regional associations with neurocognitive functioning were observed (FSIQ, Digit Span and RAVLT Encoding) that consistently involved the corpus callosum. FA had superior predictive value for functional outcome (i.e. intelligence, attention and working memory, encoding in verbal memory and internalizing problems) relative to acute CT-scanning (i.e. internalizing problems) and conventional MRI (no predictive value). We conclude that children with mild^RF+ TBI and moderate/severe TBI are at risk of persistent white matter abnormality. Furthermore, DTI has superior predictive value for neurocognitive out-come relative to conventional neuroimaging.

Increased stressor-evoked cardiovascular reactivity is associated with reduced amygdala and hippocampus volume

Exaggerated cardiovascular reactivity to acute psychological stress is associated with an increased risk of developing cardiovascular disease. The amygdala and hippocampus have been implicated in centrally mediating stressor-evoked cardiovascular reactivity. However, little is known about the associations of amygdala and hippocampus morphology with stressor-evoked cardiovascular reactivity. Forty (M_age = 19.05, SD = 0.22 years) healthy young women completed two separate testing sessions. Session 1 assessed multiple parameters of cardiovascular physiology at rest and during a validated psychological stress task (Paced Auditory Serial Addition Test), using electrocardiography, Doppler echocardiography, and blood pressure monitoring. In Session 2, 1 year later, structural MRI was conducted. Brain structural volumes were computed using automated segmentation methods. Regression analyses, following Benjamini-Hochberg correction, showed that greater heart rate and cardiac output reactivity were associated with reduced amygdala and hippocampus gray matter volume. Systolic blood pressure reactivity was associated with reduced hippocampus volume. In contrast, no associations between diastolic blood pressure, mean arterial blood pressure, stroke volume, or total peripheral resistance reactivity with amygdala or hippocampus volumes were apparent. Comparison analyses examining insula volume found no significant associations. Some indicators of greater stressor-evoked cardiovascular reactivity associate with reduced amygdala and hippocampus gray matter volume, but the mechanisms of this association warrant further study.

Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)

Magnetic resonance imaging has become an indispensable tool for studying associations of structural and functional properties of the brain with behavior in humans. However, generally recognized standards for assessing and reporting the reliability of these techniques are still lacking. Here, we introduce a new approach for assessing and reporting reliability, termed intra-class effect decomposition (ICED). ICED uses structural equation modeling of data from a repeated-measures design to decompose reliability into orthogonal sources of measurement error that are associated with different characteristics of the measurements, for example, session, day, or scanning site. This allows researchers to describe the magnitude of different error components, make inferences about error sources, and inform them in planning future studies. We apply ICED to published measurements of myelin content and resting state functional connectivity. These examples illustrate how longitudinal data can be leveraged separately or conjointly with cross-sectional data to obtain more precise estimates of reliability.

Inter-observer variation of hippocampus delineation in hippocampal avoidance prophylactic cranial irradiation

BACKGROUND

Hippocampal avoidance prophylactic cranial irradiation (HA-PCI) techniques have been developed to reduce radiation damage to the hippocampus. An inter-observer hippocampus delineation analysis was performed and the influence of the delineation variability on dose to the hippocampus was studied.

MATERIALS AND METHODS

For five patients, seven observers delineated both hippocampi on brain MRI. The intra-class correlation (ICC) with absolute agreement and the generalized conformity index (CI_gen) were computed. Median surfaces over all observers' delineations were created for each patient and regional outlining differences were analysed. HA-PCI dose plans were made from the median surfaces and we investigated whether dose constraints in the hippocampus could be met for all delineations.

RESULTS

The ICC for the left and right hippocampus was 0.56 and 0.69, respectively, while the CI_gen ranged from 0.55 to 0.70. The posterior and anterior-medial hippocampal regions had most variation with SDs ranging from approximately 1 to 2.5 mm. The mean dose (D_mean) constraint was met for all delineations, but for the dose received by 1% of the hippocampal volume (D_1%) violations were observed.

CONCLUSION

The relatively low ICC and CI_gen indicate that delineation variability among observers for both left and right hippocampus was large. The posterior and anterior-medial border have the largest delineation inaccuracy. The hippocampus D_mean constraint was not violated.

A comparison of manual tracing and FreeSurfer for estimating hippocampal volume over the adult lifespan

MRI has become an indispensable tool for brain volumetric studies, with the hippocampus an important region of interest. Automation of the MRI segmentation process has helped advance the field by facilitating the volumetric analysis of larger cohorts and more studies. FreeSurfer has emerged as the de facto standard tool for these analyses, but studies validating its output are all based on older versions. To characterize FreeSurfer's validity, we compare several versions of FreeSurfer software with traditional hand-tracing. Using MRI images of 262 males and 402 females aged 38 to 84, we directly compare estimates of hippocampal volume from multiple versions of FreeSurfer, its hippocampal subfield routines, and our manual tracing protocol. We then use those estimates to assess asymmetry and atrophy, comparing performance of different estimators with each other and with brain atrophy measures. FreeSurfer consistently reports larger volumes than manual tracing. This difference is smaller in larger hippocampi or older people, with these biases weaker in version 6.0.0 than prior versions. All methods tested agree qualitatively on rightward asymmetry and increasing atrophy in older people. FreeSurfer saves time and money, and approximates the same atrophy measures as manual tracing, but it introduces biases that could require statistical adjustments in some studies.

Development of subcortical volumes across adolescence in males and females: A multisample study of longitudinal changes

The developmental patterns of subcortical brain volumes in males and females observed in previous studies have been inconsistent. To help resolve these discrepancies, we examined developmental trajectories using three independent longitudinal samples of participants in the age-span of 8-22 years (total 216 participants and 467 scans). These datasets, including Pittsburgh (PIT; University of Pittsburgh, USA), NeuroCognitive Development (NCD; University of Oslo, Norway), and Orygen Adolescent Development Study (OADS; The University of Melbourne, Australia), span three countries and were analyzed together and in parallel using mixed-effects modeling with both generalized additive models and general linear models. For all regions and across all samples, males were found to have significantly larger volumes as compared to females, and significant sex differences were seen in age trajectories over time. However, direct comparison of sample trajectories and sex differences identified within samples were not consistent. The trajectories for the amygdala, putamen, and nucleus accumbens were most consistent between the three samples. Our results suggest that even after using similar preprocessing and analytic techniques, additional factors, such as image acquisition or sample composition may contribute to some of the discrepancies in sex specific patterns in subcortical brain changes across adolescence, and highlight region-specific variations in congruency of developmental trajectories.

Test-retest reliability and longitudinal analysis of automated hippocampal subregion volumes in healthy ageing and Alzheimer's disease populations

The hippocampal formation is a complex brain structure that is important in cognitive processes such as memory, mood, reward processing and other executive functions. Histological and neuroimaging studies have implicated the hippocampal region in neuropsychiatric disorders as well as in neurodegenerative diseases. This highly plastic limbic region is made up of several subregions that are believed to have different functional roles. Therefore, there is a growing interest in imaging the subregions of the hippocampal formation rather than modelling the hippocampus as a homogenous structure, driving the development of new automated analysis tools. Consequently, there is a pressing need to understand the stability of the measures derived from these new techniques. In this study, an automated hippocampal subregion segmentation pipeline, released as a developmental version of Freesurfer (v6.0), was applied to T1-weighted magnetic resonance imaging (MRI) scans of 22 healthy older participants, scanned on 3 separate occasions and a separate longitudinal dataset of 40 Alzheimer's disease (AD) patients. Test-retest reliability of hippocampal subregion volumes was assessed using the intra-class correlation coefficient (ICC), percentage volume difference and percentage volume overlap (Dice). Sensitivity of the regional estimates to longitudinal change was estimated using linear mixed effects (LME) modelling. The results show that out of the 24 hippocampal subregions, 20 had ICC scores of 0.9 or higher in both samples; these regions include the molecular layer, granule cell layer of the dentate gyrus, CA1, CA3 and the subiculum (ICC > 0.9), whilst the hippocampal fissure and fimbria had lower ICC scores (0.73-0.88). Furthermore, LME analysis of the independent AD dataset demonstrated sensitivity to group and individual differences in the rate of volume change over time in several hippocampal subregions (CA1, molecular layer, CA3, hippocampal tail, fissure and presubiculum). These results indicate that this automated segmentation method provides a robust method with which to measure hippocampal subregions, and may be useful in tracking disease progression and measuring the effects of pharmacological intervention.