Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain

Modelling the overlap and divergence of autistic and schizotypal traits on hippocampal subfield volumes and regional cerebral blood flow

Psychiatric disorders show high co-morbidity, including co-morbid expressions of subclinical psychopathology across multiple disease spectra. Given the limitations of classical case-control designs in elucidating this overlap, new approaches are needed to identify biological underpinnings of spectra and their interaction. We assessed autistic-like traits (using the Autism Quotient, AQ) and schizotypy - as models of subclinical expressions of disease phenotypes and examined their association with volumes and regional cerebral blood flow (rCBF) of anterior, mid- and posterior hippocampus segments from  structural MRI scans in 318 and arterial spin labelling (ASL) in 346 nonclinical subjects, which overlapped with the structural imaging sample (N = 298). We demonstrate significant interactive effects of positive schizotypy and AQ social skills as well as of positive schizotypy and AQ imagination on hippocampal subfield volume variation. Moreover, we show that AQ attention switching modulated hippocampal head rCBF, while positive schizotypy by AQ attention to detail interactions modulated hippocampal tail rCBF. In addition, we show significant correlation of hippocampal volume and rCBF in both region-of-interest and voxel-wise analyses, which were robust after removal of variance related to schizotypy and autistic traits. These findings provide empirical evidence for both the modulation of hippocampal subfield structure and function through subclinical traits, and in particular how only the interaction of phenotype facets leads to significant reductions or variations in these parameters. This makes a case for considering the synergistic impact of different (subclinical) disease spectra on transdiagnostic biological parameters in psychiatry.

Comparative validation of AI and non-AI methods in MRI volumetry to diagnose Parkinsonian syndromes

Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain MRI segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls ([Formula: see text]) and patients with PD ([Formula: see text]), multiple systemic atrophy ([Formula: see text]), and progressive supranuclear palsy ([Formula: see text]) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotated data for DL models, the representative convolutional neural network (CNN) and vision transformer (ViT)-based models. Dice scores and the area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated to determine the measure to which FS performance can be reproduced as-is while increasing speed by the DL approaches. The segmentation times of CNN and ViT for the six brain structures per patient were 51.26 ± 2.50 and 1101.82 ± 22.31 s, respectively, being 14 to 300 times faster than FS (15,735 ± 1.07 s). Dice scores of both DL models were sufficiently high (> 0.85) so their AUCs for disease classification were not inferior to that of FS. For classification of normal vs. P-plus and PD vs. P-plus (except multiple systemic atrophy - Parkinsonian type) based on all brain parts, the DL models and FS showed AUCs above 0.8, demonstrating the clinical value of DL models in addition to FS. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research.

A multimodal neuroimaging study of brain abnormalities and clinical correlates in post treatment Lyme disease

Lyme disease is the most common vector-borne infectious disease in the United States. Post-treatment Lyme disease (PTLD) is a condition affecting 10-20% of patients in which symptoms persist despite antibiotic treatment. Cognitive complaints are common among those with PTLD, suggesting that brain changes are associated with the course of the illness. However, there has been a paucity of evidence to explain the cognitive difficulties expressed by patients with PTLD. This study administered a working memory task to a carefully screened group of 12 patients with well-characterized PTLD and 18 healthy controls while undergoing functional MRI (fMRI). A subset of 12 controls and all 12 PTLD participants also received diffusion tensor imaging (DTI) to measure white matter integrity. Clinical variables were also assessed and correlated with these multimodal MRI findings. On the working memory task, the patients with PTLD responded more slowly, but no less accurately, than did controls. FMRI activations were observed in expected regions by the controls, and to a lesser extent, by the PTLD participants. The PTLD group also hypoactivated several regions relevant to the task. Conversely, novel regions were activated by the PTLD group that were not observed in controls, suggesting a compensatory mechanism. Notably, three activations were located in white matter of the frontal lobe. DTI measures applied to these three regions of interest revealed that higher axial diffusivity correlated with fewer cognitive and neurological symptoms. Whole-brain DTI analyses revealed several frontal lobe regions in which higher axial diffusivity in the patients with PTLD correlated with longer duration of illness. Together, these results show that the brain is altered by PTLD, involving changes to white matter within the frontal lobe. Higher axial diffusivity may reflect white matter repair and healing over time, rather than pathology, and cognition appears to be dynamically affected throughout this repair process.

Modified MRI Anonymization (De-Facing) for Improved MEG Coregistration

Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code. However, sharing structural MRI data often requires the face go be hidden to help protect the identity of the individuals concerned. While automated de-facing methods exist, they tend to remove the whole face, which can impair methods for coregistering the MRI data with the EEG/MEG data. We show that a new, automated de-facing method that retains the nose maintains good MRI-MEG/EEG coregistration. Importantly, behavioural data show that this "face-trimming" method does not increase levels of identification relative to a standard de-facing approach and has less effect on the automated segmentation and surface extraction sometimes used to create head models for MEG/EEG localisation. We suggest that this trimming approach could be employed for future sharing of structural MRI data, at least for those to be used in forward modelling (source reconstruction) of EEG/MEG data.

Gray matter structural plasticity in patients with basal ganglia germ cell tumors: A voxel-based morphometry study

BACKGROUND

Basal ganglia germ cell tumors (BGGCTs) are rare intracranial germ cell tumors (iGCTs) that often presents with cognitive impairment.

OBJECTIVE

To assess structural brain plasticity in the presence of unilateral basal ganglia germ cell tumors (BGGCTs), and the correlation between gray matter volume (GMV) changes and cognitive tests.

MATERIALS AND METHODS

We applied voxel-based morphometry (VBM) to structural magnetic resonance imaging (MRI) scans to compare a sample of 41 patients with BGGCTs in the left (n = 22) or right (n = 19) and a sample of 16 patients as control group using a two-sample t-test, correcting for family-wise-errors. A battery of cognitive tests was administered to all BGGCTs patients prior to MRI. We used Pearson correlation analysis to assess the correlation between cognitive test scores and GMV changes.

RESULTS

In patients with left BGGCTs, whole-brain VBM analysis revealed a large cluster of voxels reflecting an increase in GMV in the left parahippocampal region (k = 529 voxels, T = 4.18, p < 0.01), right middle cingulate cortex (k = 172 voxels, T = 3.96, p < 0.01), and a decrease in volume in the left thalamus (k = 527 voxels, T = -4.88, p < 0.01), right inferior frontal gyrus (k = 495 voxels, T = -4.29, p < 0.01). Pearson correlation analysis showed that the GMV were significantly correlated with the Integrated Visual and Auditory continuous performance test (IVA-CPT) scale (r = 0.637, P = 0.002), abstract reasoning (r = 0.597, P = 0.011), Self-rating Depression Scale (SAS) scale (r = -0.623, P = 0.004) and memory recall (r = 0.648, P = 0.003).

CONCLUSION

These results demonstrate that slow growing but destructive BGGCTs markedly and asymmetrically effect the GMV in left parahippocampal, left thalamus, right middle cingulate cortex, right inferior frontal gyrus and GMV changes were significantly associated with cognitive test.

Head CT: Toward Making Full Use of the Information the X-Rays Give

Although clinical head CT images are typically interpreted qualitatively, automated methods applied to routine clinical head CTs enable quantitative assessment of brain volume, brain parenchymal fraction, brain radiodensity, and brain radiomass. These metrics gain clinical meaning when viewed relative to a reference database and expressed as quantile regression values. Quantitative imaging data can aid in objective reporting and in the identification of outliers, with possible diagnostic implications. The comparison to a reference database necessitates standardization of head CT imaging parameters and protocols. Future research is needed to learn the effects of virtual monochromatic imaging on the quantitative characteristics of head CT images.

MRI brain volume loss, lesion burden, and clinical outcome in secondary progressive multiple sclerosis

BACKGROUND

Magnetic resonance imaging (MRI) of brain volume measures are widely used outcomes in secondary progressive multiple sclerosis (SPMS), but it is unclear whether they are associated with physical and cognitive disability.

OBJECTIVE

To investigate the association between MRI outcomes and physical and cognitive disability worsening in people with SPMS.

METHODS

We used data from ASCEND, a large randomized controlled trial (n = 889). We investigated the association of change in whole brain and gray matter volume, contrast enhancing lesions, and T2 lesions with significant worsening on the Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk (T25FW), Nine-Hole Peg Test (NHPT), and Symbol Digit Modalities Test (SDMT) with logistic regression models.

RESULTS

We found no association between MRI measures and EDSS or SDMT worsening. T25FW worsening at 48 and 96 weeks, and NHPT worsening at 96 weeks were associated with cumulative new or newly enlarging T2 lesions at 96 weeks. NHPT worsening at 48 and 96 weeks was associated with normalized brain volume loss at 48 weeks, but not with other MRI outcomes.

CONCLUSION

The association of standard MRI outcomes and disability was noticeably weak and inconsistent over 2 years of follow-up. These MRI outcomes may not be useful surrogates of disability measures in SPMS.

Engagement in Enriching Early-Life Activities Is Associated With Larger Hippocampal and Amygdala Volumes in Community-Dwelling Older Adults

OBJECTIVES

Numerous studies show benefits of mid- and late-life activity on neurocognitive health. Yet, few studies have examined how engagement in enriching activities during childhood, when the brain is most plastic, may confer long-term neurocognitive benefits that may be especially important to individuals raised in low-income settings. We examined associations between enriching early-life activities (EELAs) and hippocampal and amygdala volumes in a sample of predominantly African-American, community-dwelling older adults. We further assessed whether these associations were independent of current activity engagement.

METHODS

Ninety participants from the baseline Brain Health Substudy of the Baltimore Experience Corps Trial (mean age: 67.4) completed retrospective activity inventories and an magnetic resonance imaging scan. Volumes were segmented using FreeSurfer.

RESULTS

Each additional EELA was associated with a 2.3% (66.6 mm3) greater amygdala volume after adjusting for covariates. For men, each additional EELA was associated with a 4.1% (278.9 mm3) greater hippocampal volume. Associations were specific to these regions when compared with the thalamus, used as a control region.

DISCUSSION

Enriching lifestyle activities during an important window of childhood brain development may be a modifiable factor that impacts lifelong brain reserve, and results highlight the importance of providing access to such activities in historically underserved populations.

PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface

Accurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers a robust, efficient and generalizable pipeline for the automatic standardization of MRI surface analysis on NHP.

SeSBAT: Single Subject Brain Analysis Toolbox. Application to Huntington's Disease as a Preliminary Study

Magnetic resonance imaging (MRI) biomarkers require complex processing routines that are time-consuming and labor-intensive for clinical users. The Single Subject Brain Analysis Toolbox (SeSBAT) is a fully automated MATLAB toolbox with a graphical user interface (GUI) that offers standardized and optimized protocols for the pre-processing and analysis of anatomical MRI data at the single-subject level. In this study, the two-fold strategy provided by SeSBAT is illustrated through its application on a cohort of 42 patients with Huntington’s disease (HD), in pre-manifest and early manifest stages, as a suitable model of neurodegenerative processes. On the one hand, hypothesis-driven analysis can be used to extract biomarkers of neurodegeneration in specific brain regions of interest (ROI-based analysis). On the other hand, an exploratory voxel-based morphometry (VBM) approach can detect volume changes due to neurodegeneration throughout the whole brain (whole-brain analysis). That illustration reveals the potential of SeSBAT in providing potential prognostic biomarkers in neurodegenerative processes in clinics, which could be critical to overcoming the limitations of current qualitative evaluation strategies, and thus improve the diagnosis and monitoring of neurodegenerative disorders. Furthermore, the importance of the availability of tools for characterization at the single-subject level has been emphasized, as there is high interindividual variability in the pattern of neurodegeneration. Thus, tools like SeSBAT could pave the way towards more effective and personalized medicine.

A new virtue of phantom MRI data: explaining variance in human participant data

Background: Magnetic resonance imaging (MRI) is an important yet complex data acquisition technology for studying the brain. MRI signals can be affected by many factors and many sources of variance are often simply attributed to "noise". Unexplained variance in MRI data hinders the statistical power of MRI studies and affects their reproducibility. We hypothesized that it would be possible to use phantom data as a proxy of scanner characteristics with a simplistic model of seasonal variation to explain some variance in human MRI data. Methods: We used MRI data from human participants collected in several studies, as well as phantom data collected weekly for scanner quality assurance (QA) purposes. From phantom data we identified the variables most likely to explain variance in acquired data and assessed their statistical significance by using them to model signal-to-noise ratio (SNR), a fundamental MRI QA metric. We then included phantom data SNR in the models of morphometric measures obtained from human anatomical MRI data from the same scanner. Results: Phantom SNR and seasonal variation, after multiple comparisons correction, were statistically significant predictors of the volume of gray brain matter. However, a sweep over 16 other brain matter areas and types revealed no statistically significant predictors among phantom SNR or seasonal variables after multiple comparison correction. Conclusions: Seasonal variation and phantom SNR may be important factors to account for in MRI studies. Our results show weak support that seasonal variations are primarily caused by biological human factors instead of scanner performance variation. The phantom QA metric and scanning parameters are useful for more than just QA. Using QA metrics, scanning parameters, and seasonal variation data can help account for some variance in MRI studies, thus making them more powerful and reproducible.

Anterior vs Posterior Hippocampal Subfields in an Extended Psychosis Phenotype of Multidimensional Schizotypy in a Nonclinical Sample

Numerous studies have implicated involvement of the hippocampus in the etiology and expression of schizophrenia-spectrum psychopathology, and reduced hippocampal volume is one of the most robust brain abnormalities reported in schizophrenia. Recent studies indicate that early stages of schizophrenia are specifically characterized by reductions in anterior hippocampal volume; however, studies have not examined hippocampal volume reductions in subclinical schizotypy. The present study was the first to examine the associations of positive, negative, and disorganized schizotypy dimensions with hippocampal subfield volumes in a large sample (n = 195) of nonclinically ascertained young adults, phenotyped using the Multidimensional Schizotypy Scale (MSS). Hippocampal subfields were analyzed from high-resolution 3 Tesla structural magnetic resonance imaging scans testing anatomical models, including anterior vs posterior regions and the cornu ammonis (CA), dentate gyrus (DG), and subiculum subfields separately for the left and right hemispheres. We demonstrate differential spatial effects across anterior vs posterior hippocampus segments across different dimensions of the schizotypy risk phenotype. The interaction of negative and disorganized schizotypy robustly predicted left hemisphere volumetric reductions for the anterior and total hippocampus, and anterior CA and DG, and the largest reductions were seen in participants high in negative and disorganized schizotypy. These findings extend previous early psychosis studies and together with behavioral studies of hippocampal-related memory impairments provide the basis for a dimensional neurobiological hippocampal model of schizophrenia risk. Subtle hippocampal subfield volume reductions may be prevalent prior to the onset of detectable prodromal clinical symptoms of psychosis and play a role in the etiology and development of such conditions.

Creation of an anthropomorphic CT head phantom for verification of image segmentation

Purpose

Many methods are available to segment structural magnetic resonance (MR) images of the brain into different tissue types. These have generally been developed for research purposes but there is some clinical use in the diagnosis of neurodegenerative diseases such as dementia. The potential exists for computed tomography (CT) segmentation to be used in place of MRI segmentation, but this will require a method to verify the accuracy of CT processing, particularly if algorithms developed for MR are used, as MR has notably greater tissue contrast.

Methods

To investigate these issues we have created a three‐dimensional (3D) printed brain with realistic Hounsfield unit (HU) values based on tissue maps segmented directly from an individual T1 MRI scan of a normal subject. Several T1 MRI scans of normal subjects from the ADNI database were segmented using SPM12 and used to create stereolithography files of different tissues for 3D printing. The attenuation properties of several material blends were investigated, and three suitable formulations were used to print an object expected to have realistic geometry and attenuation properties. A skull was simulated by coating the object with plaster of Paris impregnated bandages. Using two CT scanners, the realism of the phantom was assessed by the measurement of HU values, SPM12 segmentation and comparison with the source data used to create the phantom.

Results

Realistic relative HU values were measured although a subtraction of 60 was required to obtain equivalence with the expected values (gray matter 32.9–35.8 phantom, 29.9–34.2 literature). Segmentation of images acquired at different kVps/mAs showed excellent agreement with the source data (Dice Similarity Coefficient 0.79 for gray matter). The performance of two scanners with two segmentation methods was compared, with the scanners found to have similar performance and with one segmentation method clearly superior to the other.

Conclusion

The ability to use 3D printing to create a realistic (in terms of geometry and attenuation properties) head phantom has been demonstrated and used in an initial assessment of CT segmentation accuracy using freely available software developed for MRI.

Test-retest reliability and sample size estimates after MRI scanner relocation

OBJECTIVE

Many factors can contribute to the reliability and robustness of MRI-derived metrics. In this study, we assessed the reliability and reproducibility of three MRI modalities after an MRI scanner was relocated to a new hospital facility.

METHODS

Twenty healthy volunteers (12 females, mean age (standard deviation) ​= ​41 (11) years, age range [25-66]) completed three MRI sessions. The first session (S1) was one week prior to the 3T GE HDxt scanner relocation. The second (S2) occurred nine weeks after S1 and at the new location; a third session (S3) was acquired 4 weeks after S2. At each session, we acquired structural T1-weighted, pseudo-continuous arterial spin labelled, and diffusion tensor imaging sequences. We used longitudinal processing streams to create 12 summary MRI metrics, including total gray matter (GM), cortical GM, subcortical GM, white matter (WM), and lateral ventricle volume; mean cortical thickness; total surface area; average gray matter perfusion, and average diffusion tensor metrics along principal white matter pathways. We compared mean MRI values and variance at the old scanner location to multiple sessions at the new location using Bayesian multi-level regression models. K-fold cross validation allowed identification of important predictors. Whole-brain analyses were used to investigate any regional differences. Furthermore, we calculated within-subject coefficient of variation (wsCV), intraclass correlation coefficient (ICC), and dice similarity index (SI) of cortical segmentations across scanner relocation and within-site. Additionally, we estimated sample sizes required to robustly detect a 4% difference between two groups across MRI metrics.

RESULTS

All global MRI metrics exhibited little mean difference and small variability (bar cortical gray matter perfusion) both across scanner relocation and within-site repeat. T1- and DTI-derived tissue metrics showed ​< ​|0.3|% mean difference and <1.2% variance across scanner location and <|0.4|% mean difference and <0.8% variance within the new location, with between-site intraclass correlation coefficient (ICC) ​> ​0.80 and within-subject coefficient of variation (wsCV) ​< ​1.4%. Mean cortical gray matter perfusion had the highest between-session variability (6.7% [0.3, 16.7], estimate [95% uncertainty interval]), and hence the smallest ICC (0.71 [0.44,0.92]) and largest wsCV (13.4% [5.4, 18.1]). No global metric exhibited evidence of a meaningful mean difference between scanner locations. However, surface area showed evidence of a mean difference within-site repeat (between S2 and S3). Whole-brain analyses revealed no significant areas of difference between scanner relocation or within-site. For all metrics, we found no support for a systematic difference in variance across relocation sites compared to within-site test-retest reliability. Necessary sample sizes to detect a 4% difference between two independent groups varied from a maximum of n ​= ​362 per group (cortical gray matter perfusion), to total gray matter volume (n ​= ​114), average fractional anisotropy (n ​= ​23), total gray matter volume normalized by intracranial volume (n ​= ​19), and axial diffusivity (n ​= ​3 per group).

CONCLUSION

Cortical gray matter perfusion was the most variable metric investigated (necessitating large sample sizes to identify group differences), with other metrics showing substantially less variability. Scanner relocation appeared to have a negligible effect on variability of the global MRI metrics tested. This manuscript reports within-site test-retest variability to act as a tool for calculating sample size in future investigations. Our results suggest that when all other parameters are held constant (e.g., sequence parameters and MRI processing), the effect of scanner relocation is indistinguishable from within-site variability, but may need to be considered depending on the question being investigated.

Imputation Strategy for Reliable Regional MRI Morphological Measurements

Regional morphological analysis represents a crucial step in most neuroimaging studies. Results from brain segmentation techniques are intrinsically prone to certain degrees of variability, mainly as results of suboptimal segmentation. To reduce this inherent variability, the errors are often identified through visual inspection and then corrected (semi)manually. Identification and correction of incorrect segmentation could be very expensive for large-scale studies. While identification of the incorrect results can be done relatively fast even with manual inspection, the correction step is extremely time-consuming, as it requires training staff to perform laborious manual corrections. Here we frame the correction phase of this problem as a missing data problem. Instead of manually adjusting the segmentation outputs, our computational approach aims to derive accurate morphological measures by machine learning imputation. Data imputation techniques may be used to replace missing or incorrect region average values with carefully chosen imputed values, all of which are computed based on other available multivariate information. We examined our approach of correcting segmentation outputs on a cohort of 970 subjects, which were undergone an extensive, time-consuming, manual post-segmentation correction. A random forest imputation technique recovered the gold standard results with a significant accuracy (r = 0.93, p < 0.0001; when 30% of the segmentations were considered incorrect in a non-random fashion). The random forest technique proved to be most effective for big data studies (N > 250).

Smaller anterior subgenual cingulate volume mediates the effect of girls' early sexual maturation on negative psychobehavioral outcome

Early-maturing girls are relatively likely to experience compromised psychobehavioral outcomes. Some studies have explored the association between puberty and brain morphology in adolescents, while the results were non-specific for females or the method was a region-of-interest analysis. To our knowledge, no large-scale study has comprehensively explored the effects of pubertal timing on whole-brain volumetric development or the neuroanatomical substrates of the association in girls between pubertal timing and psychobehavioral outcomes. We collected structural magnetic resonance imaging (MRI) data of a subsample (N ​= ​203, mean age 11.6 years) from a large-scale population-based birth cohort. Tanner stage, a scale of physical maturation in adolescents, was rated almost simultaneously with MRI scan. The Strengths and Difficulties Questionnaire total difficulties (SDQ-TD) scores were rated by primary parents some duration after MRI scan (mean age 12.1 years). In each sex group, we examined brain regions associated with Tanner stage using whole-brain analysis controlling for chronological age, followed by an exploration of brain regions also associated with the SDQ-TD scores. We also performed mediation analyses. In girls, Tanner stage was significantly negatively correlated with gray matter volumes (GMVs) in the anterior/middle cingulate cortex (ACC/MCC), of which the subgenual ACC (sgACC) showed a negative correlation between GMVs and SDQ-TD scores. Smaller GMVs in the sgACC mediated the association between higher Tanner stages and higher SDQ-TD scores. We found no significant results in boys. Our results from a minimally biased, large-scale sample provide new insights into neuroanatomical correlates of the effect of pubertal timing on developmental psychological difficulties emerging in adolescence.

Conditions for numerically accurate TMS electric field simulation

BACKGROUND

Computational simulations of the E-field induced by transcranial magnetic stimulation (TMS) are increasingly used to understand its mechanisms and to inform its administration. However, characterization of the accuracy of the simulation methods and the factors that affect it is lacking.

OBJECTIVE

To ensure the accuracy of TMS E-field simulations, we systematically quantify their numerical error and provide guidelines for their setup.

METHOD

We benchmark the accuracy of computational approaches that are commonly used for TMS E-field simulations, including the finite element method (FEM) with and without superconvergent patch recovery (SPR), boundary element method (BEM), finite difference method (FDM), and coil modeling methods.

RESULTS

To achieve cortical E-field error levels below 2%, the commonly used FDM and 1st order FEM require meshes with an average edge length below 0.4 mm, 1st order SPR-FEM requires edge lengths below 0.8 mm, and BEM and 2nd (or higher) order FEM require edge lengths below 2.9 mm. Coil models employing magnetic and current dipoles require at least 200 and 3000 dipoles, respectively. For thick solid-conductor coils and frequencies above 3 kHz, winding eddy currents may have to be modeled.

CONCLUSION

BEM, FDM, and FEM all converge to the same solution. Compared to the common FDM and 1st order FEM approaches, BEM and 2nd (or higher) order FEM require significantly lower mesh densities to achieve the same error level. In some cases, coil winding eddy-currents must be modeled. Both electric current dipole and magnetic dipole models of the coil current can be accurate with sufficiently fine discretization.

A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms

Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above [Formula: see text] on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism ([Formula: see text], [Formula: see text]/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.

White matter volume alterations in hair-pulling disorder (trichotillomania)

Trichotillomania (TTM) is a disorder characterized by repetitive hair-pulling resulting in hair loss. Key processes affected in TTM comprise affective, cognitive, and motor functions. Emerging evidence suggests that brain matter aberrations in fronto-striatal and fronto-limbic brain networks and the cerebellum may characterize the pathophysiology of TTM. The aim of the present voxel-based morphometry (VBM) study was to evaluate whole brain grey and white matter volume alteration in TTM and its correlation with hair-pulling severity. High-resolution magnetic resonance imaging (3 T) data were acquired from 29 TTM patients and 28 age-matched healthy controls (CTRLs). All TTM participants completed the Massachusetts General Hospital Hair-Pulling Scale (MGH-HPS) to assess illness/pulling severity. Using whole-brain VBM, between-group differences in regional brain volumes were measured. Additionally, within the TTM group, the relationship between MGH-HPS scores, illness duration and brain volumes were examined. All data were corrected for multiple comparisons using family-wise error (FWE) correction at p < 0.05. Patients with TTM showed larger white matter volumes in the parahippocampal gyrus and cerebellum compared to CTRLs. Estimated white matter volumes showed no significant association with illness duration or MGH-HPS total scores. No significant between-group differences were found for grey matter volumes. Our observations suggest regional alterations in cortico-limbic and cerebellar white matter in patients with TTM, which may underlie deficits in cognitive and affective processing. Such volumetric white matter changes may precipitate impaired cortico-cerebellar communication leading to a reduced ability to control hair pulling behavior.

Evidence for peri-ictal blood-brain barrier dysfunction in patients with epilepsy

Epilepsy has been associated with a dysfunction of the blood-brain barrier. While there is ample evidence that a dysfunction of the blood-brain barrier contributes to epileptogenesis, blood-brain barrier dysfunction as a consequence of single epileptic seizures has not been systematically investigated. We hypothesized that blood-brain barrier dysfunction is temporally and anatomically associated with epileptic seizures in patients and used a newly-established quantitative MRI protocol to test our hypothesis. Twenty-three patients with epilepsy undergoing inpatient monitoring as part of their presurgical evaluation were included in this study (10 females, mean age ± standard deviation: 28.78 ± 8.45). For each patient, we acquired quantitative T1 relaxation time maps (qT1) after both ictal and interictal injection of gadolinium-based contrast agent. The postictal enhancement of contrast agent was quantified by subtracting postictal qT1 from interictal qT1 and the resulting ΔqT1 was used as a surrogate imaging marker of peri-ictal blood-brain barrier dysfunction. Additionally, the serum concentrations of MMP9 and S100, both considered biomarkers of blood-brain barrier dysfunction, were assessed in serum samples obtained prior to and after the index seizure. Fifteen patients exhibited secondarily generalized tonic-clonic seizures and eight patients exhibited focal seizures at ictal injection of contrast agent. By comparing ΔqT1 of the generalized tonic-clonic seizures and focal seizures groups, the anatomical association between ictal epileptic activity and postictal enhancement of contrast agent could be probed. The generalized tonic-clonic seizures group showed significantly higher ΔqT1 in the whole brain as compared to the focal seizures group. Specific analysis of scans acquired later than 3 h after the onset of the seizure revealed higher ΔqT1 in the generalized tonic-clonic seizures group as compared to the focal seizures group, which was strictly lateralized to the hemisphere of seizure onset. Both MMP9 and S100 showed a significantly increased postictal concentration. The current study provides evidence for the occurrence of a blood-brain barrier dysfunction, which is temporally and anatomically associated with epileptic seizures. qT1 after ictal contrast agent injection is rendered as valuable imaging marker of seizure-associated blood-brain barrier dysfunction and may be measured hours after the seizure. The observation of the strong anatomical association of peri-ictal blood-brain barrier dysfunction may spark the development of new functional imaging modalities for the post hoc visualization of brain areas affected by the seizure.

Brain Segmentation From Computed Tomography of Healthy Aging and Geriatric Concussion at Variable Spatial Resolutions

When properly implemented and processed, anatomic T ₁-weighted magnetic resonance imaging (MRI) can be ideal for the noninvasive quantification of white matter (WM) and gray matter (GM) in the living human brain. Although MRI is more suitable for distinguishing GM from WM than computed tomography (CT), the growing clinical use of the latter technique has renewed interest in head CT segmentation. Such interest is particularly strong in settings where MRI is unavailable, logistically unfeasible or prohibitively expensive. Nevertheless, whereas MRI segmentation is a sophisticated and technically-mature research field, the task of automatically classifying soft brain tissues from CT remains largely unexplored. Furthermore, brain segmentation methods for MRI hold considerable potential for adaptation and application to CT image processing. Here we demonstrate this by combining probabilistic, atlas-based classification with topologically-constrained tissue boundary refinement to delineate WM, GM and cerebrospinal fluid (CSF) from head CT images. The feasibility and utility of this approach are revealed by comparison of MRI-only vs. CT-only segmentations in geriatric concussion victims with both MRI and CT scans. Comparison of the two segmentations yields mean Sørensen-Dice coefficients of 85.5 ± 4.6% (WM), 86.7 ± 5.6% (GM) and 91.3 ± 2.8% (CSF), as well as average Hausdorff distances of 3.76 ± 1.85 mm (WM), 3.43 ± 1.53 mm (GM) and 2.46 ± 1.27 mm (CSF). Bootstrapping results suggest that the segmentation approach is sensitive enough to yield WM, GM and CSF volume estimates within ~5%, ~4%, and ~3% of their MRI-based estimates, respectively. To our knowledge, this is the first 3D segmentation approach for CT to undergo rigorous within-subject comparison with high-resolution MRI. Results suggest that (1) standard-quality CT allows WM/GM/CSF segmentation with reasonable accuracy, and that (2) the task of soft brain tissue classification from CT merits further attention from neuroimaging researchers.

Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites

Accurate and reliable quantification of brain metabolites measured in vivo using ¹H magnetic resonance spectroscopy (MRS) is a topic of continued interest. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, spectrally edited γ-aminobutyric acid (GABA) MRS data were analyzed and GABA levels were quantified relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using GABA+ (GABA + co-edited macromolecules (MM)) and MM-suppressed GABA editing. The unsuppressed water signal from the volume of interest was acquired for concentration referencing. Whole-brain T₁-weighted structural images were acquired and segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17% for the GABA + data and 29% for the MM-suppressed GABA data. The mean within-site coefficient of variation was 10% for the GABA + data and 19% for the MM-suppressed GABA data. Vendor differences contributed 53% to the total variance in the GABA + data, while the remaining variance was attributed to site- (11%) and participant-level (36%) effects. For the MM-suppressed data, 54% of the variance was attributed to site differences, while the remaining 46% was attributed to participant differences. Results from an exploratory analysis suggested that the vendor differences were related to the unsuppressed water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA measurements exhibit similar levels of variance to creatine-referenced GABA measurements. It is concluded that quantification using internal tissue water referencing is a viable and reliable method for the quantification of in vivo GABA levels.

FuNP (Fusion of Neuroimaging Preprocessing) Pipelines: A Fully Automated Preprocessing Software for Functional Magnetic Resonance Imaging

The preprocessing of functional magnetic resonance imaging (fMRI) data is necessary to remove unwanted artifacts and transform the data into a standard format. There are several neuroimaging data processing tools that are widely used, such as SPM, AFNI, FSL, FreeSurfer, Workbench, and fMRIPrep. Different data preprocessing pipelines yield differing results, which might reduce the reproducibility of neuroimaging studies. Here, we developed a preprocessing pipeline for T1-weighted structural MRI and fMRI data by combining components of well-known software packages to fully incorporate recent developments in MRI preprocessing into a single coherent software package. The developed software, called FuNP (Fusion of Neuroimaging Preprocessing) pipelines, is fully automatic and provides both volume- and surface-based preprocessing pipelines with a user-friendly graphical interface. The reliability of the software was assessed by comparing resting-state networks (RSNs) obtained using FuNP with pre-defined RSNs using open research data (n = 90). The obtained RSNs were well-matched with the pre-defined RSNs, suggesting that the pipelines in FuNP are reliable. In addition, image quality metrics (IQMs) were calculated from the results of three different software packages (i.e., FuNP, FSL, and fMRIPrep) to compare the quality of the preprocessed data. We found that our FuNP outperformed other software in terms of temporal characteristics and artifacts removal. We validated our pipeline with independent local data (n = 28) in terms of IQMs. The IQMs of our local data were similar to those obtained from the open research data. The codes for FuNP are available online to help researchers.

Alterations of Sub-cortical Gray Matter Volume and Their Associations With Disease Duration in Patients With Restless Legs Syndrome

Object: The purpose of this study was to uncover the pathology of restless legs syndrome (RLS) by exploring brain structural alterations and their corresponding functional abnormality.

Method: Surface-based morphometry (SBM) and voxel-based morphometry (VBM) were performed to explore the alterations in cortical and sub-cortical gray matter volume (GMV) in a cohort of 20 RLS and 18 normal controls (NC). Furthermore, resting-state functional connectivity (RSFC) was also performed to identify the functional alterations in patients with RLS.

Results: We found significant alterations of sub-cortical GMV, especially the bilateral putamen (PUT), rather than alterations of cortical GMV in patients with RLS compared to NC using both SBM and VBM. Further sub-regional analysis revealed that GMV alterations of PUT was mostly located in the left dorsal caudal PUT in patients with RLS. In addition, altered RSFC patterns of PUT were identified in patients with RLS compared to NC. Moreover, correlation analyses showed that the GMV of the left caudate and the left ventral rostral PUT were positively correlated with disease duration in patients with RLS.

Conclusions: The alterations of subcortical GMV might imply that the primarily affected areas are located in sub-cortical areas especially in the sub-region of PUT by the pathologic process of RLS, which might be used as potential biomarkers for the early diagnosis of RLS.

Investigation of microstructural abnormalities in white and gray matter around hippocampus with diffusion tensor imaging (DTI) in temporal lobe epilepsy (TLE)

OBJECTIVE

The objective of this study was to apply diffusion tensor imaging (DTI) to investigate microstructural abnormalities in temporal lobe epilepsy (TLE) with and without hippocampal sclerosis (HS).

MATERIALS

Totally, 19 patients with TLE with HS and 23 patients with TLE without HS were included. Fiber tracking fibers focused on the parahippocampal cingulum (PHC), cingulate gyrus (CG), and fornix (FORX). Fractional anisotropy (FA) and mean diffusivity (MD) values were obtained, and hippocampal volumes were measured.

RESULTS

Compared with the contralateral side, for the HS group, FA values of ipsilateral CG and FORX were significantly decreased, and MD value of ipsilateral hippocampus was significantly higher, with significantly declined ipsilateral hippocampal volume. For the MRI-Neg group, FA values of ipsilateral CG, FORX, and hippocampus were significantly decreased, while MD values of ipsilateral FORX and hippocampus were significantly higher. Moreover, for the MRI-Neg group, the FA value of contralateral PHC was significantly decreased. Fractional anisotropy values of ipsilateral CG for both groups were significantly decreased, and FA value of ipsilateral FORX for the HS group was significantly decreased. Furthermore, MD value of ipsilateral hippocampus for the HS group was significantly higher, and FA value of ipsilateral hippocampus for the MRI-Neg group was significantly decreased. In addition, ipsilateral hippocampal volumes for both groups were significantly decreased. Fractional anisotropy value of ipsilateral CG and FORX had a correlation with the seizure frequency.

CONCLUSION

Diffusion tensor imaging can detect microstructural abnormalities in brain from patients with TLE, which might be hard to find with routine Magnetic Resonance Imaging (MRI) sequence.

Association of Brain Cortical Changes With Relapse in Patients With Major Depressive Disorder

IMPORTANCE

More than half of all patients with major depressive disorder (MDD) experience a relapse within 2 years after recovery. It is unclear how relapse affects brain morphologic features during the course of MDD.

OBJECTIVE

To use structural magnetic resonance imaging to identify morphologic brain changes associated with relapse in MDD.

DESIGN, SETTING, AND PARTICIPANTS

In this longitudinal case-control study, patients with acute MDD at baseline and healthy controls were recruited from the University of Münster Department of Psychiatry from March 21, 2010, to November 14, 2014, and were reassessed from November 11, 2012, to October 28, 2016. Depending on patients' course of illness during follow-up, they were subdivided into groups of patients with and without relapse. Whole-brain gray matter volume and cortical thickness of the anterior cingulate cortex, orbitofrontal cortex, middle frontal gyrus, and insula were assessed via 3-T magnetic resonance imaging at baseline and 2 years later.

MAIN OUTCOMES AND MEASURES

Gray matter was analyzed via group (no relapse, relapse, and healthy controls) by time (baseline and follow-up) analysis of covariance, controlling for age and total intracranial volume. Confounding factors of medication and depression severity were assessed.

RESULTS

This study included 37 patients with MDD and a relapse (19 women and 18 men; mean [SD] age, 37.0 [12.7] years), 23 patients with MDD and without relapse (13 women and 10 men; mean [SD] age, 32.5 [10.5] years), and 54 age- and sex-matched healthy controls (24 women and 30 men; mean [SD] age, 37.5 [8.7] years). A significant group-by-time interaction controlling for age and total intracranial volume revealed that patients with relapse showed a significant decline of insular volume (difference, -0.032; 95% CI, -0.063 to -0.002; P = .04) and dorsolateral prefrontal volume (difference, -0.079; 95% CI, -0.113 to -0.045; P < .001) from baseline to follow-up. In patients without relapse, gray matter volume in these regions did not change significantly (insula: difference, 0.027; 95% CI, -0.012 to 0.066; P = .17; and dorsolateral prefrontal volume: difference, 0.023; 95% CI, -0.020 to 0.066; P = .30). Volume changes were not correlated with psychiatric medication or with severity of depression at follow-up. Additional analysis of cortical thickness showed an increase in the anterior cingulate cortex (difference, 0.073 mm; 95% CI, 0.023-0.123 mm; P = .005) and orbitofrontal cortex (difference, 0.089 mm; 95% CI, 0.032-0.147 mm; P = .003) from baseline to follow-up in patients without relapse.

CONCLUSION AND RELEVANCE

A distinct association of relapse in MDD with brain morphologic features was revealed using a longitudinal design. Relapse is associated with brain structures that are crucial for regulation of emotions and thus needs to be prevented. This study might be a step to guide future prognosis and maintenance treatment in patients with recurrent MDD.

Revisiting subcortical brain volume correlates of autism in the ABIDE dataset: effects of age and sex

BACKGROUND

Autism spectrum disorders (ASD) are characterized by substantial clinical, etiological and neurobiological heterogeneity. Despite this heterogeneity, previous imaging studies have highlighted the role of specific cortical and subcortical structures in ASD and have forwarded the notion of an ASD specific neuroanatomy in which abnormalities in brain structures are present that can be used for diagnostic classification approaches.

METHOD

A large (N = 859, 6-27 years, IQ 70-130) multi-center structural magnetic resonance imaging dataset was examined to specifically test ASD diagnostic effects regarding (sub)cortical volumes.

RESULTS

Despite the large sample size, we found virtually no main effects of ASD diagnosis. Yet, several significant two- and three-way interaction effects of diagnosis by age by gender were found.

CONCLUSION

The neuroanatomy of ASD does not exist, but is highly age and gender dependent. Implications for approaches of stratification of ASD into more homogeneous subtypes are discussed.

An Automated Segmentation of R2* Iron-Overloaded Liver Images Using a Fuzzy C-Mean Clustering Scheme

OBJECTIVES

The objectives of this study were to develop and test an automated segmentation of R2* iron-overloaded liver images using fuzzy c-mean (FCM) clustering and to evaluate the observer variations.

MATERIALS AND METHODS

Liver R2* images and liver iron concentration (LIC) maps of 660 thalassemia examinations were randomly separated into training (70%) and testing (30%) cohorts for development and evaluation purposes, respectively. Two-dimensional FCM used R2* images, and the LIC map was implemented to segment vessels from the parenchyma. Two automated FCM variables were investigated using new echo time and membership threshold selection criteria based on the FCM centroid distance and LIC levels, respectively. The new method was developed on a training cohort and compared with manual segmentation for segmentation accuracy and to a previous semiautomated method, and a semiautomated scheme was suggested to improve unsuccessful results. The automated variables found from the training cohort were assessed for their effectiveness in the testing cohort, both quantitatively and qualitatively (the latter by 2 abdominal radiologists using a grading method, with evaluations of observer variations). A segmentation error of less than 30% was considered to be a successful result in both cohorts, whereas, in the testing cohort, a good grade obtained from satisfactory automated results was considered a success.

RESULTS

The centroid distance method has a segmentation accuracy comparable with the previous-best, semiautomated method. About 94% and 90% of the examinations in the training and testing cohorts were automatically segmented out successfully, respectively. The failed examinations were successfully segmented out with thresholding adjustment (3% and 8%) or by using alternative results from the previous 1-dimensional FCM method (3% and 2%) in the training and testing cohorts, respectively. There were no failed segmentation examinations in either cohort. The intraobserver and interobserver variabilities were found to be in substantial agreement.

CONCLUSIONS

Our new method provided a robust automated segmentation outcome with a high ease of use for routine clinical application.

Pattern recognition of magnetic resonance imaging-based gray matter volume measurements classifies bipolar disorder and major depressive disorder

BACKGROUND

Bipolar Disorder (BD) cannot be reliably distinguished from Major Depressive Disorder (MDD) until the first manic or hypomanic episode. Consequently, many patients with BD are treated with antidepressants without mood stabilizers, a strategy that is often ineffective and carries a risk of inducing a manic episode. We previously reported reduced cortical thickness in right precuneus, right caudal middle-frontal cortex and left inferior parietal cortex in BD compared with MDD.

METHODS

This study extends our previous work by performing individual level classification of BD or MDD in an expanded, currently unmedicated, cohort using gray matter volume (GMV) based on Magnetic Resonance Imaging and a Support Vector Machine. All patients were in a Major Depressive Episode and a leave-two-out analysis was performed.

RESULTS

Nineteen out of 26 BD subjects and 20 out of 26 MDD subjects were correctly identified, for a combined accuracy of 75%. The three brain regions contributing to the classification were higher GMV in bilateral supramarginal gyrus and occipital cortex indicating MDD, and higher GMV in right dorsolateral prefrontal cortex indicating BD.

LIMITATIONS

This analysis included scans performed with two different headcoils and scan sequences, which limited the interpretability of results in an independent cohort analysis.

CONCLUSIONS

Our results add to previously published data which suggest that regional gray matter volume should be investigated further as a clinical diagnostic tool to predict BD before the appearance of a manic or hypomanic episode.

Hemispheric asymmetry of the frontolimbic cortex in young adults with borderline personality disorder

OBJECTIVE

Although the frontolimbic cortex has been implicated in borderline personality disorder (BPD), information about possible asymmetries in this region in patients with BPD is limited. This study aimed to examine whether frontolimbic cortex asymmetries differ between patients with BPD and healthy individuals.

METHODS

The brains of 30 young adult patients with BPD and 32 healthy control subjects were scanned with magnetic resonance imaging (MRI). The participants completed self-report scales assessing impulsivity, affect intensity and other psychological variables. Gray matter volume, surface area, and cortical thickness in regions of interest (ROIs), namely anterior insula (AI) and anterior cingulate cortex (ACC) were determined and the data were probed for hemisphere-group interactions.

RESULTS

Relative to controls, patients with BPD had reduced cortical thickness in left ACC and less surface area and gray matter volume in left AI. Significant group-hemisphere interactions were observed for gray matter volume and surface area of AI and for cortical thickness of ACC. Post hoc analysis showed that the BPD patients had greater frontolimbic cortex asymmetry than healthy controls; furthermore, greater asymmetry of AI&ACC correlated with a higher score in attention subscale of Barratt Impulsiveness Scale.

CONCLUSION

Patients with BPD have greater frontolimbic asymmetry than healthy individuals.

Double inversion recovery imaging improves the evaluation of gray matter volume losses in patients with Alzheimer's disease and mild cognitive impairment

Our goal was to investigate whether three-dimensional (3D) double inversion recovery (DIR) images can show alterations of gray matter volume (GMV) between Alzheimer's disease (AD) patients and nondemented controls and to compare alterations of GMV between groups using DIR images and those using 3D T1-weighted (T1W) images. We included 25 subjects with mild or probable AD, 25 subjects with amnestic mild cognitive impairment (MCI), and 25 elderly cognitively normal (CN) subjects. Group differences in GMV among CN, MCI, and AD patients were tested by voxel-wise, one-way ANOVA. Additional region-of-interest-based comparisons of GMV differences among the three groups for DIR and T1WI were performed using ANCOVA. Finally, ROC curve analysis was performed. In the AD group compared with the CN and MCI groups, GMV was decreased in both DIR and T1W images. However, the areas showing GMV loss were larger in DIR images compared to those in T1W images. Amygdala had the highest area under curve value for both DIR and T1W images. DIR images were sensitive for identifying GMV loss in patients with AD compared with MCI and CN subjects and areas showing GMV loss identified with DIR were extended to more brain areas than those identified with T1W. With DIR, amygdala GMV is the most sensitive in differentiating between subject groups.

Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington's Disease

The selection of an appropriate segmentation tool is a challenge facing any researcher aiming to measure gray matter (GM) volume. Many tools have been compared, yet there is currently no method that can be recommended above all others; in particular, there is a lack of validation in disease cohorts. This work utilizes a clinical dataset to conduct an extensive comparison of segmentation tools. Our results confirm that all tools have advantages and disadvantages, and we present a series of considerations that may be of use when selecting a GM segmentation method, rather than a ranking of these tools. Seven segmentation tools were compared using 3 T MRI data from 20 controls, 40 premanifest Huntington's disease (HD), and 40 early HD participants. Segmented volumes underwent detailed visual quality control. Reliability and repeatability of total, cortical, and lobular GM were investigated in repeated baseline scans. The relationship between each tool was also examined. Longitudinal within-group change over 3 years was assessed via generalized least squares regression to determine sensitivity of each tool to disease effects. Visual quality control and raw volumes highlighted large variability between tools, especially in occipital and temporal regions. Most tools showed reliable performance and the volumes were generally correlated. Results for longitudinal within-group change varied between tools, especially within lobular regions. These differences highlight the need for careful selection of segmentation methods in clinical neuroimaging studies. This guide acts as a primer aimed at the novice or non-technical imaging scientist providing recommendations for the selection of cohort-appropriate GM segmentation software.

Lower body negative pressure reduces optic nerve sheath diameter during head-down tilt

The microgravity ocular syndrome (MOS) results in significant structural and functional ophthalmic changes during 6-mo spaceflight missions consistent with an increase in cerebrospinal fluid (CSF) pressure compared with the preflight upright position. A ground-based study was performed to assess two of the major hypothesized contributors to MOS, headward fluid shifting and increased ambient CO₂, on intracranial and periorbital CSF. In addition, lower body negative pressure (LBNP) was assessed as a countermeasure to headward fluid shifting. Nine healthy male subjects participated in a crossover design study with five head-down tilt (HDT) conditions: -6, -12, and -18° HDT, -12° HDT with -20 mmHg LBNP, and -12° HDT with a 1% CO₂ environment, each for 5 h total. A three-dimensional volumetric scan of the cranium and transverse slices of the orbita were collected with MRI, and intracranial CSF volume and optic nerve sheath diameter (ONSD) were measured after 4.5 h HDT. ONSD increased during -6° (P < 0.001), -12° (P < 0.001), and -18° HDT (P < 0.001) and intracranial CSF increased during -12° HDT (P = 0.01) compared with supine baseline. Notably, LBNP was able to reduce the increases in ONSD and intracranial CSF during HDT. The addition of 1% CO₂ during HDT, however, had no further effect on ONSD, but rather ONSD increased from baseline in a similar magnitude to -12° HDT with ambient air (P = 0.001). These findings demonstrate the ability of LBNP, a technique that targets fluid distribution in the lower limbs, to directly influence CSF and may be a promising countermeasure to help reduce increases in CSF.NEW & NOTEWORTHY This is the first study to demonstrate the ability of lower body negative pressure to directly influence cerebrospinal fluid surrounding the optic nerve, indicating potential use as a countermeasure for increased cerebrospinal fluid on Earth or in space.

Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy

INTRODUCTION

Despite the recognized importance of atrophy in multiple sclerosis (MS), methods for its quantification have been mostly restricted to the research domain. Recently, a CE labelled and FDA approved MS-specific atrophy quantification method, MSmetrix, has become commercially available. Here we perform a validation of MSmetrix against established methods in simulated and in vivo MRI data.

METHODS

Whole-brain and gray matter (GM) volume were measured with the cross-sectional pipeline of MSmetrix and compared to the outcomes of FreeSurfer (cross-sectional pipeline), SIENAX and SPM. For this comparison we investigated 20 simulated brain images, as well as in vivo data from 100 MS patients and 20 matched healthy controls. In fifty of the MS patients a second time point was available. In this subgroup, we additionally analyzed the whole-brain and GM volume change using the longitudinal pipeline of MSmetrix and compared the results with those of FreeSurfer (longitudinal pipeline) and SIENA.

RESULTS

In the simulated data, SIENAX displayed the smallest average deviation compared with the reference whole-brain volume (+ 19.56 ± 10.34 mL), followed by MSmetrix (- 38.15 ± 17.77 mL), SPM (- 42.99 ± 17.12 mL) and FreeSurfer (- 78.51 ± 12.68 mL). A similar pattern was seen in vivo. Among the cross-sectional methods, Deming regression analyses revealed proportional errors particularly in MSmetrix and SPM. The mean difference percentage brain volume change (PBVC) was lowest between longitudinal MSmetrix and SIENA (+ 0.16 ± 0.91%). A strong proportional error was present between longitudinal percentage gray matter volume change (PGVC) measures of MSmetrix and FreeSurfer (slope = 2.48). All longitudinal methods were sensitive to the MRI hardware upgrade that occurred during the time of the study.

CONCLUSION

MSmetrix, FreeSurfer, FSL and SPM show differences in atrophy measurements, even at the whole-brain level, that are large compared to typical atrophy rates observed in MS. Especially striking are the proportional errors between methods. Cross-sectional MSmetrix behaved similarly to SPM, both in terms of mean volume difference as well as proportional error. Longitudinal MSmetrix behaved most similar to SIENA. Our results indicate that brain volume measurement and normalization from T1-weighted images remains an unsolved problem that requires much more attention.

Cortical and Subcortical Structural Plasticity Associated with the Glioma Volumes in Patients with Cerebral Gliomas Revealed by Surface-Based Morphometry

Postlesional plasticity has been identified in patients with cerebral gliomas by inducing a large functional reshaping of brain networks. Although numerous non-invasive functional neuroimaging methods have extensively investigated the mechanisms of this functional redistribution in patients with cerebral gliomas, little effort has been made to investigate the structural plasticity of cortical and subcortical structures associated with the glioma volume. In this study, we aimed to investigate whether the contralateral cortical and subcortical structures are able to actively reorganize by themselves in these patients. The compensation mechanism following contralateral cortical and subcortical structural plasticity is considered. We adopted the surface-based morphometry to investigate the difference of cortical and subcortical gray matter (GM) volumes in a cohort of 14 healthy controls and 13 patients with left-hemisphere cerebral gliomas [including 1 patients with World Health Organization (WHO I), 8 WHO II, and 4 WHO III]. The glioma volume ranges from 5.1633 to 208.165 cm². Compared to healthy controls, we found significantly increased GM volume of the right cuneus and the left thalamus, as well as a trend toward enlargement in the right globus pallidus in patients with cerebral gliomas. Moreover, the GM volumes of these regions were positively correlated with the glioma volumes of the patients. These results provide evidence of cortical and subcortical enlargement, suggesting the usefulness of surface-based morphometry to investigate the structural plasticity. Moreover, the structural plasticity might be acted as the compensation mechanism to better fulfill its functions in patients with cerebral gliomas as the gliomas get larger.

Ability to Gain Control Over One's Own Brain Activity and its Relation to Spiritual Practice: A Multimodal Imaging Study

Spiritual practice, such as prayer or meditation, is associated with focusing attention on internal states and self-awareness processes. As these cognitive control mechanisms presumably are also important for neurofeedback (NF), we investigated whether people who pray frequently (N = 20) show a higher ability of self-control over their own brain activity compared to a control group of individuals who rarely pray (N = 20). All participants underwent structural magnetic resonance imaging (MRI) and one session of sensorimotor rhythm (SMR, 12–15 Hz) based NF training. Individuals who reported a high frequency of prayer showed improved NF performance compared to individuals who reported a low frequency of prayer. The individual ability to control one’s own brain activity was related to volumetric aspects of the brain. In the low frequency of prayer group, gray matter volumes in the right insula and inferior frontal gyrus were positively associated with NF performance, supporting prior findings that more general self-control networks are involved in successful NF learning. In contrast, participants who prayed regularly showed a negative association between gray matter volume in the left medial orbitofrontal cortex (Brodmann’s area (BA) 10) and NF performance. Due to their regular spiritual practice, they might have been more skillful in gating incoming information provided by the NF system and avoiding task-irrelevant thoughts.

The development and modelling of devices and paradigms for transcranial magnetic stimulation

Magnetic stimulation is a non-invasive neurostimulation technique that can evoke action potentials and modulate neural circuits through induced electric fields. Biophysical models of magnetic stimulation have become a major driver for technological developments and the understanding of the mechanisms of magnetic neurostimulation and neuromodulation. Major technological developments involve stimulation coils with different spatial characteristics and pulse sources to control the pulse waveform. While early technological developments were the result of manual design and invention processes, there is a trend in both stimulation coil and pulse source design to mathematically optimize parameters with the help of computational models. To date, macroscopically highly realistic spatial models of the brain, as well as peripheral targets, and user-friendly software packages enable researchers and practitioners to simulate the treatment-specific and induced electric field distribution in the brains of individual subjects and patients. Neuron models further introduce the microscopic level of neural activation to understand the influence of activation dynamics in response to different pulse shapes. A number of models that were designed for online calibration to extract otherwise covert information and biomarkers from the neural system recently form a third branch of modelling.

Adolescents newly diagnosed with eating disorders have structural differences in brain regions linked with eating disorder symptoms

BACKGROUND

Adults with eating disorders (ED) show brain volume reductions in the frontal, insular, cingulate, and parietal cortices, as well as differences in subcortical regions associated with reward processing. However, little is known about the structural differences in adolescents with behavioural indications of early stage ED.

AIM

This is the first study to investigate structural brain changes in adolescents newly diagnosed with ED compared to healthy controls (HC), and to study whether ED cognitions correlate with structural changes in adolescents with ED of short duration.

METHODS

Fifteen adolescent females recently diagnosed with ED, and 28 age-matched HC individuals, were scanned with structural magnetic resonance imaging (MRI). Whole-brain and region-of-interest analyses were conducted using voxel-based morphometry (VBM). ED cognitions were measured with self-report questionnaires and working memory performance was measured with a neuropsychological computerized test.

RESULTS AND CONCLUSIONS

The left superior temporal gyrus had a smaller volume in adolescents with ED than in HC, which correlated with ED cognitions (concerns about eating, weight, and shape). Working memory reaction time correlated positively with insula volumes in ED participants, but not HC. In ED, measurements of restraint and obsession was negatively correlated with temporal gyrus volumes, and positively correlated with cerebellar and striatal volumes. Thus, adolescents with a recent diagnosis of ED had volumetric variations in brain areas linked to ED cognitions, obsessions, and working memory. The findings emphasize the importance of early identification of illness, before potential long-term effects on structure and behaviour occur.

Reproducibility and Bias in Healthy Brain Segmentation: Comparison of Two Popular Neuroimaging Platforms

We evaluated and compared the performance of two popular neuroimaging processing platforms: Statistical Parametric Mapping (SPM) and FMRIB Software Library (FSL). We focused on comparing brain segmentations using Kirby21, a magnetic resonance imaging (MRI) replication study with 21 subjects and two scans per subject conducted only a few hours apart. We tested within- and between-platform segmentation reliability both at the whole brain and in 10 regions of interest (ROIs). For a range of fixed probability thresholds we found no differences between-scans within-platform, but large differences between-platforms. We have also found very large differences between- and within-platforms when probability thresholds were changed. A randomized blinded reader study indicated that: (1) SPM and FSL performed well in terms of gray matter segmentation; (2) SPM and FSL performed poorly in terms of white matter segmentation; and (3) FSL slightly outperformed SPM in terms of CSF segmentation. We also found that tissue class probability thresholds can have profound effects on segmentation results. We conclude that the reproducibility of neuroimaging studies depends on the neuroimaging software-processing platform and tissue probability thresholds. Our results suggest that probability thresholds may not be comparable across platforms and consistency of results may be improved by estimating a probability threshold correspondence function between SPM and FSL.

Patterns of Cortical and Subcortical Amyloid Burden across Stages of Preclinical Alzheimer's Disease

OBJECTIVES

We examined florbetapir positron emission tomography (PET) amyloid scans across stages of preclinical Alzheimer's disease (AD) in cortical, allocortical, and subcortical regions. Stages were characterized using empirically defined methods.

METHODS

A total of 312 cognitively normal Alzheimer's Disease Neuroimaging Initiative participants completed a neuropsychological assessment and florbetapir PET scan. Participants were classified into stages of preclinical AD using (1) a novel approach based on the number of abnormal biomarkers/cognitive markers each individual possessed, and (2) National Institute on Aging and the Alzheimer's Association (NIA-AA) criteria. Preclinical AD groups were compared to one another and to a mild cognitive impairment (MCI) sample on florbetapir standardized uptake value ratios (SUVRs) in cortical and allocortical/subcortical regions of interest (ROIs).

RESULTS

Amyloid deposition increased across stages of preclinical AD in all cortical ROIs, with SUVRs in the later stages reaching levels seen in MCI. Several subcortical areas showed a pattern of results similar to the cortical regions; however, SUVRs in the hippocampus, pallidum, and thalamus largely did not differ across stages of preclinical AD.

CONCLUSIONS

Substantial amyloid accumulation in cortical areas has already occurred before one meets criteria for a clinical diagnosis. Potential explanations for the unexpected pattern of results in some allocortical/subcortical ROIs include lack of correspondence between (1) cerebrospinal fluid and florbetapir PET measures of amyloid, or between (2) subcortical florbetapir PET SUVRs and underlying neuropathology. Findings support the utility of our novel method for staging preclinical AD. By combining imaging biomarkers with detailed cognitive assessment to better characterize preclinical AD, we can advance our understanding of who is at risk for future progression. (JINS, 2016, 22, 978-990).

Distinguishing medication-free subjects with unipolar disorder from subjects with bipolar disorder: state matters

OBJECTIVES

Recent studies have indicated that pattern recognition techniques of functional magnetic resonance imaging (fMRI) data for individual classification may be valuable for distinguishing between major depressive disorder (MDD) and bipolar disorder (BD). Importantly, medication may have affected previous classification results as subjects with MDD and BD use different classes of medication. Furthermore, almost all studies have investigated only depressed subjects. Therefore, we focused on medication-free subjects. We additionally investigated whether classification would be mood state independent by including depressed and remitted subjects alike.

METHODS

We applied Gaussian process classifiers to investigate the discriminatory power of structural MRI (gray matter volumes of emotion regulation areas) and resting-state fMRI (resting-state networks implicated in mood disorders: default mode network [DMN], salience network [SN], and lateralized frontoparietal networks [FPNs]) in depressed (n=42) and remitted (n=49) medication-free subjects with MDD and BD.

RESULTS

Depressed subjects with MDD and BD could be classified based on the gray matter volumes of emotion regulation areas as well as DMN functional connectivity with 69.1% prediction accuracy. Prediction accuracy using the FPNs and SN did not exceed chance level. It was not possible to discriminate between remitted subjects with MDD and BD.

CONCLUSIONS

For the first time, we showed that medication-free subjects with MDD and BD can be differentiated based on structural MRI as well as resting-state functional connectivity. Importantly, the results indicated that research concerning diagnostic neuroimaging tools distinguishing between MDD and BD should consider mood state as only depressed subjects with MDD and BD could be correctly classified. Future studies, in larger samples are needed to investigate whether the results can be generalized to medication-naïve or first-episode subjects.

Prefrontal cortex, temporal cortex, and hippocampus volume are affected in suicidal psychiatric patients

Suicide is a leading cause of death in America, with over 40,000 reported suicides per year. Mental illness is a major risk factor for suicidality. This study attempts to validate findings of volumetric differences from studies on suicidality. Psychiatric inpatients classified as having mildly severe or severe depression were separated into two groups: suicide attempted in the past two months (SA; n=20), non-suicidal control group (DA; n=20); these patients were all depressed and not significantly different for age, gender, race, marital status, education level, anxiety level, and substance abuse. Healthy controls (HC; n=20) were not significantly different from the suicidal groups for age and gender. Volunteers underwent MRI to assess volumes of cortical lobes, corpus callosum, and subcortical regions of interest, including the thalamus, insula, limbic structures, and basal ganglia. The right hippocampal volume of the SA group was significantly reduced compared to healthy controls. The frontal and temporal lobe volumes of the SA group were significantly decreased compared to the DA group. These volumetric reductions confirm previous findings and support the hypothesis that fronto-temporal function may be altered in suicidal patients.

Reduced Gray Matter Volume Is Associated With Poorer Instrumental Activities of Daily Living Performance in Heart Failure

BACKGROUND

Heart failure patients require assistance with instrumental activities of daily living in part because of the high rates of cognitive impairment in this population. Structural brain insult (eg, reduced gray matter volume) is theorized to underlie cognitive dysfunction in heart failure, although no study has examined the association among gray matter, cognition, and instrumental activities of daily living in heart failure.

OBJECTIVES

The aim of this study was to investigate the associations among gray matter volume, cognitive function, and functional ability in heart failure.

METHODS

A total of 81 heart failure patients completed a cognitive test battery and the Lawton-Brody self-report questionnaire to assess instrumental activities of daily living. Participants underwent magnetic resonance imaging to quantify total gray matter and subcortical gray matter volume.

RESULTS

Impairments in instrumental activities of daily living were common in this sample of HF patients. Regression analyses controlling for demographic and medical confounders showed that smaller total gray matter volume predicted decreased scores on the instrumental activities of daily living composite, with specific associations noted for medication management and independence in driving. Interaction analyses showed that reduced total gray matter volume interacted with worse attention/executive function and memory to negatively impact instrumental activities of daily living.

CONCLUSIONS

Smaller gray matter volume is associated with greater impairment in instrumental activities of daily living in persons with heart failure, possibly via cognitive dysfunction. Prospective studies are needed to clarify the utility of clinical correlates of gray matter volume (eg, cognitive dysfunction) in identifying heart failure patients at risk for functional decline and determine whether interventions that target improved brain and cognitive function can preserve functional independence in this high-risk population.

Inter-Method Discrepancies in Brain Volume Estimation May Drive Inconsistent Findings in Autism

Previous studies applying automatic preprocessing methods on Structural Magnetic Resonance Imaging (sMRI) report inconsistent neuroanatomical abnormalities in Autism Spectrum Disorder (ASD). In this study we investigate inter-method differences as a possible cause behind these inconsistent findings. In particular, we focus on the estimation of the following brain volumes: gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), and total intra cranial volume (TIV). T1-weighted sMRIs of 417 ASD subjects and 459 typically developing controls (TDC) from the ABIDE dataset were estimated using three popular preprocessing methods: SPM, FSL, and FreeSurfer (FS). Brain volumes estimated by the three methods were correlated but had significant inter-method differences; except TIVSPM vs. TIVFS, all inter-method differences were significant. ASD vs. TDC group differences in all brain volume estimates were dependent on the method used. SPM showed that TIV, GM, and CSF volumes of ASD were larger than TDC with statistical significance, whereas FS and FSL did not show significant differences in any of the volumes; in some cases, the direction of the differences were opposite to SPM. When methods were compared with each other, they showed differential biases for autism, and several biases were larger than ASD vs. TDC differences of the respective methods. After manual inspection, we found inter-method segmentation mismatches in the cerebellum, sub-cortical structures, and inter-sulcal CSF. In addition, to validate automated TIV estimates we performed manual segmentation on a subset of subjects. Results indicate that SPM estimates are closest to manual segmentation, followed by FS while FSL estimates were significantly lower. In summary, we show that ASD vs. TDC brain volume differences are method dependent and that these inter-method discrepancies can contribute to inconsistent neuroimaging findings in general. We suggest cross-validation across methods and emphasize the need to develop better methods to increase the robustness of neuroimaging findings.

MRI morphometry in Alzheimer's disease

MRI based evaluation of brain atrophy is regarded as a valid method to stage the disease and to assess progression in Alzheimer's disease (AD). Volumetric software programs have made it possible to quantify gray matter in the human brain in an automated fashion. At present, voxel based morphometry (VBM) is easily applicable to the routine clinical procedure with a short execution time. The importance of the VBM approach is that it is not biased to one particular structure and is able to assess anatomical differences throughout the brain. Stand-alone VBM software running on Windows, Voxel-based Specific Regional analysis system for AD (VSRAD), has been widely used in the clinical diagnosis of AD in Japan. On the other hand, recent application of graph theory to MRI has made it possible to analyze changes in structural connectivity in AD.