T1-weighted Data Research Articles

Deep Learning-Based Superresolution Reconstruction for Upper Abdominal Magnetic Resonance Imaging: An Analysis of Image Quality, Diagnostic Confidence, and Lesion Conspicuity.

The aim of this study was to investigate the impact of a deep learning-based superresolution reconstruction technique for T1-weighted volume-interpolated breath-hold examination (VIBESR) on image quality in comparison with standard VIBE images (VIBESD). Between May and August 2020, a total of 46 patients with various abdominal pathologies underwent contrast-enhanced upper abdominal VIBE magnetic resonance imaging (MRI) at 1.5 T. After data acquisition, the precontrast and postcontrast T1-weighted VIBE raw data were processed by a deep learning-based prototype algorithm for deblurring and denoising the images as well as for enhancing their sharpness (VIBESR). In a randomized and blinded manner, 2 radiologists independently analyzed the image data sets using the unprocessed images VIBESD as a standard reference. Outcome measures were as follows: overall image quality, anatomic clarity of organ borders, sharpness of vessels, artifacts, noise, and diagnostic confidence. All ratings were performed on an ordinal 4-point Likert scale. If the MRI examination encompassed a hepatic lesion, the maximum diameter of the largest hepatic lesion was quantified, and lesion sharpness and conspicuity were evaluated on an ordinal 4-point Likert scale. In addition, a post hoc regression analysis for lesion evaluation was computed. Finally, interrater/intrarater agreement was analyzed. The overall image quality, anatomic clarity of organ borders, and sharpness of vessels in both precontrast and postcontrast images were rated significantly higher in VIBESR than in VIBESD (P < 0.001). Similarly, diagnostic confidence was higher in VIBESR than in VIBESD (P < 0.001). Furthermore, VIBESR images were rated to have significantly less noise and fewer artifacts in comparison with VIBESD (P < 0.001). The interreader agreement was substantial with a Cohen κ of 0.72 for the precontrast analysis and a κ of 0.74 for the postcontrast analysis. A total of 28 hepatic lesions were analyzed. For both readers, lesion sharpness and conspicuity were rated significantly better in VIBESR than in VIBESD in both the precontrast and postcontrast data sets (P < 0.01), which was consistent with the post hoc regression analysis (for every 1-point increase in sharpness/conspicuity, the odds ratio revealed a positive relation with VIBESR of 13-fold to 17-fold in comparison with VIBESD; P < 0.001). In terms of lesion size, there was no significant difference between the precontrast VIBESD and VIBESR or between the postcontrast VIBESD and VIBESR for both readers. Similarly, there was an excellent interreader agreement regarding lesion size (intraclass correlation coefficient, >0.9). The data-driven superresolution reconstruction (VIBESR) is clinically feasible for precontrast and postcontrast upper abdominal VIBE MRI, providing improved image quality, diagnostic confidence, and lesion conspicuity compared with standard VIBESD images.

Prenatal and postnatal maternal anxiety and amygdala structure and function in young children

Anxiety symptoms are relatively common during pregnancy and are associated with behavioural problems in children. The amygdala is involved in emotion regulation, and its volume and function are associated with exposure to prenatal maternal depression. The associations between perinatal maternal anxiety and children’s amygdala structure and function remain unclear. The objective of this study was to determine associations between prenatal and postnatal maternal anxiety and amygdala structure and function in children. Maternal anxiety was measured during the second trimester of pregnancy and 12 weeks postpartum. T1-weighted anatomical data and functional magnetic resonance imaging data were collected from 54 children (25 females), between the ages of 3–7 years. Amygdala volume was calculated and functional connectivity maps were created between the amygdalae and the rest of the brain. Spearman correlations were used to test associations between amygdala volume/functional connectivity and maternal anxiety symptoms, controlling for maternal depression symptoms. Second trimester maternal anxiety symptoms were negatively associated with functional connectivity between the left amygdala and clusters in bilateral parietal regions; higher maternal anxiety was associated with increased negative connectivity. Postnatal maternal anxiety symptoms were positively associated with child amygdala volume, but this finding did not remain significant while controlling for total brain volume. These functional connectivity differences may underlie behavioral outcomes in children exposed to maternal anxiety during pregnancy.

Abstract PS13-44: Identifying relevant parameters that characterize the early response to NAT in breast cancer patients using a novel personalized mechanistic model integrating in vitro and in vivo imaging data

Abstract The early determination of response to neoadjuvant therapy (NAT) in triple-negative breast cancer would enable the treating oncologist to adapt the therapeutic regimen of a non-responding patient (e.g., by changing dosage, dose schedule, prescribed drugs), and thereby improve treatment outcomes while avoiding unnecessary toxicities. To address this challenge, we propose to use personalized, in silico forecasts of tumor response to therapeutic regimens via a mechanistic mathematical model calibrated with patient-specific longitudinal multi-parametric magnetic resonance imaging (MRI) data acquired early in the course of NAT. Here, we extend our mechanistic model to include a new term describing the synergistic effects of NAT drug combinations and identify the driving parameters involved in its formulation by means of a sensitivity analysis. Our model describes tumor cell dynamics as a combination of proliferation, which is regulated by a logistic term, and mobility, which is described as a diffusion process constrained by the local tumor-induced mechanical stress. Tumor cell density is extracted from diffusion-weighted MRI data, while tissue mechanical properties are defined from segmented T1-weighted MRI data. We adjust the tumor proliferation rate in response to NAT drug combinations with a recent model of drug synergy, MuSyC, which accounts for distinct types of synergistic drug effects (synergy of potency vs. synergy of efficacy). We also consider the heterogeneous intratumoral delivery of drugs by means of perfusion maps estimated from dynamic contrast-enhanced MRI data. We use Sobol’s method for the sensitivity analysis of two different tumors - one well-perfused and one poorly-perfused. We simulate a four-cycle NAT protocol in which NAT drugs are delivered every 14 days, and assess the total effect (ST) of each parameter on the mean relative difference of tumor cell density with respect to a control simulation of tumor growth without NAT. Sensitivity analysis results directly depend on the definition of the parameter space, which we construct by combining two approaches. First, we experimentally constrain parameter ranges using time-resolved, high-throughput, automated microscopy assays to capture the changes in proliferation rates of various breast cancer lines (HCC1143, SUM149, MDAMB231, and MDAMB468) caused by two standard drug combinations: paclitaxel with carboplatin and doxorubicin with perfosfamide (metabolic derivative of the pro-drug cyclophosphamide), and fitting the MuSyC model to these data. Second, we scale the resulting in vitro parameter ranges to clinically-relevant in vivo ranges by running an in silico study with our mechanistic model of breast cancer growth and NAT response. Our results show that, out of the ten parameters involved in the synergy term, three have a dominant role in the dynamics of breast cancer during NAT (ST &amp;gt; 0.1): synergistic potency, the maximal change in tumor cell proliferation by the slowest decaying drug, and its concentration producing half of maximal effects. The other parameters have marginal (0.02 &amp;lt; ST &amp;lt; 0.1) to negligible effect (ST &amp;lt; 0.02). Ongoing studies are assessing the ability of our mechanistic model to forecast NAT response over a small patient cohort after patient-specific calibration of the driving parameters identified in the present study. Citation Format: Guillermo Lorenzo, Angela M. Jarrett, Christian T. Meyer, Darren R. Tyson, Vito Quaranta, Thomas E. Yankeelov. Identifying relevant parameters that characterize the early response to NAT in breast cancer patients using a novel personalized mechanistic model integrating in vitro and in vivo imaging data [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS13-44.

Comparative evaluation of intracranial oligodendroglioma and astrocytoma of similar grades using conventional and T1-weighted DCE-MRI.

This retrospective study was performed on a 3T MRI to determine the unique conventional MR imaging and T1-weighted DCE-MRI features of oligodendroglioma and astrocytoma and investigate the utility of machine learning algorithms in their differentiation. Histologically confirmed, 81 treatment-naïve patients were classified into two groups as per WHO 2016 classification: oligodendroglioma (n = 16; grade II, n = 25; grade III) and astrocytoma (n = 10; grade II, n = 30; grade III). The differences in tumor morphology characteristics were evaluated using Z-test. T1-weighted DCE-MRI data were analyzed using an in-house built MATLAB program. The mean 90th percentile of relative cerebral blood flow, relative cerebral blood volume corrected, volume transfer rate from plasma to extracellular extravascular space, and extravascular extracellular space volume values were evaluated using independent Student's t test. Support vector machine (SVM) classifier was constructed to differentiate two groups across grade II, grade III, and grade II+III based on statistically significant features. Z-test signified only calcification among conventional MR features to categorize oligodendroglioma and astrocytoma across grade III and grade II+III tumors. No statistical significance was found in the perfusion parameters between two groups and its subtypes. SVM trained on calcification also provided moderate accuracy to differentiate oligodendroglioma from astrocytoma. We conclude that conventional MR features except calcification and the quantitative T1-weighted DCE-MRI parameters fail to discriminate between oligodendroglioma and astrocytoma. The SVM could not further aid in their differentiation. The study also suggests that the presence of more than 50% T2-FLAIR mismatch may be considered as a more conclusive sign for differentiation of IDH mutant astrocytoma.

Deep Learning-Based Automated Abdominal Organ Segmentation in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies.

The aims of this study were to train and evaluate deep learning models for automated segmentation of abdominal organs in whole-body magnetic resonance (MR) images from the UK Biobank (UKBB) and German National Cohort (GNC) MR imaging studies and to make these models available to the scientific community for analysis of these data sets. A total of 200 T1-weighted MR image data sets of healthy volunteers each from UKBB and GNC (400 data sets in total) were available in this study. Liver, spleen, left and right kidney, and pancreas were segmented manually on all 400 data sets, providing labeled ground truth data for training of a previously described U-Net-based deep learning framework for automated medical image segmentation (nnU-Net). The trained models were tested on all data sets using a 4-fold cross-validation scheme. Qualitative analysis of automated segmentation results was performed visually; performance metrics between automated and manual segmentation results were computed for quantitative analysis. In addition, interobserver segmentation variability between 2 human readers was assessed on a subset of the data. Automated abdominal organ segmentation was performed with high qualitative and quantitative accuracy on UKBB and GNC data. In more than 90% of data sets, no or only minor visually detectable qualitative segmentation errors occurred. Mean Dice scores of automated segmentations compared with manual reference segmentations were well higher than 0.9 for the liver, spleen, and kidneys on UKBB and GNC data and around 0.82 and 0.89 for the pancreas on UKBB and GNC data, respectively. Mean average symmetric surface distance was between 0.3 and 1.5 mm for the liver, spleen, and kidneys and between 2 and 2.2 mm for pancreas segmentation. The quantitative accuracy of automated segmentation was comparable with the agreement between 2 human readers for all organs on UKBB and GNC data. Automated segmentation of abdominal organs is possible with high qualitative and quantitative accuracy on whole-body MR imaging data acquired as part of UKBB and GNC. The results obtained and deep learning models trained in this study can be used as a foundation for automated analysis of thousands of MR data sets of UKBB and GNC and thus contribute to tackling topical and original scientific questions.

Sex differences in structural brain asymmetry of children with autism spectrum disorders

Previous studies have confirmed the sex difference of gray matter asymmetry in typically developing controls and the abnormal gray matter asymmetry in autism spectrum disorders. However, whether and how sex differences of gray matter asymmetry exist in autism spectrum disorders remains studied. This paper analyzes the above issues and explores correlations between gray matter asymmetry and autistic symptoms. Data from 72 children (36 males and 36 females) with autism spectrum disorders and 72 typically developing-controls (36 males and 36 females) between 8 and 14 years were included and obtained from the autism brain imaging data exchange repository (autism brain imaging data exchange I and autism brain imaging data exchange II). The voxel-based morphometry approach was used to assess gray matter asymmetry in T1-weighted brain data, and gray matter asymmetry was quantified as asymmetry index. A 2 × 2 analysis of covariance was used to identify asymmetry index differences among the four groups. Pearson correlation analysis was performed for asymmetry index values extracted from the clusters with significant differences between the four groups and autistic symptoms (social impairments, communication difficulties, and restricted, repetitive behaviors) measured by the revised autism diagnostic interview scale. Results showed that specific brain regions showed significant main effects for diagnosis in which autism spectrum disorders patients had more leftward asymmetry than typically developing-controls for the parahippocampal gyrus and the postcentral gyrus; specific brain regions showed significant main effects for sex in which females showed more rightward asymmetry for the middle temporal gyrus, inferior frontal gyrus, angular gyrus, and postcentral gyrus and minor rightward asymmetry for the superior frontal gyrus than males; significant diagnosis × sex interaction effects were identified in the angular gyrus and middle occipital gyrus. Pearson correlation analysis showed that males with autism spectrum disorders had a positive association between the asymmetry index value in the middle occipital gyrus and more significant verbal impairment measured by the revised autism diagnostic interview (r = 0.387, p = 0.026). The asymmetry index value in the parahippocampal gyrus was positively associated with more severe social impairment in females with autism spectrum disorders (r = 0.422, p = 0.020). We identified that the sex difference of gray matter asymmetry in children with autism spectrum disorders is qualitative rather than quantitative, which is relatively novel. Our findings provide the theoretical basis for conducting separate studies and using sex-specific diagnostic methods and treatments for males and females children with autism spectrum disorders.

Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach.

Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. We propose the use of a deep learning and transfer learning approach based on conventional magnetic resonance imaging (MRI) for non-invasive classification of SCLC vs. NSCLC brain metastases. Sixty-nine patients with brain metastasis of lung cancer origin were included. Of them, 44 patients had NSCLC and 25 patients had SCLC. Classification was performed with EfficientNet architecture on crop images of lesion areas and based on post-contrast T1-weighted, T2-weighted and FLAIR imaging input data. Evaluation of the model was carried out in a 5-fold cross-validation manner, and based on accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve. The best classification results were obtained with multiparametric MRI input data (T1WI+c+FLAIR+T2WI), with a mean overall accuracy of 0.90 ± 0.04, and F1 score of 0.92 ± 0.05 for NSCLC and 0.87 ± 0.08 for SCLC for the validation data and an accuracy of 0.87 ± 0.05, with an F1 score of 0.88 ± 0.05 for NSCLC and 0.85 ± 0.05 for SCLC for the test dataset. The proposed method provides an automatic noninvasive method for the classification of brain metastasis with high sensitivity and specificity for differentiation between NSCLC vs. SCLC brain metastases. It may be used as a diagnostic tool for improving decision-making in the treatment of patients with these metastases. Further studies on larger patient samples are required to validate the current results.

Simultaneous Diffusion and T1 Weighted Contrast Imaging for Human Brain Mapping

Introduction: Diffusion tensor imaging (DTI) is conventionally performed using echo planar imaging (EPI) for human brain mapping. However, EPI suffers from susceptibility distortion, requiring elaborate image post processing. Besides, DTI alone is limited in assessing gray and white matter boundaries of the brain, which is important for accurately imaging brain atrophy. The goal of this study was to design and evaluate simultaneous diffusion and T1 weighting high resolution imaging for human brain mapping. Materials and Methods: Three dimensional Magnetization-prepared rapid gradient-echo (3D MPRAGE) with T1 weighting, which is generally the method of choice for mapping gray and white matter structures in the brain, was extended to incorporate diffusion encoding using simulation and experiment to develop high resolution DTI and T1-weighted human brain data. Results: It is shown that the incorporation of DTI contrast (i.e. fractional anisotropy (FA) and mean diffusivity (MD)) into T1 weighted 3D MPRAGE using simulations as well as experimental results from in-vivo human brain studies at 4 Tesla magnetic field strength improves the contrast between gray and white matter sub-structural boundaries. Moreover, incorporating diffusion encoding into 3D MPRAGE avoids the inherent image distortions typically seen in EPI based DTI.Conclusion: This study suggests DTI weighted 3D MPRAGE combined with T1w is feasible for human brain imaging and expected to benefit improved assessment of gray/white matter boundaries.

Fully synthetic neuroimaging data for replication and exploration

Scientific transparency, data exploration, and education are advanced through data sharing. However, risk for disclosure of personal information and institutional data sharing regulations can impede human subject/patient data sharing and thus limit open science initiatives. Sharing fully synthetic data is an alternative when it is not possible to share real or observed data. Here we describe a data sharing approach that borrows principles and methods from multiple imputation to replace observed values with synthetic values, thereby creating a fully synthetic neuroimaging dataset that accurately represents the covariance structure of the observed dataset. Predictor tables composed of demographic, site, behavioral and total intracranial volume (ICV) variables from 264 pediatric cases were used to create synthetic predictor tables, which were then used to synthesize gray matter images derived from T1-weighted data. The synthetic predictor tables demonstrated pooled variance and statistical estimates that closely approximated the observed data, as reflected in measures of efficiency and statistical bias. Similarly, the synthetic gray matter data accurately represented the variance and voxel-level associations with predictor variables (age, sex, verbal IQ, and ICV). The magnitude and spatial distribution of gray matter effects in the observed imaging data were replicated in the pooled results from the synthetic datasets. This approach for generating fully synthetic neuroimaging data has widespread potential for data sharing, including replication, new discovery, and education. Fully synthetic neuroimaging datasets can enable data-sharing because it accurately represents patterns of variance in the original data, while diminishing the risk of privacy disclosures that can accompany neuroimaging data sharing.

Extensive peritumoral edema and brain-to-tumor interface MRI features enable prediction of brain invasion in meningioma: development and validation.

Brain invasion by meningioma is a stand-alone criterion for tumor atypia in the 2016 World Health Organization classification, but no imaging parameter has yet been shown to be sufficient for predicting it. The aim of this study was to develop and validate an MRI-based radiomics model from the brain-to-tumor interface to predict brain invasion by meningioma. Preoperative T2-weighted and contrast-enhanced T1-weighted imaging data were obtained from 454 patients (88 patients with brain invasion) between 2012 and 2017. Feature selection was performed from 3222 radiomics features obtained in the 1 cm thickness tumor-to-brain interface region using least absolute shrinkage and selection operator. Peritumoral edema volume, age, sex, and selected radiomics features were used to construct a random forest classifier-based diagnostic model. The performance was evaluated using the areas under the curves (AUCs) of the receiver operating characteristic in an independent cohort of 150 patients (29 patients with brain invasion) between 2018 and 2019. Volume of peritumoral edema was an independent predictor of brain invasion (P < 0.001). The top 6 interface radiomics features plus the volume of peritumoral edema were selected for model construction. The combined model showed the highest performance for prediction of brain invasion in the training (AUC 0.97; 95% CI: 0.95-0.98) and validation sets (AUC 0.91; 95% CI: 0.84-0.98), and improved diagnostic performance over volume of peritumoral edema only (AUC 0.76; 95% CI: 0.66-0.86). An imaging-based model combining interface radiomics and peritumoral edema can help to predict brain invasion by meningioma and improve the diagnostic performance of known clinical and imaging parameters.

Computational Modeling of Interstitial Fluid Pressure and Velocity in Non-small Cell Lung Cancer Brain Metastases Treated With Stereotactic Radiosurgery.

Background: Early imaging-based treatment response assessment of brain metastases following stereotactic radiosurgery (SRS) remains challenging. The aim of this study is to determine whether early (within 12 weeks) intratumoral changes in interstitial fluid pressure (IFP) and velocity (IFV) estimated from computational fluid modeling (CFM) using dynamic contrast-enhanced (DCE) MRI can predict long-term outcomes of lung cancer brain metastases (LCBMs) treated with SRS.Methods: Pre- and post-treatment T1-weighted DCE-MRI data were obtained in 41 patients treated with SRS for intact LCBMs. The imaging response was assessed using RANO-BM criteria. For each lesion, extravasation of contrast agent measured from Extended Tofts pharmacokinetic Model (volume transfer constant, Ktrans) was incorporated into a computational fluid model to estimate tumor IFP and IFV. Estimates of mean IFP and IFV and heterogeneity (skewness and kurtosis) were calculated for each lesion from pre- and post-SRS imaging. The Wilcoxon rank-sum test was utilized to assess for significant differences in IFP, IFV, and IFP/IFV change (Δ) between response groups.Results: Fifty-three lesions from 41 patients were included. Median follow-up time after SRS was 11 months. The objective response (OR) rate (partial or complete response) was 79%, with 21% demonstrating stable disease (SD) or progressive disease (PD). There were significant response group differences for multiple posttreatment and Δ CFM parameters: post-SRS IFP skewness (mean −0.405 vs. −0.691, p = 0.022), IFP kurtosis (mean 2.88 vs. 3.51, p = 0.024), and IFV mean (5.75e-09 vs. 4.19e-09 m/s, p = 0.027); and Δ IFP kurtosis (mean −2.26 vs. −0.0156, p = 0.017) and IFV mean (1.91e-09 vs. 2.38e-10 m/s, p = 0.013). Posttreatment and Δ thresholds predicted non-OR with high sensitivity (sens): post-SRS IFP skewness (−0.432, sens 84%), kurtosis (2.89, sens 84%), and IFV mean (4.93e-09 m/s, sens 79%); and Δ IFP kurtosis (−0.469, sens 74%) and IFV mean (9.90e-10 m/s, sens 74%).Conclusions: Objective response was associated with lower post-treatment tumor heterogeneity, as represented by reductions in IFP skewness and kurtosis. These results suggest that early post-treatment assessment of IFP and IFV can be used to predict long-term response of lung cancer brain metastases to SRS, allowing a timelier treatment modification.

Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0–2 years

The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.

T162. THICKER PREFRONTAL CORTEX IS ASSOCIATED WITH SUBCLINICAL NEGATIVE SYMPTOMS IN SCHIZOTYPY - AN ENIGMA CONSORTIUM META-ANALYSIS

BackgroundNegative symptoms can be seen to represent a continuum from subclinical manifestations in the general population to severe symptoms in schizophrenia. Neuroanatomical studies show evidence of fronto-striatal structural abnormalities linked to negative symptoms in patients with schizophrenia (Walton et al. 2018). However, it remains an open question whether these structural associations are also observed in ostensibly healthy individuals reporting subclinical negative symptoms. The present study used structural T1-weighted brain imaging data from the ENIGMA Schizotypy Working Group to investigate the relationship between subclinical negative symptoms and fronto-striatal structural measures.MethodsWe included 2,235 healthy unmedicated individuals with varying levels of schizotypy from 17 centers around the world. The complete sample had a weighted mean (range) age of 29.2 (15.9–39.6) and 59.4% (51–100) were male. Subclinical negative symptoms were assessed at each site separately using factor scores from self-report schizotypy questionnaires (i.e., the Community Assessment of Psychic Experiences, the Oxford-Liverpool Inventory of Feelings and Experiences, or the Schizotypal Personality Questionnaire). Based on prior studies in schizophrenia, we obtained cortical thickness from 22 frontal regions-of-interest (ROIs) and subcortical volumes from 6 striatal ROIs using FreeSurfer. We performed meta-analyses of effect sizes (standardized regression coefficients) from a model predicting mean cortical thickness by subclinical negative symptom scores, adjusting for age, sex, and site. The same analysis was repeated for subcortical volumes including intracranial volume as additional covariate.ResultsMeta-analyses revealed significant positive associations between subclinical negative symptoms and cortical thickness of the left frontal pole (βstd=0.091; pFDR=0.009), right medial orbitofrontal cortex (βstd=0.083; pFDR=0.009) and right anterior cingulate cortex (βstd=0.07; pFDR=0.011).DiscussionUsing a large sample of healthy unmedicated individuals with varying levels of schizotypal personality traits, this ENIGMA meta-analysis showed that subclinical negative symptoms are associated with thicker prefrontal cortex. The present data are contrary to previous findings in schizophrenia, which demonstrates a relationship between negative symptoms and lower prefrontal cortical thickness (Walton et al. 2018). These divergent neural correlates suggest that thicker cortex could be a potential compensatory mechanism preventing individuals with schizotypy from the clinical manifestation of severe negative symptoms. Alternatively, greater prefrontal cortical thickness could also be associated with pathological processes along the negative symptom continuum prior to clinical manifestation.

A flexible workflow for simulating transcranial electric stimulation in healthy and lesioned brains.

Simulating transcranial electric stimulation is actively researched as knowledge about the distribution of the electrical field is decisive for understanding the variability in the elicited stimulation effect. Several software pipelines comprehensively solve this task in an automated manner for standard use-cases. However, simulations for non-standard applications such as uncommon electrode shapes or the creation of head models from non-optimized T1-weighted imaging data and the inclusion of irregular structures are more difficult to accomplish. We address these limitations and suggest a comprehensive workflow to simulate transcranial electric stimulation based on open-source tools. The workflow covers the head model creation from MRI data, the electrode modeling, the modeling of anisotropic conductivity behavior of the white matter, the numerical simulation and visualization. Skin, skull, air cavities, cerebrospinal fluid, white matter, and gray matter are segmented semi-automatically from T1-weighted MR images. Electrodes of arbitrary number and shape can be modeled. The meshing of the head model is implemented in a way to preserve the feature edges of the electrodes and is free of topological restrictions of the considered structures of the head model. White matter anisotropy can be computed from diffusion-tensor imaging data. Our solver application was verified analytically and by contrasting the tDCS simulation results with that of other simulation pipelines (SimNIBS 3.0, ROAST 3.0). An agreement in both cases underlines the validity of our workflow. Our suggested solutions facilitate investigations of irregular structures in patients (e.g. lesions, implants) or new electrode types. For a coupled use of the described workflow, we provide documentation and disclose the full source code of the developed tools.

ALTERED BRAIN FUNCTIONAL CONNECTIVITY FOLLOWING A SEASON OF COMPETITIVE FEMALE HIGH SCHOOL SOCCER AND THE EFFECT OF A JUGULAR VEIN COMPRESSION COLLAR: A NETWORK-BASED STATISTICS ANALYSIS

Background:Cumulative exposure to repetitive sub-concussive head impacts in contact sports may have deleterious effects on brain function, even in the absence of acute symptoms. Moreover, anatomical and biomechanical factors may predispose female athletes to higher risk compared to males. At present, there is no effective injury prevention strategy to protect female athletes from sports-related head impact.Hypothesis/Purpose:(1). We aimed to use resting-state fMRI to investigate the effect of a full season of competitive soccer on brain functional network integrity in female high school athletes. (2). We also aimed to evaluate the efficacy of a jugular vein compression neck collar device, designed to mitigate potential injury by reducing the brain slosh effect.Methods:A total of 125 high school female soccer athletes were included in this study. These athletes were assigned randomly to a non-collar (n=55, age=16.06±1.06 yrs) or collar group (n=70, 15.81±0.95 yrs) before the season started. High resolution 3D T1-weighted images and resting-state fMRI data were collected prospectively at pre-season and again at post-season. Data processing and analysis were conducted in the MATLAB-based programs Statistical Parametric Mapping (SPM12) and Connectivity Toolbox (Conn). Functional connectivity was computed between each pair of 105 anatomically delineated regions of interest (ROI). Network Based Statistics were applied to detect coherent patterns of altered connectivity from pre- to post-season.Results:The non-collar group showed a significant pattern of altered connectivity (p-FWE = 0.047) spanning 60% of ROIs (63/105) and 1.7% of ROI-ROI connections (94/5,460). 65 of the 94 altered connections were weakened from pre-to-post season and tended to occur in the right hemisphere. 29 of the 94 altered connections were strengthened from pre-to-post season and tended to involve regions in the occipital lobe. The collar group did not show any statistically significant change (p-FWE = 0.223).Conclusion:The results of this study indicate that exposure to repetitive sub-concussive head impacts during a single season of competitive female soccer induces changes in brain functional connectivity. The observed increases and decreases of functional connectivity strength comprising the pattern of altered connectivity are congruent with a heterogeneous response to insult wherein some connections are reduced in strength due to neuronal damage and other “detour” connections are strengthened to preserve network function. Comparatively, the absence of alterations in the collar group suggest that the jugular vein compression collar may have generated a potentially protective effect to preserve brain functional network integrity during exposure to head impacts.Figure 1.Pattern of pre-to-post season altered connectivity in the non-collar group (p-FWE=0.023). Observed changes in functional connectivity strength were bimodal with decreased (blue) connectivity occurring predominantly between right hemispheric regions and increased (red) connectivity often associated with occipital lobe areas.

WEAKENED ROLE OF VENTROMEDIAL AND DORSOLATERAL PREFRONTAL CORTEX IN REGULATING AMYGDALA ACTIVITY IN LATE-LIFE DEPRESSION – A DYNAMIC CAUSAL MODELLING STUDY ON RESTING STATE FMRI

Introduction Research in older adults has generally found greater emotional well-being despite physical and cognitive decline. A literature review indicates that although older adults have structural and functional degradation in the dorsolateral and ventrolateral prefrontal cortices (dlPFC, vlPFC, key brain regions related to emotion regulation in younger adults), they may adaptively and automatically use strategies that involve alternative or additional brain regions to regulate emotion as a compensatory mechanism (for example, ventromedial prefrontal cortex, vmPFC). However, few studies have confirmed this hypothesis. More importantly, it is imperative to examine emotion regulation patterns in older adults with major depression (also known as late-life depression, LLD) to clarify the neural mechanisms of emotion regulation deficits in LLD. We hypothesize that LLD may have weakened connectivity between vmPFC and dlPFC in regulating activity of brain regions related to emotional processing (such as the amygdala). Methods We have analyzed the 7-min resting-state fMRI and T1-weighted brain imaging data which was acquired from NBOLD project conducted at University of Connecticut Health Center (UCHC). The data was from 37 healthy older subjects (9 males, age(mean±SD)= 75.5±7.5 yrs) and 102 LLD patients (32 males, age(mean±SD)=71.5 ±7.4 yrs). We first examined whether in healthy subjects, smaller dlPFC volume is associated with greater vmPFC activity measured by amplitude of low-frequency fluctuation (ALFF) using DPABI program. The dlPFC volume was measured using FreeSurfer software on T1-weighted images. We then examined whether vmPFC exert greater control on amygdala activity than dlPFC using spectral dynamic causal modelling (spDCM) program of SPM12. Finally we compared differences between healthy and LLD subjects in information flow among dlPFC, vlPFC, vmPFC and amygdala using spDCM. Results As we predicted, there was significant correlation between lower left dlPFC volume and greater ALFF in the vmPFC (r=-0.42, p Conclusions Negative correlation between the vmPFC resting activity and dlPFC volume in the healthy older adults suggest a possible compensatory activity of vmPFC. The spDCM analysis confirmed our hypothesis that vmPFC may exert a key role in regulating emotion to compensate the dlPFC volume reduction in healthy older adults, whereas the compensatory effect is weaker in the LLD group compared with healthy subjects, which could explain poor emotion regulation in LLD. Future studies using emotion regulation tasks directly are warranted to confirm the compensatory role of the vmPFC in emotion regulation in older adults and its the weakened role of emotion regulation in LLD. This research was funded by: The study was supported by NIMH grant R01 MH108578 (PI, David C. Steffens).

Glutamatergic neurometabolites and cortical thickness in treatment-resistant schizophrenia: Implications for glutamate-mediated excitotoxicity

Treatment-resistant schizophrenia may be related to structural brain alterations. However, the mechanisms underlying these changes remain unclear. The present study had two main aims: (1) to explore differences in cortical thickness between patients with treatment-resistant schizophrenia non-responsive to clozapine (ultra-treatment-resistant schizophrenia, UTRS), patients with treatment-resistant schizophrenia responsive to clozapine (Cloz-Resp), patients responsive to first-line non-clozapine antipsychotics (FL-Resp), and healthy controls (HCs); and (2) to test our hypothesis of structural compromise as a manifestation of neurotoxic effects from elevated glutamate (Glu) (i.e. glutamate-mediated excitotoxicity) by examining the relationships between glutamatergic neurometabolite levels (Glu and glutamate + glutamine (Glx)) in the dorsal anterior cingulate cortex (dACC) and cortical thickness. T1-weighted images and 1H-MRS data were obtained from UTRS (n = 24), Cloz-Resp (n = 25), FL-Resp (n = 19), and HCs (n = 26). Vertex-wise analyses showed that patients with UTRS had widespread cortical thinning in the bilateral frontal, temporal, parietal, and occipital gyri compared to HCs and FL-Resp patients. In the patient group, negative associations were found between dACC Glx levels and cortical thickness in the right dorsolateral prefrontal cortex after correcting for multiple comparisons and controlling for age, sex, antipsychotic dose, and illness severity. In conclusion, glutamate-mediated excitotoxicity may be one of the mechanisms underlying structural compromise seen in treatment-resistant schizophrenia. Future studies should longitudinally examine the associations between glutamatergic neurometabolite levels and cortical thickness in the context of treatment and illness progression.

Mean apparent propagator-MRI: A new diffusion model which improves temporal lobe epilepsy lateralization

PurposeTo compare MRI volume measurements, FLAIR image intensity, Diffusion tensor imaging (DTI) and mean apparent propagator (MAP)-MRI measurements in hippocampus ipsilateral and contralateral to the epileptogenic focus for non-invasive lateralization of temporal lobe epilepsy (TLE) and also compare these DTI and MAP-MRI measurements to cognitive function. MethodA cohort of patients with unilateral TLE and aged-and gendered-matched controls were enrolled in this retrospective study. T1-weighted MPRAGE data for the volume, FLAIR image intensity, DTI and MAP-MRI parameters were performed for bilateral hippocampi of all subjects. The sensitivity, specificity, lateralization ratios and Cohen's d effect sizes of all MR measurements were calculated. Pearson correlation analysis was performed to compare DTI and MAP-MRI measurements to cognitive function. ResultsWe evaluated 23 patients and 17 controls. The MAP-MRI parameter ‘return to the plane probability’ (RTPP) had the strongest effect size (d = −1.678, lateralization ratio = 86.36 %) for differentiating hippocampus ipsilateral to the epileptogenic focus from contralateral hippocampus when compared to all other DTI/MAP-MRI parameters, signal intensity on FLAIR and hippocampal volumes. Mean diffusivity (MD), radial diffusivity (RD), mean square displacement (MSD) were each negatively correlated to clinical measures of delayed recall (r = −0.758; r = −0.772; r = −0.684, respectively). While return to the axis probability (RTAP) return to the origin probability (RTOP) and fractional anisotropy (FA) were positively correlated (r = 0.832; r = 0.813; r = 0.717, respectively) (all P < 0.05). ConclusionMAP-MRI measurements are promising radiologic biomarkers for the non-invasive lateralization of epileptogenic foci in TLE.

Brain perfusion patterns are altered in chronic knee pain: a spatial covariance analysis of arterial spin labelling MRI.

Chronic musculoskeletal pain is a common problem globally. Current evidence suggests that maladapted central pain pathways are associated with pain chronicity, for example, in postoperative pain after knee replacement. Other factors such as low mood, anxiety, and tendency to catastrophize are also important contributors. We aimed to investigate brain imaging features that underpin pain chronicity based on multivariate pattern analysis of cerebral blood flow (CBF), as a marker of maladaptive brain changes. This was achieved by identifying CBF patterns that discriminate chronic pain from pain-free conditions and by exploring their explanatory power for factors thought to drive pain chronification. In 44 chronic knee pain and 29 pain-free participants, we acquired both CBF and T1-weighted data. Participants completed questionnaires related to affective processes and pressure and cuff algometry to assess pain sensitization. Two factor scores were extracted from these scores representing negative affect and pain sensitization. A spatial covariance principal component analysis of CBF identified 5 components that significantly discriminated chronic pain participants from controls, with the unified network achieving 0.83 discriminatory accuracy (area under the curve). In chronic knee pain, significant patterns of relative hypoperfusion were evident in anterior default-mode and salience network hubs, while hyperperfusion was seen in posterior default mode, thalamus, and sensory regions. One component correlated positively with the pain sensitization score (r = 0.43, P = 0.006), suggesting that this CBF pattern reflects neural activity changes encoding pain sensitization. Here, we report a distinct chronic knee pain-related representation of CBF, pointing toward a brain signature underpinning central aspects of pain sensitization.

Alteration of the gut microbiome in first-episode drug-naïve and chronic medicated schizophrenia correlate with regional brain volumes

The human gut microbiome plays an important role in the basic neurodevelopmental processes of the central nervous system and has been implicated in several neuropsychiatric disorders. However, the connection between the gut microbiome and the underlying pathogenesis of schizophrenia (SCZ) is poorly defined. Here we analyzed the faecal samples from 40 first-episode drug-naïve SCZ (FSCZ) patients, 85 chronically antipsychotic-treated SCZ (TSCZ) patients and 69 healthy controls (HCs) using 16S rRNA gene sequence to determine whether the alterations of the gut microbiome were associated with SCZ or antipsychotic treatment. In addition, we acquired the T1-weighted brain imaging data by using structural magnetic resonance imaging to test whether microbial composition correlated with structural brain signatures. Our analyses revealed low microbiome alpha-diversity indexes in TSCZ patients but not in FSCZ patients as compared to HCs. Importantly, both FSCZ and TSCZ patients had distinct changes in gut microbial composition at certain taxa including Christensenellaceae, Enterobacteriaceae, Pasteurellaceae, Turicibacteraceae at the family level and Escherichia at genus level as compared to HCs. We also found significant disturbances of gut microbial composition in TSCZ versus FSCZ patients (eg. Enterococcaceae and Lactobacillaceae). Most interestingly, our exploratory analyses found specific SCZ-associated microbiota to be correlated with the right middle frontal gyrus (rMFG) volume which was aberrant in SCZ patients. Our findings extend prior work and suggest a possible link between the gut microbiome and brain structure which may be implicated in the pathology of SCZ.