Diffusion-weighted Magnetic Resonance Imaging Data Research Articles

Deep Learning Role in Early Diagnosis of Prostate Cancer.

The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.

Effects of diffusion time on non-Gaussian diffusion and intravoxel incoherent motion (IVIM) MRI parameters in breast cancer and hepatocellular carcinoma xenograft models.

BackgroundPerfusion-related intravoxel incoherent motion (IVIM) and non-Gaussian diffusion magnetic resonance (MR) parameters are becoming important biomarkers for differentiating malignant from benign tumors without contrast agents. However, diffusion-time dependence has rarely been investigated in tumors.PurposeTo investigate the relationship between diffusion time and diffusion parameters in breast cancer and hepatocellular carcinoma xenograft mouse models.Material and MethodsDiffusion-weighted MR images (DWI) were obtained on a 7-T magnetic resonance imaging (MRI) scanner at two different diffusion times (9.6 ms and 27.6 ms) in human breast cancer (MDA-MB-231) and hepatocellular carcinoma (HepG2 and PLC/PRF/5) xenograft mouse models. Perfusion-related IVIM (fIVIM and D*) and non-Gaussian diffusion (ADC0 and K) parameters were estimated. Parametric maps of diffusion changes with the diffusion times were generated using a synthetic apparent diffusion coefficient (sADC) obtained from b = 438 and 2584 s/mm2.ResultsADC0 values significantly decreased when diffusion times were changed from 9.6 ms to 27.6 ms in MDA-MB-231, HepG2, and PLC/PRF/5 groups (P = 0.0163, 0.0351, and 0.0170, respectively). K values significantly increased in MDA-MB-231 and HepG2 groups (P < 0.0003 and = 0.0007, respectively); however, no significant difference was detected in the PLC/PRF/5 group. fIVIM values increased, although not significantly (P = 0.164–0.748). The maps of sADC changes showed that diffusion changes with the diffusion time were not homogeneous across tumor tissues.ConclusionDiffusion MR parameters in both breast cancer and HCC xenograft models were found to be diffusion time-dependent. Our results show that diffusion time is an important parameter to consider when interpreting DWI data.

Multi-modal imaging investigation of anterior cingulate cortex cytoarchitecture in neurodevelopment

Multi-modal imaging may improve our understanding of the relationship between cortical morphology and cytoarchitecture. To this end we integrated the analyses of several magnetic resonance imaging (MRI) and spectroscopy (MRS) metrics within the anterior cingulate cortex (ACC). Considering the ACCs role in neurodevelopmental disorders, we also investigated the association between neuropsychiatric symptoms and the various metrics. T1 and diffusion-weighted MRI and 1H-MRS (ACC voxel) data along with phenotypic information were acquired from children (8–12 years) with various neurodevelopmental disorders (n=95) and healthy controls (n=50). From within the MRS voxel mean diffusivity (MD) of the grey matter fraction, intrinsic curvature (IC) of the surface and concentrations of creatine, choline, glutamate, N-acetylaspartate and myo-inositol were extracted. Linear models were used to investigate if the neurochemicals predicted MD and IC or if MD predicted IC. Finally, measures of various symptom severities were included to determine the influence of symptoms of neurodevelopmental disorders. All five neurochemicals inversely predicted MD (all puncorrected<0.04, β=0.23–0.36). There was no association between IC and MD or IC and the neurochemicals (all p>0.05). Severity of autism symptoms related positively to MD (puncorrected=0.002, β=0.39). Our findings support the notion that the neurochemicals relate to cytoarchitecture within the cortex. Additionally, we showed that autism symptoms across participants relate to the ACC cytoarchitecture.

CSF contamination-invariant statistics in conventional diffusion-weighted MRI of the fornix

The goal of this paper is to develop a method for assessment of microstructural properties of the fornix in conventional (low resolution, single non-zero b-value) diffusion-weighted magnetic resonance imaging (DW-MRI) data. For this purpose, a bi-tensor model, comprising of an isotropic and an anisotropic diffusion compartment, was fitted to the diffusion-weighted images. Two subject-specific constraints were studied to solve the ill-posedness of the parameter estimation at a single (non-zero) b-value, namely by fixating the mean diffusivity (MD) or the axial diffusivity (AxD) of the anisotropic compartment. The bi-tensor statistics were compared to conventional diffusion statistics using simulated fiber bundles with different diameters and using fornix segmentations of 577 elderly subjects. Based on simulated fiber bundles, the anisotropy (FA) estimated by the bi-tensor model did not become biased with decreasing fiber bundle diameter, unlike conventional diffusion statistics such as FA and MD estimated by the single tensor model. In the population-based study, the bi-tensor tissue fraction decreased significantly with age, suggesting an increase of free water. The FA estimated by the bi-tensor model decreased with age, but this relation was not significant when the subject-specific values to which MD or AxD were constrained were added as covariates in the regression analysis. The distinction of an isotropic and an anisotropic diffusion compartment may allow a more sophisticated analysis of the fornix based on conventional DW-MRI data.

Fiberweb: Diffusion Visualization and Processing in the Browser.

Data visualization is one of the most important tool to explore the brain as we know it. In this work, we introduce a novel browser-based solution for medical imaging data visualization and interaction with diffusion-weighted magnetic resonance imaging (dMRI) and tractography data: Fiberweb. It uses a recent technology, WebGL, that has yet to be fully explored for medical imaging purposes. There are currently very few software tools that allow medical imaging data visualization in the browser, and none of these tools support efficient data interaction and processing, such as streamlines selection and real-time deterministic and probabilistic tractography (RTT). With Fiberweb allowing these types of interaction, it is no longer the case. We show results of the visualization of medical imaging data, and demonstrate that our new RTT probabilistic algorithm can compare to a state of the art offline algorithm. Overall, Fiberweb pushes the boundary of interaction combined with scientific visualization, which opens great perspectives for quality control and neurosurgical navigation on browser-based mobile and static devices.

A combined tract-based spatial statistics and voxel-based morphometry study of the first MRI scan after diagnosis of amyotrophic lateral sclerosis with subgroup analysis

Background and purposeThis study aims to compare the cortical and subcortical deep gray matter (GM) and white matter (WM) of ALS subjects and controls and to compare ALS subjects with (ALScog) and without (ALSnon-cog) cognitive impairment. Materials and methodsThe study was performed in 30 ALS subjects, and 19 healthy controls. Structural T1- and diffusion-weighted MRI data were analyzed using voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS). ResultsAll DTI measures and GM volume differed significantly between ALS subjects and controls. Compared to controls, greater DTI changes were present in ALScog than ALSnon-cog subjects. GM results showed reduction in the caudate nucleus volume in ALScog subjects compared to ALSnon-cog. and comparing all ALS with controls, there were changes on the right side and in a small region in the left middle frontal gyrus. ConclusionThis combined DTI and VBM study showed changes in motor and extra-motor regions. The DTI changes were more extensive in ALScog than ALSnon-cog subjects. It is likely that the inclusion of ALS subjects with cognitive impairment in previous studies resulted in extra-motor WM abnormalities being reported in ALS subjects.

Is the extrastriate body area part of the dorsal visuomotor stream?

The extrastriate body area (EBA) processes visual information about body parts, and it is considered one among a series of category-specific perceptual modules distributed across the occipito-temporal cortex. However, recent evidence raises the possibility that EBA might also provide an interface between perception and action, linking the ventral and dorsal streams of visual information processing. Here, we assess anatomical evidence supporting this possibility. We localise EBA in individual subjects using a perceptual task and compare the characteristics of its functional and structural connectivity to those of two perceptual areas, the lateral occipital complex (LOC) and the fusiform body area (FBA), separately for each hemisphere. We apply complementary analyses of resting-state fMRI and diffusion-weighted MRI data in a group of healthy right-handed human subjects (N = 31). Functional and structural connectivity profiles indicate that EBA interacts more strongly with dorsal-stream regions compared to other portions of the occipito-temporal cortex involved in processing body parts (FBA) and object identification (LOC). These findings provide anatomical ground for a revision of the functional role of EBA. Building on a number of recent observations, we suggest that EBA contributes to planning goal-directed actions, possibly by specifying a desired postural configuration to parieto-frontal areas involved in computing movement parameters.

Comparison of brain computed tomography and diffusion-weighted magnetic resonance imaging to predict early neurologic outcome before target temperature management comatose cardiac arrest survivors

AimWe previously reported that diffusion-weighted magnetic resonance imaging (DW-MRI) could be used to predict neurologic outcomes before targeted temperature management (TTM) after return of spontaneous circulation (ROSC) from cardiac arrest (CA). We compared the efficacy of brain computed tomography (CT) and DW-MRI to predict neurologic outcome before TTM in comatose cardiac arrest survivors. MethodsWe performed a retrospective study of CA patients treated with TTM. The brain CT and DW-MRI were both obtained before TTM. We analysed the grey matter to white matter ratio (GWR) on the brain CT and the presence of high signal intensity on DW-MRI, alone or in combination, to predict poor neurologic outcome (CPC 3–5). ResultsOf 47 comatose CA patients treated with TTM, 39 patients with brain CT and DW-MRI data were included. Median time from the ROSC to the brain CT and DW-MRI was 90min (52–150) and 175min (118–240), respectively. There was no significant difference in predicting poor neurologic outcome between average GWR (area under the curve [AUC] 0.891, sensitivity/specificity 78.8%/100%) and DW-MRI (AUC 0.894, sensitivity/specificity 75.8%/100%) (p=0.963). The combination of average GWR and DW-MRI (AUC 0.939, sensitivity/specificity 87.9%/100%) improved the prediction of poor neurologic outcome rather than each one alone and in other combinations. ConclusionOur preliminary finding suggests that DW-MRI is potentially useful for early prediction of neurologic outcome (i.e., before TTM) in CA patients. The combination of GWR on brain CT and that on DW-MRI, rather than on each modality alone, appears to improve the sensitivity for predicting neurologic outcome after ROSC from CA. Large prospective multicenter studies should be conducted to confirm these results.

A method for u-fiber quantification from 7 T diffusion-weighted MRI data tested in patients with nonlesional focal epilepsy.

In this methods development, we present an ultra-high-field, diffusion-weighted MRI method to quantitatively assess u-fibers and use it to compare u-fiber counts in nonlesional epilepsy patients with controls. Emerging evidence implicates white matter abnormalities in nonlesional epilepsy, including the short-range, cortical-cortical connections, or u-fibers. Eight patients with nonlesional epilepsy and eight demographically matched controls underwent 7 T MRI consisting of a T1-weighted sequence (0.7 mm isotropic resolution) and high-angular-resolved diffusion-weighted MRI (1.05 mm isotropic resolution, 68 directions). MRI data were used to quantify u-fiber counts in known u-fiber populations on the basis of an atlas and fiber tractography. From tractography, connectivity matrices summarizing the u-fiber counts were computed. Quantitative group comparisons were performed on the connectivity matrices. U-fiber counts were found to be lower on average in patients with epilepsy than in healthy controls. The results indicate that the density or the number of u-fibers is reduced in patients with nonlesional epilepsy. Future work will focus on histological validation and determining whether differences in u-fiber counts can be used clinically to noninvasively identify seizure-onset zones.

Tumor Cell Load and Heterogeneity Estimation From Diffusion-Weighted MRI Calibrated With Histological Data: an Example From Lung Cancer.

Diffusion-weighted magnetic resonance imaging (DWI) is a key non-invasive imaging technique for cancer diagnosis and tumor treatment assessment, reflecting Brownian movement of water molecules in tissues. Since densely packed cells restrict molecule mobility, tumor tissues produce usually higher signal (a.k.a. less attenuated signal) on isotropic maps compared with normal tissues. However, no general quantitative relation between DWI data and the cell density has been established. In order to link low-resolution clinical cross-sectional data with high-resolution histological information, we developed an image processing and analysis chain, which was used to study the correlation between the diffusion coefficient (D value) estimated from DWI and tumor cellularity from serial histological slides of a resected non-small cell lung cancer tumor. Color deconvolution followed by cell nuclei segmentation was performed on digitized histological images to determine local and cell-type specific 2d (two-dimensional) densities. From these, the 3d cell density was inferred by a model-based sampling technique, which is necessary for the calculation of local and global 3d tumor cell count. Next, DWI sequence information was overlaid with high-resolution CT data and the resected histology using prominent anatomical hallmarks for co-registration of histology tissue blocks and non-invasive imaging modalities' data. The integration of cell numbers information and DWI data derived from different tumor areas revealed a clear negative correlation between cell density and D value. Importantly, spatial tumor cell density can be calculated based on DWI data. In summary, our results demonstrate that tumor cell count and heterogeneity can be predicted from DWI data, which may open new opportunities for personalized diagnosis and therapy optimization.

Abstract A09: Predicting response to whole brain radiotherapy in a murine model of glioma

Abstract Introduction: Glioblastoma is a highly invasive and aggressive brain tumor which accounts for nearly 82% of all gliomas. One promising direction for improving the clinical care of cancer, in general, and glioblastomas, in particular, is the development of accurate and precise predictive mathematical models of tumor growth. Specifically, models that can accurately characterize tumor response to radiotherapy could improve the design and optimization of individual radiotherapy plans. Using non-invasive imaging measurements from magnetic resonance imaging (MRI), a biophysical model of tumor growth has been shown to accurately predict future tumor growth on an individual basis in a murine model of glioma. In this work, we have expanded upon this biophysical model to incorporate the effects of radiotherapy. Furthermore, we evaluate the accuracy of model predictions relative to in vivo measurements of tumor growth. Methods: Untreated tumor growth was modeled using a mechanically-coupled reaction-diffusion model incorporating a voxel-specific carrying capacity, a voxel-specific tumor cell proliferation rate, and a global value for tumor cell diffusion. The effects of radiotherapy were modeled as resulting in either instantaneous cell death (Md model), the reduction in the net proliferation rate (Mp model), or a combination of the two effects (Mdp ). Instantaneous cell death was incorporated into the Md and Mdp models by estimating a death rate that was applied only during treatment. Similarly, decreased proliferation was incorporated into the Mp and Mdp models by estimating a proliferation fraction that was applied during and after treatment. To evaluate these models, rats with C6 gliomas were imaged over twelve days with diffusion-weighted MRI (DW-MRI) and contrast enhanced MRI (CE-MRI). Rats were imaged at three time points prior to receiving whole brain radiation therapy with a dose of either 20 Gy (N = 5) or 40 Gy (N = 7), and were imaged an additional four times following treatment. Tumor cellularity was estimated within enhancing regions in CE-MRI images using DW-MRI data. The pre-treatment measurements of cellularity were used to estimate tumor cell proliferation rate, carrying capacity, and the tumor cell diffusion coefficient. Two post-treatment measurements were used to estimate the post-treatment model parameters. The estimated model parameters were then used in a finite difference simulation to predict tumor growth at the remaining (6th through 7th) imaging time points. Error between the predicted radiotherapy response and the observed radiotherapy response was assessed by calculating the percent error in tumor volume, percent error in voxel cell number, and the normalized root mean square error (nRMSE). Results: For the 20 Gy rats no statistically significant differences were observed for model error in tumor volume (ranging from 7.7 to 21.2%), voxel cell number (ranging from 14.0 to 18.4%), or nRMSE (ranging from 0.14 to 0.16) between models. For the 40 Gy rats, the error in tumor volume prediction was significantly lower (p &amp;lt; 0.05) for the Mp and Mdp models (ranging from 22.2 to 36.3%) compared to the Md model (error greater than 54%). Error in voxel cell number ranged from 17.2 to 23.3% for all three models. The Mp and Mdp models had statistically lower nRMSE (nRMSE &amp;lt; 0.30, p = 0.05) compared to the Md model (nRMSE = 0.64). Conclusion: For 20 Gy whole brain radiotherapy, all three models demonstrated accurate prediction of future bulk tumor growth. However, at 40 Gy the model predictions of future bulk tumor growth were poor, although the Mp and Mdp models provide lower error in predictions than the Md model. Additionally, all three models fail to capture the development of low cell density, potentially necrotic, regions following radiotherapy for both the 20 and 40 Gy groups. Further work is needed to better characterize intra-tumor response at all doses. Citation Format: David A. Hormuth, II, Jared A. Weis, Stephanie B. Eldridge, Michael I. Miga, Erin C. Rericha, Vito Quaranta, Thomas E. Yankeelov. Predicting response to whole brain radiotherapy in a murine model of glioma. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A09.

Multimodality functional imaging using DW-MRI and 18F-FDG-PET/CT during radiation therapy for human papillomavirus negative head and neck squamous cell carcinoma: Meixoeiro Hospital of Vigo Experience.

AIMTo noninvasively investigate tumor cellularity measured using diffusion-weighted magnetic resonance imaging (DW-MRI) and glucose metabolism measured by 18F-labeled fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) during radiation therapy (RT) for human papillomavirus negative (HPV-) head and neck squamous cell carcinoma (HNSCC).METHODSIn this prospective study, 6 HPV- HNSCC patients underwent a total of 34 multimodality imaging examinations DW-MRI at 1.5 T Philips MRI scanner [(n = 24) pre-, during- (2-3 wk), and post-treatment (Tx), and 18F-FDG PET/CT pre- and post-Tx (n = 10)]. All patients received RT. Monoexponential modeling of the DW-MRI data yielded the imaging metric apparent diffusion coefficient (ADC) and the mean of standardized uptake value (SUV) was measured from 18F-FDG PET uptake. All patients had a clinical follow-up as the standard of care and survival status was documented at 1 year.RESULTSThere was a strong negative correlation between the mean of pretreatment ADC (ρ = -0.67, P = 0.01) and the pretreatment 18F-FDG PET SUV. The percentage (%) change in delta (∆) ADC for primary tumors and neck nodal metastases between pre- and Wk2-3 Tx were as follows: 75.4% and 61.6%, respectively, for the patient with no evidence of disease, 27.5% and 32.7%, respectively, for those patients who were alive with disease, and 26.9% and 7.31%, respectively, for those who were dead with disease.CONCLUSIONThese results are preliminary in nature and are indicative, and not definitive, trends rendered by the imaging metrics due to the small sample size of HPV- HNSCC patients in a Meixoeiro Hospital of Vigo Experience.

Strength of Structural and Functional Frontostriatal Connectivity Predicts Self-Control in the Healthy Elderly.

Self-regulation refers to the successful use of executive functions and initiation of top-down processes to control one's thoughts, behavior, and emotions, and it is crucial to perform self-control. Self-control is needed to overcome impulses and can be assessed by delay of gratification (DoG) and delay discounting (DD) paradigms. In children/adolescents, good DoG/DD ability depends on the maturity of frontostriatal connectivity, and its decline in strength with advancing age might adversely affect self-control because prefrontal brain regions are more prone to normal age-related atrophy than other regions. Here, we aimed at highlighting the relationship between frontostriatal connectivity strength and DoG performance in advanced age. We recruited 40 healthy elderly individuals (mean age 74.0 ± 7.7 years) and assessed the DoG ability using the German version of the DoG test for adults in addition to the delay discounting (DD) paradigm. Based on diffusion-weighted and resting-state functional magnetic resonance imaging data, respectively, the structural and functional whole-brain connectome were reconstructed based on 90 different brain regions of interest in addition to a 12-node frontostriatal DoG-specific network and the resulting connectivity matrices were subjected to network-based statistics. The 90-nodes whole-brain connectome analyses revealed subnetworks significantly associated with DoG and DD with a preponderance of frontostriatal nodes involved suggesting a high specificity of the findings. Structural and functional connectivity strengths between the putamen, caudate nucleus, and nucleus accumbens on the one hand and orbitofrontal, dorsal, and ventral lateral prefrontal cortices on the other hand showed strong positive correlations with DoG and negative correlations with DD corrected for age, sex, intracranial volume, and head motion parameters. These associations cannot be explained by differences in impulsivity and executive functioning. This pattern of correlations between structural or functional frontostriatal connectivity strength and self-control suggests that, in addition to the importance of the frontostriatal nodes itself, the structural and functional properties of different connections within the frontostriatal network are crucial for self-controlled behaviors in the healthy elderly. Because high DoG/low DD is a significant predictor of willpower and wellbeing in the elderly population, interventions aiming at strengthening frontostriatal connectivity to strengthen self-controlled behavior are needed in the future.

A comprehensive non-invasive framework for diagnosing prostate cancer

Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquired at different b-values, estimates its apparent diffusion coefficients (ADC), and classifies their descriptors – empirical cumulative distribution functions (CDF) – with a trained deep learning network. To segment the prostate, an evolving geometric (level-set-based) deformable model is guided by a speed function depending on intensity attributes extracted from the DW-MRI with nonnegative matrix factorization (NMF). For a more robust evolution, the attributes are fused with a probabilistic shape prior and estimated spatial dependencies between prostate voxels. To preserve continuity, the ADCs of the segmented prostate volume at different b-values are normalized and refined using a generalized Gauss-Markov random field image model. The CDFs of the refined ADCs at different b-values are considered global water diffusion features and used to distinguish between benign and malignant prostates. A deep learning network of stacked non-negativity-constrained auto-encoders (SNCAE) is trained to classify the benign or malignant prostates on the basis of the constructed CDFs. Our experiments on 53 clinical DW-MRI data sets resulted in 92.3% accuracy, 83.3% sensitivity, and 100% specificity, indicating that the proposed CAD system could be used as a reliable non-invasive diagnostic tool.

Total variation-based method for generation of intravoxel incoherent motion parametric images in MRI.

Total variation (TV) method has been used widely for image restoration and reconstruction. In this work, we propose a TV-based algorithm for parametric image generation in intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (DW-MRI). We used simulated and real data to investigate whether the proposed TV-based method can provide reliable parametric images. Parametric images of IVIM parameters including perfusion fraction (PF), diffusion coefficient (D), and pseudo-diffusion coefficient (D*) were estimated using DW-MRI data and TV through fitting the IVIM model. The Levenberg-Marquardt (LM) method, which has often been used in the context of IVIM analysis, was employed as the standard method for comparison of the resulting parametric images. The simulation results show that the proposed method outperforms the LM algorithm in terms of precision, providing a 40-81%, 90-93%, and 68-84% improvement for PF, D and D*, respectively, at signal-to-noise ratio (SNR) of 30. For real data, the proposed method showed an average five-fold, three-fold, and four-fold improvement in the SNR for PF, D and D*, respectively. We introduced the use of TV to produce parametric images, and demonstrated that the proposed TV-based method is effective in improving the parametric image quality. Magn Reson Med 78:1383-1391, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Preserved white matter microstructure in young patients with anorexia nervosa?

A massive but reversible reduction of cortical thickness and subcortical gray matter (GM) volumes in Anorexia Nervosa (AN) has been recently reported. However, the literature on alterations in white matter (WM) volume and microstructure changes in both acutely underweight AN (acAN) and after recovery (recAN) is sparse and results are inconclusive. Here, T1-weighted and diffusion-weighted MRI data in a sizable sample of young and medication-free acAN (n = 35), recAN (n = 32), and age-matched female healthy controls (HC, n = 62) were obtained. For analysis, a well-validated global probabilistic tractography reconstruction algorithm including rigorous motion correction implemented in FreeSurfer: TRACULA (TRActs Constrained by UnderLying Anatomy) were used. Additionally, a clustering algorithm and a multivariate pattern classification technique to WM metrics to predict group membership were applied. No group differences in either WM volume or WM microstructure were detected with standard analysis procedures either in acAN or recAN relative to HC after controlling for the number of performed statistical tests. These findings were not affected by age, IQ, or psychiatric symptoms. While cluster analysis was unsuccessful at discriminating between groups, multivariate pattern classification showed some ability to separate acAN from HC (but not recAN from HC). However, these results were not compatible with a straightforward hypothesis of impaired WM microstructure. The current findings suggest that WM integrity is largely preserved in non-chronic AN. This finding stands in contrast to findings in GM, but may help to explain the relatively intact cognitive performance of young patients with AN and provide the basis for the fast recovery of GM structures. Hum Brain Mapp 37:4069-4083, 2016. © 2016 Wiley Periodicals, Inc.

S.28.02 - Top-down control in ADHD: disorder-specificity relative to CD, autism and OCD

Multi-modal imaging may improve our understanding of the relationship between cortical morphology and cytoarchitecture. To this end we integrated the analyses of several magnetic resonance imaging (MRI) and spectroscopy (MRS) metrics within the anterior cingulate cortex (ACC). Considering the ACCs role in neurodevelopmental disorders, we also investigated the association between neuropsychiatric symptoms and the various metrics. T1 and diffusion-weighted MRI and 1H-MRS (ACC voxel) data along with phenotypic information were acquired from children (8–12 years) with various neurodevelopmental disorders (n=95) and healthy controls (n=50). From within the MRS voxel mean diffusivity (MD) of the grey matter fraction, intrinsic curvature (IC) of the surface and concentrations of creatine, choline, glutamate, N-acetylaspartate and myo-inositol were extracted. Linear models were used to investigate if the neurochemicals predicted MD and IC or if MD predicted IC. Finally, measures of various symptom severities were included to determine the influence of symptoms of neurodevelopmental disorders. All five neurochemicals inversely predicted MD (all puncorrected<0.04, β=0.23–0.36). There was no association between IC and MD or IC and the neurochemicals (all p>0.05). Severity of autism symptoms related positively to MD (puncorrected=0.002, β=0.39). Our findings support the notion that the neurochemicals relate to cytoarchitecture within the cortex. Additionally, we showed that autism symptoms across participants relate to the ACC cytoarchitecture.

Connectome Disconnectivity and Cortical Gene Expression in Patients With Schizophrenia

BackgroundGenome-wide association studies have identified several common risk loci for schizophrenia (SCZ). In parallel, neuroimaging studies have shown consistent findings of widespread white matter disconnectivity in patients with SCZ. MethodsWe examined the role of genes in brain connectivity in patients with SCZ by combining transcriptional profiles of 43 SCZ risk genes identified by the recent genome-wide association study of the Schizophrenia Working Group of the Psychiatric Genomics Consortium with data on macroscale connectivity reductions in patients with SCZ. Expression profiles of 43 Psychiatric Genomics Consortium SCZ risk genes were extracted from the Allen Human Brain Atlas, and their average profile across the cortex was correlated to the pattern of cortical disconnectivity as derived from diffusion-weighted magnetic resonance imaging data of patients with SCZ (n = 48) and matched healthy controls (n = 43). ResultsThe expression profile of SCZ risk genes across cortical regions was significantly correlated with the regional macroscale disconnectivity (r = .588; p = .017). In addition, effects were found to be potentially specific to SCZ, with transcriptional profiles not related to cortical disconnectivity in patients with bipolar I disorder (diffusion-weighted magnetic resonance imaging data; 216 patients, 144 controls). Further examination of correlations across all 20,737 genes present in the Allen Human Brain Atlas showed the set of top 100 strongest correlating genes to display significant enrichment for the disorder, potentially identifying new genes involved in the pathophysiology of SCZ. ConclusionsOur results suggest that under disease conditions, cortical areas with pronounced expression of risk genes implicated in SCZ form central areas for white matter disconnectivity.

Brain connectome modularity in weight-restored anorexia nervosa and body dysmorphic disorder.

Anorexia nervosa (AN) and body dysmorphic disorder (BDD) frequently co-occur, and have several overlapping phenomenological features. Little is known about their shared neurobiology. The aim of the study was to compare modular organization of brain structural connectivity. We acquired diffusion-weighted magnetic resonance imaging data on unmedicated individuals with BDD (n = 29), weight-restored AN (n = 24) and healthy controls (HC) (n = 31). We constructed connectivity matrices using whole-brain white matter tractography, and compared modular structures across groups. AN showed abnormal modularity involving frontal, basal ganglia and posterior cingulate nodes. There was a trend in BDD for similar abnormalities, but no significant differences compared with AN. In AN, poor insight correlated with longer path length in right caudal anterior cingulate and right posterior cingulate. Abnormal network organization patterns in AN, partially shared with BDD, may have implications for understanding integration between reward and habit/ritual formation, as well as conflict monitoring/error detection.

Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma.

Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.