Fusion Of Magnetic Resonance Imaging Data Research Articles

Structure/function interrelationships and illness insight in patients with schizophrenia: a multimodal MRI data fusion study

Illness insight in schizophrenia (SZ) has an important impact on treatment outcome, integration into society and can vary over the course of the disorder. To deal with and treat reduced or absent illness insight, we need to better understand its functional and structural correlates. Previous studies showed regionally abnormal brain volume in brain areas related to cognitive control and self-reference. However, little is known about associations between illness insight and structural and functional network strength in patients with SZ. This study employed a cross-sectional design to examine structural and functional differences between patients with SZ (n = 74) and healthy controls (n = 47) using structural and resting-state functional magnetic resonance imaging (MRI). Voxel-based morphometry was performed on structural data, and the amplitude of low frequency fluctuations (ALFF) was calculated for functional data. To investigate abnormal structure/function interrelationships and their association with illness insight, we used parallel independent component analysis (pICA). Significant group (SZ vs. HC) differences were detected in distinct structural and functional networks, predominantly comprising frontoparietal, temporal and cerebellar regions. Significant associations were found between illness insight and two distinct structural networks comprising frontoparietal (pre- and postcentral gyrus, inferior parietal lobule, thalamus, and precuneus) and posterior cortical regions (cuneus, precuneus, lingual, posterior cingulate, and middle occipital gyrus). Finally, we found a significant relationship between illness insight and functional network comprising temporal regions (superior temporal gyrus). This study suggests that aberrant structural and functional integrity of neural systems subserving cognitive control, memory and self-reference are tightly coupled to illness insight in SZ.

Acute Posttraumatic Symptoms Are Associated With Multimodal Neuroimaging Structural Covariance Patterns: A Possible Role for the Neural Substrates of Visual Processing in Posttraumatic Stress Disorder

BackgroundAlthough aspects of brain morphology have been associated with chronic posttraumatic stress disorder (PTSD), limited work has investigated multimodal patterns in brain morphology that are linked to acute posttraumatic stress severity. In the present study, we utilized multimodal magnetic resonance imaging to investigate if structural covariance networks (SCNs) assessed acutely following trauma were linked to acute posttraumatic stress severity. MethodsStructural magnetic resonance imaging data were collected around 1 month after civilian trauma exposure in 78 participants. Multimodal magnetic resonance imaging data fusion was completed to identify combinations of SCNs, termed structural covariance profiles (SCPs), related to acute posttraumatic stress severity collected at 1 month. Analyses assessed the relationship between participant SCP loadings, acute posttraumatic stress severity, the change in posttraumatic stress severity from 1 to 12 months, and depressive symptoms. ResultsWe identified an SCP that reflected greater gray matter properties of the anterior temporal lobe, fusiform face area, and visual cortex (i.e., the ventral visual stream) that varied curvilinearly with acute posttraumatic stress severity and the change in PTSD symptom severity from 1 to 12 months. The SCP was not associated with depressive symptoms. ConclusionsWe identified combinations of multimodal SCNs that are related to variability in PTSD symptoms in the early aftermath of trauma. The identified SCNs may reflect patterns of neuroanatomical organization that provide unique insight into acute posttraumatic stress. Furthermore, these multimodal SCNs may be potential candidates for neural markers of susceptibility to both acute posttraumatic stress and the future development of PTSD.

Multimodal Magnetic Resonance Imaging Data Fusion Reveals Distinct Patterns of Abnormal Brain Structure and Function in Catatonia

Catatonia is a nosologically unspecific syndrome, which subsumes a plethora of mostly complex affective, motor, and behavioral phenomena. Although catatonia frequently occurs in schizophrenia spectrum disorders (SSD), specific patterns of abnormal brain structure and function underlying catatonia are unclear at present. Here, we used a multivariate data fusion technique for multimodal magnetic resonance imaging (MRI) data to investigate patterns of aberrant intrinsic neural activity (INA) and gray matter volume (GMV) in SSD patients with and without catatonia. Resting-state functional MRI and structural MRI data were collected from 87 right-handed SSD patients. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). A multivariate analysis approach was used to examine co-altered patterns of INA and GMV. Following a categorical approach, we found predominantly frontothalamic and corticostriatal abnormalities in SSD patients with catatonia (NCRS total score ≥ 3; n = 24) when compared to SSD patients without catatonia (NCRS total score = 0; n = 22) matched for age, gender, education, and medication. Corticostriatal network was associated with NCRS affective scores. Following a dimensional approach, 33 SSD patients with catatonia according to Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision were identified. NCRS behavioral scores were associated with a joint structural and functional system that predominantly included cerebellar and prefrontal/cortical motor regions. NCRS affective scores were associated with frontoparietal INA. This study provides novel neuromechanistic insights into catatonia in SSD suggesting co-altered structure/function-interactions in neural systems subserving coordinated visuospatial functions and motor behavior.

The prognostic value of repeated prostate fusion biopsy

Objective – disclosure of the main advantages of prostate biopsy under the magnetic resonance imaging (MRI) control, consideration of technical aspects of its implementation. Materials and methods. Advances in multiparametric MRI have lead to improved detection of prostate tumors. The fusion of MRI data with transrectal ultrasound enables the targeted biopsy of suspicious areas. The results of fusion-biopsy were analyzed in 38 patients. The mean age of patients was 61.3 (44–70) years. All the patients underwent at least 1 transrectal prostate biopsies. The average number of biopsy cores was 24.3 (17–30), the average value of total prostate-specific antigen before saturation biopsy was 10,4 (0.20 to 34.16) ng/ml. Results . Prostate cancer was diagnosed in 52.7 % of cases (20/38). Better prostate cancer detectability during repeated saturation biopsy generally occurred due to the localized forms of the disease (93.3 %). Conclusion. Fusion biopsy allows prediction of a pathological stage of prostate cancer, Gleason grade of a tumor and its site localization with a greaterprobability. Most tumors detectable by saturation biopsy were clinically significant, which makes it possible to recommend fusion biopsy to some cohort of high prostate cancer risk patients.

Feature-based MRI data fusion for cardiac arrhythmia studies

Current practices in studying cardiac arrhythmias primarily use electrical or optical surface recordings of a heart, spatially limited transmural recordings, and mathematical models. However, given that such arrhythmias occur on a 3D myocardial tissue, information obtained from such practices lack in dimension, completeness, and are sometimes prone to oversimplification. The combination of complementary Magnetic-Resonance Imaging (MRI)-based techniques such as Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI) could provide more depth to current practices in assessing the cardiac arrhythmia dynamics in entire cross sections of myocardium. In this work, we present an approach utilizing feature-based data fusion methods to demonstrate that complimentary information obtained from electrical current distribution and structural properties within a heart could be quantified and enhanced. Twelve (12) pairs of CDI and DTI image data sets were gathered from porcine hearts perfused through a Langendorff setup. Images were fused together using feature-based data fusion techniques such as Joint Independent Component Analysis (jICA), Canonical Correlation Analysis (CCA), and their combination (CCA+jICA). The results suggest that the complimentary information of cardiac states from CDI and DTI are enhanced and are better classified with the use of data fusion methods. For each data set, an increase in mean correlations of fused images were observed with 38% increase from CCA+jICA compared to the original images while mean mutual information of the fused images from jICA and CCA+jICA increased by approximately three-fold. We conclude that MRI-based techniques present potential viable tools in furthering studies for cardiac arrhythmias especially Ventricular Fibrillation.

MRI-based Preplanning Using CT and MRI Data Fusion in Patients With Cervical Cancer Treated With 3D-based Brachytherapy: Feasibility and Accuracy Study

Magnetic resonance imaging (MRI)-assisted radiation treatment planning enables enhanced target contouring. The purpose of this study is to analyze the feasibility and accuracy of computed tomography (CT) and MRI data fusion for MRI-based treatment planning in an institution where an MRI scanner is not available in the radiotherapy department. The registration inaccuracy of applicators and soft tissue was assessed in 42 applications with CT/MRI data fusion. The absolute positional difference of the center of the applicators was measured in four different planes from the top of the tandem to the cervix. Any inaccuracy of registration of soft tissue in relation to the position of applicators was determined and dose-volume parameters for MRI preplans and for CT/MRI fusion plans with or without target and organs at risk (OAR) adaptation were evaluated. We performed 6,132 measurements in 42 CT/MRI image fusions. Median absolute difference of the center of tandem on CT and MRI was 1.1 mm. Median distance between the center of the right ovoid on CT and MRI was 1.7 and 1.9 mm in the laterolateral and anteroposterior direction, respectively. Corresponding values for the left ovoid were 1.6 and 1.8 mm. Rotation of applicators was 3.1°. Median absolute difference in position of applicators in relation to soft tissue was 1.93, 1.50, 1.05, and 0.84 mm in the respective transverse planes, and 1.17, 1.28, 1.27, and 1.17 mm in selected angular directions. The dosimetric parameters for organs at risk on CT/MRI fusion plans without OAR adaptation were significantly impaired whereas the target coverage was not influenced. Planning without target adaptation led to overdosing of the target volume, especially high-risk clinical target volume--D₉₀ 88.2 vs. 83.1 (p < 0.05). MRI-based preplanning with consecutive CT/MRI data fusion can be safe and feasible, with an acceptable inaccuracy of soft tissue registration.

Nonlinear local electrovascular coupling. I: A theoretical model

Here we present a detailed biophysical model of how brain electrical and vascular dynamics are generated within a basic cortical unit. The model was obtained from coupling a canonical neuronal mass and an expandable vasculature. In this proposal, we address several aspects related to electroencephalographic and functional magnetic resonance imaging data fusion: (1) the impact of the cerebral architecture (at different physical levels) on the observations; (2) the physiology involved in electrovascular coupling; and (3) energetic considerations to gain a better understanding of how the glucose budget is used during neuronal activity. The model has three components. The first is the canonical neural mass model of three subpopulations of neurons that respond to incoming excitatory synaptic inputs. The generation of the membrane potentials in the somas of these neurons and the electric currents flowing in the neuropil are modeled by this component. The second and third components model the electrovascular coupling and the dynamics of vascular states in an extended balloon approach, respectively. In the first part we describe, in some detail, the biophysical model and establish its face validity using simulations of visually evoked responses under different flickering frequencies and luminous contrasts. In a second part, a recursive optimization algorithm is developed and used to make statistical inferences about this forward/generative model from actual data.