Multicenter Magnetic Resonance Imaging Study Research Articles

Associations Between Structural Covariance Network and Antipsychotic Treatment Response in Schizophrenia.

Schizophrenia is associated with widespread cortical thinning and abnormality in the structural covariance network, which may reflect connectome alterations due to treatment effect or disease progression. Notably, patients with treatment-resistant schizophrenia (TRS) have stronger and more widespread cortical thinning, but it remains unclear whether structural covariance is associated with treatment response in schizophrenia. We organized a multicenter magnetic resonance imaging study to assess structural covariance in a large population of TRS and non-TRS, who had been resistant and responsive to non-clozapine antipsychotics, respectively. Whole-brain structural covariance for cortical thickness was assessed in 102 patients with TRS, 77 patients with non-TRS, and 79 healthy controls (HC). Network-based statistics were used to examine the difference in structural covariance networks among the 3 groups. Moreover, the relationship between altered individual differentiated structural covariance and clinico-demographics was also explored. Patients with non-TRS exhibited greater structural covariance compared with HC, mainly in the fronto-temporal and fronto-occipital regions, while there were no significant differences in structural covariance between TRS and non-TRS or HC. Higher individual differentiated structural covariance was associated with lower general scores of the Positive and Negative Syndrome Scale in the non-TRS group, but not in the TRS group. These findings suggest that reconfiguration of brain networks via coordinated cortical thinning is related to treatment response in schizophrenia. Further longitudinal studies are warranted to confirm if greater structural covariance could serve as a marker for treatment response in this disease.

P.507 Machine learning classification of first-episode psychosis using cortical thickness: a large multicenter magnetic resonance imaging study

P.507 Machine learning classification of first-episode psychosis using cortical thickness: a large multicenter magnetic resonance imaging study

The modulating impact of cigarette smoking on brain structure in panic disorder: a voxel-based morphometry study.

Cigarette smoking increases the likelihood of developing anxiety disorders, among them panic disorder (PD). While brain structures altered by smoking partly overlap with morphological changes identified in PD, the modulating impact of smoking as a potential confounder on structural alterations in PD has not yet been addressed. In total, 143 PD patients (71 smokers) and 178 healthy controls (62 smokers) participated in a multicenter magnetic resonance imaging (MRI) study. T1-weighted images were used to examine brain structural alterations using voxel-based morphometry in a priori defined regions of the defensive system network. PD was associated with gray matter volume reductions in the amygdala and hippocampus. This difference was driven by non-smokers and absent in smoking subjects. Bilateral amygdala volumes were reduced with increasing health burden (neither PD nor smoking > either PD or smoking > both PD and smoking). As smoking can narrow or diminish commonly observed structural abnormalities in PD, the effect of smoking should be considered in MRI studies focusing on patients with pathological forms of fear and anxiety. Future studies are needed to determine if smoking may increase the risk for subsequent psychopathology via brain functional or structural alterations.

Subcortical Brain Volume Abnormalities in Individuals With an At-risk Mental State.

Previous structural magnetic resonance imaging studies of psychotic disorders have demonstrated volumetric alterations in subcortical (ie, the basal ganglia, thalamus) and temporolimbic structures, which are involved in high-order cognition and emotional regulation. However, it remains unclear whether individuals at high risk for psychotic disorders with minimal confounding effects of medication exhibit volumetric changes in these regions. This multicenter magnetic resonance imaging study assessed regional volumes of the thalamus, caudate, putamen, nucleus accumbens, globus pallidus, hippocampus, and amygdala, as well as lateral ventricular volume using FreeSurfer software in 107 individuals with an at-risk mental state (ARMS) (of whom 21 [19.6%] later developed psychosis during clinical follow-up [mean = 4.9 years, SD = 2.6 years]) and 104 age- and gender-matched healthy controls recruited at 4 different sites. ARMS individuals as a whole demonstrated significantly larger volumes for the left caudate and bilateral lateral ventricles as well as a smaller volume for the right accumbens compared with controls. In male subjects only, the left globus pallidus was significantly larger in ARMS individuals. The ARMS group was also characterized by left-greater-than-right asymmetries of the lateral ventricle and caudate nucleus. There was no significant difference in the regional volumes between ARMS groups with and without later psychosis onset. The present study suggested that significant volume expansion of the lateral ventricle, caudate, and globus pallidus, as well as volume reduction of the accumbens, in ARMS subjects, which could not be explained only by medication effects, might be related to general vulnerability to psychopathology.

Increased Occipital Gyrification and Development of Psychotic Disorders in Individuals With an At-Risk Mental State: A Multicenter Study

BackgroundAnomalies of brain gyrification have been reported in schizophrenia, possibly reflecting its neurodevelopmental pathology. However, it remains elusive whether individuals at risk for psychotic disorders exhibit deviated gyrification patterns, and whether such findings, if present, are predictive of transition to psychotic disorders. MethodsThis multicenter magnetic resonance imaging study investigated brain gyrification and its relationship to later transition to psychotic disorders in a large sample of at-risk mental state (ARMS) individuals. T1-weighted magnetic resonance imaging scans were obtained from 104 ARMS individuals, of whom 21 (20.2%) exhibited the transition to psychotic disorders during clinical follow-up (mean = 4.9 years, SD = 2.6 years), and 104 healthy control subjects at 4 different sites. The local gyrification index (LGI) of the entire cortex was compared across the groups using FreeSurfer software. ResultsCompared with the control subjects, ARMS individuals showed a significantly higher LGI in widespread cortical areas, including the bilateral frontal, temporal, parietal, and occipital regions, which was partly associated with prodromal symptomatology. ARMS individuals who exhibited the transition to psychotic disorders showed a significantly higher LGI in the left occipital region compared with individuals without transition. ConclusionsThese findings suggested that increased LGI in diverse cortical regions might represent vulnerability to psychopathology, while increased LGI in the left occipital cortex might be related to subsequent manifestation of florid psychotic disorders as a possible surrogate marker.

Multi-Center MRI Carotid Plaque Component Segmentation Using Feature Normalization and Transfer Learning.

Automated segmentation of plaque components in carotid artery magnetic resonance imaging (MRI) is important to enable large studies on plaque vulnerability, and for incorporating plaque composition as an imaging biomarker in clinical practice. Especially supervised classification techniques, which learn from labeled examples, have shown good performance. However, a disadvantage of supervised methods is their reduced performance on data different from the training data, for example on images acquired with different scanners. Reducing the amount of manual annotations required for each new dataset will facilitate widespread implementation of supervised methods. In this paper we segment carotid plaque components of clinical interest (fibrous tissue, lipid tissue, calcification and intraplaque hemorrhage) in a multi-center MRI study. We perform voxelwise tissue classification by traditional same-center training, and compare results with two approaches that use little or no annotated same-center data. These approaches additionally use an annotated set of different-center data. We evaluate 1) a nonlinear feature normalization approach, and 2) two transfer-learning algorithms that use same and different-center data with different weights. Results showed that the best results were obtained for a combination of feature normalization and transfer learning. While for the other approaches significant differences in voxelwise or mean volume errors were found compared with the reference same-center training, the proposed approach did not yield significant differences from that reference. We conclude that both extensive feature normalization and transfer learning can be valuable for the development of supervised methods that perform well on different types of datasets.

Abstract 428: Progression Patterns of Carotid Atherosclerotic Plaques with and Without Intraplaque Hemorrhage Monitored for 6 Months: A Multicenter Magnetic Resonance Imaging Study

Background: Carotid magnetic resonance imaging (MRI) is emerging as an effective tool for monitoring plaque progression noninvasively. However, data from multi-center studies are scarce. Intraplaque hemorrhage (IPH) has been shown to promote plaque growth. In this multi-center study over 6 months, we sought to study plaque progression in subjects with and without IPH. Methods: As the placebo arm of a multi-center clinical trial, 59 subjects with 16-79% carotid stenosis by ultrasound and a lipid core (LC) on baseline MRI completed a follow-up MRI at the end of a 6-month period. Thirty-nine (66.1%) were on concurrent rosuvastatin therapy. Lumen, wall and LC volumes, and IPH presence were measured on both scans blinded to time sequence at a core lab. Plaque progression was calculated as annualized changes within the common coverage between scans using the carotid bifurcation as a landmark. Results: IPH was present on 14 slices from 3 (5.1%) subjects at baseline. Subjects without IPH (n=56) showed a significant reduction in LC volume (p=0.031), a trend towards reduction in wall volume (p=0.215) and no apparent change in lumen volume (p=0.910). Although all subjects with IPH were on rosuvastatin during the study period, compared to subjects without IPH, they had increased LC (62.9±46.2 mm3/year vs. -8.8±29.9 mm3/year, p<0.001) and wall (100.1±78.1 mm3/year vs. -12.7±75.8 mm3/year, p=0.015) volumes, but decreased lumen volume (-93.4±114.7 mm3/year vs. 0.90±59.7 mm3/year, p=0.014). Conclusion: This multi-center study over 6 months demonstrated significant regression in LC volume in plaques without IPH. By contrast, plaques with IPH may be poorly controlled despite statin therapy.

Brain volumes as predictor of outcome in recent-onset schizophrenia: a multi-center MRI study.

Gray matter brain volume decreases have been found in patients with schizophrenia as compared to healthy control subjects measured by using Magnetic Resonance Imaging (MRI). An association has been suggested between decreased gray matter volume and poor outcome in chronically ill patients with schizophrenia. The present longitudinal multi-center study investigated whether gray matter volume at illness onset can predict poor outcome in recent-onset schizophrenia after a follow-up of approximately 2 years. An MRI calibration study was performed since scans of patients with recent-onset psychosis were conducted at three sites with 1.5 T MR scanners from two different manufacturers. Applying a linear scaling procedure on the histogram improved comparability between volume measurements acquired from images from the different scanners. Brain scans were obtained from 109 patients with recent-onset schizophrenia. Volumes of intracranium, total brain, cerebral gray and white matter, third and lateral ventricles, and cerebellum were measured. After a mean follow-up period of approximately 2 years, measurements of symptoms, functioning, need for care, and illness history variables were assessed. No significant correlations were found between the brain volume measures and any of these measures. Gray matter volume at illness onset does not predict outcome after 2 years in recent-onset schizophrenia.