Multidimensional morphometric 3D MRI analyses for detecting brain abnormalities in children: impact of control population

The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) consortium: A collaborative cognitive and neuroimaging genetics project

BACKGROUND

Schizophrenia has a large genetic component, and the pathways from genes to illness manifestation are beginning to be identified. The Genetics of Endophenotypes of Neurofunction to Understand Schizophrenia (GENUS) Consortium aims to clarify the role of genetic variation in brain abnormalities underlying schizophrenia. This article describes the GENUS Consortium sample collection.

METHODS

We identified existing samples collected for schizophrenia studies consisting of patients, controls, and/or individuals at familial high-risk (FHR) for schizophrenia. Samples had single nucleotide polymorphism (SNP) array data or genomic DNA, clinical and demographic data, and neuropsychological and/or brain magnetic resonance imaging (MRI) data. Data were subjected to quality control procedures at a central site.

RESULTS

Sixteen research groups contributed data from 5199 psychosis patients, 4877 controls, and 725 FHR individuals. All participants have relevant demographic data and all patients have relevant clinical data. The sex ratio is 56.5% male and 43.5% female. Significant differences exist between diagnostic groups for premorbid and current IQ (both p<1×10^-10). Data from a diversity of neuropsychological tests are available for 92% of participants, and 30% have structural MRI scans (half also have diffusion-weighted MRI scans). SNP data are available for 76% of participants. The ancestry composition is 70% European, 20% East Asian, 7% African, and 3% other.

CONCLUSIONS

The Consortium is investigating the genetic contribution to brain phenotypes in a schizophrenia sample collection of >10,000 participants. The breadth of data across clinical, genetic, neuropsychological, and MRI modalities provides an important opportunity for elucidating the genetic basis of neural processes underlying schizophrenia.

CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation

Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php.

Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy

Focal cortical dysplasia is a congenital abnormality of cortical development and the leading cause of surgically remediable drug-resistant epilepsy in children. Post-surgical outcome is improved by presurgical lesion detection on structural MRI. Automated computational techniques have improved detection of focal cortical dysplasias in adults but have not yet been effective when applied to developing brains. There is therefore a need to develop reliable and sensitive methods to address the particular challenges of a paediatric cohort. We developed a classifier using surface-based features to identify focal abnormalities of cortical development in a paediatric cohort. In addition to established measures, such as cortical thickness, grey-white matter blurring, FLAIR signal intensity, sulcal depth and curvature, our novel features included complementary metrics of surface morphology such as local cortical deformation as well as post-processing methods such as the "doughnut" method - which quantifies local variability in cortical morphometry/MRI signal intensity, and per-vertex interhemispheric asymmetry. A neural network classifier was trained using data from 22 patients with focal epilepsy (mean age = 12.1 ± 3.9, 9 females), after intra- and inter-subject normalisation using a population of 28 healthy controls (mean age = 14.6 ± 3.1, 11 females). Leave-one-out cross-validation was used to quantify classifier sensitivity using established features and the combination of established and novel features. Focal cortical dysplasias in our paediatric cohort were correctly identified with a higher sensitivity (73%) when novel features, based on our approach for detecting local cortical changes, were included, when compared to the sensitivity using only established features (59%). These methods may be applicable to aiding identification of subtle lesions in medication-resistant paediatric epilepsy as well as to the structural analysis of both healthy and abnormal cortical development.

Prospective detection of cortical dysplasia on clinical MRI in pediatric intractable epilepsy

BACKGROUND

Cortical dysplasia is the most common cause of pediatric refractory epilepsy. MRI detection of epileptogenic lesion is associated with good postsurgical outcome. Additional electrophysiological information is suggested to be helpful in localization of cortical dysplasia. Educational measures were taken to increase the awareness of cortical dysplasia at our institution in the context of a recent International League Against Epilepsy (ILAE 2011) classification of cortical dysplasia.

OBJECTIVE

To determine changes in the rate of prospective identification of cortical dysplasia on an initial radiology report and also evaluate the benefit of MRI review as part of a multidisciplinary epilepsy conference in identifying previously overlooked MRI findings.

MATERIALS AND METHODS

We retrospectively evaluated surgically treated children with refractory epilepsy from 2007 to 2014 with cortical dysplasia on histopathology. We analyzed the initial radiology report, preoperative MRI interpretation at multidisciplinary epilepsy conference and subsequent retrospective MRI review with knowledge of the resection site. We recorded additional electrophysiological data and the presence of lobar concordance with the MRI findings.

RESULTS

Of 78 children (44 MRI lesional) evaluated, 18 had initially overlooked MRI findings. Comparing 2007-2010 to 2011-2014, there was improvement in the rate of overlooked findings on the initial radiology report (54% vs. 13% of lesional cases, respectively; P = 0.008). The majority (72%) were identified at a multidisciplinary conference with lobar concordance of findings with at least one additional electrophysiological investigation in 89%.

CONCLUSION

Awareness of current classification schemes of cortical dysplasia and image review in the context of a multidisciplinary conference can lead to improved MRI detection of cortical dysplasia in children.

Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis

The gold standard for identifying stroke lesions is manual tracing, a method that is known to be observer dependent and time consuming, thus impractical for big data studies. We propose LINDA (Lesion Identification with Neighborhood Data Analysis), an automated segmentation algorithm capable of learning the relationship between existing manual segmentations and a single T1-weighted MRI. A dataset of 60 left hemispheric chronic stroke patients is used to build the method and test it with k-fold and leave-one-out procedures. With respect to manual tracings, predicted lesion maps showed a mean dice overlap of 0.696 ± 0.16, Hausdorff distance of 17.9 ± 9.8 mm, and average displacement of 2.54 ± 1.38 mm. The manual and predicted lesion volumes correlated at r = 0.961. An additional dataset of 45 patients was utilized to test LINDA with independent data, achieving high accuracy rates and confirming its cross-institutional applicability. To investigate the cost of moving from manual tracings to automated segmentation, we performed comparative lesion-to-symptom mapping (LSM) on five behavioral scores. Predicted and manual lesions produced similar neuro-cognitive maps, albeit with some discussed discrepancies. Of note, region-wise LSM was more robust to the prediction error than voxel-wise LSM. Our results show that, while several limitations exist, our current results compete with or exceed the state-of-the-art, producing consistent predictions, very low failure rates, and transferable knowledge between labs. This work also establishes a new viewpoint on evaluating automated methods not only with segmentation accuracy but also with brain-behavior relationships. LINDA is made available online with trained models from over 100 patients.

Fast semi-automated lesion demarcation in stroke

Lesion–behaviour mapping analyses require the demarcation of the brain lesion on each (usually transverse) slice of the individual stroke patient's brain image. To date, this is generally thought to be most precise when done manually, which is, however, both time-consuming and potentially observer-dependent. Fully automated lesion demarcation methods have been developed to address these issues, but these are often not practicable in acute stroke research where for each patient only a single image modality is available and the available image modality differs over patients. In the current study, we evaluated a semi-automated lesion demarcation approach, the so-called Clusterize algorithm, in acute stroke patients scanned in a range of common image modalities. Our results suggest that, compared to the standard of manual lesion demarcation, the semi-automated Clusterize algorithm is capable of significantly speeding up lesion demarcation in the most commonly used image modalities, without loss of either lesion demarcation precision or lesion demarcation reproducibility. For the three investigated acute datasets (CT, DWI, T2FLAIR), containing a total of 44 patient images obtained in a regular clinical setting at patient admission, the reduction in processing time was on average 17.8 min per patient and this advantage increased with increasing lesion volume (up to 60 min per patient for the largest lesion volumes in our datasets). Additionally, our results suggest that performance of the Clusterize algorithm in a chronic dataset with 11 T1 images was comparable to its performance in the acute datasets. We thus advocate the use of the Clusterize algorithm, integrated into a simple, freely available SPM toolbox, for the precise, reliable and fast preparation of imaging data for lesion–behaviour mapping analyses.

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We evaluated a semi-automated lesion demarcation approach in stroke patients.

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We compared our approach to the gold standard of manual lesion demarcation.

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We investigated typically used image modalities in acute clinical settings.

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Precision and reliability of our approach were comparable to the gold standard.

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Our approach was significantly faster than the gold standard.

Isolated assessment of translation or rotation severely underestimates the effects of subject motion in fMRI data

Subject motion has long since been known to be a major confound in functional MRI studies of the human brain. For resting-state functional MRI in particular, data corruption due to motion artefacts has been shown to be most relevant. However, despite 6 parameters (3 for translations and 3 for rotations) being required to fully describe the head's motion trajectory between timepoints, not all are routinely used to assess subject motion. Using structural (n = 964) as well as functional MRI (n = 200) data from public repositories, a series of experiments was performed to assess the impact of using a reduced parameter set (translation_only and rotation_only) versus using the complete parameter set. It could be shown that the usage of 65 mm as an indicator of the average cortical distance is a valid approximation in adults, although care must be taken when comparing children and adults using the same measure. The effect of using slightly smaller or larger values is minimal. Further, both translation_only and rotation_only severely underestimate the full extent of subject motion; consequently, both translation_only and rotation_only discard substantially fewer datapoints when used for quality control purposes (“motion scrubbing”). Finally, both translation_only and rotation_only severely underperform in predicting the full extent of the signal changes and the overall variance explained by motion in functional MRI data. These results suggest that a comprehensive measure, taking into account all available parameters, should be used to characterize subject motion in fMRI.