IT Infrastructure to Support the Secondary Use of Routinely Acquired Clinical Imaging Data for Research

We propose an infrastructure for the automated anonymization, extraction and processing of image data stored in clinical data repositories to make routinely acquired imaging data available for research purposes. The automated system, which was tested in the context of analyzing routinely acquired MR brain imaging data, consists of four modules: subject selection using PACS query, anonymization of privacy sensitive information and removal of facial features, quality assurance on DICOM header and image information, and quantitative imaging biomarker extraction. In total, 1,616 examinations were selected based on the following MRI scanning protocols: dementia protocol (246), multiple sclerosis protocol (446) and open question protocol (924). We evaluated the effectiveness of the infrastructure in accessing and successfully extracting biomarkers from routinely acquired clinical imaging data. To examine the validity, we compared brain volumes between patient groups with positive and negative diagnosis, according to the patient reports. Overall, success rates of image data retrieval and automatic processing were 82.5 %, 82.3 % and 66.2 % for the three protocol groups respectively, indicating that a large percentage of routinely acquired clinical imaging data can be used for brain volumetry research, despite image heterogeneity. In line with the literature, brain volumes were found to be significantly smaller (p-value <0.001) in patients with a positive diagnosis of dementia (915 ml) compared to patients with a negative diagnosis (939 ml). This study demonstrates that quantitative image biomarkers such as intracranial and brain volume can be extracted from routinely acquired clinical imaging data. This enables secondary use of clinical images for research into quantitative biomarkers at a hitherto unprecedented scale

Introducing a new method for classifying skull shape abnormalities related to craniosynostosis

We present a novel technique for classification of skull deformities due to most common craniosynostosis. We included 5 children of every group of the common craniosynostoses (scaphocephaly, brachycephaly, trigonocephaly, and right- and left-sided anterior plagiocephaly) and additionally 5 controls. Our outline-based classification method is described, using the software programs OsiriX, MeVisLab, and Matlab. These programs were used to identify chosen landmarks (porion and exocanthion), create a base plane and a plane at 4 cm, segment outlines, and plot resulting graphs. We measured repeatability and reproducibility, and mean curves of groups were analyzed. All raters achieved excellent intraclass correlation scores (0.994–1.000) and interclass correlation scores (0.989–1.000) for identifying the external landmarks. Controls, scaphocephaly, trigonocephaly, and brachycephaly all have the peak of the forehead in the middle of the curve (180°). In contrary, in anterior plagiocephaly, the peak is shifted (to the left of graph in right-sided and vice versa). Additionally, controls, scaphocephaly, and trigonocephaly have a high peak of the forehead; scaphocephaly has the lowest troughs; in brachycephaly, the width/frontal peak ratio has the highest valu

Mixed-location cerebral microbleeds as a biomarker of neurodegeneration in a memory clinic population

Cerebral microbleeds (CMBs) in the lobar and deep locations are associated with two distinct pathologies: cerebral amyloid angiopathy and hypertensive arteriopathy. However, the role of mixed-location CMBs in neurodegeneration remains unexplored. We investigated the associations between strictly lobar, strictly deep and mixed-location CMBs with markers of neurodegeneration. This study recruited 477 patients from a memory clinic who underwent 3T MRI scans. CMBs were categorized into strictly lobar, strictly deep and mixed-location. Cortical thickness, white matter volume and subcortical structural volumes were quantified using Free-Surfer. Linear regression models were performed to assess the association between CMBs and cerebral atrophy, and the mean difference (β) and 95% confidence intervals (CIs) were reported. In the regression analyses, mixed-location CMBs were associated with smaller cortical thickness of limbic region [β=-0.01; 95% CI=-0.02,-0.00, p=0.007) as well as with smaller accumbens volume [β=-0.01; 95% CI=-0.02,-0.00, p=0.004) and presubiculum region of hippocampus [β=-0.01; 95% CI=-0.02,-0.00, p=0.002). Strictly lobar CMBs were associated with smaller total white matter volume [β=-0.03; 95% CI=-0.04,-0.01, p<0.001] and with region specific white mat

Interethnic differences in neuroimaging markers and cognition in Asians, a population-based study

We examined interethnic differences in the prevalence of neuroimaging markers of cerebrovascular and neurodegenerative disease in 3 major Asian ethnicities (Chinese, Malays, and Indians), as well as their role in cognitive impairment. 3T MRI brain scans were acquired from 792 subjects (mean age: 70.0 ± 6.5years, 52.1% women) in the multi-ethnic Epidemiology of Dementia In Singapore study. Markers of cerebrovascular disease and neurodegeneration were identified. Cognitive performance was evaluated using Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and a neuropsychological assessment. Compared to Chinese, Malays had a higher burden of intracranial stenosis (OR: 2.28. 95%CI: 1.23-4.20) and cortical atrophy (β: -0.60. 95%CI: -0.78, -0.41), while Indians had a higher burden of subcortical atrophy (β: -0.23. 95%CI: -0.40, -0.06). Moreover, Malay and Indian ethnicities were likely to be cognitively impaired (OR for Malays: 3.79. 95%CI: 2.29-6.26; OR for Indians: 2.87. 95%CI: 1.74-4.74) and showed worse performance in global cognition (β for Malays: -0.51. 95%CI: -0.66, -0.37; and Indians: -0.32. 95%CI: -0.47, -0.17). A higher burden of cerebrovascular and neurodegenerative markers were found in Malays and Indians when compared to Chinese. Further research is required to fully elucidate the factors and pathways that contribute to these observed differences

Intracranial hypertension and cortical thickness in syndromic craniosynostosis

Aim: To evaluate the impact of risk factors for intracranial hypertension (ICH) on cerebral cortex thickness in syndromic craniosynostosis. Method: ICH risk factors including papilloedema, hydrocephalus, obstructive sleep apnea (OSA), cerebellar tonsillar position, occipitofrontal circumference (OFC) curve deflection, age, and sex were collected from the records of patients with syndromic craniosynostosis (Apert, Crouzon, Pfeiffer, Muenke, Saethre-Chotzen syndromes) and imaging. Magnetic resonance images were analysed and exported for statistical analysis. A linear mixed model was developed to determine correlations with cerebral cortex thickness changes. Results: In total, 171 scans from 107 patients (83 males, 88 females, mean age 8y 10mo, range 1y 1mo–34y, SD 5y 9mo) were evaluated. Mean cortical thickness in this cohort was 2.78mm (SD 0.17). Previous findings of papilloedema (p=0.036) and of hydrocephalus (p=0.007) were independently associated with cortical thinning. Cortical thickness did not vary significantly by sex (p=0.534), syndrome (p=0.896), OSA (p=0.464), OFC (p=0.375), or tonsillar position (p=0.682). Interpretation: Detection of papilloedema or hydrocephalus in syndromic craniosynostosis is associated with significant changes in cortical thickness, supporting the need for preventative rather than reactive treatment strategies

The Impact of Strategic White Matter Hyperintensity Lesion Location on Language

Objective: The impact of white matter hyperintensities (WMH) on language possibly depends on lesion location through disturbance of strategic white matter tracts. We examined the impact of WMH location on language in elderly Asians. Design: Cross-sectional. Setting: Population-based. Participants: Eight-hundred nineteen residents of Singapore, ages (≥65 years). Measurements: Clinical, cognitive and 3T magnetic resonance imaging assessments were performed on all participants. Language was assessed using the Modified Boston Naming Test (MBNT) and Verbal Fluency (VF). Hypothesis-free region-of-interest-based (ROI) analyses based on major white matter tracts were used to determine the association between WMH location and language. Conditional dependencies between the regional WMH volumes and language were examined using Bayesian-network analysis. Results: ROI-based analyses showed that WMH located within the anterior thalamic radiation (mean difference: −0.12, 95% confidence interval [CI]: −0.22; −0.02, p = 0.019) and uncinate fasciculus (mean difference: −0.09, 95% CI: −0.18; −0.01, p = 0.022) in the left hemisphere were significantly associated with worse VF but did not survive multiple testing. Conversely, WMH volume in the left cingulum of cingulate gyrus was significantly associated with MBNT performance (mean difference: −0.09, 95% CI: −0.17; −0.02, p = 0.016). Bayesian-network analyses confirmed the left cingulum of cingulate gyrus as a direct determinant of MBNT performance. Conclusion: Our findings identify the left cingulum of cingulate gyrus as a strategic white matter tract for MBNT, suggesting that language – is sensitive to subcortical ischemic damage. Future studies on the role of sporadic ischemic lesions and vascular cognitive impairment should not only focus on total WMH volume but should also take WMH lesion location into account when addressing language

Cortical Thickness in Crouzon-Pfeiffer Syndrome: Findings in Relation to Primary Cranial Vault Expansion

Background: Episodes of intracranial hypertension are associated with reductions in cerebral cortical thickness (CT) in syndromic craniosynostosis. Here we focus on Crouzon–Pfeiffer syndrome patients to measure CT and evaluate associations with type of primary cranial vault expansion and synostosis pattern. Methods: Records from 34 Crouzon–Pfeiffer patients were reviewed along with MRI data on CT and intracranial volume to examine associations. Patients were grouped according to initial cranial vault expansion (frontal/occipital). Data were analyzed by multiple linear regression controlled for age and brain volume to determine an association between global/lobar CT and vault expansion type. Synostosis pattern

Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations

Alzheimer's disease (AD) is the most common form of dementia and is phenotypically heterogeneous. APOE is a triallelic gene which correlates with phenotypic heterogeneity in AD. In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM). Since DEBM is a data-driven model, stratification into smaller disease subgroups would lead to more inaccurate models as compared to fitting the model on the entire dataset. Hence our secondary aim is to propose and evaluate novel approaches in which we split the different steps of DEBM into group-aspecific and group-specific parts, where the entire dataset is used to train the group-aspecific parts and only the data from a specific group is used to train the group-specific parts of the DEBM. We performed simulation experiments to benchmark the accuracy of the proposed approaches and to select the optimal approach. Subsequently, the chosen approach was applied to the baseline data of 417 cognitively normal, 235 mild cognitively impaired who convert to AD within 3 years, and 342 AD patients from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset to gain new insights into the effect of APOE carriership on the disease progression timeline of AD. In the ε4 carrier group, the model predicted with high confidence that CSF Amyloidβ42 and the cognitive score of Alzheimer's Disease Assessment Scale (ADAS) are early biomarkers. Hippocampus was the earliest volumetric biomarker to become abnormal, closely followed by the CSF Phosphorylated Tau181 (PTAU) biomarker. In the homozygous ε3 carrier group, the model predicted a similar ordering among CSF biomarkers. However, the volume of the fusiform gyrus was identified as one of the earliest volumetric biomarker. While the findings in the ε4 carrier and the homozygous ε3 carrier groups fit the current understanding of progression of AD, the finding in the ε2 carrier group did not. The model predicted, with relatively low confidence, CSF Neurogranin as one of the earliest biomarkers along with cognitive score of Mini-Mental State Examination (MMSE). Amyloid β42 was found to become abnormal after PTAU. The presented models could aid understanding of the disease, and in selecting homogeneous group of presymptomatic subjects at-risk of developing symptoms for clinical trials

Clustering of Alzheimer's and Parkinson's disease based on genetic burden of shared molecular mechanisms

One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer’s (AD) and Parkinson’s Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are disease-associated. To our knowledge this work is the first demonstration of a mechanism based stratification in the field of neurodegenerative diseases. Overall, we thus see this work as an important step towards a molecular mechanism-based taxonomy of neurological disorders, which could help in developing better targeted therapies in the future by going beyond classical phenotype based disease definitions