Cerebral cortex maldevelopment in syndromic craniosynostosis

AIM: To assess the relationship of surface area of the cerebral cortex to intracranial volume (ICV) in syndromic craniosynostosis. METHOD: Records of 140 patients (64 males, 76 females; mean age 8y 6mo [SD 5y 6mo], range 1y 2mo–24y 2mo) with syndromic craniosynostosis were reviewed to include clinical and imaging data. Two hundred and three total magnetic resonance imaging (MRI) scans were evaluated in this study (148 patients with fibroblast growth factor receptor [FGFR], 19 patients with TWIST1, and 36 controls). MRIs were processed via FreeSurfer pipeline to determine total ICV and cortical surface area (CSA). Scaling coefficients were calculated from log‐transformed data via mixed regression to account for multiple measurements, sex, syndrome, and age. Educational outcomes were reported by syndrome. RESULTS: Mean ICV was greater in patients with FGFR (1519cm(3), SD 269cm(3), p=0.016) than in patients with TWIST1 (1304cm(3), SD 145cm(3)) or controls (1405cm(3), SD 158cm(3)). CSA was related to ICV by a scaling law with an exponent of 0.68 (95% confidence interval [CI] 0.61–0.76) in patients with FGFR compared to 0.81 (95% CI 0.50–1.12) in patients with TWIST1 and 0.77 (95% CI 0.61–0.93) in controls. Lobar analysis revealed reduced scaling in the parietal (0.50, 95% CI 0.42–0.59) and occipital (0.67, 95% CI 0.54–0.80) lobes of patients with FGFR compared with controls. Modified learning environments were needed more often in patients with FGFR. INTERPRETATION: Despite adequate ICV in FGFR‐mediated craniosynostosis, CSA development is reduced, indicating maldevelopment, particularly in parietal and occipital lobes. Modified education is also more common in patients with FGFR

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 effect of hippocampal function, volume and connectivity on posterior cingulate cortex functioning during episodic memory fMRI in mild cognitive impairment

Objectives: Diminished function of the posterior cingulate cortex (PCC) is a typical finding in early Alzheimer’s disease (AD). It is hypothesized that in early stage AD, PCC functioning relates to or reflects hippocampal dysfunction or atrophy. The aim of this study was to examine the relationship between hippocampus function, volume and structural connectivity, and PCC activation during an episodic memory task-related fMRI study in mild cognitive impairment (MCI). Method: MCI patients (n = 27) underwent episodic memory task-related fMRI, 3D-T1w MRI, 2D T2-FLAIR MRI and diffusion tensor imaging. Stepwise linear regression analysis was performed to examine the relationship between PCC activation and hippocampal activation, hippocampal volume and diffusion measures within the cingulum along the hippocampus. Results: We found a significant relationship between PCC and hippocampus activation during successful episodic memory encoding and correct recognition in MCI patients. We found no relationship between the PCC and structural hippocampal predictors. Conclusions: Our results indicate a relationship between PCC and hippocampus activation during episodic memory engagement in MCI. This may suggest that during episodic memory, functional network deterioration is the most important predictor of PCC functioning in MCI. Key Points: • PCC functioning during episodic memory relates to hippocampal functioning in MCI. • PCC functioning during episodic memory does not relate to hippocampal structure in MCI. • Functional network changes are an important predictor of PCC functioning in MCI

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

Deep learning for clustering of multivariate clinical patient trajectories with missing values

BACKGROUND: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivariate and relatively short time series because (i) these diseases are multifactorial and not well described by single clinical outcome variables and (ii) disease progression needs to be monitored over time. Additionally, clinical data often additionally are hindered by the presence of many missing values, further complicating any clustering attempts. FINDINGS: The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature. In this work, we propose a deep learning-based method to address this issue, variational deep embedding with recurrence (VaDER). VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended to (i) model multivariate time series and (ii) directly deal with missing values. We validated VaDER by accurately recovering clusters from simulated and benchmark data with known ground truth clustering, while varying the degree of missingness. We then used VaDER to successfully stratify patients with Alzheimer disease and patients with Parkinson disease into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected known underlying aspects of Alzheimer disease and Parkinson disease. CONCLUSIONS: We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate time-series clustering in general

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersio

Genome-wide association studies of cerebral white matter lesion burden: The CHARGE consortium

White matter hyperintensities (WMH) detectable by magnetic resonance imaging (MRI)are part of the spectrum of vascular injury associated with aging of the brain and are thought to reflect ischemic damage to the small deep cerebral vessels. WMH are associated with an increased risk of cognitive and motor dysfunction, dementia, depression, and stroke. Despite a significant heritability, few genetic loci influencing WMH burden have been identified

Classification of Skull Shape Deformities Related to Craniosynostosis on 3D Photogrammetry

Implementation of the Utrecht Cranial Shape Quantificator (UCSQ) classification method on 3D photogrammetry in patients with different types of craniosynostosis is the aim of the present study. Five children (age <1 year) of every group of the common craniosynostoses (scaphocephaly, brachycephaly, trigonocephaly, right-sided and left-sided anterior plagiocephaly) were randomly included. The program 3-Matic (v13.0) was used to import and analyze the included 3dMD photos. Three external landmarks were placed. Using the landmarks, a base plane was created, as well as a plane 4 cm superior to the base plane. Using UCSQ, we created sinusoid curves of the patients, the resulting curves were analyzed and values were extracted for calculations. Results per patient were run through a diagnostic flowchart in order to determine correctness of the flowchart when using 3D photogrammetry. Each of the patients (n=25) of the different craniosynostosis subgroups is diagnosed correctly based on the different steps in the flowchart. This study proposes and implements a diagnostic approach of craniosynostosis based on 3D photogrammetry. By using a diagnostic flowchart based on specific characteristics for every type of craniosynostosis related to specific skull deformities, diagnosis can be established. All variables are expressed in number and are therefore objective