Biology subjects

Hajnal, J.

Publications and source records attributed to Hajnal, J..

3 recordsLinked to original sources

bioRxiv2018

Segmentation of Myelin-like Signals on Clinical MR Images for Age Estimation in Preterm Infants

Myelination is considered to be an important developmental process during human brain maturation and closely correlated with gestational age. Assessment of the myelination status requires dedicated imaging, but the conventional T2-weighted scans routinely acquired during clinical imaging of neonates carry signatures that are thought to be associated with myelination. In this work, we propose a new segmentation method for myelin-like signals on T2-weighted magnetic resonance images that could be used to assess neonatal brain maturation in clinical practice. Firstly we define a segmentation protocol for myelin-like signals, and delineate manual annotations according to this protocol. We then develop an expectation-maximization framework through which we obtain the automatic segmentations of myelin-like signals. We incorporate an explicit class for partial volume voxels whose locations are configured in relation to the composing pure tissues via second-order Markov random fields. We conduct experiments in the thalami and brainstem where the majority of myelination occurs during the perinatal period for 16 test subjects aged between 29 and 44 gestational weeks. The proposed method performs accurately and robustly in both regions with respect to the manual annotations over a range of intensity percentile thresholds that are used to generate the initial segmentation estimates. Finally, we construct spatio-temporal growth models for myelin-like signals in the thalami and brainstem to demonstrate the applicability of the proposed method for age estimation in preterm infants.

neuroscience↗

bioRxiv2018

Genes associated with neuropsychiatric disease increase vulnerability to abnormal deep grey matter development

1.BackgroundNeuropsychiatric disease has polygenic determinants but is often precipitated by environmental pressures, including adverse perinatal events. However, the way in which genetic vulnerability and early-life adversity interact remains obscure. Preterm birth is associated with abnormal brain development and psychiatric disease. We hypothesised that the extreme environmental stress of premature extra-uterine life could contribute to neuroanatomic abnormality in genetically vulnerable individuals.\n\nMethodsWe combined Magnetic Resonance Imaging (MRI) and genome-wide single nucleotide polymorphism (SNP) data from 194 infants, born before 33 weeks of gestation, to test the prediction that: the characteristic deep grey matter abnormalities seen in preterm infants are associated with polygenic risk for psychiatric illness. Summary statistics from a meta-analysis of SNP data for five psychiatric disorders were used to compute individual polygenic risk scores (PRS). The variance explained by the PRS in the relative volumes of four deep grey matter structures (caudate nucleus, thalamus, subthalamic nucleus and lentiform nucleus) was estimated using linear regression both for the full, mixed-ancestral, cohort and a subsample of European infants.\n\nResultsThe PRS was negatively associated with: lentiform volume in the full cohort ({beta}=-0.24, p=8x10-4) and the European subsample ({beta}=-0.24, p=8x10-3); and with subthalamic nuclear volume in the full cohort ({beta}=-0.18, p=0.01) and the European subsample ({beta}=-0.26, p=3x10-3).\n\nConclusionsGenetic variants associated with neuropsychiatric disease increase vulnerability to abnormal deep grey matter development and are associated with neuroanatomic changes in the perinatal period. This suggests a mechanism by which perinatal adversity leads to later neuropsychiatric disease in genetically predisposed individuals.

neuroscience↗

bioRxiv2017

Multimodal Surface Matching with Higher-Order Smoothness Constraints

The accurate alignment of brains is fundamental to the statistical sensitivity and spatial localisation of group studies in brain imaging, and cortical surface-based alignment is generally accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of cortical areas relative to these folds, which makes aligning cortical areas based on folding alone a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that using areal features such as resting state-networks and myelin maps to drive cross-subject surface alignment improves group task fMRI statistics and map sharpness. However, the initial implementation of MSMs regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits unless the regularisation strength was increased, in which case this prevented the algorithm from fully maximizing surface alignment. Here, we propose a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multi-modal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints to enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modeling of cortical development for neonates (born between 32 and 45 weeks) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.

neuroscience↗