Infants with congenital heart defects have reduced brain volumes.

Children with congenital heart defects (CHDs) have increased risk of cognitive disabilities for reasons not fully understood. Previous studies have indicated signs of disrupted fetal brain growth from mid-gestation measured with ultrasound and magnetic resonance imaging (MRI) and infants with CHDs have decreased brain volumes at birth. We measured the total and regional brain volumes of infants with and without CHDs using MRI to investigate, if certain areas of the brain are at particular risk of disrupted growth. MRI brain volumetry analyses were performed on 20 infants; 10 with- (postmenstrual age 39-54 weeks, mean 44 weeks + 5 days) and 10 without CHDs (postmenstrual age 39-52 weeks, mean 43 weeks + 5 days). In six infants with- and eight infants without CHDs grey and white matter were also differentiated. Infants with CHDs had smaller brains (48 ml smaller; 95% CI, 6.1-90; p = 0.03), cerebrums (37.8 ml smaller; 95% CI, 0.8-74.8; p = 0.04), and cerebral grey matter (25.8 ml smaller; 95% CI, 3.5-48; p = 0.03) than infants without CHD. Brain volume differences observed within weeks after birth in children with CHDs confirm that the brain impact, which increase the risk of cognitive disabilities, may begin during pregnancy.

A voxel-wise assessment of growth differences in infants developing autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a phenotypically and etiologically heterogeneous developmental disorder typically diagnosed around 4 years of age. The development of biomarkers to help in earlier, presymptomatic diagnosis could facilitate earlier identification and therefore earlier intervention and may lead to better outcomes, as well as providing information to help better understand the underlying mechanisms of ASD. In this study, magnetic resonance imaging (MRI) scans of infants at high familial risk, from the Infant Brain Imaging Study (IBIS), at 6, 12 and 24 months of age were included in a morphological analysis, fitting a mixed-effects model to Tensor Based Morphometry (TBM) results to obtain voxel-wise growth trajectories. Subjects were grouped by familial risk and clinical diagnosis at 2 years of age. Several regions, including the posterior cingulate gyrus, the cingulum, the fusiform gyrus, and the precentral gyrus, showed a significant effect for the interaction of group and age associated with ASD, either as an increased or a decreased growth rate of the cerebrum. In general, our results showed increased growth rate within white matter with decreased growth rate found mostly in grey matter. Overall, the regions showing increased growth rate were larger and more numerous than those with decreased growth rate. These results detail, at the voxel level, differences in brain growth trajectories in ASD during the first years of life, previously reported in terms of overall brain volume and surface area.

A variation in the infant oxytocin receptor gene modulates infant hippocampal volumes in association with sex and prenatal maternal anxiety.

Genetic variants in the oxytocin receptor (OTR) have been linked to distinct social phenotypes, psychiatric disorders and brain volume alterations in adults. However, to date, it is unknown how OTR genotype shapes prenatal brain development and whether it interacts with maternal prenatal environmental risk factors on infant brain volumes. In 105 Finnish mother-infant dyads (44 female, 11-54 days old), the association of offspring OTR genotype rs53576 and its interaction with prenatal maternal anxiety (revised Symptom Checklist 90, gestational weeks 14, 24, 34) on infant bilateral amygdalar, hippocampal and caudate volumes were probed. A sex-specific main effect of rs53576 on infant left hippocampal volumes was observed. In boys compared to girls, left hippocampal volumes were significantly larger in GG-homozygotes compared to A-allele carriers. Furthermore, genotype rs53576 and prenatal maternal anxiety significantly interacted on right hippocampal volumes irrespective of sex. Higher maternal anxiety was associated both with larger hippocampal volumes in A-allele carriers than GG-homozygotes, and, though statistically weak, also with smaller right caudate volumes in GG-homozygotes than A-allele carriers. Our study results suggest that OTR genotype enhances hippocampal neurogenesis in male GG-homozygotes. Further, prenatal maternal anxiety might induce brain alterations that render GG-homozygotes compared to A-allele carriers more vulnerable to depression.

Partial Support for an Interaction Between a Polygenic Risk Score for Major Depressive Disorder and Prenatal Maternal Depressive Symptoms on Infant Right Amygdalar Volumes.

Psychiatric disease susceptibility partly originates prenatally and is shaped by an interplay of genetic and environmental risk factors. A recent study has provided preliminary evidence that an offspring polygenic risk score for major depressive disorder (PRS-MDD), based on European ancestry, interacts with prenatal maternal depressive symptoms (GxE) on neonatal right amygdalar (US and Asian cohort) and hippocampal volumes (Asian cohort). However, to date, this GxE interplay has only been addressed by one study and is yet unknown for a European ancestry sample. We investigated in 105 Finnish mother-infant dyads (44 female, 11-54 days old) how offspring PRS-MDD interacts with prenatal maternal depressive symptoms (Edinburgh Postnatal Depression Scale, gestational weeks 14, 24, 34) on infant amygdalar and hippocampal volumes. We found a GxE effect on right amygdalar volumes, significant in the main analysis, but nonsignificant after multiple comparison correction and some of the control analyses, whose direction paralleled the US cohort findings. Additional exploratory analyses suggested a sex-specific GxE effect on right hippocampal volumes. Our study is the first to provide support, though statistically weak, for an interplay of offspring PRS-MDD and prenatal maternal depressive symptoms on infant limbic brain volumes in a cohort matched to the PRS-MDD discovery sample.

Network connectivity determines cortical thinning in early Parkinson's disease progression.

Here we test the hypothesis that the neurodegenerative process in Parkinson's disease (PD) moves stereotypically along neural networks, possibly reflecting the spread of toxic alpha-synuclein molecules. PD patients (n = 105) and matched controls (n = 57) underwent T1-MRI at entry and 1 year later as part of the Parkinson's Progression Markers Initiative. Over this period, PD patients demonstrate significantly greater cortical thinning than controls in parts of the left occipital and bilateral frontal lobes and right somatomotor-sensory cortex. Cortical thinning is correlated to connectivity (measured functionally or structurally) to a "disease reservoir" evaluated by MRI at baseline. The atrophy pattern in the ventral frontal lobes resembles one described in certain cases of Alzheimer's disease. Our findings suggest that disease propagation to the cortex in PD follows neuronal connectivity and that disease spread to the cortex may herald the onset of cognitive impairment.

Framework for integrated MRI average of the spinal cord white and gray matter: the MNI-Poly-AMU template.

The field of spinal cord MRI is lacking a common template, as existing for the brain, which would allow extraction of multi-parametric data (diffusion-weighted, magnetization transfer, etc.) without user bias, thereby facilitating group analysis and multi-center studies. This paper describes a framework to produce an unbiased average anatomical template of the human spinal cord. The template was created by co-registering T2-weighted images (N = 16 healthy volunteers) using a series of pre-processing steps followed by non-linear registration. A white and gray matter probabilistic template was then merged to the average anatomical template, yielding the MNI-Poly-AMU template, which currently covers vertebral levels C1 to T6. New subjects can be registered to the template using a dedicated image processing pipeline. Validation was conducted on 16 additional subjects by comparing an automatic template-based segmentation and manual segmentation, yielding a median Dice coefficient of 0.89. The registration pipeline is rapid (~15 min), automatic after one C2/C3 landmark manual identification, and robust, thereby reducing subjective variability and bias associated with manual segmentation. The template can notably be used for measurements of spinal cord cross-sectional area, voxel-based morphometry, identification of anatomical features (e.g., vertebral levels, white and gray matter location) and unbiased extraction of multi-parametric data.

A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood.

Cross-sectional analysis of longitudinal anatomical magnetic resonance imaging (MRI) data may be suboptimal as each dataset is analyzed independently. In this study, we evaluate how much variability can be reduced by analyzing structural volume changes in longitudinal data using longitudinal analysis. We propose a two-part pipeline that consists of longitudinal registration and longitudinal classification. The longitudinal registration step includes the creation of subject-specific linear and nonlinear templates that are then registered to a population template. The longitudinal classification step comprises a four-dimensional expectation-maximization algorithm, using a priori classes computed by averaging the tissue classes of all time points obtained cross-sectionally. To study the impact of these two steps, we apply the framework completely ("LL method": Longitudinal registration and Longitudinal classification) and partially ("LC method": Longitudinal registration and Cross-sectional classification) and compare these with a standard cross-sectional framework ("CC method": Cross-sectional registration and Cross-sectional classification). The three methods are applied to (1) a scan-rescan database to analyze reliability and (2) the NIH pediatric population to compare gray matter growth trajectories evaluated with a linear mixed model. The LL method, and the LC method to a lesser extent, significantly reduced the variability in the measurements in the scan-rescan study and gave the best-fitted gray matter growth model with the NIH pediatric MRI database. The results confirm that both steps of the longitudinal framework reduce variability and improve accuracy in comparison with the cross-sectional framework, with longitudinal classification yielding the greatest impact. Using the improved method to analyze longitudinal data, we study the growth trajectories of anatomical brain structures in childhood using the NIH pediatric MRI database. We report age- and gender-related growth trajectories of specific regions of the brain during childhood that could be used as a reference in studying the impact of neurological disorders on brain development.