Alterations in brain morphology by MRI in adults with neurofibromatosis 1

Objective Neurofibromatosis 1 (NF1) is a rare autosomal dominant disease that causes the dysregulated growth of Schwann cells. Most reported studies of brain morphology in NF1 patients have included only children, and clinical implications of the observed changes later in life remain unclear. In this study, we used MRI to characterize brain morphology in adults with NF1. Methods Planar (2D) MRI measurements of 29 intracranial structures were compared in 389 adults with NF1 and 112 age- and sex-matched unaffected control subjects. The 2D measurements were correlated with volumetric (3D) brain measurements in 99 of the adults with NF1 to help interpret the 2D findings. A subset (n = 70) of these NF1 patients also received psychometric testing for attention deficits and IQ and was assessed for clinical severity of NF1 features and neurological problems. Correlation analysis was performed between the MRI measurements and clinical and psychometric features of these patients. Results Four of nine corpus callosum measurements were significantly greater in adults with NF1 than in sex- and age-matched controls. All seven brainstem measurements were significantly greater in adults with NF1 than in controls. Increased corpus callosum and brainstem 2D morphology were correlated with increased total white matter volume among the NF1 patients. No robust correlations were observed between the 2D size of these structures and clinical or neuropsychometric assessments. Conclusion Our findings are consistent with the hypothesis that dysregulation of brain myelin production is an important manifestation of NF1 in adults.

Fetal brain growth and infant autistic traits

Background Research indicates that structural differences exist in the brains of individuals who later display developmental conditions (e.g., autism). To date only a handful of studies have explored the relationship between fetal brain growth and later infant outcomes, with a particular focus on fetal head circumference (HC) as a proxy for brain development. These findings have been inconsistent. We investigate whether fetal brain measurements correlate with the emergence of autistic traits in toddlers. Method 219 singleton pregnancies (104 males and 115 females) were recruited at the Rosie Hospital, Cambridge, UK. A 2D ultrasound was performed at 12-, 20- and between 26-30-weeks of pregnancy, measuring head circumference (HC), ventricular atrium (VA) and transcerebellar diameter (TCD). 178 infants were subsequently followed up at 18-20 months of age and completed the Quantitative Checklist for Autism in Toddlers (Q-CHAT) to observe early autistic traits. Results HC was larger in males than in females in both the second and third trimester. There was a significant positive association between TCD size at 20 weeks and Q-CHAT scores at 18-20 months of age, found in both univariate and multivariate analyses, and this remained significant after controlling for sex. Conclusion There is a positive relationship between cerebellar (TCD) development at 20 weeks gestation and the later emergence of autistic traits (at 18-20 months of age). Atypical neurodevelopment may start prenatally. If replicated these findings could facilities early diagnosis and improved outcomes.

Alterations in Brain Morphology by MRI in Adults with Neurofibromatosis 1

Objective: Neurofibromatosis 1 (NF1) is a rare autosomal dominant disease that causes the dysregulated growth of Schwann cells. Most studies focused on brain morphology changes in NF1 were small and only included children, making clinical implications unclear. One consistent finding in children with NF1 has been increased corpus callosum area. We aimed to characterize alterations in brain morphology by MRI in adults with neurofibromatosis 1 (NF1). Methods: Planar (2D) MRI measurements of 29 intracranial structures were compared in 389 adults with NF1 and 112 age- and sex-matched unaffected control subjects. The 2D measurements were correlated to volumetric (3D) brain measurements for 99 of the adults with NF1. A subset of adults with NF1 (n = 70) was also assessed for clinical severity of NF1 features and neurological problems and received psychometric testing for attention deficits and IQ. Correlation analyses were performed between principal components of the intracranial measurements and clinical and psychometric features of these patients. Results:Four of nine corpus callosum measurements were significantly greater in adults with NF1 than in sex- and age-matched controls. All seven brainstem measurements were significantly greater in adults with NF1 than in controls. Increased corpus callosum and brainstem 2D morphology were correlated with increased total white matter volume among the NF1 patients. No robust correlations were observed between the 2D size of these structures and clinical or neuropsychometric assessments.Interpretation:Our findings are consistent with the hypothesis that dysregulation of brain myelin production is an important manifestation of NF1 in adults.

EEG hyperscanning in motor rehabilitation: a position paper

Studying the human brain during interpersonal interaction allows us to answer many questions related to motor control and cognition. For instance, what happens in the brain when two people walking side by side begin to change their gait and match cadences? Adapted from the neuroimaging techniques used in single-brain measurements, hyperscanning (HS) is a technique used to measure brain activity from two or more individuals simultaneously. Thus far, HS has primarily focused on healthy participants during social interactions in order to characterize inter-brain dynamics. Here, we advocate for expanding the use of this electroencephalography hyperscanning (EEG-HS) technique to rehabilitation paradigms in individuals with neurological diagnoses, namely stroke, spinal cord injury (SCI), Parkinson’s disease (PD), and traumatic brain injury (TBI). We claim that EEG-HS in patient populations with impaired motor function is particularly relevant and could provide additional insight on neural dynamics, optimizing rehabilitation strategies for each individual patient. In addition, we discuss future technologies related to EEG-HS that could be developed for use in the clinic as well as technical limitations to be considered in these proposed settings.

Magnetic resonance imaging of skull and brain parameters in fetuses with intrauterine growth restriction

Objective: To compare fetuses with intrauterine growth restriction (IUGR) and those with normal growth, in terms of skull and brain measurements obtained by magnetic resonance imaging (MRI). Materials and Methods: This was a prospective cohort study including 26 single fetuses (13 with IUGR and 13 with normal growth), evaluated from 26 to 38 weeks of gestation. Using MRI, we measured skull and brain biparietal diameters (BPDs); skull and brain occipitofrontal diameters (OFDs); corpus callosum length and area; transverse cerebellar diameter; extracerebral cerebrospinal fluid (eCSF); and right and left interopercular distances (IODs). Results: The following were significantly smaller in IUGR fetuses than in control fetuses: skull BPD (76.9 vs. 78.2 mm; p = 0.0029); brain BPD (67.8 vs. 71.6 mm; p = 0.0064); skull OFD (93.6 vs. 95 mm; p = 0.0010); eCSF (5.5 vs. 8.2 mm; p = 0.0003); right IOD (9.8 vs. 13.9 mm; p = 0.0023); and left IOD (11.8 vs. 16.3 mm; p = 0.0183). The skull BPD/eCSF, brain BPD/eCSF, skull OFD/eCSF, and brain OFD/eCSF ratios were also lower in IUGR fetuses. Conclusion: IUGR fetuses had smaller OFD and BPD, both skull and brain, and less eCSF when compared to normal growth fetuses.

Effect of MRI acquisition acceleration via compressed sensing and parallel imaging on brain volumetry

Objectives To investigate the effect of compressed SENSE (CS), an acceleration technique combining parallel imaging and compressed sensing, on potential bias and precision of brain volumetry and evaluate it in the context of normative brain volumetry. Materials and methods In total, 171 scans from scan-rescan experiments on three healthy subjects were analyzed. Each subject received 3D-T1-weighted brain MRI scans at increasing degrees of acceleration (CS-factor = 1/4/8/12/16/20/32). Single-scan acquisition times ranged from 00:41 min (CS-factor = 32) to 21:52 min (CS-factor = 1). Brain segmentation and volumetry was performed using two different software tools: md.brain, a proprietary software based on voxel-based morphometry, and FreeSurfer, an open-source software based on surface-based morphometry. Four sub-volumes were analyzed: brain parenchyma (BP), total gray matter, total white matter, and cerebrospinal fluid (CSF). Coefficient of variation (CoV) of the repeated measurements as a measure of intra-subject reliability was calculated. Intraclass correlation coefficient (ICC) with regard to increasing CS-factor was calculated as another measure of reliability. Noise-to-contrast ratio as a measure of image quality was calculated for each dataset to analyze the association between acceleration factor, noise and volumetric brain measurements. Results For all sub-volumes, there is a systematic bias proportional to the CS-factor which is dependent on the utilized software and subvolume. Measured volumes deviated significantly from the reference standard (CS-factor = 1), e.g. ranging from 1 to 13% for BP. The CS-induced systematic bias is driven by increased image noise. Except for CSF, reliability of brain volumetry remains high, demonstrated by low CoV (< 1% for CS-factor up to 20) and good to excellent ICC for CS-factor up to 12. Conclusion CS-acceleration has a systematic biasing effect on volumetric brain measurements.

Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability

Variation in cognitive ability arises from subtle differences in underlying neural architectural properties. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N=1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to 4% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.Author summaryCognition is a complex and interconnected process whose underlying mechanisms are still unclear. In order to unravel this question, studies usually look at one neuroimaging modality (e.g. functional MRI) and associate the observed brain properties with individual differences in cognitive performance. However, this approach is limiting because it fails to incorporate other sources of brain information and does not generalize well to new data. Here we tackled both problems by using out-of-sample testing and a multi-level learning approach that can efficiently integrate across simultaneous brain measurements. We tested this scenario by evaluating individual differences across several cognitive domains, using five measures that represent morphological, functional and structural aspects of the brain network architecture. We predicted individual cognitive differences using each brain property group separately and then stacked these predictions, forming a new matrix with as many columns as separate brain measurements, that was then fit using a regularized regression model that isolated unique information among modalities and substantially helped enhance prediction accuracy across most of the cognitive domains. This holistic approach provides a framework for capturing non-redundant variability across different imaging modalities, opening a window to easily incorporate more sources of brain information to further understand cognitive function.