Matthew J Marzelli

Abstract

Studies of sex effects on neurodevelopment have traditionally focused on animal models investigating hormonal influences on brain anatomy. However, more recent evidence suggests that sex chromosomes may also have direct upstream effects that act independently of hormones. Sex chromosome aneuploidies provide ideal models to examine this framework in humans, including Turner syndrome (TS), where females are missing one X-chromosome (45X), and Klinefelter syndrome (KS), where males have an additional X-chromosome (47XXY). As these disorders essentially represent copy number variants of the sex chromosomes, investigation of brain structure across these disorders allows us to determine whether sex chromosome gene dosage effects exist. We used voxel-based morphometry to investigate this hypothesis in a large sample of children in early puberty, to compare regional gray matter volumes among individuals with one (45X), two (typically developing 46XX females and 46XY males), and three (47XXY) sex chromosomes. Between-group contrasts of TS and KS groups relative to respective sex-matched controls demonstrated highly convergent patterns of volumetric differences with the presence of an additional sex chromosome being associated with relatively decreased parieto-occipital gray matter volume and relatively increased temporo-insular gray matter volumes. Furthermore, z-score map comparisons between TS and KS cohorts also suggested that this effect occurs in a linear dose-dependent fashion. We infer that sex chromosome gene expression directly influences brain structure in children during early stages of puberty, extending our understanding of genotype-phenotype mechanisms underlying sex differences in the brain.

View details for DOI 10.1523/JNEUROSCI.2790-13.2014

View details for PubMedID 24599451

Abstract

The ability to lie still in an MRI scanner is essential for obtaining usable image data. To reduce motion, young children are often sedated, adding significant cost and risk.We assessed the feasibility of using a simple and affordable behavioral desensitization program to yield high-quality brain MRI scans in sedation-free children.222 children (4-9.9 years), 147 with type 1 diabetes and 75 age-matched non-diabetic controls, participated in a multi-site study focused on effects of type 1 diabetes on the developing brain. T1-weighted and diffusion-weighted imaging (DWI) MRI scans were performed. All children underwent behavioral training and practice MRI sessions using either a commercial MRI simulator or an inexpensive mock scanner consisting of a toy tunnel, vibrating mat, and video player to simulate the sounds and feel of the MRI scanner.205 children (92.3%), mean age 7 ± 1.7 years had high-quality T1-W scans and 174 (78.4%) had high-quality diffusion-weighted scans after the first scan session. With a second scan session, success rates were 100% and 92.5% for T1-and diffusion-weighted scans, respectively. Success rates did not differ between children with type 1 diabetes and children without diabetes, or between centers using a commercial MRI scan simulator and those using the inexpensive mock scanner.Behavioral training can lead to a high success rate for obtaining high-quality T1-and diffusion-weighted brain images from a young population without sedation.

View details for DOI 10.1007/s00247-013-2798-7

View details for PubMedID 24096802

Abstract

OBJECTIVE To investigate whether type 1 diabetes affects white matter (WM) structure in a large sample of young children. RESEARCH DESIGN AND METHODS Children (ages 4 to <10 years) with type 1 diabetes (n = 127) and age-matched nondiabetic control subjects (n = 67) had diffusion weighted magnetic resonance imaging scans in this multisite neuroimaging study. Participants with type 1 diabetes were assessed for HbA1c history and lifetime adverse events, and glucose levels were monitored using a continuous glucose monitor (CGM) device and standardized measures of cognition. RESULTS Between-group analysis showed that children with type 1 diabetes had significantly reduced axial diffusivity (AD) in widespread brain regions compared with control subjects. Within the type 1 diabetes group, earlier onset of diabetes was associated with increased radial diffusivity (RD) and longer duration was associated with reduced AD, reduced RD, and increased fractional anisotropy (FA). In addition, HbA1c values were significantly negatively associated with FA values and were positively associated with RD values in widespread brain regions. Significant associations of AD, RD, and FA were found for CGM measures of hyperglycemia and glucose variability but not for hypoglycemia. Finally, we observed a significant association between WM structure and cognitive ability in children with type 1 diabetes but not in control subjects. CONCLUSIONS These results suggest vulnerability of the developing brain in young children to effects of type 1 diabetes associated with chronic hyperglycemia and glucose variability.

View details for DOI 10.2337/dc13-1388

View details for PubMedID 24319123

Abstract

The neurocognitive and behavioral profile of individuals with 47,XYY is increasingly documented; however, very little is known about the effect of a supernumerary Y-chromosome on brain development. Establishing the neural phenotype associated with 47,XYY may prove valuable in clarifying the role of Y-chromosome gene dosage effects, a potential factor in several neuropsychiatric disorders that show a prevalence bias toward males, including autism spectrum disorders. Here, we investigated brain structure in 10 young boys with 47,XYY and 10 age-matched healthy controls by combining voxel-based morphometry (VBM) and surface-based morphometry (SBM). The VBM results show the existence of altered gray matter volume (GMV) in the insular and parietal regions of 47,XYY relative to controls, changes that were paralleled by extensive modifications in white matter (WM) bilaterally in the frontal and superior parietal lobes. The SBM analyses corroborated these findings and revealed the presence of abnormal surface area and cortical thinning in regions with abnormal GMV and WMV. Overall, these preliminary results demonstrate a significant impact of a supernumerary Y-chromosome on brain development, provide a neural basis for the motor, speech and behavior regulation difficulties associated with 47,XYY and may relate to sexual dimorphism in these areas.

View details for DOI 10.1111/gbb.12107

View details for PubMedID 24308542

Abstract

There is increasing evidence that genomic imprinting, a process by which certain genes are expressed in a parent-of-origin-specific manner, can influence neurogenetic and psychiatric manifestations. While some data suggest possible imprinting effects of the X chromosome on physical and cognitive characteristics in humans, there is no compelling evidence that X-linked imprinting affects brain morphology. To address this issue, we investigated regional cortical volume, thickness, and surface area in 27 healthy controls and 40 prepubescent girls with Turner syndrome (TS), a condition caused by the absence of one X chromosome. Of the young girls with TS, 23 inherited their X chromosome from their mother (X(m)) and 17 from their father (X(p)). Our results confirm the existence of significant differences in brain morphology between girls with TS and controls, and reveal the presence of a putative imprinting effect among the TS groups: girls with X(p) demonstrated thicker cortex than those with X(m) in the temporal regions bilaterally, while X(m) individuals showed bilateral enlargement of gray matter volume in the superior frontal regions compared with X(p). These data suggest the existence of imprinting effects of the X chromosome that influence both cortical thickness and volume during early brain development, and help to explain variability in cognitive and behavioral manifestations of TS with regard to the parental origin of the X chromosome.

View details for DOI 10.1523/JNEUROSCI.5810-12.2013

View details for PubMedID 23658194

Abstract

Turner syndrome (TS) offers a unique opportunity to investigate associations among genes, the brain, and cognitive phenotypes. In this study, we used 3 complementary analyses of diffusion tensor imaging (DTI) data (whole brain, region of interest, and fiber tractography) and a whole brain volumetric imaging technique to investigate white matter (WM) structure in prepubertal, nonmosaic, estrogen-naive girls with TS compared with age and sex matched typically developing controls. The TS group demonstrated significant WM aberrations in brain regions implicated in visuospatial abilities, face processing, and sensorimotor and social abilities compared with controls. Extensive spatial overlap between regions of aberrant WM structure (from DTI) and regions of aberrant WM volume were observed in TS. Our findings indicate that complete absence of an X chromosome in young females (prior to receiving exogenous estrogen) is associated with WM aberrations in specific regions implicated in characteristic cognitive features of TS.

View details for DOI 10.1093/cercor/bhr355

View details for Web of Science ID 000310965200005

View details for PubMedID 22172580

Abstract

Turner syndrome (TS) is a highly prevalent genetic condition caused by partial or complete absence of one X-chromosome in a female and is associated with a lack of endogenous estrogen during development secondary to gonadal dysgenesis. Prominent cognitive weaknesses in executive and visuospatial functions in the context of normal overall IQ also occur in affected individuals. Previous neuroimaging studies of TS point to a profile of neuroanatomical variation relative to age and sex matched controls. However, there are no neuroimaging studies focusing on young girls with TS before they receive exogenous estrogen treatment to induce puberty. Information obtained from young girls with TS may help to establish an early neural correlate of the cognitive phenotype associated with the disorder. Further, univariate analysis has predominantly been the method of choice in prior neuroimaging studies of TS. Univariate approaches examine between-group differences on the basis of individual image elements (i.e., a single voxel's intensity or the volume of an a priori defined brain region). This is in contrast to multivariate methods that can elucidate complex neuroanatomical profiles in a clinical population by determining the pattern of between-group differences from many image elements evaluated simultaneously. In this case, individual image elements might not be significantly different between groups but can still contribute to a significantly different overall spatial pattern. In this study, voxel-based morphometry (VBM) of high-resolution magnetic resonance images was used to investigate differences in brain morphology between 13 pediatric, pre-estrogen girls with monosomic TS and 13 age-matched typically developing controls (3.0 T imaging: mean age 9.1±2.1). A similar analysis was performed with an older cohort of 13 girls with monosomic TS and 13 age-matched typically developing controls (1.5 T imaging: mean age 15.8±4.5). A multivariate, linear support vector machine analysis using leave-one-out cross-validation was then employed to discriminate girls with TS from typically developing controls based on differences in neuroanatomical spatial patterns and to assess how accurately such patterns translate across heterogeneous cohorts. VBM indicated that both TS cohorts had significantly reduced gray matter volume in the precentral, postcentral, and supramarginal gyri and enlargement of the left middle and superior temporal gyri. Support vector machine (SVM) classifiers achieved high accuracy for discriminating brain morphology patterns in TS from typically developing controls and also displayed spatial patterns consistent with the VBM results. Furthermore, the SVM classifiers identified additional neuroanatomical variations in individuals with TS, localized in the hippocampus, orbitofrontal cortex, insula, caudate, and cuneus. Our results demonstrate robust spatial patterns of altered brain morphology in developmentally dynamic populations with TS, providing further insight into the neuroanatomical correlates of cognitive-behavioral features in this condition.

View details for DOI 10.1016/j.neuroimage.2010.12.054

View details for Web of Science ID 000287556200001

View details for PubMedID 21195197

Abstract

Techniques to enable direct communication between the brain and computers/machines, such as the brain-computer interface (BCI) or the brain-machine interface (BMI), are gaining momentum in the neuroscientific realm, with potential applications ranging from medicine to general consumer electronics. Noninvasive BCI techniques based on neuroimaging modalities are reviewed in terms of their methodological approaches as well as their similarities and differences. Trends in automated data interpretation through machine learning algorithms are also introduced. Applications of functional neuromodulation techniques to BCI systems would allow for bidirectional communication between the brain and the computer. Such bidirectional interfaces can relay information directly from one brain to another using a computer as a medium, ultimately leading to the concept of a brain-to-brain interface (BBI).

View details for DOI 10.1016/j.tibtech.2010.08.002

View details for Web of Science ID 000283703300003

View details for PubMedID 20810180

Abstract

Automated interpretation and classification of functional MRI (fMRI) data is an emerging research field that enables the characterization of underlying cognitive processes with minimal human intervention. In this work, we present a method for the automated classification of human thoughts reflected on a trial-based paradigm using fMRI with a significantly shortened data acquisition time (less than one minute). Based on our preliminary experience with various cognitive imagery tasks, six characteristic thoughts were chosen as target tasks for the present work: right-hand motor imagery, left-hand motor imagery, right foot motor imagery, mental calculation, internal speech/word generation, and visual imagery. These six tasks were performed by five healthy volunteers and functional images were obtained using a T(*)(2)-weighted echo planar imaging (EPI) sequence. Feature vectors from activation maps, necessary for the classification of neural activity, were automatically extracted from the regions that were consistently and exclusively activated for a given task during the training process. Extracted feature vectors were classified using the support vector machine (SVM) algorithm. Parameter optimization, using a k-fold cross validation scheme, allowed the successful recognition of the six different categories of administered thought tasks with an accuracy of 74.5% (mean)+/-14.3% (standard deviation) across all five subjects. Our proposed study for the automated classification of fMRI data may be utilized in further investigations to monitor/identify human thought processes and their potential link to hardware/computer control.

View details for DOI 10.1016/j.media.2009.01.001

View details for Web of Science ID 000267096400002

View details for PubMedID 19233711

View details for DOI 10.1002/ima.20142

View details for Web of Science ID 000257301400008