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Children with fragile X syndrome (FXS) often avoid eye contact, a behavior that is potentially related to hyperarousal. Prior studies, however, have focused on between-person associations rather than coupling of within-person changes in gaze behaviors and arousal. In addition, there is debate about whether prompts to maintain eye contact are beneficial for individuals with FXS. In a study of young females (ages 6–16), we used eye tracking to assess gaze behavior and pupil dilation during social interactions in a group with FXS (n = 32) and a developmentally similar comparison group (n = 23). Participants engaged in semi-structured conversations with a female examiner during blocks with and without verbal prompts to maintain eye contact. We identified a social–behavioral and psychophysiological profile that is specific to females with FXS; this group exhibited lower mean levels of eye contact, significantly increased mean pupil dilation during conversations that included prompts to maintain eye contact, and showed stronger positive coupling between eye contact and pupil dilation. Our findings strengthen support for the perspective that gaze aversion in FXS reflects negative reinforcement of social avoidance behavior. We also found that behavioral skills training may improve eye contact, but maintaining eye contact appears to be physiologically taxing for females with FXS.

Background Children and adolescents with fragile X syndrome (FXS) manifest significant symptoms of anxiety, particularly in response to face-to-face social interaction. In this study we used functional near-infrared spectroscopy (fNIRS) to reveal a specific pattern of brain activation and habituation in response to face stimuli in young girls with FXS, an important but understudied clinical population. Methods Participants were 32 girls with FXS (age: 11.8 ± 2.9 years) and a control group of 28 girls without FXS (age: 10.5 ± 2.3 years) matched for age, general cognitive function and autism symptoms. Functional NIRS was used to assess brain activation during a face habituation task including repeated upright/inverted faces and greeble (nonface) objects. Results Compared to the control group, girls with FXS showed significant hyper-activation in the frontopolar and dorsal lateral prefrontal cortices in response to all face stimuli (upright + inverted). Lack of neural habituation (and significant sensitization) was also observed in the FXS group in the frontopolar cortex in response to upright face stimuli. Finally, aberrant frontopolar sensitization in response to upright faces in girls with FXS was significantly correlated with notable cognitive-behavioral and social-emotional outcomes relevant to this condition including executive function, autism symptoms, depression and anxiety. Conclusions These findings strongly support a hypothesis of neural hyper-activation and accentuated sensitization during face processing in FXS, a phenomenon that could be developed as a biomarker endpoint for improving treatment trial evaluation in girls with this condition.

OBJECTIVE To assess whether previously observed brain and cognitive differences between children with type 1 diabetes and control subjects without diabetes persist, worsen, or improve as children grow into puberty and whether differences are associated with hyperglycemia. RESEARCH DESIGN AND METHODS One hundred forty-four children with type 1 diabetes and 72 age-matched control subjects without diabetes (mean ± SD age at baseline 7.0 ± 1.7 years, 46% female) had unsedated MRI and cognitive testing up to four times over 6.4 ± 0.4 (range 5.3–7.8) years; HbA1c and continuous glucose monitoring were done quarterly. FreeSurfer-derived brain volumes and cognitive metrics assessed longitudinally were compared between groups using mixed-effects models at 6, 8, 10, and 12 years. Correlations with glycemia were performed. RESULTS Total brain, gray, and white matter volumes and full-scale and verbal intelligence quotients (IQs) were lower in the diabetes group at 6, 8, 10, and 12 years, with estimated group differences in full-scale IQ of −4.15, −3.81, −3.46, and −3.11, respectively (P < 0.05), and total brain volume differences of −15,410, −21,159, −25,548, and −28,577 mm3 at 6, 8, 10, and 12 years, respectively (P < 0.05). Differences at baseline persisted or increased over time, and brain volumes and cognitive scores negatively correlated with a life-long HbA1c index and higher sensor glucose in diabetes. CONCLUSIONS Detectable changes in brain volumes and cognitive scores persist over time in children with early-onset type 1 diabetes followed longitudinally; these differences are associated with metrics of hyperglycemia. Whether these changes can be reversed with scrupulous diabetes control requires further study. These longitudinal data support the hypothesis that the brain is a target of diabetes complications in young children.

Smartphones and other modern technologies have introduced multiple new forms of distraction that color the modern driving experience. While many smartphone functions aim to improve driving by providing the driver with real-time navigation and traffic updates, others, such as texting, are not compatible with driving and are often the cause of accidents. Because both functions elicit driver attention, an outstanding question is the degree to which drivers’ naturalistic interactions with navigation and texting applications differ in regard to brain and behavioral indices of distracted driving. Here, we employed functional near-infrared spectroscopy to examine the cortical activity that occurs under parametrically increasing levels of smartphone distraction during naturalistic driving. Our results highlight a significant increase in bilateral prefrontal and parietal cortical activity that occurs in response to increasingly greater levels of smartphone distraction that, in turn, predicts changes in common indices of vehicle control.

OBJECTIVE This study examined whether a history of diabetic ketoacidosis (DKA) is associated with changes in longitudinal cognitive and brain development in young children with type 1 diabetes. RESEARCH DESIGN AND METHODS Cognitive and brain imaging data were analyzed from 144 children with type 1 diabetes, ages 4 to RESULTS In 48 of 51 subjects, the DKA event occurred at the time of onset, at an average of 2.9 years before study entry. The moderate/severe DKA group gained more total and regional white and gray matter volume over the observed 18 months compared with the none/mild group. When matched by age at time of enrollment and average HbA1c during the 18-month interval, participants who had a history of moderate/severe DKA compared with none/mild DKA were observed to have significantly lower Full Scale Intelligence Quotient scores and cognitive performance on the Detectability and Commission subtests of the Conners’ Continuous Performance Test II and the Dot Locations subtest of the Children’s Memory Scale. CONCLUSIONS A single episode of moderate/severe DKA in young children at diagnosis is associated with lower cognitive scores and altered brain growth. Further studies are needed to assess whether earlier diagnosis of type 1 diabetes and prevention of DKA may reduce the long-term effect of ketoacidosis on the developing brain.

Abnormal brain development and cognitive dysfunction have been reported both in children and in adults with fragile X syndrome (FXS). However, few studies have examined neuroanatomical abnormalities in FXS during adolescence. In this study we focus on adolescent subjects with FXS (N = 54) as compared to age- and sex-matched subjects with idiopathic intellectual disability (Comparison Group) (N = 32), to examine neuroanatomical differences during this developmental period. Brain structure was assessed with voxel-based morphometry and independent groups t-test in SPM8 software. Results showed that the FXS group, relative to the comparison group, had significantly larger gray matter volume (GMV) in only one region: the bilateral caudate nucleus, but have smaller GMV in several regions including bilateral medial frontal, pregenual cingulate, gyrus rectus, insula, and superior temporal gyrus. Group differences also were noted in white matter regions. Within the FXS group, lower FMRP levels were associated with less GMV in several regions including cerebellum and gyrus rectus, and less white matter volume (WMV) in pregenual cingulate, middle frontal gyrus, and other regions. Lower full scale IQ within the FXS group was associated with larger right caudate nucleus GMV. In conclusion, adolescents and young adults with FXS demonstrate neuroanatomical abnormalities consistent with those previously reported in children and adults with FXS. These brain variations likely result from reduced FMRP during early neurodevelopment and mediate downstream deleterious effects on cognitive function.

Significant regional differences in gray and white matter volume and subtle cognitive differences between young diabetic and nondiabetic children have been observed. Here, we assessed whether these differences change over time and the relation with dysglycemia. Children ages 4 to

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.

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 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.

Studies of brain structure in type 1 diabetes (T1D) describe widespread neuroanatomical differences related to exposure to glycemic dysregulation in adults and adolescents. In this study, we investigate the neuroanatomical correlates of dysglycemia in very young children with early-onset T1D. Structural magnetic resonance images of the brain were acquired in 142 children with T1D and 68 age-matched control subjects (mean age 7.0 ± 1.7 years) on six identical scanners. Whole-brain volumetric analyses were conducted using voxel-based morphometry to detect regional differences between groups and to investigate correlations between regional brain volumes and measures of glycemic exposure (including data from continuous glucose monitoring). Relative to control subjects, the T1D group displayed decreased gray matter volume (GMV) in bilateral occipital and cerebellar regions (P < 0.001) and increased GMV in the left inferior prefrontal, insula, and temporal pole regions (P = 0.002). Within the T1D group, hyperglycemic exposure was associated with decreased GMV in medial frontal and temporal-occipital regions and increased GMV in lateral prefrontal regions. Cognitive correlations of intelligence quotient to GMV were found in cerebellar-occipital regions and medial prefrontal cortex for control subjects, as expected, but not for the T1D group. Thus, early-onset T1D affects regions of the brain that are associated with typical cognitive development.

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.

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.

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 (Xm) and 17 from their father (Xp). 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 Xpdemonstrated thicker cortex than those with Xmin the temporal regions bilaterally, while Xmindividuals showed bilateral enlargement of gray matter volume in the superior frontal regions compared with Xp. 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.

Early-onset type 1 diabetes may affect the developing brain during a critical window of rapid brain maturation. Structural MRI was performed on 141 children with diabetes (4–10 years of age at study entry) and 69 age-matched control subjects at two time points spaced 18 months apart. For the children with diabetes, the mean (±SD) HbA1c level was 7.9 ± 0.9% (63 ± 9.8 mmol/mol) at both time points. Relative to control subjects, children with diabetes had significantly less growth of cortical gray matter volume and cortical surface area and significantly less growth of white matter volume throughout the cortex and cerebellum. For the population with diabetes, the change in the blood glucose level at the time of scan across longitudinal time points was negatively correlated with the change in gray and white matter volumes, suggesting that fluctuating glucose levels in children with diabetes may be associated with corresponding fluctuations in brain volume. In addition, measures of hyperglycemia and glycemic variation were significantly negatively correlated with the development of surface curvature. These results demonstrate that early-onset type 1 diabetes has widespread effects on the growth of gray and white matter in children whose blood glucose levels are well within the current treatment guidelines for the management of diabetes.

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 automated classification of human thoughts reflected on an event-related paradigm using fMRI modality with significantly shortened data acquisition time (less than a minute). Six distinct thoughts (right-hand motor imagery, left-hand motor imagery, right-foot motor imagery, mental calculation, internal word/speech generation, and visual imagery) were chosen as target tasks. Five healthy volunteers performed the tasks. The regions-of-interest (ROIs) were delineated from the activated regions that were consistently and exclusively activated during the training phase. Extracted feature vectors of activations were recognized using a support vector machine (SVM) classifier. With a parameter optimization using a cross-validation scheme, the classifier successfully recognized the six different categories of the given thought tasks with above 80% average accuracy from four participants. The proposed automated processing method of short-time fMRI data can be utilized for the further investigation of monitoring/identifying of human minds and their possible link to the hardware and computer control.

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).

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.