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Objective The investigators aimed to assess predictive factors of novel oppositional defiant disorder (ODD) among children and adolescents in the first 6 months following traumatic brain injury (TBI). Methods Children ages 5-14 years who experienced a TBI were recruited from consecutive admissions to five hospitals. Testing of a biopsychosocial model that may elucidate the development of novel ODD included assessment soon after injury (baseline) of preinjury characteristics, including psychiatric disorders, adaptive function, family function, psychosocial adversity, family psychiatric history, socioeconomic status, injury severity, and postinjury processing speed (which may be a proxy for brain injury). MRI analyses were also conducted to examine potential brain lesions. Psychiatric outcome, including that of novel ODD, was assessed 6 months after the injury. Results A total of 177 children and adolescents were recruited for the study, and 134 who were without preinjury ODD, conduct disorder, or disruptive behavior disorder not otherwise specified (DBD NOS) returned for the 6-month assessment. Of those who returned 6 months postinjury, 11 (8.2%) developed novel ODD, and none developed novel conduct disorder or DBD NOS. Novel ODD was significantly associated with socioeconomic status, preinjury family functioning, psychosocial adversity, and processing speed. Conclusions These findings show that an important minority of children with TBI developed ODD. Psychosocial and injury-related variables, including socioeconomic status, lower family function, psychosocial adversity, and processing speed, significantly increase risk for this outcome.

To investigate the factors predictive of novel psychiatric disorders in the interval 0-6 months following traumatic brain injury (TBI).Children ages 5-14 years consecutively hospitalized for mild to severe TBI at five hospitals were recruited. Participants were evaluated at baseline (soon after injury) for pre-injury characteristics including psychiatric disorders, socioeconomic status (SES), psychosocial adversity, family function, family psychiatric history, and adaptive function. In addition to the psychosocial variables, injury severity and lesion location detected with acquisition of a research MRI were measured to develop a biopsychosocial predictive model for development of novel psychiatric disorders. Psychiatric outcome, including occurrence of a novel psychiatric disorder, was assessed 6 months after the injury.The recruited sample numbered 177 children, and 141 children (80%) returned for the six-month assessment. Of the 141 children, 58 (41%) developed a novel psychiatric disorder. In univariable analyses, novel psychiatric disorder was significantly associated with lower SES, higher psychosocial adversity, and lesions in frontal lobe locations, such as frontal white matter, superior frontal gyrus, inferior frontal gyrus, and orbital gyrus. Multivariable analyses found that novel psychiatric disorder was independently and significantly associated with frontal-lobe white matter, superior frontal gyrus, and orbital gyrus lesions.The results demonstrate that occurrence of novel psychiatric disorders following pediatric TBI requiring hospitalization is common and has identifiable psychosocial and specific biological predictors. However, only the lesion predictors were independently related to this adverse psychiatric outcome.

The investigators examined the factors predictive of novel oppositional defiant disorder in the 6-12 months following traumatic brain injury (TBI).Children ages 5-14 years old who experienced a TBI were recruited from consecutive admissions to five hospitals. Participants were evaluated soon after injury (baseline) for preinjury characteristics, including psychiatric disorders, adaptive function, family function, psychosocial adversity, family psychiatric history, socioeconomic status, and injury severity, to develop a biopsychosocial predictive model for development of novel oppositional defiant disorder. MRI analyses were conducted to examine potential brain lesions. Psychiatric outcome, including that of novel oppositional defiant disorder, was assessed 12 months after injury.Although 177 children were recruited for the study, 120 children without preinjury oppositional defiant disorder, conduct disorder, or disruptive behavior disorder not otherwise specified (DBD NOS) returned for the 12-month assessment. Of these 120 children, seven (5.8%) exhibited novel oppositional defiant disorder, and none developed conduct disorder or DBD NOS in the 6-12 months postinjury. Novel oppositional defiant disorder was significantly associated with lower socioeconomic status, higher psychosocial adversity, and lower preinjury adaptive functioning.These results demonstrate that novel oppositional defiant disorder following TBI selectively and negatively affects an identifiable group of children. Both proximal (preinjury adaptive function) and distal (socioeconomic status and psychosocial adversity) psychosocial variables significantly increase risk for this outcome.

Neonatal brain injury may impact brain development and lead to lifelong functional impairments. Hypoxic-ischemic encephalopathy (HIE) and congenital heart disease (CHD) are two common causes of neonatal brain injury differing in timing and mechanism. Maturation of whole-brain neural networks can be quantified during development using diffusion magnetic resonance imaging (dMRI) in combination with graph theory metrics. DMRI of 35 subjects with CHD and 62 subjects with HIE were compared to understand differences in the effects of HIE and CHD on the development of network topological parameters and functional outcomes. CHD newborns had worse 12–18 month language (P

The objective of the study was to compare psychiatric outcomes in adults with and without history of pediatric traumatic brain injury (TBI). Youth ages 6 to 14 years hospitalized for TBI from 1992 to 1994 were assessed at baseline and at 3, 6, 12, and 24 months post-injury. In the current study, psychiatric assessments were repeated at 24 years post-injury with the same cohort, now adults ages 29 to 39 years. A control group of healthy adults also was recruited for one-time cross-sectional assessments. Outcome measures included: 1) presence of a psychiatric disorder since the 24-month assessment not present before the TBI ("novel psychiatric disorder," NPD), or in the control group, the presence of a psychiatric disorder that developed after the mean age of injury of the TBI group plus 2 years; and 2) Time-to-Event for onset of an NPD during the same time periods. In the TBI group, NPDs were significantly more common, and presence of a current NPD was significantly predicted by presence of a pre-injury lifetime psychiatric disorder and by abnormal day-of-injury computed tomography (CT) scan. Compared with controls, the TBI group also had significantly shorter Time-to-Event for onset of any NPD. These findings demonstrate that long-term psychiatric outcomes in adults previously hospitalized for pediatric TBI are significantly worse when compared with adult controls without history of pediatric TBI, both in terms of prevalence and earlier onset of NPD. Further, in the TBI group, long-term NPD outcome is predicted independently by presence of pre-injury psychiatric disorder and abnormal day-of-injury CT scan.

Highlights • Mindfulness training decreased anxiety symptoms in healthy adolescents. • Mindfulness training decreased structural connectivity of right amygdala in teens. • Increased structural connectivity of amygdala may underlie anxiety in teens., Mindfulness-based approaches show promise to improve emotional health in youth and may help treat and prevent adolescent depression and anxiety. However, there is a fundamental gap in understanding the neural reorganization that takes place as a result of such interventions. The Training for Awareness, Resilience, and Action (TARA) program, initially developed for depressed adolescents, uses a framework drawn from neuroscience, mindfulness, yoga, and modern psychotherapeutic techniques to promote emotional health. The goal of this study was to assess the effects of the TARA training on emotional health and structural white matter brain networks in healthy youth. We analyzed data from 23 adolescents who underwent the 12-week TARA training in a controlled within-subject study design and whose brain networks were assessed using diffusion MRI connectomics. Compared to the control time period, adolescents showed a significant decrease in anxiety symptoms with TARA (Cohen’s d = -0.961, p = 0.006); moreover, the node strength of the Right Amygdala decreased significantly after TARA (Cohen’s d = -1.026, p = 0.004). Post-hoc analyses indicated that anxiety at baseline before TARA was positively correlated with Right Amygdala node strength (r = 0.672, p = 0.001). While change in Right Amygdala node strength with TARA was not correlated with change in anxiety (r = 0.146, p = 0.51), it was associated with change in depression subscale of Anhedonia / Negative Affect (r = 0.575, p = 0.004, exploratory analysis), possibly due to overlapping constructs captured in our anxiety and depression scales. Our results suggest that increased structural connectivity of Right Amygdala may underlie increased anxiety in adolescents and be lowered through anxiety-reducing training such as TARA. The results of this study contribute to our understanding of the neural mechanisms of TARA and may facilitate neuroscience-based prevention and treatment of adolescent anxiety and depression.

Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.

INTRODUCTION: Cranial radiation therapy (CRT) has significantly benefitted pediatric brain cancer treatment. The improved prognosis has led to an increasing population of cancer survivors, but CRT is also associated with negative side effects. These include neurocognitive impairments in attention and working memory. Similar declines have been observed in adults with prior pediatric CRT and the newly-diagnosed elderly receiving CRT. Magnetic resonance imaging (MRI) studies have found that CRT has structural effects in the brain, such as altering white matter (WM). Graph network analysis of WM connectivity is a promising method to study the effects of CRT on cognitive functions that engage multiple brain regions. The method models the brain as a network of connected edges and nodes, which are quantitatively described with graph theory. This study used graph analysis to examine associations between neurocognitive deficits and WM connectivity. METHODS: Ten adolescent and young adult subjects (17.7±5yrs.) with prior CRT (7 whole-brain, 3 whole-ventricular) for pediatric brain cancers were scanned on a 7-Tesla MRI. Subjects were also assessed with a computerized cognitive battery (CogState). High resolution T1 anatomical and multiband two-shell diffusion sequences were acquired. A diffusion-based WM connectivity network was constructed and assessed with global and local graph metrics. Local analysis examined node strength at executive function and working memory areas. RESULTS: The analysis found significant negative correlations between graph metrics and cognitive measures of spatial working memory and executive function. Globally, this included measures of segregation (clustering coefficient and transitivity) and integration (efficiency); locally, this included node strength at domain-related regions. CONCLUSION: These preliminary findings suggest that cognitive performance is inversely correlated with graph metrics characterizing WM structural connectivity. The tumors’ infratentorial locations suggest that the findings were treatment-related rather than tumor-related. Our work suggests that CRT-related cognitive effects may potentially be assessed using WM structural graph analysis.

Purpose Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with cognitive behavioral therapy (CBT), a first-line treatment for adolescent MDD, and face devastating consequences of increased risk of suicide and many negative health outcomes. “Who will improve with CBT?” is a crucial question that remains unanswered, and treatment planning for adolescent depression remains biologically unguided. The purpose of this study was to utilize machine learning applied to patients' brain imaging data in order to help predict depressive symptom reduction with CBT. Methods We applied supervised machine learning to diffusion MRI-based structural connectome data in order to predict symptom reduction in 30 depressed adolescents after three months of CBT. A set of 21 attributes was chosen, including the baseline depression score, age, gender, two global network properties, and node strengths of brain regions previously implicated in depression. The practical and robust J48 pruned tree classifier was utilized with a 10-fold cross-validation. Results The classification resulted in an 83% accuracy of predicting depressive symptom reduction. The resulting tree of size seven with only three attributes highlights the role of the right thalamus in predicting depressive symptom reduction with CBT. Additional analysis showed a significant negative correlation between the change in the depressive symptoms and the node strength of the right thalamus. Conclusions Our results demonstrate that a machine learning algorithm that exclusively uses structural connectome data and the baseline depression score can predict with a high accuracy depressive symptom reduction in adolescent MDD with CBT. This knowledge can help improve treatment planning for adolescent depression., Highlights • Machine learning predicted symptom reduction in depressed teens with 83% accuracy. • Resulting prunned classification tree size was 7, with only 3 attributes. • Change in depression symptoms correlated with node strength of the right thalamus.

Graph theory analysis of structural brain networks derived from diffusion tensor imaging (DTI) has become a popular analytical method in neuroscience, enabling advanced investigations of neurological and psychiatric disorders. The purpose of this study was to investigate (1) the effects of edge weighting schemes and (2) the effects of varying interscan periods on graph metrics within the adolescent brain. We compared a binary (B) network definition with three weighting schemes: fractional anisotropy (FA), streamline count, and streamline count with density and length correction (SDL). Two commonly used global and two local graph metrics were examined. The analysis was conducted with two groups of adolescent volunteers who received DTI scans either 12 weeks apart (16.62 ± 1.10 years) or within the same scanning session (30 min apart) (16.65 ± 1.14 years). The intraclass correlation coefficient was used to assess test–retest reliability and the coefficient of variation (CV) was used to assess precision. On average, each edge scheme produced reliable results at both time intervals. Weighted measures outperformed binary measures, with SDL weights producing the most reliable metrics. All edge schemes except FA displayed high CV values, leaving FA as the only edge scheme that consistently showed high precision while also producing reliable results. Overall findings suggest that FA weights are more suited for DTI connectome studies in adolescents.

BACKGROUND AND PURPOSE: Differences in structural brain connectivity that underlie inattention have been previously investigated in adolescents with attention deficit/hyperactivity disorder, but not in the context of premature birth, which is often associated with attentional problems. The purpose of this study was to identify the neural correlates of attentional problems in adolescents born prematurely and determine neonatal predictors of those neural correlates and attention problems. MATERIALS AND METHODS: The study included 24 adolescents (12.5 ± 1.8 years of age; 12 girls, 12 boys) who were born prematurely and underwent MR imaging of the brain and cognitive assessment, both shortly after birth and as adolescents. Structural connectivity was assessed at adolescence using diffusion tensor imaging and tractography. RESULTS: Of the 24 subjects, 12 had attention deficits. A set of axonal pathways connecting the frontal, parietal, temporal, and occipital lobes had significantly lower fractional anisotropy in subjects with attentional problems. The temporoparietal connection between the left precuneus and left middle temporal gyrus was the most significantly underconnected interlobar axonal pathway. Low birth weight and ventriculomegaly, but not white matter injury or intraventricular hemorrhage on neonatal MR imaging, predicted temporoparietal hypoconnectivity in adolescence. However, neither birth weight nor other neonatal characteristics were associated with attention deficits directly. CONCLUSIONS: We identified an aberrant structural brain connectivity pattern, involving temporoparietal hypoconnectivity, in prematurely born adolescents with attentional problems. We also identified birth weight as a potential neonatal predictor of the temporoparietal hypoconnectivity. These findings add to our understanding of the neural basis and etiology of inattention in adolescents after premature birth.

Most neuroimaging studies of adolescent depression employ tasks not designed to engage brain regions necessary for the cognitive control of emotion, which is central to many behavioral therapies for depression. Depressed adults demonstrate less effective activation of these regions and greater amygdala activation during cognitive reappraisal; we examined whether depressed adolescents show similar patterns of brain activation.We collected functional magnetic resonance imaging (fMRI) data during cognitive reappraisal in 41 adolescents with major depressive disorder (MDD) and 34 matched controls (ages 13-17). We examined group differences in (1) activations associated with reappraisal and reappraisal success (i.e., negative affect reduction during reappraisal) using whole brain and amygdala region-of-interest analyses, and (2) functional connectivity of regions from the group-by-reappraisal success interaction.We found no significant group differences in whole brain or amygdala analyses during reappraisal. In the group-by-reappraisal success interaction, activations in the left dorsomedial prefrontal cortex (dmPFC) and left dorsolateral PFC (dlPFC) were associated with reappraisal success in healthy controls but not depressed adolescents. Depressed adolescents demonstrated reduced connectivity between the left dmPFC and the anterior insula/inferior frontal gyri bilaterally (AI/IFG) and between left dlPFC and left AI/IFG.Our results should be considered exploratory given our less conservative statistical threshold in the group-by-reappraisal interaction.We find preliminary evidence that depressed adolescents engage cognitive control regions less efficiently than healthy controls, suggesting delayed maturation of regulatory prefrontal cortex regions; more research is needed to determine whether cognitive therapies improve functioning of these regions in depressed youth.

Background: Adolescent anxiety and depression are highly prevalent psychiatric disorders that are associated with altered molecular and neurocircuit profiles. Recently, increased mitochondrial DNA copy number (mtDNA-cn) has been found to be associated with several psychopathologies in adults, especially anxiety and depression. The associations between mtDNA-cn and anxiety and depression have not, however, been investigated in adolescents. Moreover, to date there have been no studies examining associations between mtDNA-cn and brain network alterations in mood disorders in any age group. Methods: The first aim of this study was to compare salivary mtDNA-cn between 49 depressed and/or anxious adolescents and 35 well-matched healthy controls. The second aim of this study was to identify neural correlates of mtDNA-cn derived from diffusion tensor imaging (DTI) and tractography, in the full sample of adolescents. Results: There were no diagnosis-specific alterations in mtDNA-cn. However, there was a positive correlation between mtDNA-cn and levels of anxiety, but not depression, in the full sample of adolescents. A subnetwork of connections largely corresponding to the left fronto-occipital fasciculus had significantly lower fractional anisotropy (FA) values in adolescents with higher than median mtDNA-cn. Limitations: Undifferentiated analysis of free and intracellular mtDNA and use of DTI-based tractography represent this study's limitations. Conclusions: The results of this study help elucidate the relationships between clinical symptoms, molecular changes, and neurocircuitry alterations in adolescents with and without anxiety and depression, and they suggest that increased mtDNA-cn is associated both with increased anxiety symptoms and with decreased fronto-occipital structural connectivity in this population.

Diffusion Tensor Imaging (DTI) is adversely affected by subject motion. It is necessary to discard the corrupted images before diffusion parameter estimation. However, the consequences of rejecting those images are not well understood. In this study, we investigated the effects of excluding one or more volumes of diffusion weighted images by analyzing the changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) and the primary eigenvector (V1). Based on the full set of diffusion images acquired by the Jones30 diffusion scheme, we generated incomplete sets of at least six in three different ways: random, uniform and clustered rejections. The results showed that MD was not significantly affected by rejecting diffusion directions. In the cases of random rejections, FA, AD, RD and V1 were overestimated more greatly with increasing number of rejections and the overestimations were worse in low FA regions than high FA regions. For uniform rejections, at which the remaining diffusion directions are evenly distributed on a sphere, little change was observed in FA and in V1. Clustered rejections, on the other hand, displayed the most significant overestimation of the parameters, and the resulting accuracy depended on the relative orientation of the underlying fibers with respect to the excluded directions. In practice, if diffusion direction data is excluded, it is important to note the number and location of directions rejected, in order to make a more precise analysis of the data.

Recent evidence suggests that anterior cingulate cortex (ACC) maturation during adolescence contributes to or underlies the development of major depressive disorder (MDD) during this sensitive period. The ACC is a structure that sits at the intersection of several task-positive networks (eg, central executive network, CEN), which are still developing during adolescence. While recent work using seed-based approaches indicate that depressed adolescents show limited task-evoked vs resting-state connectivity (termed ‘inflexibility’) between the ACC and task-negative networks, no study has used network-based approaches to investigate inflexibility of the ACC in task-positive networks to understand adolescent MDD. Here, we used graph theory to compare flexibility of network-level topology in eight subregions of the ACC (spanning three task-positive networks) in 42 unmedicated adolescents with MDD and 53 well-matched healthy controls. All participants underwent fMRI scanning during resting state and a response inhibition task that robustly engages task-positive networks. Relative to controls, depressed adolescents were characterized by inflexibility in local efficiency of a key ACC node in the CEN: right dorsal anterior cingulate cortex/medial frontal gyrus (R dACC/MFG). Furthermore, individual differences in flexibility of local efficiency of R dACC/MFG significantly predicted inhibition performance, consistent with current literature demonstrating that flexible network organization affords successful cognitive control. Finally, reduced local efficiency of dACC/MFG during the task was significantly associated with an earlier age of depression onset, consistent with prior work suggesting that MDD may alter functional network development. Our results support a neurodevelopmental hypothesis of MDD wherein dysfunctional self-regulation is potentially reflected by altered ACC maturation.

Background and purposeDiffusion and fMRI has been providing insights to brain development in addition to anatomic imaging. This study aimed to evaluate the microstructure of white matter tracts underlying the default mode network in premature infants by using resting-state functional MR imaging in conjunction with diffusion tensor imaging-based tractography.Materials and methodsA cohort of 44 preterm infants underwent structural T1-weighted imaging, resting-state fMRI, and DTI at 3T, including 21 infants with brain injuries and 23 infants with normal-appearing structural imaging as controls. Neurodevelopment was evaluated with the Bayley Scales of Infant Development at 12 months' adjusted age. Probabilistic independent component analysis was applied to resting-state fMRI data to explore resting-state networks. The localized clusters of the default mode network were used as seeding for probabilistic tractography. The DTI metrics (fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) of the reconstructed primary tracts within the default mode network-cingula were measured.ResultsResults revealed decreased fractional anisotropy (0.20 ± 0.03) and elevated radial diffusivity values (1.24 ± 0.16) of the cingula in the preterm infants with brain injuries compared with controls (fractional anisotropy, 0.25 ± 0.03; P < .001; radial diffusivity, 1.06 ± 0.16; P = .001). The Bayley Scales of Infant Development cognitive scores were significantly associated with cingulate fractional anisotropy (P = .004) and radial diffusivity (P = .021); this association suggests that the microstructural properties of interconnecting axonal pathways within the default mode network are of critical importance in the early neurocognitive development of infants.ConclusionsThis study of combined resting-state fMRI and DTI at rest suggests that such studies may allow the investigation of key functional brain circuits in premature infants, which could function not only as diagnostic tools but also as biomarkers for long-term neurodevelopmental outcomes.

Background: The novel group treatment program Training for Awareness, Resilience, and Action (TARA) was developed to target specific mechanisms based on neuroscience findings in adolescent depression and framed within the National Institute of Mental Health Research Domain Criteria. TARA contains training of autonomic and emotional self-regulation, interoceptive awareness, relational skills, and value-based committed action. Methods: We performed a single-arm trial to test the feasibility and preliminary efficacy of TARA in reducing depression and anxiety levels and assessed whether the specific targeted domains of function reflected the hypothesized symptom change. Twenty-six adolescents (14–18 years old, 7 males and 19 females) participated in the 12-week group program. Assessment was performed before (T0), immediately after (T1), and 3 months after the end of TARA (T2). Results: Significant improvement was seen in depression symptoms (Reynolds Adolescent Depression Scale Second Edition) between T0–T1 (t-value = −3.56, p = 0.002, CI = −6.64, −1.77) and T0–T2 (t-value = −4.17, p < 0.001, CI = −11.20, −3.75) and anxiety symptoms (Multidimensional Anxiety Scale for Children) between T0–T1 (t-value = −2.26, p = 0.033, CI = −4.61, −0.21) and T0–T2 (t-value = −3.06, p = 0.006, 95% confidence interval = −9.02, −1.73). Significant improvements in psychological flexibility, sleep, and mindfulness skills were also found between T0 and T2. Limitations: The sample size was small without a control condition. The pilot design did not allow for testing the hypothesized brain changes and effect of TARA on relevant systemic biomarkers. Conclusion: TARA is feasible in a sample of clinically depressed and/or anxious adolescents and preliminary efficacy was demonstrated by reduced depression and anxiety symptoms. The specific symptom and behavioral outcomes corresponded well with the hypothesized mechanisms of change.

Recently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes is therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages, the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network and this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a graph alignment (GA) problem. In this work, we first define the problem formally, then we test some existing state of the art aligners on diffusion MRI-derived brain networks, and we compare the performances. The results confirm that GA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.

Adolescence is a vulnerable period for the onset of major depressive disorder (MDD). While some studies have shown white matter alterations in adolescent MDD, there is still a gap in understanding how the brain is affected at a network level.We compared diffusion tensor imaging (DTI)-based brain networks in a cohort of 57 adolescents with MDD and 41 well-matched healthy controls who completed self-reports of depression symptoms and stressful life events. Using atlas-based brain regions as network nodes and tractography streamline count or mean fractional anisotropy (FA) as edge weights, we examined weighted local and global network properties and performed Network-Based Statistic (NBS) analysis.While there were no significant group differences in the global network properties, the FA-weighted node strength of the right caudate was significantly lower in depressed adolescents and correlated positively with age across both groups. The NBS analysis revealed a cluster of lower FA-based connectivity in depressed subjects centered on the right caudate, including connections to frontal gyri, insula, and anterior cingulate. Within this cluster, the most robust difference between groups was the connection between the right caudate and middle frontal gyrus. This connection showed a significant diagnosis by stress interaction and a negative correlation with total stress in depressed adolescents.Use of DTI-based tractography, one atlas-based parcellation, and FA values to characterize brain networks represent this study's limitations.Our results allowed us to suggest caudate-centric models of dysfunctional processes underlying adolescent depression, which might guide future studies and help better understand and treat this disorder.

The MRI-based contrast-enhanced method of tooth surface digitization provides an alternative possibility for making a dental impression. It has recently been demonstrated that tooth surface digitization with an accuracy sufficient for production of dental restorations is possible using a clinical whole-body MRI scanner. However, for an in vivo application of the technique, the required high nominal resolution and signal-to- noise ratio have to be achieved within reasonable measurement times. Existing radio fre- quency (RF) volume and surface coils have not been able to fulfill this requirement. The objective of this work was to develop a dedicated RF receiver coil for contrast-enhanced dental MRI which provides a high sensitivity in the region of the whole dental arch and allows for application of the contrast media into the mouth of the patient. To achieve this goal, an intraoral dental coil design with a built-in contrast medium applicator was devel- oped and implemented. The coil was shown to enable high-resolution contrast-enhanced dental MRI in vivo. This was demonstrated on four teeth prepared for dental restorations. The average accuracy obtained for all four teeth was below 100 lm. For two of the teeth, copings were modeled based on MRI data and produced from zirconium oxide using computer-aided design and manufacturing (CAD/CAM) technology. 2008 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 33B: 244-251, 2008

MRI connectomics methods treat the brain as a network and provide new information about its organization, efficiency, and mechanisms of disruption. The most commonly used method of defining network nodes is to register the brain to a standardized anatomical atlas based on the Brodmann areas. This approach is limited by inter-subject variability and can be especially problematic in the context of brain maturation or neuroplasticity (cerebral reorganization after brain damage). In this study, we combined different image processing and network theory methods and created a novel approach that enables atlas-free construction and connection-wise comparison of diffusion MRI-based brain networks. We illustrated the proposed approach in three age groups: neonates, 6-month-old infants, and adults. First, we explored a data-driven method of determining the optimal number of equal-area nodes based on the assumption that all cortical areas of the brain are connected and, thus, no part of the brain is structurally isolated. Second, to enable a connection-wise comparison, alignment to a "reference brain" was performed in the network domain within each group using a matrix alignment algorithm with simulated annealing. The correlation coefficients after pair-wise network alignment ranged from 0.6102 to 0.6673. To test the method's reproducibility, one subject from the 6-month-old group and one from the adult group were scanned twice, resulting in correlation coefficients of 0.7443 and 0.7037, respectively. While being less than 1 due to parcellation and noise, statistically, these values were significantly higher than inter-subject values. Rotation of the parcellation largely explained the variability. Through the abstraction from anatomy, the developed framework allows for a fully network-driven analysis of structural MRI connectomes and can be applied to subjects at any stage of development and with substantial differences in cortical anatomy. © 2014 Tymofiyeva et al.

Improved understanding of how the human brain is "wired" on a macroscale may now be possible due to the emerging field of MRI connectomics. However, mapping the rapidly developing infant brain networks poses challenges. In this study, we applied an automated template-free "baby connectome" framework using diffusion MRI to non-invasively map the structural brain networks in subjects of different ages, including premature neonates, term-born neonates, six-month-old infants, and adults. We observed increasing brain network integration and decreasing segregation with age in term-born subjects. We also explored how the equal area nodes can be grouped into modules without any prior anatomical information - an important step toward a fully network-driven registration and analysis of brain connectivity. © 2013 Tymofiyeva et al.

Neonatal encephalopathy represents a heterogeneous group of conditions associated with life-long developmental disabilities and neurological deficits. Clinical measures and current anatomic brain imaging remain inadequate predictors of outcome in children with neonatal encephalopathy. Some studies have suggested that brain development and, therefore, brain connectivity may be altered in the subgroup of patients who subsequently go on to develop clinically significant neurological abnormalities. Large-scale structural brain connectivity networks constructed using diffusion tractography have been posited to reflect organizational differences in white matter architecture at the mesoscale, and thus offer a unique tool for characterizing brain development in patients with neonatal encephalopathy. In this manuscript we use diffusion tractography to construct structural networks for a cohort of patients with neonatal encephalopathy. We systematically map these networks to a high-dimensional space and then apply standard machine learning algorithms to predict neurological outcome in the cohort. Using nested cross-validation we demonstrate high prediction accuracy that is both statistically significant and robust over a broad range of thresholds. Our algorithm offers a novel tool to evaluate neonates at risk for developing neurological deficit. The described approach can be applied to any brain pathology that affects structural connectivity. © 2013 Ziv et al.

Purpose To assess the feasibility of magnetic resonance imaging (MRI) of dental abnormalities in children. Materials and Methods The study included 16 patients (mean age, 10.8 yr) prospectively selected from 1,500 orthodontic patients. The selected patients included 3 with a mesiodens, 9 with supernumerary teeth other than a mesiodens, 1 with gemination, 1 with dilacerations, 1 with transmigration, and 1 with transposition. Three-dimensional (3D) images were acquired on a 1.5-T MRI scanner using a 3D turbo spin echo pulse sequence with a voxel size of 0.8 × 0.8 × 1 mm. The measurement time was 4 to 5 minutes. Results Using natural MRI contrast, the teeth, dental pulp, mandibular canal, and cortical bone could be clearly delineated. The position and shape of malformed teeth could be assessed in all 3 spatial dimensions. Conclusion MRI was found to be a well-tolerated imaging modality for the diagnosis of dental abnormalities in children and for orthodontic treatment and surgical planning. Compared with conventional radiography, dental MRI provides the advantage of 3-dimensionality and complete elimination of ionizing radiation, which is particularly relevant for repeated examinations in children.

The purpose of this study was to investigate the achievable precision of localization of boundaries between extended uniform objects in MRI and to study the effect of zero-filling on reaching it.A theoretical model of an object boundary in the presence of noise was introduced, and the error of localization was derived. The effect of zero-filling on reaching the achievable precision was assessed by computer simulations and experimentally on an extracted tooth in a signal-giving medium.With the help of the theoretical model, the achievable precision of localization of boundaries between two uniform extended objects was shown to surpass the nominal resolution by a factor equal to the contrast-to-noise ratio. In the simulations and phantom experiments, zero-filling followed by image segmentation allowed for approaching the theoretical value. As an application example, an MRI-based dental impression was performed in vivo, and a bridge was produced and permanently fixed to the volunteer's teeth.This work demonstrates that in an MRI experiment, the achievable precision of localization of object boundaries is not limited to the nominal resolution and can surpass it by an order of magnitude. Zero-filling is a simple and effective method of reaching it.