Olga Tymofiyeva

Three-dimensional localization of impacted teeth using magnetic resonance imaging

Impacted teeth remain embedded in the jawbone beyond the normal eruption time with completed root growth. They can often get infected or damage neighboring teeth. Information about the three-dimensional position of impacted teeth is invaluable in orthodontic diagnosis and treatment planning. The purpose of this prospective study was to assess the feasibility of using magnetic resonance imaging (MRI) for the three-dimensional localization of impacted teeth in children and adults. The study included 39 patients from the pediatric age group with different tooth impactions and seven adults with impacted wisdom teeth. MRI yielded a clear separation between impacted teeth and the surrounding tissue, and the position and angulation of impacted teeth in all three spatial dimensions could be assessed. Compared to conventional radiography, dental MRI provides the advantage of full volumetric morphology accompanied by complete elimination of ionizing radiation, which is particularly relevant for repeated examinations of the pediatric group.

Towards the "baby connectome": Mapping the structural connectivity of the newborn brain

Defining the structural and functional connectivity of the human brain (the human “connectome”) is a basic challenge in neuroscience. Recently, techniques for noninvasively characterizing structural connectivity networks in the adult brain have been developed using diffusion and high-resolution anatomic MRI. The purpose of this study was to establish a framework for assessing structural connectivity in the newborn brain at any stage of development and to show how network properties can be derived in a clinical cohort of six-month old infants sustaining perinatal hypoxic ischemic encephalopathy (HIE). Two different anatomically unconstrained parcellation schemes were proposed and the resulting network metrics were correlated with neurological outcome at 6 months. Elimination and correction of unreliable data, automated parcellation of the cortical surface, and assembling the large-scale baby connectome allowed an unbiased study of the network properties of the newborn brain using graph theoretic analysis. In the application to infants with HIE, a trend to declining brain network integration and segregation was observed with increasing neuromotor deficit scores.

High-resolution 3D magnetic resonance imaging and quantification of carious lesions and dental pulp in vivo

ObjectiveThe purpose of the study was to assess the feasibility of MRI of three-dimensional visualization and quantification of carious lesions, as well as measurement of the distance between the lesion and dental pulp in vivo.Materials and methodsHigh-resolution 3D MRI was performed to measure seven carious lesions in vivo using gelatinous gadolinium-based oral contrast medium in combination with an intraoral radio frequency receiver coil on a clinical 1.5 T MRI scanner. Extension of the carious lesion in three spatial dimensions and the minimum distance between the lesion and dental pulp were quantified. When possible, the result was compared to an X-ray projection and an impression of the lesion taken using a plastic impression material before and after dental treatment.ResultsCarious lesions, including pit and fissure, approximal lesions, and occult dentin caries, could be visualized due to the MRI signal rise in the porous affected dentin. The minimum distance between the carious lesion and dental pulp could be determined in all cases.ConclusionThe results presented demonstrate the feasibility of high-resolution dental MRI to three-dimensionally visualize and quantify carious lesions, including approximal and occult caries lesions, and measure the minimum distance to the dental pulp.

A DTI-Based Template-Free Cortical Connectome Study of Brain Maturation

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.

Influence of dental materials on dental MRI

OBJECTIVES To investigate the potential influence of standard dental materials on dental MRI (dMRI) by estimating the magnetic susceptibility with the help of the MRI-based geometric distortion method and to classify the materials from the standpoint of dMRI. METHODS A series of standard dental materials was studied on a 1.5 T MRI system using spin echo and gradient echo pulse sequences and their magnetic susceptibility was estimated using the geometric method. Measurements on samples of dental materials were supported by in vivo examples obtained in dedicated dMRI procedures. RESULTS The tested materials showed a range of distortion degrees. The following materials were classified as fully compatible materials that can be present even in the tooth of interest: the resin-based sealer AH Plus(®) (Dentsply, Maillefer, Germany), glass ionomer cement, gutta-percha, zirconium dioxide and composites from one of the tested manufacturers. Interestingly, composites provided by the other manufacturer caused relatively strong distortions and were therefore classified as compatible I, along with amalgam, gold alloy, gold-ceramic crowns, titanium alloy and NiTi orthodontic wires. Materials, the magnetic susceptibility of which differed from that of water by more than 200 ppm, were classified as non-compatible materials that should not be present in the patients mouth for any dMRI applications. They included stainless steel orthodontic appliances and CoCr. CONCLUSIONS A classification of the materials that complies with the standard grouping of materials according to their magnetic susceptibility was proposed and adopted for the purposes of dMRI. The proposed classification can serve as a guideline in future dMRI research.

Resting-state functional connectivity of the amygdala and longitudinal changes in depression severity in adolescent depression

BACKGROUND The incidence of major depressive disorder (MDD) rises during adolescence, yet the neural mechanisms of MDD during this key developmental period are unclear. Altered amygdala resting-state functional connectivity (RSFC) has been associated with both adolescent and adult MDD, as well as symptom improvement in response to treatment in adults. However, no study to date has examined whether amygdala RSFC is associated with changes in depressive symptom severity in adolescents. METHOD We examined group differences in amygdala RSFC between medication-naïve depressed adolescents (N=48) and well-matched healthy controls (N=53) cross-sectionally. We then longitudinally examined whether baseline amygdala RSFC was associated with change in depression symptoms three months later in a subset of the MDD group (N=24). RESULTS Compared to healthy controls, depressed adolescents showed reduced amygdala-based RSFC with the dorsolateral prefrontal cortex (DLPFC)and the ventromedial prefrontal cortex (VMPFC). Within the depressed group, more positive baseline RSFC between the amygdala and insulae was associated with greater reduction in depression symptoms three months later. LIMITATIONS Only a subset of depressed participants was assessed at follow-up and treatment type and delivery were not standardized. CONCLUSIONS Adolescent depression may be characterized by dysfunction of frontolimbic circuits (amygdala-DLPFC, amygdala-VMPFC) underpinning emotional regulation, whereas those circuits (amygdala-insula) subserving affective integration may index changes in depression symptom severity and may therefore potentially serve as a candidate biomarker for treatment response. Furthermore, these results suggest that the biomarkers of MDD presence are distinct from those associated with change in depression symptoms over time.

Diagnosis of dental abnormalities in children using 3-dimensional magnetic resonance imaging.

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.

Large-Scale Hypoconnectivity Between Resting-State Functional Networks in Unmedicated Adolescent Major Depressive Disorder

Major depressive disorder (MDD) often emerges during adolescence, a critical period of brain development. Recent resting-state fMRI studies of adults suggest that MDD is associated with abnormalities within and between resting-state networks (RSNs). Here we tested whether adolescent MDD is characterized by abnormalities in interactions among RSNs. Participants were 55 unmedicated adolescents diagnosed with MDD and 56 matched healthy controls. Functional connectivity was mapped using resting-state fMRI. We used the network-based statistic (NBS) to compare large-scale connectivity between groups and also compared the groups on graph metrics. We further assessed whether group differences identified using nodes defined from functionally defined RSNs were also evident when using anatomically defined nodes. In addition, we examined relations between network abnormalities and depression severity and duration. Finally, we compared intranetwork connectivity between groups and assessed the replication of previously reported MDD-related abnormalities in connectivity. The NBS indicated that, compared with controls, depressed adolescents exhibited reduced connectivity (p<0.024, corrected) between a specific set of RSNs, including components of the attention, central executive, salience, and default mode networks. The NBS did not identify group differences in network connectivity when using anatomically defined nodes. Longer duration of depression was significantly correlated with reduced connectivity in this set of network interactions (p=0.020, corrected), specifically with reduced connectivity between components of the dorsal attention network. The dorsal attention network was also characterized by reduced intranetwork connectivity in the MDD group. Finally, we replicated previously reported abnormal connectivity in individuals with MDD. In summary, adolescents with MDD show hypoconnectivity between large-scale brain networks compared with healthy controls. Given that connectivity among these networks typically increases during adolescent neurodevelopment, these results suggest that adolescent depression is associated with abnormalities in neural systems that are still developing during this critical period.

DTI-based connectome analysis of adolescents with major depressive disorder reveals hypoconnectivity of the right caudate.

BACKGROUND 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. METHODS 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. RESULTS 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. LIMITATIONS Use of DTI-based tractography, one atlas-based parcellation, and FA values to characterize brain networks represent this studys limitations. CONCLUSIONS 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.

A Machine Learning Approach to Automated Structural Network Analysis: Application to Neonatal Encephalopathy

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.