SUMMARY The human brain is organized into large-scale functional modules that have been shown to evolve in childhood and adolescence. However, it remains unknown whether the underlying white matter architecture is similarly refined during development, potentially allowing for improvements in executive function. In a sample of 882 participants (ages 8–22) who underwent diffusion imaging as part of the Philadelphia Neurodevelopmental Cohort, we demonstrate that structural network modules become more segregated with age, with weaker connections between modules and stronger connections within modules. Evolving modular topology facilitates global network efficiency, and is driven by age-related strengthening of hub edges present both within and between modules. Critically, both modular segregation and network efficiency are associated with enhanced executive performance, and mediate the improvement of executive functioning with age. Together, results delineate a process of structural network maturation that supports executive function in youth.

Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.

Background Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g. motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1,601 youths ages of 8–21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics. Methods All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared to DTIPrep. Results TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of age, sex and race in a matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images. Conclusion Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a pra...

Psychosis commonly develops in adolescence or early adulthood. Youths at clinical high risk (CHR) for psychosis exhibit similar, subtle symptoms to those with schizophrenia (SZ). Malfunctioning neurotransmitter systems, such as glutamate, are implicated in the disease progression of psychosis. Yet, in vivo imaging techniques for measuring glutamate across the cortex are limited. Here we use a novel 7 Tesla MRI glutamate imaging technique (GluCEST) to estimate changes in glutamate levels across cortical and subcortical regions in young healthy individuals and ones on the psychosis spectrum. Individuals on the psychosis spectrum (PS;n=19) and healthy young individuals (HC; n=17) underwent MRI imaging at 3T and 7T. At 7T, a single slice GluCEST technique was used to estimate in vivo glutamate. GluCEST contrast was compared within and across the subcortex, frontal, parietal and occipital lobes. Subcortical [χ2 (1) = 4.65, p=0.031] and lobular [χ2 (1) = 5.17, p=0.023] GluCEST contrast levels were lower in PS compared to HC. Abnormal GluCEST contrast levels were evident in both CHR (n=14) and SZ (n=5) subjects, and correlated differentially, across regions, with clinical symptoms. Our findings describe a pattern of abnormal brain neurochemistry early in the course of psychosis. Specifically, CHR and young SZ exhibit diffuse abnormalities in GluCEST contrast attributable to a major contribution from glutamate. We suggest that neurochemical profiles of GluCEST contrast across cortex and subcortex may be considered markers of early psychosis. GluCEST methodology thus shows promise to further elucidate the progression of the psychosis disease state.

Background Measurements of olfaction may serve as useful biomarkers of incipient dementia. Here we examine the improvement in diagnostic accuracy of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) when assessing both cognitive functioning and odor identification. Objective To determine the utility of odor identification as a supplementary screening test in incipient AD. Methods Sniffin’ Sticks Odor Identification Test (SS-OIT) and the Montreal Cognitive Assessment (MoCA) were administered in 262 AD, 174 MCI [150 amnestic (aMCI), and 24 non-amnestic (naMCI)], and 292 healthy older adults (HOA). Results Odor identification scores were higher in HOA relative to MCI or AD groups, and MCI outperformed AD. Odor identification scores were higher in aMCI single domain than aMCI multiple domain. Complementing MoCA scores with the SS-OIT significantly improved diagnostic accuracy of individuals with AD and MCI, including within MCI subgroups. Discussion Odor identification is a useful supplementary screening tool that provides additional information relevant for clinical categorization of AD and MCI, including those who are at highest risk to convert to AD.

Background Diffusion tensor imaging (DTI) studies in schizophrenia report widespread aberrations in brain white matter (WM). These appear related to poorer neurocognitive performance and higher levels of negative and positive symptomatology. However, identification of the most salient WM aberrations to neurocognition and clinical symptoms is limited by relatively small samples with divergent results. Methods We examined 53 well-characterized patients with schizophrenia and 62 healthy controls. All participants were administered a computerized neurocognitive battery, which evaluated performance in several domains. Patients were assessed for negative and positive symptoms. Fractional anisotropy (FA) of WM cortical regions and WM fiber tracts were compared across the groups. FA values were also used to predict neurocognitive performance and symptoms. Results We confirm widespread aberrant WM microstructure in a relatively large sample of well-characterized patients with schizophrenia in comparison to healthy participants. Moreover, we illustrate the utility of FA measures in predicting global neurocognitive performance in healthy participants and schizophrenia patients, especially for reaction time. FA was less predictive of clinical symptomatology. Conclusions Using a standardized computerized neurocognitive battery and diffusion tensor imaging we show that behavioral performance is moderated by a particular constellation of WM microstructure in healthy individuals that differs in schizophrenia.

Background The transition from mild cognitive impairment (MCI) to Alzheimer’s disease is characterized by a decline in cognitive performance in many domains. Cognitive performance profiles in MCI are heterogeneous, however, and additional insights into markers of incipient dementia are needed. Typically, studies focus on average or mean performance, but ignore consistency of performance across domains. WIV (within-individual variability) provides an index of this consistency and is a potential marker of cognitive decline. Objective To use neurocognitive data from the Alzheimer’s Disease Neuroimaging Initiative cohort to measure neurocognitive variability. Methods The utility of WIV was measured, in addition to global neurocognitive performance (GNP), for identifying AD and MCI. In addition, the association between changes in neurocognitive variability and diagnostic transition over 12 months was measured. Results As expected, variability was higher in AD and MCI as compared to healthy controls; GNP was lower in both groups as compared to healthy subjects. Global neurocognitive performance alone best distinguished those with dementia from healthy older adults. Yet, for individuals with MCI, including variability along with GNP improved diagnostic classification. Variability was higher at baseline in individuals transitioning from MCI to AD over a 12-month period. Conclusion We conclude that variability offers complementary information about neurocognitive performance in dementia, particularly in individuals with MCI, and may provide beneficial information about disease transition.

Wariness in early childhood manifests as shy, inhibited behavior in novel social situations and is associated with increased risk for developing social anxiety. In youth with childhood wariness, exposure to a potent social stressor, such as peer victimization, may potentiate brain-based sensitivity to unpredictable social contexts, thereby increasing risk for developing social anxiety. To test brain-based associations between early childhood wariness, self-reported peer victimization, and current social anxiety symptoms, we quantified neural responses to different social contexts in low- and high-victimized pre-adolescents with varying levels of early childhood wariness. Measures of early childhood wariness were obtained annually from ages 2-to-7-years. At age 11, participants were characterized as having low (N=20) or high (N=27) peer victimization. To index their neural responses to peer evaluation, participants completed an fMRI-based Virtual School paradigm (Jarcho et al., 2013). In highly victimized, relative to low-victimized participants, wariness was differentially related to right amygdala responsivity based on the valence and predictability of peer evaluation. More specifically, in highly victimized participants, wariness was associated with greater right amygdala responsivity to unpredictably positive peer evaluation. Effects of wariness were not observed in participants who reported low levels of victimization. Moreover, in victimized participants, high wariness and right amygdala response to unpredictably positive peer evaluation was associated with more severe social anxiety symptoms. Results can be interpreted using a diathesis-stress model, which suggests that neural response to unexpectedly positive social feedback is a mechanism by which exposure to peer victimization potentiates the risk for developing social anxiety in individuals exhibiting high levels of early childhood wariness.