The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort

Heritability and Cognitive Relevance of Structural Brain Controllability

Cognition and behavior are thought to emerge from the connections and interactions among brain regions. The precise nature of these relationships remains elusive. Here we use tools provided by network control theory to determine how the structural connectivity profile of brain regions may shape individual variation in cognition. In a cohort of healthy young adults (n = 1066), we computed two fundamental brain regional control patterns, average and modal controllability, which index the degree of influence of a region over others. We first established that regional brain controllability measures were both reproducible and heritable. Regions with controllability profiles theoretically conducive to facilitating multiple cognitive operations were over-represented in higher-order resting-state networks. Finally, variation in regional controllability accounted for about 50% of interindividual variability in multiple cognitive domains. We conclude that controllability is a biologically plausible property of the structural connectome and provides a mechanistic explanation for how brain structural architecture may influence cognitive functions.

Post-acute white matter microstructure predicts post-acute and chronic post-concussive symptom severity following mild traumatic brain injury in children

INTRODUCTION

Mild traumatic brain injury (TBI) is a global public health concern that affects millions of children annually. Mild TBI tends to result in subtle and diffuse alterations in brain tissue, which challenges accurate clinical detection and prognostication. Diffusion tensor imaging (DTI) holds promise as a diagnostic and prognostic tool, but little research has examined DTI in post-acute mild TBI. The current study compared post-acute white matter microstructure in children with mild TBI versus those with mild orthopedic injury (OI), and examined whether post-acute DTI metrics can predict post-acute and chronic post-concussive symptoms (PCS).

MATERIALS AND METHODS

Children aged 8-16.99 years with mild TBI (n = 132) or OI (n = 69) were recruited at emergency department visits to two children's hospitals, during which parents rated children's pre-injury symptoms retrospectively. Children completed a post-acute (<2 weeks post-injury) assessment, which included a 3T MRI, and 3- and 6-month post-injury assessments. Parents and children rated PCS at each assessment. Mean diffusivity (MD) and fractional anisotropy (FA) were derived from diffusion-weighted MRI using Automatic Fiber Quantification software. Multiple multivariable linear and negative binomial regression models were used to test study aims, with False Discovery Rate (FDR) correction for multiple comparisons.

RESULTS

No significant group differences were found in any of the 20 white matter tracts after FDR correction. DTI metrics varied by age and sex, and site was a significant covariate. No interactions involving group, age, and sex were significant. DTI metrics in several tracts robustly predicted PCS ratings at 3- and 6-months post-injury, but only corpus callosum genu MD was significantly associated with post-acute PCS after FDR correction. Significant group by DTI metric interactions on chronic PCS ratings indicated that left cingulum hippocampus and thalamic radiation MD was positively associated with 3-month PCS in the OI group, but not in the mild TBI group.

CONCLUSIONS

Post-acute white matter microstructure did not differ for children with mild TBI versus OI after correcting for multiple comparisons, but was predictive of post-acute and chronic PCS in both injury groups. These findings support the potential prognostic utility of this advanced DTI technique.

Longitudinal stability of the brain functional connectome is associated with episodic memory performance in aging

The brain functional connectome forms a relatively stable and idiosyncratic backbone that can be used for identification or “fingerprinting” of individuals with a high level of accuracy. While previous cross‐sectional evidence has demonstrated increased stability and distinctiveness of the brain connectome during the course of childhood and adolescence, less is known regarding the longitudinal stability in middle and older age. Here, we collected structural and resting‐state functional MRI data at two time points separated by 2–3 years in 75 middle‐aged and older adults (age 49–80, SD = 6.91 years) which allowed us to assess the long‐term stability of the functional connectome. We show that the connectome backbone generally remains stable over a 2–3 years period in middle and older age. Independent of age, cortical volume was associated with the connectome stability of several canonical resting‐state networks, suggesting that the connectome backbone relates to structural properties of the cortex. Moreover, the individual longitudinal stability of subcortical and default mode networks was associated with individual differences in cross‐sectional and longitudinal measures of episodic memory performance, providing new evidence for the importance of these networks in maintaining mnemonic processing in middle and old age. Together, the findings encourage the use of within‐subject connectome stability analyses for understanding individual differences in brain function and cognition in aging.

Developmental Changes in the Integration of Affective and Cognitive Corticostriatal Pathways are Associated with Reward-Driven Behavior

The relative influence of affective and cognitive processes on behavior is increasingly understood to transform through development, from adolescence into adulthood, but the neuroanatomical mechanisms underlying this change are not well understood. We analyzed diffusion magnetic resonance imaging in 115 10- to 28-year-old participants to identify convergent corticostriatal projections from cortical systems involved in affect and cognitive control and determined the age-related differences in their relative structural integrity. Results indicate that the relative integrity of affective projections, in relation to projections from cognitive control systems, decreases with age and is positively associated with reward-driven task performance. Together, these findings provide new evidence that developmental differences in the integration of corticostriatal networks involved in affect and cognitive control underlie known developmental decreases in the propensity for reward-driven behavior into adulthood.

Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates cross-study harmonisation and reduces pipeline-related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data.

Development of a computerized adaptive screening tool for overall psychopathology ("p")

A substantial body of work supports the existence of a general psychopathology factor ("p"). Psychometrically, this is important because it implies that there is a psychological phenomenon (overall psychopathology) that can be measured and potentially used in clinical research or treatment. The present study aimed to construct, calibrate, and begin to validate a computerized adaptive (CAT) screener for "p". In a large community sample (N = 4544; age 11-21), we modeled 114 clinical items using a bifactor multidimensional item response theory (MIRT) model and constructed a fully functional (and public) CAT for assessing "p" called the Overall mental illness (OMI) screener. In a random, non-overlapping sample (N = 1019) with extended phenotyping (neuroimaging) from the same community cohort, adaptive versions of the OMI screener (10-, 20-, and 40-item) were simulated and compared to the full 114-item test in their ability to predict demographic characteristics, common mental disorders, and brain parameters. The OMI screener performed almost as well as the full test, despite being only a small fraction of the length. For prediction of 13 mental disorders, the mid-length (20-item) adaptive version showed mean area under the receiver operating characteristic curve of 0.76, compared to 0.79 for the full version. For prediction of brain parameters, mean absolute standardized relationship was 0.06 for the 20-item adaptive version, compared to 0.07 for the full form. This brief, public tool may facilitate the rapid and accurate measurement of overall psychopathology in large-scale studies and in clinical practice.

Burden of Environmental Adversity Associated With Psychopathology, Maturation, and Brain Behavior Parameters in Youths

IMPORTANCE

Low socioeconomic status (L-SES) and the experience of traumatic stressful events (TSEs) are environmental factors implicated in behavioral deficits, abnormalities in brain development, and accelerated maturation. However, the relative contribution of these environmental factors is understudied.

OBJECTIVE

To compare the association of L-SES and TSEs with psychopathology, puberty, neurocognition, and multimodal neuroimaging parameters in brain maturation.

DESIGN, SETTING, AND PARTICIPANTS

The Philadelphia Neurodevelopmental Cohort is a community-based study examining psychopathology, neurocognition, and neuroimaging among participants recruited through the Children's Hospital of Philadelphia pediatric network. Participants are youths aged 8 to 21 years at enrollment with stable health and fluency in English. The sample of 9498 participants was racially (5298 European ancestry [55.8%], 3124 African ancestry [32.9%], and 1076 other [11.4%]) and economically diverse. A randomly selected subsample (n = 1601) underwent multimodal neuroimaging. Data were collected from November 5, 2009, through December 30, 2011, and analyzed from February 1 through November 7, 2018.

MAIN OUTCOMES AND MEASURES

The following domains were examined: (1) clinical, including psychopathology, assessed with a structured interview based on the Schedule for Affective Disorders and Schizophrenia for School-Age Children, and puberty, assessed with the Tanner scale; (2) neurocognition, assessed by the Penn Computerized Neurocognitive Battery; and (3) multimodal magnetic resonance imaging parameters of brain structure and function.

RESULTS

A total of 9498 participants were included in the analysis (4906 [51.7%] female; mean [SD] age, 14.2 [3.7] years). Clinically, L-SES and TSEs were associated with greater severity of psychiatric symptoms across the psychopathology domains of anxiety/depression, fear, externalizing behavior, and the psychosis spectrum. Low SES showed small effect sizes (highest for externalizing behavior, 0.306 SD; 95% CI, 0.269 to 0.342), whereas TSEs had large effect sizes, with the highest in females for anxiety/depression (1.228 SD; 95% CI, 1.156 to 1.300) and in males for the psychosis spectrum (1.099 SD; 95% CI, 1.032 to 1.166). Both were associated with early puberty. Cognitively, L-SES had moderate effect sizes on poorer performance, the greatest being on complex cognition (-0.500 SD 95% CI, -0.536 to -0.464), whereas TSEs were associated with slightly better memory (0.129 SD; 95% CI, 0.084 to 0.174) and poorer complex reasoning (-0.109 SD; 95% CI, -0.154 to -0.064). Environmental factors had common and distinct associations with brain structure and function. Structurally, both were associated with lower volume, but L-SES had correspondingly lower gray matter density, whereas TSEs were associated with higher gray matter density. Functionally, both were associated with lower regional cerebral blood flow and coherence and with accelerated brain maturation.

CONCLUSIONS AND RELEVANCE

Low SES and TSEs are associated with common and unique differences in symptoms, neurocognition, and structural and functional brain parameters. Both environmental factors are associated with earlier completion of puberty by physical features and brain parameters. These findings appear to underscore the need for identifying and preventing adverse environmental conditions associated with neurodevelopment.

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.

Diffusion MRI: Assessment of the Impact of Acquisition and Preprocessing Methods Using the BrainVISA-Diffuse Toolbox

Diffusion MR images are prone to severe geometric distortions induced by head movement, eddy-current and inhomogeneity of magnetic susceptibility. Various correction methods have been proposed that depend on the choice of the acquisition settings and potentially provide highly different data quality. However, the impact of this choice has not been evaluated in terms of the ratio between scan time and preprocessed data quality. This study aims at investigating the impact of six well-known preprocessing methods, each associated to specific acquisition settings, on the outcome of diffusion analyses. For this purpose, we developed a comprehensive toolbox called Diffuse which automatically guides the user to the best preprocessing pipeline according to the input data. Using MR images of 20 subjects from the HCP dataset, we compared the six pre-processing pipelines regarding the following criteria: the ability to recover brain’s true geometry, the tensor model estimation and derived indices in the white matter, and finally the spatial dispersion of six well known connectivity pathways. As expected the pipeline associated to the longer acquisition fully repeated with reversed phase-encoding (RPE) yielded the higher data quality and was used as a reference to evaluate the other pipelines. In this way, we highlighted several significant aspects of other pre-processing pipelines. Our results first established that eddy-current correction improves the tensor-fitting performance with a localized impact especially in the corpus callosum. Concerning susceptibility distortions, we showed that the use of a field map is not sufficient and involves additional smoothing, yielding to an artificial decrease of tensor-fitting error. Of most importance, our findings demonstrate that, for an equivalent scan time, the acquisition of a b0 volume with RPE ensures a better brain’s geometry reconstruction and local improvement of tensor quality, without any smoothing of the image. This was found to be the best scan time/data quality compromise. To conclude, this study highlights and attempts to quantify the strong dependence of diffusion metrics on acquisition settings and preprocessing methods.

Altered frontostriatal white matter microstructure is associated with familial alcoholism and future binge drinking in adolescence

Adolescence is a time of significant neurobiological development, including changes in white matter microstructure. Familial alcoholism and adolescent binge-drinking have both been associated with altered white matter microstructure; however, the temporal nature of these effects, and their interaction, is unclear. Using diffusion-weighted imaging and voxel-wise multilevel modeling, the effects of familial alcoholism and future binge-drinking on white matter microstructural development were assessed in 45 adolescents, who went on to binge-drink (but were alcohol-naive at baseline), and 68 adolescents, who remained largely alcohol-naive, all with varying degrees of familial alcoholism. Both future binge-drinking and familial alcoholism were associated with altered frontostriatal white matter microstructure early in adolescence, prior to alcohol use. While several binge-drinking-related effects persisted throughout adolescence (in the posterior limb of the internal capsule, superior corona radiata, and cerebellar peduncles), the association between familial alcoholism and altered white matter microstructure dissipated across adolescence in all regions. There were no white matter regions identified where future binge-drinking or familial alcoholism were significantly associated with emergent or exacerbated alterations in white matter microstructure. Altogether, these findings suggest that alterations in frontostiatal white matter microstructure, some of which are associated with familial alcoholism, may be used to predict which adolescents are more likely to go on and engage in alcohol use. Meanwhile, a reduction in family history-related associations with altered white matter microstructure by late-adolescence is encouraging for future prevention work targeted at at-risk youth.

Test-retest reliability of Diffusion Tensor Imaging metrics in neonates

Diffusion tensor imaging (DTI) has been widely used in children and adults to study the microstructural features of the brain. Its use in neonate brains has been limited. Neonate brains are almost completely unmyelinated, and this together with the tendency for babies to move during a scanning session may affect the reliability of the measurements. Here we divided a 96 direction acquisition into three segments, and analysed the intra scan test-retest reliability for pairs of segments. Each segment was subjected to a rigorous quality control, and from the surviving data we chose 25 diffusion encoding directions from each segment, and assessed the pairwise reliability of the most common DTI metrics. This pairwise reliability was assessed for data from 86 infants. We used tract-based spatial statistics (TBSS), voxelwise and ROI analysis schemes, to see potential differential effects of analysis strategy and post processing on the obtained DTI metrics. We found that intra class correlation coefficient (ICC) values were generally high (ICC > 0.80). Residual motion in the data, after quality control, was not found to associate with the diffusion metrics. The results indicate that DTI metrics from neonate data can be reliable, even at relatively low angular resolution that are common for neonate scans. The results lend confidence to the use of neonate DTI data in cross sectional and longitudinal analyses in brain white matter skeleton. Future studies should assess the reliability of fiber tracking techniques in neonate data.

A review of diffusion MRI of typical white matter development from early childhood to young adulthood

Understanding typical, healthy brain development provides a baseline from which to detect and characterize brain anomalies associated with various neurological or psychiatric disorders and diseases. Diffusion MRI is well suited to study white matter development, as it can virtually extract individual tracts and yield parameters that may reflect alterations in the underlying neural micro-structure (e.g. myelination, axon density, fiber coherence), though it is limited by its lack of specificity and other methodological concerns. This review summarizes the last decade of diffusion imaging studies of healthy white matter development spanning childhood to early adulthood (4-35 years). Conclusions about anatomical location, rates, and timing of white matter development with age are discussed, as well as the influence of image acquisition, analysis, age range/sample size, and statistical model. Despite methodological variability between studies, some consistent findings have emerged from the literature. Specifically, diffusion studies of neurodevelopment overwhelmingly demonstrate regionally varying increases of fractional anisotropy and decreases of mean diffusivity during childhood and adolescence, some of which continue into adulthood. While most studies use linear fits to model age-related changes, studies with sufficient sample sizes and age range provide clear evidence that white matter development (as indicated by diffusion) is non-linear. Several studies further suggest that maturation in association tracts with frontal-temporal connections continues later than commissural and projection tracts. The emerging contributions of more advanced diffusion methods are also discussed, as they may reveal new aspects of white matter development. Although non-specific, diffusion changes may reflect increases of myelination, axonal packing, and/or coherence with age that may be associated with changes in cognition.

Laterality of the frontal aslant tract (FAT) explains externalizing behaviors through its association with executive function

We investigated the development of a recently identified white matter pathway, the frontal aslant tract (FAT) and its association with executive function and externalizing behaviors in a sample of 129 neurotypical male and female human children ranging in age from 7 months to 19 years. We found that the FAT could be tracked in 92% of those children, and that the pathway showed age-related differences into adulthood. The change in white matter microstructure was very rapid until about 6 years, and then plateaued, only to show age-related increases again after the age of 11 years. In a subset of those children (5-18 years; n = 70), left laterality of the microstructural properties of the FAT was associated with greater attention problems as measured by the Child Behavior Checklist (CBCL). However, this relationship was fully mediated by higher executive dysfunction as measured by the Behavior Rating Inventory of Executive Function (BRIEF). This relationship was specific to the FAT-we found no relationship between laterality of a control pathway, or of the white matter of the brain in general, and attention and executive function. These findings suggest that the degree to which the developing brain favors a right lateralized structural dominance of the FAT is directly associated with executive function and attention. This novel finding provides a new potential structural biomarker to assess attention deficit hyperactivity disorder (ADHD) and associated executive dysfunction during development.

Sex differences in network controllability as a predictor of executive function in youth

Executive function is a quintessential human capacity that emerges late in development and displays different developmental trends in males and females. Sex differences in executive function in youth have been linked to vulnerability to psychopathology as well as to behaviors that impinge on health, wellbeing, and longevity. Yet, the neurobiological basis of these differences is not well understood, in part due to the spatiotemporal complexity inherent in patterns of brain network maturation supporting executive function. Here we test the hypothesis that sex differences in impulsivity in youth stem from sex differences in the controllability of structural brain networks as they rewire over development. Combining methods from network neuroscience and network control theory, we characterize the network control properties of structural brain networks estimated from diffusion imaging data acquired in males and females in a sample of 879 youth aged 8-22 years. We summarize the control properties of these networks by estimating average and modal controllability, two statistics that probe the ease with which brain areas can drive the network towards easy versus difficult-to-reach states. We find that females have higher modal controllability in frontal, parietal, and subcortical regions while males have higher average controllability in frontal and subcortical regions. Furthermore, controllability profiles in males are negatively related to the false positive rate on a continuous performance task, a common measure of impulsivity. Finally, we find associations between average controllability and individual differences in activation during an n-back working memory task. Taken together, our findings support the notion that sex differences in the controllability of structural brain networks can partially explain sex differences in executive function. Controllability of structural brain networks also predicts features of task-relevant activation, suggesting the potential for controllability to represent context-specific constraints on network state more generally.

A fiber coherence index for quality control of B-table orientation in diffusion MRI scans

PURPOSE

The diffusion MRI "b-vector" table describing the diffusion sensitization direction can be flipped and permuted in dimension due to different orientation conventions used in scanners and incorrect or improperly utilized file formats. This can lead to incorrect fiber orientation estimates and subsequent tractography failure. Here, we present an automated quality control procedure to detect when the b-table is flipped and/or permuted incorrectly.

METHODS

We define a "fiber coherence index" to describe how well fibers are connected to each other, and use it to automatically detect the correct configuration of b-vectors. We examined the performance on 3981 research subject scans (Baltimore Longitudinal Study of Aging), 1065 normal subject scans of high image quality (Human Connectome Project), and 202 patient scans (Vanderbilt University Medical Center), as well as 9 in-vivo and 9 ex-vivo animal data.

RESULTS

The coherence index resulted in a 99.9% (3979/3981) and 100% (1065/1065) success rate in normal subject scans, 98% (198/202) in patient scans, and 100% (18/18) in both in-vivo and ex-vivo animal data in detecting the correct gradient table in datasets without severe image artifacts. The four failing cases (4/202) in patient scans, and two failures in healthy subject scans (2/3981), all showed prominent motion or signal dropout artifacts.

CONCLUSIONS

The fiber coherence measure can be used as an automatic quality assurance check in any diffusion analysis pipeline. Additionally, the success of this fiber coherence measure suggests potential broader applications, including evaluating data quality, or even providing diagnostic value as a biomarker of white matter integrity.

Head motion: the dirty little secret of neuroimaging in psychiatry

Psychiatry is at a crossroads when choosing final samples for analysis of neuroimaging data. Many patient populations exhibit significantly increased motion in the scanner compared with healthy controls, suggesting that more patients would need to be excluded to obtain a clean sample. However, this need is often overshadowed by the extensive amount of time and effort required to recruit these valuable and uncommon samples. This commentary sheds light on the impact of motion on imaging studies, drawing examples from psychiatric patient samples to better understand how head motion can confound interpretation of clinically oriented questions. We discuss the impact of even subtle motion artifacts on the interpretation of results as well as how different levels of stringency in quality control can affect findings within nearly identical samples. We also summarize recent initiatives toward harmonization of quality-control procedures as well as tools to prospectively and retrospectively correct for motion artifacts.

Longitudinal Effects of Everolimus on White Matter Diffusion in Tuberous Sclerosis Complex

OBJECTIVE

We studied the longitudinal effects of everolimus, an inhibitor of the mammalian target of rapamycin (mTOR), on callosal white matter diffusion tensor imaging (DTI) in patients with tuberous sclerosis complex (TSC).

METHODS

Serial imaging data spanning nine years were used from the open label, Phase I/II trial (NCT00411619) and open-ended extension phase of everolimus for the treatment of subependymal giant cell astrocytoma associated with TSC. From 28 patients treated with everolimus and 25 untreated control patients, 481 MRI scans were available. Rigorous quality control resulted in omission of all scans with diffusion weighted imaging data in less than 15 directions or more than eight artifacted volumes, and all postsurgical scans. We applied a linear mixed-effects model to the remaining 125 scans (17 treated, 24 controls) for longitudinal analysis of each DTI metric of manually drawn callosal regions of interest.

RESULTS

On a population level, mTOR inhibition was associated with a decrease in mean diffusivity. In addition, in treated patients only, a decrease of radial diffusivity was observed; in untreated patients only, an increase of axial diffusivity was seen. In patients below age 10, effect-sizes were consistently greater, and longer treatment was associated with greater rate of diffusion change. There was no correlation between DTI metrics and reduction of subependymal giant cell astrocytoma volume, or everolimus serum levels.

CONCLUSIONS

Effects from mTOR overactivity on white matter microstructural integrity in TSC were modified through pharmacologic inhibition of mTOR. These changes sustained over time, were greater with longer treatment and in younger patients during a time of rapid white matter maturation.

Prenatal stress and the developing brain: Risks for neurodevelopmental disorders

The prenatal period is increasingly considered as a crucial target for the primary prevention of neurodevelopmental and psychiatric disorders. Understanding their pathophysiological mechanisms remains a great challenge. Our review reveals new insights from prenatal brain development research, involving (epi)genetic research, neuroscience, recent imaging techniques, physical modeling, and computational simulation studies. Studies examining the effect of prenatal exposure to maternal distress on offspring brain development, using brain imaging techniques, reveal effects at birth and up into adulthood. Structural and functional changes are observed in several brain regions including the prefrontal, parietal, and temporal lobes, as well as the cerebellum, hippocampus, and amygdala. Furthermore, alterations are seen in functional connectivity of amygdalar-thalamus networks and in intrinsic brain networks, including default mode and attentional networks. The observed changes underlie offspring behavioral, cognitive, emotional development, and susceptibility to neurodevelopmental and psychiatric disorders. It is concluded that used brain measures have not yet been validated with regard to sensitivity, specificity, accuracy, or robustness in predicting neurodevelopmental and psychiatric disorders. Therefore, more prospective long-term longitudinal follow-up studies starting early in pregnancy should be carried out, in order to examine brain developmental measures as mediators in mediating the link between prenatal stress and offspring behavioral, cognitive, and emotional problems and susceptibility for disorders.

A computational network control theory analysis of depression symptoms

Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction

Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts.

•

Two tools to automatically perform QC of diffusion MRI data.

•

Automated generation of single subject reports for visual inspection and database.

•

Group databases and reports allow to compare subjects within and between studies.

•

Categorical and continuous variables can be used to update the reports.

White matter aberrations and age-related trajectories in patients with schizophrenia and bipolar disorder revealed by diffusion tensor imaging

Supported by histological and genetic evidence implicating myelin, neuroinflammation and oligodendrocyte dysfunction in schizophrenia spectrum disorders (SZ), diffusion tensor imaging (DTI) studies have consistently shown white matter (WM) abnormalities when compared to healthy controls (HC). The diagnostic specificity remains unclear, with bipolar disorders (BD) frequently conceptualized as a less severe clinical manifestation along a psychotic spectrum. Further, the age-related dynamics and possible sex differences of WM abnormalities in SZ and BD are currently understudied. Using tract-based spatial statistics (TBSS) we compared DTI-based microstructural indices between SZ (n = 128), BD (n = 61), and HC (n = 293). We tested for age-by-group and sex-by-group interactions, computed effect sizes within different age-bins and within genders. TBSS revealed global reductions in fractional anisotropy (FA) and increases in radial (RD) diffusivity in SZ compared to HC, with strongest effects in the body and splenium of the corpus callosum, and lower FA in SZ compared to BD in right inferior longitudinal fasciculus and right inferior fronto-occipital fasciculus, and no significant differences between BD and HC. The results were not strongly dependent on age or sex. Despite lack of significant group-by-age interactions, a sliding-window approach supported widespread WM involvement in SZ with most profound differences in FA from the late 20 s.

Understanding the Emergence of Neuropsychiatric Disorders With Network Neuroscience

Major neuropsychiatric disorders such as psychosis are increasingly acknowledged to be disorders of brain connectivity. Yet tools to map, model, predict, and change connectivity are difficult to develop, largely because of the complex, dynamic, and multivariate nature of interactions between brain regions. Network neuroscience (NN) provides a theoretical framework and mathematical toolset to address these difficulties. Building on areas of mathematics such as graph theory, NN in its simplest form summarizes neuroimaging data by treating brain regions as nodes in a graph and by treating interactions or connections between nodes as edges in the graph. Network metrics can then be used to quantitatively describe the architecture of the graph, which in turn reflects the network's function. We review evidence supporting the utility of NN in understanding psychiatric disorders, with a focus on normative brain network development and abnormalities associated with psychosis. We also emphasize relevant methodological challenges, such as motion artifact correction, which are particularly important to consider when applying network tools to developmental neuroimaging data. We close with a discussion of several emerging frontiers of NN in psychiatry, including generative network modeling and network control theory. We aim to offer an accessible introduction to this emerging field and motivate further work that uses NN to better understand the normative development of brain networks and alterations in that development that accompany or foreshadow psychiatric disease.

Weighted Stochastic Block Models of the Human Connectome across the Life Span

The human brain can be described as a complex network of anatomical connections between distinct areas, referred to as the human connectome. Fundamental characteristics of connectome organization can be revealed using the tools of network science and graph theory. Of particular interest is the network's community structure, commonly identified by modularity maximization, where communities are conceptualized as densely intra-connected and sparsely inter-connected. Here we adopt a generative modeling approach called weighted stochastic block models (WSBM) that can describe a wider range of community structure topologies by explicitly considering patterned interactions between communities. We apply this method to the study of changes in the human connectome that occur across the life span (between 6-85 years old). We find that WSBM communities exhibit greater hemispheric symmetry and are spatially less compact than those derived from modularity maximization. We identify several network blocks that exhibit significant linear and non-linear changes across age, with the most significant changes involving subregions of prefrontal cortex. Overall, we show that the WSBM generative modeling approach can be an effective tool for describing types of community structure in brain networks that go beyond modularity.

Resilience to Risk for Psychopathology: The Role of White Matter Microstructural Development in Adolescence

BACKGROUND

One major risk factor for the development of psychopathology is a family history of psychopathology (FHP). Cross-sectional studies have shown that FHP is associated with alterations in white matter microstructure in adolescents without current psychopathology; however, whether these associations persist throughout adolescence, particularly in those who remain resilient to developing psychopathology, is unclear.

METHODS

Sixty-six adolescents underwent diffusion-weighted imaging at baseline (12-16 years of age) and at one or two follow-up visits (142 total scans). Adolescents' parents completed a modified Family History Assessment Module to calculate FHP density (FHPD) based on familial alcohol use, substance use, and major depressive, generalized anxiety, substance-induced mood, and antisocial personality disorders. The relationship between FHPD and white matter microstructural development was examined using multilevel modeling.

RESULTS

FHPD was associated with significant alterations in white matter microstructure at baseline; in the bilateral superior corona radiata and left superior longitudinal fasciculus, these effects were transient (FHPD was associated with altered white matter microstructure only in early adolescence), while effects in the posterior limb of the internal capsule were persistent. Associations between FHPD and white matter microstructure in the body of the corpus callosum emerged later in adolescence.

CONCLUSIONS

This prospective, longitudinal study provides novel information indicating that the association between FHP and white matter microstructure previously observed in adolescents is transient in most regions but may persist into late adolescence in other regions, despite current resilience to developing psychopathology. Future studies are necessary to determine if these persistent alterations are associated with onset of psychopathology later in life.

The impact of in-scanner head motion on structural connectivity derived from diffusion MRI

Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion magnetic resonance imaging (dMRI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency- and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for network edges with high inter-subject consistency, which included both short- and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.

Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression

BACKGROUND

Depression is a complex disorder with large interindividual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains, such as anxiety. A dimensional and symptom-based approach may help refine the characterization of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We use resting-state functional magnetic resonance imaging to assess the brain functional connectivity correlates of a symptom-based clustering of individuals.

METHODS

We assessed symptoms using the Beck Depression and Beck Anxiety Inventories in individuals with or without a history of depression (N = 1084) and high-dimensional data clustering to form subgroups based on symptom profiles. We compared dynamic and static functional connectivity between subgroups in a subset of the total sample (n = 252).

RESULTS

We identified five subgroups with distinct symptom profiles, which cut across diagnostic boundaries with different total severity, symptom patterns, and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroups 1, 2, and 3, respectively. The distribution of individuals was 32%, 25%, 22%, 10%, and 11% in subgroups 1 to 5, respectively. These subgroups showed evidence of differential static brain-connectivity patterns, in particular comprising a frontotemporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity, or global connectivity.

CONCLUSIONS

Adding to the pursuit of individual-based treatment, subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct patterns of static functional connectivity in the brain.

Amygdala Functional and Structural Connectivity Predicts Individual Risk Tolerance

Risk tolerance, the degree to which an individual is willing to tolerate risk in order to achieve a greater expected return, influences a variety of financial choices and health behaviors. Here we identify intrinsic neural markers for risk tolerance in a large (n = 108) multimodal imaging dataset of healthy young adults, which includes anatomical and resting-state functional MRI and diffusion tensor imaging. Using a data-driven approach, we found that higher risk tolerance was most strongly associated with greater global functional connectivity (node strength) of and greater gray matter volume in bilateral amygdala. Further, risk tolerance was positively associated with functional connectivity between amygdala and medial prefrontal cortex and negatively associated with structural connectivity between these regions. These findings show how the intrinsic functional and structural architecture of the amygdala, and amygdala-medial prefrontal pathways, which have previously been implicated in anxiety, are linked to individual differences in risk tolerance during economic decision making.

Functional network integration and attention skills in young children

Children acquire attention skills rapidly during early childhood as their brains undergo vast neural development. Attention is well studied in the adult brain, yet due to the challenges associated with scanning young children, investigations in early childhood are sparse. Here, we examined the relationship between age, attention and functional connectivity (FC) during passive viewing in multiple intrinsic connectivity networks (ICNs) in 60 typically developing girls between 4 and 7 years whose sustained, selective and executive attention skills were assessed. Visual, auditory, sensorimotor, default mode (DMN), dorsal attention (DAN), ventral attention (VAN), salience, and frontoparietal ICNs were identified via Independent Component Analysis and subjected to a dual regression. Individual spatial maps were regressed against age and attention skills, controlling for age. All ICNs except the VAN showed regions of increasing FC with age. Attention skills were associated with FC in distinct networks after controlling for age: selective attention positively related to FC in the DAN; sustained attention positively related to FC in visual and auditory ICNs; and executive attention positively related to FC in the DMN and visual ICN. These findings suggest distributed network integration across this age range and highlight how multiple ICNs contribute to attention skills in early childhood.

High-resolution whole-brain diffusion MRI at 7T using radiofrequency parallel transmission

PURPOSE

Investigating the utility of RF parallel transmission (pTx) for Human Connectome Project (HCP)-style whole-brain diffusion MRI (dMRI) data at 7 Tesla (7T).

METHODS

Healthy subjects were scanned in pTx and single-transmit (1Tx) modes. Multiband (MB), single-spoke pTx pulses were designed to image sagittal slices. HCP-style dMRI data (i.e., 1.05-mm resolutions, MB2, b-values = 1000/2000 s/mm² , 286 images and 40-min scan) and data with higher accelerations (MB3 and MB4) were acquired with pTx.

RESULTS

pTx significantly improved flip-angle detected signal uniformity across the brain, yielding ∼19% increase in temporal SNR (tSNR) averaged over the brain relative to 1Tx. This allowed significantly enhanced estimation of multiple fiber orientations (with ∼21% decrease in dispersion) in HCP-style 7T dMRI datasets. Additionally, pTx pulses achieved substantially lower power deposition, permitting higher accelerations, enabling collection of the same data in 2/3 and 1/2 the scan time or of more data in the same scan time.

CONCLUSION

pTx provides a solution to two major limitations for slice-accelerated high-resolution whole-brain dMRI at 7T; it improves flip-angle uniformity, and enables higher slice acceleration relative to current state-of-the-art. As such, pTx provides significant advantages for rapid acquisition of high-quality, high-resolution truly whole-brain dMRI data.

Association of Heritable Cognitive Ability and Psychopathology With White Matter Properties in Children and Adolescents

IMPORTANCE

Many mental disorders emerge during adolescence, which may reflect a cost of the potential for brain plasticity offered during this period. Brain dysconnectivity has been proposed as a common factor across diagnostic categories.

OBJECTIVE

To investigate the hypothesis that brain dysconnectivity is a transdiagnostic phenotype in adolescence with increased susceptibility and symptoms of psychiatric disease.

DESIGN, SETTING, AND PARTICIPANTS

We investigated clinical symptoms as well as cognitive function in 6487 individuals aged 8 to 21 years from November 1, 2009, to November 30, 2011, in the Philadelphia Neurodevelopmental Cohort and analyzed diffusion magnetic resonance imaging brain scans for 748 of the participants.

MAIN OUTCOMES AND MEASURES

Independent component analysis was used to derive dimensional psychopathology scores, and genome-wide complex trait analysis was used to estimate its heritability. Multimodal fusion simultaneously modeled contributions of the diffusion magnetic resonance imaging metrics fractional anisotropy, mean diffusivity, radial diffusivity, L1 (the principal diffusion tensor imaging eigen value), mode of anisotropy, as well as dominant and secondary fiber orientations, and structural connectivity density, and their association with general psychopathology and cognition.

RESULTS

Machine learning with 10-fold cross-validation and permutation testing in 729 individuals (aged 8 to 22 years; mean [SD] age, 15.1 [3.3] years; 343 females [46%]) revealed significant association with general psychopathology levels (r = 0.24, P < .001) and cognition (r = 0.39, P < .001). A brain white matter pattern reflecting frontotemporal connectivity and crossing fibers in the uncinate fasciculus was the most associated feature for both traits. Univariate analysis across a range of clinical domains and cognitive test scores confirmed its transdiagnostic importance. Both the general psychopathology (16%; SE, 0.095; P = .05) and cognitive (18%; SE, 0.09; P = .01) factor were heritable and showed a negative genetic correlation.

CONCLUSION AND RELEVANCE

Dimensional and heritable general cognitive and psychopathology factors are associated with specific patterns of white matter properties, suggesting that dysconnectivity is a transdiagnostic brain-based phenotype in individuals with increased susceptibility and symptoms of psychiatric disorders.

Socioeconomic disparities in academic achievement: A multi-modal investigation of neural mechanisms in children and adolescents

Growing evidence suggests that childhood socioeconomic status (SES) influences neural development, which may contribute to the well-documented SES-related disparities in academic achievement. However, the particular aspects of SES that impact neural structure and function are not well understood. Here, we investigate associations of childhood SES and a potential mechanism-degree of cognitive stimulation in the home environment-with cortical structure, white matter microstructure, and neural function during a working memory (WM) task across development. Analyses included 53 youths (age 6-19 years). Higher SES as reflected in the income-to-needs ratio was associated with higher parent-reported achievement, WM performance, and cognitive stimulation in the home environment. Although SES was not significantly associated with cortical thickness, children raised in more cognitively stimulating environments had thicker cortex in the frontoparietal network and cognitive stimulation mediated the assocation between SES and cortical thickness in the frontoparietal network. Higher family SES was associated with white matter microstructure and neural activation in the frontoparietal network during a WM task, including greater fractional anisotropy (FA) in the right and left superior longitudinal fasciculi (SLF), and greater BOLD activation in multiple regions of the prefrontal cortex during WM encoding and maintenance. Greater FA and activation in these regions was associated higher parent-reported achievement. Together, cognitive stimulation, WM performance, FA in the SLF, and prefrontal activation during WM encoding and maintenance significantly mediated the association between SES and parent-reported achievement. These findings highlight potential neural, cognitive, and environmental mechanisms linking SES with academic achievement and suggest that enhancing cognitive stimulation in the home environment might be one effective strategy for reducing SES-related disparities in academic outcomes.

Connectivity-enhanced diffusion analysis reveals white matter density disruptions in first episode and chronic schizophrenia

Reduced fractional anisotropy (FA) is a well-established correlate of schizophrenia, but it remains unclear whether these tensor-based differences are the result of axon damage and/or organizational changes and whether the changes are progressive in the adult course of illness. Diffusion MRI data were collected in 81 schizophrenia patients (54 first episode and 27 chronic) and 64 controls. Analysis of FA was combined with “fixel-based” analysis, the latter of which leverages connectivity and crossing-fiber information to assess both fiber bundle density and organizational complexity (i.e., presence and magnitude of off-axis diffusion signal). Compared with controls, patients with schizophrenia displayed clusters of significantly lower FA in the bilateral frontal lobes, right dorsal centrum semiovale, and the left anterior limb of the internal capsule. All FA-based group differences overlapped substantially with regions containing complex fiber architecture. FA within these clusters was positively correlated with principal axis fiber density, but inversely correlated with both secondary/tertiary axis fiber density and voxel-wise fiber complexity. Crossing fiber complexity had the strongest (inverse) association with FA (r = −0.82). When crossing fiber structure was modeled in the MRtrix fixel-based analysis pipeline, patients exhibited significantly lower fiber density compared to controls in the dorsal and posterior corpus callosum (central, postcentral, and forceps major). Findings of lower FA in patients with schizophrenia likely reflect two inversely related signals: reduced density of principal axis fiber tracts and increased off-axis diffusion sources. Whereas the former confirms at least some regions where myelin and or/axon count are lower in schizophrenia, the latter indicates that the FA signal from principal axis fiber coherence is broadly contaminated by macrostructural complexity, and therefore does not necessarily reflect microstructural group differences. These results underline the need to move beyond tensor-based models in favor of acquisition and analysis techniques that can help disambiguate different sources of white matter disruptions associated with schizophrenia.

•

MRtrix3's fixel-based analysis pipeline performs crossing-fiber modeling and anatomically precise whole-brain white matter registration

•

Axon bundle density and organizational complexity appear to underlie white matter alterations in first episode and chronic schizophrenia

•

Results underline the need to incorporate techniques that can help disambiguate sources of white matter alterations

Brain structural connectivity during adrenarche: Associations between hormone levels and white matter microstructure

Levels of the adrenal hormones dehydroepiandrosterone (DHEA), its sulfate (DHEAS), and testosterone, have all been linked to behavior and mental health during adrenarche, and preclinical studies suggest that these hormones influence brain development. However, little is known about how variation in these hormones is associated with white matter structure during this period of life. The current study aimed to examine associations between DHEA, DHEAS, and testosterone, and white matter microstructure during adrenarche. To avoid the confounding effect of age on hormone levels, we tested these associations in 87 children within a narrow age range (mean age 9.56 years, SD=0.34) but varying in hormone levels. All children provided saliva samples directly after waking and completed a diffusion-weighted MRI scan. Higher levels of DHEA were associated with higher mean diffusivity (MD) in a widespread cluster of white matter tracts, which was partially explained by higher radial diffusivity (RD) and partially by higher axial diffusivity (AD). In addition, there was an interaction between DHEA and testosterone, with higher levels of testosterone being associated with higher fractional anisotropy (FA) and lower MD and RD when DHEA levels were relatively high, but with lower FA and higher MD and RD when DHEA levels were low. These findings suggest that relatively early exposure to DHEA, as well as an imbalance between the adrenal hormones, may be associated with alterations in white matter microstructure. These findings highlight the potential relevance of adrenarcheal hormones for structural brain development.

White matter maturation during 12 months in individuals at ultra-high-risk for psychosis

OBJECTIVE

The neurodevelopmental hypothesis of psychosis suggests that disrupted white matter (WM) maturation underlies disease onset. In this longitudinal study, we investigated WM connectivity and compared WM changes between individuals at ultra-high-risk for psychosis (UHR) and healthy controls (HCs).

METHOD

Thirty UHR individuals and 23 HCs underwent MR diffusion tensor imaging before and after 12 months of non-manualized standard care. Positive and negative symptoms and level of functioning were assessed. Tract-based spatial statistics were employed.

RESULTS

During 12 months, none of the UHR individuals transitioned to psychosis. Both UHR individuals and HCs increased significantly in fractional anisotropy (FA). UHR individuals showed significant FA increases predominantly in the left superior longitudinal fasciculus (SLF) (P = 0.01), and HCs showed significant FA increases in the left uncinate fasciculus (P = 0.03). Within UHR individuals, a significant positive correlation between FA change and age was observed predominantly in the left SLF (P = 0.02). Within HCs, no significant correlation between FA change and age was observed. No significant correlations between baseline FA and clinical outcomes were observed; however, FA changes were significantly positively correlated to changes in negative symptoms (P = 0.04).

CONCLUSION

As normal brain maturation occurs in a posterior to frontal direction, our findings could suggest disturbed WM maturation in UHR individuals.

Quantitative assessment of structural image quality

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.

Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress

Diffusion magnetic resonance imaging (dMRI) continues to grow in popularity as a useful neuroimaging method to study brain development, and longitudinal studies that track the same individuals over time are emerging. Over the last decade, seminal work using dMRI has provided new insights into the development of brain white matter (WM) microstructure, connections and networks throughout childhood and adolescence. This review provides an introduction to dMRI, both diffusion tensor imaging (DTI) and other dMRI models, as well as common acquisition and analysis approaches. We highlight the difficulties associated with ascribing these imaging measurements and their changes over time to specific underlying cellular and molecular events. We also discuss selected methodological challenges that are of particular relevance for studies of development, including critical choices related to image acquisition, image analysis, quality control assessment, and the within-subject and longitudinal reliability of dMRI measurements. Next, we review the exciting progress in the characterization and understanding of brain development that has resulted from dMRI studies in childhood and adolescence, including brief overviews and discussions of studies focusing on sex and individual differences. Finally, we outline future directions that will be beneficial to the field.

Family Income, Cumulative Risk Exposure, and White Matter Structure in Middle Childhood

Family income is associated with gray matter morphometry in children, but little is known about the relationship between family income and white matter structure. In this paper, using Tract-Based Spatial Statistics, a whole brain, voxel-wise approach, we examined the relationship between family income (assessed by income-to-needs ratio) and white matter organization in middle childhood (N = 27, M = 8.66 years). Results from a non-parametric, voxel-wise, multiple regression (threshold-free cluster enhancement, p < 0.05 FWE corrected) indicated that lower family income was associated with lower white matter organization [assessed by fractional anisotropy (FA)] for several clusters in white matter tracts involved in cognitive and emotional functions including fronto-limbic circuitry (uncinate fasciculus and cingulum bundle), association fibers (inferior longitudinal fasciculus, superior longitudinal fasciculus), and corticospinal tracts. Further, we examined the possibility that cumulative risk (CR) exposure might function as one of the potential pathways by which family income influences neural outcomes. Using multiple regressions, we found lower FA in portions of these tracts, including those found in the left cingulum bundle and left superior longitudinal fasciculus, was significantly related to greater exposure to CR (β = -0.47, p < 0.05 and β = -0.45, p < 0.05).

Patterns of white matter microstructure in individuals at ultra-high-risk for psychosis: associations to level of functioning and clinical symptoms

BACKGROUND

Individuals at ultra-high-risk (UHR) for psychosis present with emerging symptoms and decline in functioning. Previous univariate analyses have indicated widespread white matter (WM) aberrations in multiple brain regions in UHR individuals and patients with schizophrenia. Using multivariate statistics, we investigated whole brain WM microstructure and associations between WM, clinical symptoms, and level of functioning in UHR individuals.

METHODS

Forty-five UHR individuals and 45 matched healthy controls (HCs) underwent magnetic resonance diffusion tensor imaging (DTI) at 3 Tesla. UHR individuals were assessed with the Comprehensive Assessment of At-Risk Mental States, Scale for the Assessment of Negative Symptoms, and Social and Occupational Functioning Assessment Scale. Partial least-squares correlation analysis (PLSC) was used as statistical method.

RESULTS

PLSC group comparisons revealed one significant latent variable (LV) accounting for 52% of the cross-block covariance. This LV indicated a pattern of lower fractional anisotropy (FA), axial diffusivity (AD), and mode of anisotropy (MO) concomitant with higher radial diffusivity (RD) in widespread brain regions in UHR individuals compared with HCs. Within UHR individuals, PLSC revealed five significant LVs associated with symptoms and level of functioning. The first LV accounted for 31% of the cross-block covariance and indicated a pattern where higher symptom score and lower level of functioning correlated to lower FA, AD, MO, and higher RD.

CONCLUSIONS

UHR individuals demonstrate complex brain patterns of WM abnormalities. Despite the subtle psychopathology of UHR individuals, aberrations in WM appear associated with positive and negative symptoms as well as level of functioning.

Developmental increases in white matter network controllability support a growing diversity of brain dynamics

As the human brain develops, it increasingly supports coordinated control of neural activity. The mechanism by which white matter evolves to support this coordination is not well understood. Here we use a network representation of diffusion imaging data from 882 youth ages 8-22 to show that white matter connectivity becomes increasingly optimized for a diverse range of predicted dynamics in development. Notably, stable controllers in subcortical areas are negatively related to cognitive performance. Investigating structural mechanisms supporting these changes, we simulate network evolution with a set of growth rules. We find that all brain networks are structured in a manner highly optimized for network control, with distinct control mechanisms predicted in child vs. older youth. We demonstrate that our results cannot be explained by changes in network modularity. This work reveals a possible mechanism of human brain development that preferentially optimizes dynamic network control over static network architecture.

Functional brain imaging in neuropsychology over the past 25 years

OBJECTIVE

Outline effects of functional neuroimaging on neuropsychology over the past 25 years.

METHOD

Functional neuroimaging methods and studies will be described that provide a historical context, offer examples of the utility of neuroimaging in specific domains, and discuss the limitations and future directions of neuroimaging in neuropsychology.

RESULTS

Tracking the history of publications on functional neuroimaging related to neuropsychology indicates early involvement of neuropsychologists in the development of these methodologies. Initial progress in neuropsychological application of functional neuroimaging has been hampered by costs and the exposure to ionizing radiation. With rapid evolution of functional methods-in particular functional MRI (fMRI)-neuroimaging has profoundly transformed our knowledge of the brain. Its current applications span the spectrum of normative development to clinical applications. The field is moving toward applying sophisticated statistical approaches that will help elucidate distinct neural activation networks associated with specific behavioral domains. The impact of functional neuroimaging on clinical neuropsychology is more circumscribed, but the prospects remain enticing.

CONCLUSIONS

The theoretical insights and empirical findings of functional neuroimaging have been led by many neuropsychologists and have transformed the field of behavioral neuroscience. Thus far they have had limited effects on the clinical practices of neuropsychologists. Perhaps it is time to add training in functional neuroimaging to the clinical neuropsychologist's toolkit and from there to the clinic or bedside. (PsycINFO Database Record

White matter microstructural deficits in 22q11.2 deletion syndrome

Diffusion tensor imaging (DTI) studies in 22q11.2 deletion syndrome (22q11DS), a neurogenetic condition associated with psychosis, report brain white matter (WM) microstructure aberrations. Several studies report that WM disruptions in 22q11DS are similar to deficits in idiopathic schizophrenia. Yet, DTI results in 22q11DS are inconsistent. We used DTI to compare WM structure in 22q11DS individuals to healthy controls (HC) and explored WM differences in 22q11DS with (+) and without (-) psychosis spectrum symptoms. We examined 39 22q11DS individuals and 39 age, sex and race equivalent HC. DTI was performed at 3T using a 64-direction protocol. Fractional anisotropy (FA) was lower, while radial diffusivity was higher in 22q11DS within the cingulum bundle. Mean diffusivity was lower in the inferior longitudinal fasciculus, while axial diffusivity (AD) was lower in the cingulum bundle, forceps major, and several posterior to anterior fasciculi. 22q11DS+ had lower FA in the cingulum bundle and lower AD in the uncinate fasciculus compared to 22q11DS-. Overall, we found aberrant WM microstructure in individuals with 22q11DS compared to age and sex matched HC and exploratory analysis indicated subtle WM deficits associated with psychosis. The findings highlight the dysfunction of WM microstructure in 22q11DS and its potential importance in elucidating WM abnormalities in psychosis.

White matter microstructure is associated with functional, cognitive and emotional symptoms 12 months after mild traumatic brain injury

Identifying patients at risk of poor outcome after mild traumatic brain injury (MTBI) is essential to aid prognostics and treatment. Diffuse axonal injury (DAI) may be the primary pathologic feature of MTBI but is normally not detectable by conventional imaging technology. This lack of sensitivity of clinical imaging techniques has impeded a pathophysiologic understanding of the long-term cognitive and emotional consequences of MTBI, which often remain unnoticed and are attributed to factors other than the injury. Diffusion tensor imaging (DTI) is sensitive to microstructural properties of brain tissue and has been suggested to be a promising candidate for the detection of DAI in vivo. In this study, we report strong associations between brain white matter DTI and self-reported cognitive, somatic and emotional symptoms at 12 months post-injury in 134 MTBI patients. The anatomical distribution suggested global associations, in line with the diffuse symptomatology, although the strongest effects were found in frontal regions including the genu of the corpus callosum and the forceps minor. These findings support the hypothesis that DTI may provide increased sensitivity to the diffuse pathophysiology of MTBI and suggest an important role of advanced Magnetic Resonance Imaging (MRI) in trauma care.

Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature

The development of human cognition results from the emergence of coordinated activity between distant brain areas. Network science, combined with non-invasive functional imaging, has generated unprecedented insights regarding the adult brain's functional organization, and promises to help elucidate the development of functional architectures supporting complex behavior. Here we review what is known about functional network development from birth until adulthood, particularly as understood through the use of resting-state functional connectivity MRI (rs-fcMRI). We attempt to synthesize rs-fcMRI findings with other functional imaging techniques, with macro-scale structural connectivity, and with knowledge regarding the development of micro-scale structure. We highlight a number of outstanding conceptual and technical barriers that need to be addressed, as well as previous developmental findings that may need to be revisited. Finally, we discuss key areas ripe for future research in order to (1) better characterize normative developmental trajectories, (2) link these trajectories to biologic mechanistic events, as well as component behaviors and (3) better understand the clinical implications and pathophysiological basis of aberrant network development.

Glutamate imaging (GluCEST) reveals lower brain GluCEST contrast in patients on the psychosis spectrum

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 3 and 7 T. At 7 T, 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 with 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.

Sex differences in associations between white matter microstructure and gonadal hormones in children and adolescents with prenatal alcohol exposure

Despite accumulating evidence from animal models demonstrating that prenatal alcohol exposure (PAE) results in life-long neuroendocrine dysregulation, very little is known on this topic among humans with fetal alcohol spectrum disorders (FASD). We expected that alterations in gonadal hormones might interfere with the typical development of white matter (WM) myelination, and in a sex-dependent manner, in human adolescents with FASD. In order to investigate this hypothesis, we used diffusion tensor imaging (DTI) to assess: 1) whether or not sex moderates the impact of PAE on WM microstructure; and 2) how gonadal hormones relate to alterations in WM microstructure in children and adolescents affected by PAE.

METHODS

61 youth (9 to 16 yrs.; 49% girls; 50% PAE) participated as part of the Collaborative Initiative on Fetal Alcohol Spectrum Disorders (CIFASD). DTI scans and passive drool samples were obtained to examine neurodevelopmental associations with testosterone (T) and dehydroepiandrosterone (DHEA) levels in boys and girls, and estradiol (E2) and progesterone (P) levels in girls. Tract-based spatial statistics were utilized to generate fractional anisotropy (FA) and mean diffusivity (MD) for 9 a priori WM regions of interest (ROIs).

RESULTS

As predicted, alterations in FA were observed in adolescents with PAE relative to controls, and these differences varied by sex. Girls with PAE exhibited lower FA (Inferior fronto-occipital and Uncinate fasciculi) while boys with PAE exhibited higher FA (Callosal body, Cingulum, Corticospinal tract, Optic radiation, Superior longitudinal fasciculus) relative to age-matched controls. When gonadal hormone levels were examined in relation to DTI measures, additional group differences in FA were revealed, demonstrating that neuroendocrine factors are associated with PAE-related brain alterations.

CONCLUSIONS

These findings provide human evidence that PAE relates to sex-specific differences in WM microstructure, and underlying alterations in gonadal hormone function may, in part, contribute to these effects. Determining PAE-effects on neuroendocrine function among humans is an essential first step towards developing novel clinical (e.g., assessment or intervention) tools that target hormone systems to improve on-going brain development among children and adolescents with FASD.

Consistent Functional Connectivity Alterations in Schizophrenia Spectrum Disorder: A Multisite Study

Schizophrenia (SZ) is a severe mental illness with high heritability and complex etiology. Mounting evidence from neuroimaging has implicated disrupted brain network connectivity in the pathophysiology. However, previous findings are inconsistent, likely due to a combination of methodological and clinical variability and relatively small sample sizes. Few studies have used a data-driven approach for characterizing pathological interactions between regions in the whole brain and evaluated the generalizability across independent samples. To overcome this issue, we collected resting-state functional magnetic resonance imaging data from 3 independent samples (1 from Norway and 2 from Sweden) consisting of 182 persons with a SZ spectrum diagnosis and 348 healthy controls. We used a whole-brain data-driven definition of network nodes and regularized partial correlations to evaluate and compare putatively direct brain network node interactions between groups. The clinical utility of the functional connectivity features and the generalizability of effects across samples were evaluated by training and testing multivariate classifiers in the independent samples using machine learning. Univariate analyses revealed 14 network edges with consistent reductions in functional connectivity encompassing frontal, somatomotor, visual, auditory, and subcortical brain nodes in patients with SZ. We found a high overall accuracy in classifying patients and controls (up to 80%) using independent training and test samples, strongly supporting the generalizability of connectivity alterations across different scanners and heterogeneous samples. Overall, our findings demonstrate robust reductions in functional connectivity in SZ spectrum disorders, indicating disrupted information flow in sensory, subcortical, and frontal brain regions.

Diffusion-weighted tractography in the common marmoset monkey at 9.4T

The common marmoset (Callithrix jacchus) is a small New World primate that is becoming increasingly popular in the neurosciences as an animal model of preclinical human disease. With several major disorders characterized by alterations in neural white matter (e.g., multiple sclerosis, Alzheimer's disease, schizophrenia), proposed to be transgenically modeled using marmosets, the ability to isolate and characterize reliably major white matter fiber tracts with MRI will be of use for evaluating structural brain changes related to disease processes and symptomatology. Here, we propose protocols for isolating major white matter fiber tracts in the common marmoset using in vivo ultrahigh-field MRI (9.4T) diffusion-weighted imaging (DWI) data. With the use of a high angular-resolution DWI (256 diffusion-encoding directions) sequence, collected on four anesthetized marmosets, we provide guidelines for manually drawing fiber-tracking regions of interest, based on easily identified anatomical landmarks in DWI native space. These fiber-tract isolation protocols are expected to be experimentally useful for visualization and quantification of individual white matter fiber tracts in both control and experimental groups of marmosets (e.g., transgenic models). As disease models in the marmoset advance, the determination of how macroscopic white matter anatomy is altered as a function of disease state will be relevant in bridging the existing translational gap between preclinical rodent models and human patients.NEW & NOTEWORTHY Although significant progress has been made in mapping white matter connections in the marmoset brain using ex vivo tracing techniques, the application of in vivo virtual dissection of major white matter fiber tracts has been established by few studies in the marmoset literature. Here, we demonstrate the feasibility of whole-brain diffusion-weighted tractography in anesthetized marmosets at ultrahigh-field MRI (9.4T) and propose protocols for isolating nine major white matter fiber tracts in the marmoset brain.

Modular Segregation of Structural Brain Networks Supports the Development of Executive Function in Youth

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.

Exploring the role of white matter connectivity in cortex maturation

The maturation of the cortical gray matter (GM) and white matter (WM) are described as sequential processes following multiple, but distinct rules. However, neither the mechanisms driving brain maturation processes, nor the relationship between GM and WM maturation are well understood. Here we use connectomics and two MRI measures reflecting maturation related changes in cerebral microstructure, namely the Apparent Diffusion Coefficient (ADC) and the T1 relaxation time (T1), to study brain development. We report that the advancement of GM and WM maturation are inter-related and depend on the underlying brain connectivity architecture. Particularly, GM regions and their incident WM connections show corresponding maturation levels, which is also observed for GM regions connected through a WM tract. Based on these observations, we propose a simple computational model supporting a key role for the connectome in propagating maturation signals sequentially from external stimuli, through primary sensory structures to higher order functional cortices.