Imaging the "At-Risk" Brain: Future Directions

A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories substudy (N = 369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.

Effects of the SNAP25 on Integration Ability of Brain Functions in Children With ADHD

OBJECTIVE

The present study aimed to examine the effects of SNAP25 on the integration ability of intrinsic brain functions in children with ADHD, and whether the integration ability was associated with working memory (WM).

METHODS

A sliding time window method was used to calculate the spatial and temporal concordance among five rs-fMRI regional indices in 55 children with ADHD and 20 healthy controls.

RESULTS

The SNAP25 exhibited significant interaction effects with ADHD diagnosis on the voxel-wise concordance in the right posterior central gyrus, fusiform gyrus and lingual gyrus. Specifically, for children with ADHD, G-carriers showed increased voxel-wise concordance in comparison to TT homozygotes in the right precentral gyrus, superior frontal gyrus, postcentral gyrus, and middle frontal gyrus. The voxel-wise concordance was also found to be related to WM.

CONCLUSION

Our findings provided a new insight into the neural mechanisms of the brain function of ADHD children.

Effects of the DRD4 -521 C/T SNP on Local Neural Activity and Functional Connectivity in Children With ADHD

Objective: The present study aimed to investigate the effects of the dopamine receptor D4 (DRD4) -521 C/T single-nucleotide polymorphism on brain function among children with attention deficit hyperactivity disorder (ADHD) and to evaluate whether brain function is associated with behavioral performance among this demographic. Methods: Using regional homogeneity, fractional amplitude low-frequency fluctuation, and functional connectivity as measurement indices, we compared differences in resting-state brain function between 34 boys with ADHD in the TT homozygous group and 37 boys with ADHD in the C-allele carrier group. The Conners' Parent Rating Scale, the SNAP-IV Rating Scale, the Stroop Color Word Test, the go/no-go task, the n-back task, and the working memory index within the Wechsler Intelligence Scale for Children-Fourth Edition were selected as comparative indicators in order to test effects on behavioral performance. Results: We found that TT homozygotes had low behavioral performance as compared with C-allele carriers. The regional homogeneity for TT homozygotes decreased in the right middle occipital gyrus and increased in the right superior frontal gyrus as compared with C-allele carriers. In addition, the right middle occipital gyrus and the right superior frontal gyrus were used as the seeds of functional connectivity, and we found that the functional connectivity between the right middle occipital gyrus and the right cerebellum decreased, as did the functional connectivity between the right superior frontal gyrus and the angular gyrus. No statistically significant differences were observed in the respective brain regions when comparing the fractional amplitudes for low-frequency fluctuation between the two groups. Correlation analyses demonstrated that the fractional amplitude low-frequency fluctuation in the precentral gyrus for TT homozygotes were statistically significantly correlated with working memory. Conclusions: We found differing effects of DRD4 -521 C/T polymorphisms on brain function among boys with ADHD. These findings promote our understanding of the genetic basis for neurobiological differences observed among children with ADHD, but they must be confirmed in larger samples.

Identifying individuals with autism spectrum disorder based on the principal components of whole-brain phase synchrony

Autism spectrum disorder (ASD) is a brain disorder that develops during an early stage of childhood. Previous neuroimaging-based diagnostic models for ASD were based on static functional connectivity (FC). The nonlinear complexity of brain connectivity remains unexplored for ASD diagnosis. This study aimed to build intelligent discriminative models for ASD based on phase synchrony (PS). To this end, data from 49 patients with ASD and 41 healthy controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) project. PS between brain regions was determined using Hilbert transform. Principal component analysis (PCA) and support vector machines (SVMs) were used to build the discriminative models. PS-based models (AUC = 0.81) outperformed static FC-based models (AUC = 0.71). Furthermore, embedded functional biomarkers were discovered. Moreover, significant correlations were found between PCA-PS and the clinical severity of ASD. Together, intelligent discriminative models based on PS were established for ASD identification. The performance of the diagnostic models suggested the potential benefits of PS for clinical applications. The discriminative patterns indicated that PCA-PS features could be additional biomarkers for ASD research. Furthermore, the significant relationships between the PCA-PS features and clinical scores implied their potential use for personalized medication strategies.

Estimating individual scores of inattention and impulsivity based on dynamic features of intrinsic connectivity network

Inattention and impulsivity are the two most important indices for evaluations of ADHD. Currently, inattention and impulsivity were evaluated by clinical scales. The intelligent evaluation of the two indices using machine learning remains largely unexplored. This paper aimed to build regression modes for inattention and impulsivity based on resting state fMRI and additional measures, and discover the associating features for the two indices. To achieve these goals, a cohort of 95 children with ADHD as well as 105 healthy controls were selected from the ADHD-200 database. The raw features were consisted of univariate dynamic estimators of intrinsic connectivity network (ICNs), head motion, and additional measures. The regression models were solved using support vector regression (SVR). The performance of the regression models was evaluated by cross-validations. The performance of regression models based on ICNs outperformed that based on regional measures. The estimated clinical scores were significantly correlated to inattention (r = 0.4 ± 0.02, p < 0.01) and impulsivity (r = 0.31 ± 0.02, p < 0.01). The most associating ICNs are sensorimotor network (SMN) for inattention and executive control network (ECN) for impulsivity. The results suggested that inattention and impulsivity could be estimated using machine learning, and the intra-ICN dynamics could be supplementary features for regression models of clinical scores of ADHD.

Frontoinsular Network Markers of Current and Future Adolescent Mood Health

BACKGROUND

Adolescence is a developmental period in which depression and related mood syndromes often emerge, but few objective markers exist to guide diagnosis or predict symptoms. One potential mood marker is the functioning of frontoinsular networks, which undergo substantial development in adolescence and have been implicated in adult depression. To test this hypothesis, we used task-based neuroimaging to evaluate whether frontoinsular network dysfunction was linked to current and prospective mood health in adolescents.

METHODS

Adolescents (n = 40, 13-19 years of age) reporting varying levels of depressive symptom severity performed an emotional working memory task with neuroimaging. Next, teens completed a 2-week follow-up consisting of a daily diary report of negative affect and final report of depressive symptoms (n = 28 adherent). Analyses tested associations between task-related functional connectivity in frontoinsular networks and baseline or prospective measures of mood health over 2-week follow-up.

RESULTS

Frontoinsular task response was associated with higher current depression severity (p = .049, η_p² = .12), increases in future depression severity (p = .018, η_p² = .23), and more intense and labile negative affect in daily life (ps = .015 to .040, η_p² = .22 to .30). In particular, hypoconnectivity between insula and lateral prefrontal regions of the frontoparietal network was related to both baseline and prospective mood health, and hyperconnectivity between insula and midline or temporal regions of the default network was related to prospective mood health.

CONCLUSIONS

These findings indicate that frontoinsular imbalances are related to both current depression and changes in mood health in the near future and suggest that frontoinsular markers may hold promise as translational tools for risk prediction.

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.

Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative

Individuals often interpret the same event in different ways. How do personality traits modulate brain activity evoked by a complex stimulus? Here we report results from a naturalistic paradigm designed to draw out both neural and behavioral variation along a specific dimension of interest, namely paranoia. Participants listen to a narrative during functional MRI describing an ambiguous social scenario, written such that some individuals would find it highly suspicious, while others less so. Using inter-subject correlation analysis, we identify several brain areas that are differentially synchronized during listening between participants with high and low trait-level paranoia, including theory-of-mind regions. Follow-up analyses indicate that these regions are more active to mentalizing events in high-paranoia individuals. Analyzing participants’ speech as they freely recall the narrative reveals semantic and syntactic features that also scale with paranoia. Results indicate that a personality trait can act as an intrinsic “prime,” yielding different neural and behavioral responses to the same stimulus across individuals.

Reactions to the same event can vary vastly based on multiple factors. Here the authors show that people with high trait-level paranoia process ambiguous information in a narrative differently and this can be attributed to greater activity in mentalizing brain regions during the moments of ambiguity.

Can brain state be manipulated to emphasize individual differences in functional connectivity?

While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within- to between-subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within- and between-subject variability across conditions using data from the Human Connectome Project, and outline questions for future study.

Clinically useful brain imaging for neuropsychiatry: How can we get there?

Despite decades of research, visions of transforming neuropsychiatry through the development of brain imaging-based "growth charts" or "lab tests" have remained out of reach. In recent years, there is renewed enthusiasm about the prospect of achieving clinically useful tools capable of aiding the diagnosis and management of neuropsychiatric disorders. The present work explores the basis for this enthusiasm. We assert that there is no single advance that currently has the potential to drive the field of clinical brain imaging forward. Instead, there has been a constellation of advances that, if combined, could lead to the identification of objective brain imaging-based markers of illness. In particular, we focus on advances that are helping to (1) elucidate the research agenda for biological psychiatry (e.g., neuroscience focus, precision medicine), (2) shift research models for clinical brain imaging (e.g., big data exploration, standardization), (3) break down research silos (e.g., open science, calls for reproducibility and transparency), and (4) improve imaging technologies and methods. Although an arduous road remains ahead, these advances are repositioning the brain imaging community for long-term success.

A Novel Way to Measure and Predict Development: A Heuristic Approach to Facilitate the Early Detection of Neurodevelopmental Disorders

PURPOSE OF REVIEW

Substantial research exists focusing on the various aspects and domains of early human development. However, there is a clear blind spot in early postnatal development when dealing with neurodevelopmental disorders, especially those that manifest themselves clinically only in late infancy or even in childhood.

RECENT FINDINGS

This early developmental period may represent an important timeframe to study these disorders but has historically received far less research attention. We believe that only a comprehensive interdisciplinary approach will enable us to detect and delineate specific parameters for specific neurodevelopmental disorders at a very early age to improve early detection/diagnosis, enable prospective studies and eventually facilitate randomised trials of early intervention. In this article, we propose a dynamic framework for characterising neurofunctional biomarkers associated with specific disorders in the development of infants and children. We have named this automated detection 'Fingerprint Model', suggesting one possible approach to accurately and early identify neurodevelopmental disorders.