Subcortical volumes in offspring with a multigenerational family history of depression - A study across two cohorts.

BACKGROUND: Having multiple previous generations with depression in the family increases offspring risk for psychopathology. Parental depression has been associated with smaller subcortical brain volumes in their children, but whether two prior generations with depression is associated with further decreases is unclear. METHODS: Using two independent cohorts, 1) a Three-Generation Study (TGS, N = 65) with direct clinical interviews of adults and children across all three generations, and 2) the Adolescent Brain Cognitive Development Study (ABCD, N = 10,626) of 9-10 year-old children with family history assessed by a caregiver, we tested whether having more generations of depression in the family was associated with smaller subcortical volumes (using structural MRI). RESULTS: In TGS, caudate, pallidum and putamen showed decreasing volumes with higher familial risk for depression. Having a parent and a grandparent with depression was associated with decreased volume compared to having no familial depression in these regions. Putamen volume was associated with depression at eight-year follow-up. In ABCD, smaller pallidum and putamen were associated with family history, which was driven by parental depression, regardless of grandparental depression. LIMITATIONS: Discrepancies between cohorts could be due to interview type (clinical or self-report) and informant (individual or common informant), sample size or age. Future analyses of follow-up ABCD waves will be able to assess whether effects of grandparental depression on brain markers become more apparent as the children enter young adulthood. CONCLUSIONS: Basal ganglia regional volumes are significantly smaller in offspring with a family history of depression in two independent cohorts.

Reduced dorsal fronto-striatal connectivity at rest in anorexia nervosa.

BACKGROUND: Anorexia nervosa (AN) is a serious psychiatric illness that remains difficult to treat. Elucidating the neural mechanisms of AN is necessary to identify novel treatment targets and improve outcomes. A growing body of literature points to a role for dorsal fronto-striatal circuitry in the pathophysiology of AN, with increasing evidence of abnormal task-based fMRI activation within this network among patients with AN. Whether these abnormalities are present at rest and reflect fundamental differences in brain organization is unclear. METHODS: The current study combined resting-state fMRI data from patients with AN (n = 89) and healthy controls (HC; n = 92) across four studies, removing site effects using ComBat harmonization. First, the a priori hypothesis that dorsal fronto-striatal connectivity strength - specifically between the anterior caudate and dlPFC - differed between patients and HC was tested using seed-based functional connectivity analysis with small-volume correction. To assess specificity of effects, exploratory analyses examined anterior caudate whole-brain connectivity, amplitude of low-frequency fluctuations (ALFF), and node centrality. RESULTS: Compared to HC, patients showed significantly reduced right, but not left, anterior caudate-dlPFC connectivity (p = 0.002) in small-volume corrected analyses. Whole-brain analyses also identified reduced connectivity between the right anterior caudate and left superior frontal and middle frontal gyri (p = 0.028) and increased connectivity between the right anterior caudate and right occipital cortex (p = 0.038). No group differences were found in analyses of anterior caudate ALFF and node centrality. CONCLUSIONS: Decreased coupling of dorsal fronto-striatal regions indicates that circuit-based abnormalities persist at rest and suggests this network may be a potential treatment target.

Large-Scale Exploration of Whole-Brain Structural Connectivity in Anorexia Nervosa: Alterations in the Connectivity of Frontal and Subcortical Networks.

BACKGROUND: Anorexia nervosa (AN) is characterized by disturbances in cognition and behavior surrounding eating and weight. The severity of AN combined with the absence of localized brain abnormalities suggests distributed, systemic underpinnings that may be identified using diffusion-weighted magnetic resonance imaging and tractography to reconstruct white matter pathways. METHODS: Diffusion-weighted magnetic resonance imaging data acquired from female patients with AN (n= 147) and female healthy control (HC) participants (n = 119), ages 12 to 40 years, were combined across 5 studies. Probabilistic tractography was completed, and full-cortex connectomes describing streamline counts between 84 brain regions were generated and harmonized. Graph theory methods were used to describe alterations in network organization in AN. The network-based statistic tested between-group differences in brain subnetwork connectivity. The metrics strength and efficiency indexed the connectivity of brain regions (network nodes) and were compared between groups using multiple linear regression. RESULTS: Individuals with AN, relative to HC peers, had reduced connectivity in a network comprising subcortical regions and greater connectivity between frontal cortical regions (p < .05, familywise error corrected). Node-based analyses indicated reduced connectivity of the left hippocampus in patients relative to HC peers (p < .05, permutation corrected). Severity of illness, assessed by body mass index, was associated with subcortical connectivity (p < .05, uncorrected). CONCLUSIONS: Analyses identified reduced structural connectivity of subcortical networks and regions, and stronger cortical network connectivity, among individuals with AN relative to HC peers. These findings are consistent with alterations in feeding, emotion, and executive control circuits in AN, and may direct hypothesis-driven research into mechanisms of persistent restrictive eating behavior.

PTNet3D: A 3D High-Resolution Longitudinal Infant Brain MRI Synthesizer Based on Transformers.

An increased interest in longitudinal neurodevelopment during the first few years after birth has emerged in recent years. Noninvasive magnetic resonance imaging (MRI) can provide crucial information about the development of brain structures in the early months of life. Despite the success of MRI collections and analysis for adults, it remains a challenge for researchers to collect high-quality multimodal MRIs from developing infant brains because of their irregular sleep pattern, limited attention, inability to follow instructions to stay still during scanning. In addition, there are limited analytic approaches available. These challenges often lead to a significant reduction of usable MRI scans and pose a problem for modeling neurodevelopmental trajectories. Researchers have explored solving this problem by synthesizing realistic MRIs to replace corrupted ones. Among synthesis methods, the convolutional neural network-based (CNN-based) generative adversarial networks (GANs) have demonstrated promising performance. In this study, we introduced a novel 3D MRI synthesis framework- pyramid transformer network (PTNet3D)- which relies on attention mechanisms through transformer and performer layers. We conducted extensive experiments on high-resolution Developing Human Connectome Project (dHCP) and longitudinal Baby Connectome Project (BCP) datasets. Compared with CNN-based GANs, PTNet3D consistently shows superior synthesis accuracy and superior generalization on two independent, large-scale infant brain MRI datasets. Notably, we demonstrate that PTNet3D synthesized more realistic scans than CNN-based models when the input is from multi-age subjects. Potential applications of PTNet3D include synthesizing corrupted or missing images. By replacing corrupted scans with synthesized ones, we observed significant improvement in infant whole brain segmentation.

Automated Multiclass Artifact Detection in Diffusion MRI Volumes via 3D Residual Squeeze-and-Excitation Convolutional Neural Networks.

Diffusion MRI (dMRI) is widely used to investigate neuronal and structural development of brain. dMRI data is often contaminated with various types of artifacts. Hence, artifact type identification in dMRI volumes is an essential pre-processing step prior to carrying out any further analysis. Manual artifact identification amongst a large pool of dMRI data is a highly labor-intensive task. Previous attempts at automating this process are often limited to a binary classification ("poor" vs. "good" quality) of the dMRI volumes or focus on detecting a single type of artifact (e.g., motion, Eddy currents, etc.). In this work, we propose a deep learning-based automated multiclass artifact classifier for dMRI volumes. Our proposed framework operates in 2 steps. In the first step, the model predicts labels associated with 3D mutually exclusive collectively exhaustive (MECE) sub-volumes or "slabs" extracted from whole dMRI volumes. In the second step, through a voting process, the model outputs the artifact class present in the whole volume under investigation. We used two different datasets for training and evaluating our model. Specifically, we utilized 2,494 poor-quality dMRI volumes from the Adolescent Brain Cognitive Development (ABCD) and 4,226 from the Healthy Brain Network (HBN) dataset. Our results demonstrate accurate multiclass volume-level main artifact type prediction with 96.61 and 97.52% average accuracies on the ABCD and HBN test sets, respectively. Finally, in order to demonstrate the effectiveness of the proposed framework in dMRI pre-processing pipelines, we conducted a proof-of-concept dMRI analysis exploring the relationship between whole-brain fractional anisotropy (FA) and participant age, to test whether the use of our model improves the brain-age association.

Organization of the social cognition network predicts future depression and interpersonal impairment: a prospective family-based study.

Deficits in social cognition and functioning are common in major depressive disorder (MDD). Still, no study into the pathophysiology of MDD has examined the social cognition-related neural pathways through which familial risk for MDD leads to depression and interpersonal impairments. Using resting-state fMRI, we applied a graph theoretical analysis to quantify the influence of nodes within the fronto-temporo-parietal cortical social cognition network in 108 generation 2 and generation 3 offspring at high and low-risk for MDD, defined by the presence or absence, respectively, of moderate to severe MDD in generation 1. New MDD episodes, future depressive symptoms, and interpersonal impairments were tested for associations with social cognition nodal influence, using regression analyses applied in a generalized estimating equations approach. Increased familial risk was associated with reduced nodal influence within the network, and this predicted new depressive episodes, worsening depressive symptomatology, and interpersonal impairments, 5-8 years later. Findings remained significant after controlling for current depressive/anxiety symptoms and current/lifetime MDD and anxiety disorders. Path-analysis models indicate that increased familial risk impacted offspring's brain function in two ways. First, high familial risk was indirectly associated with future depression, both new MDD episodes and symptomatology, via reduced nodal influence of the right posterior superior temporal gyrus (pSTG). Second, high familial risk was indirectly associated with future interpersonal impairments via reduced nodal influence of right inferior frontal gyrus (IFG). Finally, reduced nodal influence was associated with high familial risk in (1) those who had never had MDD at the time of scanning and (2) a subsample (n = 52) rescanned 8 years later. Together, findings reveal a potential pathway for the intergenerational transmission of vulnerability via the aberrant social cognition network organization and suggest using the connectome of neural network related to social cognition to identify intervention and prevention targets for those particularly at risk.

Automatic Volumetric Quality Assessment of Diffusion MR Images via Convolutional Neural Network Classifiers.

Diffusion Tensor Imaging (DTI) is widely used to find brain biomarkers for various stages of brain structural and neuronal development. Processing DTI data requires a detailed Quality Assessment (QA) to detect artifactual volumes amongst a large pool of data. Since large cohorts of brain DTI data are often used in different studies, manual QA of such images is very labor-intensive. In this paper, a deep learning-based tool is developed for quick automatic QA of 3D raw diffusion MR images. We propose a 2-step framework to automate the process of binary (i.e., 'good' vs 'poor') quality classification of diffusion MR images. In the first step, using two separately trained 3D convolutional neural networks with different input sizes, quality labels for individual Regions of Interest (ROIs) sampled from whole DTI volumes are predicted. In the second step, two distinct novel voting systems are designed and fine-tuned to predict the quality label of whole brain DTI volumes using the individual ROI labels predicted in the previous step. Our results demonstrate the validity and practicality of our tool. Specifically, using a balanced dataset of 6,940 manually-labeled 3D DTI volumes from 85 unique subjects for training, validation, and testing, our model achieves 100% accuracy via one voting system, and 98% accuracy via another voting system on the same test set.

Intra-session test-retest reliability of functional connectivity in infants.

Resting functional MRI studies of the infant brain are increasingly becoming an important tool in developmental neuroscience. Whereas the test-retest reliability of functional connectivity (FC) measures derived from resting fMRI data have been characterized in the adult and child brain, similar assessments have not been conducted in infants. In this study, we examined the intra-session test-retest reliability of FC measures from 119 infant brain MRI scans from four neurodevelopmental studies. We investigated edge-level and subject-level reliability within one MRI session (between and within runs) measured by the Intraclass correlation coefficient (ICC). First, using an atlas-based approach, we examined whole-brain connectivity as well as connectivity within two common resting fMRI networks - the default mode network (DMN) and the sensorimotor network (SMN). Second, we examined the influence of run duration, study site, and scanning manufacturer (e.g., Philips and General Electric) on ICCs. Lastly, we tested spatial similarity using the Jaccard Index from networks derived from independent component analysis (ICA). Consistent with resting fMRI studies from adults, our findings indicated poor edge-level reliability (ICC = 0.14-0.18), but moderate-to-good subject-level intra-session reliability for whole-brain, DMN, and SMN connectivity (ICC = 0.40-0.78). We also found significant effects of run duration, site, and scanning manufacturer on reliability estimates. Some ICA-derived networks showed strong spatial reproducibility (e.g., DMN, SMN, and Visual Network), and were labelled based on their spatial similarity to analogous networks measured in adults. These networks were reproducibly found across different study sites. However, other ICA-networks (e.g. Executive Control Network) did not show strong spatial reproducibility, suggesting that the reliability and/or maturational course of functional connectivity may vary by network. In sum, our findings suggest that developmental scientists may be on safe ground examining the functional organization of some major neural networks (e.g. DMN and SMN), but judicious interpretation of functional connectivity is essential to its ongoing success.

Depression Risk Is Associated With Weakened Synchrony Between the Amygdala and Experienced Emotion.

BACKGROUND: Major depressive disorder (MDD) is associated with aberrant limbic neural responses to emotional stimuli. We assessed how self-generated emotions modulate trial-by-trial limbic activity and whether this brain-emotion synchrony varies by familial MDD risk (regardless of personal MDD history) and neuroticism. METHODS: Participants (n = 74, mean age = 34 years) were later-generation family members of depressed or nondepressed probands as part of a longitudinal cohort study. Using an emotion induction task, we examined participant-specific modulation of anatomically defined limbic neurobiology. Neuroticism, mental health, and familial parenting style were assessed, and MDD assessments were routinely collected throughout the previous longitudinal assessments of the study. RESULTS: Participant-specific emotional arousal modulated amygdala and hippocampal activity. Lasso regression identified attenuated right amygdala arousal modulation as being relatively more associated with neuroticism (even though neuroticism was not associated with arousal ratings). Attenuated amygdala modulation and neuroticism were significantly more likely in offspring of parents with MDD. Parental MDD, but not personal history of MDD, predicted attenuated amygdala modulation. CONCLUSIONS: Attenuated right amygdala modulation by emotional arousal was associated with neuroticism, indicating that the amygdala was less synchronous with emotional experiences in individuals higher in neuroticism. This neurophenotype was predicted by participants' parental MDD history but not by their own MDD history; that is, it was observed in unaffected and affected offspring of parents with MDD. These data suggest that weak amygdala-emotion synchrony may be a predisposing risk factor for MDD, rather than a result of the illness, and they suggest pathways by which this risk factor for depression is passed intergenerationally.

Altered Dentate Gyrus Microstructure in Individuals at High Familial Risk for Depression Predicts Future Symptoms.

BACKGROUND: Offspring of individuals with major depressive disorder (MDD) are at increased risk for developing MDD themselves. Altered hippocampal, and specifically dentate gyrus (DG), structure and function may be involved in depression development. However, hippocampal abnormalities could also be a consequence of the disease. For the first time, we tested whether abnormal DG micro- and macrostructure were present in offspring of individuals with MDD and whether these abnormalities predicted future symptomatology. METHODS: We measured the mean diffusivity of gray matter, a measure of microstructure, via diffusion tensor imaging and volume of the DG via structural magnetic resonance imaging in 102 generation 2 and generation 3 offspring at high and low risk for depression, defined by the presence or absence, respectively, of moderate to severe MDD in generation 1. Prior, current, and future depressive symptoms were tested for association with hippocampal structure. RESULTS: DG mean diffusivity was higher in individuals at high risk for depression, regardless of a lifetime history of MDD. While DG mean diffusivity was not associated with past or current depressive symptoms, higher mean diffusivity predicted higher symptom scores 8 years later. DG microstructure partially mediated the association between risk and future symptoms. DG volume was smaller in high-risk generation 2 but not in high-risk generation 3. CONCLUSIONS: Together, these findings suggest that the DG has a role in the development of depression. Furthermore, DG microstructure, more than macrostructure, is a sensitive risk marker for depression and partially mediates future depressive symptoms.

Stability of Cortical Thinning in Persons at Increased Familial Risk for Major Depressive Disorder Across 8 Years.

BACKGROUND: A biological marker of vulnerability should precede onset of illness and be independent of disease course. We previously reported that cortical thinning may serve as a potential biomarker for risk for familial depression. We now test stability of the cortical thinning across 8 years, and whether thinning mediates associations between familial risk and depressive traits. METHOD: Participants were from a 3-generation family study of depression, where 2nd and 3rd generation offspring were characterized as being at high- or low-risk for depression based on the presence/absence of major depression in the 1st generation. The analysis includes 82 offspring with anatomical MRI scans across two assessment waves, 7.8 (S.D.1.3, range: 5.2-10.9) years apart. RESULTS: High-risk offspring had thinner bilateral superior and middle frontal gyri, and left inferior parietal lobule, at both time-points. High intra-subject correlation (0.60

Effects of prenatal exposure to air pollutants (polycyclic aromatic hydrocarbons) on the development of brain white matter, cognition, and behavior in later childhood.

IMPORTANCE: Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous and neurotoxic environmental contaminants. Prenatal PAH exposure is associated with subsequent cognitive and behavioral disturbances in childhood. OBJECTIVES: To identify the effects of prenatal PAH exposure on brain structure and to assess the cognitive and behavioral correlates of those abnormalities in school-age children. DESIGN, SETTING, AND PARTICIPANTS: Cross-sectional imaging study in a representative community-based cohort followed up prospectively from the fetal period to ages 7 to 9 years. The setting was urban community residences and an academic imaging center. Participants included a sample of 40 minority urban youth born to Latina (Dominican) or African American women. They were recruited between February 2, 1998, and March 17, 2006. MAIN OUTCOMES AND MEASURES: Morphological measures that index local volumes of the surface of the brain and of the white matter surface after cortical gray matter was removed. RESULTS: We detected a dose-response relationship between increased prenatal PAH exposure (measured in the third trimester but thought to index exposure for all of gestation) and reductions of the white matter surface in later childhood that were confined almost exclusively to the left hemisphere of the brain and that involved almost its entire surface. Reduced left hemisphere white matter was associated with slower information processing speed during intelligence testing and with more severe externalizing behavioral problems, including attention-deficit/hyperactivity disorder symptoms and conduct disorder problems. The magnitude of left hemisphere white matter disturbances mediated the significant association of PAH exposure with slower processing speed. In addition, measures of postnatal PAH exposure correlated with white matter surface measures in dorsal prefrontal regions bilaterally when controlling for prenatal PAH. CONCLUSIONS AND RELEVANCE: Our findings suggest that prenatal exposure to PAH air pollutants contributes to slower processing speed, attention-deficit/hyperactivity disorder symptoms, and externalizing problems in urban youth by disrupting the development of left hemisphere white matter, whereas postnatal PAH exposure contributes to additional disturbances in the development of white matter in dorsal prefrontal regions.