Abstract 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 MRI (dMRI) and tractography to reconstruct white matter pathways. Methods dMRI data acquired from female patients with AN (n = 147) and female healthy controls (HC; n = 119), aged 12-40 years, were combined across five studies. Probabilistic tractography was completed, and full cortex connectomes describing streamline counts between 84 brain regions 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, had reduced connectivity in a network comprising subcortical regions and greater connectivity between frontal cortical regions (p < 0.05, FWE corrected). Node-based analyses indicated reduced connectivity of the left hippocampus in patients relative to HC (p < 0.05, permutation corrected). Severity of illness, assessed by BMI, was associated with subcortical connectivity (p < 0.05, uncorrected). Conclusions Analyses identified reduced structural connectivity of subcortical networks and regions, and stronger cortical network connectivity, amongst individuals with AN relative to HC. 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.
Backgroundwhile the increased risk of major depressive disorder (MDD) in offspring of depressed parents is one of the best-replicated findings in psychiatry, their long-term outcomes are less well known. The clinical outcomes of biological offspring of depressed (high-risk) and not depressed (low-risk) parents who have been directly interviewed over the years are presented.Methodsa longitudinal retrospective cohort study began in 1982, and 276 biological offspring of moderately-to-severely depressed or non-depressed parents from the same community were followed up to 38 years. Rates of psychiatric disorders for offspring were collected by clinically trained interviewers. Final diagnoses were made by M.D. or Ph.D. clinicians. Mortality and cause of death were obtained from relatives and registries.Findingshigh- compared to low-risk offspring continue to have about a three-fold increased risk of MDD, increased rates of anxiety disorder, substance dependence, and poorer functioning over the life course. Adolescence and early adulthood remain prime age of first onsets. Within high-risk group only, the death rate due to unnatural causes, suicides and overdose was 4·97/100 in the offspring and 5·36/100 in their parents. This subsample of White, lower-educated, often unemployed persons, who died by unnatural causes are similar demographically to those described as having a recent increase in 'deaths of despair'.Interpretationfamily history of MDD continues to be a powerful predictor of clinical course and mortality and should be probed in clinical visits, especially in youth when depression usually first appears.FundingThis work is supported by NIMH grant R01MH-036,197 (P.I.s: Weissman, Posner).
Magnetic resonance imaging (MRI) noninvasively provides critical information about how human brain structures develop across stages of life. Developmental scientists are particularly interested in the first few years of neurodevelopment. Despite the success of MRI collection and analysis for adults, it is a challenge for researchers to collect high-quality multimodal MRIs from developing infants mainly because of their irregular sleep pattern, limited attention, inability to follow instructions to stay still, and a lack of analysis approaches. These challenges often lead to a significant reduction of usable data. To address this issue, researchers have explored various solutions to replace corrupted scans through synthesizing realistic MRIs. Among them, the convolution neural network (CNN) based generative adversarial network has demonstrated promising results and achieves state-of-the-art performance. However, adversarial training is unstable and may need careful tuning of regularization terms to stabilize the training. In this study, we introduced a novel MRI synthesis framework - Pyramid Transformer Net (PTNet). PTNet consists of transformer layers, skip-connections, and multi-scale pyramid representation. Compared with the most widely used CNN-based conditional GAN models (namely pix2pix and pix2pixHD), our model PTNet shows superior performance in terms of synthesis accuracy and model size. Notably, PTNet does not require any type of adversarial training and can be easily trained using the simple mean squared error loss.
Abstract Early postnatal period brain magnetic resonance imaging (MRI) is becoming an important approach to measure the impact of prenatal exposures on neurodevelopment and to investigate early biomarkers for risk. Among brain structures, Limbic structures are particular of interest in psychiatric disorder-related research. However, despite the promise of infant neuroimaging and the success of initial infant MRI studies, assessing limbic regions’ structure and function remains a significant challenge due to low inter-regional intensity contrast and high curvature (e.g., hippocampus). In addition, the agreement between existing automatic techniques and manual segmentation remains either untested or insufficient, particularly for the amygdala and hippocampus. In this work, we developed an accurate (based on three segmentation evaluation metrics), reliable and efficient infant deep learning segmentation framework (ID-Seg) to address the aforementioned challenges. Specifically, we leveraged a large dataset of 473 infant MRI scans to train ID-Seg and rigorously evaluated ID-Seg’s performance on internal and external datasets with manual segmentations. Compared with a state-of-the-art segmentation pipeline, we demonstrated that ID-Seg significantly improved the segmentation accuracy of limbic structures (hippocampus and amygdala) in newborn infants. Moreover, in a medium-size dataset, we found that ID-Seg-derived morphometric measures yield strong brain-behavior associations. As such, our ID-Seg may improve our capacity and efficiency to measure MRI-based brain features relevant to neuropsychological development and ultimately advance the success of quantitative analyses on large-scale datasets.
Background Adolescence is a critical developmental period for the study of anorexia nervosa (AN), an illness characterized by extreme restriction of food intake. The maturation of the reward system during adolescence combined with recent neurobiological models of AN led to the hypothesis that early on in illness, restrictive food choices would be associated with activity in nucleus accumbens reward regions, rather than caudate regions identified among adults with AN. Methods Healthy adolescents (HC, n = 41) and adolescents with AN or atypical AN (atypAN, n = 76) completed a Food Choice Task during fMRI scanning. Selection of high‐fat foods and choice‐related activation in nucleus accumbens and anterior caudate regions‐of‐interest (ROIs) were compared between individuals with AN/atypAN and HC. Associations were examined between choice‐related activation and choice preferences among the AN group. Exploratory analyses examined associations between choice‐related activation and psychological assessments among the patient group. Results Adolescents with AN or atypAN selected fewer high‐fat foods than HC ( t = −5.92, p < .001). Counter to predictions, there were no significant group differences in choice‐related activation in the ROIs. Among individuals with AN or atypAN, choice‐related neural activity in the anterior caudate was significantly negatively associated with high‐fat food selections in the task ( r = −.32, p = .024). In exploratory analyses, choice‐related anterior caudate activation was positively associated with psychological measures of illness severity among patients ( p 's < .05, uncorrected). Conclusions In this large cohort of adolescents with AN/atypAN, there was no evidence of altered reward system engagement during food choice. While there was no group difference in choice‐related caudate activation, the associations with choices and psychological measures continue to suggest that this neural region is implicated in illness. Longitudinal analyses will clarify whether neural variability relates to longer‐term course.
Importance Being born either small for gestational age (SGA) or large for gestational age (LGA) and experiencing rapid or slow growth after birth are associated with later-life obesity. Understanding the associations of dietary quality during pregnancy with infant growth may inform obesity prevention strategies. Objective To evaluate the associations of prenatal dietary quality according to the Healthy Eating Index (HEI) and the Empirical Dietary Inflammatory Pattern (EDIP) with infant size at birth and infant growth from birth to age 24 months. Design, Setting, and Participants This cohort study used data from birthing parent–child dyads in 8 cohorts participating in the Environmental influences on Child Health Outcomes program between 2007 and 2021. Data were analyzed from March 2021 to August 2024. Exposures The HEI and the EDIP dietary patterns. Main Outcomes and Measures Outcomes of interest were infant birth weight, categorized as SGA, reference range, or LGA, and infant growth from birth to ages 6, 12, and 24 months, categorized as slow growth (weight-for-length z score [WLZ] score difference &lt;−0.67), within reference range (WLZ score difference −0.67 to 0.67), or rapid (WLZ score difference, &gt;0.67). Results The study included 2854 birthing parent–child dyads (median [IQR] maternal age, 30 [25-34] years; 1464 [51.3%] male infants). The cohort was racially and ethnically diverse, including 225 Asian or Pacific Islander infants (7.9%), 640 Black infants (22.4%), 1022 Hispanic infants (35.8%), 664 White infants (23.3%), and 224 infants (7.8%) with other race or multiple races. A high HEI score (&gt;80), indicative of a healthier diet, was associated with lower odds of LGA (adjusted odds ratio [aOR], 0.88 [95% CI, 0.79-0.98]), rapid growth from birth to age 6 months (aOR, 0.80 [95% CI, 0.37-0.94]) and age 24 months (aOR 0.82 [95% CI, 0.70- 0.96]), and slow growth from birth to age 6 months (aOR, 0.65 [95% CI, 0.50-0.84]), 12 months (aOR, 0.74 [95% CI, 0.65-0.83]), and 24 months (OR, 0.65 [95% CI, 0.56-0.76]) compared with an HEI score 80 or lower. There was no association between high HEI and SGA (aOR, 1.14 [95% CI, 0.95-1.35]). A low EDIP score (ie, ≤63.6), indicative of a less inflammatory diet, was associated with higher odds of LGA (aOR, 1.24 [95% CI, 1.13-1.36]) and rapid infant growth from birth to age 12 months (aOR, 1.50 [95% CI, 1.18-1.91]) and lower odds of rapid growth to age 6 months (aOR, 0.77 [95% CI, 0.71-0.83]), but there was no association with SGA (aOR, 0.80 [95% CI, 0.51-1.25]) compared with an EDIP score of 63.6 or greater. Conclusions and Relevance In this cohort study, a prenatal diet that aligned with the US Dietary Guidelines was associated with reduced patterns of rapid and slow infant growth, known risk factors associated with obesity. Future research should examine whether interventions to improve prenatal diet are also beneficial in improving growth trajectory in children.
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