Biology subjects

Spann, M. N.

Publications and source records attributed to Spann, M. N..

3 recordsLinked to original sources

bioRxiv2022

The effects of ethnoracial-related stressors during pregnancy on the developing offspring brain.

We are increasingly aware of the effects of ethnoracial stress on health, with emerging interest in the potential for intergenerational transmission before birth. Here, we investigate the effect of maternal prenatal discrimination and acculturation experiences on fetal growth, birth outcomes, and functional connectivity in the infant brain. In pregnant adolescent women, we collected self-report measures of acculturation (tailored to an adolescent and Latinx population), discrimination, and maternal distress (i.e., stress and depressive symptoms; n=165). Fetal growth were obtained via electronic health records (n=92), and infant amygdala seed connectivity was assessed using functional magnetic resonance imaging (n=38). We found that greater maternal prenatal assimilation to the host culture was associated with slower fetal growth, lower gestational age at birth, and weaker amygdala-fusiform connectivity. Maternal prenatal discrimination was associated with weaker amygdala-prefrontal connectivity. Together, these results suggest intergenerational effects of ethnoracial stressors on the growth and neural development of future generations.

neuroscience↗

bioRxiv2019

Task-Based Functional Connectomes Predict Cognitive Phenotypes Across Psychiatric Disease

ImportanceWe show that three common approaches to clinical deficits (cognitive phenotype, disease group, disease severity) each offer useful and perhaps complimentary explanations for the brains underlying functional architecture as affected by psychiatric disease.\n\nObjectiveTo understand how different clinical frameworks are represented in the brains functional connectome.\n\nDesignWe use an openly available dataset to create predictive models based on multiple connectomes built from task-based functional MRI data. We use these models to predict individual traits corresponding to multiple cognitive constructs across disease category. We also show that these same connectomes statistically differ depending on disease category and symptom burden.\n\nSettingThis was a population-based study with data collected in UCLA.\n\nParticipantsHealthy adults were recruited by community advertisements from the Los Angeles area. Participants with adult ADHD, bipolar disorder, and schizophrenia were recruited using a patient-oriented strategy involving outreach to local clinics and online portals (separate from the methods used to recruit healthy volunteers)

neuroscience↗

bioRxiv2018

The functional brain organization of an individual predicts measures of social abilities in autism spectrum disorder

Autism Spectrum Disorder (ASD) is associated with multiple complex abnormalities in functional brain connectivity measured with functional magnetic resonance imaging (fMRI). Despite much research in this area, to date, neuroimaging-based models are not able to characterize individuals with ASD with sufficient sensitivity and specificity; this is likely due to the heterogeneity and complexity of this disorder. Here we apply a data-driven subject-level approach, connectome-based predictive modeling, to resting-state fMRI data from a set of individuals from the Autism Brain Imaging Data Exchange. Using leave-one-subject-out and split-half analyses, we define two functional connectivity networks that predict continuous scores on the Social Responsiveness Scale (SRS) and Autism Diagnostic Observation Schedule (ADOS) and confirm that these networks generalize to novel subjects. Notably, these networks were found to share minimal anatomical overlap. Further, our results generalize to individuals for whom SRS/ADOS scores are unavailable, predicting worse scores for ASD than typically developing individuals. In addition, predicted SRS scores for individuals with attention-deficit/hyperactivity disorder (ADHD) from the ADHD-200 Consortium are linked to ADHD symptoms, supporting the hypothesis that the functional brain organization changes relevant to ASD severity share a component associated with attention. Finally, we explore the membership of predictive connections within conventional (atlas-based) functional networks. In summary, our results suggest that an individuals functional connectivity profile contains information that supports dimensional, non-binary classification in ASD, aligning with the goals of precision medicine and individual-level diagnosis.

neuroscience↗