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Gestational exposure to environmental toxins and socioeconomic stressors is epidemiologically linked to neurodevelopmental disorders with strong male bias, such as autism. We model these prenatal risk factors in mice by co-exposing pregnant dams to an environmental pollutant and limited-resource stress, which robustly activates the maternal immune system. Only male offspring display long-lasting behavioral abnormalities and alterations in the activity of brain networks encoding social interactions. Cellularly, prenatal stressors diminish microglial function within the anterior cingulate cortex, a central node of the social coding network, in males during early postnatal development. Precise inhibition of microglial phagocytosis within the anterior cingulate cortex (ACC) of wild-type (WT) mice during the same critical period mimics the impact of prenatal stressors on a male-specific behavior, indicating that environmental stressors alter neural circuit formation in males via impairing microglia function during development.

The architecture whereby activity across many brain regions integrates to encode individual appetitive social behavior remains unknown. Here we measure electrical activity from eight brain regions as mice engage in a social preference assay. We then use machine learning to discover a network that encodes the extent to which individual mice engage another mouse. This network is organized by theta oscillations leading from prelimbic cortex and amygdala that converge on the ventral tegmental area. Network activity is synchronized with cellular firing, and frequency-specific activation of a circuit within this network increases social behavior. Finally, the network generalizes, on a mouse-by-mouse basis, to encode individual differences in social behavior in healthy animals but fails to encode individual behavior in a 'high confidence' genetic model of autism. Thus, our findings reveal the architecture whereby the brain integrates distributed activity across timescales to encode an appetitive brain state underlying individual differences in social behavior.

Gestational exposure to environmental toxins and socioeconomic stressors are epidemiologically linked to neurodevelopmental disorders with strong male-bias, such as autism. We modeled these prenatal risk factors in mice, by co-exposing pregnant dams to an environmental pollutant and limited-resource stress, which robustly activated the maternal immune system. Only male offspring displayed long-lasting behavioral abnormalities and alterations in the activity of brain networks encoding social interactions. Cellularly, prenatal stressors diminished microglial function within the anterior cingulate cortex, a central node of the social coding network, in males during early postnatal development. Genetic ablation of microglia during the same critical period mimicked the impact of prenatal stressors on a male-specific behavior, indicating that environmental stressors alter neural circuit formation in males via impairing microglia function during development.

Many cortical and subcortical regions contribute to complex social behavior; nevertheless, the network level architecture whereby the brain integrates this information to encode appetitive socioemotional behavior remains unknown. Here we measure electrical activity from eight brain regions as mice engage in a social preference assay. We then use machine learning to discover an explainable brain network that encodes the extent to which mice chose to engage another mouse. This socioemotional network is organized by theta oscillations leading from prelimbic cortex and amygdala that converge on ventral tegmental area, and network activity is synchronized with brain-wide cellular firing. The network generalizes, on a mouse-by-mouse basis, to encode socioemotional behaviors in healthy animals, but fails to encode an appetitive socioemotional state in a ‘high confidence’ genetic mouse model of autism. Thus, our findings reveal the architecture whereby the brain integrates spatially distributed activity across timescales to encode an appetitive socioemotional brain state in health and disease.

Introduction Much attention has been given to stress-related symptoms including insomnia related to the COVID-19 pandemic. Exposure to racially targeted police violence and the disproportionate impact of COVID on their communities have been particularly salient stressors for African Americans. Aims for this presentation are to illuminate the emotional impact of COVID and racism on young adult African Americans and their relationship to sleep problems. Methods An online survey was sent to participants in an ongoing study focusing on sleep and health along with new participants recruited through a snowball method. The survey included the Insomnia Severity Index (ISI), Impact of Event scale (IES), as well as items to assess experiences with COVID, exposure to racially targeted violence, and disparate community impact. Results Respondents were African Americans age 18–35. 45% endorsed worsening sleep during the pandemic. 25% had ISI scores of probable insomnia and 61% had IES scores above the symptom threshold for clinical concern. Racism-related distress was endorsed at higher levels than worries related to COVID. There was a significant relationship of insomnia severity with the number of COVID related stressors and a trend level relationship with COVID worries. Sleep outcomes were not correlated with racism-related distress. Conclusion While distress related to racism was endorsed more than COVID worries, our preliminary findings suggest COVID worries but not racism exposure negatively impact sleep health. This may be a consequence of habituation to the chronic stress of racism in contrast with the novel stress of COVID. Support (if any) 5R01HL136626 from the National Heart Lung and Blood Institute

Anticipation of an upcoming stimulus induces neural activity across cortical and subcortical regions and influences subsequent behavior. Nevertheless, the network mechanism whereby the brain integrates this information to signal the anticipation of rewards remains relatively unexplored. Here we employ multi-circuit electrical recordings from six brain regions as mice perform a sample-to-match task in which reward anticipation is operationalized as their progress towards obtaining a potential reward. We then use machine learning to discover the naturally occurring network patterns that integrate this neural activity across timescales. Only one of the networks that we uncovered signals responses linked to reward anticipation, specifically relative proximity and reward magnitude. Activity in this Electome (electrical functional connectivity) network is dominated by theta oscillations leading from prelimbic cortex and striatum that converge on ventral tegmental area, and by beta oscillations leading from striatum that converge on prelimbic cortex. Network activity is also synchronized with brain-wide cellular firing. Critically, this network generalizes to new groups of healthy mice, as well as a mouse line that models aberrant neural circuitry observed in brain disorders that show altered reward anticipation. Thus, our findings reveal the network-level architecture whereby the brain integrates spatially distributed activity across timescales to signal reward anticipation.

The neurobiology of schizophrenia (SZ) may be altered in older versus younger adults with SZ, as less frequent episodes of symptom exacerbation and increased sensitivity to medications are observed in older age. The goal of this study was to examine the effect of age and diagnosis on glutamate and cerebral blood flow (rCBF) in adults with SZ and healthy controls. Young and older adults with SZ and healthy controls were recruited to participate in this study. Participants completed a neuropsychological battery and neuroimaging that included optimized magnetic resonance spectroscopy to measure anterior cingulate (AC) glutamate (Glu) and glutamine (Gln) and arterial spin labeling evaluation for rCBF. Regression analyses revealed significant effects of age with Glu, Gln, Gln/Glu, and AC white matter (WM) rCBF. Glu and WM rCBF decreased linearly with age while Gln and Gln/Glu increased linearly with age. Glu was lower in adults with SZ compared with healthy controls and in older adults versus younger adults but there was no interaction. Glu and WM rCBF were correlated with the UCSD Performance-Based Skills Assessment (UPSA) and processing speed, and the correlations were stronger in the SZ group. In the largest sample to date, lower Glu and elevated Gln/Glu levels were observed in adults with SZ and in older subjects. Contrary to expectation, these results do not show evidence of accelerated Glu aging in the anterior cingulate region in SZ compared with healthy controls.

Brain-wide fluctuations in local field potential oscillations reflect emergent network-level signals that mediate behavior. Cracking the code whereby these oscillations coordinate in time and space (spatiotemporal dynamics) to represent complex behaviors would provide fundamental insights into how the brain signals emotional pathology. Using machine learning, we discover a spatiotemporal dynamic network that predicts the emergence of major depressive disorder (MDD)-related behavioral dysfunction in mice subjected to chronic social defeat stress. Activity patterns in this network originate in prefrontal cortex and ventral striatum, relay through amygdala and ventral tegmental area, and converge in ventral hippocampus. This network is increased by acute threat, and it is also enhanced in three independent models of MDD vulnerability. Finally, we demonstrate that this vulnerability network is biologically distinct from the networks that encode dysfunction after stress. Thus, these findings reveal a convergent mechanism through which MDD vulnerability is mediated in the brain.

The neurobiology of schizophrenia (SZ) may be altered in older versus younger adults with SZ, as less frequent episodes of symptom exacerbation and increased sensitivity to medications are observed in older age. The goal of this study was to examine the effect of age and diagnosis on glutamate and cerebral blood flow (rCBF) in adults with SZ and healthy controls. Young and older adults with SZ and healthy controls were recruited to participate in this study. Participants completed a neuropsychological battery and neuroimaging that included optimized magnetic resonance spectroscopy to measure anterior cingulate (AC) glutamate (Glu) and glutamine (Gln) and arterial spin labeling evaluation for rCBF. Regression analyses revealed significant effects of age with Glu, Gln, Gln/Glu, and AC white matter (WM) rCBF. Glu and WM rCBF decreased linearly with age while Gln and Gln/Glu increased linearly with age. Glu was lower in adults with SZ compared with healthy controls and in older adults versus younger adults but there was no interaction. Glu and WM rCBF were correlated with the UCSD Performance-Based Skills Assessment (UPSA) and processing speed, and the correlations were stronger in the SZ group. In the largest sample to date, lower Glu and elevated Gln/Glu levels were observed in adults with SZ and in older subjects. Contrary to expectation, these results do not show evidence of accelerated Glu aging in the anterior cingulate region in SZ compared with healthy controls.