Examining litter specific variability in mice and its impact on neurodevelopmental studies.

Our current understanding of litter variability in neurodevelopmental studies using mice may limit translation of neuroscientific findings. Higher variance of measures across litters than within, often termed intra-litter likeness, may be attributable to both pre- and postnatal environment. This study aimed to assess the litter-effect within behavioral assessments (2 timepoints) and anatomy using T1-weighted magnetic resonance images across 72 brain region volumes (4 timepoints) (36 C57bl/6J inbred mice; 7 litters: 19F/17M).

Retinal nerve fiber layer in frontotemporal lobar degeneration and amyotrophic lateral sclerosis.

Purpose: Tauopathy and transactive response DNA binding protein 43 (TDP-43) proteinopathy are associated with neurodegenerative diseases. These proteinopathies are difficult to detect . This study examined if spectral-domain optical coherence tomography (SD-OCT) can differentiate the difference in peripapillary retinal nerve fibre layer (pRNFL) thickness and macular retinal thickness between participants with presumed tauopathy (progressive supranuclear palsy) and those with presumed TDP-43 proteinopathy (amyotrophic lateral sclerosis and semantic variant primary progressive aphasia).

Differential effects of early or late exposure to prenatal maternal immune activation on mouse embryonic neurodevelopment.

Exposure to maternal immune activation (MIA) in utero is a risk factor for neurodevelopmental and psychiatric disorders. MIA-induced deficits in adolescent and adult offspring have been well characterized; however, less is known about the effects of MIA exposure on embryo development. To address this gap, we performed high-resolution ex vivo MRI to investigate the effects of early (gestational day [GD]9) and late (GD17) MIA exposure on embryo (GD18) brain structure.

Evaluating the perceived utility of an artificial intelligence-powered clinical decision support system for depression treatment using a simulation center.

Aifred is a clinical decision support system (CDSS) that uses artificial intelligence to assist physicians in selecting treatments for major depressive disorder (MDD) by providing probabilities of remission for different treatment options based on patient characteristics. We evaluated the utility of the CDSS as perceived by physicians participating in simulated clinical interactions. Twenty physicians who were either staff or residents in psychiatry or family medicine completed a study in which they had three 10-minute clinical interactions with standardized patients portraying mild, moderate, and severe episodes of MDD.

Subtle alterations in neonatal neurodevelopment following early or late exposure to prenatal maternal immune activation in mice.

Prenatal exposure to maternal immune activation (MIA) is a risk factor for a variety of neurodevelopmental and psychiatric disorders. The timing of MIA-exposure has been shown to affect adolescent and adult offspring neurodevelopment, however, less is known about these effects in the neonatal period. To better understand the impact of MIA-exposure on neonatal brain development in a mouse model, we assess neonate communicative abilities with the ultrasonic vocalization task, followed by high-resolution ex vivo magnetic resonance imaging (MRI) on the neonatal (postnatal day 8) mouse brain.

Early or Late Gestational Exposure to Maternal Immune Activation Alters Neurodevelopmental Trajectories in Mice: An Integrated Neuroimaging, Behavioral, and Transcriptional Study.

Background: Exposure to maternal immune activation (MIA) in utero is a risk factor for neurodevelopmental disorders later in life. The impact of the gestational timing of MIA exposure on downstream development remains unclear.

Methods: We characterized neurodevelopmental trajectories of mice exposed to the viral mimetic poly I:C (polyinosinic:polycytidylic acid) either on gestational day 9 (early) or on day 17 (late) using longitudinal structural magnetic resonance imaging from weaning to adulthood.

Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician-patient interaction.

Background: Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.

Aims: Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression.

A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder.

Introduction: The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response.