PUBLICATIONS

Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects 2.4 million people world-wide, and up to 60% experience anxiety. We investigated how anxiety in MS is associated with white matter lesion burden in the uncinate fasciculus (UF). This is a retrospective case-control study of participants who received research-quality 3-tesla (3T) neuroimaging as part of MS clinical care. We first evaluated whether MS+severeA patients had greater lesion burden in the UF than MS+noA. Next, we examined whether increasing anxiety severity was associated with greater UF lesion burden. We demonstrated that overall lesion burden in UF was associated with the presence and severity of anxiety in patients with MS, suggesting that UF burden may contribute to anxiety comorbidity in MS.

Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects nearly one million people in the United States. Up to 50% of patients with MS experience depression. We provide new evidence supporting a relationship between white matter lesions and depression in MS. MS lesions disproportionately impacted fascicles in the depression network. MS+Depression had more disease than MS-Depression, which was driven by disease within the depression network. Future studies relating lesion location to personalized depression interventions are warranted.

Depression is a common psychiatric illness that often begins in youth, and is sometimes associated with cognitive deficits. However, there is significant variability in cognitive dysfunction, likely reflecting biological heterogeneity. We sought to identify neurocognitive subtypes and their neurofunctional signatures in a large cross-sectional sample of depressed youth. Participants were drawn from the Philadelphia Neurodevelopmental Cohort, including 712 youth with a lifetime history of a major depressive episode and 712 typically developing (TD) youth matched on age and sex. A subset (MDD n = 368, TD n = 200) also completed neuroimaging. Cognition was assessed with the Penn Computerized Neurocognitive Battery. A recently developed semi-supervised machine learning algorithm was used to delineate neurocognitive subtypes. Subtypes were evaluated for differences in both clinical psychopathology and brain activation during an n-back working memory fMRI task. We identified three neurocognitive subtypes in the depressed group. Subtype 1 was high-performing (high accuracy, moderate speed), Subtype 2 was cognitively impaired (low accuracy, slow speed), and Subtype 3 was impulsive (low accuracy, fast speed). While subtypes did not differ in clinical psychopathology, they diverged in their activation profiles in regions critical for executive function, which mirrored differences in cognition. Taken together, these data suggest disparate mechanisms of cognitive vulnerability and resilience in depressed youth, which may inform the identification of biomarkers for prognosis and treatment response.

To investigate the neural substrate of premenstrual dysphoric disorder (PMDD), the authors used [15O]H2O positron emission tomography (PET) regional cerebral blood flow (rCBF) and blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal measurements during working memory in conjunction with a 6-month hormone manipulation protocol. Abnormal working memory activation in PMDD, specifically in the dorsolateral prefrontal cortex, is related to PMDD severity, symptoms, age at onset, and disease burden. These results support the clinical relevance of the findings and the proposal that dorsolateral prefrontal cortex dysfunction represents a substrate of risk for PMDD. The concordance of the fMRI and PET data attests to the neurobiological validity of the results.

Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects one million people in the United States. Up to 50% of patients with MS experience depression, yet the mechanisms of depression in MS remain under-investigated. MS is characterized by white matter lesions, suggesting that brain network disruption may underlie depression symptoms. Studies of medically healthy participants with depression have described associations between white matter variability and depressive symptoms, but frequently exclude participants with medical comorbidities and thus cannot be extrapolated to people with intracranial diseases. The purpose of this current study is to investigate how brain network disruption underlies depression by learning from the example of multiple sclerosis.

The functions of the human brain are metabolically expensive and reliant on coupling between cerebral blood flow (CBF) and neural activity, yet how this coupling evolves over development remains unexplored. Here, we examine the relationship between CBF, measured by arterial spin labeling, and the amplitude of low-frequency fluctuations (ALFF) from resting-state magnetic resonance imaging across a sample of 831 children (478 females, aged 8-22 years) from the Philadelphia Neurodevelopmental Cohort. We first use locally weighted regressions on the cortical surface to quantify CBF-ALFF coupling. We relate coupling to age, sex, and executive functioning with generalized additive models and assess network enrichment via spin testing. We demonstrate regionally specific changes in coupling over age and show that variations in coupling are related to biological sex and executive function. Our results highlight the importance of CBF-ALFF coupling throughout development; we discuss its potential as a future target for the study of neuropsychiatric diseases.

The pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as one of the biggest health threats of our generation. A significant portion of patients are presenting with delirium and neuropsychiatric sequelae of the disease. Unique examination findings and responses to treatment have been identified. In this article, we seek to provide pharmacologic and treatment recommendations specific to delirium in patients with COVID-19.