Mark T. Curtis, M.D., Thomas Jefferson University, 1982, PhD - US grants

Project Summary/Abstract Impaired selective attention is a fundamental cognitive deficit present in the first episode of psychosis (FEP). Auditory selective attention can be measured with electroencephalography (EEG). Late sensory processing of a sound is measured by the event-related negativity present ~100ms post-stimulus (N1). When individuals pay attention to the sound, the amplitude of the N1 increases. The difference between N1 amplitudes on trials of attend vs ignore produces a negative difference (Nd), a specific measure of modulation with selective attention. Previous data from our lab suggests that FEP are impaired in modulating the N1 with selective attention (i.e. reduced Nd), and pilot data in our lab suggests that the Nd is associated with gray matter (GM) deficits in executive control areas. Neuronal recordings and human fMRI studies suggest an executive control network that involves the prefrontal cortex (PFC) and posterior parietal cortex (PPC), which modulates sensory cortices such as auditory cortex (AC). This project will investigate the network underlying the executive control of auditory attention modulation and structural abnormalities of this network in FEP. This proposal takes advantage of infrastructure in place from an ongoing study in the lab, in which healthy participants and patients with FEP perform a standard two-tone oddball task with attend and ignore conditions, while concurrent EEG and magnetoencephalography (MEG) are recorded. In Aim 1, the EEG and MEG will be merged with individual structural MRI scans to source resolve activity during the time window of the Nd (~100ms post stimulus). This project has also collected structural MR and resting fMRI to quasi-functionally parcellate cortex to estimate GM within nodes of the auditory attention network. The nodes of the network will be defined by applying the Glasser multimodal cortical parcellation to regions of PFC, PPC, and AC with the greatest activity in all participants. In Aim 2, high resolution MR data will be processed through the advanced Human Connectome Project (HCP) pipelines to significantly improve the registration of cortical areas. GM thickness will be estimated within the implicated cortical regions and compared between HC and FEP. GM thickness will be correlated with cognitive performance and symptom scores. The results from this project will give insight to the GM abnormalities in the auditory attention network underlying selective attention deficits seen in FEP. Knowing where abnormalities exist will allow therapies to target very specific brain areas. My training will focus on gaining expertise in advanced MRI processing and analytic techniques capitalizing on the HCP?s emerging state-of-the-art methods. In Aim 1, I will learn how to use EEG and MEG data to estimate source resolved activity. In Aim 2, I will gain expertise in the advanced processing methods used in the HCP pipeline to analyze structural data. I will also learn Diffusion Spectrum Imaging (DSI) as part of my MRI training, and I will gain expertise in working with a population with psychosis and understanding the structural abnormalities of early psychosis. This experience will help me accomplish my career objective of becoming a primary investigator in the field of clinical cognitive neuroscience.

Project Summary/Abstract - no change to original. Impaired selective attention is a fundamental cognitive deficit present in the first episode of psychosis (FEP). Auditory selective attention can be measured with electroencephalography (EEG). Late sensory processing of a sound is measured by the event-related negativity present ~100ms post-stimulus (N1). When individuals pay attention to the sound, the amplitude of the N1 increases. The difference between N1 amplitudes on trials of attend vs ignore produces a negative difference (Nd), a specific measure of modulation with selective attention. Previous data from our lab suggests that FEP are impaired in modulating the N1 with selective attention (i.e. reduced Nd), and pilot data in our lab suggests that the Nd is associated with gray matter (GM) deficits in executive control areas. Neuronal recordings and human fMRI studies suggest an executive control network that involves the prefrontal cortex (PFC) and posterior parietal cortex (PPC), which modulates sensory cortices such as auditory cortex (AC). This project will investigate the network underlying the executive control of auditory attention modulation and structural abnormalities of this network in FEP. This proposal takes advantage of infrastructure in place from an ongoing study in the lab, in which healthy participants and patients with FEP perform a standard two-tone oddball task with attend and ignore conditions, while concurrent EEG and magnetoencephalography (MEG) are recorded. In Aim 1, the EEG and MEG will be merged with individual structural MRI scans to source resolve activity during the time window of the Nd (~100ms post stimulus). This project has also collected structural MR and resting fMRI to quasi-functionally parcellate cortex to estimate GM within nodes of the auditory attention network. The nodes of the network will be defined by applying the Glasser multimodal cortical parcellation to regions of PFC, PPC, and AC with the greatest activity in all participants. In Aim 2, high resolution MR data will be processed through the advanced Human Connectome Project (HCP) pipelines to significantly improve the registration of cortical areas. GM thickness will be estimated within the implicated cortical regions and compared between HC and FEP. GM thickness will be correlated with cognitive performance and symptom scores. The results from this project will give insight to the GM abnormalities in the auditory attention network underlying selective attention deficits seen in FEP. Knowing where abnormalities exist will allow therapies to target very specific brain areas. My training will focus on gaining expertise in advanced MRI processing and analytic techniques capitalizing on the HCP?s emerging state-of-the-art methods. In Aim 1, I will learn how to use EEG and MEG data to estimate source resolved activity. In Aim 2, I will gain expertise in the advanced processing methods used in the HCP pipeline to analyze structural data. I will also learn Diffusion Spectrum Imaging (DSI) as part of my MRI training, and I will gain expertise in working with a population with psychosis and understanding the structural abnormalities of early psychosis. This experience will help me accomplish my career objective of becoming a primary investigator in the field of clinical cognitive neuroscience.