Our group focuses on developing complex biomarkers for neuropsychiatric disorders, particularly schizophrenia and Alzheimer's disease, using the tools of computational anatomy developed in conjunction with our collaborators. To achieve this goal, we develop and implement precise measures of neuroanatomical structure and then relate these measures to cognition, brain activation and specific elements of clinical psychopathology. One of our primary focuses is to describe changes in any of these measures that may occur during disease progression or treatment. As an adjunct to such studies, we also conduct clinical trials with special emphasis on interventions that aim to improve cognition.
In addition to neuropathology, our interests also lie in the neuroanatomical and functional consequences of normal genetic variation. Therefore, some of our studies employ juvenile subjects so that developmental aspects of brain structure and function may be studied.
Students in our laboratory have the opportunity to learn a wide variety of experimental techniques, working with structural and functional magnetic resonance imaging, diffusion tensor imaging and quantitative image analysis. They become familiar with scientific software such as MATLAB, in addition to learning specific software packages used for brain mapping and computational anatomy.
NIACAL welcomes research volunteers. Please visit the Psychiatry Department volunteer page to find out how to participate.
Because of the diverse interests of the NIACAL team, we are constantly exploring novel means of studying neuological disorders. Below are some of the projects with which we're currently involved.
Complex Neuroimaging Biomarkers
- Using Spatial Patterns by Structural MRI to Predict Specific Neuropathologies in Dementia (PI: Wang): We propose to develop novel antemortem biomarkers for specific neurodegenerative diseases using structural MRI. We will develop this technology using the hippocampus as an anatomic “test bed” and demonstrate its sensitivity and specificity in large neuroimaging datasets.
- Multimodal neuroimaging biomarkers for early detection of frontotemporal dementia using machine learning and large-scale multisite longitudinal datasets (PI: Wang): We propose to use machine learning to develop multimodal neuroimaging biomarkers for early detection and monitoring progression of FTD, using neuroimaging data, clinical severity, and cognitive assessments provided by multiple consortium efforts on FTD research, while capitalizing on unique opportunities for collecting new imaging data to further increase sample size.
- LitCog MRI: brain network markers and intervention targets for the cognitive decline to health literacy & self-management (HLSM) skills loss to poor health outcomes cascade (PI: Wang): Poor health outcomes have been associated with cognitive declines and loss of health literacy & self-management skills. To date, we don’t yet understand of the underlying causal links between changes in cognition that may lead to loss of health skills and ultimately poorer physical health. We will conduct a longitudinal neuroimaging study and develop brain network biomarkers that may explain these causal links.
- HippoPCI: Hippocampal Predictors of Cognitive Impairment in Breast Cancer Patients (PI: Wang): This is a longitudinal magnetic resonance imaging (MRI) study where we identify predictors and mechanisms of cognitive impairment in breast cancer patients receiving hormonal treatment by using structural and functional assessments that are sensitive to the integrity of the hippocampal-cortical circuitry.
- The ENDURES Study: Environmental Dynamics Underlying Responsive Extreme Survivors of Glioblastoma (PI: Lei Wang, PhD and Kristin Swanson, PhD): This project is a collaboration between the laboratories of Professor Lei Wang (Neuroimaging and Applied Computational Anatomy Lab) and Professor Kristin Swanson (Mathematical Neuro-Oncology Lab). We plan to assess neural capacity and functional recovery in the setting of injury; specifically, the ability of the brain to recover/remodel in long-term glioblastoma multiforme patients.
- Childhood Origins of CHD Disparities: Neural & Immune Pathways (PI: Lei Wang PhD and Greg Miller PhD (Psychology)): Our lab’s role is to map out the relationship between socioeconomic status (SES) and the development of brain’s gray matter and white matter. The overall study of 250 youth from economically diverse backgrounds poses questions about SES disparities in immunologic, neural, and psychosocial development, and the implications for early Congenital Heart Defect (CHD) risk.
- The role of the hippocampal-prefrontal network in cancer-related cognitive impairment; a multimodal cross sectional study (PI: Alexandra Apple, F31 Predoctoral NRSA): This project investigates the role of the hippocampal-prefrontal network in cognitive impairment due to cancer and its treatments using structural and functional imaging data.
- Cortical thickness, subcortical deformation, and structural covariance networks in youth with perinatally-acquired HIV: associations with HIV disease severity and cognition (PI: C. Paula Lewis de los Angeles, F30 Predoctoral NRSA): This project uses structural MRI data from youth with perinatally-acquired HIV (PHIV) in combination anti-retroviral therapy (cART) to characterize neuro-structure and structural covariance networks and its correlation to HIV disease severity and cognitive performance.
- Using cognitive neuroscience approaches on memory and attention to elucidate brain network deficits underlying cognitive complaints in breast cancer patients receiving adjuvant therapy (PI: Wang): Cancer patients often report a significant reduction in cognitive abilities after receiving adjuvant cancer therapy. However, assessments using standard neuropsychological battery has not been able to accurately pinpoint the nature of these cancer treatment-related cognitive impairments (CRCI) in patients. We propose to use cognitive neuroscience-based assessments of episodic memory and attention/executive function to study the neural mechanism of CRCI.
- Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry Software (PI: Lei Wang PhD and Michael Miller PhD (JHU)): Our lab’s role is to use SchizConnect to retrieve schizophrenia imaging data for pipeline processing; implement the subcortical software developed and hardened at JHU, with remote processing being carried out at MriCloud. The overall project extends and hardens powerful computational anatomy and computer science software to analyze large datasets from neuroimaging studies of neurodevelopmental and neurodegeneration disorders including Huntington’s Disease, Schizophrenia and Attention Deficit Hyperactive Disorders.
- BD Spokes: SPOKE MIDWEST Collaborative: Advanced Computational Neuroscience Network (ACNN) (PI: Lei Wang PhD and Franco Pestilli PhD (Indiana)): The SchizConnect technology is being leveraged to enable cross-center neuroimaging data and high-level accessibility, and provide an integrated virtual database with a well-defined consistent schema. The overall ACNN project is aimed at building broad consensus on the core requirements, infrastructure, and components needed to develop a new generation of sustainable interdisciplinary neuroscience big data research. Six major universities in the Midwest including Northwestern will coordinate activities across more than 25 other universities, industry partners, neuroscience research centers and hospitals.
- SchizConnect: Large-Scale Schizophrenia Neuroimaging Data Mediation & Federation (PI: Lei Wang (Contact, NU), Jose Luis Ambite (USC), Steven Potkin (UCI), Jessica Turner (MRN), David Keator (UCI)): Large-scale data sharing and integration is needed to further the state-of-the-art schizophrenia research, but is presently not possible due to practical limitations in the way in which data are being shared. SchizConnect is a data mediation and integration resource to overcome these limitations in a low-cost manner and deliver a web portal to interact with the federated databases. Access SchizConnect at http://schizconnect.org.
- Northwestern University Schizophrenia Data and Software Tool (NUSDAST) (PI: Wang): To make structural magnetic resonance (MR) imaging data, genotyping data, and neurocognitive data as well as analysis tools available to the schizophrenic research community. Please note that this dataset is also accessible through the SchizConnect project at: http://schizconnect.org. Here, you will be able to get additional schizophrenia neuroimaging data from FBIRN and COINS.
- Northwestern University Neuroimaging Data Archive (NUNDA) (PI: Wang): To establish a data archive for the neuroimaging community at Northwestern University, which has the capacity to efficiently and securely store collected MR scans, and to later retrieve them in time-sensitive datasets.
At NIACAL, we are incredibly grateful for grant support.
- NICHD: NRSA F30: Cortical thickness, subcortical deformation, and structural covariance networks in youth with perinatally-acquired HIV: associations with HIV disease severity and cognition (PI: de los Angeles) - C. Paula Lewis-de los Angeles was awarded an NRSA F30 Predoctoral Fellowship for MD/PhD students from the NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The project focuses on characterizing brain morphology and networks in youth with perinatally-acquired HIV using neuroimaging. The fellowship will support research and clinical training. (12/01/2016 - 12/01/2018). Associated project: Cortical thickness, subcortical deformation, and structural covariance networks in youth with perinatally-acquired HIV: associations with HIV disease severity and cognition
- NSF: SP0037646 National Science Foundation (PI: Pestilli, MPI: Wang, Saykin, Sporns) - BD Spokes: SPOKE: MIDWEST: Collaborative: Advanced Computational Neuroscience Network (ACNN) (09/01/2016 - 08/31/2019). Associated project: BD Spokes: SPOKE MIDWEST Collaborative: Advanced Computational Neuroscience Network (ACNN)
- NCI: The role of the hippocampal-prefrontal network in cancer-related cognitive impairment; a multimodal cross sectional study (PI: Apple) - Alexandra Apple was awarded an NRSA F31 Predoctoral Fellowship from the National Cancer Institute. Her project investigates the role of the hippocampal-prefrontal network in cognitive impairment due to cancer and its treatments using structural and functional imaging data. (07/01/2016 - 06/30/2018). Associated project: The role of the hippocampal-prefrontal network in cancer-related cognitive impairment; a multimodal cross sectional study
- NIBIB: 1R01EB020062-01A1 NIBIB (Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry Software) (PI: Miller, MPI: Paulsen, Mostfosky, Wang) - Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry Software (09/01/2015 - 08/31/2019). Associated project: Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry Software
- NICHD: R01 HL122328 NHLBI/NICHD (Childhood Origins of CHD Disparities: Neural & Immune Pathways) (PI: Wang, Miller) - Childhood Origins of CHD Disparities: Neural & Immune Pathways (11/01/2014 - 08/31/2019). Associated project: Childhood Origins of CHD Disparities: Neural & Immune Pathways
- James S. McDonnell Foundation: “The ENDURES Study: Environmental Dynamics Underlying Responsive Extreme Survivors of Glioblastoma” (PI: Kristin Swanson, PhD; Collaborating-PI: Lei Wang) - Tumor biologists and clinical oncologists are searching for new ways of developing and testing treatment approaches that consider cancer in the context of its environment. Borrowing concepts from ecosystems science and mathematical biology, researchers are investigating the dynamic and adaptive changes that make tumors less susceptible or resistant to mainline therapies and more invasive and aggressive upon recurrence. The collaborative activity proposal submitted by Swanson and collaborators will study extreme long-term survivors of Glioblastoma Multiforma (GBM) to characterize treatment responses and the causes of disease progression. An expanded use of imagederived cancer dynamics could reveal environmental contributions to long-term survival. Extreme long-term survivors are typically patients surviving five years or more beyond initial diagnosis and treatment. The team will also collect data on the GBM microenvironment, interactions between the tumor and the rest of the body and the role of other environmental influences. As stated in the proposal “…current approaches are akin to trying to understand the germination of a seed by looking at its genetic profile but ignoring the impact of the surrounding environment.” (09/01/2014 - 08/31/2017). Associated project: The ENDURES Study: Environmental Dynamics Underlying Responsive Extreme Survivors of Glioblastoma
- 1U01 MH097435: SchizConnect: Large-Scale Schizophrenia Neuroimaging Data Mediation & Federation (PI: Lei Wang (Contact, NU), Jose Luis Ambite (USC), Steven Potkin (UCI), Jessica Turner (MRN)) - We propose a data mediation and integration resource to overcome limitations in large-scale data sharing and integration in state-of-the-art schizophrenia research in a low-cost manner and deliver a web portal to interact with the federated databases. (03/19/2013 - 01/31/2017). Associated project: SchizConnect: Large-Scale Schizophrenia Neuroimaging Data Mediation & Federation
- 1R01 NR014182: HippoPCI Hippocampal Predictors of Cognitive Impairment in Breast Cancer Patients (PI: Wang) - Post-surgery adjuvant therapy produces changes in cognitive function in up 10-70% of women with breast cancer. To date, no investigation has assessed the neural correlates of adjuvant hormonal therapy, nor have any studies determined how to identify individuals at risk for treatment-related cognitive impairment. We propose to use longitudinal magnetic resonance imaging (MRI) to identify predictors and mechanisms of cognitive impairment in breast cancer patients receiving hormonal treatment. (09/26/2012 - 06/30/2017). Associated project: HippoPCI: Hippocampal Predictors of Cognitive Impairment in Breast Cancer Patients
Journal Club Papers
- 2016-02-12 (Lei Wang, PhD): Feature Selection Based on the SVM Weight Vector for Classification of Dementia View PDF
- 2016-02-12 (Lei Wang, PhD): A Robust Deep Model for Improved Classification of AD/MCI Patients View PDF
- 2016-03-11 (Lei Wang, PhD): Dissociating hippocampal and basal ganglia contributions to category learning using stimulus novelty and subjective judgments View PDF
- 2016-05-06 (Anna Varentsova): Data Visualization in the Neurosciences: Overcoming the Curse of Dimensionality View PDF