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

  • Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry Software (PI: Michael I. Miller (JHU, contact), Wang): 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.
  • 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.
  • PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis (PI: Wang (contact), Howie Rosen): Accurate diagnosis of frontotemporal dementia (FTD) is difficult because the relationships between clinical syndromes, and pathological and genetic causes are complex. In this project, we propose to use machine learning technologies to develop powerful predictive biomarkers that can distinguish bvFTD, AD and healthy controls, using well-characterized clinical, neurological and neuroanatomical data from multiple national databases. As treatment agents with potential disease-modifying effects are developed, sensitive and specific biomarkers can be tested and then eventually used in the appropriate patient populations.

Clinical Applications

  • 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: Kristin Swanson (Mayo), Wang): 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: Greg Miller (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.
  • Pilot for A Systems-based Understanding and REmediation of Cancer and Cancer-Related Cognitive Impairment (ASURE) (PI: Wang (Contact), Penedo, Cella): This pilot project will collect preliminary data toward the goal of developing a program aimed at studying precise mechanisms of cancer and cancer-treatment related cognitive impairments (CRCI) and developing treatment strategies targeting the mechanisms. We will study prostate cancer and breast cancer survivors by collecting and analyzing neuroimaging, cognitive, psychosocial stress, and inflammation data before and after initiation of adjuvant therapy.
  • The New Tics Study: A Novel Approach to Pathophysiology and Cause of TIC Disorder (PI: Kevin Black (contact), Brad Schlaggar WUSTL): At least 20% of all children have tics at some time in their life, making tic disorders a subject of substantial public health interest. In many children the tics will disappear before they’ve lasted as long as a year, but in others they go on to become Tourette syndrome or Chronic Tic Disorder (TS/CTD). Prior research generally has not clarified whether abnormalities of brain structure and function in children with TS/CTD are related to tic appearance or to the more important process of tic disappearance. This project will study children with recent-onset tics using clinical evaluation, MRI, and neuropsychological measures, and repeat these measures at the earliest time point that TS/CTD can be diagnosed. The results from the first assessment will be compared to results from two matching comparison groups: children with no personal or family history of tics, and children who have had tics for more than a year (Existing TS/CTD). This new approach may provide a new angle on research into Tourette syndrome, in addition to providing sorely needed information about prognosis for children who just started ticcing.
  • Cerebral Small Vessels in Motor and Cognitive Decline (PI: Farzaneh Sorond (Neurology)): This study is to identify vascular measures of cerebral small vessels which precede the onset of cognitive and motor decline and are predictive of clinical and radiographic outcomes in small vessel disease.

Big Data Neuroscience

  • 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
  • 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: 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.
  • NCS-FO: Connectome mapping algorithms with application to community services for big data neuroscience (PI: Pestilli, Garyfallidis, Henschel, Wang, Dinov): The project focuses on providing seamless public access to data, computing, and reproducible algorithms, while promoting code sharing and upcycling the long tail of neuroscience data.


At NIACAL, we are incredibly grateful for grant support.

Journal Club Papers