Research

The Margaret Hackett Family Program (MHFP) supports and engages in research dedicated to improving treatments and bettering surgical outcomes for patients with CNS congenital anomalies. Learn more about the MHFP’s research below.

The MHFP Database

The primary goal of the Margaret Hackett Family Program (MHFP) Database is to gather comprehensive patient medical histories into a database to monitor and store information that may be helpful in bettering patient care. The MHFP Database uses REDCap to collect extensive information about patient symptoms, treatments, and surgical history. By collecting this information, the MHFP and its collaborating institutions will have access to data that may help better understand and improve patient treatments and surgical outcomes. Current collaborating sites are located in Los Angeles, CA, Boston, MA, and Charlotte, NC.

Chiari I Malformation: Are Surgical Outcomes linked to Morphological Changes?

The MHFP has partnered up with Dr. Francis Loth of the University of Akron Conquer Chiari Research Center on a study to determine if individual patient skull morphology (or the structure of the skull), and/or the morphological changes that result from surgery, can predict a patient’s surgical outcome. This study has the potential to link a patient’s morphology to their surgical outcome, which will provide surgeons with valuable data to help determine patient selection for surgery, and whether or not good outcomes can be expected. The impact of this work can extend to the advancement of techniques for quantifying brain morphometrics and its relationship to diagnosis, prognosis, and/or treatment development of a broad spectrum of diseases.

Learn More about Dr. Francis Loth

Learn More about the Conquer Chiari Research Center

How can we predict and improve shunt outcomes?

Cerebrospinal fluid shunt failure in patients with hydrocephalus is a complex process. This study aims to use the MHFP database to evaluate various factors that regulate shunt outcomes. These factors include patient characteristics such as clinical signs and symptoms, valve opening pressure, and catheter route, as well as morphometric parameters such as Evan’s ratio and brain and ventricle volume. Subsequently, machine learning models will be used to define data analysis techniques allowing accurate shunt failure risk stratification. These models will be used to predict shunt survival based on the contribution of nonlinear effective parameters. This investigation has great potential to help neurosurgeons predict shunt outcome that can aid in decision-making regarding hydrocephalus patients.

Hydrocephalus: Can intracranial compliance predict clinical oscillations?

This study aims to measure the long-term changes in intracranial compliance and recovery behavior of brain tissue in hydrocephalus patients after cerebrospinal fluid shunting. This investigation is trying to suggest a new definition for the concept of intracranial compliance noninvasively that can reflect the oscillatory behaviors, non-uniformities, and clinical variations of hydrocephalus patients during shunt treatment. This project has great potential to connect the clinical fluctuations of hydrocephalus patients with a quantitative index to open a window on clarifying the ambiguous points related to the mechanism and pathophysiology of hydrocephalus. The impact of this study can gain insight into the quantitative evaluation of material property and relaxation behavior of brain tissue and their relationships with diagnosis, prognosis, and management of hydrocephalus patients. This investigation will provide neurosurgeons with valuable data to help predict shunting outcomes as well as development of novel treatment of hydrocephalus patients.

Questions to solve when Blood, Brain, and Cerebrospinal fluid interact.

This study tries to understand the interaction of cerebral blood and cerebrospinal fluid with brain tissue to alleviate the pathophysiological ambiguities in cerebrospinal fluid dyscrasia. This work has good potential to help gain insight into the controversial and unknown points in the movement of cerebrospinal fluid and blood circulation and their close relationships with the interstitial fluid flow. This study will present a comprehensive and flexible framework to provide a robust and effective computational approach for understanding the physiological function of intracranial fluid dynamics. This investigation will provide key data to improve our understanding regarding brain fluids transport and their hydrodynamic changes. This project can help neurosurgeons prevent misdiagnoses due to clinical overlaps in cerebrospinal fluid disorders by using optimal parameters in brain fluids dynamics as the diagnostic indexes.

Skull base drilling: How coolants can reduce thermal and physical damage?

This project will study novel gas coolants to decrease the risk of thermal death in cells of the skull, blood coagulation, adjacent nerves injury, and micro-fractures during skull base drilling. This study will address the optimal operating variables that can lead to minimizing temperature, force, and torque during the drilling process to improve surgeries efficiency and prevent prolongation of treatment. This work evaluates the superiority and inferiority in skull base drilling to increase the safety of surgeries. This investigation has great potential to produce a guideline for neurosurgeons to control thermal and physical damage during skull base drilling/grinding improving the chances of successful surgery. The impact of this study may extend to the advanced navigation systems and robotic drilling to improve the performance of skull base surgeries.

Meet Seif Gholampour

Post-Doctoral Researcher, MHFP

Dr. Seif Gholampour currently works for the Margaret Hackett Family Program in the Department of Neurological Surgery at the University of Chicago. He has more than 13 years of research experience in biomedical modeling of CNS, computational and experimental neuroscience, cerebral blood hemodynamics, CSF hydrodynamics, machine learning, brain morphology, brain biomechanics, image-based fluid-structure interaction/computational fluid dynamics/finite element analysis simulations in neuroscience, and neurosurgical drilling systems. Currently, he is working on changes in intracranial fluid dynamics and recovery behavior of brain tissue of hydrocephalus patients after shunting to improve shunt outcomes, as well as the development of machine learning models to predict the risk of shunt failure.