Quantitative Automated Lesion Detection of Traumatic Brain Injury
Condition: Traumatic Brain Injury
MeSH terms: Brain Injuries; Brain Injuries, Traumatic
Study type: Observational

Summary: 
      The investigators propose to develop quantitative automated lesion detection (QALD)
      procedures to identify brain damage following traumatic brain injury more accurately than is
      possible with a normal magnetic resonance imaging (MRI) scans. These procedures require about
      1 hour of imaging in an MRI scanner. Subjects will also undergo about 2 hours of cognitive
      tests. The investigators will compare the results of the cognitive tests with those from MRI
      scanning to determine what brain regions are responsible for superior performance and for
      performance decrements.
    

Detailed description: 
      Because of their non-focal nature, TBI-related brain lesions are difficult to detect and
      quantify with traditional MRI. In the current research program the investigators propose to
      develop quantitative automated lesion detection (QALD) procedures to (1) clarify the nature
      and distribution of tissue damage following mild, moderate and severe TBI (2) improve the
      capability of detecting, quantifying, and localizing TBI brain damage in individual patients
      and (3) correlate quantitative measures of brain damage in individual TBI patients with
      neuropsychological deficits in attention, memory, and executive function.

      QALD detects abnormal tissue parameters in the diseased brain through statistical comparisons
      with a normative database. Preliminary results show that QALD is capable of detecting highly
      significant abnormalities in the brains of TBI patients with normal clinical MRI scans. QALD
      will be further enhanced and tested with a larger database and including brain images
      acquired with four different imaging sequences (T1, T2, DTI and fluid-attenuated inversion
      recovery or FLAIR) from 100 control subjects. Data analysis will incorporate advanced
      cortical surface mapping techniques to quantify gray matter tissue parameters and thickness
      in 34 distinct cortical regions in each hemisphere. In addition, cortical fiber projections
      will be quantified with DTI and FLAIR analysis of white matter lying below the cortical
      surface. Subcortical fiber tracts critical for complex cognitive operations will be analyzed
      with voxel-based morphometry and with improved region of interest algorithms to define fiber
      tract boundaries. Tissue properties in critical subcortical structures (e.g., the
      hippocampus) will be quantified after automatic parcellation of these brain regions. The
      investigators will also test the control subjects on a battery of neuropsychological tests
      (NPTs) and correlate variations in the size, myelination, and tissue properties of normal
      cortical and subcortical structures with cognitive performance. Then, the investigators will
      gather identical imaging data in 99 TBI patients divided into three groups (mild, moderate
      and severe TBI) in order to characterize the average pattern of damage caused by TBIs of
      different severity. Next, the investigators will quantify lesions in individual TBI patients
      and describe the variability of lesion patterns in the different severity groups. In
      parallel, the investigators will develop further multimodal analysis techniques to combine
      statistical information from different imaging sequences to improve lesion-detection
      sensitivity to co-localized abnormalities evident with different imaging protocols. In
      addition, the investigators will test patients with NPTs and analyze the relationship between
      brain damage, cognitive performance and self-assessments of outcome in order to improve the
      prognostic value of neuroradiological studies of TBI.
    

Observational model: Case-Control
Time perspective: Retrospective
Sampling method: Probability Sample
Study population: 
        Our targeted population is veterans from OEF/OIF. Other subjects will be recruited as
        needed to fulfill recruitment targets.
      
Biospecimen description: 
      No biospecimen, only digital data.
    
Keywords: behavioral data acquisition; MRI