Quantitative Automated Lesion Detection of Traumatic Brain Injury

Traumatic Brain Injury Clinical Trial

— QALD  

Official title:

Quantitative Automated Lesion Detection of TBI

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT01022307
• Other study ID #: B6120-R
• Status: Completed
• Phase: N/A
• First received: May 8, 2009
• Last updated: December 8, 2015
• Start date: May 2009
• Est. completion date: October 2014

Study information

• Verified date: December 2015
• Source: VA Office of Research and Development
• Contact: n/a
• Is FDA regulated: No
• Health authority: United States: Federal Government
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 212
• Est. completion date: October 2014
• Est. primary completion date: October 2014
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Both
• Age group: 18 Years to 50 Years

Eligibility

Inclusion Criteria:

• Control subjects from 18-50.

• Patients from 18-50 who have suffered TBI.

Exclusion Criteria:

• Substance abuse.

• Irremedial sensory deficits (blindness, deafness).

• Primary psychiatric disorder.

• Neurological disease unrelated to TBI.

Study Design

Observational Model: Case Control, Time Perspective: Retrospective

Related Conditions & MeSH terms

• Brain Injuries
• Traumatic Brain Injury

Locations

Country	Name	City	State
United States	VA Northern California HCS	Martinez	California

Sponsors (1)

Lead Sponsor	Collaborator
VA Office of Research and Development

Country where clinical trial is conducted

United States, 

References & Publications (18)

Alho K, Rinne T, Herron TJ, Woods DL. Stimulus-dependent activations and attention-related modulations in the auditory cortex: a meta-analysis of fMRI studies. Hear Res. 2014 Jan;307:29-41. doi: 10.1016/j.heares.2013.08.001. Epub 2013 Aug 11. Review. — View Citation

Cate AD, Herron TJ, Kang X, Yund EW, Woods DL. Intermodal attention modulates visual processing in dorsal and ventral streams. Neuroimage. 2012 Nov 15;63(3):1295-304. doi: 10.1016/j.neuroimage.2012.08.026. Epub 2012 Aug 16. — View Citation

Herron TJ, Kang X, Woods DL. Automated measurement of the human corpus callosum using MRI. Front Neuroinform. 2012 Sep 12;6:25. doi: 10.3389/fninf.2012.00025. eCollection 2012. — View Citation

Kang X, Herron TJ, Cate AD, Yund EW, Woods DL. Hemispherically-unified surface maps of human cerebral cortex: reliability and hemispheric asymmetries. PLoS One. 2012;7(9):e45582. doi: 10.1371/journal.pone.0045582. Epub 2012 Sep 18. — View Citation

Kang X, Herron TJ, Ettlinger M, Woods DL. Hemispheric asymmetries in cortical and subcortical anatomy. Laterality. 2015;20(6):658-84. doi: 10.1080/1357650X.2015.1032975. Epub 2015 Apr 20. — View Citation

Kang X, Herron TJ, Turken AU, Woods DL. Diffusion properties of cortical and pericortical tissue: regional variations, reliability and methodological issues. Magn Reson Imaging. 2012 Oct;30(8):1111-22. doi: 10.1016/j.mri.2012.04.004. Epub 2012 Jun 12. — View Citation

Kang X, Herron TJ, Woods DL. Regional variation, hemispheric asymmetries and gender differences in pericortical white matter. Neuroimage. 2011 Jun 15;56(4):2011-23. doi: 10.1016/j.neuroimage.2011.03.016. Epub 2011 Mar 22. — View Citation

Kang X, Herron TJ, Woods DL. Validation of the anisotropy index ellipsoidal area ratio in diffusion tensor imaging. Magn Reson Imaging. 2010 May;28(4):546-56. doi: 10.1016/j.mri.2009.12.015. Epub 2010 Jan 21. — View Citation

Turken AU, Herron TJ, Kang X, O'Connor LE, Sorenson DJ, Baldo JV, Woods DL. Multimodal surface-based morphometry reveals diffuse cortical atrophy in traumatic brain injury. BMC Med Imaging. 2009 Dec 31;9:20. doi: 10.1186/1471-2342-9-20. — View Citation

Whitaker KJ, Kang X, Herron TJ, Woods DL, Robertson LC, Alvarez BD. White matter microstructure throughout the brain correlates with visual imagery in grapheme-color synesthesia. Neuroimage. 2014 Apr 15;90:52-9. doi: 10.1016/j.neuroimage.2013.12.054. Epub — View Citation

Woods DL, Herron TJ, Cate AD, Kang X, Yund EW. Phonological processing in human auditory cortical fields. Front Hum Neurosci. 2011 Apr 20;5:42. doi: 10.3389/fnhum.2011.00042. eCollection 2011. — View Citation

Woods DL, Herron TJ, Cate AD, Yund EW, Stecker GC, Rinne T, Kang X. Functional properties of human auditory cortical fields. Front Syst Neurosci. 2010 Dec 3;4:155. doi: 10.3389/fnsys.2010.00155. eCollection 2010. — View Citation

Woods DL, Wyma JM, Herron TJ, Yund EW. An improved spatial span test of visuospatial memory. Memory. 2015 Sep 11:1-14. [Epub ahead of print] — View Citation

Woods DL, Wyma JM, Herron TJ, Yund EW. The Effects of Aging, Malingering, and Traumatic Brain Injury on Computerized Trail-Making Test Performance. PLoS One. 2015 Jun 10;10(6):e0124345. doi: 10.1371/journal.pone.0124345. eCollection 2015. — View Citation

Woods DL, Wyma JM, Yund EW, Herron TJ, Reed B. Age-related slowing of response selection and production in a visual choice reaction time task. Front Hum Neurosci. 2015 Apr 23;9:193. doi: 10.3389/fnhum.2015.00193. eCollection 2015. Erratum in: Front Hum Ne — View Citation

Woods DL, Wyma JM, Yund EW, Herron TJ, Reed B. Factors influencing the latency of simple reaction time. Front Hum Neurosci. 2015 Mar 26;9:131. doi: 10.3389/fnhum.2015.00131. eCollection 2015. — View Citation

Woods DL, Yund EW, Wyma JM, Ruff R, Herron TJ. Measuring executive function in control subjects and TBI patients with question completion time (QCT). Front Hum Neurosci. 2015 May 19;9:288. doi: 10.3389/fnhum.2015.00288. eCollection 2015. — View Citation

Zhang S, Cate AD, Herron TJ, Kang X, Yund EW, Bao S, Woods DL. Functional and anatomical properties of human visual cortical fields. Vision Res. 2015 Apr;109(Pt A):107-21. doi: 10.1016/j.visres.2015.01.015. Epub 2015 Feb 4. — View Citation

* Note: There are 18 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Performance on Trail-making Test, Part B	z-score based on response time, regressed for age and computer use	Single session generally several years after TBI depending on time of recruitment of subjects.	No