Diffusion Anisotropy Imaging Shows More Than Just Axonal Connectivity<br />Pratik Mukherjee, MD PhD<br />Associate Profess...
Mild TBI: the Need for Biomarkers<br />Mild Traumatic Brain Injury (mTBI)<br />Standard clinical measures of injury severi...
Diffusion Tensor Imaging<br />Anisotropy (Microstructure):<br />FA: 0 (spherical) to 1 (linear)<br />is CV of DTI eigenval...
cingulum bundle<br />superior<br />longitudinal<br />fasciculus<br />centrum<br />semiovale<br />corpus<br />callosum,<br ...
Diffusion Analytics<br /><ul><li>Manual ROI-based Measurements of Diffusion Anisotropy</li></ul>– advantages: simple to pe...
Cornell - UCSF Study:  3T MRI-DTI of Mild TBI<br />Is Extent of Microstructural White Matter Injury <br />Related to Globa...
3T DTI of Mild TBI<br />Niogi S, Mukherjee P, Ghajar J et al., AJNR 2008; 29:967-73. <br />
Spatial Extent of White Matter Injury on DTI <br />Correlates with Cognitive Processing Speed in Mild TBI<br />Niogi S, Mu...
Cornell - UCSF Study:  3T MRI-DTI of Mild TBI<br />Are Attentional and Memory Impairment Related to Damage in Specific Whi...
DTI of Blast-Induced Mild TBI<br />
Tract-Based Spatial Statistics (TBSS) –<br />Voxel-Wise DTI Analysis using FA Skeletonization<br />Smith, Jenkinson, Johan...
UCSF Prospective Longitudinal Study <br />of Mild TBI with 3T MRI-DTI<br />31 mild TBI patients prospectively enrolled in ...
Longitudinal DTI of Mild TBI vs Controls: TBSS<br />MD<br />mTBI > controls<br />FA<br />mTBI < controls<br />RD<br />mTBI...
Longitudinal DTI of Mild TBI vs Controls: TBSS<br />< 2 weeks<br />1 month<br />1 year<br />FA(mTBI < controls)<br /> P<0....
Neuroplasticity: Training-Induced Changes in WM Microstructure<br />Scholz et al., <br />Nat Neurosci 2008; 6(7):e159. <br />
ICA decomposition for group DTI data<br />Microstructural Correlation Maps<br />Voxel<br />A<br />Y<br />X<br />         N...
ICA of Group DTI Data<br />53 Normal Adult Volunteers:<br />31 men, 22 women<br />mean age 30.7 ± 8.8 years<br />44 right-...
ICA of Group DTI Data<br />supratentorial<br />projection<br />WM tracts<br />Li, Yang, Nguyen, Cooper, LaHue, Venugopal, ...
ICA of <br />Group DTI Data<br />neocortical<br />association<br />WM tracts<br />Li, Yang, Nguyen, Cooper, LaHue, Venugop...
Reproducible Quantitative Fiber Tracking:<br />3T DTI with 55 diffusion directions @ b=1000<br />CB<br />AF<br />IFO<br />...
“Connectomics”: Global Connectivity, not Local Microstructure<br />Hagmann P et al., PLoS Biology 2008; 6(7):e159. <br />N...
“Importance Maps”: Loss of Connectivity due to Mild TBI<br />Kuceyeski A et al., Neuroimage 2011; doi:10.1016/jneuroimage....
Summary:  DTI of Mild TBI<br /><ul><li>Assessment of local white matter microstructure (anisotropy)</li></ul>-- proven to ...
Acknowledgements<br />UCSF Neurosurgery<br />Geoffrey T. Manley, MD PhD<br />Diane Morabito, RN<br />Sara C. LaHue<br />Sh...
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Mukherjee, Pratik

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  • Spatial temporal decomposition on fMRI can reveal the brain functional connectivity.Start from seed voxel correlation;Data arranged as Y;Can we connectivity maps without ROI?Multiple maps and time course --- composes a linear mixture model.
  • Spatial temporal decomposition on fMRI can reveal the brain functional connectivity.Start from seed voxel correlation;Data arranged as Y;Can we connectivity maps without ROI?Multiple maps and time course --- composes a linear mixture model.
  • Spatial temporal decomposition on fMRI can reveal the brain functional connectivity.Start from seed voxel correlation;Data arranged as Y;Can we connectivity maps without ROI?Multiple maps and time course --- composes a linear mixture model.
  • Mukherjee, Pratik

    1. 1. Diffusion Anisotropy Imaging Shows More Than Just Axonal Connectivity<br />Pratik Mukherjee, MD PhD<br />Associate Professor of <br />Radiology & Bioengineering<br />
    2. 2. Mild TBI: the Need for Biomarkers<br />Mild Traumatic Brain Injury (mTBI)<br />Standard clinical measures of injury severity such as GCS, duration of loss of consciousness (LOC), and duration of post-traumatic amnesia (PTA) do not predict the development of persistent PCS, especially in mild TBI<br />A prognostic biomarker for PCS is needed for early patient counseling and for medicolegal purposes<br />A predictive biomarker is needed for triaging patients to interventions as well as for monitoring the effectiveness of the intervention<br />cognitive & occupational rehabilitation<br />cognitive enhancement pharmacotherapy<br />experimental trials of drugs to reduce secondary injury after TBI<br />
    3. 3. Diffusion Tensor Imaging<br />Anisotropy (Microstructure):<br />FA: 0 (spherical) to 1 (linear)<br />is CV of DTI eigenvalues<br />Connectivity:<br />Fiber orientation is the<br />primary DTI eigenvector<br />Mukherjee P, et al., AJNR 2008; 29:632-41 <br />
    4. 4. cingulum bundle<br />superior<br />longitudinal<br />fasciculus<br />centrum<br />semiovale<br />corpus<br />callosum,<br />body<br />3 Tesla Diffusion Tensor Imaging (DTI)<br />1.8 mm isotropic <br />spatial resolution<br />
    5. 5. Diffusion Analytics<br /><ul><li>Manual ROI-based Measurements of Diffusion Anisotropy</li></ul>– advantages: simple to perform<br />– disadvantages: labor intensive, covers tiny part of tract, may not be reproducible<br /><ul><li>Voxel-based Analysis: e.g. Tract-Based Spatial Statistics (TBSS)</li></ul>– advantages: fully automated, high spatial resolution, covers entire brain<br />– disadvantages: requires spatial normalization, poor sensitivity compared to ROIs or tractography due to multiple comparisons problem, interpretation of results<br /><ul><li>Source-Based Analysis: Independent Component Analysis of DTI</li></ul>– advantages: fully automated, high spatial resolution, covers entire brain<br />– disadvantages: requires spatial normalization, interpretation of results<br /><ul><li>Quantitative Fiber Tractography </li></ul>– advantages: covers “entire” tract, excellent inter-rater reliability<br />– disadvantages: labor intensive, coarse spatial scale, fiber tracking errors<br /><ul><li>Connectomics: Graph Theoretical Analysis of Fiber Connectivity</li></ul>– advantages: fully automated, uses global brain connectivity information<br />– disadvantages: unproven, loses local microstructural information<br />
    6. 6. Cornell - UCSF Study: 3T MRI-DTI of Mild TBI<br />Is Extent of Microstructural White Matter Injury <br />Related to Global Cognitive Processing Speed?<br />34 chronic symptomatic mild TBI patients prospectively enrolled 1-65 months after injury, both in NY & SF<br />All with only a single episode of head trauma (predominantly MVAs, assaults, & falls)<br />All with no history of chronic medical or neuropsychiatric illness (including drug or EtOH abuse)<br />All presented with GCS 13-15 in the Emergency Dept.<br />All presented with post-traumatic amnesia<br />All with persistent post-concussive symptoms<br />26 normal volunteers from NY & SF matched for:<br />age<br />gender<br />handedness<br />years of education<br />Niogi S, Mukherjee P, Ghajar J et al., AJNR 2008; 29:967-73. <br />
    7. 7. 3T DTI of Mild TBI<br />Niogi S, Mukherjee P, Ghajar J et al., AJNR 2008; 29:967-73. <br />
    8. 8. Spatial Extent of White Matter Injury on DTI <br />Correlates with Cognitive Processing Speed in Mild TBI<br />Niogi S, Mukherjee P, Ghajar J et al., AJNR 2008; 29:967-73. <br />
    9. 9. Cornell - UCSF Study: 3T MRI-DTI of Mild TBI<br />Are Attentional and Memory Impairment Related to Damage in Specific White Matter Tracts?<br />43 chronic symptomatic mild TBI patients prospectively enrolled 1-65 months after injury, both in NY & SF<br />All with only a single episode of head trauma (predominantly MVAs, assaults, & falls)<br />All with no history of chronic medical or neuropsychiatric illness (including drug or EtOH abuse)<br />All presented with GCS 13-15 in the Emergency Dept.<br />All presented with post-traumatic amnesia<br />All with persistent post-concussive symptoms<br />23 normal volunteers from NY & SF matched for:<br />age<br />gender<br />handedness<br />years of education<br />Niogi S, Mukherjee P, Ghajar J et al., Brain 2008; 131:3209-21. <br />
    10. 10.
    11. 11. DTI of Blast-Induced Mild TBI<br />
    12. 12. Tract-Based Spatial Statistics (TBSS) –<br />Voxel-Wise DTI Analysis using FA Skeletonization<br />Smith, Jenkinson, Johansen-Berg et al. Neuroimage 2006; 31:1487-1505<br />
    13. 13. UCSF Prospective Longitudinal Study <br />of Mild TBI with 3T MRI-DTI<br />31 mild TBI patients prospectively enrolled in Emergency Dept.<br />All with only a single episode of head trauma (predominantly MVAs, assaults, & falls)<br />All with no history of chronic medical or neuropsychiatric illness (including drug or EtOH abuse)<br />All presented with GCS 13-15 in the Emergency Dept.<br />All presented with witnessed loss of consciousness (LOC)<br />All presented with post-traumatic amnesia<br />Patients scanned serially with 3T MRI and DTI at acute (< 2 wks), 1-month, and 1-year time points after injury<br />30 age-, gender-, & education-matched normal volunteers<br />Yang FG, Manley GT, Mukherjee P et al., ISMRM (2011) <br />
    14. 14. Longitudinal DTI of Mild TBI vs Controls: TBSS<br />MD<br />mTBI > controls<br />FA<br />mTBI < controls<br />RD<br />mTBI > controls<br />
    15. 15. Longitudinal DTI of Mild TBI vs Controls: TBSS<br />< 2 weeks<br />1 month<br />1 year<br />FA(mTBI < controls)<br /> P<0.01 P=0<br />
    16. 16. Neuroplasticity: Training-Induced Changes in WM Microstructure<br />Scholz et al., <br />Nat Neurosci 2008; 6(7):e159. <br />
    17. 17. ICA decomposition for group DTI data<br />Microstructural Correlation Maps<br />Voxel<br />A<br />Y<br />X<br /> Noise<br />.<br />+<br />=<br />Subject<br />Localized spatial maps<br />Subject courses<br />Li, Yang, Nguyen, Cooper, LaHue, Venugopal, Mukherjee. Hum Brain Mapp (2011)<br />
    18. 18. ICA of Group DTI Data<br />53 Normal Adult Volunteers:<br />31 men, 22 women<br />mean age 30.7 ± 8.8 years<br />44 right-handed<br />supratentorial commissural<br />WM tracts<br />Reproducibility (Rep):<br />measure of algorithmic stability across 30 Monte Carlo trials<br />Percentage Explained Variance (PEV): proportion of total data variance explained by the IC<br />Li, Yang, Nguyen, Cooper, LaHue, Venugopal, Mukherjee. <br />Hum Brain Mapp (2011)<br />
    19. 19. ICA of Group DTI Data<br />supratentorial<br />projection<br />WM tracts<br />Li, Yang, Nguyen, Cooper, LaHue, Venugopal, Mukherjee. <br />Hum Brain Mapp (2011).<br />
    20. 20. ICA of <br />Group DTI Data<br />neocortical<br />association<br />WM tracts<br />Li, Yang, Nguyen, Cooper, LaHue, Venugopal, Mukherjee. <br />Hum Brain Mapp (2011).<br />
    21. 21. Reproducible Quantitative Fiber Tracking:<br />3T DTI with 55 diffusion directions @ b=1000<br />CB<br />AF<br />IFO<br />ILF<br />UF<br />CST<br />
    22. 22. “Connectomics”: Global Connectivity, not Local Microstructure<br />Hagmann P et al., PLoS Biology 2008; 6(7):e159. <br />Network Metrics<br />Characteristic Path Length<br />Clustering Coefficient<br />Small Worldness<br />Network Efficiency<br />Eigenvector Centrality <br />
    23. 23. “Importance Maps”: Loss of Connectivity due to Mild TBI<br />Kuceyeski A et al., Neuroimage 2011; doi:10.1016/jneuroimage.2011.05.087<br />
    24. 24. Summary: DTI of Mild TBI<br /><ul><li>Assessment of local white matter microstructure (anisotropy)</li></ul>-- proven to be sensitive to mild TBI (civilian & military)<br />-- requires only DTI; no current advantage of HARDI of DSI<br />-- correlates with neurocognitive outcome (attention/memory)<br />-- can be used to detect neuroplasticity due to cognitive training<br />-- exciting new analytics on the horizon (source-based analysis)<br />-- however, may not be sensitive to global brain network effects<br /><ul><li>Assessment of global white matter connectivity (connectomics)</li></ul>-- very promising new technology; however, largely unproven<br />-- benefits from HARDI or DSI, rather than DTI<br />-- analytics in flux: area of very active technical development<br />-- importance maps may detect global network effects<br />-- however, may not be sensitive to local microstructure known to be sensitive to neurocognitive status and neuroplasticity<br />
    25. 25. Acknowledgements<br />UCSF Neurosurgery<br />Geoffrey T. Manley, MD PhD<br />Diane Morabito, RN<br />Sara C. LaHue<br />Shelly R. Cooper<br />Hana A. Lee<br />Michele Meeker, RN<br />UCSF Radiology<br />Alisa Gean, MD<br />Fanpei Gloria Yang, PhD<br />Yi-Ou Leo Li, PhD<br />Michael Wahl<br />Joshua Ng<br />Sandya Venugopal, MD<br />Brain Trauma Foundation<br />Jam Ghajar, MD PhD<br />Cornell<br />Sumit N. Niogi, PhD<br />Bruce D. McCandliss, PhD<br />Supported by grants from the McDonnell Foundation, the Dana Foundation, and <br />NIH R01 NS060776 & RC2 NS069409<br />

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