Individualized assessments are needed to guide personalized therapeutic interventions 
. Personalized medicine is generally understood to encompass genotype-tailored treatment 
, but other unique manifestations of disease demand individualized diagnostic and therapeutic approaches. Individualized assessment of DTI has been reported in only a few studies of TBI, which applied group-wise methods to individuals 
. However, individualized assessments are especially relevant to TBI, where the nature of the injury and its pathologic manifestations will be unique in each individual 
. Additionally, the analytic methods we have described could, after appropriate validation, be generalized to the assessment of many brain diseases.
We carefully addressed several important considerations in the implementation and validation of our approach. First, any study must in practice employ a control group that is a small subset of the population against which determinations of abnormality are to be inferred. This sampling limitation may lead to underestimation of variance in Z-scores and consequent erroneous inferences. EZ-MAP accounts for this potential additional variance by bootstrap, a nonparametric method which resamples the Z-score (i.e., by resampling the composition of the control group). An alternative bootstrap method would first resample the deviation of an individual FA from the mean FA obtained from a control group arriving at a new bootstrap SD quantity, which would then be used in computing the individual patient Z-scores. We employed the former approach because it yields a more robust and stable approximation of the true distribution 
We did not incorporate bias (variability of the control group mean based on its composition) in calculating the EZ-MAP, assuming that bias would be very close to zero. The narrow distribution (particularly around zero) of the mean of resampled Z-scores at each voxel confirms the validity of this assumption (Figure S2
We further confirmed that differences between the patient and control groups were carefully assessed and accounted for to minimize the chance that covariates such as age, gender and education would be detected as real effects. Notably, we found that significant effects of age, gender and education were modest in magnitude and spatial extent.
EZ-MAP discriminated mTBI patients from normals, showing statistical significance on assessment of area under the ROC curve and significant differences between patients and controls in the number of abnormal voxels detected. In terms of discrimination power, the standard Z-score and “one vs. many” t-test approaches also attained significance while an approach employing FWER-control did not. Although all three methods achieved significant discrimination power, the extent of abnormal regions varied among the methods. Inferences based on the standard Z-score approach tend to produce more false positive findings and those identified with the “one vs. many” t-test yield more false negative inferences. EZ-MAP inferences fall in between these two extremes. Because EZ-MAP is a data-adaptive approach, it is inherently less sensitive to underlying assumptions regarding the composition of the reference group than standard Z-score approach and “one vs. many” t-test.
Several potential limitations of our study should be considered. The assumption that the distribution of FA at each voxel would be symmetric, implicit in the standard Z-score, EZ-MAP and the “one vs. many” t-test, was explored because we suspected that such assumptions would lead to erroneous inferences. The density functions of EZ-scores from each normal control subject (Figure S3
, top) and all subjects (Figure S3
, bottom), were estimated by mixture modeling 
. In the estimated density functions for individuals, we found a thicker tail to the right (highly positive EZ-scores). The estimated density function for all control subjects also showed a thicker tail to the right in comparison to the standard Gaussian distribution. As a consequence of this asymmetry, abnormally high FA voxels (i.e., the right tail) were likely to be identified in “normal control subjects”. Such “abnormalities” might be detected due to deviation from underlying assumptions about the control group (i.e., the absence of a symmetric distribution across subjects at each voxel) not due to actual abnormalities. Therefore, a further improvement for analysis of DTI images in individual patients beyond the EZ-MAP may be achieved by developing methods to account for potential asymmetry in the FA distribution.
It is also important to critically assess the likelihood that the effects seen in the mTBI patients we studied are due to mTBI rather than some other white matter abnormality. Although, strictly speaking, it would never be possible to accept a null hypothesis that our patient and control subjects do not differ other than due to mTBI, we went to great lengths to ensure, to the maximum extent possible, that differences between patients and controls are reasonably attributable to mTBI. The mTBI patients enrolled in this study were carefully screened to exclude pre-injury medical, neurological or psychiatric disorders, including substance use, which could possibly cause white matter pathology. In addition to adjusting for age, we excluded patients at extremes of the lifespan, where developmental or senescent changes may affect FA. Although a significant difference in education was found between patients and controls, we found only minimal effects on FA, which were adjusted for in our analyses. Finally, abnormal findings occurred in areas expected to be affected in TBI (e.g., 
) and are consistent with numerous prior studies of DTI in TBI (e.g.,