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Beatriz Hernandez

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YNICL Journal 2025 Journal Article

Network-targeted transcranial magnetic stimulation (TMS) for mild cognitive impairment (MCI)

Joy L. Taylor
Priyanka Bhatt
Beatriz Hernandez
Michael Iv
Maheen M. Adamson
Alesha Heath
Jerome A. Yesavage
Margaret Windy McNerney

BACKGROUND: Transcranial magnetic stimulation (TMS) is a promising non-pharmacological intervention for treatment of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). Yet, we know little about precisely where stimulation would be ideal to improve cognitive function. OBJECTIVE: To examine the network functional connectivity (fc) characteristics of prefrontal and parietal stimulation sites, given that these sites have led to improved cognitive function in TMS studies involving MCI-AD and unimpaired participants. METHODS: Resting-state functional MRI data were acquired from 32 MCI participants at the baseline visit of an ongoing TMS trial and used to compute connectivity with prefrontal and parietal stimulation locations, selected on the basis of previous TMS studies. The TMS seed maps were examined for extent of spatial overlap with eight canonical networks. After identifying the network most likely to be targeted by TMS, we applied strategies that may provide purer targeting. Finally, we examined network connectivity in relation to participants' behavioral characteristics because of the potential for TMS treatment to be personalized. RESULTS: The prefrontal TMS seed map overlapped primarily with the salience network. The prefrontal site is also notable for its anti-correlated connectivity with the AD-vulnerable posterior cingulate cortex (PCC). The parietal TMS seed map showed the expected strong positive connectivity with the PCC and other default network regions. Nonetheless, this particular parietal site may simultaneously modulate the fronto-parietal network. Strategies to improve network targeting and to personalize TMS are reported as secondary findings. CONCLUSION: These results can be applied to network-targeted brain stimulation for MCI and early AD treatment. Greater precision and personalization of TMS offer the promise of achieving better outcomes for individuals with MCI or mild AD dementia.

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YNICL Journal 2017 Journal Article

DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans

Keith L. Main
Salil Soman
Franco Pestilli
Ansgar Furst
Art Noda
Beatriz Hernandez
Jennifer Kong
Jauhtai Cheng

Standard MRI methods are often inadequate for identifying mild traumatic brain injury (TBI). Advances in diffusion tensor imaging now provide potential biomarkers of TBI among white matter fascicles (tracts). However, it is still unclear which tracts are most pertinent to TBI diagnosis. This study ranked fiber tracts on their ability to discriminate patients with and without TBI. We acquired diffusion tensor imaging data from military veterans admitted to a polytrauma clinic (Overall n =109; Age: M =47. 2, SD =11. 3; Male: 88%; TBI: 67%). TBI diagnosis was based on self-report and neurological examination. Fiber tractography analysis produced 20 fiber tracts per patient. Each tract yielded four clinically relevant measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity). We applied receiver operating characteristic (ROC) analyses to identify the most diagnostic tract for each measure. The analyses produced an optimal cutpoint for each tract. We then used kappa coefficients to rate the agreement of each cutpoint with the neurologist's diagnosis. The tract with the highest kappa was most diagnostic. As a check on the ROC results, we performed a stepwise logistic regression on each measure using all 20 tracts as predictors. We also bootstrapped the ROC analyses to compute the 95% confidence intervals for sensitivity, specificity, and the highest kappa coefficients. The ROC analyses identified two fiber tracts as most diagnostic of TBI: the left cingulum (LCG) and the left inferior fronto-occipital fasciculus (LIF). Like ROC, logistic regression identified LCG as most predictive for the FA measure but identified the right anterior thalamic tract (RAT) for the MD, RD, and AD measures. These findings are potentially relevant to the development of TBI biomarkers. Our methods also demonstrate how ROC analysis may be used to identify clinically relevant variables in the TBI population.

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