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Bruce P. Hermann

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4 papers

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YNIMG Journal 2023 Journal Article

Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance

Moo K. Chung
Camille Garcia Ramos
Felipe Branco de Paiva
Jedidiah Mathis
Vivek Prabhakaran
Veena A. Nair
Mary E. Meyerand
Bruce P. Hermann

Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.

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YNICL Journal 2015 Journal Article

The relationship between carotid artery plaque stability and white matter ischemic injury

Sara E. Berman
Xiao Wang
Carol C. Mitchell
Bornali Kundu
Daren C. Jackson
Stephanie M. Wilbrand
Tomy Varghese
Bruce P. Hermann

Higher local carotid artery strain has previously been shown to be a characteristic of unstable carotid plaques. These plaques may be characterized by microvascular changes that predispose to intraplaque hemorrhage, increasing the likelihood of embolization. Little is known however, about how these strain indices correspond with imaging markers of brain health and metrics of brain structure. White matter hyperintensities (WMHs), which are bright regions seen on T2-weighted brain MRI imaging, are postulated to result from cumulative ischemic vascular injury. Consequently, we hypothesized that plaques that are more prone to microvascular changes and embolization, represented by higher strain indices on ultrasound, would be associated with an increased amount of WMH lesion volume. This relationship would suggest not only emboli as a cause for the brain degenerative changes, but more importantly, a common microvascular etiology for large and small vessel contributions to this process. Subjects scheduled to undergo a carotid endarterectomy were recruited from a neurosurgery clinic. Prior to surgery, participating subjects underwent both ultrasound strain imaging and brain MRI scans as part of a larger clinical study on vascular health and cognition. A linear regression found that maximum absolute strain and peak to peak strain in the surgical side carotid artery were predictive of WMH burden. Furthermore, the occurrence of microembolic signals monitored using transcranial Doppler (TCD) ultrasound examinations also correlated with increasing lesion burden. It is becoming increasingly recognized that cognitive decline is often multifactorial in nature. One contributing extra-brain factor may be changes in the microvasculature that produce unstable carotid artery plaques. In this study, we have shown that higher strain indices in carotid artery plaques are significantly associated with an increased WMH burden, a marker of vascular mediated brain damage.

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YNICL Journal 2014 Journal Article

Individual classification of children with epilepsy using support vector machine with multiple indices of diffusion tensor imaging

Ishmael Amarreh
Mary E. Meyerand
Carl Stafstrom
Bruce P. Hermann
Rasmus M. Birn

INTRODUCTION: Support vector machines (SVM) have recently been demonstrated to be useful for voxel-based MR image classification. In the present study we sought to evaluate whether this method is feasible in the classification of childhood epilepsy intractability based on diffusion tensor imaging (DTI), with adequate accuracy. We applied SVM in conjunction DTI indices of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). DTI studies have reported white matter abnormalities in childhood-onset epilepsy, but the mechanisms underlying these abnormalities are not well understood. The aim of this study was to examine the relationship between epileptic seizures and cerebral white matter abnormalities identified by DTI in children with active compared to remitted epilepsy utilizing an automated and unsupervised classification method. METHODS: The DTI data were tensor-derived indices including FA, MD, AD and RD in 49 participants including 20 children with epilepsy 5-6 years after seizure onset as compared to healthy controls. To determine whether there was normalization of white matter diffusion behavior following cessation of seizures and treatment, the epilepsy subjects were grouped into those with active versus remitted epilepsy. Group comparisons were previously made examining FA, MD and RD via whole-brain tract-based spatial statistics (TBSS). The SVM analysis was undertaken with the WEKA software package with 10-fold cross validation. Weighted sensitivity, specificity and accuracy were measured for all the DTI indices for two classifications: (1) controls vs. all children with epilepsy and (2) controls vs. children with remitted epilepsy vs. children with active epilepsy. RESULTS: Using TBSS, significant differences were identified between controls and all children with epilepsy, between controls and children with active epilepsy, and also between the active and remitted epilepsy groups. There were no significant differences between the remitted epilepsy and controls on any DTI measure. In the SVM analysis, the best predictor between controls and all children with epilepsy was MD, with a sensitivity of 90-100% and a specificity between 96.6 and 100%. For the three-way classification, the best results were for FA with 100% sensitivity and specificity. CONCLUSION: DTI-based SVM classification appears promising for distinguishing children with active epilepsy from either those with remitted epilepsy or controls, and the question that arises is whether it will prove useful as a prognostic index of seizure remission. While SVM can correctly identify children with active epilepsy from other groups' diagnosis, further research is needed to determine the efficacy of SVM as a prognostic tool in longitudinal clinical studies.

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YNIMG Journal 2004 Journal Article

Voxel-based morphometry of unilateral temporal lobe epilepsy reveals abnormalities in cerebral white matter

Alan B. McMillan
Bruce P. Hermann
Sterling C. Johnson
Russ R. Hansen
Michael Seidenberg
Mary E. Meyerand

Voxel-based morphometric (VBM) investigations of temporal lobe epilepsy have focused on the presence and distribution of gray matter abnormalities. VBM studies to date have identified the expected abnormalities in hippocampus and extrahippocampal temporal lobe, as well as more diffuse abnormalities in the thalamus, cerebellum, and extratemporal neocortical areas. To date, there has not been a comprehensive VBM investigation of cerebral white matter in nonlesional temporal lobe epilepsy. This study examined 25 lateralized temporal lobe epilepsy patients (13 left, 12 right) and 62 healthy controls in regard to both temporal and extratemporal lobe gray and white matter. Consistent with prior reports, gray matter abnormalities were evident in ipsilateral hippocampus and ipsilateral thalamus. Temporal and extratemporal white matter was affected ipsilateral to the side of seizure onset, in both left and right temporal lobe epilepsy groups. These findings indicate that chronic temporal lobe epilepsy is associated not only with abnormalities in gray matter, but also with concomitant abnormalities in cerebral white matter regions that may affect connectivity both within and between the cerebral hemispheres.

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