Biology subjects

Fischl, B.

Publications and source records attributed to Fischl, B..

26 records · Page 2Linked to original sources

bioRxiv2020

A novel algorithm to optimize generalized gamma distributed multiplicative noise with implications on speckle removal from OCT images

Optical coherence tomography (OCT) images are corrupted by multiplicative generalized gamma distributed speckle noise that significantly degrades the contrast to noise ratio (CNR) of microstructural compartments in biological applications. This work proposes a novel algorithm to optimize the negative log likelihood of the spatial distribution of speckle. Specifically, the proposed method formulates a penalized negative log likelihood (P-NLL) cost function and proposes a majorize-minimize-based optimization method that removes speckle from OCT images. The optimization reduces to solving an iterative gradient descent problem. We demonstrate the usefulness of the proposed method by removing speckle in OCT images of uniform phantoms with varying scattering coefficients and human brain tissue.

neuroscience↗

bioRxiv2020

Multi-Atlas Image Soft-Segmentation via Computation of the Expected Label Value

The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible atlas-to-image transformations and compute the expected label value (ELV), thereby not relying merely on the transformation deemed "optimal" by the registration method. Moreover, we do so without actually performing deformable registration, thus avoiding the associated computational costs. We evaluate our ELV computation approach by applying it to brain, liver, and pancreas segmentation on datasets of magnetic resonance and computed tomography images.

bioengineering↗

bioRxiv2020

Conductance-Based Structural Brain Connectivity in Aging and Dementia

BackgroundStructural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimers disease (AD) progression. MethodsIn this work, we used our recently proposed structural connectivity quantification measure derived from diffusion MRI, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the ADNI-2 and OASIS-3 datasets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these datasets to the HCP dataset, as a reference, and eventually validated externally on two cohorts of the EDSD database. ResultsOur analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among sub-cortical regions, and cingulate and temporal cortices. DiscussionResults indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anti-correlation in structural connections. Impact statementThis work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from dMRI, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and AD.

neuroscience↗

bioRxiv2020

Tractography-Pathology Correlations in Traumatic Brain Injury: A TRACK-TBI Study

Diffusion tractography MRI can infer changes in network connectivity in patients with traumatic brain injury (TBI), but pathological substrates of disconnected tracts have not been well-defined due to a lack of high-resolution imaging with histopathological validation. We developed an ex vivo MRI protocol to analyze tract terminations at 750 m resolution, followed by histopathologic evaluation of white matter pathology, and applied these methods to a 60-year-old man who died 26 days after TBI. Analysis of 74 cerebral hemispheric white matter regions revealed a heterogeneous distribution of tract disruptions. Associated histopathology identified variable white matter injury with patchy deposition of amyloid precursor protein and loss of neurofilament-positive axonal processes, myelin dissolution, astrogliosis, microgliosis, and perivascular hemosiderin-laden macrophages. Multiple linear regression revealed that tract disruption strongly correlated with neurofilament loss. Ex vivo diffusion MRI can detect tract disruptions in the human brain that reflect axonal injury.

neuroscience↗

bioRxiv2019

Optimizing the Accuracy of Cortical Volumetric Analysis in Traumatic Brain Injury

Cortical volumetric analysis is widely used to study the anatomic basis of neurological deficits in patients with traumatic brain injury (TBI). However, patients with TBI-related lesions are often excluded from analysis, because cortical lesions may compromise the accuracy of reconstructed surfaces upon which volumetric measurements are based. Here, we propose a novel FreeSurfer-based lesion correction method and illustrate its impact on cortical volume measures in patients with chronic moderate-to-severe TBI. We performed MRI in 87 patients at mean+/-SD 10.9+/-9.1 years post-injury using a T1-weighted multi-echo MPRAGE sequence at 1 mm resolution. Following surface reconstruction, we parcellated the cerebral cortex into seven functional networks using FreeSurfers standard pipeline. Next, we manually labeled vertices on the cortical surface where lesions caused inaccuracies and removed them from network-based cortical volumetric measures. After performing this lesion correction procedure, we measured the surface area of lesion overlap with each network and the percent volume of each network affected by lesions. We identified 120 lesions that caused inaccuracies in the cortical surface in 46 patients. In these 46 patients, the most commonly lesioned networks were the limbic and default mode networks (95.7% each), followed by the executive control (78.3%), and salience (71.7%) networks. The limbic network had the largest average surface area of lesion overlap (4.4+/-3.7%) and the largest percent volume affected by lesions (12.7+/-9.7%). The lesion correction method has the potential to improve the accuracy of cortical volumetric measurements and permit inclusion of patients with lesioned brains in quantitative analyses, providing new opportunities to elucidate network-based mechanisms of neurological deficits in patients with TBI.

neuroscience↗

bioRxiv2019

7 Tesla MRI of the ex vivo human brain at 100 micron resolution

We present an ultra-high resolution MRI dataset of an ex vivo human brain specimen. The brain specimen was donated by a 58-year-old woman who had no history of neurological disease and died of non-neurological causes. After fixation in 10% formalin, the specimen was imaged on a 7 Tesla MRI scanner at 100 m isotropic resolution using a custom-built 31-channel receive array coil. Single-echo multi-flip Fast Low-Angle SHot (FLASH) data were acquired over 100 hours of scan time (25 hours per flip angle), allowing derivation of a T1 parameter map and synthesized FLASH volumes. This dataset provides an unprecedented view of the three-dimensional neuroanatomy of the human brain. To optimize the utility of this resource, we warped the dataset into standard stereotactic space. We now distribute the dataset in both native space and stereotactic space to the academic community via multiple platforms. We envision that this dataset will have a broad range of investigational, educational, and clinical applications that will advance understanding of human brain anatomy in health and disease.\n\n\n\nO_TBL View this table:\norg.highwire.dtl.DTLVardef@17d0265org.highwire.dtl.DTLVardef@285366org.highwire.dtl.DTLVardef@17b53c6org.highwire.dtl.DTLVardef@1b90277org.highwire.dtl.DTLVardef@150ea14_HPS_FORMAT_FIGEXP M_TBL C_TBL

neuroscience↗

bioRxiv2018

Quantification of Structural Brain Connectivityvia a Conductance Model

Connectomics has proved promising in quantifying and understanding the effects of development, aging and an array of diseases on the brain. In this work, we propose a new structural connectivity measure that allows us to incorporate direct brain connections as well as indirect ones that would not be otherwise accounted for by standard techniques and that may be key for the better understanding of function from structure. From our experiments on the Human Connectome Project dataset, we find that our measure of structural connectivity better correlates with functional connectivity than streamline tractography does, meaning that it provides new structural information related to function. Through additional experiments on the ADNI-2 dataset, we demonstrate the ability of this new measure to better discriminate different stages of Alzheimers disease. Our findings suggest that this measure is useful in the study of the normal brain function and structure, and for quantifying the effects of disease on brain structure.

neuroscience↗

bioRxiv2018

Accurate Nonlinear Mapping between MNI Volumetric and FreeSurfer Surface Coordinate Systems

The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization, or projecting resting-state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group-average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF-ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF-ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF-ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).

neuroscience↗