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John Pluta

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Ulrike Prof. Dr. Halsband

Albert-Ludwigs-Universität Freiburg

Alexandra Golby

Mount Sinai School of Medicine

Guillermo Verduzco

New York Medical College

Jennifer L Cuzzocreo

Yale University School of Medicine

Nancy Honeycutt

Andreas Schulze-bonhage

Albert-Ludwigs-Universität Freiburg

Donna Rose Addis

The University of Auckland

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Papers by John Pluta

Spatial Correspondence Based Asymmetry Analysis in Hippocampus: Application to Temporal Lobe Epilepsy

Quantification of functional and structural asymmetry in the brain can provide clinically useful ... more Quantification of functional and structural asymmetry in the brain can provide clinically useful information. In the study of temporal lobe epilepsy (TLE), such analyses are often carried out within the hippocampus. Functional asymmetry is typically expressed in terms of differences in the number of suprathreshold voxels activated, normalized to total activation, within the structure of interest, while the subjects perform a cognitive task in a functional magnetic resonance imaging (fMRI) experiment. Structural asymmetry is usually expressed in terms of normalized, relative hippocampal volume differences between hemispheres. We introduce methodologies for carrying out asymmetry analysis for region of interest (ROI) based studies that take into account information about spatial correspondence of voxels on two sides of the brain. We apply this methodology to make determination of hemispheric specialization during a memory encoding task in patients with refractory TLE. Memory lateralization is an important step in the presurgical evaluation of such patients for temporal lobectomy. Our functional asymmetry scores in hippocampus are found to have a strong correspondence with hemispheric dominance given by Intracarotid Amobarbital Testing (IAT), which is the widely accepted gold standard for determining laterality. We also use local thickness measurements to study structural asymmetry within hippocampus. Regional variation in thickness differences between different subgroups are revealed using the correspondence based approach.

The correlation between functional connectivity and cerebral blood flow and its reliability

Background / Purpose: Here, we aimed to test the association between resting functional connectiv... more Background / Purpose: Here, we aimed to test the association between resting functional connectivity (FC) and quantitative cerbral blood flow (CBF) and its reliability. Main conclusion: We found reliable correlations between regional CBF and the posterior cingulate cortex (PCC), FC and the dorsal anterior cingulate cortex (dACC) and FC in the significant FC areas.

Relationship of Contextual Cueing and Hippocampal Volume in Amnestic Mild Cognitive Impairment Patients and Cognitively Normal Older Adults

Journal of the International Neuropsychological Society : JINS, Jan 20, 2015

There is currently some debate as to whether hippocampus mediates contextual cueing. In the prese... more There is currently some debate as to whether hippocampus mediates contextual cueing. In the present study, we examined contextual cueing in patients diagnosed with mild cognitive impairment (MCI) and healthy older adults, with the main goal of investigating the role of hippocampus in this form of learning. Amnestic MCI (aMCI) patients and healthy controls completed the contextual cueing task, in which they were asked to search for a target (a horizontal T) in an array of distractors (rotated L's). Unbeknownst to them, the spatial arrangement of elements on some displays was repeated thus making the configuration a contextual cue to the location of the target. In contrast, the configuration for novel displays was generated randomly on each trial. The difference in response times between repeated and novel configurations served as a measure of contextual learning. aMCI patients, as a group, were able to learn spatial contextual cues as well as healthy older adults. However, better...

Anterior and posterior MTL networks in aging and MCI

by John Pluta, Paul Yushkevich, and David Wolk

Two neuroanatomically dissociable, large-scale cortical memory networks, referred to as the anter... more Two neuroanatomically dissociable, large-scale cortical memory networks, referred to as the anterior and posterior medial temporal lobe (MTL) networks have recently been described in young adults using resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI)-based functional connectivity (fc-BOLD). They have been hypothesized to subserve distinct mnemonic and non-memory cognitive functions and are thought to be associated with differential vulnerability in neurological disorders. In this article, we demonstrate the existence of these functional networks in an older adult population and in a cohort of patients diagnosed with amnestic mild cognitive impairment (aMCI). Anatomic subregions of interest in the MTL were defined using high-resolution T2-weighted MRI and used as seeds for defining the putative networks using fc-BOLD. Although the literature has suggested that the posterior MTL network is particularly vulnerable to early Alzheimer&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s disease, we show that both the networks are affected in MCI, to varying degrees, compared with the control group. Furthermore, cortical thickness in the brain regions defined by these networks was reduced in MCI.

Disrupted structural connectome is associated with both psychometric and real-world neuropsychological impairment in diffuse traumatic brain injury

Journal of the International Neuropsychological Society : JINS, 2014

Traumatic brain injury (TBI) is likely to disrupt structural network properties due to diffuse wh... more Traumatic brain injury (TBI) is likely to disrupt structural network properties due to diffuse white matter pathology. The present study aimed to detect alterations in structural network topology in TBI and relate them to cognitive and real-world behavioral impairment. Twenty-two people with moderate to severe TBI with mostly diffuse pathology and 18 demographically matched healthy controls were included in the final analysis. Graph theoretical network analysis was applied to diffusion tensor imaging (DTI) data to characterize structural connectivity in both groups. Neuropsychological functions were assessed by a battery of psychometric tests and the Frontal Systems Behavior Scale (FrSBe). Local connection-wise analysis demonstrated reduced structural connectivity in TBI arising from subcortical areas including thalamus, caudate, and hippocampus. Global network metrics revealed that shortest path length in participants with TBI was longer compared to controls, and that this reduced ...

Shape-based semi-automatic hippocampal subfield segmentation with learning-based bias removal

2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings, 2010

We develop a semi-automatic technique for segmentation of hippocampal subfields in T2-weighted in... more We develop a semi-automatic technique for segmentation of hippocampal subfields in T2-weighted in vivo brain MRI. The technique takes the binary segmentation of the whole hippocampus as input, and automatically labels the subfields inside the hippocampus segmentation. Shape priors for the hippocampal subfields are generated from shape-based normalization of whole hippocampi via the continuous medial representation method. To combine the shape priors with appearance features, we use a machine learning based method. The key novelty is that we treat the mistakes made by the shape priors as bias, which can be detected and corrected via learning. The main advantage of this formulation is that it significantly simplifies the learning problem by taking full advantage of current segmentations and focusing on only improving their drawbacks. Experiments show that the bias removal approach achieves significant improvement in all subfields. Our bias removal idea is general, and can be applied to improve other segmentation methods as well.

Optimal weights for multi-atlas label fusion

by John Pluta, Paul Yushkevich, and J. Suh

Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusio... more Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusion, previous studies show that image similarity-based local weighting techniques produce the most accurate results. However, these methods ignore the correlations between results produced by different atlases. Furthermore, they rely on preselected weighting models and ad hoc methods to choose model parameters. We propose a novel label fusion method to address these limitations. Our formulation directly aims at reducing the expectation of the combined error and can be efficiently solved in a closed form. In our hippocampus segmentation experiment, our method significantly outperforms similarity-based local weighting. Using 20 atlases, we produce results with 0.898 ± 0.019 Dice overlap to manual labelings for controls.

Hippocampus segmentation using a stable maximum likelihood classifier ensemble algorithm

by John Pluta, J. Suh, and Paul Yushkevich

We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new class... more We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas based segmentation method. Our classifier ensemble algorithm searches for the maximum likelihood solution via gradient ascent optimization. Compared to the additive regression based algorithm, LogitBoost, our algorithm avoids the numerical instability problem. Experiments on a hippocampus segmentation problem using the ADNI data show that our algorithm consistently converges faster and generalizes better than AdaBoost.

In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI

by Paul Yushkevich and John Pluta

The measurement of hippocampal volumes using MRI is a useful in-vivo biomarker for detection and ... more The measurement of hippocampal volumes using MRI is a useful in-vivo biomarker for detection and monitoring of early Alzheimer's Disease (AD), including during the amnestic Mild Cognitive Impairment (a-MCI) stage. The pathology underlying AD has regionally selective effects within the hippocampus. As such, we predict that hippocampal subfields are more sensitive in discriminating prodromal AD (i.e., a-MCI) from cognitively normal controls than whole hippocampal volumes, and attempt to demonstrate this using a semi-automatic method that can accurately segment hippocampal subfields. High-resolution coronal-oblique T2-weighted images of the hippocampal formation were acquired in 45 subjects (28 controls and 17 a-MCI (mean age: 69.5 ± 9.2; 70.2 ± 7.6)). CA1, CA2, CA3, and CA4/DG subfields, along with head and tail regions, were segmented using an automatic algorithm. CA1 and CA4/DG segmentations were manually edited. Whole hippocampal volumes were obtained from the subjects' T1-weighted anatomical images. Automatic segmentation produced significant group differences in the following subfields: CA1 (left: p=0.001, right: p=0.038), CA4/DG (left: p=0.002, right: p=0.043), head (left: p=0.018, right: p=0.002), and tail (left: p=0.019). After manual correction, differences were increased in CA1 (left: p<0.001, right: p=0.002), and reduced in CA4/DG (left: p=0.029, right: p=0.221). Whole hippocampal volumes significantly differed bilaterally (left: p=0.028, right: p=0.009). This pattern of atrophy in a-MCI is consistent with the topography of AD pathology observed in postmortem studies, and corrected left CA1 provided stronger discrimination than whole hippocampal volume (p=0.03). These results suggest that semiautomatic segmentation of hippocampal subfields is efficient and may provide additional sensitivity beyond whole hippocampal volumes.

Spatial correspondence based asymmetry analysis in fMRI

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI, 2008

Quantification of functional and structural asymmetry in the brain can provide clinically useful ... more Quantification of functional and structural asymmetry in the brain can provide clinically useful information. In the study of temporal lobe epilepsy (TLE), such analyses are often carried out within the hippocampus. Functional asymmetry is typically expressed in terms of differences in the number of suprathreshold voxels activated, normalized to total activation, within the structure of interest, while the subjects perform a cognitive task in a functional magnetic resonance imaging (fMRI) experiment. Structural asymmetry is usually expressed in terms of normalized, relative hippocampal volume differences between hemispheres. We introduce methodologies for carrying out asymmetry analysis for region of interest (ROI) based studies that take into account information about spatial correspondence of voxels on two sides of the brain. We apply this methodology to make determination of hemispheric specialization during a memory encoding task in patients with refractory TLE. Memory lateralization is an important step in the presurgical evaluation of such patients for temporal lobectomy. Our functional asymmetry scores in hippocampus are found to have a strong correspondence with hemispheric dominance given by Intracarotid Amobarbital Testing (IAT), which is the widely accepted gold standard for determining laterality. We also use local thickness measurements to study structural asymmetry within hippocampus. Regional variation in thickness differences between different subgroups are revealed using the correspondence based approach.

Shape-based alignment of hippocampal subfields: evaluation in postmortem MRI

by Paul Yushkevich and John Pluta

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2008

This paper estimates the accuracy of hippocampal subfield alignment via shape-based normalization... more This paper estimates the accuracy of hippocampal subfield alignment via shape-based normalization. Evaluation takes place in postmortem MRI dataset acquired at 9.4 Tesla with many averages and approximately 0.01 mm3 voxel resolution. Continuous medial representations (cm-reps) are used to establish geometrical correspondences between hippocampal formations in different images; the extent to which these correspondences match up subfields is evaluated and compared to normalization driven by image forces. Shape-based normalization is shown to perform only slightly worse than image-based normalization; this is encouraging because the former is more applicable to in vivo MRI, which typically lacks features that distinguish hippocampal subfields.

Building an atlas of hippocampal subfields using postmortem MRI

by Paul Yushkevich and John Pluta

This paper presents preliminary work on the construction of a computational anatomical atlas of t... more This paper presents preliminary work on the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus (cornu Ammonis fields CA1, CA2/3 and CA4; dentate gyrus; and the vestigial hippocampal sulcus) are labeled in the images manually using a combination of distinguishable image features and geometrical features. A synthetic average image is derived from the MRI of the samples using shape and intensity averaging in the diffeomorphic non-linear registration framework, and a consensus labeling of the template is generated. The agreement of the consensus labeling with manual labeling of each sample is measured, and the effect of aiding registration with landmarks and manually generated mask images is evaluated.

From label fusion to correspondence fusion: a new approach to unbiased groupwise registration

by Paul Yushkevich and John Pluta

Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012

Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consen... more Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by registering the image to a set of atlases [19, 11, 8]. Effective label fusion strategies, such as local similarity-weighted voting [1, 13] substantially reduce segmentation errors compared to single-atlas segmentation. This paper extends the label fusion idea to the problem of finding correspondences across a set of images. Instead of computing a consensus segmentation, weighted voting is used to estimate a consensus coordinate map between a target image and a reference space. Two variants of the problem are considered: (1) where correspondences between a set of atlases are known and are propagated to the target image; (2) where correspondences are estimated across a set of images without prior knowledge. Evaluation in synthetic data shows that correspondences recovered by fusion methods are more accurate th...

Reconstruction of the human hippocampus in 3D from histology and high-resolution ex-vivo MRI

by Paul Yushkevich and John Pluta

In this paper, we present methods for the reconstruction of 3D histological volumes of the human ... more In this paper, we present methods for the reconstruction of 3D histological volumes of the human hippocampal formation from histology slices. Inter-slice alignment is guided by a graph-theoretic approach that minimizes the impact of badly distorted slices. The reconstruction is refined by iterative affine and deformable co-registration with a high-resolution MRI of the postmortem tissue sample. We present an evaluation of reconstruction accuracy that is based on measures of similarity between boundaries drawn on both histology and MRI. Our methodology is currently being applied to an MRI atlas of the human hippocampal formation, in which atlas anatomical labels are derived from segmentation of reconstructed histology.

Contrast-enhanced ultrasound-guided Sentinel lymph node biopsy of the ocular conjunctiva

The Laryngoscope, 2014

Sentinel lymph node biopsy (SLNB) has been utilized for cutaneous melanoma and other malignancies... more Sentinel lymph node biopsy (SLNB) has been utilized for cutaneous melanoma and other malignancies arising from the eye and ocular adnexa. Currently, SLNB requires blue dyes and/or radiopharmaceuticals; both of which have significant shortcomings. This study sought to evaluate the feasibility of SLNB with the use of real-time, contrast-enhanced ultrasound (CEUS) as an alternative technique for tumors arising in the conjunctiva. Prospective feasibility study in a porcine model. Twelve experiments were performed on six non-tumor-bearing Yorkshire swine. An ultrasound contrast agent, Sonazoid (GE Healthcare, Oslo, Norway), (99m) technetium ((99m) Tc), and methylene blue (MB) (Covidien, Mansfield, MA) were injected in the ocular conjunctiva. Sentinel lymph nodes (SLNs) were localized with CEUS and findings were compared to that of MB and (99m) Tc. Fisher exact test was used. Contrast-enhanced SLNs were identified within an average of 6.2 minutes from time of injection of Sonazoid. A total of 17 SLNs were identified by at least one of the three techniques. Correlation between Sonazoid and (99m) Tc was 94.1% (16/17 SLNs). Correlation between (99m) Tc and MB was 88.2% (15/17). One SLN that was positive for (99m) Tc but negative for Sonazoid and was considered to be a false positive (1/17); findings were similar for MB (1/17). Differences between the three techniques were not significant (P = .886). CEUS-guided injection of conjunctiva for SLNB is technically feasible and correlates well with standard detection techniques. This technique shows promise for rapid, real-time, intraoperative imaging for SLNB, using a widely available imaging modality and avoiding the need for radiopharmaceuticals. NA

Structure specific analysis of the hippocampus in temporal lobe epilepsy

Hippocampus, 2009

The hippocampus is a major structure of interest affected by temporal lobe epilepsy (TLE). Region... more The hippocampus is a major structure of interest affected by temporal lobe epilepsy (TLE). Region of interest (ROI)-based analysis has traditionally been used to study hippocampal involvement in TLE, although spatial variation of structural and functional pathology have been known to exist within the ROI. In this article, structure-specific analysis Neuroimage 35:1516-1530) is applied to the study of both structure and function in TLE patients. This methodology takes into account information about the spatial correspondence of voxels within ROIs on left and right sides of the same subject as well as between subjects. Hippocampal thickness is studied as a measure of structural integrity, and functional activation in a functional magnetic resonance imaging (fMRI) experiment in which subjects performed a memory encoding task is studied as a measure of functional integrity. Pronounced disease-related decrease in thickness is found in posterior and anterior hippocampus. A region in the body also shows increased thickness in patients' healthy hippocampi compared with controls. Functional activation in diseased hippocampi is reduced in the body region compared to controls, whereas a region in the tail showing greater right-lateralized activation in controls also shows greater activation in healthy hippocampi compared with the diseased side in patients. Summary measurements generated by integrating quantities of interest over the entire hippocampus can also be used, as is done in conventional ROI analysis.

Appearance and incomplete label matching for diffeomorphic template based hippocampus segmentation

Hippocampus, 2009

We present a robust, high-throughput, semi-automated template based protocol for segmenting the h... more We present a robust, high-throughput, semi-automated template based protocol for segmenting the hippocampus in temporal lobe epilepsy (TLE). The semi-automated component of this approach, which minimizes user effort while maximizing the benefit of human input to the algorithm, relies on "incomplete labeling." Incomplete labeling requires the user to quickly and approximately segment a few key regions of the hippocampus through a user-interface. Subsequently, this partial labeling of the hippocampus is combined with image similarity terms to guide volumetric diffeomorphic normalization between an individual brain and an unbiased disease-specific template, with fully labeled hippocampi. We solve this many-to-few and few-to-many matching problem, and gain robustness to inter and intra-rater variability and small errors in user labeling, by embedding the template-based normalization within a probabilistic framework that examines both label geometry and appearance data at each label. We evaluate the reliability of this framework with respect to manual labeling and show that it increases minimum performance levels relative to fully automated approaches and provides high inter-rater reliability. Thus, this approach does not require expert neuroanatomical training and is viable for high-throughput studies of both the normal and the highly atrophic hippocampus.

Increased functional connectivity within medial temporal lobe in mild cognitive impairment

by Paul Yushkevich and John Pluta

Pathology at preclinical and prodromal stages of Alzheimer's disease (AD) may manifest itself as ... more Pathology at preclinical and prodromal stages of Alzheimer's disease (AD) may manifest itself as measurable functional change in neuronal networks earlier than detectable structural change. Functional connectivity as measured using resting-state fMRI (functional Magnetic Resonance Imaging) has emerged as a useful tool for studying disease effects on baseline states of neuronal networks. In this study, we use high resolution MRI to label subregions within the medial temporal lobe (MTL), a site of early pathology in AD, and report an increase in functional connectivity in amnesic MCI (aMCI) between entorhinal cortex and subregions of the MTL, with the strongest effect in the anterior hippocampus. However, our data also replicated the effects of decreased connectivity of the MTL to other nodes of the default mode network reported by other researchers. This dissociation of changes in functional connectivity within the MTL versus the MTL's connection with other neocortical structures can help enrich the characterization of early stages of disease progression in AD.

Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI

NeuroImage, 2014

Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields usi... more Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200 μm spacing and 5 μm slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4 Tesla MRI scan of the intact, whole hippocampal formation acquired with 160 μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1 cm-thick tissue sub-blocks acquired with 200 μm isotropic resolution. These 1 cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation.

A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T

by Paul Yushkevich and John Pluta

Neuroimage, 2009

This paper describes the construction of a computational anatomical atlas of the human hippocampu... more

Spatial Correspondence Based Asymmetry Analysis in Hippocampus: Application to Temporal Lobe Epilepsy

Quantification of functional and structural asymmetry in the brain can provide clinically useful ... more Quantification of functional and structural asymmetry in the brain can provide clinically useful information. In the study of temporal lobe epilepsy (TLE), such analyses are often carried out within the hippocampus. Functional asymmetry is typically expressed in terms of differences in the number of suprathreshold voxels activated, normalized to total activation, within the structure of interest, while the subjects perform a cognitive task in a functional magnetic resonance imaging (fMRI) experiment. Structural asymmetry is usually expressed in terms of normalized, relative hippocampal volume differences between hemispheres. We introduce methodologies for carrying out asymmetry analysis for region of interest (ROI) based studies that take into account information about spatial correspondence of voxels on two sides of the brain. We apply this methodology to make determination of hemispheric specialization during a memory encoding task in patients with refractory TLE. Memory lateralization is an important step in the presurgical evaluation of such patients for temporal lobectomy. Our functional asymmetry scores in hippocampus are found to have a strong correspondence with hemispheric dominance given by Intracarotid Amobarbital Testing (IAT), which is the widely accepted gold standard for determining laterality. We also use local thickness measurements to study structural asymmetry within hippocampus. Regional variation in thickness differences between different subgroups are revealed using the correspondence based approach.

The correlation between functional connectivity and cerebral blood flow and its reliability

Background / Purpose: Here, we aimed to test the association between resting functional connectiv... more Background / Purpose: Here, we aimed to test the association between resting functional connectivity (FC) and quantitative cerbral blood flow (CBF) and its reliability. Main conclusion: We found reliable correlations between regional CBF and the posterior cingulate cortex (PCC), FC and the dorsal anterior cingulate cortex (dACC) and FC in the significant FC areas.

Relationship of Contextual Cueing and Hippocampal Volume in Amnestic Mild Cognitive Impairment Patients and Cognitively Normal Older Adults

Journal of the International Neuropsychological Society : JINS, Jan 20, 2015

There is currently some debate as to whether hippocampus mediates contextual cueing. In the prese... more There is currently some debate as to whether hippocampus mediates contextual cueing. In the present study, we examined contextual cueing in patients diagnosed with mild cognitive impairment (MCI) and healthy older adults, with the main goal of investigating the role of hippocampus in this form of learning. Amnestic MCI (aMCI) patients and healthy controls completed the contextual cueing task, in which they were asked to search for a target (a horizontal T) in an array of distractors (rotated L's). Unbeknownst to them, the spatial arrangement of elements on some displays was repeated thus making the configuration a contextual cue to the location of the target. In contrast, the configuration for novel displays was generated randomly on each trial. The difference in response times between repeated and novel configurations served as a measure of contextual learning. aMCI patients, as a group, were able to learn spatial contextual cues as well as healthy older adults. However, better...

Anterior and posterior MTL networks in aging and MCI

by John Pluta, Paul Yushkevich, and David Wolk

Two neuroanatomically dissociable, large-scale cortical memory networks, referred to as the anter... more Two neuroanatomically dissociable, large-scale cortical memory networks, referred to as the anterior and posterior medial temporal lobe (MTL) networks have recently been described in young adults using resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI)-based functional connectivity (fc-BOLD). They have been hypothesized to subserve distinct mnemonic and non-memory cognitive functions and are thought to be associated with differential vulnerability in neurological disorders. In this article, we demonstrate the existence of these functional networks in an older adult population and in a cohort of patients diagnosed with amnestic mild cognitive impairment (aMCI). Anatomic subregions of interest in the MTL were defined using high-resolution T2-weighted MRI and used as seeds for defining the putative networks using fc-BOLD. Although the literature has suggested that the posterior MTL network is particularly vulnerable to early Alzheimer&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s disease, we show that both the networks are affected in MCI, to varying degrees, compared with the control group. Furthermore, cortical thickness in the brain regions defined by these networks was reduced in MCI.

Disrupted structural connectome is associated with both psychometric and real-world neuropsychological impairment in diffuse traumatic brain injury

Journal of the International Neuropsychological Society : JINS, 2014

Traumatic brain injury (TBI) is likely to disrupt structural network properties due to diffuse wh... more Traumatic brain injury (TBI) is likely to disrupt structural network properties due to diffuse white matter pathology. The present study aimed to detect alterations in structural network topology in TBI and relate them to cognitive and real-world behavioral impairment. Twenty-two people with moderate to severe TBI with mostly diffuse pathology and 18 demographically matched healthy controls were included in the final analysis. Graph theoretical network analysis was applied to diffusion tensor imaging (DTI) data to characterize structural connectivity in both groups. Neuropsychological functions were assessed by a battery of psychometric tests and the Frontal Systems Behavior Scale (FrSBe). Local connection-wise analysis demonstrated reduced structural connectivity in TBI arising from subcortical areas including thalamus, caudate, and hippocampus. Global network metrics revealed that shortest path length in participants with TBI was longer compared to controls, and that this reduced ...

Shape-based semi-automatic hippocampal subfield segmentation with learning-based bias removal

2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings, 2010

We develop a semi-automatic technique for segmentation of hippocampal subfields in T2-weighted in... more We develop a semi-automatic technique for segmentation of hippocampal subfields in T2-weighted in vivo brain MRI. The technique takes the binary segmentation of the whole hippocampus as input, and automatically labels the subfields inside the hippocampus segmentation. Shape priors for the hippocampal subfields are generated from shape-based normalization of whole hippocampi via the continuous medial representation method. To combine the shape priors with appearance features, we use a machine learning based method. The key novelty is that we treat the mistakes made by the shape priors as bias, which can be detected and corrected via learning. The main advantage of this formulation is that it significantly simplifies the learning problem by taking full advantage of current segmentations and focusing on only improving their drawbacks. Experiments show that the bias removal approach achieves significant improvement in all subfields. Our bias removal idea is general, and can be applied to improve other segmentation methods as well.

Optimal weights for multi-atlas label fusion

by John Pluta, Paul Yushkevich, and J. Suh

Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusio... more Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusion, previous studies show that image similarity-based local weighting techniques produce the most accurate results. However, these methods ignore the correlations between results produced by different atlases. Furthermore, they rely on preselected weighting models and ad hoc methods to choose model parameters. We propose a novel label fusion method to address these limitations. Our formulation directly aims at reducing the expectation of the combined error and can be efficiently solved in a closed form. In our hippocampus segmentation experiment, our method significantly outperforms similarity-based local weighting. Using 20 atlases, we produce results with 0.898 ± 0.019 Dice overlap to manual labelings for controls.

Hippocampus segmentation using a stable maximum likelihood classifier ensemble algorithm

by John Pluta, J. Suh, and Paul Yushkevich

We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new class... more We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas based segmentation method. Our classifier ensemble algorithm searches for the maximum likelihood solution via gradient ascent optimization. Compared to the additive regression based algorithm, LogitBoost, our algorithm avoids the numerical instability problem. Experiments on a hippocampus segmentation problem using the ADNI data show that our algorithm consistently converges faster and generalizes better than AdaBoost.

In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI

by Paul Yushkevich and John Pluta

The measurement of hippocampal volumes using MRI is a useful in-vivo biomarker for detection and ... more The measurement of hippocampal volumes using MRI is a useful in-vivo biomarker for detection and monitoring of early Alzheimer's Disease (AD), including during the amnestic Mild Cognitive Impairment (a-MCI) stage. The pathology underlying AD has regionally selective effects within the hippocampus. As such, we predict that hippocampal subfields are more sensitive in discriminating prodromal AD (i.e., a-MCI) from cognitively normal controls than whole hippocampal volumes, and attempt to demonstrate this using a semi-automatic method that can accurately segment hippocampal subfields. High-resolution coronal-oblique T2-weighted images of the hippocampal formation were acquired in 45 subjects (28 controls and 17 a-MCI (mean age: 69.5 ± 9.2; 70.2 ± 7.6)). CA1, CA2, CA3, and CA4/DG subfields, along with head and tail regions, were segmented using an automatic algorithm. CA1 and CA4/DG segmentations were manually edited. Whole hippocampal volumes were obtained from the subjects' T1-weighted anatomical images. Automatic segmentation produced significant group differences in the following subfields: CA1 (left: p=0.001, right: p=0.038), CA4/DG (left: p=0.002, right: p=0.043), head (left: p=0.018, right: p=0.002), and tail (left: p=0.019). After manual correction, differences were increased in CA1 (left: p<0.001, right: p=0.002), and reduced in CA4/DG (left: p=0.029, right: p=0.221). Whole hippocampal volumes significantly differed bilaterally (left: p=0.028, right: p=0.009). This pattern of atrophy in a-MCI is consistent with the topography of AD pathology observed in postmortem studies, and corrected left CA1 provided stronger discrimination than whole hippocampal volume (p=0.03). These results suggest that semiautomatic segmentation of hippocampal subfields is efficient and may provide additional sensitivity beyond whole hippocampal volumes.

Spatial correspondence based asymmetry analysis in fMRI

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI, 2008

Quantification of functional and structural asymmetry in the brain can provide clinically useful ... more Quantification of functional and structural asymmetry in the brain can provide clinically useful information. In the study of temporal lobe epilepsy (TLE), such analyses are often carried out within the hippocampus. Functional asymmetry is typically expressed in terms of differences in the number of suprathreshold voxels activated, normalized to total activation, within the structure of interest, while the subjects perform a cognitive task in a functional magnetic resonance imaging (fMRI) experiment. Structural asymmetry is usually expressed in terms of normalized, relative hippocampal volume differences between hemispheres. We introduce methodologies for carrying out asymmetry analysis for region of interest (ROI) based studies that take into account information about spatial correspondence of voxels on two sides of the brain. We apply this methodology to make determination of hemispheric specialization during a memory encoding task in patients with refractory TLE. Memory lateralization is an important step in the presurgical evaluation of such patients for temporal lobectomy. Our functional asymmetry scores in hippocampus are found to have a strong correspondence with hemispheric dominance given by Intracarotid Amobarbital Testing (IAT), which is the widely accepted gold standard for determining laterality. We also use local thickness measurements to study structural asymmetry within hippocampus. Regional variation in thickness differences between different subgroups are revealed using the correspondence based approach.

Shape-based alignment of hippocampal subfields: evaluation in postmortem MRI

by Paul Yushkevich and John Pluta

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2008

This paper estimates the accuracy of hippocampal subfield alignment via shape-based normalization... more This paper estimates the accuracy of hippocampal subfield alignment via shape-based normalization. Evaluation takes place in postmortem MRI dataset acquired at 9.4 Tesla with many averages and approximately 0.01 mm3 voxel resolution. Continuous medial representations (cm-reps) are used to establish geometrical correspondences between hippocampal formations in different images; the extent to which these correspondences match up subfields is evaluated and compared to normalization driven by image forces. Shape-based normalization is shown to perform only slightly worse than image-based normalization; this is encouraging because the former is more applicable to in vivo MRI, which typically lacks features that distinguish hippocampal subfields.

Building an atlas of hippocampal subfields using postmortem MRI

by Paul Yushkevich and John Pluta

This paper presents preliminary work on the construction of a computational anatomical atlas of t... more This paper presents preliminary work on the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples. The main subfields of the hippocampus (cornu Ammonis fields CA1, CA2/3 and CA4; dentate gyrus; and the vestigial hippocampal sulcus) are labeled in the images manually using a combination of distinguishable image features and geometrical features. A synthetic average image is derived from the MRI of the samples using shape and intensity averaging in the diffeomorphic non-linear registration framework, and a consensus labeling of the template is generated. The agreement of the consensus labeling with manual labeling of each sample is measured, and the effect of aiding registration with landmarks and manually generated mask images is evaluated.

From label fusion to correspondence fusion: a new approach to unbiased groupwise registration

by Paul Yushkevich and John Pluta

Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012

Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consen... more Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by registering the image to a set of atlases [19, 11, 8]. Effective label fusion strategies, such as local similarity-weighted voting [1, 13] substantially reduce segmentation errors compared to single-atlas segmentation. This paper extends the label fusion idea to the problem of finding correspondences across a set of images. Instead of computing a consensus segmentation, weighted voting is used to estimate a consensus coordinate map between a target image and a reference space. Two variants of the problem are considered: (1) where correspondences between a set of atlases are known and are propagated to the target image; (2) where correspondences are estimated across a set of images without prior knowledge. Evaluation in synthetic data shows that correspondences recovered by fusion methods are more accurate th...

Reconstruction of the human hippocampus in 3D from histology and high-resolution ex-vivo MRI

by Paul Yushkevich and John Pluta

In this paper, we present methods for the reconstruction of 3D histological volumes of the human ... more In this paper, we present methods for the reconstruction of 3D histological volumes of the human hippocampal formation from histology slices. Inter-slice alignment is guided by a graph-theoretic approach that minimizes the impact of badly distorted slices. The reconstruction is refined by iterative affine and deformable co-registration with a high-resolution MRI of the postmortem tissue sample. We present an evaluation of reconstruction accuracy that is based on measures of similarity between boundaries drawn on both histology and MRI. Our methodology is currently being applied to an MRI atlas of the human hippocampal formation, in which atlas anatomical labels are derived from segmentation of reconstructed histology.

Contrast-enhanced ultrasound-guided Sentinel lymph node biopsy of the ocular conjunctiva

The Laryngoscope, 2014

Sentinel lymph node biopsy (SLNB) has been utilized for cutaneous melanoma and other malignancies... more Sentinel lymph node biopsy (SLNB) has been utilized for cutaneous melanoma and other malignancies arising from the eye and ocular adnexa. Currently, SLNB requires blue dyes and/or radiopharmaceuticals; both of which have significant shortcomings. This study sought to evaluate the feasibility of SLNB with the use of real-time, contrast-enhanced ultrasound (CEUS) as an alternative technique for tumors arising in the conjunctiva. Prospective feasibility study in a porcine model. Twelve experiments were performed on six non-tumor-bearing Yorkshire swine. An ultrasound contrast agent, Sonazoid (GE Healthcare, Oslo, Norway), (99m) technetium ((99m) Tc), and methylene blue (MB) (Covidien, Mansfield, MA) were injected in the ocular conjunctiva. Sentinel lymph nodes (SLNs) were localized with CEUS and findings were compared to that of MB and (99m) Tc. Fisher exact test was used. Contrast-enhanced SLNs were identified within an average of 6.2 minutes from time of injection of Sonazoid. A total of 17 SLNs were identified by at least one of the three techniques. Correlation between Sonazoid and (99m) Tc was 94.1% (16/17 SLNs). Correlation between (99m) Tc and MB was 88.2% (15/17). One SLN that was positive for (99m) Tc but negative for Sonazoid and was considered to be a false positive (1/17); findings were similar for MB (1/17). Differences between the three techniques were not significant (P = .886). CEUS-guided injection of conjunctiva for SLNB is technically feasible and correlates well with standard detection techniques. This technique shows promise for rapid, real-time, intraoperative imaging for SLNB, using a widely available imaging modality and avoiding the need for radiopharmaceuticals. NA

Structure specific analysis of the hippocampus in temporal lobe epilepsy

Hippocampus, 2009

The hippocampus is a major structure of interest affected by temporal lobe epilepsy (TLE). Region... more The hippocampus is a major structure of interest affected by temporal lobe epilepsy (TLE). Region of interest (ROI)-based analysis has traditionally been used to study hippocampal involvement in TLE, although spatial variation of structural and functional pathology have been known to exist within the ROI. In this article, structure-specific analysis Neuroimage 35:1516-1530) is applied to the study of both structure and function in TLE patients. This methodology takes into account information about the spatial correspondence of voxels within ROIs on left and right sides of the same subject as well as between subjects. Hippocampal thickness is studied as a measure of structural integrity, and functional activation in a functional magnetic resonance imaging (fMRI) experiment in which subjects performed a memory encoding task is studied as a measure of functional integrity. Pronounced disease-related decrease in thickness is found in posterior and anterior hippocampus. A region in the body also shows increased thickness in patients' healthy hippocampi compared with controls. Functional activation in diseased hippocampi is reduced in the body region compared to controls, whereas a region in the tail showing greater right-lateralized activation in controls also shows greater activation in healthy hippocampi compared with the diseased side in patients. Summary measurements generated by integrating quantities of interest over the entire hippocampus can also be used, as is done in conventional ROI analysis.

Appearance and incomplete label matching for diffeomorphic template based hippocampus segmentation

Hippocampus, 2009

We present a robust, high-throughput, semi-automated template based protocol for segmenting the h... more We present a robust, high-throughput, semi-automated template based protocol for segmenting the hippocampus in temporal lobe epilepsy (TLE). The semi-automated component of this approach, which minimizes user effort while maximizing the benefit of human input to the algorithm, relies on "incomplete labeling." Incomplete labeling requires the user to quickly and approximately segment a few key regions of the hippocampus through a user-interface. Subsequently, this partial labeling of the hippocampus is combined with image similarity terms to guide volumetric diffeomorphic normalization between an individual brain and an unbiased disease-specific template, with fully labeled hippocampi. We solve this many-to-few and few-to-many matching problem, and gain robustness to inter and intra-rater variability and small errors in user labeling, by embedding the template-based normalization within a probabilistic framework that examines both label geometry and appearance data at each label. We evaluate the reliability of this framework with respect to manual labeling and show that it increases minimum performance levels relative to fully automated approaches and provides high inter-rater reliability. Thus, this approach does not require expert neuroanatomical training and is viable for high-throughput studies of both the normal and the highly atrophic hippocampus.

Increased functional connectivity within medial temporal lobe in mild cognitive impairment

by Paul Yushkevich and John Pluta

Pathology at preclinical and prodromal stages of Alzheimer's disease (AD) may manifest itself as ... more Pathology at preclinical and prodromal stages of Alzheimer's disease (AD) may manifest itself as measurable functional change in neuronal networks earlier than detectable structural change. Functional connectivity as measured using resting-state fMRI (functional Magnetic Resonance Imaging) has emerged as a useful tool for studying disease effects on baseline states of neuronal networks. In this study, we use high resolution MRI to label subregions within the medial temporal lobe (MTL), a site of early pathology in AD, and report an increase in functional connectivity in amnesic MCI (aMCI) between entorhinal cortex and subregions of the MTL, with the strongest effect in the anterior hippocampus. However, our data also replicated the effects of decreased connectivity of the MTL to other nodes of the default mode network reported by other researchers. This dissociation of changes in functional connectivity within the MTL versus the MTL's connection with other neocortical structures can help enrich the characterization of early stages of disease progression in AD.

Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI

NeuroImage, 2014

Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields usi... more Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200 μm spacing and 5 μm slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4 Tesla MRI scan of the intact, whole hippocampal formation acquired with 160 μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1 cm-thick tissue sub-blocks acquired with 200 μm isotropic resolution. These 1 cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation.

A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T

by Paul Yushkevich and John Pluta

Neuroimage, 2009

This paper describes the construction of a computational anatomical atlas of the human hippocampu... more

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