Publications by authors named "Nagesh Adluru"

79 Publications

Individual variation in white matter microstructure is related to better recovery from negative stimuli.

Emotion 2021 Sep 30. Epub 2021 Sep 30.

Center for Healthy Minds.

The uncinate fasciculus is a white matter tract that may facilitate emotion regulation by carrying connections from the prefrontal cortex to regions of the temporal lobe, including the amygdala. Depression and anxiety are associated with reduced uncinate fasciculus fractional anisotropy (FA)-a diffusion tensor imaging measure related to white matter integrity. In the current study, we tested whether FA in the uncinate fasciculus is associated with individual differences in emotional recovery measured with corrugator supercilii electromyography in response to negative, neutral, and positive images in 108 participants from the Midlife in the US (MIDUS; http://midus.wisc.edu) Refresher study. Corrugator activity is linearly associated with changes in affect, and differentiated negative, neutral, and positive emotional responses. Higher uncinate fasciculus FA was associated with lower corrugator activity 4-8 seconds after negative image offset, indicative of better recovery from negative provocation. In an exploratory analysis, we found a similar association for the inferior fronto-occipital, inferior longitudinal and superior longitudinal fasciculi. These results suggest that the microstructural features of the uncinate fasciculus, and these other association white matter fibers, may support emotion regulatory processes with greater white matter integrity facilitating healthier affective functioning. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/emo0000996DOI Listing
September 2021

Genetic and environmental influences of variation in diffusion MRI measures of white matter microstructure.

Brain Struct Funct 2021 Sep 28. Epub 2021 Sep 28.

Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.

Quantitative neuroimaging studies in twin samples can investigate genetic contributions to brain structure and microstructure. Diffusion tensor imaging (DTI) studies with twin samples have shown moderate to high heritability in white matter microstructure. This study investigates the genetic and environmental contributions of another widely used diffusion MRI model not yet applied to twin studies, neurite orientation dispersion and density imaging (NODDI). The NODDI model is a multicompartment model of the diffusion-weighted MRI signal, providing estimates of neurite density (ND) and the orientation dispersion index (ODI). A cohort of monozygotic (MZ) and same-sex dizygotic (DZ) twins (N = 460 individuals) between 13 and 24 years of age were scanned with a multi-shell diffusion weighted imaging protocol. Select white matter (WM) regions of interest (ROI) were extracted. Biometric structural equation modeling estimated the relative contributions from additive genetic (A) and common (C) and unique environmental (E) factors. Genetic factors for the NODDI measures accounted for 91% and 65% of the variation of global ND and ODI, respectively, compared with 83% for FA. We observed higher heritability for ND than both FA and ODI in 25 of 30 discrete white matter regions that we examined, suggesting ND may be more sensitive to underlying genetic sources of variation. This study demonstrated that genetic factors play a key role in the development of white matter microstructure using both DTI and NODDI.
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http://dx.doi.org/10.1007/s00429-021-02393-7DOI Listing
September 2021

The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI.

Semin Ultrasound CT MR 2021 Oct 1;42(5):507-522. Epub 2021 Aug 1.

Department of Radiology, University of Wisconsin-Madison, Madison, WI. Electronic address:

A comprehensive mapping of the structural and functional circuitry of the brain is a major unresolved problem in contemporary neuroimaging research. Diffusion-weighted and functional MRI have provided investigators with the capability to assess structural and functional connectivity in-vivo, driven primarily by methods of white matter tractography and resting-state fMRI, respectively. These techniques have paved the way for the construction of the functional and structural connectomes, which are quantitative representations of brain architecture as neural networks, comprised of nodes and edges. The connectomes, typically depicted as matrices or graphs, possess topological properties that inherently characterize the strength, efficiency, and organization of the connections between distinct brain regions. Graph theory, a general mathematical framework for analyzing networks, can be implemented to derive metrics from the connectomes that are sensitive to changes in brain connectivity associated with age, sex, cognitive function, and disease. These quantities can be assessed at either the global (whole brain) or local levels, allowing for the identification of distinct regional connectivity hubs and associated localized brain networks, which together serve crucial roles in establishing the structural and functional architecture of the brain. As a result, structural and functional connectomes have each been employed to study the brain circuitry underlying early brain development, neuroplasticity, developmental disorders, psychopathology, epilepsy, aging, neurodegenerative disorders, and traumatic brain injury. While these studies have yielded important insights into brain structure, function, and pathology, a precise description of the innate relationship between functional and structural networks across the brain remains unachieved. To date, connectome research has merely scratched the surface of potential clinical applications and related characterizations of brain-wide connectivity. Continued advances in diffusion and functional MRI acquisition, the delineation of functional and structural networks, and the quantification of neural network properties in specific brain regions, will be invaluable to future progress in neuroimaging science.
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http://dx.doi.org/10.1053/j.sult.2021.07.007DOI Listing
October 2021

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

Neuroimage 2021 Nov 22;243:118502. Epub 2021 Aug 22.

Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos, Madrid, Spain.

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118502DOI Listing
November 2021

Effects of simvastatin on white matter integrity in healthy middle-aged adults.

Ann Clin Transl Neurol 2021 08 18;8(8):1656-1667. Epub 2021 Jul 18.

Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.

Background: The brain is the most cholesterol-rich organ and myelin contains 70% of total brain cholesterol. Statins are potent cholesterol-lowing medications used by millions of adults for prevention of vascular disease, yet the effect of statins on cholesterol-rich brain white matter (WM) is largely unknown.

Methods: We used longitudinal neuroimaging data acquired from 73 healthy, cognitively unimpaired, statin-naïve, middle-aged adults during an 18-month randomized controlled trial of simvastatin 40 mg daily (n = 35) or matching placebo (n = 38). ANCOVA models (covariates: age, sex, APOE-ɛ4) tested the effect of treatment group on percent change in WM, gray matter (GM), and WM hyperintensity (WMH) neuroimaging measures at each study visit. Mediation analysis tested the indirect effects of simvastatin on WM microstructure through change in serum total cholesterol levels.

Results: At 18 months, the simvastatin group showed a significant preservation in global WM fractional anisotropy (β = 0.88%, 95% CI 0.27 to 1.50, P = 0.005), radial diffusivity (β = -1.10%, 95% CI -2.13 to -0.06, P = 0.039), and WM volume (β = 0.72%, 95% CI 0.13 to 1.32, P = 0.018) relative to the placebo group. There was no significant effect of simvastatin on GM or WMH volume. Change in serum total cholesterol mediated approximately 30% of the effect of simvastatin on WM microstructure.

Conclusions: Simvastatin treatment in healthy, middle-aged adults resulted in preserved WM microstructure and volume at 18 months. The partial mediation by serum cholesterol reduction suggests both peripheral and central mechanisms. Future studies are needed to determine whether these effects persist and translate to cognitive outcomes.

Trial Registration: NCT00939822 (ClinicalTrials.gov).
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http://dx.doi.org/10.1002/acn3.51421DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8351379PMC
August 2021

Interaction of amyloid and tau on cortical microstructure in cognitively unimpaired adults.

Alzheimers Dement 2021 May 13. Epub 2021 May 13.

Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.

Introduction: Neurite orientation dispersion and density imaging (NODDI), a multi-compartment diffusion-weighted imaging (DWI) model, may be useful for detecting early cortical microstructural alterations in Alzheimer's disease prior to cognitive impairment.

Methods: Using neuroimaging (NODDI and T1-weighted magnetic resonance imaging [MRI]) and cerebrospinal fluid (CSF) biomarker data (measured using Elecsys® CSF immunoassays) from 219 cognitively unimpaired participants, we tested the main and interactive effects of CSF amyloid beta (Aβ) /Aβ and phosphorylated tau (p-tau) on cortical NODDI metrics and cortical thickness, controlling for age, sex, and apolipoprotein E ε4.

Results: We observed a significant CSF Aβ /Aβ × p-tau interaction on cortical neurite density index (NDI), but not orientation dispersion index or cortical thickness. The directionality of these interactive effects indicated: (1) among individuals with lower CSF p-tau, greater amyloid burden was associated with higher cortical NDI; and (2) individuals with greater amyloid and p-tau burden had lower cortical NDI, consistent with cortical neurodegenerative changes.

Discussion: NDI is a particularly sensitive marker for early cortical changes that occur prior to gross atrophy or development of cognitive impairment.
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http://dx.doi.org/10.1002/alz.12364DOI Listing
May 2021

A 16-year study of longitudinal volumetric brain development in males with autism.

Neuroimage 2021 08 18;236:118067. Epub 2021 Apr 18.

Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA; Department of Neurology, University of Utah, Salt Lake City, UT USA; Department of Pediatrics, University of Utah, Salt Lake City, UT, USA.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with unknown brain etiology. Our knowledge to date about structural brain development across the lifespan in ASD comes mainly from cross-sectional studies, thereby limiting our understanding of true age effects within individuals with the disorder that can only be gained through longitudinal research. The present study describes FreeSurfer-derived volumetric findings from a longitudinal dataset consisting of 607 T1-weighted magnetic resonance imaging (MRI) scans collected from 105 male individuals with ASD (349 MRIs) and 125 typically developing male controls (258 MRIs). Participants were six to forty-five years of age at their first scan, and were scanned up to 5 times over a period of 16 years (average inter-scan interval of 3.7 years). Atypical age-related volumetric trajectories in ASD included enlarged gray matter volume in early childhood that approached levels of the control group by late childhood, an age-related increase in ventricle volume resulting in enlarged ventricles by early adulthood and reduced corpus callosum age-related volumetric increase resulting in smaller corpus callosum volume in adulthood. Larger corpus callosum volume was related to a lower (better) ADOS score at the most recent study visit for the participants with ASD. These longitudinal findings expand our knowledge of volumetric brain-based abnormalities in males with ASD, and highlight the need to continue to examine brain structure across the lifespan and well into adulthood.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8489006PMC
August 2021

A 3D Fully Convolutional Neural Network With Top-Down Attention-Guided Refinement for Accurate and Robust Automatic Segmentation of Amygdala and Its Subnuclei.

Front Neurosci 2020 21;14:260. Epub 2020 May 21.

Waisman Brain Imaging Laboratory, University of Wisconsin-Madison, Madison, WI, United States.

Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most existing deep learning based approaches in neuroimaging do not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the subnuclei of the amygdala. To tackle this challenging task, we developed a dual-branch dilated residual 3D fully convolutional network with parallel convolutions to extract more global context and alleviate the class imbalance issue by maintaining a small receptive field that is just the size of the regions of interest (ROIs). We also conduct multi-scale feature fusion in both parallel and series to compensate the potential information loss during convolutions, which has been shown to be important for small objects. The serial feature fusion enabled by residual connections is further enhanced by a proposed top-down attention-guided refinement unit, where the high-resolution low-level spatial details are selectively integrated to complement the high-level but coarse semantic information, enriching the final feature representations. As a result, the segmentations resulting from our method are more accurate both volumetrically and morphologically, compared with other deep learning based approaches. To the best of our knowledge, this work is the first deep learning-based approach that targets the subregions of the amygdala. We also demonstrated the feasibility of using a cycle-consistent generative adversarial network (CycleGAN) to harmonize multi-site MRI data, and show that our method generalizes well to challenging traumatic brain injury (TBI) datasets collected from multiple centers. This appears to be a promising strategy for image segmentation for multiple site studies and increased morphological variability from significant brain pathology.
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http://dx.doi.org/10.3389/fnins.2020.00260DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7253589PMC
May 2020

BrainAGE and regional volumetric analysis of a Buddhist monk: a longitudinal MRI case study.

Neurocase 2020 04 26;26(2):79-90. Epub 2020 Feb 26.

Center for Healthy Minds, UW-Madison, USA.

Yongey Mingyur Rinpoche (YMR) is a Tibetan Buddhist monk, and renowned meditation practitioner and teacher who has spent an extraordinary number of hours of his life meditating. The brain-aging profile of this expert meditator in comparison to a control population was examined using a machine learning framework, which estimates "brain-age" from brain imaging. YMR's brain-aging rate appeared slower than that of controls suggesting early maturation and delayed aging. At 41 years, his brain resembled that of a 33-year-old. Specific regional changes did not differentiate YMR from controls, suggesting that the brain-aging differences may arise from coordinated changes spread throughout the gray matter.
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http://dx.doi.org/10.1080/13554794.2020.1731553DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7150651PMC
April 2020

Cortical Microstructural Alterations in Mild Cognitive Impairment and Alzheimer's Disease Dementia.

Cereb Cortex 2020 05;30(5):2948-2960

Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53792 USA.

In Alzheimer's disease (AD), neurodegenerative processes are ongoing for years prior to the time that cortical atrophy can be reliably detected using conventional neuroimaging techniques. Recent advances in diffusion-weighted imaging have provided new techniques to study neural microstructure, which may provide additional information regarding neurodegeneration. In this study, we used neurite orientation dispersion and density imaging (NODDI), a multi-compartment diffusion model, in order to investigate cortical microstructure along the clinical continuum of mild cognitive impairment (MCI) and AD dementia. Using gray matter-based spatial statistics (GBSS), we demonstrated that neurite density index (NDI) was significantly lower throughout temporal and parietal cortical regions in MCI, while both NDI and orientation dispersion index (ODI) were lower throughout parietal, temporal, and frontal regions in AD dementia. In follow-up ROI analyses comparing microstructure and cortical thickness (derived from T1-weighted MRI) within the same brain regions, differences in NODDI metrics remained, even after controlling for cortical thickness. Moreover, for participants with MCI, gray matter NDI-but not cortical thickness-was lower in temporal, parietal, and posterior cingulate regions. Taken together, our results highlight the utility of NODDI metrics in detecting cortical microstructural degeneration that occurs prior to measurable macrostructural changes and overt clinical dementia.
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http://dx.doi.org/10.1093/cercor/bhz286DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197091PMC
May 2020

Mindfulness video game improves connectivity of the fronto-parietal attentional network in adolescents: A multi-modal imaging study.

Sci Rep 2019 12 10;9(1):18667. Epub 2019 Dec 10.

Center for Healthy Minds, University of Wisconsin - Madison, 625W. Washington Avenue, Madison, WI, 53703, USA.

Mindfulness training has been shown to improve attention and change the underlying brain substrates in adults. Most mindfulness training programs involve a myriad of techniques, and it is difficult to attribute changes to any particular aspect of the program. Here, we created a video game, Tenacity, which models a specific mindfulness technique - focused attention on one's breathing - and assessed its potential to train an attentional network in adolescents. A combined analysis of resting state functional connectivity (rs-FC) and diffusion tensor imaging (DTI) yielded convergent results - change in communication within the left fronto-parietal network after two weeks of playing Tenacity compared to a control game. Rs-FC analysis showed greater connectivity between left dorsolateral prefrontal cortex (dlPFC) and left inferior parietal cortex (IPC) in the Tenacity group. Importantly, changes in left dlPFC - IPC rs-FC and changes in structural connectivity of the white matter tract that connects these regions -left superior longitudinal fasiculus (SLF) - were associated with changes in performance on an attention task. Finally, changes in left dlPFC - IPC rs-FC correlated with the change in left SLF structural connectivity as measured by fractional anisotropy (FA) in the Tenacity group only.
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http://dx.doi.org/10.1038/s41598-019-53393-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6904443PMC
December 2019

Optimizing the intrinsic parallel diffusivity in NODDI: An extensive empirical evaluation.

PLoS One 2019 25;14(9):e0217118. Epub 2019 Sep 25.

Department of Medical Physics, University of Wisconsin - Madison, Madison, WI, United States of America.

Purpose: NODDI is widely used in parameterizing microstructural brain properties. The model includes three signal compartments: intracellular, extracellular, and free water. The neurite compartment intrinsic parallel diffusivity (d∥) is set to 1.7 μm2⋅ms-1, though the effects of this assumption have not been extensively explored. This work investigates the optimality of d∥ = 1.7 μm2⋅ms-1 under varying imaging protocol, age groups, sex, and tissue type in comparison to other biologically plausible values of d∥.

Methods: Model residuals were used as the optimality criterion. The model residuals were evaluated in function of d∥ over the range from 0.5 to 3.0 μm2⋅ms-1. This was done with respect to tissue type (i.e., white matter versus gray matter), sex, age (infancy to late adulthood), and diffusion-weighting protocol (maximum b-value). Variation in the estimated parameters with respect to d∥ was also explored.

Results: Results show d∥ = 1.7 μm2⋅ms-1 is appropriate for adult brain white matter but it is suboptimal for gray matter with optimal values being significantly lower. d∥ = 1.7 μm2⋅ms-1 was also suboptimal in the infant brain for both white and gray matter with optimal values being significantly lower. Minor optimum d∥ differences were observed versus diffusion protocol. No significant sex effects were observed. Additionally, changes in d∥ resulted in significant changes to the estimated NODDI parameters.

Conclusion: The default (d∥) of 1.7 μm2⋅ms-1 is suboptimal in gray matter and infant brains.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0217118PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6760776PMC
March 2020

Mindfulness-Based Stress Reduction-related changes in posterior cingulate resting brain connectivity.

Soc Cogn Affect Neurosci 2019 07;14(7):777-787

Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI 53703, USA.

Mindfulness meditation training has been shown to increase resting-state functional connectivity between nodes of the frontoparietal executive control network (dorsolateral prefrontal cortex [DLPFC]) and the default mode network (posterior cingulate cortex [PCC]). We investigated whether these effects generalized to a Mindfulness-Based Stress Reduction (MBSR) course and tested for structural and behaviorally relevant consequences of change in connectivity. Healthy, meditation-naïve adults were randomized to either MBSR (N = 48), an active (N = 47) or waitlist (N = 45) control group. Participants completed behavioral testing, resting-state fMRI scans and diffusion tensor scans at pre-randomization (T1), post-intervention (T2) and ~5.5 months later (T3). We found increased T2-T1 PCC-DLPFC resting connectivity for MBSR relative to control groups. Although these effects did not persist through long-term follow-up (T3-T1), MBSR participants showed a significantly stronger relationship between days of practice (T1 to T3) and increased PCC-DLPFC resting connectivity than participants in the active control group. Increased PCC-DLPFC resting connectivity in MBSR participants was associated with increased microstructural connectivity of a white matter tract connecting these regions and increased self-reported attention. These data show that MBSR increases PCC-DLPFC resting connectivity, which is related to increased practice time, attention and structural connectivity.
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http://dx.doi.org/10.1093/scan/nsz050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778831PMC
July 2019

Predicting Motor Outcomes in Stroke Patients Using Diffusion Spectrum MRI Microstructural Measures.

Front Neurol 2019 18;10:72. Epub 2019 Feb 18.

Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.

Improved understanding of neuroimaging signal changes and their relation to patient outcomes after ischemic stroke is needed to improve ability to predict motor improvement and make therapy recommendations. The posterior limb of the internal capsule (PLIC) is a hub of afferent and efferent motor signaling and this work proposes new, image-based methods for prognosis based on interhemispheric differences in the PLIC. In this work, nine acute supratentorial ischemic stroke patients with motor impairment received a baseline, 203-direction diffusion brain MRI and a clinical assessment 3-12 days post-stroke and were compared to nine age-matched healthy controls. Asymmetries based on the mean and Kullback-Leibler divergence in the ipsilesional and contralesional PLIC were calculated for diffusion tensor imaging (DTI) and diffusion spectrum imaging (DSI) measures from the baseline MRI. Predictions of upper extremity Fugl-Meyer (FM) scores at 5-weeks follow-up from baseline measures of PLIC asymmetry in diffusion tensor imaging (DTI) and diffusion spectrum imaging (DSI) models were evaluated. For the stroke participants, the baseline asymmetry measures in the PLIC for the orientation dispersion index of the neurite orientation dispersion and density imaging (NODDI) model were highly correlated with upper extremity FM outcomes ( = 0.83). Use of DSI and the NODDI orientation dispersion index parameter shows promise of being more predictive of stroke recovery and to help better understand white matter changes in stroke, beyond DTI measures. The new finding that baseline interhemispheric differences in the PLIC calculated from the orientation dispersion index of the NODDI model are highly correlated with upper extremity functional outcomes may lead to improved image-based motor-outcome prediction after middle cerebral artery ischemic stroke.
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http://dx.doi.org/10.3389/fneur.2019.00072DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387951PMC
February 2019

Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: A multi-resolution graph analysis.

Neuroimage Clin 2019 23;21:101586. Epub 2018 Oct 23.

Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, USA; Department of Computer Science, University of Wisconsin - Madison, Madison, WI, USA.

In addition to the development of beta amyloid plaques and neurofibrillary tangles, Alzheimer's disease (AD) involves the loss of connecting structures including degeneration of myelinated axons and synaptic connections. However, the extent to which white matter tracts change longitudinally, particularly in the asymptomatic, preclinical stage of AD, remains poorly characterized. In this study we used a novel graph wavelet algorithm to determine the extent to which microstructural brain changes evolve in concert with the development of AD neuropathology as observed using CSF biomarkers. A total of 118 participants with at least two diffusion tensor imaging (DTI) scans and one lumbar puncture for CSF were selected from two observational and longitudinally followed cohorts. CSF was assayed for pathology specific to AD (Aβ42 and phosphorylated-tau), neurodegeneration (total-tau), axonal degeneration (neurofilament light chain protein; NFL), and synaptic degeneration (neurogranin). Tractography was performed on DTI scans to obtain structural connectivity networks with 160 nodes where the nodes correspond to specific brain regions of interest (ROIs) and their connections were defined by DTI metrics (i.e., fractional anisotropy (FA) and mean diffusivity (MD)). For the analysis, we adopted a multi-resolution graph wavelet technique called Wavelet Connectivity Signature (WaCS) which derives higher order representations from DTI metrics at each brain connection. Our statistical analysis showed interactions between the CSF measures and the MRI time interval, such that elevated CSF biomarkers and longer time were associated with greater longitudinal changes in white matter microstructure (decreasing FA and increasing MD). Specifically, we detected a total of 17 fiber tracts whose WaCS representations showed an association between longitudinal decline in white matter microstructure and both CSF p-tau and neurogranin. While development of neurofibrillary tangles and synaptic degeneration are cortical phenomena, the results show that they are also associated with degeneration of underlying white matter tracts, a process which may eventually play a role in the development of cognitive decline and dementia.
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http://dx.doi.org/10.1016/j.nicl.2018.10.024DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6411581PMC
December 2019

Heritability of nested hierarchical structural brain network.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:554-557

When a brain network is constructed by an existing parcellation method, the topological structure of the network changes depending on the scale of the parcellation. To avoid the scale dependency, we propose to construct a nested hierarchical structural brain network by subdividing the existing parcellation hierarchically. The method is applied in diffusion tensor imaging study of 111 twins in characterizing the topology of the brain network. The genetic contribution of the whole brain structural connectivity is determined and shown to be robustly present over different network scales.
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http://dx.doi.org/10.1109/EMBC.2018.8512359DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6241281PMC
July 2018

Associations Between Positron Emission Tomography Amyloid Pathology and Diffusion Tensor Imaging Brain Connectivity in Pre-Clinical Alzheimer's Disease.

Brain Connect 2019 03 7;9(2):162-173. Epub 2019 Jan 7.

1 Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin.

Characterizing Alzheimer's disease (AD) at pre-clinical stages is crucial for initiating early treatment strategies. It is widely accepted that amyloid accumulation is a primary pathological event in AD. Also, loss of connectivity between brain regions is suspected of contributing to cognitive decline, but studies that test these associations using either local (i.e., individual edges) or global (i.e., modularity) connectivity measures may be limited. In this study, we utilized data acquired from 139 cognitively unimpaired participants. Sixteen gray matter (GM) regions known to be affected by AD were selected for analysis. For each of the 16 regions, the effect of amyloid burden, measured using Pittsburgh Compound B (PiB) positron emission tomography, on each of the 1761 brain network connections derived from diffusion tensor imaging (DTI) connecting 162 GM regions, was investigated. Applying our unique multiresolution statistical analysis called the Wavelet Connectivity Signature (WaCS), this study demonstrates the relationship between amyloid burden and structural brain connectivity as assessed with DTI. Our statistical analysis using WaCS shows that in 15 of 16 GM regions, statistically significant relationships between amyloid burden in those regions and structural connectivity networks were observed. After applying multiple testing correction, 10 unique structural brain connections were found to be significantly associated with amyloid accumulation. For 7 of those 10 network connections, the decrease in their network connection strength indexed by fractional anisotropy was, in turn, associated with lower cognitive function, providing evidence that AD-related structural connectivity loss is a correlate of cognitive decline.
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http://dx.doi.org/10.1089/brain.2018.0590DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909755PMC
March 2019

Association of Prenatal Maternal Depression and Anxiety Symptoms With Infant White Matter Microstructure.

JAMA Pediatr 2018 10;172(10):973-981

Waisman Center, University of Wisconsin, Madison.

Importance: Maternal depression and anxiety can have deleterious and lifelong consequences on child development. However, many aspects of the association of early brain development with maternal symptoms remain unclear. Understanding the timing of potential neurobiological alterations holds inherent value for the development and evaluation of future therapies and interventions.

Objective: To examine the association between exposure to prenatal maternal depression and anxiety symptoms and offspring white matter microstructure at 1 month of age.

Design, Setting, And Participants: This cohort study of 101 mother-infant dyads used a composite of depression and anxiety symptoms measured in mothers during the third trimester of pregnancy and measures of white matter microstructure characterized in the mothers' 1-month offspring using diffusion tensor imaging and neurite orientation dispersion and density imaging performed from October 1, 2014, to November 30, 2016. Magnetic resonance imaging was performed at an academic research facility during natural, nonsedated sleep.

Main Outcomes And Measures: Brain mapping algorithms and statistical models were used to evaluate the association between maternal depression and anxiety and 1-month infant white matter microstructure as measured by diffusion tensor imaging and neurite orientation dispersion and density imaging findings.

Results: In the 101 mother-infant dyads (mean [SD] age of mothers, 33.22 [3.99] years; mean age of infants at magnetic resonance imaging, 33.07 days [range, 18-50 days]; 92 white mothers [91.1%]; 53 male infants [52.5%]), lower 1-month white matter microstructure (decreased neurite density and increased mean, radial, and axial diffusivity) was associated in right frontal white matter microstructure with higher prenatal maternal symptoms of depression and anxiety. Significant sex × symptom interactions with measures of white matter microstructure were also observed, suggesting that white matter development may be differentially sensitive to maternal depression and anxiety symptoms in males and females during the prenatal period.

Conclusions And Relevance: These data highlight the importance of the prenatal period to early brain development and suggest that the underlying white matter microstructure is associated with the continuum of prenatal maternal depression and anxiety symptoms.
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http://dx.doi.org/10.1001/jamapediatrics.2018.2132DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6190835PMC
October 2018

A Natural Language Interface for Dissemination of Reproducible Biomedical Data Science.

Med Image Comput Comput Assist Interv 2018 Sep 13;11073:197-205. Epub 2018 Sep 13.

University of Wisconsin-Madison, Madison, USA.

Computational tools in the form of software packages are burgeoning in the field of medical imaging and biomedical research. These tools enable biomedical researchers to analyze a variety of data using modern machine learning and statistical analysis techniques. While these publicly available software packages are a great step towards a multiplicative increase in the biomedical research productivity, there are still many open issues related to validation and reproducibility of the results. A key gap is that while scientists can validate domain insights that are implicit in the analysis, the analysis itself is coded in a programming language and that domain scientist may not be a programmer. Thus, there is no/limited direct validation of the program that carries out the desired analysis. We propose a novel solution, building upon recent successes in natural language understanding, to address this problem. Our platform allows researchers to perform, share, reproduce and interpret the analysis pipelines and results via natural language. While this approach still requires users to have a conceptual understanding of the techniques, it removes the burden of programming syntax and thus lowers the barriers to advanced and reproducible neuroimaging and biomedical research.
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http://dx.doi.org/10.1007/978-3-030-00937-3_23DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224401PMC
September 2018

Efficient Relative Attribute Learning using Graph Neural Networks.

Comput Vis ECCV 2018 Sep 9;11218:575-590. Epub 2018 Oct 9.

University of Wisconsin - Madison.

A sizable body of work on relative attributes provides evidence that relating pairs of images along a continuum of strength pertaining to a visual attribute yields improvements in a variety of vision tasks. In this paper, we show how emerging ideas in graph neural networks can yield a solution to various problems that broadly fall under relative attribute learning. Our main idea is the observation that relative attribute learning naturally benefits from exploiting the graph of dependencies among the different relative attributes of images, especially when only partial ordering is provided at training time. We use message passing to perform end to end learning of the image representations, their relationships as well as the interplay between different attributes. Our experiments show that this simple framework is effective in achieving competitive accuracy with specialized methods for both relative attribute learning and binary attribute prediction, while relaxing the requirements on the training data and/or the number of parameters, or both.
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http://dx.doi.org/10.1007/978-3-030-01264-9_34DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173331PMC
September 2018

Longitudinal white matter microstructural change in Parkinson's disease.

Hum Brain Mapp 2018 10 27;39(10):4150-4161. Epub 2018 Jun 27.

William S. Middleton Memorial Veterans Hospital, Madison, Wisconsin.

Postmortem studies of Parkinson's disease (PD) suggest that Lewy body pathology accumulates in a predictable topographical sequence, beginning in the olfactory bulb, followed by caudal brainstem, substantia nigra, limbic cortex, and neocortex. Diffusion-weighted imaging (DWI) is sensitive, if not specific, to early disease-related white matter (WM) change in a variety of traumatic and degenerative brain diseases. Although numerous cross-sectional studies have reported DWI differences in cerebral WM in PD, only a few longitudinal studies have investigated whether DWI change exceeds that of normal aging or coincides with regional Lewy body accumulation. This study mapped regional differences in the rate of DWI-based microstructural change between 29 PD patients and 43 age-matched controls over 18 months. Iterative within- and between-subject tensor-based registration was completed on motion- and eddy current-corrected DWI images, then baseline versus follow-up difference maps of fractional anisotropy, mean, radial, and axial diffusivity were analyzed in the Biological Parametric Mapping toolbox for MATLAB. This analysis showed that PD patients had a greater decline in WM integrity in the rostral brainstem, caudal subcortical WM, and cerebellar peduncles, compared with controls. In addition, patients with unilateral clinical signs at baseline experienced a greater rate of WM change over the 18-month study than patients with bilateral signs. These findings suggest that rate of WM microstructural change in PD exceeds that of normal aging and is maximal during early stage disease. In addition, the neuroanatomic locations (rostral brainstem and subcortical WM) of accelerated WM change fit with current theories of topographic disease progression.
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http://dx.doi.org/10.1002/hbm.24239DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6128734PMC
October 2018

Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging.

Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 2017 Jul 9;2017:5777-5786. Epub 2017 Nov 9.

University of Wisconsin-Madison.

Statistical machine learning models that operate on manifold-valued data are being extensively studied in vision, motivated by applications in activity recognition, feature tracking and medical imaging. While non-parametric methods have been relatively well studied in the literature, efficient formulations for parametric models (which may offer benefits in small sample size regimes) have only emerged recently. So far, manifold-valued regression models (such as geodesic regression) are restricted to the analysis of cross-sectional data, i.e., the so-called "fixed effects" in statistics. But in most "longitudinal analysis" (e.g., when a participant provides multiple measurements, over time) the application of fixed effects models is problematic. In an effort to answer this need, this paper generalizes non-linear mixed effects model to the regime where the response variable is manifold-valued, i.e., f : → ℳ. We derive the underlying model and estimation schemes and demonstrate the immediate benefits such a model can provide - both for group level and individual level analysis - on longitudinal brain imaging data. The direct consequence of our results is that longitudinal analysis of manifold-valued measurements (especially, the symmetric positive definite manifold) can be conducted in a computationally tractable manner.
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http://dx.doi.org/10.1109/CVPR.2017.612DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5805155PMC
July 2017

Gut microbiome populations are associated with structure-specific changes in white matter architecture.

Transl Psychiatry 2018 01 10;8(1). Epub 2018 Jan 10.

Department of Radiology, University of Wisconsin School of Medicine and Public Health, E3/366 Clinical Science Center, 600 Highland Avenue, M/C 3252, Madison, WI, 53792-3252, USA.

Altered gut microbiome populations are associated with a broad range of neurodevelopmental disorders including autism spectrum disorder and mood disorders. In animal models, modulation of gut microbiome populations via dietary manipulation influences brain function and behavior and has been shown to ameliorate behavioral symptoms. With striking differences in microbiome-driven behavior, we explored whether these behavioral changes are also accompanied by corresponding changes in neural tissue microstructure. Utilizing diffusion tensor imaging, we identified global changes in white matter structural integrity occurring in a diet-dependent manner. Analysis of 16S ribosomal RNA sequencing of gut bacteria also showed changes in bacterial populations as a function of diet. Changes in brain structure were found to be associated with diet-dependent changes in gut microbiome populations using a machine learning classifier for quantitative assessment of the strength of microbiome-brain region associations. These associations allow us to further test our understanding of the gut-brain-microbiota axis by revealing possible links between altered and dysbiotic gut microbiome populations and changes in brain structure, highlighting the potential impact of diet and metagenomic effects in neuroimaging.
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http://dx.doi.org/10.1038/s41398-017-0022-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5802560PMC
January 2018

Longitudinal development of thalamic and internal capsule microstructure in autism spectrum disorder.

Autism Res 2018 03 18;11(3):450-462. Epub 2017 Dec 18.

Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin.

The thalamus is a key sensorimotor relay area that is implicated in autism spectrum disorder (ASD). However, it is unknown how the thalamus and white-matter structures that contain thalamo-cortical fiber connections (e.g., the internal capsule) develop from childhood into adulthood and whether this microstructure relates to basic motor challenges in ASD. We used diffusion weighted imaging in a cohort-sequential design to assess longitudinal development of the thalamus, and posterior- and anterior-limbs of the internal capsule (PLIC and ALIC, respectively) in 89 males with ASD and 56 males with typical development (3-41 years; all verbal). Our results showed that the group with ASD exhibited different developmental trajectories of microstructure in all regions, demonstrating childhood group differences that appeared to approach and, in some cases, surpass the typically developing group in adolescence and adulthood. The PLIC (but not ALIC nor thalamus) mediated the relation between age and finger-tapping speed in both groups. Yet, the gap in finger-tapping speed appeared to widen at the same time that the between-group gap in the PLIC appeared to narrow. Overall, these results suggest that childhood group differences in microstructure of the thalamus and PLIC become less robust in adolescence and adulthood. Further, finger-tapping speed appears to be mediated by the PLIC in both groups, but group differences in motor speed that widen during adolescence and adulthood suggest that factors beyond the microstructure of the thalamus and internal capsule may contribute to atypical motor profiles in ASD. Autism Res 2018, 11: 450-462. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Lay Summary: Microstructure of the thalamus, a key sensory and motor brain area, appears to develop differently in individuals with autism spectrum disorder (ASD). Microstructure is important because it informs us of the density and organization of different brain tissues. During childhood, thalamic microstructure was distinct in the ASD group compared to the typically developing group. However, these group differences appeared to narrow with age, suggesting that the thalamus continues to dynamically change in ASD into adulthood.
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http://dx.doi.org/10.1002/aur.1909DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5867209PMC
March 2018

Evaluation of striatonigral connectivity using probabilistic tractography in Parkinson's disease.

Neuroimage Clin 2017 9;16:557-563. Epub 2017 Sep 9.

William S. Middleton Memorial Veterans Hospital, Madison, WI, USA.

The cardinal movement abnormalities of Parkinson's disease (PD), including tremor, muscle rigidity, and reduced speed and frequency of movements, are caused by degeneration of dopaminergic neurons in the substantia nigra that project to the putamen, compromising information flow through frontal-subcortical circuits. Typically, the nigrostriatal pathway is more severely affected on the side of the brain opposite (contralateral) to the side of the body that manifests initial symptoms. Several studies have suggested that PD is also associated with changes in white matter microstructural integrity. The goal of the present study was to further develop methods for measuring striatonigral connectivity differences between PD patients and age-matched controls using diffusion weighted magnetic resonance imaging (MRI). In this cross-sectional study, 40 PD patients and 44 controls underwent diffusion weighted imaging (DWI) using a 40-direction MRI sequence as well as an optimized 60-direction sequence with overlapping slices. Regions of interest (ROIs) encompassing the putamen and substantia nigra were hand drawn in the space of the 40-direction data using high-contrast structural images and then coregistered to the 60-direction data. Probabilistic tractography was performed in the native space of each dataset by seeding the putamen ROI with an ipsilateral substantia nigra classification target. The effect of disease group (PD versus control) on mean putamen-SN connection probability and streamline density were then analyzed using generalized linear models controlling for age, gender, education, as well as seed and target region characteristics. Mean putamen-SN streamline density was lower in PD on both sides of the brain and in both 40- and 60-direction data. The optimized sequence provided a greater separation between PD and control means; however, individual values overlapped between groups. The 60-direction data also yielded mean connection probability values either trending (ipsilateral) or significantly (contralateral) lower in the PD group. There were minor between-group differences in average diffusion measures within the substantia nigra ROIs that did not affect the results of the GLM analyses when included as covariates. Based on these results, we conclude that mean striatonigral structural connectivity differs between PD and control groups and that use of an optimized 60-direction DWI sequence with overlapping slices increases the sensitivity of the technique to putative disease-related differences. However, overlap in individual values between disease groups limits its use as a classifier.
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http://dx.doi.org/10.1016/j.nicl.2017.09.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608174PMC
June 2018

A geometric framework for statistical analysis of trajectories with distinct temporal spans.

Proc IEEE Int Conf Comput Vis 2017 Oct 25;2017:172-181. Epub 2017 Dec 25.

Department of CISE, University of Florida.

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http://dx.doi.org/10.1109/iccv.2017.28DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7278111PMC
October 2017

A Novel Registration-Based Semiautomatic Mandible Segmentation Pipeline Using Computed Tomography Images to Study Mandibular Development.

J Comput Assist Tomogr 2018 Mar/Apr;42(2):306-316

Objective: We present a registration-based semiautomatic mandible segmentation (SAMS) pipeline designed to process a large number of computed tomography studies to segment 3-dimensional mandibles.

Method: The pipeline consists of a manual preprocessing step, an automatic segmentation step, and a final manual postprocessing step. The automatic portion uses a nonlinear diffeomorphic method to register each preprocessed input computed tomography test scan on 54 reference templates, ranging in age from birth to 19 years. This creates 54 segmentations, which are then combined into a single composite mandible.

Results: This pipeline was assessed using 20 mandibles from computed tomography studies with ages 1 to 19 years, segmented using both SAMS-processing and manual segmentation. Comparisons between the SAMS-processed and manually-segmented mandibles revealed 97% similarity agreement with comparable volumes. The resulting 3-dimensional mandibles were further enhanced with manual postprocessing in specific regions.

Conclusions: Findings are indicative of a robust pipeline that reduces manual segmentation time by 75% and increases the feasibility of large-scale mandibular growth studies.
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http://dx.doi.org/10.1097/RCT.0000000000000669DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5847411PMC
April 2018

Anxiety-related experience-dependent white matter structural differences in adolescence: A monozygotic twin difference approach.

Sci Rep 2017 08 18;7(1):8749. Epub 2017 Aug 18.

Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Madison, WI, 53705, USA.

Anxiety is linked to deficits in structural and functional connectivity between limbic structures and pre-frontal cortices. We employed a monozygotic (MZ) twin difference design to examine the relationship between structural characteristics of the uncinate fasciculus (UF) measured by Diffusion Tensor Imaging (DTI) and anxiety symptoms in a sample of N = 100 monozygotic (genetically identical), adolescent twins. The MZ difference design allowed us focus on environmental factors that vary within twin pairs while controlling for genetic and environmental factors shared by twin pairs. Twins aged 13-18 years reported on symptoms of generalized anxiety and social phobia prior to participating in a neuroimaging visit. Regions of interest from the JHU ICBM atlas, including uncinate fasciculus and sagittal stratum as a control tract, were registered to the study template. We incorporated multiple diffusion tensor measures to characterize the white matter differences. Within twin pairs, the more anxious twin exhibited decreased fractional anisotropy (t = -2.22, p = 0.032) and axial diffusivity (t = -2.38, p = 0.022) in the left UF compared to the less anxious twin, controlling for age and gender. This study demonstrated the feasibility and advantages of adopting the MZ twin design for DTI measures in neuroimaging research.
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http://dx.doi.org/10.1038/s41598-017-08107-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5562810PMC
August 2017

Riemannian Variance Filtering: An Independent Filtering Scheme for Statistical Tests on Manifold-valued Data.

Conf Comput Vis Pattern Recognit Workshops 2017 Jul 24;2017:699-708. Epub 2017 Aug 24.

University of Wisconsin-Madison.

Performing large scale hypothesis testing on brain imaging data to identify group-wise differences (e.g., between healthy and diseased subjects) typically leads to a large number of tests (one per voxel). Multiple testing adjustment (or correction) is necessary to control false positives, which may lead to lower detection power in detecting true positives. Motivated by the use of so-called "independent filtering" techniques in statistics (for genomics applications), this paper investigates the use of independent filtering for manifold-valued data (e.g., Diffusion Tensor Imaging, Cauchy Deformation Tensors) which are broadly used in neuroimaging studies. Inspired by the concept of variance of a Riemannian Gaussian distribution, a type of non-specific data-dependent Riemannian variance filter is proposed. In practice, the filter will select a subset of the full set of voxels for performing the statistical test, leading to a more appropriate multiple testing correction. Our experiments on synthetic/simulated manifold-valued data show that the detection power is improved when the statistical tests are performed on the voxel locations that "pass" the filter. Given the broadening scope of applications where manifold-valued data are utilized, the scheme can serve as a general feature selection scheme.
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http://dx.doi.org/10.1109/CVPRW.2017.99DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7191643PMC
July 2017

Integrative Structural Brain Network Analysis in Diffusion Tensor Imaging.

Brain Connect 2017 08 28;7(6):331-346. Epub 2017 Jun 28.

6 Department of Psychiatry, University of Wisconsin , Madison, Wisconsin.

In diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length, and FA values into the connectivity model. Using various node-degree-based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in postinstitutional settings before being adopted by families in the United States.
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http://dx.doi.org/10.1089/brain.2016.0481DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5567603PMC
August 2017
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