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https://www.readbyqxmd.com/read/28928638/affective-circuitry-alterations-in-patients-with-trigeminal-neuralgia
#1
Dave J Hayes, David Q Chen, Jidan Zhong, Ariel Lin, Brendan Behan, Matthew Walker, Mojgan Hodaie
Trigeminal neuralgia (TN) is a severe chronic neuropathic facial pain disorder. Affect-related behavioral and structural brain changes have been noted across chronic pain disorders, but have not been well-studied in TN. We examined the potential impact of TN (37 patients: 23 with right-sided TN, 14 with left-sided TN), compared to age- and sex-matched healthy controls, on three major white matter tracts responsible for carrying affect-related signals-i.e., cingulum, fornix, and medial forebrain bundle. Diffusion magnetic resonance imaging (dMRI), deterministic multi-tensor tractography for tract modeling, and a model-driven region-of-interest approach was used...
2017: Frontiers in Neuroanatomy
https://www.readbyqxmd.com/read/28915311/can-increased-spatial-resolution-solve-the-crossing-fiber-problem-for-diffusion-mri
#2
Kurt Schilling, Yurui Gao, Vaibhav Janve, Iwona Stepniewska, Bennett A Landman, Adam W Anderson
It is now widely recognized that voxels with crossing fibers or complex geometrical configurations present a challenge for diffusion MRI (dMRI) reconstruction and fiber tracking, as well as microstructural modeling of brain tissues. This "crossing fiber" problem has been estimated to affect anywhere from 30% to as many as 90% of white matter voxels, and it is often assumed that increasing spatial resolution will decrease the prevalence of voxels containing multiple fiber populations. The aim of this study is to estimate the extent of the crossing fiber problem as we progressively increase the spatial resolution, with the goal of determining whether it is possible to mitigate this problem with higher resolution spatial sampling...
September 15, 2017: NMR in Biomedicine
https://www.readbyqxmd.com/read/28910696/tissue-microstructure-estimation-using-a-deep-network-inspired-by-a-dictionary-based-framework
#3
Chuyang Ye
Diffusion magnetic resonance imaging (dMRI) captures the anisotropic pattern of water displacement in the neuronal tissue and allows noninvasive investigation of the complex tissue microstructure. A number of biophysical models have been proposed to relate the tissue organization with the observed diffusion signals, so that the tissue microstructure can be inferred. The Neurite Orientation Dispersion and Density Imaging (NODDI) model has been a popular choice and has been widely used for many neuroscientific studies...
September 6, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28904382/multidimensional-encoding-of-brain-connectomes
#4
Cesar F Caiafa, Franco Pestilli
The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. Advances in network neuroscience may benefit from developing new frameworks for mapping brain connectomes. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data using multidimensional arrays. The framework integrates the relation between connectome nodes, edges, white matter fascicles and diffusion data...
September 13, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28871500/computational-neuroanatomy-of-human-stratum-proprium-of-interparietal-sulcus
#5
Maiko Uesaki, Hiromasa Takemura, Hiroshi Ashida
Recent advances in diffusion-weighted MRI (dMRI) and tractography have enabled identification of major long-range white matter tracts in the human brain. Yet, our understanding of shorter tracts, such as those within the parietal lobe, remains limited. Over a century ago, a tract connecting the superior and inferior parts of the parietal cortex was identified in a post-mortem study: stratum proprium of interparietal sulcus (SIPS; Sachs, Das hemisphärenmark des menschlichen grosshirns. Verlag von georg thieme, Leipzig, 1892)...
September 4, 2017: Brain Structure & Function
https://www.readbyqxmd.com/read/28868000/fiberweb-diffusion-visualization-and-processing-in-the-browser
#6
Louis-Philippe Ledoux, Felix C Morency, Martin Cousineau, Jean-Christophe Houde, Kevin Whittingstall, Maxime Descoteaux
Data visualization is one of the most important tool to explore the brain as we know it. In this work, we introduce a novel browser-based solution for medical imaging data visualization and interaction with diffusion-weighted magnetic resonance imaging (dMRI) and tractography data: Fiberweb. It uses a recent technology, WebGL, that has yet to be fully explored for medical imaging purposes. There are currently very few software tools that allow medical imaging data visualization in the browser, and none of these tools support efficient data interaction and processing, such as streamlines selection and real-time deterministic and probabilistic tractography (RTT)...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28842386/complementary-contributions-of-concurrent-eeg-and-fmri-connectivity-for-predicting-structural-connectivity
#7
Jonathan Wirsich, Ben Ridley, Pierre Besson, Viktor Jirsa, Christian Bénar, Jean-Philippe Ranjeva, Maxime Guye
While averaged dynamics of brain function are known to estimate the underlying structure, the exact relationship between large-scale function and structure remains an unsolved issue in network neuroscience. These complex functional dynamics, measured by EEG and fMRI, are thought to arise from a shared underlying structural architecture, which can be measured by diffusion MRI (dMRI). While simulation and data transformation (e.g. graph theory measures) have been proposed to refine the understanding of the underlying function-structure relationship, the potential complementary and/or independent contribution of EEG and fMRI to this relationship is still poorly understood...
August 24, 2017: NeuroImage
https://www.readbyqxmd.com/read/28840098/white-matter-microstructure-of-6-year-old-children-born-preterm-and-full-term
#8
Cory K Dodson, Katherine E Travis, Michal Ben-Shachar, Heidi M Feldman
AIM: We previously observed a complex pattern of differences in white matter (WM) microstructure between preterm-born (PT) and full-term-born (FT) children and adolescents age 9-17 years. The aim of this study was to determine if the same differences exist as early as age 6 years. METHOD: We obtained diffusion MRI (dMRI) scans in children born PT at age 6 years (n = 20; 11 males) and FT (n = 38; 14 males), using two scanning protocols: 30 diffusion directions (b = 1000 s/mm(2)) and 96 diffusion directions (b = 2500 s/mm(2))...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/28748504/whole-brain-analyses-of-age-related-microstructural-changes-quantified-using-different-diffusional-magnetic-resonance-imaging-methods
#9
Miho Ota, Noriko Sato, Norihide Maikusa, Daichi Sone, Hiroshi Matsuda, Hiroshi Kunugi
PURPOSE: The new diffusional magnetic resonance imaging (dMRI) techniques, diffusional kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) have been developed to clarify the microstructural changes. To our knowledge, however, there is little information on the similarities and differences of these metrics evaluated by the image-by-image paired t test. MATERIALS AND METHODS: Twenty-three healthy subjects underwent dMRI. We estimated the relationships of these metrics evaluated by the image-by-image paired t-test and compared aging effects on each metric...
July 26, 2017: Japanese Journal of Radiology
https://www.readbyqxmd.com/read/28732674/3d-mb-muse-a-robust-3d-multi-slab-multi-band-and-multi-shot-reconstruction-approach-for-ultrahigh-resolution-diffusion-mri
#10
Iain P Bruce, Hing-Chiu Chang, Christopher Petty, Nan-Kuei Chen, Allen W Song
Recent advances in achieving ultrahigh spatial resolution (e.g. sub-millimeter) diffusion MRI (dMRI) data have proven highly beneficial in characterizing tissue microstructures in organs such as the brain. However, the routine acquisition of in-vivo dMRI data at such high spatial resolutions has been largely prohibited by factors that include prolonged acquisition times, motion induced artifacts, and low SNR. To overcome these limitations, we present here a framework for acquiring and reconstructing 3D multi-slab, multi-band and interleaved multi-shot EPI data, termed 3D-MB-MUSE...
July 18, 2017: NeuroImage
https://www.readbyqxmd.com/read/28721357/whole-mouse-brain-imaging-using-optical-coherence-tomography-reconstruction-normalization-segmentation-and-comparison-with-diffusion-mri
#11
Joël Lefebvre, Alexandre Castonguay, Philippe Pouliot, Maxime Descoteaux, Frédéric Lesage
An automated massive histology setup combined with an optical coherence tomography (OCT) microscope was used to image a total of [Formula: see text] whole mouse brains. Each acquisition generated a dataset of thousands of OCT volumetric tiles at a sampling resolution of [Formula: see text]. This paper describes techniques for reconstruction and segmentation of the sliced brains. In addition to the measured OCT optical reflectivity, a single scattering photon model was used to compute the attenuation coefficients within each tissue slice...
October 2017: Neurophotonics
https://www.readbyqxmd.com/read/28713174/fusing-multiple-neuroimaging-modalities-to-assess-group-differences-in-perception-action-coupling
#12
Jordan Muraskin, Jason Sherwin, Gregory Lieberman, Javier O Garcia, Timothy Verstynen, Jean M Vettel, Paul Sajda
In the last few decades, non-invasive neuroimaging has revealed macro-scale brain dynamics that underlie perception, cognition and action. Advances in non-invasive neuroimaging target two capabilities; 1) increased spatial and temporal resolution of measured neural activity, and 2) innovative methodologies to extract brain-behavior relationships from evolving neuroimaging technology. We target the second. Our novel methodology integrated three neuroimaging methodologies and elucidated expertise-dependent differences in functional (fused EEG-fMRI) and structural (dMRI) brain networks for a perception-action coupling task...
January 2017: Proceedings of the IEEE
https://www.readbyqxmd.com/read/28708547/multimodal-fusion-with-reference-searching-for-joint-neuromarkers-of-working-memory-deficits-in-schizophrenia
#13
Shile Qi, Vince D Calhoun, Theo G M van Erp, Juan Bustillo, Eswar Damaraju, Jessica A Turner, Yuhui Du, Jian Yang, Jiayu Chen, Qingbao Yu, Daniel H Mathalon, Judith M Ford, James Voyvodic, Bryon A Mueller, Aysenil Belger, Sarah McEwen, Steven G Potkin, Adrian Preda, Tianzi Jiang, Jing Sui
By exploiting cross-information among multiple imaging data, multimodal fusion has often been used to better understand brain diseases. However, most current fusion approaches are blind, without adopting any prior information. There is increasing interest to uncover the neurocognitive mapping of specific clinical measurements on enriched brain imaging data; hence, a supervised, goal-directed model that employs prior information as a reference to guide multimodal data fusion is much needed and becomes a natural option...
July 11, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28702836/sequential-language-learning-and-language-immersion-in-bilingualism-diffusion-mri-connectometry-reveals-microstructural-evidence
#14
Farzaneh Rahmani, Soheila Sobhani, Mohammad Hadi Aarabi
Study of bilingual brain has provided evidence for probable advantageous outcomes of early second language learning and brain structural correlates to these outcomes. DMRI connectometry is a novel approach that tracts fibers based on correlation of the adjacent voxels with a variable of interest or group differences. Using the data deposited by Pliatsikas et al., we investigated through diffusion MRI connectometry and correlation analysis, the structural differences in white matter tracts of 20 healthy sequential bilingual adults who used English as a second language on a daily basis, compared to 25 age matched in fiber differentiation analyses...
July 12, 2017: Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
https://www.readbyqxmd.com/read/28692970/a-kernel-based-low-rank-klr-model-for-low-dimensional-manifold-recovery-in-highly-accelerated-dynamic-mri
#15
Ukash Nakarmi, Yanhua Wang, Jingyuan Lyu, Dong Liang, Leslie Ying
While many low rank and sparsity based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub- Nyquist data. Within this framework, many existing algorithms can be extended to kernel framework with nonlinear models. In particular, we have developed a novel algorithm with a kernel-based low-rank (KLR) model generalizing the conventional low rank formulation...
July 5, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28692678/bilateral-effects-of-unilateral-cerebellar-lesions-as-detected-by-voxel-based-morphometry-and-diffusion-imaging
#16
Giusy Olivito, Michael Dayan, Valentina Battistoni, Silvia Clausi, Mara Cercignani, Marco Molinari, Maria Leggio, Marco Bozzali
Over the last decades, the importance of cerebellar processing for cortical functions has been acknowledged and consensus was reached on the strict functional and structural cortico-cerebellar interrelations. From an anatomical point of view strictly contralateral interconnections link the cerebellum to the cerebral cortex mainly through the middle and superior cerebellar peduncle. Diffusion MRI (dMRI) based tractography has already been applied to address cortico-cerebellar-cortical loops in healthy subjects and to detect diffusivity alteration patterns in patients with neurodegenerative pathologies of the cerebellum...
2017: PloS One
https://www.readbyqxmd.com/read/28684331/fiberprint-a-subject-fingerprint-based-on-sparse-code-pooling-for-white-matter-fiber-analysis
#17
Kuldeep Kumar, Christian Desrosiers, Kaleem Siddiqi, Olivier Colliot, Matthew Toews
White matter characterization studies use the information provided by diffusion magnetic resonance imaging (dMRI) to draw cross-population inferences. However, the structure, function, and white matter geometry vary across individuals. Here, we propose a subject fingerprint, called Fiberprint, to quantify the individual uniqueness in white matter geometry using fiber trajectories. We learn a sparse coding representation for fiber trajectories by mapping them to a common space defined by a dictionary. A subject fingerprint is then generated by applying a pooling function for each bundle, thus providing a vector of bundle-wise features describing a particular subject's white matter geometry...
July 3, 2017: NeuroImage
https://www.readbyqxmd.com/read/28672181/corpus-callosum-macro-and-microstructure-in-late-life-depression
#18
Louise Emsell, Christopher Adamson, François-Laurent De Winter, Thibo Billiet, Daan Christiaens, Filip Bouckaert, Katarzyna Adamczuk, Rik Vandenberghe, Marc L Seal, Pascal Sienaert, Stefan Sunaert, Mathieu Vandenbulcke
BACKGROUND: Differences in corpus callosum (CC) morphology and microstructure have been implicated in late-life depression and may distinguish between late and early-onset forms of the illness. However, a multimodal approach using complementary imaging techniques is required to disentangle microstructural alterations from macrostructural partial volume effects. METHODS: 107 older adults were assessed: 55 currently-depressed patients without dementia and 52 controls without cognitive impairment...
November 2017: Journal of Affective Disorders
https://www.readbyqxmd.com/read/28669918/estimation-of-white-matter-fiber-parameters-from-compressed-multiresolution-diffusion-mri-using-sparse-bayesian-learning
#19
Pramod Kumar Pisharady, Stamatios N Sotiropoulos, Julio M Duarte-Carvajalino, Guillermo Sapiro, Christophe Lenglet
We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions...
June 29, 2017: NeuroImage
https://www.readbyqxmd.com/read/28664183/the-virtual-mouse-brain-a-computational-neuroinformatics-platform-to-study-whole-mouse-brain-dynamics
#20
Francesca Melozzi, Marmaduke M Woodman, Viktor K Jirsa, Christophe Bernard
Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brain's structure-function relationship, necessitating the close integration of diverse neuroinformatics fields. Here we extend the open-source simulation software The Virtual Brain (TVB) to whole mouse brain network modeling based on individual diffusion magnetic resonance imaging (dMRI)-based or tracer-based detailed mouse connectomes. We provide practical examples on how to use The Virtual Mouse Brain (TVMB) to simulate brain activity, such as seizure propagation and the switching behavior of the resting state dynamics in health and disease...
May 2017: ENeuro
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