92 results on '"Christophe Lenglet"'
Search Results
2. Improved Diagnostic Accuracy and Sensitivity to Longitudinal Change in ALS with Multimodal MRI of the Brain and Cervical Cord
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Pramod Pisharady, Lynn Eberly, Isaac Adanyeguh, Georgios Manousakis, Gaurav Guliani, David Walk, and Christophe Lenglet
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We demonstrate high sensitivity for detecting longitudinal change as well as diagnostic sensitivity in ALS by applying recent advances in MRI data acquisition and analysis to multimodal brain and cervical spinal cord data. We acquired high quality diffusion MRI data from the brain and cervical cord, and high quality T1 data from the brain, of 20 participants with ALS and 20 healthy control participants. Ten participants with ALS and 14 healthy control participants, and 11 participants with ALS and 13 healthy control participants were re-scanned at 6-month and 12-month follow-up visits respectively. We analyzed cross-sectional differences and longitudinal changes in brain diffusion metrics and cortical thickness to identify white and gray matter areas affected by the disease. We also used fixel-based microstructure measures, i.e. fiber density and fiber cross-section, that are found more sensitive to longitudinal changes. Combining the brain metrics with our previously reported diffusion and cross-sectional area measures of the spinal cord, we demonstrate improved disease diagnostic accuracy and sensitivity through multimodal analysis of cross-sectional data, including high sensitivity for diagnosis of lower motor neuron-predominant ALS. Fiber density and cross-section provided the greatest sensitivity for change in our longitudinal dataset. We demonstrate evidence of progression in a cohort of 11 participants with slowly progressive ALS, including in participants with very slow change in ALSFRS-R (less than 0.5 points per month). More importantly, we demonstrate that longitudinal change is detectable at a six-month follow-up visit. Our findings suggest that fixel-based measures may serve as potential biomarkers of disease progression in clinical trials. We also provide a comprehensive list of affected areas both in the white matter and cortical gray matter, and report correlations between ALSFRS-R and the fiber density and cross-section. more...
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- 2022
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Catalog
3. Progressive Spinal Cord Degeneration in Friedreich's Ataxia: Results from ENIGMA-Ataxia
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Thiago J.R. Rezende, Isaac M. Adanyeguh, Filippo Arrigoni, Benjamin Bender, Fernando Cendes, Louise A. Corben, Andreas Deistung, Martin Delatycki, Imis Dogan, Gary F. Egan, Sophia L. Göricke, Nellie Georgiou‐Karistianis, Pierre‐Gilles Henry, Diane Hutter, Neda Jahanshad, James M. Joers, Christophe Lenglet, Tobias Lindig, Alberto R.M. Martinez, Andrea Martinuzzi, Gabriella Paparella, Denis Peruzzo, Kathrin Reetz, Sandro Romanzetti, Ludger Schöls, Jörg B. Schulz, Matthis Synofzik, Sophia I. Thomopoulos, Paul M. Thompson, Dagmar Timmann, Ian H. Harding, and Marcondes C. França more...
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Movement Disorders ,pathology [Friedreich Ataxia] ,Pyramidal Tracts ,Medizin ,Friedreich's ataxia ,spinal cord ,complications [Friedreich Ataxia] ,methods [Magnetic Resonance Imaging] ,Neurology ,Humans ,Ataxia ,ddc:610 ,Neurology (clinical) ,ENIGMA-ataxia ,MRI ,SCT - Abstract
Movement disorders 38(1), 45-56 (2023). doi:10.1002/mds.29261, Published by Wiley, New York, NY
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- 2022
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4. White matter microstructure and longitudinal relaxation time anisotropy in human brain at 3 and 7 T
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Risto A. Kauppinen, Jeromy Thotland, Pramod K. Pisharady, Christophe Lenglet, and Michael Garwood
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Molecular Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,White Matter ,Spectroscopy - Abstract
A high degree of structural order by white matter (WM) fibre tracts creates a physicochemical environment where water relaxations are rendered anisotropic. Recently, angularly dependent longitudinal relaxation has been reported in human WM. We have characterised interrelationships between T1 relaxation and diffusion MRI microstructural indices at 3 and 7 T. Eleven volunteers consented to participate in the study. Multishell diffusion MR images were acquired with b-values of 0/1500/3000 and 0/1000/2000 s/mm more...
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- 2022
5. Spinal cord damage in Friedreich’s ataxia: Results from the ENIGMA-Ataxia
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Thiago JR Rezende, Isaac M Adanyeguh, Filippo Arrigoni, Benjamin Bender, Fernando Cendes, Louise A Corben, Andreas Deistung, Martin Delatycki, Imis Dogan, Gary F Egan, Sophia L Göricke, Nellie Georgiou-Karistianis, Pierre-Gilles Henry, Diane Hutter, Neda Jahanshad, James M Joers, Christophe Lenglet, Tobias Lindig, Alberto RM Martinez, Andrea Martinuzzi, Gabriella Paparella, Denis Peruzzo, Kathrin Reetz, Sandro Romanzetti, Ludger Schöls, Jörg B Schulz, Matthis Synofzik, Sophia I Thomopoulos, Paul M Thompson, Dagmar Timmann, Ian H Harding, and Marcondes C. França more...
- Abstract
ObjectiveSpinal cord damage is a hallmark of Friedreich ataxia (FRDA), but its progression and clinical correlates remain unclear. Here we performed a characterization of cervical spinal cord structural abnormalities in a large multisite FRDA cohort.MethodsWe performed a cross-sectional analysis of cervical spinal cord (C1 to C4) cross-sectional area (CSA) and eccentricity using MRI data from eight sites within the ENIGMA-Ataxia initiative, including 256 individuals with FRDA and 223 age- and sex-matched controls. Correlations and subgroup analyses within the FRDA cohort were undertaken based on disease duration, ataxia severity, and onset age.ResultsIndividuals with FRDA, relative to controls, had significantly reduced CSA at all examined levels, with large effect sizes (d>2.1) and significant correlations with disease severity (rd>1.2), but without significant clinical correlations. Subgroup analyses showed that CSA and eccentricity are abnormal at all disease stages. However, while CSA appears to decrease progressively, eccentricity remains stable over time.InterpretationPrevious research has shown that increased eccentricity reflects dorsal column (DC) damage, while decreased CSA reflects either DC or corticospinal tract (CST) damage or both. Hence, our data support the hypothesis that damage to DC and CST follow distinct courses in FRDA: developmental abnormalities likely define the DC, whereas CST alterations may be both developmental and degenerative. These results provide new insights about FRDA pathogenesis and indicate that CSA of the cervical spinal cord should be investigated further as a potential biomarker of disease progression. more...
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- 2022
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6. Diffusion Imaging in the Post HCP Era
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Christophe Lenglet, Steen Moeller, Pramod Pisharady Kumar, Mehmet Akcakaya, Xiaoping Wu, Ruoyun Emily Ma, Jesper L. R. Andersson, Noam Harel, Kamil Ugurbil, and Essa Yacoub
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Computer science ,Context (language use) ,Article ,030218 nuclear medicine & medical imaging ,Diffusion ,03 medical and health sciences ,0302 clinical medicine ,Ultra high field ,Component (UML) ,Connectome ,Image Processing, Computer-Assisted ,Humans ,Radiology, Nuclear Medicine and imaging ,Transmit array ,Large field of view ,Human Connectome Project ,business.industry ,Deep learning ,Brain ,Magnetic Resonance Imaging ,Diffusion imaging ,Diffusion Magnetic Resonance Imaging ,Magnetic Fields ,Computer engineering ,Artificial intelligence ,business - Abstract
Diffusion imaging is a critical component in the pursuit of developing a better understanding of the human brain. Recent technical advances promise enabling the advancement in the quality of data that can be obtained. In this review the context for different approaches relative to the Human Connectome Project are compared. Significant new gains are anticipated from the use of high-performance head gradients. These gains can be particularly large when the high-performance gradients are employed together with ultrahigh magnetic fields. Transmit array designs are critical in realizing high accelerations in diffusion-weighted (d)MRI acquisitions, while maintaining large field of view (FOV) coverage, and several techniques for optimal signal-encoding are now available. Reconstruction and processing pipelines that precisely disentangle the acquired neuroanatomical information are established and provide the foundation for the application of deep learning in the advancement of dMRI for complex tissues. Level of Evidence: 3 Technical Efficacy Stage: Stage 3. more...
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- 2020
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7. Self‐navigation for 3D multishot EPI with data‐reference
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Kamil Ugurbil, Sudhir Ramanna, Edward J. Auerbach, Lance DelaBarre, Steen Moeller, Xiaoping Wu, Pramod Kumar Pisharady, Christophe Lenglet, and Mehmet Akcakaya
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Large field of view ,Echo-Planar Imaging ,Computer science ,business.industry ,Resolution (electron density) ,Phase (waves) ,Brain ,Reproducibility of Results ,Stability (probability) ,Article ,Self navigation ,Diffusion Magnetic Resonance Imaging ,Image Interpretation, Computer-Assisted ,Spin echo ,Radiology, Nuclear Medicine and imaging ,Computer vision ,Artificial intelligence ,Sensitivity (control systems) ,business ,Algorithms ,Tractography - Abstract
Purpose In this study, we sought to develop a self-navigation strategy for improving the reconstruction of diffusion weighted 3D multishot echo planar imaging (EPI). We propose a method for extracting the phase correction information from the acquisition itself, eliminating the need for a 2D navigator, further accelerating the acquisition. Methods In-vivo acquisitions at 3T with 0.9 mm and 1.5 mm isotropic resolutions were used to evaluate the performance of the self-navigation strategy. Sensitivity to motion was tested using a large difference in pitch position of the head. Using a multishell diffusion weighted acquisition, tractography results were obtained at (0.9 mm)3 to validate the quality with conventional acquisition. Results The use of 3D multislab EPI with self-navigation enables 3D diffusion-weighted spin echo EPI acquisitions that have the same efficiency as 2D single-shot acquisition. For matched acquisition time the image signal-to-noise ratio (SNR) between 3D and 2D acquisition is shown to be comparable for whole-brain coverage with (1.5 mm)3 resolution and for (0.9 mm)3 resolution the 3D acquisition has higher SNR than what can be obtained with 2D acquisitions using current state-of-art multiband techniques. The self-navigation technique was shown to be stable under inter-volume motion. In tractography analysis, the higher resolution afforded by our technique enabled clear delineation of the tapetum and posterior corona radiata. Conclusion The proposed self-navigation approach utilized a self-consistent phase in 3D diffusion weighted acquisitions. Its efficiency and stability were demonstrated for a plurality of common acquisitions. The proposed self-navigation approach allows for faster acquisition of 3D multishot EPI desirable for large field of view and/or higher resolution. more...
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- 2020
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8. Spinal cord magnetic resonance imaging and spectroscopy detect early-stage alterations and disease progression in Friedreich ataxia
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James M Joers, Isaac M Adanyeguh, Dinesh K Deelchand, Diane H Hutter, Lynn E Eberly, Isabelle Iltis, Khalaf O Bushara, Christophe Lenglet, and Pierre-Gilles Henry
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Cellular and Molecular Neuroscience ,Psychiatry and Mental health ,Neurology ,Biological Psychiatry - Abstract
Friedreich ataxia is the most common hereditary ataxia. Atrophy of the spinal cord is one of the hallmarks of the disease. MRI and magnetic resonance spectroscopy are powerful and non-invasive tools to investigate pathological changes in the spinal cord. A handful of studies have reported cross-sectional alterations in Friedreich ataxia using MRI and diffusion MRI. However, to our knowledge no longitudinal MRI, diffusion MRI or MRS results have been reported in the spinal cord. Here, we investigated early-stage cross-sectional alterations and longitudinal changes in the cervical spinal cord in Friedreich ataxia, using a multimodal magnetic resonance protocol comprising morphometric (anatomical MRI), microstructural (diffusion MRI), and neurochemical (1H-MRS) assessments.We enrolled 28 early-stage individuals with Friedreich ataxia and 20 age- and gender-matched controls (cross-sectional study). Disease duration at baseline was 5.5 ± 4.0 years and Friedreich Ataxia Rating Scale total neurological score at baseline was 42.7 ± 13.6. Twenty-one Friedreich ataxia participants returned for 1-year follow-up, and 19 of those for 2-year follow-up (cohort study). Each visit consisted in clinical assessments and magnetic resonance scans. Controls were scanned at baseline only. At baseline, individuals with Friedreich ataxia had significantly lower spinal cord cross-sectional area (−31%, P = 8 × 10−17), higher eccentricity (+10%, P = 5 × 10−7), lower total N-acetyl-aspartate (tNAA) (−36%, P = 6 × 10−9) and higher myo-inositol (mIns) (+37%, P = 2 × 10−6) corresponding to a lower ratio tNAA/mIns (−52%, P = 2 × 10−13), lower fractional anisotropy (−24%, P = 10−9), as well as higher radial diffusivity (+56%, P = 2 × 10−9), mean diffusivity (+35%, P = 10−8) and axial diffusivity (+17%, P = 4 × 10−5) relative to controls. Longitudinally, spinal cord cross-sectional area decreased by 2.4% per year relative to baseline (P = 4 × 10−4), the ratio tNAA/mIns decreased by 5.8% per year (P = 0.03), and fractional anisotropy showed a trend to decrease (−3.2% per year, P = 0.08). Spinal cord cross-sectional area correlated strongly with clinical measures, with the strongest correlation coefficients found between cross-sectional area and Scale for the Assessment and Rating of Ataxia (R = −0.55, P = 7 × 10−6) and between cross-sectional area and Friedreich ataxia Rating Scale total neurological score (R = −0.60, P = 4 × 10−7). Less strong but still significant correlations were found for fractional anisotropy and tNAA/mIns. We report here the first quantitative longitudinal magnetic resonance results in the spinal cord in Friedreich ataxia. The largest longitudinal effect size was found for spinal cord cross-sectional area, followed by tNAA/mIns and fractional anisotropy. Our results provide direct evidence that abnormalities in the spinal cord result not solely from hypoplasia, but also from neurodegeneration, and show that disease progression can be monitored non-invasively in the spinal cord. more...
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- 2022
9. Temporomandibular disorders cases with high-impact pain are more likely to experience short-term pain fluctuations
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Alberto Herrero Babiloni, Fernando G. Exposto, Connor M. Peck, Bruce R. Lindgren, Marc O. Martel, Christophe Lenglet, David A. Bereiter, Lynn E. Eberly, and Estephan J. Moana-Filho
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Adult ,Male ,Pain Threshold ,Time Factors ,Science ,SLEEP QUALITY ,Pain ,Chronic pain ,AXIS II ,Proof of Concept Study ,Article ,OROFACIAL PAIN ,Young Adult ,Cost of Illness ,Facial Pain ,Humans ,Pain Measurement ,Multidisciplinary ,INTENSITY ,FLARE ,Middle Aged ,Temporomandibular Joint Disorders ,RHEUMATOID-ARTHRITIS ,RESEARCH DIAGNOSTIC-CRITERIA ,stomatognathic diseases ,Jaw ,Case-Control Studies ,Medicine ,Female ,EVOKED PAIN ,HORMONAL FLUCTUATIONS ,LOW-BACK-PAIN - Abstract
Temporomandibular disorders (TMD) patients can present clinically significant jaw pain fluctuations which can be debilitating and lead to poor global health. The Graded Chronic Pain Scale evaluates pain-related disability and its dichotomous grading (high/low impact pain) can determine patient care pathways and in general high-impact pain patients have worse treatment outcomes. Individuals with low-impact TMD pain are thought to have better psychosocial functioning, more favorable disease course, and better ability to control pain, while individuals with high-impact pain can present with higher levels of physical and psychological symptoms. Thereby, there is reason to believe that individuals with low- and high-impact TMD pain could experience different pain trajectories over time. Our primary objective was to determine if short-term jaw pain fluctuations serve as a clinical marker for the impact status of TMD pain. To this end, we estimated the association between high/low impact pain status and jaw pain fluctuations over three visits (≤ 21-day-period) in 30 TMD cases. Secondarily, we measured the association between jaw pain intensity and pressure pain thresholds (PPT) over the face and hand, the latter measurements compared to matched pain-free controls (n = 17). Jaw pain fluctuations were more frequent among high-impact pain cases (n = 15) than low-impact pain cases (n = 15) (OR 5.5; 95% CI 1.2, 26.4; p value = 0.033). Jaw pain ratings were not associated with PPT ratings (p value > 0.220), suggesting different mechanisms for clinical versus experimental pain. Results from this proof-of-concept study suggest that targeted treatments to reduce short-term pain fluctuations in high-impact TMD pain is a potential strategy to achieve improved patient perception of clinical pain management outcomes. more...
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- 2022
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10. Spinal cord MRI and MRS Detect Early-stage Alterations and Disease Progression in Friedreich Ataxia
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James M. Joers, Isaac M. Adanyeguh, Dinesh K. Deelchand, Diane H. Hutter, Lynn E. Eberly, Isabelle Iltis, Khalaf O. Bushara, Christophe Lenglet, and Pierre-Gilles Henry
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BackgroundFriedreich Ataxia (FRDA) is the most common hereditary ataxia. Atrophy of the spinal cord is one of the hallmarks of the disease. Magnetic resonance imaging (MRI) and spectroscopy (MRS) are powerful and non-invasive tools to investigate pathological changes in the spinal cord. A handful of studies have reported cross-sectional alterations in FRDA using MRI and diffusion MRI (dMRI) in FRDA. However, to our knowledge no longitudinal MRI, dMRI or MRS results have been reported in the spinal cord in FRDA.ObjectiveTo investigate early-stage cross-sectional alterations and longitudinal changes in the cervical spinal cord in FRDA, using a multimodal magnetic resonance (MR) protocol comprising morphometric (anatomical MRI), microstructural (dMRI), and neurochemical (1H MRS) assessments.DesignWe enrolled 28 early-stage individuals with FRDA and 20 age- and gender-matched controls (cross-sectional study). Disease duration at baseline was 5.5±4.0 years and Friedreich Ataxia Rating Scale (FARS) total neurological score at baseline was 42.7±13.6. Twenty-one FRDA participants returned for 1-year follow-up, and 19 of those for 2-year follow-up (cohort study). Each visit consisted in clinical assessments and MR scans. Controls were scanned at baseline only.ResultsAt baseline, individuals with FRDA had significantly lower spinal cord cross-sectional area (−31%, p=4.10−17), higher eccentricity (+10%, p=5.10−7), lower total N-acetyl-aspartate (−36%, p=6.10−9) and higher myo-inositol (+37%, p=2.10−6) corresponding to a lower ratio tNAA/mIns (−52%, p=2.10−13), lower fractional anisotropy (−24%, p=10−9) as well as higher radial diffusivity (+56%, p=2.10−9), mean diffusivity (+35%, p=10−8) and axial diffusivity (+17%, p=4.10−5) relative to controls.Longitudinally, spinal cord cross-sectional area decreased by 2.4% per year relative to baseline (p=4.10−4), the ratio tNAA/mIns decreased by 5.8% per year (p=0.03), and fractional anisotropy showed a trend to decrease (−3.2% per year, p=0.08).Spinal cord cross-sectional area correlated strongly with clinical measures, with the strongest correlation coefficients found between cross-sectional area and Scale for the Assessment and Rating of Ataxia (SARA) (R=-0.55, p=7.10−6) and between cross-sectional area and FARS total neurological score (R=-0.60, p=4.10−7). Less strong but still significant correlations were found for fractional anisotropy and tNAA/mIns.ConclusionWe report here the first quantitative longitudinal MR results in the spinal cord in FRDA. The largest longitudinal effect size was found for spinal cord cross-sectional area, followed by tNAA/mIns and fractional anisotropy. Our results provide direct evidence that abnormalities in the spinal cord result not solely from hypoplasia, but also from neurodegeneration, and show that disease progression can be monitored non-invasively in the spinal cord. more...
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- 2022
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11. Efficacy and Safety of Leriglitazone in Patients With Friedreich Ataxia: A Phase 2 Double-Blind, Randomized Controlled Trial (FRAMES)
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Massimo Pandolfo, Kathrin Reetz, Alejandra Darling, Francisco Javier Rodriguez de Rivera, Pierre-Gilles Henry, James Joers, Christophe Lenglet, Isaac Adanyeguh, Dinesh Deelchand, Fanny Mochel, Françoise Pousset, Sílvia Pascual, Delphine Van den Eede, Itziar Martin-Ugarte, Anna Vilà-Brau, Adriana Mantilla, María Pascual, Marc Martinell, Uwe Meya, and Alexandra Durr more...
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Neurology (clinical) ,Genetics (clinical) - Abstract
Neurology : official journal of the American Academy of Neurology / Genetics 8(6), e200034 (2022). doi:10.1212/NXG.0000000000200034, Published by [Verlag nicht ermittelbar], Minneapolis, Minn.
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- 2022
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12. White matter microstructure in Parkinson’s disease with and without elevated REM sleep muscle tone
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Michael J. Howell, Paul J. Tuite, E. Holker, Aleksandar Videnovic, Matthew N. Petrucci, J. De Kam, Jae Woo Chung, Maria E. Linn-Evans, Remi Patriat, Christophe Lenglet, Sommer L Amundsen-Huffmaster, Pramod Kumar Pisharady, Colum D. MacKinnon, and Noam Harel more...
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medicine.medical_specialty ,Parkinson's disease ,medicine.diagnostic_test ,business.industry ,Eye movement ,Polysomnography ,Audiology ,medicine.disease ,Corpus callosum ,Sleep in non-human animals ,White matter ,Muscle tone ,medicine.anatomical_structure ,Fractional anisotropy ,medicine ,business - Abstract
The mechanisms contributing to increased expression of motor and cognitive impairment in people with Parkinson9s disease who lack muscle atonia during rapid eye movement (REM) sleep compared to those with muscle atonia are poorly understood. This study used tract-based spatial statistics to compare diffusion measures of white matter microstructure between people with Parkinson9s disease with and without REM sleep without atonia as well as the relationships of these measures to motor and cognitive function. Thirty-eight individuals with mild-to-moderate Parkinson9s disease and twenty-one matched control subjects underwent ultra-high-field MRI (7Tesla), quantitative motor assessments of gait and bradykinesia, and neuropsychological testing. The Parkinson9s disease cohort was separated post-hoc into those with and without elevated chin or leg muscle activity during REM sleep based on polysomnography findings. Fractional anisotropy was significantly higher, and radial and mean diffusivity significantly lower, in diffuse regions of the corpus callosum, projection, and association white matter pathways in the Parkinson9s group with normal REM sleep compared to controls. In contrast, there was no significant difference in fractional anisotropy between the Parkinson9s group with elevated muscle tone and controls. Fractional anisotropy was also significantly higher in a subset of pathways in the Parkinson9s disease group with normal REM sleep muscle tone compared to those with elevated REM sleep muscle tone. The group with elevated REM sleep muscle tone had significant impairments in gait and upper arm speed compared to controls and significantly worse scores in specific cognitive domains (executive function, visuospatial memory) compared to the Parkinson9s disease group with normal REM sleep muscle tone. Regression analyses showed that gait speed and step length in the Parkinson9s disease cohort were predicted by measures of mean fractional anisotropy of the anterior corona radiata, whereas elbow flexion velocity was predicted by fractional anisotropy of the superior corona radiata. Visuospatial memory task performance was predicted by the radial diffusivity of the posterior corona radiata. These findings demonstrate that people with mild-to-moderate severity of Parkinson9s disease who have normal muscle tone during REM sleep show alterations in white matter microstructure that are associated with preserved motor and cognitive function, but these adaptations are reduced or absent in those with increased REM sleep motor tone. more...
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- 2021
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13. Altered brain responses to noxious dentoalveolar stimuli in high-impact temporomandibular disorder pain patients
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Connor Peck, David A. Bereiter, Lynn E. Eberly, Christophe Lenglet, and Estephan Moana-Filho
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stomatognathic diseases ,Multidisciplinary ,Brain ,Humans ,Female ,Chronic Pain ,Temporomandibular Joint Disorders ,Magnetic Resonance Imaging ,human activities ,Pain Measurement - Abstract
High-impact temporomandibular disorder (TMD) pain may involve brain mechanisms related to maladaptive central pain modulation. We investigated brain responses to stimulation of trigeminal sites not typically associated with TMD pain by applying noxious dentoalveolar pressure to high- and low-impact TMD pain cases and pain-free controls during functional magnetic resonance imaging (fMRI). Fifty female participants were recruited and assigned to one of three groups based on the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) and Graded Chronic Pain Scale: controls (n = 17), low-impact (n = 17) and high-impact TMD (n = 16). Multimodal whole-brain MRI was acquired following the Human Connectome Project Lifespan protocol, including stimulus-evoked fMRI scans during which painful dentoalveolar pressure was applied to the buccal gingiva of participants. Group analyses were performed using non-parametric permutation tests for parcellated cortical and subcortical neuroimaging data. There were no significant between-group differences for brain activations/deactivations evoked by the noxious dentoalveolar pressure. For individual group mean activations/deactivations, a gradient in the number of parcels surviving thresholding was found according to the TMD pain grade, with the highest number seen in the high-impact group. Among the brain regions activated in chronic TMD pain groups were those previously implicated in sensory-discriminative and motivational-affective pain processing. These results suggest that dentoalveolar pressure pain evokes abnormal brain responses to sensory processing of noxious stimuli in high-impact TMD pain participants, which supports the presence of maladaptive brain plasticity in chronic TMD pain. more...
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- 2022
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14. White matter microstructure in Parkinson's disease with and without elevated rapid eye movement sleep muscle tone
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Rémi Patriat, Pramod K. Pisharady, Sommer Amundsen-Huffmaster, Maria Linn-Evans, Michael Howell, Jae Woo Chung, Matthew N. Petrucci, Aleksandar Videnovic, Erin Holker, Joshua De Kam, Paul Tuite, Christophe Lenglet, Noam Harel, and Colum D. MacKinnon more...
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General Engineering - Abstract
People with Parkinson’s disease who have elevated muscle activity during rapid eye movement sleep (REM sleep without atonia) typically have a worse motor and cognitive impairment compared with those with normal muscle atonia during rapid eye movement sleep. This study used tract-based spatial statistics to compare diffusion MRI measures of fractional anisotropy, radial, mean and axial diffusivity (measures of axonal microstructure based on the directionality of water diffusion) in white matter tracts between people with Parkinson’s disease with and without rapid eye movement sleep without atonia and controls and their relationship to measures of motor and cognitive function. Thirty-eight individuals with mild-to-moderate Parkinson’s disease and 21 matched control subjects underwent ultra-high field MRI (7 T), quantitative motor assessments of gait and bradykinesia and neuropsychological testing. The Parkinson’s disease cohort was separated post hoc into those with and without elevated chin or leg muscle activity during rapid eye movement sleep based on polysomnography findings. Fractional anisotropy was significantly higher, and diffusivity significantly lower, in regions of the corpus callosum, projection and association white matter pathways in the Parkinson’s group with normal rapid eye movement sleep muscle tone compared with controls, and in a subset of pathways relative to the Parkinson’s disease group with rapid eye movement sleep without atonia. The Parkinson’s disease group with elevated rapid eye movement sleep muscle tone showed significant impairments in the gait and upper arm speed compared with controls and significantly worse scores in specific cognitive domains (executive function, visuospatial memory) compared with the Parkinson’s disease group with normal rapid eye movement sleep muscle tone. Regression analyses showed that gait speed and step length in the Parkinson’s disease cohort were predicted by measures of fractional anisotropy of the anterior corona radiata, whereas elbow flexion velocity was predicted by fractional anisotropy of the superior corona radiata. Visuospatial memory task performance was predicted by the radial diffusivity of the posterior corona radiata. These findings show that people with mild-to-moderate severity of Parkinson’s disease who have normal muscle tone during rapid eye movement sleep demonstrate compensatory-like adaptations in axonal microstructure that are associated with preserved motor and cognitive function, but these adaptations are reduced or absent in those with increased rapid eye movement sleep motor tone. more...
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- 2021
15. Functional Magnetic Resonance Imaging and Oculomotor Dysfunction in Mild Traumatic Brain Injury
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Gaylan L. Rockswold, Walter C. Low, Essa Yacoub, Andrea Grant, Philip C. Burton, Nova McNally, Christophe Lenglet, Amy Chang, Sarah B. Rockswold, and Lynn E. Eberly
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Adult ,Male ,030506 rehabilitation ,medicine.medical_specialty ,Eye Movements ,Traumatic brain injury ,Brain activity and meditation ,Neuropsychological Tests ,Audiology ,Cuneus ,Lingual gyrus ,03 medical and health sciences ,Ocular Motility Disorders ,0302 clinical medicine ,Humans ,Medicine ,Prospective Studies ,Brain Concussion ,Blood-oxygen-level dependent ,medicine.diagnostic_test ,Resting state fMRI ,business.industry ,Functional Neuroimaging ,Brain ,Original Articles ,medicine.disease ,Magnetic Resonance Imaging ,medicine.anatomical_structure ,Female ,Neurology (clinical) ,0305 other medical science ,business ,Functional magnetic resonance imaging ,030217 neurology & neurosurgery ,Parahippocampal gyrus ,Spatial Navigation - Abstract
Mild traumatic brain injury (mTBI) is a significant cause of disability, especially when symptoms become chronic. This chronicity is often linked to oculomotor dysfunction (OMD). To our knowledge, this is the first prospective study to localize aberrations in brain function between mTBI cohorts, by comparing patients with mTBI with OMD with an mTBI control group without OMD, using task and resting-state functional magnetic resonance imaging (fMRI). Ten subjects with mTBI who had OMD (OMD group) were compared with nine subjects with mTBI who had no findings of OMD (control group). These groups were determined by a developmental optometrist using objective testing for OMD. The (convergence) task fMRI data demonstrated significantly decreased brain activity, measured as decreases in the blood oxygen level dependent (BOLD) signal, in the OMD group compared with the control group in three brain regions: the left posterior lingual gyrus, the bilateral anterior lingual gyrus and cuneus, and the parahippocampal gyrus. When doing a seed-based resting state fMRI analysis in the lingual/parahippocampal region, a large cluster covering the left middle frontal gyrus and the dorsolateral pre-frontal cortex (Brodmann areas 9 and 10), with decreased functional correlation in the OMD group, was identified. Together these observations provide evidence for neural networks of interactions involving the control of eye movement for visual processing, reading comprehension, spatial localization and navigation, and spatial working memory that appear to be decreased in mTBI patients with OMD compared with mTBI patients without OMD. The clinical symptomatology associated with post-traumatic OMD correlates well with these MRI findings. more...
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- 2019
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16. Ultra-high field (10.5T) diffusion-weighted MRI of the macaque brain
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Mark D. Grier, Essa Yacoub, Gregor Adriany, Russell L. Lagore, Noam Harel, Ru-Yuan Zhang, Christophe Lenglet, Kâmil Uğurbil, Jan Zimmermann, and Sarah R. Heilbronner
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Diffusion Magnetic Resonance Imaging ,Neurology ,Echo-Planar Imaging ,Cognitive Neuroscience ,Image Processing, Computer-Assisted ,Animals ,Brain ,Humans ,Macaca ,Magnetic Resonance Imaging - Abstract
Diffu0sion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging technique that provides information about the barriers to the diffusion of water molecules in tissue. In the brain, this information can be used in several important ways, including to examine tissue abnormalities associated with brain disorders and to infer anatomical connectivity and the organization of white matter bundles through the use of tractography algorithms. However, dMRI also presents certain challenges. For example, historically, the biological validation of tractography models has shown only moderate correlations with anatomical connectivity as determined through invasive tract-tracing studies. Some of the factors contributing to such issues are low spatial resolution, low signal-to-noise ratios, and long scan times required for high-quality data, along with modeling challenges like complex fiber crossing patterns. Leveraging the capabilities provided by an ultra-high field scanner combined with denoising, we have acquired whole-brain, 0.58 mm isotropic resolution dMRI with a 2D-single shot echo planar imaging sequence on a 10.5 Tesla scanner in anesthetized macaques. These data produced high-quality tractograms and maps of scalar diffusion metrics in white matter. This work demonstrates the feasibility and motivation for in-vivo dMRI studies seeking to benefit from ultra-high fields. more...
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- 2022
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17. Diffusion magnetic resonance imaging reveals tract‐specific microstructural correlates of electrophysiological impairments in non‐myelopathic and myelopathic spinal cord compression
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René Labounek, Tomáš Rohan, Josef Bednařík, Zdeněk Kadaňka, Miloš Keřkovský, Eva Vlčková, Magda Horáková, Alena Svátková, Petr Hluštík, Christophe Lenglet, Jan Valošek, Petr Bednařík, Julien Cohen-Adad, Jan Kočica, and Tomáš Horák more...
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Pathology ,medicine.medical_specialty ,medicine.diagnostic_test ,business.industry ,Magnetic resonance imaging ,Sensory system ,Electromyography ,medicine.disease ,Asymptomatic ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Electrophysiology ,Myelopathy ,0302 clinical medicine ,Neurology ,Spinal cord compression ,medicine ,Neurology (clinical) ,medicine.symptom ,business ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
BACKGROUND AND PURPOSE Non-myelopathic degenerative cervical spinal cord compression (NMDC) frequently occurs throughout aging and may progress to potentially irreversible degenerative cervical myelopathy (DCM). Whereas standard clinical magnetic resonance imaging (MRI) and electrophysiological measures assess compression severity and neurological dysfunction, respectively, underlying microstructural deficits still have to be established in NMDC and DCM patients. The study aims to establish tract-specific diffusion MRI markers of electrophysiological deficits to predict the progression of asymptomatic NMDC to symptomatic DCM. METHODS High-resolution 3 T diffusion MRI was acquired for 103 NMDC and 21 DCM patients compared to 60 healthy controls to reveal diffusion alterations and relationships between tract-specific diffusion metrics and corresponding electrophysiological measures and compression severity. Relationship between the degree of DCM disability, assessed by the modified Japanese Orthopaedic Association scale, and tract-specific microstructural changes in DCM patients was also explored. RESULTS The study identified diffusion-derived abnormalities in the gray matter, dorsal and lateral tracts congruent with trans-synaptic degeneration and demyelination in chronic degenerative spinal cord compression with more profound alterations in DCM than NMDC. Diffusion metrics were affected in the C3-6 area as well as above the compression level at C3 with more profound rostral deficits in DCM than NMDC. Alterations in lateral motor and dorsal sensory tracts correlated with motor and sensory evoked potentials, respectively, whereas electromyography outcomes corresponded with gray matter microstructure. DCM disability corresponded with microstructure alteration in lateral columns. CONCLUSIONS Outcomes imply the necessity of high-resolution tract-specific diffusion MRI for monitoring degenerative spinal pathology in longitudinal studies. more...
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- 2021
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18. Assessment of Cerebral and Cerebellar White Matter Microstructure in Spinocerebellar Ataxias 1, 2, 3, and 6 Using Diffusion MRI
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Young Woo Park, James M. Joers, Bin Guo, Diane Hutter, Khalaf Bushara, Isaac M. Adanyeguh, Lynn E. Eberly, Gülin Öz, and Christophe Lenglet
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Ataxia ,lcsh:RC346-429 ,diffusion MRI ,White matter ,SCA1 ,03 medical and health sciences ,SCA2 ,0302 clinical medicine ,SCA3 ,Region of interest ,SCA6 ,Fractional anisotropy ,medicine ,Middle cerebellar peduncle ,lcsh:Neurology. Diseases of the nervous system ,Original Research ,030304 developmental biology ,Spinocerebeflar ataxias ,0303 health sciences ,business.industry ,medicine.disease ,medicine.anatomical_structure ,Neurology ,Corticospinal tract ,Spinocerebellar ataxia ,Neurology (clinical) ,medicine.symptom ,Nuclear medicine ,business ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Development of imaging biomarkers for rare neurodegenerative diseases such as spinocerebellar ataxia (SCA) is important to non-invasively track progression of disease pathology and monitor response to interventions. Diffusion MRI (dMRI) has been shown to identify cross-sectional degeneration of white matter (WM) microstructure and connectivity between healthy controls and patients with SCAs, using various analysis methods. In this paper, we present dMRI data in SCAs type 1, 2, 3, and 6 and matched controls, including longitudinal acquisitions at 12–24-month intervals in a subset of the cohort, with up to 5 visits. The SCA1 cohort also contained 3 premanifest patients at baseline, with 2 showing ataxia symptoms at the time of the follow-up scans. We focused on two aspects: first, multimodal evaluation of the dMRI data in a cross-sectional approach, and second, longitudinal trends in dMRI data in SCAs. Three different pipelines were used to perform cross-sectional analyses in WM: region of interest (ROI), tract-based spatial statistics (TBSS), and fixel-based analysis (FBA). We further analyzed longitudinal changes in dMRI metrics throughout the brain using ROI-based analysis. Both ROI and TBSS analyses identified higher mean (MD), axial (AD), and radial (RD) diffusivity and lower fractional anisotropy (FA) in the cerebellum for all SCAs compared to controls, as well as some cerebral alterations in SCA1, 2, and 3. FBA showed lower fiber density (FD) and fiber crossing (FC) regions similar to those identified by ROI and TBSS analyses. FBA also highlighted corticospinal tract (CST) abnormalities, which was not detected by the other two pipelines. Longitudinal ROI-based analysis showed significant increase in AD in the middle cerebellar peduncle (MCP) for patients with SCA1, suggesting that the MCP may be a good candidate region to monitor disease progression. The patient who remained symptom-free throughout the study displayed no microstructural abnormalities. On the other hand, the two patients who were at the premanifest stage at baseline, and showed ataxia symptoms in their follow-up visits, displayed AD values in the MCP that were already in the range of symptomatic patients with SCA1 at their baseline visit, demonstrating that microstructural abnormalities are detectable prior to the onset of ataxia. more...
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- 2020
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19. Individualized tractography-based parcellation of the globus pallidus pars interna using 7T MRI in movement disorder patients prior to DBS surgery
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Scott E. Cooper, Remi Patriat, Noam Harel, Christophe Lenglet, Jacob Niederer, Jerrold L. Vitek, Yuval Duchin, Michael C. Park, and Joshua E Aman
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Adult ,Male ,medicine.medical_specialty ,Movement disorders ,Deep brain stimulation ,Deep Brain Stimulation ,Cognitive Neuroscience ,medicine.medical_treatment ,Thalamus ,Striatum ,Globus Pallidus ,Surgical planning ,Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Cortex (anatomy) ,Preoperative Care ,Image Processing, Computer-Assisted ,medicine ,Humans ,Aged ,Movement Disorders ,business.industry ,Reproducibility of Results ,Parkinson Disease ,Middle Aged ,Magnetic Resonance Imaging ,Corpus Striatum ,Surgery ,Diffusion Tensor Imaging ,medicine.anatomical_structure ,Globus pallidus ,nervous system ,Neurology ,Dystonic Disorders ,Female ,medicine.symptom ,business ,030217 neurology & neurosurgery ,Tractography - Abstract
The success of deep brain stimulation (DBS) surgeries for the treatment of movement disorders relies on the accurate placement of an electrode within the motor portion of subcortical brain targets. However, the high number of electrodes requiring relocation indicates that today's methods do not ensure sufficient accuracy for all patients. Here, with the goal of aiding DBS targeting, we use 7 Tesla (T) MRI data to identify the functional territories and parcellate the globus pallidus pars interna (GPi) into motor, associative and limbic regions in individual subjects. 7 T MRI scans were performed in seventeen patients (prior to DBS surgery) and one healthy control. Tractography-based parcellation of each patient's GPi was performed. The cortex was divided into four masks representing motor, limbic, associative and “other” regions. Given that no direct connections between the GPi and the cortex have been shown to exist, the parcellation was carried out in two steps: 1) The thalamus was parcellated based on the cortical targets, 2) The GPi was parcellated using the thalamus parcels derived from step 1. Reproducibility, via repeated scans of a healthy subject, and validity of the findings, using different anatomical pathways for parcellation, were assessed. Lastly, post-operative imaging data was used to validate and determine the clinical relevance of the parcellation. The organization of the functional territories of the GPi observed in our individual patient population agrees with that previously reported in the literature: the motor territory was located posterolaterally, followed anteriorly by the associative region, and further antero-ventrally by the limbic territory. While this organizational pattern was observed across patients, there was considerable variability among patients. The organization of the functional territories of the GPi was remarkably reproducible in intra-subject scans. Furthermore, the organizational pattern was observed consistently by performing the parcellation of the GPi via the thalamus and via a different pathway, going through the striatum. Finally, the active therapeutic contact of the DBS electrode, identified with a combination of post-operative imaging and post-surgery DBS programming, overlapped with the high-probability “motor” region of the GPi as defined by imaging-based methods. The consistency, validity, and clinical relevance of our findings have the potential for improving DBS targeting, by increasing patient-specific knowledge of subregions of the GPi to be targeted or avoided, at the stage of surgical planning, and later, at the stage when stimulation is adjusted. more...
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- 2018
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20. AutoVOI: real‐time automatic prescription of volume‐of‐interest for single voxel spectroscopy
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Peter B. Barker, Christophe Lenglet, Joseph S. Gillen, Brian Hanna, HyunWook Park, Dinesh K. Deelchand, James M. Joers, Brian J. Soher, Gülin Öz, Adam Berrington, Young W. Park, and Kejal Kantarci
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Adult ,Male ,Scanner ,Volume of interest ,Computer science ,Pipeline (computing) ,Minimum bounding box algorithms ,Article ,030218 nuclear medicine & medical imaging ,Young Adult ,03 medical and health sciences ,Imaging, Three-Dimensional ,0302 clinical medicine ,Consistency (statistics) ,Humans ,Spatial consistency ,Radiology, Nuclear Medicine and imaging ,Computer vision ,business.industry ,Brain ,Magnetic Resonance Imaging ,Automation ,Single voxel spectroscopy ,Female ,Artificial intelligence ,business ,Algorithms ,030217 neurology & neurosurgery - Abstract
PURPOSE: To develop a fast and automated volume-of-interest (VOI) prescription pipeline (AutoVOI) for single-voxel MR spectroscopy (MRS) that removes the need for manual VOI placement, allows flexible VOI planning in any brain region, and enables high inter- and intra-subject consistency of VOI prescription. METHODS: AutoVOI was designed to transfer pre-defined VOIs from an atlas to the 3D anatomical data of the subject during the scan. The AutoVOI pipeline was optimized for consistency in VOI placement (precision), enhanced coverage of the targeted tissue (accuracy) and fast computation speed. The tool was evaluated against manual VOI placement using existing T(1)-weighted datasets and corresponding VOI prescriptions. Finally, it was implemented on two scanner platforms to acquire MRS data from clinically-relevant VOIs that span the cerebrum, cerebellum and the brainstem. RESULTS: The AutoVOI pipeline includes skull stripping, non-linear registration of the atlas to the subject’s brain, and computation of the VOI coordinates and angulations using a minimum oriented bounding box algorithm. When compared against manual prescription, AutoVOI showed higher intra- and inter-subject spatial consistency, as quantified by generalized Dice coefficients (GDC), lower intra- and inter-subject variability in tissue composition (gray matter, white matter and cerebrospinal fluid) and higher or equal accuracy, as quantified by GDC of prescribed VOI with targeted tissues. High quality spectra were obtained on Siemens and Philips 3T systems from 6 automatically prescribed VOIs by the tool. CONCLUSION: Robust automatic VOI prescription is feasible and can help facilitate clinical adoption of MRS by avoiding operator dependence of manual selection. more...
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- 2018
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21. High‐resolution whole‐brain diffusion MRI at 7T using radiofrequency parallel transmission
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Sebastian Schmitter, Xiaoping Wu, Christophe Lenglet, Pierre-Francois Van de Moortele, Kâmil Uğurbil, Edward J. Auerbach, An T. Vu, Essa Yacoub, and Steen Moeller
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Adult ,Male ,Materials science ,Adolescent ,High resolution ,Signal ,Article ,030218 nuclear medicine & medical imaging ,Scan time ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Connectome ,Image Processing, Computer-Assisted ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Aged ,Human Connectome Project ,Power deposition ,Brain ,Middle Aged ,Sagittal plane ,Diffusion Magnetic Resonance Imaging ,medicine.anatomical_structure ,Parallel communication ,Female ,Algorithms ,030217 neurology & neurosurgery ,Biomedical engineering ,Diffusion MRI - Abstract
PURPOSE: Investigating the utility of RF parallel transmission (pTx) for Human Connectome Project (HCP)-style whole-brain diffusion MRI (dMRI) data at 7 Tesla (7T). METHODS: Healthy subjects were scanned in pTx and single-transmit (1Tx) modes. Multiband (MB), single-spoke pTx pulses were designed to image sagittal slices. HCP-style dMRI data (i.e., 1.05-mm resolutions, MB2, b-values=1000/2000 s/mm(2), 286 images and 40-minute scan) and data with higher accelerations (MB3 and MB4) were acquired with pTx. RESULTS: pTx significantly improved flip-angle detected signal uniformity across the brain, yielding ~19% increase in temporal signal-to-noise ratio (tSNR) averaged over the brain relative to 1Tx. This allowed significantly enhanced estimation of multiple fiber orientations (with ~21% decrease in dispersion) in HCP-style 7T dMRI datasets. Additionally, pTx pulses achieved substantially lower power deposition, permitting higher accelerations, enabling collection of the same data in 2/3 and 1/2 the scan time or of more data in the same scan time. CONCLUSION: pTx provides a solution to two major limitations for slice-accelerated high-resolution whole-brain dMRI at 7T; it improves flip-angle uniformity, and enables higher slice acceleration relative to current state-of-the-art. As such, pTx provides significant advantages for rapid acquisition of high-quality, high-resolution truly whole brain dMRI data. more...
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- 2018
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22. Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning
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Christophe Lenglet, Pramod Kumar Pisharady, Stamatios N. Sotiropoulos, Julio M. Duarte-Carvajalino, and Guillermo Sapiro
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Computer science ,Cognitive Neuroscience ,Neuroimaging ,Sparse signal recovery ,Bayesian inference ,Nerve Fibers, Myelinated ,Article ,Diffusion MRI ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Image Interpretation, Computer-Assisted ,Prior probability ,Humans ,Linear unmixing ,Hyperparameter ,K-SVD ,business.industry ,Estimation theory ,Fiber orientation ,Bayes Theorem ,Pattern recognition ,Sparse approximation ,Compressive sensing ,Models, Theoretical ,White Matter ,Diffusion Magnetic Resonance Imaging ,Compressed sensing ,Neurology ,Sparse Bayesian learning ,Artificial intelligence ,Deconvolution ,business ,Algorithms ,030217 neurology & neurosurgery - Abstract
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. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates. more...
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- 2018
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23. Brain Structure and Connectivity Mapping for Deep Brain Stimulation Using Ultrahigh Field (7 T) MRI
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Jerrold L. Vitek, Noam Harel, Christophe Lenglet, and Remi Patriat
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Dystonia ,Deep brain stimulation ,Parkinson's disease ,Essential tremor ,medicine.diagnostic_test ,Computer science ,medicine.medical_treatment ,Magnetic resonance imaging ,medicine.disease ,Tourette syndrome ,medicine.anatomical_structure ,Neuroimaging ,medicine ,Neuroscience ,Neuroanatomy - Abstract
This chapter reviews recent advances in neuroimaging methods for the visualization of brain structures, as well as the connectivity patterns that play key roles for deep brain stimulation therapy. The advantages of utilizing ultrahigh field 7 T magnetic resonance imaging (MRI) for visualizing anatomical structures will likely propel our clinical and scientific abilities to investigate, develop, and enhance therapies for patients with neurological disorders such as Parkinson’s disease, essential tremor, dystonia, and Tourette syndrome. Treatment approaches are becoming more patient-specific, thus more therapies will rely on highly accurate treatment delivery capabilities to the target area. As such, 7 T (and stronger fields) MRI systems will provide such abilities by producing enhanced input data with superior resolution and image contrast. Further development of better imaging acquisition strategies and postprocessing methods are needed, as the data being generated will open new possibilities for exploring brain function in both healthy and disease conditions at resolution levels never before observed. more...
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- 2019
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24. List of Contributors
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Cristina S. Albott, Rajagopal N. Aravalli, Dawn Bardot, Michael G. Bateman, Jazmin Camchong, Mo Chen, Wei Chen, Christopher C. Cline, Andrew Crane, Georgette Danczyk, Federico De Martino, William K. Durfee, Michael D. Eggen, Arthur G. Erdman, Kate L. Frost, Erik N. Gaasedelen, Bernadette T. Gillick, Noam Harel, Bin He, Mikayle A. Holm, Rebecca A. Hortensius, Brian T. Howard, Stephen J. Huddleston, Paul A. Iaizzo, Tinen L. Iles, Ranjit John, Rosemary F. Kelly, Natalie Kerns, Teresa J. Kimberley, Kanchan Kulkarni, Steven W. Lee, Christophe Lenglet, Kenneth K. Liao, Kelvin O. Lim, Wei-Han Lin, Walter C. Low, Ming Lu, Wei-Cheng Lu, Lars M. Mattison, Alexander R. Mattson, Zachary D. Miller, Teerapat Nantsupawat, Samuel T. Nemanich, Theoden I. Netoff, Anh Tuan Nguyen, Brenda M. Ogle, Matthew Olson, Remi Patriat, Clairice Pearce, Cecilia N. Prudente, Henri Roukoz, Abhrajeet V. Roy, Jorge D. Zhingre Sanchez, Neeraj Sathnur, Randy Schiestl, Andrew W. Shaffer, Maple Shiao, Christopher Sipe, John R. Spratt, Clifford J. Steer, Elena G. Tolkacheva, Nikolas G. Toman, Kamil Ugurbil, Jerrold L. Vitek, Joseph Voth, Hui Xie, Jian Xu, Essa Yacoub, Zhi Yang, and Xiao-Hong Zhu more...
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- 2019
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25. Intraspinal space restriction at the occipito-cervical junction alters cervical spinal cord diffusion MRI metrics in mucopolysacharidoses patients
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René Labounek, Christophe Lenglet, Carol Nguyen, Julien Cohen-Adad, Ivan Krasovec, Igor Nestrasil, Chester B. Whitley, Jan Valošek, and Alena Svátková
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Endocrinology ,medicine.anatomical_structure ,business.industry ,Endocrinology, Diabetes and Metabolism ,Genetics ,medicine ,Anatomy ,Spinal cord ,Space (mathematics) ,business ,Molecular Biology ,Biochemistry ,Diffusion MRI - Published
- 2020
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26. Altered Structural Connection Between Hippocampus and Insula in Adolescent Major Depressive Disorder using DTI
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Bonnie Klimes-Dougan, Shu-Hsien Chu, Kathryn R. Cullen, Keshab K. Parhi, Christophe Lenglet, and Mindy Westlund Schreiner
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business.industry ,05 social sciences ,Hippocampus ,Grey matter ,Insular cortex ,behavioral disciplines and activities ,Lateralization of brain function ,White matter ,03 medical and health sciences ,0302 clinical medicine ,medicine.anatomical_structure ,nervous system ,Fractional anisotropy ,medicine ,0501 psychology and cognitive sciences ,business ,Insula ,Neuroscience ,030217 neurology & neurosurgery ,Parahippocampal gyrus ,050104 developmental & child psychology - Abstract
The adolescent major depressive disorder is one of the top 10 debilitating psychiatric illnesses and the effectiveness of current treatment methods are constrained by the limited understanding of biological causes. In this paper, we use diffusion tensor imaging to explore changes in anatomical connectivity between the MDD group (n=37) and control group (n=27). Furthermore, along-track analysis is performed to identify locations of alterations along connections with significant connectivity change. For the connection between the hippocampus and the insular cortex in the right hemisphere, decreased connectivity in axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) was discovered. Additionally, the number of tracks for the same connection is increased for the MDD group. Moreover, for the connection between the parahippocampal gyrus and the insular cortex in the left hemisphere, increased connectivity is observed in fractional anisotropy (FA), AD, MD, and RD. Furthermore, the locations of significant alterations are identified to be between 65% to 100% from the insular cortex to the hippocampus in the right hemisphere and at the 80% location from the insular cortex to the parahippocampal gyrus in the left hemisphere. The significant and consistent white matter changes at the hippocampus end of the insula-hippocampus connection suggest potential correlations to the previously reported grey matter shrinkage and functional abnormalities. more...
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- 2018
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27. Classifying Adolescent Major Depressive Disorder using Linear SVM with Anatomical Features from Diffusion Weighted Imaging
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Keshab K. Parhi, Kathryn R. Cullen, Mindy Westlund Schreiner, Shu-Hsien Chu, Christophe Lenglet, and Bonnie Klimes-Dougan
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Multivariate statistics ,business.industry ,Linear svm ,Univariate ,Pattern recognition ,medicine.disease ,behavioral disciplines and activities ,030227 psychiatry ,03 medical and health sciences ,0302 clinical medicine ,Betweenness centrality ,mental disorders ,Medicine ,Major depressive disorder ,Right lingual gyrus ,Artificial intelligence ,business ,030217 neurology & neurosurgery ,Statistical hypothesis testing ,Diffusion MRI - Abstract
Adolescence is a period of rapid brain maturation and a critical period for the onset of Major Depressive Disorder (MDD) that usually leads to serious outcomes such as suicide. Although changes in anatomical connectivity in MDD have been reported, changes in network topology for MDD remain unclear. Additionally, whether the changes are the same for adolescent MDD and adult MDD remains unclear as well. This paper explores anatomical features including: a) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and b) topological measurements from anatomical networks, and apply machine learning approaches to identify responsive biomarkers distinguishing MDD patients from healthy subjects. In addition to statistical tests, univariate classifiers are designed to evaluate the discriminating power of features. Furthermore, multivariate classifiers are trained for distinguishing healthy subjects from MDD patients. The best classifier achieves an accuracy of 76.56%, 81.08% sensitivity, 70.37% specificity and 78.95% precision for 64 subjects (37 MDD and 27 matched healthy control). The selected features include: 1) betweenness centrality of the right lingual gyrus of the ADC network at 12% sparsity, 2) participation coefficient of the right pars opercularis of the AD network at 16% sparsity, 3) participation coefficient of the left insular cortex of the MD network at 21% sparsity, and 4) participation coefficient of the right lateral orbitofrontal cortex in the ADC network at 10% sparsity. These features reflect changes in the topological structure of the brain anatomical network in MDD. more...
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- 2018
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28. Biomarkers for Adolescent MDD from Anatomical Connectivity and Network Topology Using Diffusion MRI
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Bonnie Klimes-Dougan, Keshab K. Parhi, Kathryn R. Cullen, Mindy Westlund Schreiner, Shu-Hsien Chu, and Christophe Lenglet
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medicine.medical_specialty ,Adolescent ,Hippocampus ,Lateralization of brain function ,03 medical and health sciences ,symbols.namesake ,0302 clinical medicine ,Text mining ,Internal medicine ,Cortex (anatomy) ,medicine ,Humans ,Depressive Disorder, Major ,business.industry ,Case-control study ,Brain ,medicine.disease ,030227 psychiatry ,Diffusion Magnetic Resonance Imaging ,Bonferroni correction ,medicine.anatomical_structure ,Case-Control Studies ,symbols ,Cardiology ,Major depressive disorder ,business ,Biomarkers ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Due to the high resistance (35%) to the current treatment methods in adolescent Major Depressive Disorder (MDD) and its tragic outcomes, the discovery of treatmentrelated responders is critical to developing effective treatments. In this paper, the permutation test is performed to identify statistically significant changes in anatomical characteristics during pairwise comparisons among the control group (n=27), treated MDD group (n=37), and untreated MDD group (n=15). The anatomical characteristics include: 1) anatomical connectivity defined using DTI metrics between a pair of brain regions, and 2) topological measurements of anatomical networks. With the Bonferroni correction for multiple-comparison, significant alterations in community structure and local topology were identified as the p-value < 5%, which include: 1) a reduced nodal centrality (degree and strength) on right hippocampus for treated compared to untreated group, 2) an elevated clustering coefficient and local efficiency on right lateral orbitofrontal cortex for untreated compared to the combination of control and treated groups, 3) an increased participation coefficient for untreated patients on left insula cortex in the meandiffusivity network compared to the combination of control and treated groups, and 4) a degraded module degree z-score on right caudate nucleus for all the patients compared to the control group. Two connections, hippocampus-insula in the right hemisphere and parahippocampal-insula in the left hemisphere, were found significantly altered in TR, AD, and FA due to MDD. more...
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- 2018
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29. Anatomical Biomarkers for Adolescent Major Depressive Disorder from Diffusion Weighted Imaging using SVM Classifier
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Bonnie Klimes-Dougan, Kathryn R. Cullen, Keshab K. Parhi, Mindy Westlund Schreiner, Christophe Lenglet, and Shu-Hsien Chu
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Support Vector Machine ,Adolescent ,Computer science ,Feature extraction ,03 medical and health sciences ,Svm classifier ,0302 clinical medicine ,Text mining ,Betweenness centrality ,medicine ,Humans ,Depressive Disorder, Major ,business.industry ,Brain ,Pattern recognition ,Mental illness ,medicine.disease ,030227 psychiatry ,Support vector machine ,Diffusion Tensor Imaging ,Major depressive disorder ,Artificial intelligence ,business ,Biomarkers ,030217 neurology & neurosurgery ,Pars opercularis ,Diffusion MRI - Abstract
Adolescent Major Depressive Disorder (MDD) is a common and serious mental illness that could lead to tragic outcomes including chronic adult disability and suicide. In this paper, we explore anatomical features and apply machine learning approaches to identify responsive biomarkers distinguishing MDD patients from healthy subjects. The features of interest include metrics in two categories: a) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and b) topological measurements from anatomical networks. A combination of p-value based filtering and minimum redundancy maximum relevance method is performed to select features for optimal classification accuracy. A leave-one-out cross-validation method is used for the classification performance evaluation. The proposed methodology achieves an improved accuracy of 78%, 90.39% sensitivity, and 79.66% precision for 79 subjects. The most distinguishing features are the betweenness centrality of the right lingual gyrus of the ADC network at 12% sparsity, the participation coefficient of the right lateral occipital sulcus of the ADC network at 22% sparsity, the participation coefficient of the right pars opercularis of the AD network at 16% sparsity, and the participation coefficient of the right lateral orbitofrontal cortex in the ADC network at 10% sparsity. Those network measures reflect the change of connectivity between the regions and their associated anatomical subnetworks. more...
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- 2018
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30. Classifying Treated vs. Untreated MDD Adolescents from Anatomical Connectivity using Nonlinear SVM
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Christophe Lenglet, Shu-Hsien Chu, Mindy Westlund Schreiner, Kathryn R. Cullen, Keshab K. Parhi, and Bonnie Klimes-Dougan
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Male ,Support Vector Machine ,Adolescent ,Hippocampus ,Feature selection ,Corpus callosum ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Text mining ,Betweenness centrality ,medicine ,Humans ,Effective diffusion coefficient ,Anterior cingulate cortex ,Mathematics ,Depressive Disorder, Major ,business.industry ,Brain ,Pattern recognition ,030227 psychiatry ,Diffusion Tensor Imaging ,medicine.anatomical_structure ,Case-Control Studies ,Female ,Artificial intelligence ,business ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Identification of the treatment-related responders for adolescent Major Depressive Disorder (MDD) is urgently needed to develop effective treatments. In this paper, machine learning based classifiers are used to reveal anatomical features as responders for distinguishing MDD patients who have received treatment from those who never received any treatment. The features are drawn from two sets of measurements: 1) anatomical connectivity defined by diffusion tensor imaging measurements between a pair of brain regions, and 2) topological measurements from anatomical networks. Feature selection was performed based on p-value and minimum redundancy maximum relevance (mRMR) method to achieve improved classification accuracy. The classification performance is evaluated with a leave-one-out cross-validation method using 37 treated and 15 untreated subjects. The proposed methodology achieves 73% accuracy, 100% specificity, and 100% precision for 52 subjects. The most distinguishing features are the strength of the right hippocampus of the mean diffusivity (MD) network at 18% density and of the track-count (TR) network, the participation coefficient of the left middle temporal gyrus of the radial diffusivity (RD) network at 20% density, the axial diffusivity (AD) connectivity between right middle temporal gyrus and right supramarginal gyrus, the betweenness centrality of the right hippocampus of the TR network at 11% density, the apparent diffusion coefficient (ADC) connectivity between the left pars opercularis and the left rostral anterior cingulate cortex, the clustering coefficient of the middle anterior corpus callosum of the TR network at 11% density, and the AD connectivity between the left pars opercularis and the left rostral anterior cingulate cortex. more...
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- 2018
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31. Fast In Vivo High-Resolution Diffusion MRI of the Human Cervical Spinal Cord Microstructure
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Zuzana Piskořová, René Labounek, Josef Bednařík, Jakub Zimolka, Pavel Hok, Petr Bednařík, Jan Valošek, Petr Hluštík, Christophe Lenglet, Lubomír Vojtíšek, Alena Svátková, and Tomáš Horák
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Scanner ,Materials science ,medicine.diagnostic_test ,Resolution (electron density) ,Magnetic resonance imaging ,030218 nuclear medicine & medical imaging ,White matter ,03 medical and health sciences ,0302 clinical medicine ,medicine.anatomical_structure ,Fractional anisotropy ,medicine ,Image resolution ,030217 neurology & neurosurgery ,Diffusion MRI ,Biomedical engineering ,Interpolation - Abstract
Diffusion Magnetic Resonance Imaging (dMRI) is a widely-utilized method for assessment of microstructural properties in the central nervous system i.e., the brain and spinal cord (SC). In the SC, almost all previous human studies utilized Diffusion Tensor Imaging (DTI), which cannot accurately model areas where white matter (WM) pathways cross or diverge. While High Angular Diffusion Resolution Imaging (HARDI) can overcome some of these limitations, longer acquisition times critically limit its applicability to clinical human studies. In addition, previous human HARDI studies have used limited spatial resolution, with typically a few slices and voxel size ~1 × 1 × 5 mm3 being acquired in tens of minutes. Thus, we have optimized a novel fast HARDI protocol that allows collecting dMRI data at high angular and spatial resolutions in clinically-feasible time. Our data was acquired, using a 3T Siemens Prisma scanner, in less than 9 min. It has a total of 75 diffusion-weighted volumes and high spatial resolution of 0.67 × 0.67 × 3 mm3 (after interpolation in Fourier space) covering the cervical segments C4–C6. Our preliminary results demonstrate applicability of our technique in healthy individuals with good correspondence between low fractional anisotropy (FA) gray matter areas from the dMRI scans, and the same regions delineated on T2-weighted MR images with spatial resolution of 0.35 × 0.35 × 2.5 mm3. Our data also allows the detection of crossing fibers that were previously shown in vivo only in animal studies. more...
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- 2018
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32. Brain Parcellation and Connectivity Mapping Using Wasserstein Geometry
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Yongxin Chen, Tryphon T. Georgiou, Christophe Lenglet, and Hamza Farooq
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Human Connectome Project ,Computer science ,Voxel ,Wasserstein metric ,Metric (mathematics) ,Probabilistic logic ,Probability distribution ,Geometry ,computer.software_genre ,computer ,Diffusion MRI ,Tractography - Abstract
Several studies have used structural connectivity information to parcellate brain areas like the corpus callosum, thalamus, substantia nigra or motor cortex, which is otherwise difficult to achieve using conventional MRI techniques. They typically employ diffusion MRI (dMRI) tractography and compare connectivity profiles from individual voxels using correlation. However, this is potentially limiting since the profile signals (e.g. probabilistic connectivity maps) have non-zero values only in restricted areas of the brain, and correlation coefficients do not fully capture differences between connectivity profiles . Our first contribution is to introduce the Wasserstein distance as a metric to compare connectivity profiles, viewed as distributions. The Wasserstein metric (also known as Optimal Mass Transport cost or, Earth Mover’s distance) is natural as it allows a global comparison between probability distributions. Thereby, it relies not only on non-zero values but also takes into account their spatial pattern, which is crucial for the comparison of the brain connectivity profiles. Once a brain area is parcellated into anatomically relevant sub-regions, it is of interest to determine how voxels within each sub-region are collectively connected to the rest of the brain. The commonly used arithmetic mean of connectivity profiles fails to account for anatomical features and can easily over-emphasize spurious pathways. Therefore, our second contribution is to introduce the concept of Wasserstein barycenters of distributions, to estimate “average” connectivity profiles, and assess whether these are more representative of the neuroanatomy. We demonstrate the benefits of using the Wasserstein geometry to parcellate and “average” probabilistic tractography results from a realistic phantom dataset, as well as in vivo data from the Human Connectome Project. more...
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- 2018
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33. High resolution whole brain diffusion imaging at 7 T for the Human Connectome Project
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Essa Yacoub, Edward J. Auerbach, Stamatios N. Sotiropoulos, Jesper L. R. Andersson, Christophe Lenglet, Steen Moeller, An T. Vu, Saâd Jbabdi, and Kamil Ugurbil
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Signal processing ,Human Connectome Project ,Computer science ,business.industry ,Cognitive Neuroscience ,Brain ,Signal Processing, Computer-Assisted ,Image processing ,Pattern recognition ,Signal-To-Noise Ratio ,Article ,Temporal lobe ,Diffusion Magnetic Resonance Imaging ,Neurology ,Connectome ,Image Processing, Computer-Assisted ,Humans ,Artificial intelligence ,Artifacts ,business ,Simulation ,Diffusion MRI - Abstract
Mapping structural connectivity in healthy adults for the Human Connectome Project (HCP) benefits from high quality, high resolution, multiband (MB)-accelerated whole brain diffusion MRI (dMRI). Acquiring such data at ultrahigh fields (7 T and above) can improve intrinsic signal-to-noise ratio (SNR), but suffers from shorter T2 and T2* relaxation times, increased B1+ inhomogeneity (resulting in signal loss in cerebellar and temporal lobe regions), and increased power deposition (i.e. Specific Absorption Rate (SAR)), thereby limiting our ability to reduce the repetition time (TR). Here, we present recent developments and optimizations in 7 T image acquisitions for the HCP that allow us to efficiently obtain high-quality, high-resolution whole brain in-vivo dMRI data at 7 T. These data show spatial details typically seen only in ex-vivo studies and complement already very high quality 3 T HCP data in the same subjects. The advances are the result of intensive pilot studies aimed at mitigating the limitations of dMRI at 7 T. The data quality and methods described here are representative of the datasets that will be made freely available to the community in 2015. more...
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- 2015
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34. Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data
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Bennett A. Landman, Steen Moeller, Ian J. Deary, Thomas E. Nichols, Jessika E. Sussmann, David C. Glahn, Joanna M. Wardlaw, Rene L. Olvera, Stamatios N. Sotiropoulos, Susan N. Wright, David C. Van Essen, Rachel M. Brouwer, Binish Patel, John M. Starr, Dennis van 't Ent, Douglas E. Williamson, Christophe Lenglet, Nicholas G. Martin, Laura Almasy, Charles P. Peterson, Anouk den Braber, Saad Jbabdi, Katie L. McMahon, Peter Kochunov, Margie Wright, John Blangero, Braxton D. Mitchell, Hilleke E. Hulshoff Pol, Edward J. Auerbach, Jesper L. R. Andersson, Paul M. Thompson, Eco J. C. de Geus, Andrew M. McIntosh, Daniel S. Marcus, Stuart J. Ritchie, Ahmad R. Hariri, Greig I. deZubicaray, Emma Sprooten, Timothy E.J. Behrens, Joanne E. Curran, Peter T. Fox, Neda Jahanshad, Essa Yacoub, Dorret I. Boomsma, Mark E. Bastin, Kimm J. E. van Hulzen, Anderson M. Winkler, Marcel P. Zwiers, Kamil Ugurbil, L. Elliot Hong, René S. Kahn, Ravindranath Duggirala, Herve Lemaitre, Biological Psychology, Neuroscience Campus Amsterdam - Neurobiology of Mental Health, Neuroscience Campus Amsterdam - Brain Imaging Technology, Neurology, NCA - Neurobiology of mental health, and NCA - Brain imaging technology more...
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Adult ,Male ,Netherlands Twin Register (NTR) ,Cognitive Neuroscience ,Twin Study ,Research Support ,Article ,N.I.H ,Cohort Studies ,Young Adult ,Research Support, N.I.H., Extramural ,Fractional anisotropy ,Connectome ,Journal Article ,Humans ,Comparative Study ,Genetic variability ,Registries ,Non-U.S. Gov't ,Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7] ,Human Connectome Project ,Research Support, Non-U.S. Gov't ,Extramural ,Heritability ,Twin study ,White Matter ,Diffusion Tensor Imaging ,Neurology ,Evolutionary biology ,Nerve tract ,Anisotropy ,Female ,Genetic Phenomena ,Nerve Net ,Psychology ,Neuroscience ,Diffusion MRI - Abstract
Item does not contain fulltext The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h(2)=0.53-0.90, p more...
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- 2015
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35. A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI
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Guillermo Sapiro, Stamatios N. Sotiropoulos, Pramod Kumar Pisharady, and Christophe Lenglet
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Computer science ,computer.software_genre ,Bayesian inference ,Article ,030218 nuclear medicine & medical imaging ,White matter ,03 medical and health sciences ,0302 clinical medicine ,Voxel ,medicine ,Gamma distribution ,Humans ,Hyperparameter ,Human Connectome Project ,business.industry ,Estimation theory ,Brain ,Pattern recognition ,Bayes Theorem ,Image Enhancement ,White Matter ,medicine.anatomical_structure ,Diffusion Magnetic Resonance Imaging ,Artificial intelligence ,business ,Algorithm ,computer ,030217 neurology & neurosurgery ,Algorithms ,Diffusion MRI - Abstract
We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and in-vivo data from the Human Connectome Project demonstrate the improvements. more...
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- 2017
36. A vectorial total variation model for denoising high angular resolution diffusion images corrupted by Rician noise
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Yunho Kim, Liang Zhan, Christophe Lenglet, M. Tong, Bryon A. Mueller, Guillermo Sapiro, Paul M. Thompson, and Luminita A. Vese
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Diffusion imaging ,Optics ,business.industry ,Noise reduction ,Rician noise ,Total variation model ,Angular resolution ,Diffusion (business) ,business ,Mathematics - Published
- 2014
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37. Estimation of the CSA-ODF using Bayesian compressed sensing of multi-shell HARDI
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Christophe Lenglet, Steen Moeller, Essa Yacoub, Lawrence Carin, Guillermo Sapiro, Julio M. Duarte-Carvajalino, Junqian Xu, and Kamil Ugurbil
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Computer science ,business.industry ,Orientation (computer vision) ,Dirichlet process ,Reduction (complexity) ,Bayes' theorem ,Redundancy (information theory) ,Sampling (signal processing) ,Radiology, Nuclear Medicine and imaging ,Computer vision ,Artificial intelligence ,business ,Algorithm ,Data compression ,Diffusion MRI - Abstract
Purpose Diffusion MRI provides important information about the brain white matter structures and has opened new avenues for neuroscience and translational research. However, acquisition time needed for advanced applications can still be a challenge in clinical settings. There is consequently a need to accelerate diffusion MRI acquisitions. Methods A multi-task Bayesian compressive sensing (MT-BCS) framework is proposed to directly estimate the constant solid angle orientation distribution function (CSA-ODF) from under-sampled (i.e., accelerated image acquisition) multi-shell high angular resolution diffusion imaging (HARDI) datasets, and accurately recover HARDI data at higher resolution in q-space. The proposed MT-BCS approach exploits the spatial redundancy of the data by modeling the statistical relationships within groups (clusters) of diffusion signal. This framework also provides uncertainty estimates of the computed CSA-ODF and diffusion signal, directly computed from the compressive measurements. Experiments validating the proposed framework are performed using realistic multi-shell synthetic images and in vivo multi-shell high angular resolution HARDI datasets. Results Results indicate a practical reduction in the number of required diffusion volumes (q-space samples) by at least a factor of four to estimate the CSA-ODF from multi-shell data. Conclusion This work presents, for the first time, a multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions. It demonstrates improvement of the quality of acquired datasets by means of CS de-noising, and accurate estimation of the CSA-ODF, as well as enables a reduction in the acquisition time by a factor of two to four, especially when “staggered” q-space sampling schemes are used. The proposed MT-BCS framework can naturally be combined with parallel MR imaging to further accelerate HARDI acquisitions. Magn Reson Med 72:1471–1485, 2014. © 2013 Wiley Periodicals, Inc. more...
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- 2013
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38. Design of multishell sampling schemes with uniform coverage in diffusion MRI
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Rachid Deriche, Guillermo Sapiro, Emmanuel Caruyer, and Christophe Lenglet
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Orientation (computer vision) ,Design of experiments ,Sampling (statistics) ,Discrete Fourier transform ,030218 nuclear medicine & medical imaging ,law.invention ,03 medical and health sciences ,0302 clinical medicine ,Nuclear magnetic resonance ,law ,Radiology, Nuclear Medicine and imaging ,Cartesian coordinate system ,Angular resolution ,Diffusion (business) ,Algorithm ,030217 neurology & neurosurgery ,Diffusion MRI ,Mathematics - Abstract
PURPOSE: In diffusion MRI, a technique known as diffusion spectrum imaging reconstructs the propagator with a discrete Fourier transform, from a Cartesian sampling of the diffusion signal. Alternatively, it is possible to directly reconstruct the orientation distribution function in q-ball imaging, providing so-called high angular resolution diffusion imaging. In between these two techniques, acquisitions on several spheres in q-space offer an interesting trade-off between the angular resolution and the radial information gathered in diffusion MRI. A careful design is central in the success of multishell acquisition and reconstruction techniques. METHODS: The design of acquisition in multishell is still an open and active field of research, however. In this work, we provide a general method to design multishell acquisition with uniform angular coverage. This method is based on a generalization of electrostatic repulsion to multishell. RESULTS: We evaluate the impact of our method using simulations, on the angular resolution in one and two bundles of fiber configurations. Compared to more commonly used radial sampling, we show that our method improves the angular resolution, as well as fiber crossing discrimination. DISCUSSION: We propose a novel method to design sampling schemes with optimal angular coverage and show the positive impact on angular resolution in diffusion MRI. more...
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- 2013
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39. Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE
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Edward J. Auerbach, David A. Feinberg, Kamil Ugurbil, Saad Jbabdi, Jesper L. R. Andersson, Lawrence L. Wald, Junqian Xu, Christophe Lenglet, Stamatios N. Sotiropoulos, Kawin Setsompop, Timothy E.J. Behrens, Essa Yacoub, and Steen Moeller more...
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Orientation (computer vision) ,Computer science ,business.industry ,Reconstruction algorithm ,Iterative reconstruction ,Noise floor ,Signal ,Signal-to-noise ratio ,Radiology, Nuclear Medicine and imaging ,Computer vision ,Artificial intelligence ,business ,Tractography ,Diffusion MRI - Abstract
Purpose: To examine the effects of the reconstruction algorithm of magnitude images from multi-channel diffusion MRI on fibre orientation estimation. Theory and Methods: It is well established that the method used to combine signals from different coil elements in multi-channel MRI can have an impact on the properties of the reconstructed magnitude image. Utilising a root-sum-of-squares (RSoS) approach results in a magnitude signal that follows an effective non-central-distribution. As a result, the noise floor, the minimum measurable in the absence of any true signal, is elevated. This is particularly relevant for diffusion-weighted MRI, where the signal attenuation is of interest. Results: In this study, we illustrate problems that such image reconstruction characteristics may cause in the estimation of fibre orientations, both for model-based and model-free approaches, when modern 32-channel coils are employed. We further propose an alternative image reconstruction method that is based on sensitivity encoding (SENSE) and preserves the Rician nature of the single-channel, magnitude MR signal. We show that for the same k-space data, RSoS can cause excessive overfitting and reduced precision in orientation estimation compared to the SENSE-based approach. Conclusion: These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multi-channel receiver coils for diffusion MRI acquisition. more...
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- 2013
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40. Motion Detection in Diffusion MRI via Online ODF Estimation
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Guillermo Sapiro, Rachid Deriche, Christophe Lenglet, Emmanuel Caruyer, Iman Aganj, Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratory for Information and Decision Systems - Massachusetts Institute of Technology (LIDS), Massachusetts Institute of Technology (MIT), Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School [Boston] (HMS)-Massachusetts General Hospital [Boston], Center for Magnetic Resonance Research [Minneapolis] (CMRR), University of Minnesota Medical School, University of Minnesota System-University of Minnesota System, Department of Electrical and Computer Engineering [Durham] (ECE), Duke University [Durham], Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology. Laboratory for Information and Decision Systems, Aganj, Iman, and Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS) more...
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lcsh:Medical physics. Medical radiology. Nuclear medicine ,lcsh:Medical technology ,Article Subject ,Computer science ,lcsh:R895-920 ,ODF ,Diffusion MRI ,030218 nuclear medicine & medical imaging ,Scan time ,Motion ,03 medical and health sciences ,0302 clinical medicine ,Robustness (computer science) ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,Radiology, Nuclear Medicine and imaging ,Computer vision ,business.industry ,Orientation Distribution Function ,Solid angle ,Motion detection ,Kalman filter ,3. Good health ,Distribution function ,lcsh:R855-855.5 ,Likelihood-ratio test ,Artificial intelligence ,business ,Kalman Filter ,030217 neurology & neurosurgery ,Research Article - Abstract
The acquisition of high angular resolution diffusion MRI is particularly long and subject motion can become an issue. The orientation distribution function (ODF) can be reconstructed online incrementally from diffusion-weighted MRI with a Kalman filtering framework. This online reconstruction provides real-time feedback throughout the acquisition process. In this article, the Kalman filter is first adapted to the reconstruction of the ODF in constant solid angle. Then, a method called STAR (STatistical Analysis of Residuals) is presented and applied to the online detection of motion in high angular resolution diffusion images. Compared to existing techniques, this method is image based and is built on top of a Kalman filter. Therefore, it introduces no additional scan time and does not require additional hardware. The performance of STAR is tested on simulated and real data and compared to the classical generalized likelihood ratio test. Successful detection of small motion is reported (rotation under 2°) with no delay and robustness to noise., National Institutes of Health (U.S.) (NIH grant Grant P41 RR008079), National Institutes of Health (U.S.) (NIH grant P41 EB015894), National Institutes of Health (U.S.) (NIH grant P30 NS057091), National Institutes of Health (U.S.) (Human Connectome Project U54 MH091657), United States. Air Force Office of Scientific Research (NSSEFF), National Science Foundation (U.S.), United States. Army Research Office, United States. Defense Advanced Research Projects Agency, United States. National Geospatial-Intelligence Agency, France. Agence nationale de la recherche (ANR NucleiPark), Institut national de recherche en informatique et en automatique (France), National Institutes of Health (U.S.) (Human Connectome Project, Grant R01 EB008432) more...
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- 2013
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41. Microstructure Imaging of Crossing (MIX) White Matter Fibers from diffusion MRI
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Jung Who Nam, Hamza Farooq, Tryphon T. Georgiou, Junqian Xu, Essa Yacoub, Christophe Lenglet, and Daniel F. Keefe
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Computer science ,Anisotropic diffusion ,Models, Neurological ,Bioengineering ,Corpus callosum ,Article ,030218 nuclear medicine & medical imaging ,Corpus Callosum ,White matter ,03 medical and health sciences ,0302 clinical medicine ,Data acquisition ,Models ,medicine ,Humans ,Fiber ,Multidisciplinary ,Orientation (computer vision) ,Neurosciences ,Microstructure ,White Matter ,Axons ,Other Physical Sciences ,medicine.anatomical_structure ,Diffusion Magnetic Resonance Imaging ,Networking and Information Technology R&D ,Networking and Information Technology R&D (NITRD) ,Neurological ,Biomedical Imaging ,Biochemistry and Cell Biology ,Biological system ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
Diffusion MRI (dMRI) reveals microstructural features of the brain white matter by quantifying the anisotropic diffusion of water molecules within axonal bundles. Yet, identifying features such as axonal orientation dispersion, density, diameter, etc., in complex white matter fiber configurations (e.g. crossings) has proved challenging. Besides optimized data acquisition and advanced biophysical models, computational procedures to fit such models to the data are critical. However, these procedures have been largely overlooked by the dMRI microstructure community and new, more versatile, approaches are needed to solve complex biophysical model fitting problems. Existing methods are limited to models assuming single fiber orientation, relevant to limited brain areas like the corpus callosum, or multiple orientations but without the ability to extract detailed microstructural features. Here, we introduce a new and versatile optimization technique (MIX), which enables microstructure imaging of crossing white matter fibers. We provide a MATLAB implementation of MIX, and demonstrate its applicability to general microstructure models in fiber crossings using synthetic as well as ex-vivo and in-vivo brain data. more...
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- 2016
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42. Some geometric ideas for feature enhancement of diffusion tensor fields
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Yongxin Chen, Hamza Farooq, Tryphon T. Georgiou, and Christophe Lenglet
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Tensor contraction ,Weyl tensor ,0209 industrial biotechnology ,Riemann curvature tensor ,Mathematical analysis ,Ricci flow ,02 engineering and technology ,Fundamental theorem of Riemannian geometry ,symbols.namesake ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Ricci decomposition ,020201 artificial intelligence & image processing ,Tensor density ,Ricci curvature ,Mathematics - Abstract
Diffusion Tensor Imaging (DTI) generates a 3- dimensional 2-tensor field that encapsulates properties of diffusing water molecules. We present two complementing ideas that may be used to enhance and highlight geometric features that are present. The first is based on Ricci flow and can be understood as a nonlinear bandpass filtering technique that takes into account directionality of the spectral content. More specifically, we view the data as a Riemannian metric and, in manner reminiscent to reversing the heat equation, we regularize the Ricci flow so as to taper off the growth of the higher-frequency speckle-type of irregularities. The second approach, in which we again view data as defining a Riemannian structure, relies on averaging nearby values of the tensor field by weighing the summands in a manner which is inversely proportional to their corresponding distances of the tensors. The effect of this particular averaging is to enhance consensus among neighboring cells, regarding the principle directions and the values of the corresponding eigenvalues of the tensor field. This consensus is amplified along directions where distances in the Riemannian metric are short. more...
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- 2016
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43. Brain Tissue Micro-Structure Imaging from Diffusion MRI Using Least Squares Variable Separation
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Essa Yacoub, Christophe Lenglet, Tryphon T. Georgiou, Hamza Farooq, and Junqian Xu
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Computational complexity theory ,Estimation theory ,Computer science ,Least squares ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Nuclear magnetic resonance ,Search algorithm ,Non-linear least squares ,Hyperparameter optimization ,Curve fitting ,Algorithm ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
We introduce a novel data fitting procedure of multi compartment models of the brain white matter for diffusion MRI (dMRI) data. These biophysical models aim to characterize important micro-structure quantities like axonal radius, density and orientations. In order to describe the underlying tissue properties, a variety of models for intra-/extra-axonal diffusion signals have been proposed. Combinations of these analytic models are used to predict the diffusion MRI signal in multi-compartment settings. However, parameter estimation from these multi-compartment models is an ill-posed problem. Consequently, many existing fitting algorithms either rely on an initial grid search to find a good start point, or have strong assumptions like single fiber orientation to estimate some of these parameters from simpler models like the diffusion tensor (DT). In both cases, there is a trade-off between computational complexity and accuracy of the estimated parameters. Here, we describe a novel algorithm based on the separation of the Nonlinear Least Squares (NLLS) fitting problem, via Variable Projection Method , to search for non-linearly and linearly entering parameters independently. We use stochastic global search algorithms to find a global minimum, while estimating non-linearly entering parameters. The approach is independent of any starting point, and does not rely on estimates from simpler models. We show that the suggested algorithm is faster than algorithms involving grid search, and its greater accuracy and robustness are demonstrated on synthetic as well as ex-/in-vivo data. more...
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- 2016
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44. A 3D wavelet fusion approach for the reconstruction of isotropic-resolution MR images from orthogonal anisotropic-resolution scans
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Iman Aganj, Noam Harel, Christophe Lenglet, Guillermo Sapiro, and Essa Yacoub
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Image fusion ,Computer science ,business.industry ,Nonuniform sampling ,Wavelet transform ,Real-time MRI ,Image segmentation ,computer.software_genre ,Wavelet ,Signal-to-noise ratio ,Voxel ,Radiology, Nuclear Medicine and imaging ,Computer vision ,Artificial intelligence ,business ,computer - Abstract
Hardware constraints, scanning time limitations, patient movement, and signal-to-noise ratio (SNR) considerations, restrict the slice-selection and the in-plane resolutions of MRI differently, generally resulting in anisotropic voxels. This nonuniform sampling can be problematic, especially in image segmentation and clinical examination. To alleviate this, the acquisition is divided into (two or) three separate scans, with higher in-plane resolutions and thick slices, yet orthogonal slice-selection directions. In this work, a noniterative wavelet-based approach for combining the three orthogonal scans is adopted, and its advantages compared with other existing methods, such as Fourier techniques, are discussed, including the consideration of the actual pulse response of the MRI scanner, and its lower computational complexity. Experimental results are shown on simulated and real 7 T MRI data. more...
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- 2011
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45. A nonparametric Riemannian framework for processing high angular resolution diffusion images and its applications to ODF-based morphometry
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Alvina Goh, Paul M. Thompson, René Vidal, and Christophe Lenglet
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Cognitive Neuroscience ,Population ,Article ,Functional Laterality ,Rats, Sprague-Dawley ,Nerve Fibers ,Image Processing, Computer-Assisted ,Animals ,Humans ,Computer vision ,education ,Mathematics ,Principal Component Analysis ,education.field_of_study ,Models, Statistical ,Phantoms, Imaging ,Orientation (computer vision) ,business.industry ,Brain ,Spherical harmonics ,Riemannian manifold ,Image Enhancement ,Rats ,Orthant ,Diffusion Magnetic Resonance Imaging ,Neurology ,Data Interpretation, Statistical ,Linear Models ,Anisotropy ,Artificial intelligence ,Principal geodesic analysis ,Gradient descent ,business ,Algorithm ,Algorithms ,Interpolation - Abstract
High angular resolution diffusion imaging (HARDI) has become an important technique for imaging complex oriented structures in the brain and other anatomical tissues. This has motivated the recent development of several methods for computing the orientation probability density function (PDF) at each voxel. However, much less work has been done on developing techniques for filtering, interpolation, averaging and principal geodesic analysis of orientation PDF fields. In this paper, we present a Riemannian framework for performing such operations. The proposed framework does not require that the orientation PDFs be represented by any fixed parameterization, such as a mixture of von Mises-Fisher distributions or a spherical harmonic expansion. Instead, we use a nonparametric representation of the orientation PDF. We exploit the fact that under the square-root re-parameterization, the space of orientation PDFs forms a Riemannian manifold: the positive orthant of the unit Hilbert sphere. We show that various orientation PDF processing operations, such as filtering, interpolation, averaging and principal geodesic analysis, may be posed as optimization problems on the Hilbert sphere, and can be solved using Riemannian gradient descent. We illustrate these concepts with numerous experiments on synthetic, phantom and real datasets. We show their application to studying left/right brain asymmetries. more...
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- 2011
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46. A finite element modeling of thermo-hydro-mechanical behavior and numerical simulations of progressing spalling front
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S. Rigobert, M. T. Phan, S. Dal Pont, Christophe Lenglet, P. Autuori, and F. Meftah
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Fully coupled ,Materials science ,Finite element limit analysis ,business.industry ,Smoothed finite element method ,General Medicine ,Structural engineering ,Spall ,business ,Finite element method ,Engineering(all) ,Extended finite element method - Abstract
This paper presents a coupled thermo-hydro-mechanical (THM) model enriched with a buckling-type criterion for progressive spalling. In the first part of the paper, a general fully coupled multi-phase THM model describing the behaviour of concrete at moderate and high temperatures is presented. Then the spalling criterion and its numerical implementation in the framework of the finite element method are presented. Finally, a simple 1D numerical example will illustrate the effectiveness of the implemented numerical approach. more...
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- 2011
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47. Neuroimaging of Oculomotor Dysfunction in Mild Traumatic Brain Injury
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Amy Chang, Christophe Lenglet, Nova McNally, Essa Yacoub, Sarah B. Rockswold, Lynn E. Eberly, Walter C. Low, and Philip C. Burton
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Neuroimaging ,Traumatic brain injury ,business.industry ,Rehabilitation ,medicine ,Physical Therapy, Sports Therapy and Rehabilitation ,medicine.disease ,business ,Neuroscience - Published
- 2018
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48. Joint brain connectivity estimation from diffusion and functional MRI data
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Christophe Lenglet, Shu-Hsien Chu, and Keshab K. Parhi
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Human Connectome Project ,medicine.diagnostic_test ,business.industry ,Computer science ,Node (networking) ,Functional connectivity ,Pattern recognition ,Statistical model ,Grey matter ,Machine learning ,computer.software_genre ,Independent component analysis ,White matter ,medicine.anatomical_structure ,medicine ,Artificial intelligence ,Functional magnetic resonance imaging ,business ,computer ,Tractography ,Diffusion MRI - Abstract
Estimating brain wiring patterns is critical to better understand the brain organization and function. Anatomical brain connectivity models axonal pathways, while the functional brain connectivity characterizes the statistical dependencies and correlation between the activities of various brain regions. The synchronization of brain activity can be inferred through the variation of blood-oxygen-level dependent (BOLD) signal from functional MRI (fMRI) and the neural connections can be estimated using tractography from diffusion MRI (dMRI). Functional connections between brain regions are supported by anatomical connections, and the synchronization of brain activities arises through sharing of information in the form of electro-chemical signals on axon pathways. Jointly modeling fMRI and dMRI data may improve the accuracy in constructing anatomical connectivity as well as functional connectivity. Such an approach may lead to novel multimodal biomarkers potentially able to better capture functional and anatomical connectivity variations. We present a novel brain network model which jointly models the dMRI and fMRI data to improve the anatomical connectivity estimation and extract the anatomical subnetworks associated with specific functional modes by constraining the anatomical connections as structural supports to the functional connections. The key idea is similar to a multi-commodity flow optimization problem that minimizes the cost or maximizes the efficiency for flow configuration and simultaneously fulfills the supply-demand constraint for each commodity. In the proposed network, the nodes represent the grey matter (GM) regions providing brain functionality, and the links represent white matter (WM) fiber bundles connecting those regions and delivering information. The commodities can be thought of as the information corresponding to brain activity patterns as obtained for instance by independent component analysis (ICA) of fMRI data. The concept of information flow is introduced and used to model the propagation of information between GM areas through WM fiber bundles. The link capacity , i.e., ability to transfer information, is characterized by the relative strength of fiber bundles, e.g., fiber count gathered from the tractography of dMRI data. The node information demand is considered to be proportional to the correlation between neural activity at various cortical areas involved in a particular functional mode (e.g. visual, motor, etc.). These two properties lead to the link capacity and node demand constraints in the proposed model. Moreover, the information flow of a link cannot exceed the demand from either end node. This is captured by the feasibility constraints . Two different cost functions are considered in the optimization formulation in this paper. The first cost function, the reciprocal of fiber strength represents the unit cost for information passing through the link. In the second cost function, a min-max (minimizing the maximal link load) approach is used to balance the usage of each link. Optimizing the first cost function selects the pathway with strongest fiber strength for information propagation. In the second case, the optimization procedure finds all the possible propagation pathways and allocates the flow proportionally to their strength. Additionally, a penalty term is incorporated with both the cost functions to capture the possible missing and weak anatomical connections. With this set of constraints and the proposed cost functions, solving the network optimization problem recovers missing and weak anatomical connections supported by the functional information and provides the functional-associated anatomical subnetworks. Feasibility is demonstrated using realistic diffusion and functional MRI phantom data. It is shown that the proposed model recovers the maximum number of true connections, with fewest number of false connections when compared with the connectivity derived from a joint probabilistic model using the expectation-maximization (EM) algorithm presented in a prior work. We also apply the proposed method to data provided by the Human Connectome Project (HCP). more...
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- 2015
- Full Text
- View/download PDF
49. ODF RECONSTRUCTION IN Q-BALL IMAGING WITH SOLID ANGLE CONSIDERATION
- Author
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Iman Aganj, Christophe Lenglet, and Guillermo Sapiro
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Computer science ,Nano ,Medical imaging ,Nanotechnology ,Macro ,Article - Abstract
Q-ball imaging (QBI) is a high angular resolution diffusion imaging (HARDI) technique which has been proven very successful in resolving multiple intravoxel fiber orientations in MR images. The standard computation of the orientation distribution function (ODF, the probability of diffusion in a given direction) from q-ball uses linear radial projection, neglecting the change in the volume element along the ray, thereby resulting in distributions different from the true ODFs. For instance, they are not normalized or as sharp as expected, and generally require post-processing, such as sharpening or spherical deconvolution. In this paper, we consider the mathematically correct definition of the ODF and derive a closed-form expression for it in QBI. The derived ODF is dimensionless and normalized, and can be efficiently computed from q-ball acquisition protocols. We describe our proposed method and demonstrate its significantly improved performance on artificial data and real HARDI volumes. more...
- Published
- 2015
50. Sparse Bayesian Inference of White Matter Fiber Orientations from Compressed Multi-resolution Diffusion MRI
- Author
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Guillermo Sapiro, Christophe Lenglet, Stamatios N. Sotiropoulos, Julio M. Duarte-Carvajalino, and Pramod Kumar Pisharady
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Computer science ,business.industry ,Fiber (mathematics) ,Partial volume ,Pattern recognition ,Bayesian inference ,Article ,White matter ,Compressed sensing ,medicine.anatomical_structure ,Neuroimaging ,medicine ,Artificial intelligence ,Deconvolution ,Focus (optics) ,business ,Parametric statistics ,Diffusion MRI - Abstract
The RubiX [1] algorithm combines high SNR characteristics of low resolution data with high spacial specificity of high resolution data, to extract microstructural tissue parameters from diffusion MRI. In this paper we focus on estimating crossing fiber orientations and introduce sparsity to the RubiX algorithm, making it suitable for reconstruction from compressed (under-sampled) data. We propose a sparse Bayesian algorithm for estimation of fiber orientations and volume fractions from compressed diffusion MRI. The data at high resolution is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible directions. Volume fractions of fibers along these orientations define the dictionary weights. The data at low resolution is modeled using a spatial partial volume representation. The proposed dictionary representation and sparsity priors consider the dependence between fiber orientations and the spatial redundancy in data representation. Our method exploits the sparsity of fiber orientations, therefore facilitating inference from under-sampled data. Experimental results show improved accuracy and decreased uncertainty in fiber orientation estimates. For under-sampled data, the proposed method is also shown to produce more robust estimates of fiber orientations. more...
- Published
- 2015
- Full Text
- View/download PDF
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