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Saluja S, Qiu L, Wang AR, Campos G, Seilheimer R, McNab JA, Haber SN, Barbosa DAN, Halpern CH. Diffusion Magnetic Resonance Imaging Tractography Guides Investigation of the Zona Incerta: A Novel Target for Deep Brain Stimulation. Biol Psychiatry 2024:S0006-3223(24)01105-3. [PMID: 38401802 DOI: 10.1016/j.biopsych.2024.02.1004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 02/02/2024] [Accepted: 02/16/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND The zona incerta (ZI) is a subcortical structure primarily investigated in rodents that is implicated in various behaviors, ranging from motor control to survival-associated activities, partly due to its integration in multiple neural circuits. In the current study, we used diffusion magnetic resonance imaging tractography to segment the ZI and gain insight into its connectivity in various circuits in humans. METHODS We performed probabilistic tractography in 7T diffusion MRI on 178 participants from the Human Connectome Project to validate the ZI's anatomical subdivisions and their respective tracts. K-means clustering segmented the ZI based on each voxel's connectivity profile. We further characterized the connections of each ZI subregion using probabilistic tractography with each subregion as a seed. RESULTS We identified 2 dominant clusters that delineated the whole ZI into rostral and caudal subregions. The caudal ZI primarily connected with motor regions, while the rostral ZI received a topographic distribution of projections from prefrontal areas, notably the anterior cingulate and medial prefrontal cortices. We generated a probabilistic ZI atlas that was registered to a patient-participant's magnetic resonance imaging scan for placement of stereoencephalographic leads for electrophysiology-guided deep brain stimulation to treat their obsessive-compulsive disorder. Rostral ZI stimulation improved the patient's core symptoms (mean improvement 21%). CONCLUSIONS We present a tractography-based atlas of the rostral and caudal ZI subregions constructed using high-resolution diffusion magnetic resonance imaging from 178 healthy participants. Our work provides an anatomical foundation to explore the rostral ZI as a novel target for deep brain stimulation to treat refractory obsessive-compulsive disorder and other disorders associated with dysfunctional reward circuitry.
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Affiliation(s)
- Sabir Saluja
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Liming Qiu
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Allan R Wang
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gustavo Campos
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Robert Seilheimer
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jennifer A McNab
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Suzanne N Haber
- Department of Pharmacology & Physiology, University of Rochester School of Medicine and Dentistry, Rochester, New York; Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, Massachusetts
| | - Daniel A N Barbosa
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, Pennsylvania.
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Hegarty JP, Monterrey JC, Tian Q, Cleveland SC, Gong X, Phillips JM, Wolke ON, McNab JA, Hallmayer JF, Reiss AL, Hardan AY, Lazzeroni LC. A Twin Study of Altered White Matter Heritability in Youth With Autism Spectrum Disorder. J Am Acad Child Adolesc Psychiatry 2024; 63:65-79. [PMID: 37406770 PMCID: PMC10802971 DOI: 10.1016/j.jaac.2023.05.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 05/08/2023] [Accepted: 06/26/2023] [Indexed: 07/07/2023]
Abstract
OBJECTIVE White matter alterations are frequently reported in autism spectrum disorder (ASD), yet the etiology is currently unknown. The objective of this investigation was to examine, for the first time, the impact of genetic and environmental factors on white matter microstructure in twins with ASD compared to control twins without ASD. METHOD Diffusion-weighted MRIs were obtained from same-sex twin pairs (6-15 years of age) in which at least 1 twin was diagnosed with ASD or neither twin exhibited a history of neurological or psychiatric disorders. Fractional anisotropy (FA) and mean diffusivity (MD) were examined across different white matter tracts in the brain, and statistical and twin modeling were completed to assess the proportion of variation associated with additive genetic (A) and common/shared (C) or unique (E) environmental factors. We also developed a novel Twin-Pair Difference Score analysis method that produces quantitative estimates of the genetic and environmental contributions to shared covariance between different brain and behavioral traits. RESULTS Good-quality data were available from 84 twin pairs, 50 ASD pairs (32 concordant for ASD [16 monozygotic; 16 dizygotic], 16 discordant for ASD [3 monozygotic; 13 dizygotic], and 2 pairs in which 1 twin had ASD and the other exhibited some subthreshold symptoms [1 monozygotic; 1 dizygotic]) and 34 control pairs (20 monozygotic; 14 dizygotic). Average FA and MD across the brain, respectively, were primarily genetically mediated in both control twins (A = 0.80, 95% CI [0.57, 1.02]; A = 0.80 [0.55, 1.04]) and twins concordant for having ASD (A = 0.71 [0.33, 1.09]; A = 0.84 [0.32,1.36]). However, there were also significant tract-specific differences between groups. For instance, genetic effects on commissural fibers were primarily associated with differences in general cognitive abilities and perhaps some diagnostic differences for ASD because Twin-Pair Difference-Score analysis indicated that genetic factors may have contributed to ∼40% to 50% of the covariation between IQ scores and FA of the corpus callosum. Conversely, the increased impact of environmental factors on some projection and association fibers were primarily associated with differences in symptom severity in twins with ASD; for example, our analyses suggested that unique environmental factors may have contributed to ∼10% to 20% of the covariation between autism-related symptom severity and FA of the cerebellar peduncles and external capsule. CONCLUSION White matter alterations in youth with ASD are associated with both genetic contributions and potentially increased vulnerability or responsivity to environmental influences. DIVERSITY & INCLUSION STATEMENT We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as living with a disability. The author list of this paper includes contributors from the location and/or community where the research was conducted and they participated in the data collection, design, analysis, and/or interpretation of the work.
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Affiliation(s)
- John P Hegarty
- Stanford University School of Medicine, Stanford, California.
| | | | - Qiyuan Tian
- Tsinghua University School of Medicine, Beijing, China
| | - Sue C Cleveland
- Stanford University School of Medicine, Stanford, California
| | - Xinyi Gong
- Stanford University School of Medicine, Stanford, California
| | | | - Olga N Wolke
- Stanford University School of Medicine, Stanford, California
| | | | | | - Allan L Reiss
- Stanford University School of Medicine, Stanford, California
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Chau Loo Kung G, Knowles JK, Batra A, Ni L, Rosenberg J, McNab JA. Quantitative MRI reveals widespread, network-specific myelination change during generalized epilepsy progression. Neuroimage 2023; 280:120312. [PMID: 37574120 PMCID: PMC11095339 DOI: 10.1016/j.neuroimage.2023.120312] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 06/17/2023] [Accepted: 08/04/2023] [Indexed: 08/15/2023] Open
Abstract
Activity-dependent myelination is a fundamental mode of brain plasticity which significantly influences network function. We recently discovered that absence seizures, which occur in multiple forms of generalized epilepsy, can induce activity-dependent myelination, which in turn promotes further progression of epilepsy. Structural alterations of myelin are likely to be widespread, given that absence seizures arise from an extensive thalamocortical network involving frontoparietal regions of the bilateral hemispheres. However, the temporal course and spatial extent of myelin plasticity is unknown, due to limitations of gold-standard histological methods such as electron microscopy (EM). In this study, we leveraged magnetization transfer and diffusion MRI for estimation of g-ratios across major white matter tracts in a mouse model of generalized epilepsy with progressive absence seizures. EM was performed on the same brains after MRI. After seizure progression, we found increased myelination (decreased g-ratios) throughout the anterior portion (genu-to-body) of the corpus callosum but not in the posterior portion (body-splenium) nor in the fornix or the internal capsule. Curves obtained from averaging g-ratio values at every longitudinal point of the corpus callosum were statistically different with p<0.001. Seizure-associated myelin differences found in the corpus callosum body with MRI were statistically significant (p = 0.0027) and were concordant with EM in the same region (p = 0.01). Notably, these differences were not detected by diffusion tensor imaging. This study reveals widespread myelin structural change that is specific to the absence seizure network. Furthermore, our findings demonstrate the potential utility and importance of MRI-based g-ratio estimation to non-invasively detect myelin plasticity.
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Affiliation(s)
- Gustavo Chau Loo Kung
- Bioengineering Department, Stanford University, 443 Via Ortega, Stanford, CA 94305, United States; Radiology Department, Stanford University, 1201 Welch Rd, Stanford, CA 94305, United States.
| | - Juliet K Knowles
- Neurology Department, 1701 Page Mill Road, Palo Alto, CA 94304, United States.
| | - Ankita Batra
- Neurology Department, 1701 Page Mill Road, Palo Alto, CA 94304, United States.
| | - Lijun Ni
- Neurology Department, SIM1 G3035, Stanford, CA 94305, United States.
| | - Jarrett Rosenberg
- Radiology Department, Stanford University, 1201 Welch Rd, Stanford, CA 94305, United States.
| | - Jennifer A McNab
- Radiology Department, Stanford University, 1201 Welch Rd, Stanford, CA 94305, United States.
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Dai E, Zhu A, Yang GK, Quah K, Tan ET, Fiveland E, Foo TKF, McNab JA. Frequency-dependent diffusion kurtosis imaging in the human brain using an oscillating gradient spin echo sequence and a high-performance head-only gradient. Neuroimage 2023; 279:120328. [PMID: 37586445 PMCID: PMC10529993 DOI: 10.1016/j.neuroimage.2023.120328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/17/2023] [Accepted: 08/12/2023] [Indexed: 08/18/2023] Open
Abstract
Measuring the time/frequency dependence of diffusion MRI is a promising approach to distinguish between the effects of different tissue microenvironments, such as membrane restriction, tissue heterogeneity, and compartmental water exchange. In this study, we measure the frequency dependence of diffusivity (D) and kurtosis (K) with oscillating gradient diffusion encoding waveforms and a diffusion kurtosis imaging (DKI) model in human brains using a high-performance, head-only MAGNUS gradient system, with a combination of b-values, oscillating frequencies (f), and echo time that has not been achieved in human studies before. Frequency dependence of diffusivity and kurtosis are observed in both global and local white matter (WM) and gray matter (GM) regions and characterized with a power-law model ∼Λ*fθ. The frequency dependences of diffusivity and kurtosis (including changes between fmin and fmax, Λ, and θ) vary over different WM and GM regions, indicating potential microstructural differences between regions. A trend of decreasing kurtosis over frequency in the short-time limit is successfully captured for in vivo human brains. The effects of gradient nonlinearity (GNL) on frequency-dependent diffusivity and kurtosis measurements are investigated and corrected. Our results show that the GNL has prominent scaling effects on the measured diffusivity values (3.5∼5.5% difference in the global WM and 6∼8% difference in the global cortex) and subsequently affects the corresponding power-law parameters (Λ, θ) while having a marginal influence on the measured kurtosis values (<0.05% difference) and power-law parameters (Λ, θ). This study expands previous OGSE studies and further demonstrates the translatability of frequency-dependent diffusivity and kurtosis measurements to human brains, which may provide new opportunities to probe human brain microstructure in health and disease.
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Affiliation(s)
- Erpeng Dai
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | | | - Grant K Yang
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Kristin Quah
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Ek T Tan
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, USA
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Crockett RA, Wilkins KB, Zeineh MM, McNab JA, Henderson JM, Buch VP, Brontë-Stewart HM. An Individualized Tractography Pipeline for the Nucleus Basalis of Meynert Lateral Tract. medRxiv 2023:2023.08.31.23294922. [PMID: 37693520 PMCID: PMC10491381 DOI: 10.1101/2023.08.31.23294922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background At the center of the cortical cholinergic network, the nucleus basalis of Meynert (NBM) is crucial for the cognitive domains most vulnerable in PD. Preclinical evidence has demonstrated the positive impact of NBM deep brain stimulation (DBS) on cognition but early human trials have had mixed results. It is possible that DBS of the lateral NBM efferent white matter fiber bundle may be more effective at improving cognitive-motor function. However, precise tractography modelling is required to identify the optimal target for neurosurgical planning. Individualized tractography approaches have been shown to be highly effective for accurately identifying DBS targets but have yet to be developed for the NBM. Methods Using structural and diffusion weighted imaging, we developed a tractography pipeline for precise individualized identification of the lateral NBM target tract. Using dice similarity coefficients, the reliability of the tractography outputs was assessed across three cohorts to investigate: 1) whether this manual pipeline is more reliable than an existing automated pipeline currently used in the literature; 2) the inter- and intra-rater reliability of our pipeline in research scans of patients with PD; and 3) the reliability and practicality of this pipeline in clinical scans of DBS patients. Results The individualized manual pipeline was found to be significantly more reliable than the existing automated pipeline for both the segmentation of the NBM region itself (p<0.001) and the reconstruction of the target lateral tract (p=0.002). There was also no significant difference between the reliability of two different raters in the PD cohort (p=0.25), which showed high inter- (mean Dice coefficient >0.6) and intra-rater (mean Dice coefficient >0.7) reliability across runs. Finally, the pipeline was shown to be highly reliable within the clinical scans (mean Dice coefficient = 0.77). However, accurate reconstruction was only evident in 7/10 tracts. Conclusion We have developed a reliable tractography pipeline for the identification and analysis of the NBM lateral tract in research and clinical grade imaging of healthy young adult and PD patient scans.
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Affiliation(s)
- Rachel A. Crockett
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, California, USA
| | - Kevin B. Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, California, USA
| | - Michael M. Zeineh
- Department of Radiology, Stanford University School of Medicine, California, USA
- Wu Tsai Neurosciences Institute, Stanford University, California, USA
- Bio-X, Stanford University, California, USA
| | - Jennifer A. McNab
- Department of Radiology, Stanford University School of Medicine, California, USA
- Wu Tsai Neurosciences Institute, Stanford University, California, USA
- Bio-X, Stanford University, California, USA
| | - Jaimie M. Henderson
- Wu Tsai Neurosciences Institute, Stanford University, California, USA
- Bio-X, Stanford University, California, USA
- Department of Neurosurgery, Stanford University School of Medicine, California, USA
| | - Vivek P. Buch
- Wu Tsai Neurosciences Institute, Stanford University, California, USA
- Bio-X, Stanford University, California, USA
- Department of Neurosurgery, Stanford University School of Medicine, California, USA
| | - Helen M. Brontë-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, California, USA
- Wu Tsai Neurosciences Institute, Stanford University, California, USA
- Bio-X, Stanford University, California, USA
- Department of Neurosurgery, Stanford University School of Medicine, California, USA
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Barbosa DAN, Gattas S, Salgado JS, Kuijper FM, Wang AR, Huang Y, Kakusa B, Leuze C, Luczak A, Rapp P, Malenka RC, Hermes D, Miller KJ, Heifets BD, Bohon C, McNab JA, Halpern CH. An orexigenic subnetwork within the human hippocampus. Nature 2023; 621:381-388. [PMID: 37648849 PMCID: PMC10499606 DOI: 10.1038/s41586-023-06459-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 07/20/2023] [Indexed: 09/01/2023]
Abstract
Only recently have more specific circuit-probing techniques become available to inform previous reports implicating the rodent hippocampus in orexigenic appetitive processing1-4. This function has been reported to be mediated at least in part by lateral hypothalamic inputs, including those involving orexigenic lateral hypothalamic neuropeptides, such as melanin-concentrating hormone5,6. This circuit, however, remains elusive in humans. Here we combine tractography, intracranial electrophysiology, cortico-subcortical evoked potentials, and brain-clearing 3D histology to identify an orexigenic circuit involving the lateral hypothalamus and converging in a hippocampal subregion. We found that low-frequency power is modulated by sweet-fat food cues, and this modulation was specific to the dorsolateral hippocampus. Structural and functional analyses of this circuit in a human cohort exhibiting dysregulated eating behaviour revealed connectivity that was inversely related to body mass index. Collectively, this multimodal approach describes an orexigenic subnetwork within the human hippocampus implicated in obesity and related eating disorders.
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Affiliation(s)
- Daniel A N Barbosa
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandra Gattas
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, USA
| | - Juliana S Salgado
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Fiene Marie Kuijper
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
- Université Paris Cité, Paris, France
- Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Allan R Wang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuhao Huang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Bina Kakusa
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Christoph Leuze
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Artur Luczak
- Canadian Centre for Behavioral Neuroscience, University of Lethbridge, Lethbridge, Alberta, Canada
| | - Paul Rapp
- Department of Military & Emergency Medicine, Uniformed Services University, Bethesda, MD, USA
| | - Robert C Malenka
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Nancy Pritzker Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Dora Hermes
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA
| | - Boris D Heifets
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Cara Bohon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA.
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Wang AR, Kuijper FM, Barbosa DAN, Hagan KE, Lee E, Tong E, Choi EY, McNab JA, Bohon C, Halpern CH. Human habit neural circuitry may be perturbed in eating disorders. Sci Transl Med 2023; 15:eabo4919. [PMID: 36989377 DOI: 10.1126/scitranslmed.abo4919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/03/2023] [Indexed: 03/31/2023]
Abstract
Circuit-based mechanisms mediating the development and execution of habitual behaviors involve complex cortical-striatal interactions that have been investigated in animal models and more recently in humans. However, how human brain circuits implicated in habit formation may be perturbed in psychiatric disorders remains unclear. First, we identified the locations of the sensorimotor putamen and associative caudate in the human brain using probabilistic tractography from Human Connectome Project data. We found that multivariate connectivity of the sensorimotor putamen was altered in humans with binge eating disorder and bulimia nervosa and that the degree of alteration correlated with severity of disordered eating behavior. Furthermore, the extent of this circuit aberration correlated with mean diffusivity in the sensorimotor putamen and decreased basal dopamine D2/3 receptor binding potential in the striatum, consistent with previously reported microstructural changes and dopamine signaling mediating habit learning in animal models. Our findings suggest a neural circuit that links habit learning and binge eating behavior in humans, which could, in part, explain the treatment-resistant behavior common to eating disorders and other psychiatric conditions.
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Affiliation(s)
- Allan R Wang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Fiene Marie Kuijper
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
- Université Paris Cité, Paris 75006, France
- Assistance Publique des Hôpitaux de Paris, Paris 75012, France
| | - Daniel A N Barbosa
- Department of Neurosurgery, Perelman School of Medicine, Richards Medical Research Laboratories, Pennsylvania Hospital, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kelsey E Hagan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Eric Lee
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Elizabeth Tong
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305 USA
| | - Eun Young Choi
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305 USA
| | - Cara Bohon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, Richards Medical Research Laboratories, Pennsylvania Hospital, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
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Thaler C, Tian Q, Wintermark M, Ghanouni P, Halpern CH, Henderson JM, Airan RD, Zeineh M, Goubran M, Leuze C, Fiehler J, Butts Pauly K, McNab JA. Changes in the Cerebello-Thalamo-Cortical Network After Magnetic Resonance-Guided Focused Ultrasound Thalamotomy. Brain Connect 2023; 13:28-38. [PMID: 35678063 PMCID: PMC9942176 DOI: 10.1089/brain.2021.0157] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Objective: In recent years, transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) has been established as a potential treatment option for movement disorders, including essential tremor (ET). So far, however, little is known about the impact of tcMRgFUS on structural connectivity. The objective of this study was to detect microstructural changes in tremor- and motor-related white matter tracts in ET patients treated with tcMRgFUS thalamotomy. Methods: Eleven patients diagnosed with ET were enrolled in this tcMRgFUS thalamotomy study. For each patient, 3 Tesla magnetic resonance imaging (3T MRI) including structural and diffusion MRI were acquired and the Clinical Rating Scale for Tremor was assessed before the procedure as well as 1 year after the treatment. Diffusion MRI tractography was performed to identify the cerebello-thalamo-cortical tract (CTCT), the medial lemniscus, and the corticospinal tract in both hemispheres on pre-treatment data. Pre-treatment tractography results were co-registered to post-treatment diffusion data. Diffusion tensor imaging (DTI) metrics, including fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD), were averaged across the tracts in the pre- and post-treatment data. Results: The mean value of tract-specific DTI metrics changed significantly within the thalamic lesion and in the CTCT on the treated side (p < 0.05). Changes of DTI-derived indices within the CTCT correlated well with lesion overlap (FA: r = -0.54, p = 0.04; MD: r = 0.57, p = 0.04); RD: r = 0.67, p = 0.036). Further, a trend was seen for the correlation between changes of DTI-derived indices within the CTCT and clinical improvement (FA: r = 0.58; p = 0.062; MD: r = -0.52, p = 0.64; RD: r = -0.61 p = 0.090). Conclusions: Microstructural changes were detected within the CTCT after tcMRgFUS, and these changes correlated well with lesion-tract overlap. Our results show that diffusion MRI is able to detect the microstructural effects of tcMRgFUS, thereby further elucidating the treatment mechanism, and ultimately to improve targeting prospectively. Impact statement The results of this study demonstrate microstructural changes within the cerebello-thalamo-cortical pathways 1 year after MR-guided focused ultrasound thalamotomy. Even more, microstructural changes within the cerebello-thalamo-cortical pathways correlated significantly with clinical outcome. These findings do not only highly emphasize the need of new targeting strategies for MR-guided focused ultrasound thalamotomy but also help to elucidate the treatment mechanism of it.
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Affiliation(s)
- Christian Thaler
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Qiyuan Tian
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Pejman Ghanouni
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Casey H. Halpern
- Department of Neurosurgery, Stanford University, Stanford, California, USA
| | | | - Raag D. Airan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Maged Goubran
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Christoph Leuze
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kim Butts Pauly
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Jennifer A. McNab
- Department of Radiology, Stanford University, Stanford, California, USA
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Dai E, Mani M, McNab JA. Multi-band multi-shot diffusion MRI reconstruction with joint usage of structured low-rank constraints and explicit phase mapping. Magn Reson Med 2023; 89:95-111. [PMID: 36063492 PMCID: PMC9887994 DOI: 10.1002/mrm.29422] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 02/02/2023]
Abstract
PURPOSE To develop a joint reconstruction method for multi-band multi-shot diffusion MRI. THEORY AND METHODS Multi-band multi-shot EPI acquisition is an effective approach for high-resolution diffusion MRI, but requires specific algorithms to correct the inter-shot phase variations. The phase correction can be done by first estimating the explicit phase map and then feeding it into the k-space signal formulation model. Alternatively, the phase information can be used indirectly as structured low-rank constraints in k-space. The 2 methods differ in reconstruction accuracy and efficiency. We aim to combine the 2 different approaches for improved image quality and reconstruction efficiency simultaneously, termed "joint usage of structured low-rank constraints and explicit phase mapping" (JULEP). The proposed JULEP reconstruction is tested on both single-band and multi-band, multi-shot diffusion data, with different resolutions and b values. The results of JULEP are compared with conventional methods with explicit phase mapping (i.e., multiplexed sensitivity-encoding [MUSE]) and structured low-rank constraints (i.e., MUSSELS), and another joint reconstruction method (i.e., network estimated artifacts for tempered reconstruction [NEATR]). RESULTS JULEP improves the quality of the navigator and subsequently facilitates the reconstruction of final diffusion images. Compared with all 3 other methods (MUSE, MUSSELS, and NEATR), JULEP mitigates residual structural bias and improves temporal SNRs in the final diffusion image, particularly at high multi-band factors. Compared with MUSSELS, JULEP also improves computational efficiency. CONCLUSION The proposed JULEP method significantly improves the image quality and reconstruction efficiency of multi-band multi-shot diffusion MRI, which can promote a broader application of high-resolution diffusion MRI.
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Affiliation(s)
- Erpeng Dai
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Merry Mani
- Department of Radiology, University of Iowa, Iowa City, IA, United States
- Department of Biomedical Engineering, University of Iowa, Iowa City, IA, United States
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, CA, United States
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10
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Barbosa DAN, Kuijper FM, Duda J, Wang AR, Cartmell SCD, Saluja S, Cunningham T, Shivacharan RS, Bhati MT, Safer DL, Lock JD, Malenka RC, de Oliveira-Souza R, Williams NR, Grossman M, Gee JC, McNab JA, Bohon C, Halpern CH. Aberrant impulse control circuitry in obesity. Mol Psychiatry 2022; 27:3374-3384. [PMID: 35697760 PMCID: PMC9192250 DOI: 10.1038/s41380-022-01640-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/11/2022] [Accepted: 05/18/2022] [Indexed: 01/09/2023]
Abstract
The ventromedial prefrontal cortex (vmPFC) to nucleus accumbens (NAc) circuit has been implicated in impulsive reward-seeking. This disinhibition has been implicated in obesity and often manifests as binge eating, which is associated with worse treatment outcomes and comorbidities. It remains unclear whether the vmPFC-NAc circuit is perturbed in impulsive eaters with obesity. Initially, we analyzed publicly available, high-resolution, normative imaging data to localize where vmPFC structural connections converged within the NAc. These structural connections were found to converge ventromedially in the presumed NAc shell subregion. We then analyzed multimodal clinical and imaging data to test the a priori hypothesis that the vmPFC-NAc shell circuit is linked to obesity in a sample of female participants that regularly engaged in impulsive eating (i.e., binge eating). Functionally, vmPFC-NAc shell resting-state connectivity was inversely related to body mass index (BMI) and decreased in the obese state. Structurally, vmPFC-NAc shell structural connectivity and vmPFC thickness were inversely correlated with BMI; obese binge-prone participants exhibited decreased vmPFC-NAc structural connectivity and vmPFC thickness. Finally, to examine a causal link to binge eating, we directly probed this circuit in one binge-prone obese female using NAc deep brain stimulation in a first-in-human trial. Direct stimulation of the NAc shell subregion guided by local behaviorally relevant electrophysiology was associated with a decrease in number of weekly episodes of uncontrolled eating and decreased BMI. This study unraveled vmPFC-NAc shell circuit aberrations in obesity that can be modulated to restore control over eating behavior in obesity.
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Affiliation(s)
- Daniel A N Barbosa
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Fiene Marie Kuijper
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey Duda
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allan R Wang
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Samuel C D Cartmell
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sabir Saluja
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tricia Cunningham
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Rajat S Shivacharan
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Mahendra T Bhati
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Debra L Safer
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - James D Lock
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert C Malenka
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Nancy Pritzker Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Ricardo de Oliveira-Souza
- Department of Specialized Medicine, Gaffrée e Guinle University Hospital, The Federal University of the State of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Nolan R Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Murray Grossman
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - James C Gee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Cara Bohon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Casey H Halpern
- Department of Neurosurgery, Pennsylvania Hospital, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, PA, Philadelphia, USA.
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11
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Parvizi J, Veit MJ, Barbosa DA, Kucyi A, Perry C, Parker JJ, Shivacharan RS, Chen F, Yih J, Gross JJ, Fisher R, McNab JA, Falco-Walter J, Halpern CH. Complex negative emotions induced by electrical stimulation of the human hypothalamus. Brain Stimul 2022; 15:615-623. [DOI: 10.1016/j.brs.2022.04.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 03/21/2022] [Accepted: 04/05/2022] [Indexed: 11/02/2022] Open
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12
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Dai E, Lee PK, Dong Z, Fu F, Setsompop K, McNab JA. Distortion-Free Diffusion Imaging Using Self-Navigated Cartesian Echo-Planar Time Resolved Acquisition and Joint Magnitude and Phase Constrained Reconstruction. IEEE Trans Med Imaging 2022; 41:63-74. [PMID: 34383645 PMCID: PMC8799377 DOI: 10.1109/tmi.2021.3104291] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Echo-planar time resolved imaging (EPTI) is an effective approach for acquiring high-quality distortion-free images with a multi-shot EPI (ms-EPI) readout. As with traditional ms-EPI acquisitions, inter-shot phase variations present a main challenge when incorporating EPTI into a diffusion-prepared pulse sequence. The aim of this study is to develop a self-navigated Cartesian EPTI-based (scEPTI) acquisition together with a magnitude and phase constrained reconstruction for distortion-free diffusion imaging. A self-navigated Cartesian EPTI-based diffusion-prepared pulse sequence is designed. The different phase components in EPTI diffusion signal are analyzed and an approach to synthesize a fully phase-matched navigator for the inter-shot phase correction is demonstrated. Lastly, EPTI contains richer magnitude and phase information than conventional ms-EPI, such as the magnitude and phase correlations along the temporal dimension. The potential of these magnitude and phase correlations to enhance the reconstruction is explored. The reconstruction results with and without phase matching and with and without phase or magnitude constraints are compared. Compared with reconstruction without phase matching, the proposed phase matching method can improve the accuracy of inter-shot phase correction and reduce signal corruption in the final diffusion images. Magnitude constraints further improve image quality by suppressing the background noise and thereby increasing SNR, while phase constraints can mitigate possible image blurring from adding magnitude constraints. The high-quality distortion-free diffusion images and simultaneous diffusion-relaxometry imaging capacity provided by the proposed EPTI design represent a highly valuable tool for both clinical and neuroscientific assessments of tissue microstructure.
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13
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Neves CA, Leuze C, Gomez AM, Navab N, Blevins N, Vaisbuch Y, McNab JA. Augmented Reality for Retrosigmoid Craniotomy Planning. Skull Base Surg 2021; 83:e564-e573. [DOI: 10.1055/s-0041-1735509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
AbstractWhile medical imaging data have traditionally been viewed on two-dimensional (2D) displays, augmented reality (AR) allows physicians to project the medical imaging data on patient's bodies to locate important anatomy. We present a surgical AR application to plan the retrosigmoid craniotomy, a standard approach to access the posterior fossa and the internal auditory canal. As a simple and accurate alternative to surface landmarks and conventional surgical navigation systems, our AR application augments the surgeon's vision to guide the optimal location of cortical bone removal. In this work, two surgeons performed a retrosigmoid approach 14 times on eight cadaver heads. In each case, the surgeon manually aligned a computed tomography (CT)-derived virtual rendering of the sigmoid sinus on the real cadaveric heads using a see-through AR display, allowing the surgeon to plan and perform the craniotomy accordingly. Postprocedure CT scans were acquired to assess the accuracy of the retrosigmoid craniotomies with respect to their intended location relative to the dural sinuses. The two surgeons had a mean margin of davg = 0.6 ± 4.7 mm and davg = 3.7 ± 2.3 mm between the osteotomy border and the dural sinuses over all their cases, respectively, and only positive margins for 12 of the 14 cases. The intended surgical approach to the internal auditory canal was successfully achieved in all cases using the proposed method, and the relatively small and consistent margins suggest that our system has the potential to be a valuable tool to facilitate planning a variety of similar skull-base procedures.
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Affiliation(s)
- Caio A. Neves
- Department of Otolaryngology, Stanford School of Medicine, Stanford, United States
- Faculty of Medicine, University of Brasília, Brasília, Brazil
| | - Christoph Leuze
- Department of Radiology, Stanford School of Medicine, Stanford, United States
| | - Alejandro M. Gomez
- Chair for Computer Aided Medical Procedures and Augmented Reality, Department of Informatics, Technical University of Munich, Germany
- Laboratory for Computer Aided Medical Procedures, Whiting School of Engineering, Johns Hopkins University, Baltimore, USA
| | - Nassir Navab
- Chair for Computer Aided Medical Procedures and Augmented Reality, Department of Informatics, Technical University of Munich, Germany
- Laboratory for Computer Aided Medical Procedures, Whiting School of Engineering, Johns Hopkins University, Baltimore, USA
| | - Nikolas Blevins
- Department of Otolaryngology, Stanford School of Medicine, Stanford, United States
| | - Yona Vaisbuch
- Department of Otolaryngology, Stanford School of Medicine, Stanford, United States
| | - Jennifer A. McNab
- Department of Radiology, Stanford School of Medicine, Stanford, United States
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14
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Leuze C, Goubran M, Barakovic M, Aswendt M, Tian Q, Hsueh B, Crow A, Weber EMM, Steinberg GK, Zeineh M, Plowey ED, Daducci A, Innocenti G, Thiran JP, Deisseroth K, McNab JA. Comparison of diffusion MRI and CLARITY fiber orientation estimates in both gray and white matter regions of human and primate brain. Neuroimage 2021; 228:117692. [PMID: 33385546 PMCID: PMC7953593 DOI: 10.1016/j.neuroimage.2020.117692] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 12/16/2020] [Accepted: 12/18/2020] [Indexed: 11/30/2022] Open
Abstract
Diffusion MRI (dMRI) represents one of the few methods for mapping brain fiber orientations non-invasively. Unfortunately, dMRI fiber mapping is an indirect method that relies on inference from measured diffusion patterns. Comparing dMRI results with other modalities is a way to improve the interpretation of dMRI data and help advance dMRI technologies. Here, we present methods for comparing dMRI fiber orientation estimates with optical imaging of fluorescently labeled neurofilaments and vasculature in 3D human and primate brain tissue cuboids cleared using CLARITY. The recent advancements in tissue clearing provide a new opportunity to histologically map fibers projecting in 3D, which represents a captivating complement to dMRI measurements. In this work, we demonstrate the capability to directly compare dMRI and CLARITY in the same human brain tissue and assess multiple approaches for extracting fiber orientation estimates from CLARITY data. We estimate the three-dimensional neuronal fiber and vasculature orientations from neurofilament and vasculature stained CLARITY images by calculating the tertiary eigenvector of structure tensors. We then extend CLARITY orientation estimates to an orientation distribution function (ODF) formalism by summing multiple sub-voxel structure tensor orientation estimates. In a sample containing part of the human thalamus, there is a mean angular difference of 19o±15o between the primary eigenvectors of the dMRI tensors and the tertiary eigenvectors from the CLARITY neurofilament stain. We also demonstrate evidence that vascular compartments do not affect the dMRI orientation estimates by showing an apparent lack of correspondence (mean angular difference = 49o±23o) between the orientation of the dMRI tensors and the structure tensors in the vasculature stained CLARITY images. In a macaque brain dataset, we examine how the CLARITY feature extraction depends on the chosen feature extraction parameters. By varying the volume of tissue over which the structure tensor estimates are derived, we show that orientation estimates are noisier with more spurious ODF peaks for sub-voxels below 30 µm3 and that, for our data, the optimal gray matter sub-voxel size is between 62.5 µm3 and 125 µm3. The example experiments presented here represent an important advancement towards robust multi-modal MRI-CLARITY comparisons.
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Affiliation(s)
- C Leuze
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | - M Goubran
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - M Barakovic
- Department of Radiology, Stanford University, Stanford, CA, USA; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - M Aswendt
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Q Tian
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - B Hsueh
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - A Crow
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - E M M Weber
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - G K Steinberg
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - M Zeineh
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - E D Plowey
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - A Daducci
- Department of Computer Science, University of Verona, Verona, Italy
| | - G Innocenti
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Brain and Mind Institute, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - J-P Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - K Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - J A McNab
- Department of Radiology, Stanford University, Stanford, CA, USA
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15
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Kakusa B, Saluja S, Barbosa DAN, Cartmell S, Espil FM, Williams NR, McNab JA, Halpern CH. Evidence for the role of the dorsal ventral lateral posterior thalamic nucleus connectivity in deep brain stimulation for Gilles de la Tourette syndrome. J Psychiatr Res 2021; 132:60-64. [PMID: 33045620 DOI: 10.1016/j.jpsychires.2020.09.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 09/08/2020] [Accepted: 09/25/2020] [Indexed: 12/16/2022]
Abstract
Gilles de la Tourette syndrome (GTS) can manifest as debilitating, medically-refractory tics for which deep brain stimulation (DBS) of the centromedian-parafascicular complex (CM) can provide effective treatment. However, patients have reported benefit with activation of contacts dorsal to the CM and likely in the ventro-lateral thalamus (VL). At our institution, a case of a robust and durable response in a GTS patient required stimulation in the CM and more dorsally. We explore the structural connectivity of thalamic subregions associated with GTS using diffusion MRI tractography. Diffusion weighted images from 40 healthy Human Connectome Project (HCP) subjects and our GTS patient were analyzed. The VL posterior nucleus (VLp) and the CM were used as seeds for whole-brain probabilistic tractography. Leads were localized via linear registration of pre-/post-operative imaging and cross-referenced with the DBS Intrinsic Template Atlas. Tractography revealed high streamline probability from the CM and VLp to the superior frontal gyrus, rostral middle frontal gyrus, brainstem, and ventral diencephalon. Given reported variable responses to DBS along the thalamus, we segmented the VLp based on its connectivity profile. Ventral and dorsal subdivisions emerged, with streamline probability patterns differing between the dorsal VLp and CM. The CM, the most reported DBS target for GTS, and the dorsal VLp have different but seemingly complimentary connectivity profiles as evidenced by our patient who, at 1-year post-operatively, had significant therapeutic benefit. Stimulation of both regions may better target reward and motor circuits, resulting in enhanced symptom control for GTS.
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Affiliation(s)
- Bina Kakusa
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Sabir Saluja
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Daniel A N Barbosa
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Sam Cartmell
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Flint M Espil
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Nolan R Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.
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Saluja S, Barbosa DAN, Parker JJ, Huang Y, Jensen MR, Ngo V, Santini VE, Pauly KB, Ghanouni P, McNab JA, Halpern CH. Case Report on Deep Brain Stimulation Rescue After Suboptimal MR-Guided Focused Ultrasound Thalamotomy for Essential Tremor: A Tractography-Based Investigation. Front Hum Neurosci 2020; 14:191. [PMID: 32676015 PMCID: PMC7333679 DOI: 10.3389/fnhum.2020.00191] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 04/28/2020] [Indexed: 12/15/2022] Open
Abstract
Essential tremor (ET) is the most prevalent movement disorder in adults, and can often be medically refractory, requiring surgical intervention. MRI-guided focused ultrasound (MRgFUS) is a less invasive procedure that uses ultrasonic waves to induce lesions in the ventralis intermedius nucleus (VIM) to treat refractory ET. As with all procedures for treating ET, optimal targeting during MRgFUS is essential for efficacy and durability. Various studies have reported cases of tremor recurrence following MRgFUS and long-term outcome data is limited to 3–4 years. We present a tractography-based investigation on a case of DBS rescue for medically refractory ET that was treated with MRgFUS that was interrupted due to the development of dysarthria during the procedure. After initial improvement, her hand tremor started to recur within 6 months after treatment, and bilateral DBS was performed targeting the VIM 24 months after MRgFUS. DBS induced long-term tremor control with monopolar stimulation. Diffusion MRI tractography was used to reconstruct the dentatorubrothalamic (DRTT) and corticothalmic (CTT) tracts being modulated by the procedures to understand the variability in efficacy between MRgFUS and DBS in treating ET in our patient. By comparing the MRgFUS lesion and DBS volume of activated tissue (VAT), we found that the MRgFUS lesion was located ventromedially to the VAT, and was less than 10% of the size of the VAT. While the lesion encompassed the same proportion of DRTT streamlines, it encompassed fewer CTT streamlines than the VAT. Our findings indicate the need for further investigation of targeting the CTT when using neuromodulatory procedures to treat refractory ET for more permanent tremor relief.
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Affiliation(s)
- Sabir Saluja
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Daniel A N Barbosa
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Jonathon J Parker
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Yuhao Huang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Michael R Jensen
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Vyvian Ngo
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
| | - Veronica E Santini
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Kim Butts Pauly
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Pejman Ghanouni
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Jennifer A McNab
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
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17
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Neves CA, Vaisbuch Y, Leuze C, McNab JA, Daniel B, Blevins NH, Hwang PH. Application of holographic augmented reality for external approaches to the frontal sinus. Int Forum Allergy Rhinol 2020; 10:920-925. [PMID: 32362076 DOI: 10.1002/alr.22546] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 01/11/2020] [Accepted: 02/05/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND External approaches to the frontal sinus such as osteoplastic flaps are challenging because they require blind entry into the sinus, posing risks of injury to the brain or orbit. Intraoperative computed tomography (CT)-based navigation is the current standard for planning the approach, but still necessitates blind entry into the sinus. The aim of this work was to describe a novel technique for external approaches to the frontal sinus using a holographic augmented reality (AR) application. METHODS Our team developed an AR system to create a 3-dimensional (3D) hologram of key anatomical structures, based on CT scans images. Using Magic Leap AR goggles for visualization, the frontal sinus hologram was aligned to the surface anatomy in 6 fresh cadaveric heads' anatomic boundaries, and the boundaries of the frontal sinus were demarcated based on the margins of the fused image. Trephinations and osteoplastic flap approaches were performed. The specimens were re-scanned to assess the accuracy of the osteotomy with respect to the actual frontal sinus perimeter. RESULTS Registration and surgery were completed successfully in all specimens. Registration required an average of 2 minutes. The postprocedure CT showed a mean difference of 1.4 ± 4.1 mm between the contour of the osteotomy and the contour of the frontal sinus. One surgical complication (posterior table perforation) occurred (16%). CONCLUSION We describe proof of concept of a novel technique utilizing AR to enhance external approaches to the frontal sinus. Holographic AR-enhanced surgical navigation holds promise for enhanced visualization of target structures during surgical approaches to the sinuses.
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Affiliation(s)
- Caio A Neves
- Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, CA.,Faculty of Medicine, University of Brasília, Brasilia, Brazil
| | - Yona Vaisbuch
- Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, CA.,Department of Otolaryngology-Head & Neck Surgery, Rambam Medical Center, Haifa, Israel
| | - Christoph Leuze
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Jennifer A McNab
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Bruce Daniel
- Department of Radiology, Stanford University School of Medicine, Stanford, CA
| | - Nikolas H Blevins
- Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, CA
| | - Peter H Hwang
- Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, CA
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18
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Tan ET, Shih RY, Mitra J, Sprenger T, Hua Y, Bhushan C, Bernstein MA, McNab JA, DeMarco JK, Ho VB, Foo TKF. Oscillating diffusion-encoding with a high gradient-amplitude and high slew-rate head-only gradient for human brain imaging. Magn Reson Med 2020; 84:950-965. [PMID: 32011027 DOI: 10.1002/mrm.28180] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 12/09/2019] [Accepted: 01/02/2020] [Indexed: 12/22/2022]
Abstract
PURPOSE We investigate the importance of high gradient-amplitude and high slew-rate on oscillating gradient spin echo (OGSE) diffusion imaging for human brain imaging and evaluate human brain imaging with OGSE on the MAGNUS head-gradient insert (200 mT/m amplitude and 500 T/m/s slew rate). METHODS Simulations with cosine-modulated and trapezoidal-cosine OGSE at various gradient amplitudes and slew rates were performed. Six healthy subjects were imaged with the MAGNUS gradient at 3T with OGSE at frequencies up to 100 Hz and b = 450 s/mm2 . Comparisons were made against standard pulsed gradient spin echo (PGSE) diffusion in vivo and in an isotropic diffusion phantom. RESULTS Simulations show that to achieve high frequency and b-value simultaneously for OGSE, high gradient amplitude, high slew rates, and high peripheral nerve stimulation limits are required. A strong linear trend for increased diffusivity (mean: 8-19%, radial: 9-27%, parallel: 8-15%) was observed in normal white matter with OGSE (20 Hz to 100 Hz) as compared to PGSE. Linear fitting to frequency provided excellent correlation, and using a short-range disorder model provided radial long-term diffusivities of D∞,MD = 911 ± 72 µm2 /s, D∞,PD = 1519 ± 164 µm2 /s, and D∞,RD = 640 ± 111 µm2 /s and correlation lengths of lc ,MD = 0.802 ± 0.156 µm, lc ,PD = 0.837 ± 0.172 µm, and lc ,RD = 0.780 ± 0.174 µm. Diffusivity changes with OGSE frequency were negligible in the phantom, as expected. CONCLUSION The high gradient amplitude, high slew rate, and high peripheral nerve stimulation thresholds of the MAGNUS head-gradient enables OGSE acquisition for in vivo human brain imaging.
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Affiliation(s)
- Ek T Tan
- GE Research, Niskayuna, New York.,Department of Radiology and Imaging, Hospital for Special Surgery, New York, New York
| | - Robert Y Shih
- Uniformed Services University of the Health Sciences, Bethesda, Maryland.,Walter Reed National Military Medical Center, Bethesda, Maryland
| | | | | | - Yihe Hua
- GE Research, Niskayuna, New York
| | | | | | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, California
| | - J Kevin DeMarco
- Uniformed Services University of the Health Sciences, Bethesda, Maryland.,Walter Reed National Military Medical Center, Bethesda, Maryland
| | - Vincent B Ho
- Uniformed Services University of the Health Sciences, Bethesda, Maryland.,Walter Reed National Military Medical Center, Bethesda, Maryland
| | - Thomas K F Foo
- GE Research, Niskayuna, New York.,Uniformed Services University of the Health Sciences, Bethesda, Maryland
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19
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Goubran M, Leuze C, Hsueh B, Aswendt M, Ye L, Tian Q, Cheng MY, Crow A, Steinberg GK, McNab JA, Deisseroth K, Zeineh M. Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI. Nat Commun 2019; 10:5504. [PMID: 31796741 PMCID: PMC6890789 DOI: 10.1038/s41467-019-13374-0] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 11/04/2019] [Indexed: 01/21/2023] Open
Abstract
3D histology, slice-based connectivity atlases, and diffusion MRI are common techniques to map brain wiring. While there are many modality-specific tools to process these data, there is a lack of integration across modalities. We develop an automated resource that combines histologically cleared volumes with connectivity atlases and MRI, enabling the analysis of histological features across multiple fiber tracts and networks, and their correlation with in-vivo biomarkers. We apply our pipeline in a murine stroke model, demonstrating not only strong correspondence between MRI abnormalities and CLARITY-tissue staining, but also uncovering acute cellular effects in areas connected to the ischemic core. We provide improved maps of connectivity by quantifying projection terminals from CLARITY viral injections, and integrate diffusion MRI with CLARITY viral tracing to compare connectivity maps across scales. Finally, we demonstrate tract-level histological changes of stroke through this multimodal integration. This resource can propel investigations of network alterations underlying neurological disorders.
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Affiliation(s)
- Maged Goubran
- Department of Radiology, Stanford University, Stanford, CA, 94035, USA.
| | - Christoph Leuze
- Department of Radiology, Stanford University, Stanford, CA, 94035, USA
| | - Brian Hsueh
- Department of Bioengineering, Stanford University, Stanford, CA, 94035, USA
- CNC Program, Stanford University, Stanford, CA, 94035, USA
| | - Markus Aswendt
- Department of Neurosurgery and Stanford Stroke Center, Stanford University, Stanford, CA, 94035, USA
| | - Li Ye
- Department of Bioengineering, Stanford University, Stanford, CA, 94035, USA
- CNC Program, Stanford University, Stanford, CA, 94035, USA
| | - Qiyuan Tian
- Department of Radiology, Stanford University, Stanford, CA, 94035, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94035, USA
| | - Michelle Y Cheng
- Department of Neurosurgery and Stanford Stroke Center, Stanford University, Stanford, CA, 94035, USA
| | - Ailey Crow
- CNC Program, Stanford University, Stanford, CA, 94035, USA
| | - Gary K Steinberg
- Department of Neurosurgery and Stanford Stroke Center, Stanford University, Stanford, CA, 94035, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, CA, 94035, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, 94035, USA
- CNC Program, Stanford University, Stanford, CA, 94035, USA
- Department of Psychiatry, Stanford University, Stanford, CA, 94035, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, 94035, USA
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, CA, 94035, USA.
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20
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McKavanagh R, Torso M, Jenkinson M, Kolasinski J, Stagg CJ, Esiri MM, McNab JA, Johansen‐Berg H, Miller KL, Chance SA. Relating diffusion tensor imaging measurements to microstructural quantities in the cerebral cortex in multiple sclerosis. Hum Brain Mapp 2019; 40:4417-4431. [PMID: 31355989 PMCID: PMC6772025 DOI: 10.1002/hbm.24711] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 05/20/2019] [Accepted: 05/31/2019] [Indexed: 12/13/2022] Open
Abstract
To investigate whether the observed anisotropic diffusion in cerebral cortex may reflect its columnar cytoarchitecture and myeloarchitecture, as a potential biomarker for disease-related changes, we compared postmortem diffusion magnetic resonance imaging scans of nine multiple sclerosis brains with histology measures from the same regions. Histology measurements assessed the cortical minicolumnar structure based on cell bodies and associated axon bundles in dorsolateral prefrontal cortex (Area 9), Heschl's gyrus (Area 41), and primary visual cortex (V1). Diffusivity measures included mean diffusivity, fractional anisotropy of the cortex, and three specific measures that may relate to the radial minicolumn structure: the angle of the principal diffusion direction in the cortex, the component that was perpendicular to the radial direction, and the component that was parallel to the radial direction. The cellular minicolumn microcircuit features were correlated with diffusion angle in Areas 9 and 41, and the axon bundle features were correlated with angle in Area 9 and to the parallel component in V1 cortex. This may reflect the effect of minicolumn microcircuit organisation on diffusion in the cortex, due to the number of coherently arranged membranes and myelinated structures. Several of the cortical diffusion measures showed group differences between MS brains and control brains. Differences between brain regions were also found in histology and diffusivity measurements consistent with established regional variation in cytoarchitecture and myeloarchitecture. Therefore, these novel measures may provide a surrogate of cortical organisation as a potential biomarker, which is particularly relevant for detecting regional changes in neurological disorders.
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Affiliation(s)
- Rebecca McKavanagh
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Mario Torso
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - James Kolasinski
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Charlotte J. Stagg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Margaret M. Esiri
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Jennifer A. McNab
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Heidi Johansen‐Berg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Steven A. Chance
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
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21
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Ito M, Aswendt M, Lee AG, Ishizaka S, Cao Z, Wang EH, Levy SL, Smerin DL, McNab JA, Zeineh M, Leuze C, Goubran M, Cheng MY, Steinberg GK. RNA-Sequencing Analysis Revealed a Distinct Motor Cortex Transcriptome in Spontaneously Recovered Mice After Stroke. Stroke 2019; 49:2191-2199. [PMID: 30354987 DOI: 10.1161/strokeaha.118.021508] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background and Purpose- Many restorative therapies have been used to study brain repair after stroke. These therapeutic-induced changes have revealed important insights on brain repair and recovery mechanisms; however, the intrinsic changes that occur in spontaneously recovery after stroke is less clear. The goal of this study is to elucidate the intrinsic changes in spontaneous recovery after stroke, by directly investigating the transcriptome of primary motor cortex in mice that naturally recovered after stroke. Methods- Male C57BL/6J mice were subjected to transient middle cerebral artery occlusion. Functional recovery was evaluated using the horizontal rotating beam test. A novel in-depth lesion mapping analysis was used to evaluate infarct size and locations. Ipsilesional and contralesional primary motor cortices (iM1 and cM1) were processed for RNA-sequencing transcriptome analysis. Results- Cluster analysis of the stroke mice behavior performance revealed 2 distinct recovery groups: a spontaneously recovered and a nonrecovered group. Both groups showed similar lesion profile, despite their differential recovery outcome. RNA-sequencing transcriptome analysis revealed distinct biological pathways in the spontaneously recovered stroke mice, in both iM1 and cM1. Correlation analysis revealed that 38 genes in the iM1 were significantly correlated with improved recovery, whereas 74 genes were correlated in the cM1. In particular, ingenuity pathway analysis highlighted the involvement of cAMP signaling in the cM1, with selective reduction of Adora2a (adenosine receptor A2A), Drd2 (dopamine receptor D2), and Pde10a (phosphodiesterase 10A) expression in recovered mice. Interestingly, the expressions of these genes in cM1 were negatively correlated with behavioral recovery. Conclusions- Our RNA-sequencing data revealed a panel of recovery-related genes in the motor cortex of spontaneously recovered stroke mice and highlighted the involvement of contralesional cortex in spontaneous recovery, particularly Adora2a, Drd2, and Pde10a-mediated cAMP signaling pathway. Developing drugs targeting these candidates after stroke may provide beneficial recovery outcome.
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Affiliation(s)
- Masaki Ito
- From the Department of Neurosurgery (M.I., M.A., S.I., Z.C., E.H.W., S.L.L., D.L.S., M.Y.C., G.K.S.)
| | - Markus Aswendt
- From the Department of Neurosurgery (M.I., M.A., S.I., Z.C., E.H.W., S.L.L., D.L.S., M.Y.C., G.K.S.)
| | | | - Shunsuke Ishizaka
- From the Department of Neurosurgery (M.I., M.A., S.I., Z.C., E.H.W., S.L.L., D.L.S., M.Y.C., G.K.S.)
| | - Zhijuan Cao
- From the Department of Neurosurgery (M.I., M.A., S.I., Z.C., E.H.W., S.L.L., D.L.S., M.Y.C., G.K.S.)
| | - Eric H Wang
- From the Department of Neurosurgery (M.I., M.A., S.I., Z.C., E.H.W., S.L.L., D.L.S., M.Y.C., G.K.S.)
| | - Sabrina L Levy
- From the Department of Neurosurgery (M.I., M.A., S.I., Z.C., E.H.W., S.L.L., D.L.S., M.Y.C., G.K.S.)
| | - Daniel L Smerin
- From the Department of Neurosurgery (M.I., M.A., S.I., Z.C., E.H.W., S.L.L., D.L.S., M.Y.C., G.K.S.)
| | - Jennifer A McNab
- Department of Radiology (J.A.M., M.Z., C.L., M.G.), Stanford University School of Medicine, CA
| | - Michael Zeineh
- Department of Radiology (J.A.M., M.Z., C.L., M.G.), Stanford University School of Medicine, CA
| | - Christoph Leuze
- Department of Radiology (J.A.M., M.Z., C.L., M.G.), Stanford University School of Medicine, CA
| | - Maged Goubran
- Department of Radiology (J.A.M., M.Z., C.L., M.G.), Stanford University School of Medicine, CA
| | - Michelle Y Cheng
- From the Department of Neurosurgery (M.I., M.A., S.I., Z.C., E.H.W., S.L.L., D.L.S., M.Y.C., G.K.S.)
| | - Gary K Steinberg
- From the Department of Neurosurgery (M.I., M.A., S.I., Z.C., E.H.W., S.L.L., D.L.S., M.Y.C., G.K.S.)
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22
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Huang SY, Tian Q, Fan Q, Witzel T, Wichtmann B, McNab JA, Daniel Bireley J, Machado N, Klawiter EC, Mekkaoui C, Wald LL, Nummenmaa A. High-gradient diffusion MRI reveals distinct estimates of axon diameter index within different white matter tracts in the in vivo human brain. Brain Struct Funct 2019; 225:1277-1291. [PMID: 31563995 DOI: 10.1007/s00429-019-01961-2] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 09/19/2019] [Indexed: 12/01/2022]
Abstract
Axon diameter and density are important microstructural metrics that offer valuable insight into the structural organization of white matter throughout the human brain. We report the systematic acquisition and analysis of a comprehensive diffusion MRI data set acquired with 300 mT/m maximum gradient strength in a cohort of 20 healthy human subjects that yields distinct and consistent patterns of axon diameter index in white matter tracts of arbitrary orientation. We use a straightforward, previously validated approach to estimating indices of axon diameter and volume fraction that involves interpolating the diffusion signal perpendicular to the principal fiber orientation and fitting a three-compartment model of intra-axonal, extra-axonal and free water diffusion. The resultant maps confirm the presence of larger diameter indices in the body of corpus callosum compared to the genu and splenium, as previously reported, and show larger axon diameter index in the corticospinal tracts compared to adjacent white matter tracts such as the cingulum. An anterior-to-posterior gradient in axon diameter index is also observed, with smaller diameter indices in the frontal lobes and larger diameter indices in the parieto-occipital white matter. These observations are consistent with known trends from prior histologic studies in humans and non-human primates. Rather than serving as fully quantitative measures of axon diameter and density, our results may be considered as axon diameter- and volume fraction-weighted images that appear to be modulated by the underlying microstructure and may capture broad trends in axonal size and packing density, acknowledging that the precise origin of such modulation requires further investigation that will be facilitated by the availability of high gradient strengths for in vivo human imaging.
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Affiliation(s)
- Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Barbara Wichtmann
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jennifer A McNab
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, USA
| | - J Daniel Bireley
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Natalya Machado
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Choukri Mekkaoui
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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23
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Cartmell SC, Tian Q, Thio BJ, Leuze C, Ye L, Williams NR, Yang G, Ben-Dor G, Deisseroth K, Grill WM, McNab JA, Halpern CH. Multimodal characterization of the human nucleus accumbens. Neuroimage 2019; 198:137-149. [PMID: 31077843 PMCID: PMC7341972 DOI: 10.1016/j.neuroimage.2019.05.019] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 04/27/2019] [Accepted: 05/07/2019] [Indexed: 01/03/2023] Open
Abstract
Dysregulation of the nucleus accumbens (NAc) is implicated in numerous neuropsychiatric disorders. Treatments targeting this area directly (e.g. deep brain stimulation) demonstrate variable efficacy, perhaps owing to non-specific targeting of a functionally heterogeneous nucleus. Here we provide support for this notion, first observing disparate behavioral effects in response to direct simulation of different locations within the NAc in a human patient. These observations motivate a segmentation of the NAc into subregions, which we produce from a diffusion-tractography based analysis of 245 young, unrelated healthy subjects. We further explore the mechanism of these stimulation-induced behavioral responses by identifying the most probable subset of axons activated using a patient-specific computational model. We validate our diffusion-based segmentation using evidence from several modalities, including MRI-based measures of function and microstructure, human post-mortem immunohistochemical staining, and cross-species comparison of cortical-NAc projections that are known to be conserved. Finally, we visualize the passage of individual axon bundles through one NAc subregion in a post-mortem human sample using CLARITY 3D histology corroborated by 7T tractography. Collectively, these findings extensively characterize human NAc subregions and provide insight into their structural and functional distinctions with implications for stereotactic treatments targeting this region.
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Affiliation(s)
- Samuel Cd Cartmell
- Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA
| | - Qiyuan Tian
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Brandon J Thio
- Department of Biomedical Engineering, Duke University, Stanford University, Stanford, CA, 94305, USA
| | - Christoph Leuze
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Li Ye
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Nolan R Williams
- Department of Psychiatry, Stanford University, Stanford, CA, 94305, USA
| | - Grant Yang
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Gabriel Ben-Dor
- Department of Psychiatry, Stanford University, Stanford, CA, 94305, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA; Department of Psychiatry, Stanford University, Stanford, CA, 94305, USA
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Stanford University, Stanford, CA, 94305, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA.
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24
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Lee S, Polimeni JR, Price CM, Edlow BL, McNab JA. Characterizing Signals Within Lesions and Mapping Brain Network Connectivity After Traumatic Axonal Injury: A 7 Tesla Resting-State FMRI Study. Brain Connect 2019; 8:288-298. [PMID: 29665699 DOI: 10.1089/brain.2017.0499] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Resting-state functional magnetic resonance imaging (RS-FMRI) has been widely used to map brain functional connectivity, but it is unclear how to probe connectivity within and around lesions. In this study, we characterize RS-FMRI signal time course properties and evaluate different seed placements within and around hemorrhagic traumatic axonal injury (hTAI) lesions. RS-FMRI was performed on a 7 Tesla scanner in a patient who recovered consciousness after traumatic coma and in three healthy controls. Eleven lesions in the patient were characterized in terms of (1) temporal signal-to-noise ratio (tSNR); (2) physiological noise, through comparison of noise regressors derived from the white matter (WM), cerebrospinal fluid (CSF), and gray matter (GM); and (3) seed-based functional connectivity. Temporal SNR at the center of the lesions was 38.3% and 74.1% lower compared with the same region in the contralesional hemisphere of the patient and in the ipsilesional hemispheres of the controls, respectively. Within the lesions, WM noise was more prominent than CSF and GM noise. Lesional seeds did not produce discernable networks, but seeds in the contralesional hemisphere revealed networks whose nodes appeared to be shifted or obscured due to overlapping or nearby lesions. Single-voxel seed analysis demonstrated that placing a seed within a lesion's periphery was necessary to identify networks associated with the lesion region. These findings provide evidence of resting-state network changes in the human brain after recovery from traumatic coma. Furthermore, we show that seed placement within a lesion's periphery or in the contralesional hemisphere may be necessary for network identification in patients with hTAI.
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Affiliation(s)
- Seul Lee
- 1 Department of Electrical Engineering, Stanford University , Stanford, California.,2 Department of Radiology, Stanford University , Stanford, California
| | - Jonathan R Polimeni
- 3 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital , Charlestown, Massachusetts.,4 Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Collin M Price
- 5 Department of Neurology, Stanford University , Stanford, California
| | - Brian L Edlow
- 3 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital , Charlestown, Massachusetts.,6 Department of Neurology, Center for Neurotechnology and Neurorecovery , Massachusetts General Hospital, Boston, Massachusetts
| | - Jennifer A McNab
- 2 Department of Radiology, Stanford University , Stanford, California
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25
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Yang G, McNab JA. Eddy current nulled constrained optimization of isotropic diffusion encoding gradient waveforms. Magn Reson Med 2019; 81:1818-1832. [PMID: 30368913 PMCID: PMC6347544 DOI: 10.1002/mrm.27539] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2018] [Revised: 08/02/2018] [Accepted: 08/29/2018] [Indexed: 12/15/2022]
Abstract
PURPOSE Isotropic diffusion encoding efficiently encodes additional microstructural information in combination with conventional linear diffusion encoding. However, the gradient-intensive isotropic diffusion waveforms generate significant eddy currents, which cause image distortions. The purpose of this study is to present a method for designing isotropic diffusion encoding waveforms with intrinsic eddy current nulling. METHODS Eddy current nulled gradient waveforms were designed using a constrained optimization waveform for a 3T GE Premier MRI system. Encoding waveforms were designed for a variety of eddy current null times and sequence timings to evaluate the achievable b-value. Waveforms were also tested with both eddy current nulling and concomitant field compensation. Distortion reduction was tested in both phantoms and the in vivo human brain. RESULTS The feasibility of isotropic diffusion encoding with intrinsic correction of eddy current distortion and signal bias from concomitant fields was demonstrated. The constrained optimization algorithm produced gradient waveforms with the specified eddy current null times. The reduction in the achievable diffusion weighting was dependent on the number of eddy current null times. A reduction in the eddy current-induced image distortions was observed in both phantoms and in vivo human subjects. CONCLUSION The proposed framework allows the design of isotropic diffusion-encoding sequences with reduced image distortion.
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Affiliation(s)
- Grant Yang
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
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26
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Hu Y, Levine EG, Tian Q, Moran CJ, Wang X, Taviani V, Vasanawala S, McNab JA, Daniel BL, Hargreaves BA. Motion-robust reconstruction of multishot diffusion-weighted images without phase estimation through locally low-rank regularization. Magn Reson Med 2019; 81:1181-1190. [PMID: 30346058 PMCID: PMC6289606 DOI: 10.1002/mrm.27488] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 07/11/2018] [Accepted: 07/18/2018] [Indexed: 11/12/2022]
Abstract
PURPOSE The goal of this work is to propose a motion robust reconstruction method for diffusion-weighted MRI that resolves shot-to-shot phase mismatches without using phase estimation. METHODS Assuming that shot-to-shot phase variations are slowly varying, spatial-shot matrices can be formed using a local group of pixels to form columns, in which each column is from a different shot (excitation). A convex model with a locally low-rank constraint on the spatial-shot matrices is proposed. In vivo brain and breast experiments were performed to evaluate the performance of the proposed method. RESULTS The proposed method shows significant benefits when the motion is severe, such as for breast imaging. Furthermore, the resulting images can be used for reliable phase estimation in the context of phase-estimation-based methods to achieve even higher image quality. CONCLUSION We introduced the shot-locally low-rank method, a reconstruction technique for multishot diffusion-weighted MRI without explicit phase estimation. In addition, its motion robustness can be beneficial to neuroimaging and body imaging.
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Affiliation(s)
- Yuxin Hu
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Evan G. Levine
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Xiaole Wang
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | | | | | - Jennifer A. McNab
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Bruce L. Daniel
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
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Tian Q, Yang G, Leuze C, Rokem A, Edlow BL, McNab JA. Generalized diffusion spectrum magnetic resonance imaging (GDSI) for model-free reconstruction of the ensemble average propagator. Neuroimage 2019; 189:497-515. [PMID: 30684636 DOI: 10.1016/j.neuroimage.2019.01.038] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2018] [Revised: 12/06/2018] [Accepted: 01/14/2019] [Indexed: 01/14/2023] Open
Abstract
Diffusion spectrum MRI (DSI) provides model-free estimation of the diffusion ensemble average propagator (EAP) and orientation distribution function (ODF) but requires the diffusion data to be acquired on a Cartesian q-space grid. Multi-shell diffusion acquisitions are more flexible and more commonly acquired but have, thus far, only been compatible with model-based analysis methods. Here, we propose a generalized DSI (GDSI) framework to recover the EAP from multi-shell diffusion MRI data. The proposed GDSI approach corrects for q-space sampling density non-uniformity using a fast geometrical approach. The EAP is directly calculated in a preferable coordinate system by multiplying the sampling density corrected q-space signals by a discrete Fourier transform matrix, without any need for gridding. The EAP is demonstrated as a way to map diffusion patterns in brain regions such as the thalamus, cortex and brainstem where the tissue microstructure is not as well characterized as in white matter. Scalar metrics such as the zero displacement probability and displacement distances at different fractions of the zero displacement probability were computed from the recovered EAP to characterize the diffusion pattern within each voxel. The probability averaged across directions at a specific displacement distance provides a diffusion property based image contrast that clearly differentiates tissue types. The displacement distance at the first zero crossing of the EAP averaged across directions orthogonal to the primary fiber orientation in the corpus callosum is found to be larger in the body (5.65 ± 0.09 μm) than in the genu (5.55 ± 0.15 μm) and splenium (5.4 ± 0.15 μm) of the corpus callosum, which corresponds well to prior histological studies. The EAP also provides model-free representations of angular structure such as the diffusion ODF, which allows estimation and comparison of fiber orientations from both the model-free and model-based methods on the same multi-shell data. For the model-free methods, detection of crossing fibers is found to be strongly dependent on the maximum b-value and less sensitive compared to the model-based methods. In conclusion, our study provides a generalized DSI approach that allows flexible reconstruction of the diffusion EAP and ODF from multi-shell diffusion data and data acquired with other sampling patterns.
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Affiliation(s)
- Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States.
| | - Grant Yang
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States
| | - Christoph Leuze
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, WA, United States
| | - Brian L Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Jennifer A McNab
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Richard M. Lucas Center for Imaging, Stanford, CA, United States
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Tian Q, Wintermark M, Jeffrey Elias W, Ghanouni P, Halpern CH, Henderson JM, Huss DS, Goubran M, Thaler C, Airan R, Zeineh M, Pauly KB, McNab JA. Diffusion MRI tractography for improved transcranial MRI-guided focused ultrasound thalamotomy targeting for essential tremor. Neuroimage Clin 2018; 19:572-580. [PMID: 29984165 PMCID: PMC6029558 DOI: 10.1016/j.nicl.2018.05.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2017] [Revised: 05/03/2018] [Accepted: 05/08/2018] [Indexed: 01/07/2023]
Abstract
Purpose To evaluate the use of diffusion magnetic resonance imaging (MRI) tractography for neurosurgical guidance of transcranial MRI-guided focused ultrasound (tcMRgFUS) thalamotomy for essential tremor (ET). Materials and methods Eight patients with medication-refractory ET were treated with tcMRgFUS targeting the ventral intermediate nucleus (Vim) of the thalamus contralateral to their dominant hand. Diffusion and structural MRI data and clinical evaluations were acquired pre-treatment and post-treatment. To identify the optimal target location, tractography was performed on pre-treatment diffusion MRI data between the treated thalamus and the hand-knob region of the ipsilateral motor cortex, the entire ipsilateral motor cortex and the contralateral dentate nucleus. The tractography-identified locations were compared to the lesion location delineated on 1 year post-treatment T2-weighted MR image. Their overlap was correlated with the clinical outcomes measured by the percentage change of the Clinical Rating Scale for Tremor scores acquired pre-treatment, as well as 1 month, 3 months, 6 months and 1 year post-treatment. Results The probabilistic tractography was consistent from subject-to-subject and followed the expected anatomy of the thalamocortical radiation and the dentatothalamic tract. Higher overlap between the tractography-identified location and the tcMRgFUS treatment-induced lesion highly correlated with better treatment outcome (r = −0.929, −0.75, −0.643, p = 0.00675, 0.0663, 0.139 for the tractography between the treated thalamus and the hand-knob region of the ipsilateral motor cortex, the entire ipsilateral motor cortex and the contralateral dentate nucleus, respectively, at 1 year post-treatment). The correlation for the tractography between the treated thalamus and the hand-knob region of the ipsilateral motor cortex is the highest for all time points (r = −0.719, −0.976, −0.707, −0.929, p = 0.0519, 0.000397, 0.0595, 0.00675 at 1 month, 3 months, 6 months and 1 year post-treatment, respectively). Conclusion Our data support the use of diffusion tractography as a complementary approach to current targeting methods for tcMRgFUS thalamotomy. Retrospectively used tractography to define a target for MRgFUS thalamotomy for ET. Larger overlap between tractography and lesion correlates with better outcomes. Strongest correlations for tract between the thalamus and motor hand-knob region Diffusion tractography is a complementary approach to current targeting methods.
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Affiliation(s)
- Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Department of Radiology, Stanford University, Stanford, CA, United States.
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - W Jeffrey Elias
- Department of Neurosurgery, University of Virginia, Charlottesville, VA, United States
| | - Pejman Ghanouni
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
| | - Jaimie M Henderson
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
| | - Diane S Huss
- Department of Physical Therapy, University of Virginia, Charlottesville, VA, United States
| | - Maged Goubran
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Christian Thaler
- Department of Radiology, Stanford University, Stanford, CA, United States; Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Raag Airan
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Michael Zeineh
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Kim Butts Pauly
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States; Department of Radiology, Stanford University, Stanford, CA, United States
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, CA, United States
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29
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Edlow BL, Keene CD, Perl DP, Iacono D, Folkerth RD, Stewart W, Mac Donald CL, Augustinack J, Diaz-Arrastia R, Estrada C, Flannery E, Gordon WA, Grabowski TJ, Hansen K, Hoffman J, Kroenke C, Larson EB, Lee P, Mareyam A, McNab JA, McPhee J, Moreau AL, Renz A, Richmire K, Stevens A, Tang CY, Tirrell LS, Trittschuh EH, van der Kouwe A, Varjabedian A, Wald LL, Wu O, Yendiki A, Young L, Zöllei L, Fischl B, Crane PK, Dams-O'Connor K. Multimodal Characterization of the Late Effects of Traumatic Brain Injury: A Methodological Overview of the Late Effects of Traumatic Brain Injury Project. J Neurotrauma 2018; 35:1604-1619. [PMID: 29421973 DOI: 10.1089/neu.2017.5457] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Epidemiological studies suggest that a single moderate-to-severe traumatic brain injury (TBI) is associated with an increased risk of neurodegenerative disease, including Alzheimer's disease (AD) and Parkinson's disease (PD). Histopathological studies describe complex neurodegenerative pathologies in individuals exposed to single moderate-to-severe TBI or repetitive mild TBI, including chronic traumatic encephalopathy (CTE). However, the clinicopathological links between TBI and post-traumatic neurodegenerative diseases such as AD, PD, and CTE remain poorly understood. Here, we describe the methodology of the Late Effects of TBI (LETBI) study, whose goals are to characterize chronic post-traumatic neuropathology and to identify in vivo biomarkers of post-traumatic neurodegeneration. LETBI participants undergo extensive clinical evaluation using National Institutes of Health TBI Common Data Elements, proteomic and genomic analysis, structural and functional magnetic resonance imaging (MRI), and prospective consent for brain donation. Selected brain specimens undergo ultra-high resolution ex vivo MRI and histopathological evaluation including whole-mount analysis. Co-registration of ex vivo and in vivo MRI data enables identification of ex vivo lesions that were present during life. In vivo signatures of postmortem pathology are then correlated with cognitive and behavioral data to characterize the clinical phenotype(s) associated with pathological brain lesions. We illustrate the study methods and demonstrate proof of concept for this approach by reporting results from the first LETBI participant, who despite the presence of multiple in vivo and ex vivo pathoanatomic lesions had normal cognition and was functionally independent until her mid-80s. The LETBI project represents a multidisciplinary effort to characterize post-traumatic neuropathology and identify in vivo signatures of postmortem pathology in a prospective study.
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Affiliation(s)
- Brian L Edlow
- 1 Department of Neurology, Massachusetts General Hospital and Harvard Medical School , Boston, Massachusetts.,2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - C Dirk Keene
- 3 Department of Pathology, University of Washington , Seattle, Washington
| | - Daniel P Perl
- 4 Brain Tissue Repository and Neuropathology Core, Uniformed Services University of the Health Sciences , Bethesda, Maryland.,5 Department of Pathology, Uniformed Services University of the Health Sciences , Bethesda, Maryland
| | - Diego Iacono
- 4 Brain Tissue Repository and Neuropathology Core, Uniformed Services University of the Health Sciences , Bethesda, Maryland.,5 Department of Pathology, Uniformed Services University of the Health Sciences , Bethesda, Maryland.,6 Department of Neurology, Uniformed Services University of the Health Sciences , Bethesda, Maryland.,7 The Henry M. Jackson Foundation for the Advancement of Military Medicine , Bethesda, Maryland
| | - Rebecca D Folkerth
- 8 Department of Pathology, Brigham and Women's Hospital , Harvard Medical School, Boston, Massachusetts.,9 City of New York Office of the Chief Medical Examiner and New York University School of Medicine , New York, New York
| | - William Stewart
- 10 Department of Neuropathology, Queen Elizabeth University Hospital and Institute of Neuroscience and Psychology, University of Glasgow , United Kingdom
| | | | - Jean Augustinack
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Ramon Diaz-Arrastia
- 12 Department of Neurology and Center for Brain Injury and Repair, Hospital of the University of Pennsylvania , Philadelphia
| | - Camilo Estrada
- 13 Kaiser Permanente Washington Health Research Institute , Seattle, Washington
| | - Elissa Flannery
- 14 Department of Rehabilitation Medicine, Icahn School of Medicine at Mount Sinai , New York, New York
| | - Wayne A Gordon
- 14 Department of Rehabilitation Medicine, Icahn School of Medicine at Mount Sinai , New York, New York
| | - Thomas J Grabowski
- 15 Department of Neurology, University of Washington , Seattle, Washington.,16 Department of Radiology, University of Washington , Seattle, Washington
| | - Kelly Hansen
- 13 Kaiser Permanente Washington Health Research Institute , Seattle, Washington
| | - Jeanne Hoffman
- 17 Department of Rehabilitation Medicine, University of Washington , Seattle, Washington
| | - Christopher Kroenke
- 18 Advanced Imaging Research Center, Oregon Health and Science University , Portland, Oregon
| | - Eric B Larson
- 13 Kaiser Permanente Washington Health Research Institute , Seattle, Washington
| | - Patricia Lee
- 4 Brain Tissue Repository and Neuropathology Core, Uniformed Services University of the Health Sciences , Bethesda, Maryland.,7 The Henry M. Jackson Foundation for the Advancement of Military Medicine , Bethesda, Maryland
| | - Azma Mareyam
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Jennifer A McNab
- 19 Department of Radiology, Stanford University , Stanford, California
| | - Jeanne McPhee
- 14 Department of Rehabilitation Medicine, Icahn School of Medicine at Mount Sinai , New York, New York
| | - Allison L Moreau
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Anne Renz
- 13 Kaiser Permanente Washington Health Research Institute , Seattle, Washington
| | - KatieRose Richmire
- 13 Kaiser Permanente Washington Health Research Institute , Seattle, Washington
| | - Allison Stevens
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Cheuk Y Tang
- 20 Department of Radiology, Icahn School of Medicine at Mount Sinai , New York, New York
| | - Lee S Tirrell
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Emily H Trittschuh
- 21 Department of Psychiatry and Behavioral Sciences, University of Washington , Seattle, Washington.,22 Geriatric Research Education and Clinical Center , VA Puget Sound Health Care System, Seattle, Washington
| | - Andre van der Kouwe
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Ani Varjabedian
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Lawrence L Wald
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Ona Wu
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Anastasia Yendiki
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Liza Young
- 16 Department of Radiology, University of Washington , Seattle, Washington
| | - Lilla Zöllei
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Bruce Fischl
- 2 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School , Charlestown, Massachusetts
| | - Paul K Crane
- 23 Department of Medicine, University of Washington , Seattle, Washington
| | - Kristen Dams-O'Connor
- 14 Department of Rehabilitation Medicine, Icahn School of Medicine at Mount Sinai , New York, New York.,24 Department of Neurology, Icahn School of Medicine at Mount Sinai , New York, New York
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30
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Kenney K, Iacono D, Edlow BL, Katz DI, Diaz-Arrastia R, Dams-O'Connor K, Daneshvar DH, Stevens A, Moreau AL, Tirrell LS, Varjabedian A, Yendiki A, van der Kouwe A, Mareyam A, McNab JA, Gordon WA, Fischl B, McKee AC, Perl DP. Dementia After Moderate-Severe Traumatic Brain Injury: Coexistence of Multiple Proteinopathies. J Neuropathol Exp Neurol 2018; 77:50-63. [PMID: 29155947 DOI: 10.1093/jnen/nlx101] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 10/24/2017] [Indexed: 12/14/2022] Open
Abstract
We report the clinical, neuroimaging, and neuropathologic characteristics of 2 patients who developed early onset dementia after a moderate-severe traumatic brain injury (TBI). Neuropathological evaluation revealed abundant β-amyloid neuritic and cored plaques, diffuse β-amyloid plaques, and frequent hyperphosphorylated-tau neurofibrillary tangles (NFT) involving much of the cortex, including insula and mammillary bodies in both cases. Case 1 additionally showed NFTs in both the superficial and deep cortical layers, occasional perivascular and depth-of-sulci NFTs, and parietal white matter rarefaction, which corresponded with decreased parietal fiber tracts observed on ex vivo MRI. Case 2 additionally showed NFT predominance in the superficial layers of the cortex, hypothalamus and brainstem, diffuse Lewy bodies in the cortex, amygdala and brainstem, and intraneuronal TDP-43 inclusions. The neuropathologic diagnoses were atypical Alzheimer disease (AD) with features of chronic traumatic encephalopathy and white matter loss (Case 1), and atypical AD, dementia with Lewy bodies and coexistent TDP-43 pathology (Case 2). These findings support an epidemiological association between TBI and dementia and further characterize the variety of misfolded proteins that may accumulate after TBI. Analyses with comprehensive clinical, imaging, genetic, and neuropathological data are required to characterize the full clinicopathological spectrum associated with dementias occurring after moderate-severe TBI.
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Affiliation(s)
- Kimbra Kenney
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Diego Iacono
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Brian L Edlow
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Douglas I Katz
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Ramon Diaz-Arrastia
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Kristen Dams-O'Connor
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Daniel H Daneshvar
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Allison Stevens
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Allison L Moreau
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Lee S Tirrell
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Ani Varjabedian
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Anastasia Yendiki
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Andre van der Kouwe
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Azma Mareyam
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Jennifer A McNab
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Wayne A Gordon
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Bruce Fischl
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Ann C McKee
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
| | - Daniel P Perl
- Department of Neurology; Department of Pathology, F. Edward Hébert School of Medicine; Center for Neuroscience and Regenerative Medicine (CNRM), Uniformed Services University of the Health Sciences (USUHS), Bethesda, Maryland; The Henry M. Jackson Foundation for the Advancement of Military Research (HJF); Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston Massachusetts; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts; Department of Neurology; Alzheimer's Disease Center and CTE Program, Boston University School of Medicine, Boston, Massachusetts; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania;Department of Rehabilitation Medicine; Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York; Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, California; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts; VA Boston Healthcare System, Boston, Massachusetts; and Department of Pathology, Boston University School of Medicine, Boston, Massachusetts
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Yang G, Tian Q, Leuze C, Wintermark M, McNab JA. Double diffusion encoding MRI for the clinic. Magn Reson Med 2017; 80:507-520. [PMID: 29266375 DOI: 10.1002/mrm.27043] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Revised: 11/17/2017] [Accepted: 11/18/2017] [Indexed: 12/13/2022]
Abstract
PURPOSE The purpose of this study is to develop double diffusion encoding (DDE) MRI methods for clinical use. Microscopic diffusion anisotropy measurements from DDE promise greater specificity to changes in tissue microstructure compared with conventional diffusion tensor imaging, but implementation of DDE sequences on whole-body MRI scanners is challenging because of the limited gradient strengths and lengthy acquisition times. METHODS A custom single-refocused DDE sequence was implemented on a 3T whole-body scanner. The DDE gradient orientation scheme and sequence parameters were optimized based on a Gaussian diffusion assumption. Using an optimized 5-min DDE acquisition, microscopic fractional anisotropy (μFA) maps were acquired for the first time in multiple sclerosis patients. RESULTS Based on simulations and in vivo human measurements, six parallel and six orthogonal diffusion gradient pairs were found to be the minimum number of diffusion gradient pairs necessary to produce a rotationally invariant measurement of μFA. Simulations showed that optimal precision and accuracy of μFA measurements were obtained using b-values between 1500 and 3000 s/mm2 . The μFA maps showed improved delineation of multiple sclerosis lesions compared with conventional fractional anisotropy and distinct contrast from T2 -weighted fluid attenuated inversion recovery and T1 -weighted imaging. CONCLUSION The μFA maps can be measured using DDE in a clinical setting and may provide new opportunities for characterizing multiple sclerosis lesions and other types of tissue degeneration. Magn Reson Med 80:507-520, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Grant Yang
- Department of Electrical Engineering, Stanford University, Stanford, California, USA.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, California, USA.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Christoph Leuze
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, California, USA
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Leuze C, Aswendt M, Ferenczi E, Liu CW, Hsueh B, Goubran M, Tian Q, Steinberg G, Zeineh MM, Deisseroth K, McNab JA. The separate effects of lipids and proteins on brain MRI contrast revealed through tissue clearing. Neuroimage 2017; 156:412-422. [PMID: 28411157 DOI: 10.1016/j.neuroimage.2017.04.021] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 03/20/2017] [Accepted: 04/08/2017] [Indexed: 01/01/2023] Open
Abstract
Despite the widespread use of magnetic resonance imaging (MRI) of the brain, the relative contribution of different biological components (e.g. lipids and proteins) to structural MRI contrasts (e.g., T1, T2, T2*, proton density, diffusion) remains incompletely understood. This limitation can undermine the interpretation of clinical MRI and hinder the development of new contrast mechanisms. Here, we determine the respective contribution of lipids and proteins to MRI contrast by removing lipids and preserving proteins in mouse brains using CLARITY. We monitor the temporal dynamics of tissue clearance via NMR spectroscopy, protein assays and optical emission spectroscopy. MRI of cleared brain tissue showed: 1) minimal contrast on standard MRI sequences; 2) increased relaxation times; and 3) diffusion rates close to free water. We conclude that lipids, present in myelin and membranes, are a dominant source of MRI contrast in brain tissue.
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Affiliation(s)
- Christoph Leuze
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | - Markus Aswendt
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Emily Ferenczi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Corey W Liu
- Stanford Magnetic Resonance Laboratory, Stanford University, Stanford, CA, USA
| | - Brian Hsueh
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Maged Goubran
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Qiyuan Tian
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Gary Steinberg
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | | | - Karl Deisseroth
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
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Chiew M, Graedel NN, McNab JA, Smith SM, Miller KL. Accelerating functional MRI using fixed-rank approximations and radial-cartesian sampling. Magn Reson Med 2016; 76:1825-1836. [PMID: 26777798 PMCID: PMC4847647 DOI: 10.1002/mrm.26079] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 11/17/2015] [Accepted: 11/18/2015] [Indexed: 11/17/2022]
Abstract
PURPOSE Recently, k-t FASTER (fMRI Accelerated in Space-time by means of Truncation of Effective Rank) was introduced for rank-constrained acceleration of fMRI data acquisition. Here we demonstrate improvements achieved through a hybrid three-dimensional radial-Cartesian sampling approach that allows posthoc selection of acceleration factors, as well as incorporation of coil sensitivity encoding in the reconstruction. METHODS The multicoil rank-constrained reconstruction used hard thresholding and shrinkage on matrix singular values of the space-time data matrix, using sensitivity encoding and the nonuniform Fast Fourier Transform to enforce data consistency in the multicoil non-Cartesian k-t domain. Variable acceleration factors were made possible using a radial increment based on the golden ratio. Both retrospective and prospectively under-sampled data were used to assess the fidelity of the enhancements to the k-t FASTER technique in resting and task-fMRI data. RESULTS The improved k-t FASTER is capable of tailoring acceleration factors for recovery of different signal components, achieving up to R = 12.5 acceleration in visual-motor task data. The enhancements reduce data matrix reconstruction errors even at much higher acceleration factors when compared directly with the original k-t FASTER approach. CONCLUSION We have shown that k-t FASTER can be used to significantly accelerate fMRI data acquisition with little penalty to data quality. Magn Reson Med 76:1825-1836, 2016. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Mark Chiew
- FMRIB CentreUniversity of OxfordOxfordUnited Kingdom
| | | | - Jennifer A. McNab
- R.M. Lucas Center for ImagingStanford UniversityStanfordCaliforniaUSA
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Tian Q, Rokem A, Folkerth RD, Nummenmaa A, Fan Q, Edlow BL, McNab JA. Q-space truncation and sampling in diffusion spectrum imaging. Magn Reson Med 2016; 76:1750-1763. [PMID: 26762670 PMCID: PMC4942411 DOI: 10.1002/mrm.26071] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Revised: 10/30/2015] [Accepted: 11/05/2015] [Indexed: 11/11/2022]
Abstract
PURPOSE To characterize the q-space truncation and sampling on the spin-displacement probability density function (PDF) in diffusion spectrum imaging (DSI). METHODS DSI data were acquired using the MGH-USC connectome scanner (Gmax = 300 mT/m) with bmax = 30,000 s/mm2 , 17 × 17 × 17, 15 × 15 × 15 and 11 × 11 × 11 grids in ex vivo human brains and bmax = 10,000 s/mm2 , 11 × 11 × 11 grid in vivo. An additional in vivo scan using bmax =7,000 s/mm2 , 11 × 11 × 11 grid was performed with a derated gradient strength of 40 mT/m. PDFs and orientation distribution functions (ODFs) were reconstructed with different q-space filtering and PDF integration lengths, and from down-sampled data by factors of two and three. RESULTS Both ex vivo and in vivo data showed Gibbs ringing in PDFs, which becomes the main source of artifact in the subsequently reconstructed ODFs. For down-sampled data, PDFs interfere with the first replicas or their ringing, leading to obscured orientations in ODFs. CONCLUSION The minimum required q-space sampling density corresponds to a field-of-view approximately equal to twice the mean displacement distance (MDD) of the tissue. The 11 × 11 × 11 grid is suitable for both ex vivo and in vivo DSI experiments. To minimize the effects of Gibbs ringing, ODFs should be reconstructed from unfiltered q-space data with the integration length over the PDF constrained to around the MDD. Magn Reson Med 76:1750-1763, 2016. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Qiyuan Tian
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Ariel Rokem
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Rebecca D. Folkerth
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Brian L. Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jennifer A. McNab
- Department of Radiology, Stanford University, Stanford, California, USA
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Graedel NN, McNab JA, Chiew M, Miller KL. Motion correction for functional MRI with three-dimensional hybrid radial-Cartesian EPI. Magn Reson Med 2016; 78:527-540. [PMID: 27604503 PMCID: PMC5516130 DOI: 10.1002/mrm.26390] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Revised: 07/26/2016] [Accepted: 07/27/2016] [Indexed: 11/13/2022]
Abstract
Purpose Subject motion is a major source of image degradation for functional MRI (fMRI), especially when using multishot sequences like three‐dimensional (3D EPI). We present a hybrid radial‐Cartesian 3D EPI trajectory enabling motion correction in k‐space for functional MRI. Methods The EPI “blades” of the 3D hybrid radial‐Cartesian EPI sequence, called TURBINE, are rotated about the phase‐encoding axis to fill out a cylinder in 3D k‐space. Angular blades are acquired over time using a golden‐angle rotation increment, allowing reconstruction at flexible temporal resolution. The self‐navigating properties of the sequence are used to determine motion parameters from a high temporal‐resolution navigator time series. The motion is corrected in k‐space as part of the image reconstruction, and evaluated for experiments with both cued and natural motion. Results We demonstrate that the motion correction works robustly and that we can achieve substantial artifact reduction as well as improvement in temporal signal‐to‐noise ratio and fMRI activation in the presence of both severe and subtle motion. Conclusion We show the potential for hybrid radial‐Cartesian 3D EPI to substantially reduce artifacts for application in fMRI, especially for subject groups with significant head motion. The motion correction approach does not prolong the scan, and no extra hardware is required. Magn Reson Med 78:527–540, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Nadine N Graedel
- FMRIB Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Mark Chiew
- FMRIB Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Karla L Miller
- FMRIB Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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Abstract
Homeostatic adaptations to stress are regulated by interactions between the brainstem and regions of the forebrain, including limbic sites related to respiratory, autonomic, affective, and cognitive processing. Neuroanatomic connections between these homeostatic regions, however, have not been thoroughly identified in the human brain. In this study, we perform diffusion spectrum imaging tractography using the MGH-USC Connectome MRI scanner to visualize structural connections in the human brain linking autonomic and cardiorespiratory nuclei in the midbrain, pons, and medulla oblongata with forebrain sites critical to homeostatic control. Probabilistic tractography analyses in six healthy adults revealed connections between six brainstem nuclei and seven forebrain regions, several over long distances between the caudal medulla and cerebral cortex. The strongest evidence for brainstem-homeostatic forebrain connectivity in this study was between the brainstem midline raphe and the medial temporal lobe. The subiculum and amygdala were the sampled forebrain nodes with the most extensive brainstem connections. Within the human brainstem-homeostatic forebrain connectome, we observed that a lateral forebrain bundle, whose connectivity is distinct from that of rodents and nonhuman primates, is the primary conduit for connections between the brainstem and medial temporal lobe. This study supports the concept that interconnected brainstem and forebrain nodes form an integrated central homeostatic network (CHN) in the human brain. Our findings provide an initial foundation for elucidating the neuroanatomic basis of homeostasis in the normal human brain, as well as for mapping CHN disconnections in patients with disorders of homeostasis, including sudden and unexpected death, and epilepsy.
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Affiliation(s)
- Brian L. Edlow
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Jennifer A. McNab
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
- Department of Radiology, R.M. Lucas Center for Imaging, Stanford University, Stanford, California
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Hannah C. Kinney
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
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Huang SY, Tobyne SM, Nummenmaa A, Witzel T, Wald LL, McNab JA, Klawiter EC. Characterization of Axonal Disease in Patients with Multiple Sclerosis Using High-Gradient-Diffusion MR Imaging. Radiology 2016; 280:244-51. [PMID: 26859256 DOI: 10.1148/radiol.2016151582] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To evaluate the ability of high-gradient-diffusion magnetic resonance (MR) imaging by using gradient strengths of up to 300 mT/m to depict axonal disease in lesions and normal-appearing white matter (NAWM) in patients with multiple sclerosis (MS) and to compare high-gradient-diffusion MR findings in these patients with those in healthy control subjects. Materials and Methods In this HIPAA-compliant institutional review board-approved prospective study in which all subjects provided written informed consent, six patients with relapsing-remitting MS and six healthy control subjects underwent diffusion-weighted imaging with a range of diffusion weightings performed with a 3-T human MR imager by using gradient strengths of up to 300 mT/m. A model of intra-axonal, extra-axonal, and free water diffusion was fitted to obtain estimates of axon diameter and density. Differences in axon diameter and density between lesions and NAWM in patients with MS were assessed by using the nonparametric Wilcoxon matched-pairs signed rank test, and differences between NAWM in subjects with MS and white matter in healthy control subjects were assessed by using the Mann-Whitney U test. Results MS lesions showed increased mean axon diameter (10.3 vs 7.9 μm in the genu, 10.4 vs 9.3 μm in the body, and 10.6 vs 8.2 μm in the splenium; P < .05) and decreased axon density ([0.48 vs 1.1] × 10(10)/m(2) in the genu, [0.40 vs 0.70] × 10(10)/m(2) in the body, and [0.35 vs 1.1] × 10(10)/m(2) in the splenium; P < .05) compared with adjacent NAWM. No significant difference in mean axon diameter or axon density was detected between NAWM in subjects with MS and white matter in healthy control subjects. Conclusion High-gradient-diffusion MR imaging using gradient strengths of up to 300 mT/m can be used to characterize axonal disease in patients with MS, with results that agree with known trends from neuropathologic data showing increased axon diameter and decreased axon density in MS lesions when compared with NAWM. (©) RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Susie Y Huang
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129 (S.Y.H., A.N., T.W., L.L.W.); Department of Neurology, Massachusetts General Hospital, Boston, Mass (S.M.T., E.C.K.); and Richard M. Lucas Center for Imaging, Department of Radiology, Stanford University, Stanford, Calif (J.A.M.)
| | - Sean M Tobyne
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129 (S.Y.H., A.N., T.W., L.L.W.); Department of Neurology, Massachusetts General Hospital, Boston, Mass (S.M.T., E.C.K.); and Richard M. Lucas Center for Imaging, Department of Radiology, Stanford University, Stanford, Calif (J.A.M.)
| | - Aapo Nummenmaa
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129 (S.Y.H., A.N., T.W., L.L.W.); Department of Neurology, Massachusetts General Hospital, Boston, Mass (S.M.T., E.C.K.); and Richard M. Lucas Center for Imaging, Department of Radiology, Stanford University, Stanford, Calif (J.A.M.)
| | - Thomas Witzel
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129 (S.Y.H., A.N., T.W., L.L.W.); Department of Neurology, Massachusetts General Hospital, Boston, Mass (S.M.T., E.C.K.); and Richard M. Lucas Center for Imaging, Department of Radiology, Stanford University, Stanford, Calif (J.A.M.)
| | - Lawrence L Wald
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129 (S.Y.H., A.N., T.W., L.L.W.); Department of Neurology, Massachusetts General Hospital, Boston, Mass (S.M.T., E.C.K.); and Richard M. Lucas Center for Imaging, Department of Radiology, Stanford University, Stanford, Calif (J.A.M.)
| | - Jennifer A McNab
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129 (S.Y.H., A.N., T.W., L.L.W.); Department of Neurology, Massachusetts General Hospital, Boston, Mass (S.M.T., E.C.K.); and Richard M. Lucas Center for Imaging, Department of Radiology, Stanford University, Stanford, Calif (J.A.M.)
| | - Eric C Klawiter
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129 (S.Y.H., A.N., T.W., L.L.W.); Department of Neurology, Massachusetts General Hospital, Boston, Mass (S.M.T., E.C.K.); and Richard M. Lucas Center for Imaging, Department of Radiology, Stanford University, Stanford, Calif (J.A.M.)
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Duval T, McNab JA, Setsompop K, Witzel T, Schneider T, Huang SY, Keil B, Klawiter EC, Wald LL, Cohen-Adad J. In vivo mapping of human spinal cord microstructure at 300mT/m. Neuroimage 2015; 118:494-507. [PMID: 26095093 DOI: 10.1016/j.neuroimage.2015.06.038] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Revised: 05/27/2015] [Accepted: 06/11/2015] [Indexed: 11/19/2022] Open
Abstract
The ability to characterize white matter microstructure non-invasively has important applications for the diagnosis and follow-up of several neurological diseases. There exists a family of diffusion MRI techniques, such as AxCaliber, that provide indices of axon microstructure, such as axon diameter and density. However, to obtain accurate measurements of axons with small diameters (<5μm), these techniques require strong gradients, i.e. an order of magnitude higher than the 40-80mT/m currently available in clinical systems. In this study we acquired AxCaliber diffusion data at a variety of different q-values and diffusion times in the spinal cord of five healthy subjects using a 300mT/m whole body gradient system. Acquisition and processing were optimized using state-of-the-art methods (e.g., 64-channel coil, template-based analysis). Results consistently show an average axon diameter of 4.5+/-1.1μm in the spinal cord white matter. Diameters ranged from 3.0μm (gracilis) to 5.9μm (spinocerebellar tracts). Values were similar across laterality (left-right), but statistically different across spinal cord pathways (p<10(-5)). The observed trends are similar to those observed in animal histology. This study shows, for the first time, in vivo mapping of axon diameter in the spinal cord at 300mT/m, thus creating opportunities for applications in spinal cord diseases.
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Affiliation(s)
- Tanguy Duval
- Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Jennifer A McNab
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Kawin Setsompop
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Thomas Witzel
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Torben Schneider
- NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Institute of Neurology, London, London, United Kingdom
| | - Susie Yi Huang
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Boris Keil
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Eric C Klawiter
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Lawrence L Wald
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - Julien Cohen-Adad
- Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
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Huang SY, Nummenmaa A, Witzel T, Duval T, Cohen-Adad J, Wald LL, McNab JA. The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter. Neuroimage 2014; 106:464-72. [PMID: 25498429 DOI: 10.1016/j.neuroimage.2014.12.008] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Revised: 11/01/2014] [Accepted: 12/03/2014] [Indexed: 10/24/2022] Open
Abstract
Diffusion magnetic resonance imaging (MRI) methods for axon diameter mapping benefit from higher maximum gradient strengths than are currently available on commercial human scanners. Using a dedicated high-gradient 3T human MRI scanner with a maximum gradient strength of 300 mT/m, we systematically studied the effect of gradient strength on in vivo axon diameter and density estimates in the human corpus callosum. Pulsed gradient spin echo experiments were performed in a single scan session lasting approximately 2h on each of three human subjects. The data were then divided into subsets with maximum gradient strengths of 77, 145, 212, and 293 mT/m and diffusion times encompassing short (16 and 25 ms) and long (60 and 94 ms) diffusion time regimes. A three-compartment model of intra-axonal diffusion, extra-axonal diffusion, and free diffusion in cerebrospinal fluid was fitted to the data using a Markov chain Monte Carlo approach. For the acquisition parameters, model, and fitting routine used in our study, it was found that higher maximum gradient strengths decreased the mean axon diameter estimates by two to three fold and decreased the uncertainty in axon diameter estimates by more than half across the corpus callosum. The exclusive use of longer diffusion times resulted in axon diameter estimates that were up to two times larger than those obtained with shorter diffusion times. Axon diameter and density maps appeared less noisy and showed improved contrast between different regions of the corpus callosum with higher maximum gradient strength. Known differences in axon diameter and density between the genu, body, and splenium of the corpus callosum were preserved and became more reproducible at higher maximum gradient strengths. Our results suggest that an optimal q-space sampling scheme for estimating in vivo axon diameters should incorporate the highest possible gradient strength. The improvement in axon diameter and density estimates that we demonstrate from increasing maximum gradient strength will inform protocol development and encourage the adoption of higher maximum gradient strengths for use in commercial human scanners.
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Affiliation(s)
- Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Thomas Witzel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Tanguy Duval
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, QC, Canada
| | - Julien Cohen-Adad
- Institute of Biomedical Engineering, Ecole Polytechnique de Montreal, Montreal, QC, Canada
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Jennifer A McNab
- Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, United States
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Janssens T, Keil B, Serano P, Mareyam A, McNab JA, Wald LL, Vanduffel W. A 22-channel receive array with Helmholtz transmit coil for anesthetized macaque MRI at 3 T. NMR Biomed 2013; 26:1431-40. [PMID: 23703859 DOI: 10.1002/nbm.2970] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2013] [Revised: 04/02/2013] [Accepted: 04/04/2013] [Indexed: 05/15/2023]
Abstract
The macaque monkey is an important model for cognitive and sensory neuroscience that has been used extensively in behavioral, electrophysiological, molecular and, more recently, neuroimaging studies. However, macaque MRI has unique technical differences relative to human MRI, such as the geometry of highly parallel receive arrays, which must be addressed to optimize imaging performance. A 22-channel receive coil array was constructed specifically for rapid high-resolution anesthetized macaque monkey MRI at 3 T. A local Helmholtz transmit coil was used for excitation. Signal-to-noise ratios (SNRs) and noise amplification for parallel imaging were compared with those of single- and four-channel receive coils routinely used for macaque MRI. The 22-channel coil yielded significant improvements in SNR throughout the brain. Using this coil, the SNR in peripheral brain was 2.4 and 1.7 times greater than that obtained with single- or four-channel coils, respectively. In the central brain, the SNR gain was 1.5 times that of both the single- and four-channel coils. Finally, the performance of the array for functional, anatomical and diffusion-weighted imaging was evaluated. For all three modalities, the use of the 22-channel array allowed for high-resolution and accelerated image acquisition.
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Affiliation(s)
- Thomas Janssens
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Laboratory for Neuro- and Psychophysiology, K.U. Leuven Medical School, Campus Gasthuisberg, Leuven, Belgium
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41
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Nummenmaa A, McNab JA, Savadjiev P, Okada Y, Hämäläinen MS, Wang R, Wald LL, Pascual-Leone A, Wedeen VJ, Raij T. Targeting of white matter tracts with transcranial magnetic stimulation. Brain Stimul 2013; 7:80-4. [PMID: 24220599 DOI: 10.1016/j.brs.2013.10.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2013] [Revised: 10/02/2013] [Accepted: 10/09/2013] [Indexed: 10/26/2022] Open
Abstract
BACKGROUND TMS activations of white matter depend not only on the distance from the coil, but also on the orientation of the axons relative to the TMS-induced electric field, and especially on axonal bends that create strong local field gradient maxima. Therefore, tractography contains potentially useful information for TMS targeting. OBJECTIVE/METHODS Here, we utilized 1-mm resolution diffusion and structural T1-weighted MRI to construct large-scale tractography models, and localized TMS white matter activations in motor cortex using electromagnetic forward modeling in a boundary element model (BEM). RESULTS As expected, in sulcal walls, pyramidal cell axonal bends created preferred sites of activation that were not found in gyral crowns. The model agreed with the well-known coil orientation sensitivity of motor cortex, and also suggested unexpected activation distributions emerging from the E-field and tract configurations. We further propose a novel method for computing the optimal coil location and orientation to maximally stimulate a pre-determined axonal bundle. CONCLUSIONS Diffusion MRI tractography with electromagnetic modeling may improve spatial specificity and efficacy of TMS.
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Affiliation(s)
- Aapo Nummenmaa
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, MA, USA; Harvard Medical School, MA, USA
| | - Jennifer A McNab
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, MA, USA; Harvard Medical School, MA, USA; Department of Radiology, Stanford University, CA, USA
| | - Peter Savadjiev
- Harvard Medical School, MA, USA; Brigham and Women's Hospital, MA, USA
| | - Yoshio Okada
- Harvard Medical School, MA, USA; Department of Neurology, Boston Children's Hospital, MA, USA
| | - Matti S Hämäläinen
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, MA, USA; Harvard Medical School, MA, USA; Harvard-MIT Division of Health Sciences and Technology, MA, USA
| | - Ruopeng Wang
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, MA, USA; Harvard Medical School, MA, USA
| | - Lawrence L Wald
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, MA, USA; Harvard Medical School, MA, USA; Harvard-MIT Division of Health Sciences and Technology, MA, USA
| | - Alvaro Pascual-Leone
- Harvard Medical School, MA, USA; Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, MA, USA
| | - Van J Wedeen
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, MA, USA; Harvard Medical School, MA, USA
| | - Tommi Raij
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, MA, USA; Harvard Medical School, MA, USA.
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Setsompop K, Kimmlingen R, Eberlein E, Witzel T, Cohen-Adad J, McNab JA, Keil B, Tisdall MD, Hoecht P, Dietz P, Cauley SF, Tountcheva V, Matschl V, Lenz VH, Heberlein K, Potthast A, Thein H, Van Horn J, Toga A, Schmitt F, Lehne D, Rosen BR, Wedeen V, Wald LL. Pushing the limits of in vivo diffusion MRI for the Human Connectome Project. Neuroimage 2013; 80:220-33. [PMID: 23707579 DOI: 10.1016/j.neuroimage.2013.05.078] [Citation(s) in RCA: 353] [Impact Index Per Article: 32.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2013] [Revised: 05/07/2013] [Accepted: 05/13/2013] [Indexed: 11/25/2022] Open
Abstract
Perhaps more than any other "-omics" endeavor, the accuracy and level of detail obtained from mapping the major connection pathways in the living human brain with diffusion MRI depend on the capabilities of the imaging technology used. The current tools are remarkable; allowing the formation of an "image" of the water diffusion probability distribution in regions of complex crossing fibers at each of half a million voxels in the brain. Nonetheless our ability to map the connection pathways is limited by the image sensitivity and resolution, and also the contrast and resolution in encoding of the diffusion probability distribution. The goal of our Human Connectome Project (HCP) is to address these limiting factors by re-engineering the scanner from the ground up to optimize the high b-value, high angular resolution diffusion imaging needed for sensitive and accurate mapping of the brain's structural connections. Our efforts were directed based on the relative contributions of each scanner component. The gradient subsection was a major focus since gradient amplitude is central to determining the diffusion contrast, the amount of T2 signal loss, and the blurring of the water PDF over the course of the diffusion time. By implementing a novel 4-port drive geometry and optimizing size and linearity for the brain, we demonstrate a whole-body sized scanner with G(max) = 300 mT/m on each axis capable of the sustained duty cycle needed for diffusion imaging. The system is capable of slewing the gradient at a rate of 200 T/m/s as needed for the EPI image encoding. In order to enhance the efficiency of the diffusion sequence we implemented a FOV shifting approach to Simultaneous MultiSlice (SMS) EPI capable of unaliasing 3 slices excited simultaneously with a modest g-factor penalty allowing us to diffusion encode whole brain volumes with low TR and TE. Finally we combine the multi-slice approach with a compressive sampling reconstruction to sufficiently undersample q-space to achieve a DSI scan in less than 5 min. To augment this accelerated imaging approach we developed a 64-channel, tight-fitting brain array coil and show its performance benefit compared to a commercial 32-channel coil at all locations in the brain for these accelerated acquisitions. The technical challenges of developing the over-all system are discussed as well as results from SNR comparisons, ODF metrics and fiber tracking comparisons. The ultra-high gradients yielded substantial and immediate gains in the sensitivity through reduction of TE and improved signal detection and increased efficiency of the DSI or HARDI acquisition, accuracy and resolution of diffusion tractography, as defined by identification of known structure and fiber crossing.
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Affiliation(s)
- K Setsompop
- AA Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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McNab JA, Edlow BL, Witzel T, Huang SY, Bhat H, Heberlein K, Feiweier T, Liu K, Keil B, Cohen-Adad J, Tisdall MD, Folkerth RD, Kinney HC, Wald LL. The Human Connectome Project and beyond: initial applications of 300 mT/m gradients. Neuroimage 2013; 80:234-45. [PMID: 23711537 DOI: 10.1016/j.neuroimage.2013.05.074] [Citation(s) in RCA: 248] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2013] [Revised: 05/07/2013] [Accepted: 05/13/2013] [Indexed: 01/01/2023] Open
Abstract
The engineering of a 3 T human MRI scanner equipped with 300 mT/m gradients - the strongest gradients ever built for an in vivo human MRI scanner - was a major component of the NIH Blueprint Human Connectome Project (HCP). This effort was motivated by the HCP's goal of mapping, as completely as possible, the macroscopic structural connections of the in vivo healthy, adult human brain using diffusion tractography. Yet, the 300 mT/m gradient system is well suited to many additional types of diffusion measurements. Here, we present three initial applications of the 300 mT/m gradients that fall outside the immediate scope of the HCP. These include: 1) diffusion tractography to study the anatomy of consciousness and the mechanisms of brain recovery following traumatic coma; 2) q-space measurements of axon diameter distributions in the in vivo human brain and 3) postmortem diffusion tractography as an adjunct to standard histopathological analysis. We show that the improved sensitivity and diffusion-resolution provided by the gradients are rapidly enabling human applications of techniques that were previously possible only for in vitro and animal models on small-bore scanners, thereby creating novel opportunities to map the microstructure of the human brain in health and disease.
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Affiliation(s)
- Jennifer A McNab
- Department of Radiology, Stanford University, RM Lucas Center for Imaging, Stanford, CA, USA.
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McNab JA, Polimeni JR, Wang R, Augustinack JC, Fujimoto K, Stevens A, Triantafyllou C, Janssens T, Farivar R, Folkerth RD, Vanduffel W, Wald LL. Corrigendum to "Surface based analysis of diffusion orientation for identifying architectonic domains in the in vivo human cortex" [NeuroImage 69 (2013) 87-100]. Neuroimage 2013; 81:505. [PMID: 30180375 DOI: 10.1016/j.neuroimage.2013.04.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Jennifer A McNab
- R.M. Lucas Center for Imaging, Radiology, Stanford University, Stanford, CA, USA; A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Jonathan R Polimeni
- A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ruopeng Wang
- A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jean C Augustinack
- A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kyoko Fujimoto
- A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Allison Stevens
- A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christina Triantafyllou
- A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thomas Janssens
- A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Laboratory for Neuro- and Psychophysiology, K.U. Leuven Medical School, Campus Gasthuisberg, Leuven, Belgium
| | - Reza Farivar
- A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; McGill Vision Research Unit, Department of Opthalmology, McGill University, Montreal, Canada
| | - Rebecca D Folkerth
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Children's Hospital Boston, Harvard Medical School, Boston, MA, USA
| | - Wim Vanduffel
- A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Laboratory for Neuro- and Psychophysiology, K.U. Leuven Medical School, Campus Gasthuisberg, Leuven, Belgium
| | - Lawrence L Wald
- A.A. Martinos Center for Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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McNab JA, Polimeni JR, Wang R, Augustinack JC, Fujimoto K, Stevens A, Triantafyllou C, Janssens T, Farivar R, Folkerth RD, Vanduffel W, Wald LL. Surface based analysis of diffusion orientation for identifying architectonic domains in the in vivo human cortex. Neuroimage 2013; 69:87-100. [PMID: 23247190 PMCID: PMC3557597 DOI: 10.1016/j.neuroimage.2012.11.065] [Citation(s) in RCA: 113] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2012] [Revised: 11/27/2012] [Accepted: 11/30/2012] [Indexed: 11/15/2022] Open
Abstract
Diffusion tensor MRI is sensitive to the coherent structure of brain tissue and is commonly used to study large-scale white matter structure. Diffusion in gray matter is more isotropic, however, several groups have observed coherent patterns of diffusion anisotropy within the cerebral cortical gray matter. We extend the study of cortical diffusion anisotropy by relating it to the local coordinate system of the folded cerebral cortex. We use 1mm and sub-millimeter isotropic resolution diffusion imaging to perform a laminar analysis of the principal diffusion orientation, fractional anisotropy, mean diffusivity and partial volume effects. Data from 6 in vivo human subjects, a fixed human brain specimen and an anesthetized macaque were examined. Large regions of cortex show a radial diffusion orientation. In vivo human and macaque data displayed a sharp transition from radial to tangential diffusion orientation at the border between primary motor and somatosensory cortex, and some evidence of tangential diffusion in secondary somatosensory cortex and primary auditory cortex. Ex vivo diffusion imaging in a human tissue sample showed some tangential diffusion orientation in S1 but mostly radial diffusion orientations in both M1 and S1.
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Affiliation(s)
- Jennifer A McNab
- R.M. Lucas Center for Imaging, Radiology, Stanford University, Stanford, CA 94305, USA.
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Kolasinski J, Stagg CJ, Chance SA, Deluca GC, Esiri MM, Chang EH, Palace JA, McNab JA, Jenkinson M, Miller KL, Johansen-Berg H. A combined post-mortem magnetic resonance imaging and quantitative histological study of multiple sclerosis pathology. ACTA ACUST UNITED AC 2013; 135:2938-51. [PMID: 23065787 PMCID: PMC3470716 DOI: 10.1093/brain/aws242] [Citation(s) in RCA: 106] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Multiple sclerosis is a chronic inflammatory neurological condition characterized by focal and diffuse neurodegeneration and demyelination throughout the central nervous system. Factors influencing the progression of pathology are poorly understood. One hypothesis is that anatomical connectivity influences the spread of neurodegeneration. This predicts that measures of neurodegeneration will correlate most strongly between interconnected structures. However, such patterns have been difficult to quantify through post-mortem neuropathology or in vivo scanning alone. In this study, we used the complementary approaches of whole brain post-mortem magnetic resonance imaging and quantitative histology to assess patterns of multiple sclerosis pathology. Two thalamo-cortical projection systems were considered based on their distinct neuroanatomy and their documented involvement in multiple sclerosis: lateral geniculate nucleus to primary visual cortex and mediodorsal nucleus of the thalamus to prefrontal cortex. Within the anatomically distinct thalamo-cortical projection systems, magnetic resonance imaging derived cortical thickness was correlated significantly with both a measure of myelination in the connected tract and a measure of connected thalamic nucleus cell density. Such correlations did not exist between these markers of neurodegeneration across different thalamo-cortical systems. Magnetic resonance imaging lesion analysis depicted clearly demarcated subcortical lesions impinging on the white matter tracts of interest; however, quantitation of the extent of lesion-tract overlap failed to demonstrate any appreciable association with the severity of markers of diffuse pathology within each thalamo-cortical projection system. Diffusion-weighted magnetic resonance imaging metrics in both white matter tracts were correlated significantly with a histologically derived measure of tract myelination. These data demonstrate for the first time the relevance of functional anatomical connectivity to the spread of multiple sclerosis pathology in a ‘tract-specific’ pattern. Furthermore, the persisting relationship between metrics from post-mortem diffusion-weighted magnetic resonance imaging and histological measures from fixed tissue further validates the potential of imaging for future neuropathological studies.
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Affiliation(s)
- James Kolasinski
- Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, OX3 9DU, UK
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47
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Janssens T, Keil B, Farivar R, McNab JA, Polimeni JR, Gerits A, Arsenault JT, Wald LL, Vanduffel W. An implanted 8-channel array coil for high-resolution macaque MRI at 3T. Neuroimage 2012; 62:1529-36. [PMID: 22609793 DOI: 10.1016/j.neuroimage.2012.05.028] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2012] [Accepted: 05/10/2012] [Indexed: 10/28/2022] Open
Abstract
An 8-channel receive coil array was constructed and implanted adjacent to the skull in a male rhesus monkey in order to improve the sensitivity of (functional) brain imaging. The permanent implant was part of an acrylic headpost assembly and only the coil element loop wires were implanted. The tuning, matching, and preamplifier circuitry was connected via a removable external assembly. Signal-to-noise ratio (SNR) and noise amplification for parallel imaging were compared to single-, 4-, and 8-channel external receive-only coils routinely used for macaque fMRI. In vivo measurements showed significantly improved SNR within the brain for the implanted versus the external coils. Within a region-of-interest covering the cerebral cortex, we observed a 5.4-, 3.6-fold, and 3.4-fold increase in SNR compared to the external single-, 4-, and 8-channel coils, respectively. In the center of the brain, the implanted array maintained a 2.4×, 2.5×, and 2.1× higher SNR, respectively compared to the external coils. The array performance was evaluated for anatomical, diffusion tensor and functional brain imaging. This study suggests that a stable implanted phased-array coil can be used in macaque MRI to substantially increase the spatial resolution for anatomical, diffusion tensor, and functional imaging.
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Affiliation(s)
- T Janssens
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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48
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Kolasinski J, Stagg CJ, Chance S, Esiri M, Chang E, Palace JA, McNab JA, Jenkinson M, Miller K, Johansen-Berg H. 139 Comparison of histological and diffusion-weighted MRI techniques in the analysis of post mortem multiple sclerosis brains. J Neurol Neurosurg Psychiatry 2012. [DOI: 10.1136/jnnp-2011-301993.181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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49
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Cohen-Adad J, Polimeni JR, Helmer KG, Benner T, McNab JA, Wald LL, Rosen BR, Mainero C. T₂* mapping and B₀ orientation-dependence at 7 T reveal cyto- and myeloarchitecture organization of the human cortex. Neuroimage 2012; 60:1006-14. [PMID: 22270354 DOI: 10.1016/j.neuroimage.2012.01.053] [Citation(s) in RCA: 124] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2011] [Revised: 12/08/2011] [Accepted: 01/05/2012] [Indexed: 11/18/2022] Open
Abstract
Ultra-high field MRI (≥ 7 T) has recently shown great sensitivity to depict patterns of tissue microarchitecture. Moreover, recent studies have demonstrated a dependency between T₂* and orientation of white matter fibers with respect to the main magnetic field B₀. In this study we probed the potential of T₂* mapping at 7 T to provide new markers of cortical architecture. We acquired multi-echo measurements at 7 T and mapped T₂* over the entire cortex of eight healthy individuals using surface-based analysis. B₀ dependence was tested by computing the angle θ(z) between the normal of the surface and the direction of B₀, then fitting T₂*(θ(z)) using model from the literature. Average T₂* in the cortex was 32.20 +/- 1.35 ms. Patterns of lower T₂* were detected in the sensorimotor, visual and auditory cortices, likely reflecting higher myelin content. Significantly lower T₂* was detected in the left hemisphere of the auditory region (p<0.005), suggesting higher myelin content, in accordance with previous investigations. B₀ orientation dependence was detected in some areas of the cortex, the strongest being in the primary motor cortex (∆R₂*=4.10 Hz). This study demonstrates that quantitative T₂* measures at 7 T MRI can reveal patterns of cytoarchitectural organization of the human cortex in vivo and that B₀ orientation dependence can probe the coherency and orientation of gray matter fibers in the cortex, shedding light into the potential use of this type of contrast to characterize cyto-/myeloarchitecture and to understand the pathophysiology of diseases associated with changes in iron and/or myelin concentration.
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Affiliation(s)
- J Cohen-Adad
- A.A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA.
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Miller KL, Stagg CJ, Douaud G, Jbabdi S, Smith SM, Behrens TEJ, Jenkinson M, Chance SA, Esiri MM, Voets NL, Jenkinson N, Aziz TZ, Turner MR, Johansen-Berg H, McNab JA. Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner. Neuroimage 2011; 57:167-181. [PMID: 21473920 PMCID: PMC3115068 DOI: 10.1016/j.neuroimage.2011.03.070] [Citation(s) in RCA: 195] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2011] [Revised: 03/12/2011] [Accepted: 03/25/2011] [Indexed: 11/05/2022] Open
Abstract
Diffusion imaging of post mortem brains has great potential both as a reference for brain specimens that undergo sectioning, and as a link between in vivo diffusion studies and "gold standard" histology/dissection. While there is a relatively mature literature on post mortem diffusion imaging of animals, human brains have proven more challenging due to their incompatibility with high-performance scanners. This study presents a method for post mortem diffusion imaging of whole, human brains using a clinical 3-Tesla scanner with a 3D segmented EPI spin-echo sequence. Results in eleven brains at 0.94 × 0.94 × 0.94 mm resolution are presented, and in a single brain at 0.73 × 0.73 × 0.73 mm resolution. Region-of-interest analysis of diffusion tensor parameters indicate that these properties are altered compared to in vivo (reduced diffusivity and anisotropy), with significant dependence on post mortem interval (time from death to fixation). Despite these alterations, diffusion tractography of several major tracts is successfully demonstrated at both resolutions. We also report novel findings of cortical anisotropy and partial volume effects.
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Affiliation(s)
- Karla L Miller
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Charlotte J Stagg
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Gwenaëlle Douaud
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stephen M Smith
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Timothy E J Behrens
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Mark Jenkinson
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Steven A Chance
- Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Margaret M Esiri
- Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Natalie L Voets
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ned Jenkinson
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Tipu Z Aziz
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Martin R Turner
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Heidi Johansen-Berg
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jennifer A McNab
- A.A.Martinos Centre, Massachusetts General Hospital, Boston, USA
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