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Calabro FJ, Parr AC, Sydnor VJ, Hetherington H, Prasad KM, Ibrahim TS, Sarpal DK, Famalette A, Verma P, Luna B. Leveraging ultra-high field (7T) MRI in psychiatric research. Neuropsychopharmacology 2024:10.1038/s41386-024-01980-6. [PMID: 39251774 DOI: 10.1038/s41386-024-01980-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/21/2024] [Accepted: 07/23/2024] [Indexed: 09/11/2024]
Abstract
Non-invasive brain imaging has played a critical role in establishing our understanding of the neural properties that contribute to the emergence of psychiatric disorders. However, characterizing core neurobiological mechanisms of psychiatric symptomatology requires greater structural, functional, and neurochemical specificity than is typically obtainable with standard field strength MRI acquisitions (e.g., 3T). Ultra-high field (UHF) imaging at 7 Tesla (7T) provides the opportunity to identify neurobiological systems that confer risk, determine etiology, and characterize disease progression and treatment outcomes of major mental illnesses. Increases in scanner availability, regulatory approval, and sequence availability have made the application of UHF to clinical cohorts more feasible than ever before, yet the application of UHF approaches to the study of mental health remains nascent. In this technical review, we describe core neuroimaging methodologies which benefit from UHF acquisition, including high resolution structural and functional imaging, single (1H) and multi-nuclear (e.g., 31P) MR spectroscopy, and quantitative MR techniques for assessing brain tissue iron and myelin. We discuss advantages provided by 7T MRI, including higher signal- and contrast-to-noise ratio, enhanced spatial resolution, increased test-retest reliability, and molecular and neurochemical specificity, and how these have begun to uncover mechanisms of psychiatric disorders. Finally, we consider current limitations of UHF in its application to clinical cohorts, and point to ongoing work that aims to overcome technical hurdles through the continued development of UHF hardware, software, and protocols.
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Affiliation(s)
- Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Ashley C Parr
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Valerie J Sydnor
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Konasale M Prasad
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
- Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Tamer S Ibrahim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Deepak K Sarpal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Alyssa Famalette
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Piya Verma
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
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2
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Wolffsohn JS, Berkow D, Chan KY, Chaurasiya SK, Fadel D, Haddad M, Imane T, Jones L, Sheppard AL, Vianya-Estopa M, Walsh K, Woods J, Zeri F, Morgan PB. BCLA CLEAR Presbyopia: Evaluation and diagnosis. Cont Lens Anterior Eye 2024; 47:102156. [PMID: 38641525 DOI: 10.1016/j.clae.2024.102156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
It is important to be able to measure the range of clear focus in clinical practice to advise on presbyopia correction techniques and to optimise the correction power. Both subjective and objective techniques are necessary: subjective techniques (such as patient reported outcome questionnaires and defocus curves) assess the impact of presbyopia on a patient and how the combination of residual objective accommodation and their natural DoF work for them; objective techniques (such as autorefraction, corneal topography and lens imaging) allow the clinician to understand how well a technique is working optically and whether it is the right choice or how adjustments can be made to optimise performance. Techniques to assess visual performance and adverse effects must be carefully conducted to gain a reliable end-point, considering the target size, contrast and illumination. Objective techniques are generally more reliable, can help to explain unexpected subjective results and imaging can be a powerful communication tool with patients. A clear diagnosis, excluding factors such as binocular vision issues or digital eye strain that can also cause similar symptoms, is critical for the patient to understand and adapt to presbyopia. Some corrective options are more permanent, such as implanted inlays / intraocular lenses or laser refractive surgery, so the optics can be trialled with contact lenses in advance (including differences between the eyes) to better communicate with the patient how the optics will work for them so they can make an informed choice.
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Affiliation(s)
- James S Wolffsohn
- School of Optometry, Health and Life Sciences, Aston University, Birmingham, United Kingdom.
| | - David Berkow
- Department of Ophthalmology, Rambam Health Care Campus, Haifa, Israel
| | - Ka Yin Chan
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong
| | - Suraj K Chaurasiya
- Department of Contact Lens and Anterior Segment, CL Gupta Eye Institute, Moradabad, India; Department of Optometry and Vision Science, CL Gupta Eye Institute, Moradabad, India
| | - Daddi Fadel
- Centre for Ocular Research & Education (CORE), School of Optometry & Vision Science, University of Waterloo, Waterloo, Canada
| | - Mera Haddad
- Faculty of Applied Medical Sciences, Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Tarib Imane
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, College of Medicine, University of Illinois at Chicago, United States
| | - Lyndon Jones
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong; Centre for Ocular Research & Education (CORE), School of Optometry & Vision Science, University of Waterloo, Waterloo, Canada
| | - Amy L Sheppard
- School of Optometry, Health and Life Sciences, Aston University, Birmingham, United Kingdom
| | - Marta Vianya-Estopa
- Vision and Hearing Research Centre, Anglia Ruskin University, Cambridge, United Kingdom
| | - Karen Walsh
- CooperVision Inc., San Ramon, CA, United States
| | - Jill Woods
- Centre for Ocular Research & Education (CORE), School of Optometry & Vision Science, University of Waterloo, Waterloo, Canada
| | - Fabrizio Zeri
- School of Optometry, Health and Life Sciences, Aston University, Birmingham, United Kingdom; University of Milano-Bicocca, Department of Materials Science, Milan, Italy
| | - Philip B Morgan
- Eurolens Research, Division of Pharmacy and Optometry, University of Manchester, United Kingdom
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Serrano-Sponton L, Lange F, Dauth A, Krenzlin H, Perez A, Januschek E, Schumann S, Jussen D, Czabanka M, Ringel F, Keric N, Gonzalez-Escamilla G. Harnessing the frontal aslant tract's structure to assess its involvement in cognitive functions: new insights from 7-T diffusion imaging. Sci Rep 2024; 14:17455. [PMID: 39075100 PMCID: PMC11286763 DOI: 10.1038/s41598-024-67013-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 07/08/2024] [Indexed: 07/31/2024] Open
Abstract
The first therapeutical goal followed by neurooncological surgeons dealing with prefrontal gliomas is attempting supramarginal tumor resection preserving relevant neurological function. Therefore, advanced knowledge of the frontal aslant tract (FAT) functional neuroanatomy in high-order cognitive domains beyond language and speech processing would help refine neurosurgeries, predicting possible relevant cognitive adverse events and maximizing the surgical efficacy. To this aim we performed the recently developed correlational tractography analyses to evaluate the possible relationship between FAT's microstructural properties and cognitive functions in 27 healthy subjects having ultra-high-field (7-Tesla) diffusion MRI. We independently assessed FAT segments innervating the dorsolateral prefrontal cortices (dlPFC-FAT) and the supplementary motor area (SMA-FAT). FAT microstructural robustness, measured by the tract's quantitative anisotropy (QA), was associated with a better performance in episodic memory, visuospatial orientation, cognitive processing speed and fluid intelligence but not sustained selective attention tests. Overall, the percentual tract volume showing an association between QA-index and improved cognitive scores (pQACV) was higher in the SMA-FAT compared to the dlPFC-FAT segment. This effect was right-lateralized for verbal episodic memory and fluid intelligence and bilateralized for visuospatial orientation and cognitive processing speed. Our results provide novel evidence for a functional specialization of the FAT beyond the known in language and speech processing, particularly its involvement in several higher-order cognitive domains. In light of these findings, further research should be encouraged to focus on neurocognitive deficits and their impact on patient outcomes after FAT damage, especially in the context of glioma surgery.
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Affiliation(s)
- Lucas Serrano-Sponton
- Department of Neurosurgery, Sana Clinic Offenbach, Johann Wolfgang Goethe University Frankfurt am Main Academic Hospitals, Starkenburgring 66, 63069, Offenbach am Main, Germany
| | - Felipa Lange
- Department of Neurosurgery, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeck Str. 1, 55131, Mainz, Germany
| | - Alice Dauth
- Department of Neurosurgery, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeck Str. 1, 55131, Mainz, Germany
| | - Harald Krenzlin
- Department of Neurosurgery, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeck Str. 1, 55131, Mainz, Germany
| | - Ana Perez
- Department of Neurology, Oslo University Hospital HF, Sognsvannsveien 20, 0372, Oslo, Norway
| | - Elke Januschek
- Department of Neurosurgery, Sana Clinic Offenbach, Johann Wolfgang Goethe University Frankfurt am Main Academic Hospitals, Starkenburgring 66, 63069, Offenbach am Main, Germany
| | - Sven Schumann
- Institute of Anatomy, University Medical Center of the Johannes Gutenberg-University Mainz, Johann-Joachim-Becher-Weg 13, 55128, Mainz, Germany
| | - Daniel Jussen
- Department of Neurosurgery, University Medical Center of the Johann Wolfgang Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - Marcus Czabanka
- Department of Neurosurgery, University Medical Center of the Johann Wolfgang Goethe University Frankfurt am Main, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany
| | - Florian Ringel
- Department of Neurosurgery, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeck Str. 1, 55131, Mainz, Germany
| | - Naureen Keric
- Department of Neurosurgery, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeck Str. 1, 55131, Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Rhine Main Neuroscience Network, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeck Str. 1, 55131, Mainz, Germany.
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Prasad AA, Wallén-Mackenzie Å. Architecture of the subthalamic nucleus. Commun Biol 2024; 7:78. [PMID: 38200143 PMCID: PMC10782020 DOI: 10.1038/s42003-023-05691-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 12/11/2023] [Indexed: 01/12/2024] Open
Abstract
The subthalamic nucleus (STN) is a major neuromodulation target for the alleviation of neurological and neuropsychiatric symptoms using deep brain stimulation (DBS). STN-DBS is today applied as treatment in Parkinson´s disease, dystonia, essential tremor, and obsessive-compulsive disorder (OCD). STN-DBS also shows promise as a treatment for refractory Tourette syndrome. However, the internal organization of the STN has remained elusive and challenges researchers and clinicians: How can this small brain structure engage in the multitude of functions that renders it a key hub for therapeutic intervention of a variety of brain disorders ranging from motor to affective to cognitive? Based on recent gene expression studies of the STN, a comprehensive view of the anatomical and cellular organization, including revelations of spatio-molecular heterogeneity, is now possible to outline. In this review, we focus attention to the neurobiological architecture of the STN with specific emphasis on molecular patterns discovered within this complex brain area. Studies from human, non-human primate, and rodent brains now reveal anatomically defined distribution of specific molecular markers. Together their spatial patterns indicate a heterogeneous molecular architecture within the STN. Considering the translational capacity of targeting the STN in severe brain disorders, the addition of molecular profiling of the STN will allow for advancement in precision of clinical STN-based interventions.
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Affiliation(s)
- Asheeta A Prasad
- University of Sydney, School of Medical Sciences, Faculty of Medicine and Health, Sydney, NSW, Australia.
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Welton T, Hartono S, Shih YC, Schwarz ST, Xing Y, Tan EK, Auer DP, Harel N, Chan LL. Ultra-high-field 7T MRI in Parkinson's disease: ready for clinical use?-a narrative review. Quant Imaging Med Surg 2023; 13:7607-7620. [PMID: 37969629 PMCID: PMC10644128 DOI: 10.21037/qims-23-509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 09/15/2023] [Indexed: 11/17/2023]
Abstract
Background and Objective The maturation of ultra-high-field magnetic resonance imaging (MRI) [≥7 Tesla (7T)] has improved our capability to depict and characterise brain structures efficiently, with better signal-to-noise ratio (SNR) and spatial resolution. We evaluated whether these improvements benefit the clinical detection and management of Parkinson's disease (PD). Methods We performed a literature search in March 2023 in PubMed (MEDLINE), EMBASE and Google Scholar for articles on "7T MRI" AND "Parkinson*", written in English, published between inception and 1st March, 2023, which we synthesised in narrative form. Key Content and Findings In deep-brain stimulation (DBS) surgical planning, early studies show that 7T MRI can distinguish anatomical substructures, and that this results in reduced adverse effects. In other areas, while there is strong evidence for improved accuracy and precision of 7T MRI-based measurements for PD, there is limited evidence for meaningful clinical translation. In particular, neuromelanin-iron complex quantification and visualisation in midbrain nuclei is enhanced, enabling depiction of nigrosomes 1-5, improved morphometry and vastly improved radiological assessments; however, studies on the related clinical outcomes, diagnosis, subtyping, differentiation of atypical parkinsonisms, and monitoring of treatment response using 7T MRI are lacking. Moreover, improvements in clinical utility must be great enough to justify the additional costs. Conclusions Together, current evidence supports feasible future clinical implementation of 7T MRI for PD. Future impacts to clinical decision making for diagnosis, differentiation, and monitoring of progression or treatment response are likely; however, to achieve this, further longitudinal studies using 7T MRI are needed in prodromal, early-stage PD and parkinsonism cohorts focusing on clinical translational potential.
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Affiliation(s)
- Thomas Welton
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Septian Hartono
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Yao-Chia Shih
- Duke-NUS Medical School, Singapore, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
- Graduate Institute of Medicine, Yuan Ze University and National Taiwan University, Taipei
| | - Stefan T. Schwarz
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Radiology, Cardiff and Vale University Health Board, Cardiff, Wales, UK
| | - Yue Xing
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Eng-King Tan
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Dorothee P. Auer
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Ling-Ling Chan
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
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Schott B, Choksi D, Tran K, Karmonik C, Salazar B, Boone T, Khavari R. Is the Brainstem Activation Different Between Healthy Young Male and Female Volunteers at Initiation of Voiding? A High Definition 7-Tesla Magnetic Resonance Imaging Study. Int Neurourol J 2023; 27:174-181. [PMID: 37798884 PMCID: PMC10556429 DOI: 10.5213/inj.2346104.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 07/05/2023] [Indexed: 10/07/2023] Open
Abstract
PURPOSE Assessing brainstem function in humans through typical neuroimaging modalities has been challenging. Our objective was to evaluate brain and brainstem activation patterns during initiation of voiding in healthy males and females utilizing a 7 Tesla magnetic resonance imaging (MRI) scanner and a noninvasive brain-bladder functional MRI (fMRI) protocol. METHODS Twenty healthy adult volunteers (10 males and 10 females) with no history of urinary symptoms were recruited. Each volunteer underwent a clinic uroflow and postvoid residual assessment and was asked to consume water prior to entering the scanner. Anatomical and diffusion tensor images were obtained first, followed by a blood oxygenation level dependent (BOLD) resting-state fMRI (rs-fMRI) during the empty bladder. Subjects indicated when they felt the urge to void, and a full bladder rs-fMRI was obtained. Once completed, the subjects began 5 voiding cycles, where the first 7.5 seconds of each voiding cycle was identified as "initiation of voiding." BOLD activation maps were generated, and regions of interests with a t-value greater than 2.1 were deemed statistically significant. RESULTS We present 5 distinct regions within the periaqueductal gray (PAG) and pontine micturition center (PMC) with statistically significant activation associated with an initiation of voiding in both men and women, 3 within the PAG and 2 within the PMC. Several additional areas in the brain also demonstrated activation as well. When comparing males to females, there was an overall lower BOLD activation seen in females throughout all regions, with the exception of the caudate lobe. CONCLUSION Our study effectively defines regions within the PAG and PMC involved in initiation of voiding in healthy volunteers. To our knowledge, this is the first study investigating differences between male and female brainstem activation utilizing an ultra-high definition 7T MRI.
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Affiliation(s)
- Bradley Schott
- Interdisciplinary College of Engineering Medicine, Texas A&M, Houston, TX, USA
| | - Darshil Choksi
- Interdisciplinary College of Engineering Medicine, Texas A&M, Houston, TX, USA
| | - Khue Tran
- Interdisciplinary College of Engineering Medicine, Texas A&M, Houston, TX, USA
| | | | - Betsy Salazar
- Department of Urology, Houston Methodist Hospital, Houston, TX, USA
| | - Timothy Boone
- Department of Urology, Houston Methodist Hospital, Houston, TX, USA
| | - Rose Khavari
- Department of Urology, Houston Methodist Hospital, Houston, TX, USA
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7
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Doss DJ, Johnson GW, Narasimhan S, Shless JS, Jiang JW, González HFJ, Paulo DL, Lucas A, Davis KA, Chang C, Morgan VL, Constantinidis C, Dawant BM, Englot DJ. Deep Learning Segmentation of the Nucleus Basalis of Meynert on 3T MRI. AJNR Am J Neuroradiol 2023; 44:1020-1025. [PMID: 37562826 PMCID: PMC10494939 DOI: 10.3174/ajnr.a7950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 06/25/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND AND PURPOSE The nucleus basalis of Meynert is a key subcortical structure that is important in arousal and cognition and has been explored as a deep brain stimulation target but is difficult to study due to its small size, variability among patients, and lack of contrast on 3T MR imaging. Thus, our goal was to establish and evaluate a deep learning network for automatic, accurate, and patient-specific segmentations with 3T MR imaging. MATERIALS AND METHODS Patient-specific segmentations can be produced manually; however, the nucleus basalis of Meynert is difficult to accurately segment on 3T MR imaging, with 7T being preferred. Thus, paired 3T and 7T MR imaging data sets of 21 healthy subjects were obtained. A test data set of 6 subjects was completely withheld. The nucleus was expertly segmented on 7T, providing accurate labels for the paired 3T MR imaging. An external data set of 14 patients with temporal lobe epilepsy was used to test the model on brains with neurologic disorders. A 3D-Unet convolutional neural network was constructed, and a 5-fold cross-validation was performed. RESULTS The novel segmentation model demonstrated significantly improved Dice coefficients over the standard probabilistic atlas for both healthy subjects (mean, 0.68 [SD, 0.10] versus 0.45 [SD, 0.11], P = .002, t test) and patients (0.64 [SD, 0.10] versus 0.37 [SD, 0.22], P < .001). Additionally, the model demonstrated significantly decreased centroid distance in patients (1.18 [SD, 0.43] mm, 3.09 [SD, 2.56] mm, P = .007). CONCLUSIONS We developed the first model, to our knowledge, for automatic and accurate patient-specific segmentation of the nucleus basalis of Meynert. This model may enable further study into the nucleus, impacting new treatments such as deep brain stimulation.
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Affiliation(s)
- D J Doss
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
| | - G W Johnson
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
| | - S Narasimhan
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - J S Shless
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - J W Jiang
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - H F J González
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
| | - D L Paulo
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - A Lucas
- Department of Bioengineering (A.L.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - K A Davis
- Department of Neuroscience (K.A.D.), University of Pennsylvania, Philadelphia, Pennsylvania
- Center for Neuroengineering and Therapeutics (K.A.D.), University of Pennsylvania, Philadelphia, Pennsylvania
- Neurology (K.A.D.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - C Chang
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
- Department of Electrical and Computer Engineering (C. Chang, B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Department of Computer Science (C. Chang), Vanderbilt University, Nashville, Tennessee
| | - V L Morgan
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Neurology (V.L.M.), Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Radiological Sciences (V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
| | - C Constantinidis
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Department of Ophthalmology and Visual Sciences (C. Constantinidis), Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Neuroscience (C. Constantinidis), Vanderbilt University, Nashville, Tennessee
| | - B M Dawant
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
- Department of Electrical and Computer Engineering (C. Chang, B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
| | - D J Englot
- From the Department of Biomedical Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang., V.L.M., C. Constantinidis, D.J.E.), Vanderbilt University, Nashville, Tennessee
- Institute of Imaging Science (D.J.D., G.W.J., S.N., J.S.S., J.W.J., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering (D.J.D., G.W.J., S.N., H.F.J.G., C. Chang, V.L.M., B.M.D., D.J.E.), Nashville, Tennessee
- Department of Neurological Surgery (S.N., J.S.S., J.W.J., D.L.P., V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Electrical and Computer Engineering (C. Chang, B.M.D., D.J.E.), Vanderbilt University, Nashville, Tennessee
- Department of Radiological Sciences (V.L.M., D.J.E.), Vanderbilt University Medical Center, Nashville, Tennessee
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8
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Wolters AF, Heijmans M, Priovoulos N, Jacobs HIL, Postma AA, Temel Y, Kuijf ML, Michielse S. Neuromelanin related ultra-high field signal intensity of the locus coeruleus differs between Parkinson's disease and controls. Neuroimage Clin 2023; 39:103479. [PMID: 37494758 PMCID: PMC10394012 DOI: 10.1016/j.nicl.2023.103479] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/05/2023] [Accepted: 07/18/2023] [Indexed: 07/28/2023]
Abstract
INTRODUCTION Neuromelanin related signal changes in catecholaminergic nuclei are considered as a promising MRI biomarker in Parkinson's disease (PD). Until now, most studies have investigated the substantia nigra (SN), while signal changes might be more prominent in the locus coeruleus (LC). Ultra-high field MRI improves the visualisation of these small brainstem regions and might support the development of imaging biomarkers in PD. OBJECTIVES To compare signal intensity of the SN and LC on Magnetization Transfer MRI between PD patients and healthy controls (HC) and to explore its association with cognitive performance in PD. METHODS This study was conducted using data from the TRACK-PD study, a longitudinal 7T MRI study. A total of 78 early-stage PD patients and 36 HC were included. A mask for the SN and LC was automatically segmented and manually corrected. Neuromelanin related signal intensity of the SN and LC was compared between PD and HC. RESULTS PD participants showed a lower contrast-to-noise ratio (CNR) in the right SN (p = 0.029) and left LC (p = 0.027). After adding age as a confounder, the CNR of the right SN did not significantly differ anymore between PD and HC (p = 0.055). Additionally, a significant positive correlation was found between the SN CNR and memory function. DISCUSSION This study confirms that neuromelanin related signal intensity of the LC differs between early-stage PD patients and HC. No significant difference was found in the SN. This supports the theory of bottom-up disease progression in PD. Furthermore, loss of SN integrity might influence working memory or learning capabilities in PD patients.
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Affiliation(s)
- Amée F Wolters
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands; School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.
| | - Margot Heijmans
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Nikos Priovoulos
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands; Computational Cognitive Neuroscience and Neuroimaging, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Heidi I L Jacobs
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Alida A Postma
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, The Netherlands
| | - Yasin Temel
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands; Department of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Mark L Kuijf
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands; School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Stijn Michielse
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
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9
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de Godoy LL, Studart-Neto A, de Paula DR, Green N, Halder A, Arantes P, Chaim KT, Moraes NC, Yassuda MS, Nitrini R, Dresler M, da Costa Leite C, Panovska-Griffiths J, Soddu A, Bisdas S. Phenotyping Superagers Using Resting-State fMRI. AJNR Am J Neuroradiol 2023; 44:424-433. [PMID: 36927760 PMCID: PMC10084893 DOI: 10.3174/ajnr.a7820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 02/19/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND AND PURPOSE Superagers are defined as older adults with episodic memory performance similar or superior to that in middle-aged adults. This study aimed to investigate the key differences in discriminative networks and their main nodes between superagers and cognitively average elderly controls. In addition, we sought to explore differences in sensitivity in detecting these functional activities across the networks at 3T and 7T MR imaging fields. MATERIALS AND METHODS Fifty-five subjects 80 years of age or older were screened using a detailed neuropsychological protocol, and 31 participants, comprising 14 superagers and 17 cognitively average elderly controls, were included for analysis. Participants underwent resting-state-fMRI at 3T and 7T MR imaging. A prediction classification algorithm using a penalized regression model on the measurements of the network was used to calculate the probabilities of a healthy older adult being a superager. Additionally, ORs quantified the influence of each node across preselected networks. RESULTS The key networks that differentiated superagers and elderly controls were the default mode, salience, and language networks. The most discriminative nodes (ORs > 1) in superagers encompassed areas in the precuneus posterior cingulate cortex, prefrontal cortex, temporoparietal junction, temporal pole, extrastriate superior cortex, and insula. The prediction classification model for being a superager showed better performance using the 7T compared with 3T resting-state-fMRI data set. CONCLUSIONS Our findings suggest that the functional connectivity in the default mode, salience, and language networks can provide potential imaging biomarkers for predicting superagers. The 7T field holds promise for the most appropriate study setting to accurately detect the functional connectivity patterns in superagers.
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Affiliation(s)
- L L de Godoy
- From the Departments of Radiology and Oncology (L.L.d.G., P.A., K.T.C., C.d.C.L.)
- Lysholm Department of Neuroradiology (L.L.d.G., S.B.), The National Hospital of Neurology and Neurosurgery
| | - A Studart-Neto
- Neurology (A.S.-N., N.C.M., M.S.Y., R.N.), Hospital das Clinicas, Faculdade de Medicina da Universidade de Sao Paulo, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - D R de Paula
- Donders Institute for Brain Cognition and Behavior (D.R.d.P., M.D.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - N Green
- Department of Statistics (N.G.), University College London, London, UK
| | - A Halder
- Departments of Medical Biophysics (A.H.)
| | - P Arantes
- From the Departments of Radiology and Oncology (L.L.d.G., P.A., K.T.C., C.d.C.L.)
| | - K T Chaim
- From the Departments of Radiology and Oncology (L.L.d.G., P.A., K.T.C., C.d.C.L.)
| | - N C Moraes
- Neurology (A.S.-N., N.C.M., M.S.Y., R.N.), Hospital das Clinicas, Faculdade de Medicina da Universidade de Sao Paulo, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - M S Yassuda
- Neurology (A.S.-N., N.C.M., M.S.Y., R.N.), Hospital das Clinicas, Faculdade de Medicina da Universidade de Sao Paulo, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - R Nitrini
- Neurology (A.S.-N., N.C.M., M.S.Y., R.N.), Hospital das Clinicas, Faculdade de Medicina da Universidade de Sao Paulo, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - M Dresler
- Donders Institute for Brain Cognition and Behavior (D.R.d.P., M.D.), Radboud University Medical Centre, Nijmegen, the Netherlands
| | - C da Costa Leite
- From the Departments of Radiology and Oncology (L.L.d.G., P.A., K.T.C., C.d.C.L.)
| | - J Panovska-Griffiths
- The Big Data Institute and the Pandemic Sciences Institute (J.P.-G.)
- The Queen's College (J.P.-G.), University of Oxford, Oxford, UK
| | - A Soddu
- Physics and Astronomy (A.S.), University of Western Ontario, London, Ontario, Canada
| | - S Bisdas
- Lysholm Department of Neuroradiology (L.L.d.G., S.B.), The National Hospital of Neurology and Neurosurgery
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10
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Kikuchi H, Jitsuishi T, Hirono S, Yamaguchi A, Iwadate Y. 2D and 3D structures of the whole-brain, directly visible from 100-micron slice 7TMRI images. INTERDISCIPLINARY NEUROSURGERY 2023. [DOI: 10.1016/j.inat.2023.101755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023] Open
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11
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Rosenberg JT, Grant SC, Topgaard D. Nonparametric 5D D-R 2 distribution imaging with single-shot EPI at 21.1 T: Initial results for in vivo rat brain. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 341:107256. [PMID: 35753184 PMCID: PMC9339475 DOI: 10.1016/j.jmr.2022.107256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 05/27/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
In vivo human diffusion MRI is by default performed using single-shot EPI with greater than 50-ms echo times and associated signal loss from transverse relaxation. The individual benefits of the current trends of increasing B0 to boost SNR and employing more advanced signal preparation schemes to improve the specificity for selected microstructural properties eventually may be cancelled by increased relaxation rates at high B0 and echo times with advanced encoding. Here, initial attempts to translate state-of-the-art diffusion-relaxation correlation methods from 3 T to 21.1 T are made to identify hurdles that need to be overcome to fulfill the promises of both high SNR and readily interpretable microstructural information.
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Affiliation(s)
- Jens T Rosenberg
- National High Magnetic Field Laboratory, Florida State University, Tallahassee FL, United States.
| | - Samuel C Grant
- National High Magnetic Field Laboratory, Florida State University, Tallahassee FL, United States; Chemical and Biomedical Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL, United States.
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12
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Manual delineation approaches for direct imaging of the subcortex. Brain Struct Funct 2021; 227:219-297. [PMID: 34714408 PMCID: PMC8741717 DOI: 10.1007/s00429-021-02400-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 09/26/2021] [Indexed: 11/20/2022]
Abstract
The growing interest in the human subcortex is accompanied by an increasing number of parcellation procedures to identify deep brain structures in magnetic resonance imaging (MRI) contrasts. Manual procedures continue to form the gold standard for parcellating brain structures and is used for the validation of automated approaches. Performing manual parcellations is a tedious process which requires a systematic and reproducible approach. For this purpose, we created a series of protocols for the anatomical delineation of 21 individual subcortical structures. The intelligibility of the protocols was assessed by calculating Dice similarity coefficients for ten healthy volunteers. In addition, dilated Dice coefficients showed that manual parcellations created using these protocols can provide high-quality training data for automated algorithms. Here, we share the protocols, together with three example MRI datasets and the created manual delineations. The protocols can be applied to create high-quality training data for automated parcellation procedures, as well as for further validation of existing procedures and are shared without restrictions with the research community.
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13
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Isaacs BR, Heijmans M, Kuijf ML, Kubben PL, Ackermans L, Temel Y, Keuken MC, Forstmann BU. Variability in subthalamic nucleus targeting for deep brain stimulation with 3 and 7 Tesla magnetic resonance imaging. NEUROIMAGE-CLINICAL 2021; 32:102829. [PMID: 34560531 PMCID: PMC8463907 DOI: 10.1016/j.nicl.2021.102829] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/12/2021] [Accepted: 09/12/2021] [Indexed: 12/13/2022]
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective surgical treatment for Parkinson's disease (PD). Side-effects may, however, be induced when the DBS lead is placed suboptimally. Currently, lower field magnetic resonance imaging (MRI) at 1.5 or 3 Tesla (T) is used for targeting. Ultra-high-field MRI (7 T and above) can obtain superior anatomical information and might therefore be better suited for targeting. This study aims to test whether optimized 7 T imaging protocols result in less variable targeting of the STN for DBS compared to clinically utilized 3 T images. Three DBS-experienced neurosurgeons determined the optimal STN DBS target site on three repetitions of 3 T-T2, 7 T-T2*, 7 T-R2* and 7 T-QSM images for five PD patients. The distance in millimetres between the three repetitive coordinates was used as an index of targeting variability and was compared between field strength, MRI contrast and repetition with a Bayesian ANOVA. Further, the target coordinates were registered to MNI space, and anatomical coordinates were compared between field strength, MRI contrast and repetition using a Bayesian ANOVA. The results indicate that the neurosurgeons are stable in selecting the DBS target site across MRI field strength, MRI contrast and repetitions. The analysis of the coordinates in MNI space however revealed that the actual selected location of the electrode is seemingly more ventral when using the 3 T scan compared to the 7 T scans.
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Affiliation(s)
- Bethany R Isaacs
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands; Translational Neuroscience, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Margot Heijmans
- Translational Neuroscience, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands.
| | - Mark L Kuijf
- Translational Neuroscience, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Neurology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Pieter L Kubben
- Translational Neuroscience, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Neurosurgery, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Linda Ackermans
- Translational Neuroscience, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Neurosurgery, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Yasin Temel
- Translational Neuroscience, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Neurosurgery, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Max C Keuken
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - Birte U Forstmann
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
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14
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Ohta H, Vo NMV, Hata J, Terawaki K, Shirakawa T, Okano HJ. Utilizing Dynamic Phosphorous-31 Magnetic Resonance Spectroscopy for the Early Detection of Acute Compartment Syndrome: A Pilot Study on Rats. Diagnostics (Basel) 2021; 11:586. [PMID: 33805144 PMCID: PMC8064087 DOI: 10.3390/diagnostics11040586] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/20/2021] [Accepted: 03/21/2021] [Indexed: 01/15/2023] Open
Abstract
INTRODUCTION Disasters, including terrorism and earthquakes, are significant threats to people and may lead to many people requiring rescue. The longer the rescue takes, the higher the chances of an individual contracting acute compartment syndrome (ACS). ACS is fatal if diagnosed too late, and early diagnosis and treatment are essential. OBJECTIVE To assess the ability of dynamic phosphorus magnetic resonance spectroscopy (31P-MRS) in the early detection of muscular damage in ACS. MATERIALS AND METHODS Six ACS model rats were used for serial 31P-MRS scanning (9.4 Tesla). Skeletal muscle metabolism, represented by the levels of phosphocreatine (PCr), inorganic phosphate (Pi), and adenosine triphosphate (ATP), was assessed. The PCr/(Pi + PCr) ratio, which decreases with ischemia, was compared with simultaneously sampled plasma creatine phosphokinase (CPK), a muscle damage marker. RESULTS The PCr/(Pi + PCr) ratio significantly decreased after inducing ischemia (from 0.86 ± 0.10 to 0.18 ± 0.06; p < 0.05), while CPK did not change significantly (from 89 ± 29.46 to 241.50 ± 113.28; p > 0.05). The intracellular and arterial pH index decreased over time, revealing significant differences at 120 min post-ischemia (from 7.09 ± 0.01 to 6.43 ± 0.13, and from 7.47 ± 0.03 to 7.39 ± 0.04, respectively). In the reperfusion state, the spectra and pH did not return to the original values. CONCLUSIONS The dynamic 31P-MRS technique can rapidly detect changes in muscle bioenergetics. This technique is a promising non-invasive method for determining early muscular damage in ACS.
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Affiliation(s)
- Hiroki Ohta
- Division of Regenerative Medicine, Research Center for Medical Sciences, The Jikei University School of Medicine, Tokyo 105-8461, Japan; (H.O.); (N.-M.V.V.); (J.H.); (K.T.)
| | - Nhat-Minh Van Vo
- Division of Regenerative Medicine, Research Center for Medical Sciences, The Jikei University School of Medicine, Tokyo 105-8461, Japan; (H.O.); (N.-M.V.V.); (J.H.); (K.T.)
- Department of Radiological Sciences, Tokyo Metropolitan University, Tokyo 116-0012, Japan;
| | - Junichi Hata
- Division of Regenerative Medicine, Research Center for Medical Sciences, The Jikei University School of Medicine, Tokyo 105-8461, Japan; (H.O.); (N.-M.V.V.); (J.H.); (K.T.)
| | - Koshiro Terawaki
- Division of Regenerative Medicine, Research Center for Medical Sciences, The Jikei University School of Medicine, Tokyo 105-8461, Japan; (H.O.); (N.-M.V.V.); (J.H.); (K.T.)
- Department of Radiological Sciences, Tokyo Metropolitan University, Tokyo 116-0012, Japan;
| | - Takako Shirakawa
- Department of Radiological Sciences, Tokyo Metropolitan University, Tokyo 116-0012, Japan;
| | - Hirotaka James Okano
- Division of Regenerative Medicine, Research Center for Medical Sciences, The Jikei University School of Medicine, Tokyo 105-8461, Japan; (H.O.); (N.-M.V.V.); (J.H.); (K.T.)
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15
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Ebneabbasi A, Mahdipour M, Nejati V, Li M, Liebe T, Colic L, Leutritz AL, Vogel M, Zarei M, Walter M, Tahmasian M. Emotion processing and regulation in major depressive disorder: A 7T resting-state fMRI study. Hum Brain Mapp 2021; 42:797-810. [PMID: 33151031 PMCID: PMC7814754 DOI: 10.1002/hbm.25263] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 09/18/2020] [Accepted: 10/14/2020] [Indexed: 12/23/2022] Open
Abstract
Dysfunctions in bottom-up emotion processing (EP), as well as top-down emotion regulation (ER) are prominent features in pathophysiology of major depressive disorder (MDD). Nonetheless, it is not clear whether EP- and ER-related areas are regionally and/or connectively disturbed in MDD. In addition, it is yet to be known how EP- and ER-related areas are interactively linked to regulatory behavior, and whether this interaction is disrupted in MDD. In our study, regional amplitude of low frequency fluctuations (ALFF) and whole-brain functional connectivity (FC) of meta-analytic-driven EP- and ER-related areas were compared between 32 healthy controls (HC) and 20 MDD patients. Then, we aimed to investigate whether the EP-related areas can predict the ER-related areas and regulatory behavior in both groups. Finally, the brain-behavior correlations between the EP- and ER-related areas and depression severity were assessed. We found that: (a) affective areas are regionally and/or connectively disturbed in MDD; (b) EP-ER interaction seems to be disrupted in MDD; overburden of emotional reactivity in amygdala may inversely affect cognitive control processes in prefrontal cortices, which leads to diminished regulatory actions. (c) Depression severity is correlated with FC of affective areas. Our findings shed new lights on the neural underpinning of affective dysfunctions in depression.
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Affiliation(s)
- Amir Ebneabbasi
- Institute of Medical Science and TechnologyShahid Beheshti UniversityTehranIran
- Department of Psychology, Faculty of Psychology and Educational SciencesShahid Beheshti UniversityTehranIran
| | - Mostafa Mahdipour
- Institute of Medical Science and TechnologyShahid Beheshti UniversityTehranIran
| | - Vahid Nejati
- Department of Psychology, Faculty of Psychology and Educational SciencesShahid Beheshti UniversityTehranIran
| | - Meng Li
- Clinical Affective Neuroimaging LaboratoryOtto von Guericke UniversityMagdeburgGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
| | - Thomas Liebe
- Clinical Affective Neuroimaging LaboratoryOtto von Guericke UniversityMagdeburgGermany
| | - Lejla Colic
- Clinical Affective Neuroimaging LaboratoryOtto von Guericke UniversityMagdeburgGermany
| | - Anna Linda Leutritz
- Clinical Affective Neuroimaging LaboratoryOtto von Guericke UniversityMagdeburgGermany
- Department of Psychiatry, Psychosomatic Medicine and PsychotherapyUniversity Hospital, University of WürzburgWürzburgGermany
| | - Matthias Vogel
- Clinical Affective Neuroimaging LaboratoryOtto von Guericke UniversityMagdeburgGermany
- University Clinic for Psychosomatic Medicine and PsychotherapyMagdeburgGermany
| | - Mojtaba Zarei
- Institute of Medical Science and TechnologyShahid Beheshti UniversityTehranIran
| | - Martin Walter
- Clinical Affective Neuroimaging LaboratoryOtto von Guericke UniversityMagdeburgGermany
- Department of Psychiatry and PsychotherapyJena University HospitalJenaGermany
- Leibniz Institute for NeurobiologyMagdeburgGermany
| | - Masoud Tahmasian
- Institute of Medical Science and TechnologyShahid Beheshti UniversityTehranIran
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16
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A probabilistic atlas of the human ventral tegmental area (VTA) based on 7 Tesla MRI data. Brain Struct Funct 2021; 226:1155-1167. [PMID: 33580320 PMCID: PMC8036186 DOI: 10.1007/s00429-021-02231-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 01/26/2021] [Indexed: 12/12/2022]
Abstract
Functional magnetic resonance imaging (fMRI) BOLD signal is commonly localized by using neuroanatomical atlases, which can also serve for region of interest analyses. Yet, the available MRI atlases have serious limitations when it comes to imaging subcortical structures: only 7% of the 455 subcortical nuclei are captured by current atlases. This highlights the general difficulty in mapping smaller nuclei deep in the brain, which can be addressed using ultra-high field 7 Tesla (T) MRI. The ventral tegmental area (VTA) is a subcortical structure that plays a pivotal role in reward processing, learning and memory. Despite the significant interest in this nucleus in cognitive neuroscience, there are currently no available, anatomically precise VTA atlases derived from 7 T MRI data that cover the full region of the VTA. Here, we first provide a protocol for multimodal VTA imaging and delineation. We then provide a data description of a probabilistic VTA atlas based on in vivo 7 T MRI data.
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