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Eidex Z, Wang J, Safari M, Elder E, Wynne J, Wang T, Shu HK, Mao H, Yang X. High-resolution 3T to 7T ADC map synthesis with a hybrid CNN-transformer model. Med Phys 2024; 51:4380-4388. [PMID: 38630982 DOI: 10.1002/mp.17079] [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: 10/12/2023] [Revised: 02/13/2024] [Accepted: 03/23/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. PURPOSE To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI. METHODS The Vision CNN-Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided into 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). In addition, RESULTS: The results are as follows: PSNR: 27.0 ± 0.9 dB, SSIM: 0.945 ± 0.010, and MSE: 2.0E-3 ± 0.4E-3. Both qualitative and quantitative results demonstrate that VCT performs favorably against other state-of-the-art methods. We have introduced various efficiency improvements, including the implementation of flash attention and training on 176×208 resolution images. These enhancements have resulted in the reduction of parameters and training time per epoch by 50% in comparison to ResViT. Specifically, the training time per epoch has been shortened from 7.67 min to 3.86 min. CONCLUSION We propose a novel method to predict high-resolution 7T ADC maps from low-resolution 3T ADC maps and T1w MRI. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high-quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, producing higher resolution and conformal contours, and as an intermediate step in generating synthetic CT for radiation therapy, especially when 7T MRI scanners are unavailable.
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Affiliation(s)
- Zach Eidex
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Jing Wang
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Mojtaba Safari
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Eric Elder
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
- Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Jacob Wynne
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Hui-Kuo Shu
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
- Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Hui Mao
- Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University, Atlanta, Georgia, USA
- School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
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2
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Liebe T, Danyeli LV, Sen ZD, Li M, Kaufmann J, Walter M. Subanesthetic Ketamine Suppresses Locus Coeruleus-Mediated Alertness Effects: A 7T fMRI Study. Int J Neuropsychopharmacol 2024; 27:pyae022. [PMID: 38833581 PMCID: PMC11187989 DOI: 10.1093/ijnp/pyae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 06/03/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND The NMDA antagonist S-ketamine is gaining increasing use as a rapid-acting antidepressant, although its exact mechanisms of action are still unknown. In this study, we investigated ketamine in respect to its properties toward central noradrenergic mechanisms and how they influence alertness behavior. METHODS We investigated the influence of S-ketamine on the locus coeruleus (LC) brain network in a placebo-controlled, cross-over, 7T functional, pharmacological MRI study in 35 healthy male participants (25.1 ± 4.2 years) in conjunction with the attention network task to measure LC-related alertness behavioral changes. RESULTS We could show that acute disruption of the LC alertness network to the thalamus by ketamine is related to a behavioral alertness reduction. CONCLUSION The results shed new light on the neural correlates of ketamine beyond the glutamatergic system and underpin a new concept of how it may unfold its antidepressant effects.
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Affiliation(s)
- Thomas Liebe
- Department of Psychiatry and Psychotherapy, University of Jena, Jena, Germany
- University Clinic for Dermatology, Magdeburg, Germany
| | - Lena Vera Danyeli
- Department of Psychiatry and Psychotherapy, University of Jena, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Halle-Jena-Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Tübingen, Tübingen, Germany
- German Center for Mental Health (DZPG), Partner site Halle-Jena-Magdeburg, Germany
| | - Zümrüt Duygu Sen
- Department of Psychiatry and Psychotherapy, University of Jena, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Halle-Jena-Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Tübingen, Tübingen, Germany
| | - Meng Li
- Department of Psychiatry and Psychotherapy, University of Jena, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Halle-Jena-Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Tübingen, Tübingen, Germany
| | - Jörn Kaufmann
- Department of Psychiatry and Psychotherapy, University of Jena, Jena, Germany
- Department of Neurology, University of Magdeburg, Magdeburg, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, University of Jena, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Halle-Jena-Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University Tübingen, Tübingen, Germany
- German Center for Mental Health (DZPG), Partner site Halle-Jena-Magdeburg, Germany
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3
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Watanabe K, Jogia J, Yoshimura R. Editorial: Recent developments in neuroimaging in mood disorders. Front Psychiatry 2024; 15:1371347. [PMID: 38487582 PMCID: PMC10938263 DOI: 10.3389/fpsyt.2024.1371347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/15/2024] [Indexed: 03/17/2024] Open
Affiliation(s)
- Keita Watanabe
- Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Jigar Jogia
- School of Psychology, University of Birmingham, Dubai, United Arab Emirates
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Kitakyushu, Japan
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Roalf DR, Figee M, Oathes DJ. Elevating the field for applying neuroimaging to individual patients in psychiatry. Transl Psychiatry 2024; 14:87. [PMID: 38341414 PMCID: PMC10858949 DOI: 10.1038/s41398-024-02781-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 12/06/2023] [Accepted: 01/15/2024] [Indexed: 02/12/2024] Open
Abstract
Although neuroimaging has been widely applied in psychiatry, much of the exuberance in decades past has been tempered by failed replications and a lack of definitive evidence to support the utility of imaging to inform clinical decisions. There are multiple promising ways forward to demonstrate the relevance of neuroimaging for psychiatry at the individual patient level. Ultra-high field magnetic resonance imaging is developing as a sensitive measure of neurometabolic processes of particular relevance that holds promise as a new way to characterize patient abnormalities as well as variability in response to treatment. Neuroimaging may also be particularly suited to the science of brain stimulation interventions in psychiatry given that imaging can both inform brain targeting as well as measure changes in brain circuit communication as a function of how effectively interventions improve symptoms. We argue that a greater focus on individual patient imaging data will pave the way to stronger relevance to clinical care in psychiatry. We also stress the importance of using imaging in symptom-relevant experimental manipulations and how relevance will be best demonstrated by pairing imaging with differential treatment prediction and outcome measurement. The priorities for using brain imaging to inform psychiatry may be shifting, which compels the field to solidify clinical relevance for individual patients over exploratory associations and biomarkers that ultimately fail to replicate.
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Affiliation(s)
- David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Martijn Figee
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Desmond J Oathes
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Brain Imaging and Stimulation, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Neuromodulation in Depression and Stress, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Brain Science Translation, Innovation, and Modulation Center, University of Pennsylvania, Philadelphia, PA, USA.
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Choi CH, Felder J, Lerche C, Shah NJ. MRI Coil Development Strategies for Hybrid MR-PET Systems: A Review. IEEE Rev Biomed Eng 2024; 17:342-350. [PMID: 37015609 DOI: 10.1109/rbme.2022.3227337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Simultaneously operating MR-PET systems have the potential to provide synergetic multi-parametric information, and, as such, interest surrounding their use and development is increasing. However, despite the potential advantages offered by fully combined MR-PET systems, implementing this hybrid integration is technically laborious, and any factors degrading the quality of either modality must be circumvented to ensure optimal performance. In order to attain the best possible quality from both systems, most full MR-PET integrations tend to place the shielded PET system inside the MRI system, close to the target volume of the subject. The radiofrequency (RF) coil used in MRI systems is a key factor in determining the quality of the MR images, and, in simultaneous acquisition, it is generally positioned inside the PET system and PET imaging region, potentially resulting in attenuation and artefacts in the PET images. Therefore, when designing hybrid MR-PET systems, it is imperative that consideration be given to the RF coils inside the PET system. In this review, we present current state-of-the-art RF coil designs used for hybrid MR-PET experiments and discuss various design strategies for constructing PET transparent RF coils.
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McFadyen J, Dolan RJ. Spatiotemporal Precision of Neuroimaging in Psychiatry. Biol Psychiatry 2023; 93:671-680. [PMID: 36376110 DOI: 10.1016/j.biopsych.2022.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/20/2022] [Accepted: 08/12/2022] [Indexed: 12/23/2022]
Abstract
Aberrant patterns of cognition, perception, and behavior seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between spatial and temporal resolutions inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography, often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illnesses such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications that seek to drive a mechanistic understanding of psychopathology and the realization of preclinical translation.
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Affiliation(s)
- Jessica McFadyen
- UCL Max Planck Centre for Computational Psychiatry and Ageing Research and Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Raymond J Dolan
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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Chen ZS, Kulkarni P(P, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100602. [PMID: 36419447 PMCID: PMC9676543 DOI: 10.1016/j.patter.2022.100602] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
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8
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Liebe T, Kaufmann J, Hämmerer D, Betts M, Walter M. In vivo tractography of human locus coeruleus-relation to 7T resting state fMRI, psychological measures and single subject validity. Mol Psychiatry 2022; 27:4984-4993. [PMID: 36117208 PMCID: PMC9763100 DOI: 10.1038/s41380-022-01761-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/10/2022] [Accepted: 08/18/2022] [Indexed: 01/14/2023]
Abstract
The locus coeruleus (LC) in the brainstem as the main regulator of brain noradrenaline gains increasing attention because of its involvement in neurologic and psychiatric diseases and its relevance in general to brain function. In this study, we created a structural connectome of the LC nerve fibers based on in vivo MRI tractography to gain an understanding into LC connectivity and its impact on LC-related psychological measures. We combined our structural results with ultra-high field resting-state functional MRI to learn about the relationship between in vivo LC structural and functional connections. Importantly, we reveal that LC brain fibers are strongly associated with psychological measures of anxiety and alertness indicating that LC-noradrenergic connectivity may have an important role on brain function. Lastly, since we analyzed all our data in subject-specific space, we point out the potential of structural LC connectivity to reveal individual characteristics of LC-noradrenergic function on the single-subject level.
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Affiliation(s)
- Thomas Liebe
- grid.9613.d0000 0001 1939 2794Department of Psychiatry and Psychotherapy, University of Jena, D-07743 Jena, Germany ,grid.9613.d0000 0001 1939 2794Department of Radiology, University of Jena, D-07743 Jena, Germany ,Clinical Affective Neuroimaging Laboratory (CANLAB), D-39120 Magdeburg, Germany ,grid.418723.b0000 0001 2109 6265Leibniz Institute for Neurobiology, D-39118 Magdeburg, Germany
| | - Jörn Kaufmann
- grid.5807.a0000 0001 1018 4307Department of Neurology, University of Magdeburg, D-39120 Magdeburg, Germany
| | - Dorothea Hämmerer
- grid.5771.40000 0001 2151 8122Department of Psychology, University of Innsbruck, A-6020 Innsbruck, Austria ,grid.83440.3b0000000121901201Institute of Cognitive Neuroscience, University College London, London, UK-WC1E 6BT UK ,grid.5807.a0000 0001 1018 4307Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, D-39120 Magdeburg, Germany ,grid.418723.b0000 0001 2109 6265CBBS Center for Behavioral Brain Sciences, D-39120 Magdeburg, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), D-39120 Magdeburg, Germany
| | - Matthew Betts
- grid.5807.a0000 0001 1018 4307Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, D-39120 Magdeburg, Germany ,grid.418723.b0000 0001 2109 6265CBBS Center for Behavioral Brain Sciences, D-39120 Magdeburg, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE), D-39120 Magdeburg, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, University of Jena, D-07743, Jena, Germany. .,Clinical Affective Neuroimaging Laboratory (CANLAB), D-39120, Magdeburg, Germany. .,Leibniz Institute for Neurobiology, D-39118, Magdeburg, Germany. .,Department of Psychiatry and Psychotherapy, University Tuebingen, D-72076, Tuebingen, Germany. .,Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), D-07743 Jena, Germany. .,German Center for Mental Health (DZPG), Site Jena-Magdeburg-Halle, D-07743 Jena, Germany.
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Lavigne KM, Kanagasabai K, Palaniyappan L. Ultra-high field neuroimaging in psychosis: A narrative review. Front Psychiatry 2022; 13:994372. [PMID: 36506432 PMCID: PMC9730890 DOI: 10.3389/fpsyt.2022.994372] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/08/2022] [Indexed: 11/27/2022] Open
Abstract
Schizophrenia and related psychoses are complex neuropsychiatric diseases representing dysconnectivity across multiple scales, through the micro (cellular), meso (brain network), manifest (behavioral), and social (interpersonal) levels. In vivo human neuroimaging, particularly at ultra-high field (UHF), offers unprecedented opportunity to examine multiscale dysconnectivity in psychosis. In this review, we provide an overview of the literature to date on UHF in psychosis, focusing on microscale findings from magnetic resonance spectroscopy (MRS), mesoscale studies on structural and functional magnetic resonance imaging (fMRI), and multiscale studies assessing multiple neuroimaging modalities and relating UHF findings to behavior. We highlight key insights and considerations from multiscale and longitudinal studies and provide recommendations for future research on UHF neuroimaging in psychosis.
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Affiliation(s)
- Katie M Lavigne
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.,Montreal Neurological Institute-Hospital, McGill University, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Kesavi Kanagasabai
- Robarts Research Institute, Western University, London, ON, Canada.,Department of Medical Biophysics, Western University, London, ON, Canada
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montreal, QC, Canada.,Robarts Research Institute, Western University, London, ON, Canada.,Department of Medical Biophysics, Western University, London, ON, Canada
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