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Tarrano C, Zito G, Galléa C, Delorme C, McGovern EM, Atkinson-Clement C, Brochard V, Thobois S, Tranchant C, Grabli D, Degos B, Corvol JC, Pedespan JM, Krystkowiak P, Houeto JL, Degardin A, Defebvre L, Didier M, Valabrègue R, Apartis E, Vidailhet M, Roze E, Worbe Y. Microstructure of the cerebellum and its afferent pathways underpins dystonia in myoclonus dystonia. Eur J Neurol 2024:e16460. [PMID: 39254064 DOI: 10.1111/ene.16460] [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: 05/21/2024] [Revised: 08/14/2024] [Accepted: 08/20/2024] [Indexed: 09/11/2024]
Abstract
BACKGROUND AND PURPOSE Myoclonus dystonia due to a pathogenic variant in SGCE (MYC/DYT-SGCE) is a rare condition involving a motor phenotype associating myoclonus and dystonia. Dysfunction within the networks relying on the cortex, cerebellum, and basal ganglia was presumed to underpin the clinical manifestations. However, the microarchitectural abnormalities within these structures and related pathways are unknown. Here, we investigated the microarchitectural brain abnormalities related to the motor phenotype in MYC/DYT-SGCE. METHODS We used neurite orientation dispersion and density imaging, a multicompartment tissue model of diffusion neuroimaging, to compare microarchitectural neurite organization in MYC/DYT-SGCE patients and healthy volunteers (HVs). Neurite density index (NDI), orientation dispersion index (ODI), and isotropic volume fraction (ISOVF) were derived and correlated with the severity of motor symptoms. Fractional anisotropy (FA) and mean diffusivity (MD) derived from the diffusion tensor approach were also analyzed. In addition, we studied the pathways that correlated with motor symptom severity using tractography analysis. RESULTS Eighteen MYC/DYT-SGCE patients and 24 HVs were analyzed. MYC/DYT-SGCE patients showed an increase of ODI and a decrease of FA within their motor cerebellum. More severe dystonia was associated with lower ODI and NDI and higher FA within motor cerebellar cortex, as well as with lower NDI and higher ISOVF and MD within the corticopontocerebellar and spinocerebellar pathways. No association was found between myoclonus severity and diffusion parameters. CONCLUSIONS In MYC/DYT-SGCE, we found microstructural reorganization of the motor cerebellum. Structural change in the cerebellar afferent pathways that relay inputs from the spinal cord and the cerebral cortex were specifically associated with the severity of dystonia.
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Affiliation(s)
- Clément Tarrano
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Department of Neurology, Clinical Investigation Center for Neurosciences, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
- Department of Clinical Neurophysiology, Saint-Antoine Hospital, Paris, France
| | | | - Cécile Galléa
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Paris Brain Institute, Centre de NeuroImagerie de Recherche, UMRS 975, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Cécile Delorme
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Department of Neurology, Clinical Investigation Center for Neurosciences, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
| | - Eavan M McGovern
- Department of Neurology, Beaumont Hospital, Dublin, Ireland
- School of Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Cyril Atkinson-Clement
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- School of Medicine, University of Nottingham, Nottingham, UK
| | - Vanessa Brochard
- Department of Neurology, Clinical Investigation Center for Neurosciences, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
| | - Stéphane Thobois
- Neurological Department C, Hopital Neurologique Pierre Wertheimer, Hospices Civils de Lyon, Bron, France
- Faculté de Medecine Lyon Sud Charles Mérieux, Université Claude Bernard Lyon 1, Lyon, France
| | - Christine Tranchant
- Département de Neurologie, Hôpitaux Universitaires de Strasbourg, Hôpital de Hautepierre, Strasbourg, France
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, INSERM U964/CNRS UMR7104, Université de Strasbourg, Illkirch, France
- Fédération de Médecine Translationnelle de Strasbourg, Université de Strasbourg, Strasbourg, France
| | - David Grabli
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Department of Neurology, Clinical Investigation Center for Neurosciences, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
| | - Bertrand Degos
- Department of Neurology, Assistance Publique-Hôpitaux de Paris, Avicenne Hospital, Sorbonne Paris Nord, Bobigny, France
| | - Jean Christophe Corvol
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Department of Neurology, Clinical Investigation Center for Neurosciences, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
| | | | | | - Jean-Luc Houeto
- Department of Neurology, Centre Hospitalier Universitaire de Limoges, INSERM U1094, IRD U270, Epidemiology of Chronic Diseases in Tropical Zone, Institute of Epidemiology and Tropical Neurology, OmegaHealth, University of Limoges, Limoges, France
| | - Adrian Degardin
- Department of Neurology, Tourcoing Hospital, Tourcoing, France
| | - Luc Defebvre
- Centre Hospitalier Universitaire de Lille, INSERM U1172, Troubles Cognitifs Dégénératifs et Vasculaires, University of Lille, Lille, France
- Lille Center of Excellence for Neurodegenerative Diseases, Lille, France
| | - Mélanie Didier
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Paris Brain Institute, Centre de NeuroImagerie de Recherche, UMRS 975, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Romain Valabrègue
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Paris Brain Institute, Centre de NeuroImagerie de Recherche, UMRS 975, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Emmanuelle Apartis
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Department of Clinical Neurophysiology, Saint-Antoine Hospital, Paris, France
| | - Marie Vidailhet
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Department of Neurology, Clinical Investigation Center for Neurosciences, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
| | - Emmanuel Roze
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Department of Neurology, Clinical Investigation Center for Neurosciences, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
| | - Yulia Worbe
- Paris Brain Institute, INSERM, CNRS, Sorbonne Université, Paris, France
- Department of Neurology, Clinical Investigation Center for Neurosciences, Assistance Publique-Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Paris, France
- Department of Clinical Neurophysiology, Saint-Antoine Hospital, Paris, France
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Shamir I, Assaf Y. Tutorial: a guide to diffusion MRI and structural connectomics. Nat Protoc 2024:10.1038/s41596-024-01052-5. [PMID: 39232202 DOI: 10.1038/s41596-024-01052-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 07/09/2024] [Indexed: 09/06/2024]
Abstract
Diffusion magnetic resonance imaging (dMRI) is a versatile imaging technique that has gained popularity thanks to its sensitive ability to measure displacement of water molecules within a living tissue on a micrometer scale. Although dMRI has been around since the early 1990s, its applications are constantly evolving, primarily regarding the inference of structural connectomics from nerve fiber trajectories. However, these applications require expertise in image processing and statistics, and it can be difficult for a newcomer to choose an appropriate pipeline to fit their research needs, not least because dMRI is such a flexible methodology that dozens of acquisition and analysis pipelines have been developed over the years. This introductory guide is designed for graduate students and researchers in the neuroscience community who are interested in integrating this new methodology regardless of their background in neuroimaging and computational tools. The guide provides a brief overview of the basic dMRI methodologies but focuses on its applications in neuroplasticity and connectomics. The guide starts with dMRI experimental designs and a complete step-by-step pipeline for structural connectomics. The following section covers the basics of dMRI, including parameters and clinical applications (apparent diffusion coefficient, mean diffusivity, fractional anisotropy and microscopic fractional anisotropy), as well as different approaches and models. The final section focuses on structural connectomics, covering subjects from fiber tracking (techniques, evaluation and limitations) to structural networks (constructing, analyzing and visualizing a network).
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Affiliation(s)
- Ittai Shamir
- Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Yaniv Assaf
- Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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3
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Mills EP, Bosma RL, Rogachov A, Cheng JC, Osborne NR, Kim JA, Besik A, Bhatia A, Davis KD. Pretreatment Brain White Matter Integrity Associated With Neuropathic Pain Relief and Changes in Temporal Summation of Pain Following Ketamine. THE JOURNAL OF PAIN 2024; 25:104536. [PMID: 38615801 DOI: 10.1016/j.jpain.2024.104536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/07/2024] [Accepted: 04/09/2024] [Indexed: 04/16/2024]
Abstract
Neuropathic pain (NP) is a prevalent condition often associated with heightened pain responsiveness suggestive of central sensitization. Neuroimaging biomarkers of treatment outcomes may help develop personalized treatment strategies, but white matter (WM) properties have been underexplored for this purpose. Here we assessed whether WM pathways of the default mode network (DMN: medial prefrontal cortex [mPFC], posterior cingulate cortex, and precuneus) and descending pain modulation system (periaqueductal gray [PAG]) are associated with ketamine analgesia and attenuated temporal summation of pain (TSP, reflecting central sensitization) in NP. We used a fixel-based analysis of diffusion-weighted imaging data to evaluate WM microstructure (fiber density [FD]) and macrostructure (fiber bundle cross-section) within the DMN and mPFC-PAG pathways in 70 individuals who underwent magnetic resonance imaging and TSP testing; 35 with NP who underwent ketamine treatment and 35 age- and sex-matched pain-free individuals. Individuals with NP were assessed before and 1 month after treatment; those with ≥30% pain relief were considered responders (n = 18), or otherwise as nonresponders (n = 17). We found that WM structure within the DMN and mPFC-PAG pathways did not differentiate responders from nonresponders. However, pretreatment FD in the anterior limb of the internal capsule correlated with pain relief (r=.48). Moreover, pretreatment FD in the DMN (left mPFC-precuneus/posterior cingulate cortex; r=.52) and mPFC-PAG (r=.42) negatively correlated with changes in TSP. This suggests that WM microstructure in the DMN and mPFC-PAG pathway is associated with the degree to which ketamine reduces central sensitization. Thus, fixel metrics of WM structure may hold promise to predict ketamine NP treatment outcomes. PERSPECTIVE: We used advanced fixel-based analyses of MRI diffusion-weighted imaging data to identify pretreatment WM microstructure associated with ketamine outcomes, including analgesia and markers of attenuated central sensitization. Exploring associations between brain structure and treatment outcomes could contribute to a personalized approach to treatment for individuals with NP.
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Affiliation(s)
- Emily P Mills
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
| | - Rachael L Bosma
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
| | - Anton Rogachov
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Joshua C Cheng
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Natalie R Osborne
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Junseok A Kim
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Ariana Besik
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
| | - Anuj Bhatia
- Department of Anesthesia and Pain Management, University Health Network, Toronto, Ontario, Canada; Department of Anesthesia, University of Toronto, Toronto, Ontario, Canada
| | - Karen D Davis
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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Andrews L, Keller S, Ratcliffe C, Osman-Farah J, Shepherd H, Bhojak M, Macerollo A. Exploring White Matter Microstructure with Symptom Severity and Outcomes Following Deep Brain Stimulation in Tremor Syndromes. Tremor Other Hyperkinet Mov (N Y) 2024; 14:43. [PMID: 39220675 PMCID: PMC11363889 DOI: 10.5334/tohm.904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 04/17/2024] [Indexed: 09/04/2024] Open
Abstract
Background Essential tremor (ET) and dystonic tremor (DT) are movement disorders that cause debilitating symptoms, significantly impacting daily activities and quality of life. A poor understanding of their pathophysiology, as well as the mediators of clinical outcomes following deep brain stimulation (DBS), highlights the need for biomarkers to accurately characterise and optimally treat patients. Objectives We assessed the white matter microstructure of pathways implicated in the pathophysiology and therapeutic intervention in a retrospective cohort of patients with DT (n = 17) and ET (n = 19). We aimed to identity associations between white matter microstructure, upper limb tremor severity, and tremor improvement following DBS. Methods A fixel-based analysis pipeline was implemented to investigate white matter microstructural metrics in the whole brain, cerebello-thalamic pathways and tracts connected to stimulation volumes following DBS. Associations with preoperative and postoperative severity were analysed within each disorder group and across combined disorder groups. Results DBS led to significant improvements in both groups. No group differences in stimulation positions were identified. When white matter microstructural data was aligned according to the maximally affected upper limb, increased fiber density, and combined fiber density & cross-section of fixels in the left cerebellum were associated with greater tremor severity across DT and ET patients. White matter microstructure did not show associations with postoperative changes in cerebello-thalamic pathways, or tracts connected to stimulation volumes. Discussion Diffusion changes of the cerebellum are associated with the severity of upper limb tremor and appear to overlap in essential or dystonic tremor disorders.
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Affiliation(s)
- Luke Andrews
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | - Simon Keller
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | - Corey Ratcliffe
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
| | - Jibril Osman-Farah
- The Walton Centre NHS Foundation Trust for Neurology and Neurosurgery, Liverpool, UK
| | - Hilary Shepherd
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
- The Walton Centre NHS Foundation Trust for Neurology and Neurosurgery, Liverpool, UK
| | - Maneesh Bhojak
- The Walton Centre NHS Foundation Trust for Neurology and Neurosurgery, Liverpool, UK
| | - Antonella Macerollo
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, UK
- The Walton Centre NHS Foundation Trust for Neurology and Neurosurgery, Liverpool, UK
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5
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Bjork J, Kenley JK, Gardner C, Latham A, Smyser TA, Miller JP, Shimony JJ, Neil JJ, Warner B, Luby J, Barch DM, Rogers CE, Smyser CD, Lean RE. Associations between prenatal adversity and neonatal white matter microstructure on language outcomes at age 2 years. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.02.24311434. [PMID: 39211873 PMCID: PMC11361255 DOI: 10.1101/2024.08.02.24311434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Background Early life adversity is associated with microstructural alterations in white matter regions that subserve language. However, the mediating and moderating pathways between adversities experienced in utero and key neonatal white matter tracts including the corpus callosum (CC), superior longitudinal fasciculus (SLF), arcuate fasciculus (AF), inferior fronto- occipital fasciculus (IFOF), and uncinate on early language outcomes remains unknown. Methods This longitudinal study includes 160 neonates, oversampled for prenatal exposure to adversity, who underwent diffusion MRI (dMRI) in the first weeks of life. dMRI parameters were obtained using probabilistic tractography in FSL. Maternal Social Disadvantage and Psychosocial Stress was assessed throughout pregnancy. At age 2 years, the Bayley Scales of Infant and Toddler Development-III evaluated language outcomes. Linear regression, mediation, and moderation assessed associations between prenatal adversities and neonatal white matter on language outcomes. Results Prenatal exposure to Social Disadvantage (p<.001) and Maternal Psychosocial Stress (p<.001) were correlated with poorer language outcomes. When Social Disadvantage and maternal Psychosocial Stress were modeled simultaneously in relation to language outcomes, only Social Disadvantage was significant (p<.001). Independent of Social Disadvantage (p<.001), lower neonatal CC fractional anisotropy (FA) was related to poorer global (p=.02) and receptive (p=.02) language outcomes. CC FA did not mediate the association between Social Disadvantage and language outcomes (indirect effect 95% CIs -0.96-0.15), and there was no interaction between Social Disadvantage and CC FA on language outcomes (p>.05). Bilateral SLF/AF, IFOF, and uncinate were not significant (p>.05). Conclusions Prenatal exposure to Social Disadvantage and neonatal CC FA were independently related to language problems by age 2, with no evidence of mediating or moderating associations with language outcomes. These findings elucidate the early neural underpinnings of language development and suggest that the prenatal period may be an important time to provide poverty- reducing support to expectant mothers to promote offspring neurodevelopmental outcomes.
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Calixto C, Soldatelli MD, Li B, Pierotich L, Gholipour A, Warfield SK, Karimi D. White matter tract crossing and bottleneck regions in the fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.20.603804. [PMID: 39091823 PMCID: PMC11291018 DOI: 10.1101/2024.07.20.603804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been extensively researched for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 59 fetal brain scans and extracted a set of 51 distinct white tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75-80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. The results of this study highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.
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Affiliation(s)
- Camilo Calixto
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Matheus D Soldatelli
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Bo Li
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lana Pierotich
- Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Davood Karimi
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Maldonado IL, Descoteaux M, Rheault F, Zemmoura I, Benn A, Margulies D, Boré A, Duffau H, Mandonnet E. Multimodal study of multilevel pulvino-temporal connections: a new piece in the puzzle of lexical retrieval networks. Brain 2024; 147:2245-2257. [PMID: 38243610 PMCID: PMC11146422 DOI: 10.1093/brain/awae021] [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: 06/19/2023] [Revised: 11/18/2023] [Accepted: 12/30/2023] [Indexed: 01/21/2024] Open
Abstract
Advanced methods of imaging and mapping the healthy and lesioned brain have allowed for the identification of the cortical nodes and white matter tracts supporting the dual neurofunctional organization of language networks in a dorsal phonological and a ventral semantic stream. Much less understood are the anatomical correlates of the interaction between the two streams; one hypothesis being that of a subcortically mediated interaction, through crossed cortico-striato-thalamo-cortical and cortico-thalamo-cortical loops. In this regard, the pulvinar is the thalamic subdivision that has most regularly appeared as implicated in the processing of lexical retrieval. However, descriptions of its connections with temporal (language) areas remain scarce. Here we assess this pulvino-temporal connectivity using a combination of state-of-the-art techniques: white matter stimulation in awake surgery and postoperative diffusion MRI (n = 4), virtual dissection from the Human Connectome Project 3 and 7 T datasets (n = 172) and operative microscope-assisted post-mortem fibre dissection (n = 12). We demonstrate the presence of four fundamental fibre contingents: (i) the anterior component (Arnold's bundle proper) initially described by Arnold in the 19th century and destined to the anterior temporal lobe; (ii) the optic radiations-like component, which leaves the pulvinar accompanying the optical radiations and reaches the posterior basal temporal cortices; (iii) the lateral component, which crosses the temporal stem orthogonally and reaches the middle temporal gyrus; and (iv) the auditory radiations-like component, which leaves the pulvinar accompanying the auditory radiations to the superomedial aspect of the temporal operculum, just posteriorly to Heschl's gyrus. Each of those components might correspond to a different level of information processing involved in the lexical retrieval process of picture naming.
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Affiliation(s)
- Igor Lima Maldonado
- UMR 1253, iBrain, Université de Tours, Inserm, 37000 Tours, France
- Department of Neurosurgery, CHRU de Tours, 37000 Tours, France
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory, Department of Computer Science, Faculty of Sciences, Université de Sherbrooke, J1K 2X9 Sherbrooke, Quebec, Canada
- Imeka Solutions, J1H 4A7 Sherbrooke, Quebec, Canada
| | | | - Ilyess Zemmoura
- UMR 1253, iBrain, Université de Tours, Inserm, 37000 Tours, France
- Department of Neurosurgery, CHRU de Tours, 37000 Tours, France
| | - Austin Benn
- CNRS, Integrative Neuroscience and Cognition Center (UMR 8002), Université de Paris Cité, 75006 Paris, France
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, OX1 3QD Oxford, UK
| | - Daniel Margulies
- CNRS, Integrative Neuroscience and Cognition Center (UMR 8002), Université de Paris Cité, 75006 Paris, France
| | - Arnaud Boré
- Sherbrooke Connectivity Imaging Laboratory, Department of Computer Science, Faculty of Sciences, Université de Sherbrooke, J1K 2X9 Sherbrooke, Quebec, Canada
| | - Hugues Duffau
- Department of Neurosurgery, Gui de Chauliac Hospital, Montpellier University Medical Center, 34090 Montpellier, France
- Team ‘Plasticity of Central Nervous System, Stem Cells and Glial Tumors’, U1191 Laboratory, Institute of Functional Genomics, National Institute for Health and Medical Research (INSERM), University of Montpellier, 34000, Montpellier, France
| | - Emmanuel Mandonnet
- Department of Neurosurgery, Lariboisière Hospital, AP-HP, 75010 Paris, France
- Frontlab, CNRS UMR 7225, INSERM U1127, Paris Brain Institute (ICM), 75013 Paris, France
- UFR Médecine, Université de Paris Cité, 75006 Paris, France
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Poirier SE, Suskin NG, Khaw AV, Thiessen JD, Shoemaker JK, Anazodo UC. Probing Evidence of Cerebral White Matter Microstructural Disruptions in Ischemic Heart Disease Before and Following Cardiac Rehabilitation: A Diffusion Tensor MR Imaging Study. J Magn Reson Imaging 2024; 59:2137-2149. [PMID: 37589418 DOI: 10.1002/jmri.28964] [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: 05/05/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND Ischemic heart disease (IHD) is linked to brain white matter (WM) breakdown but how age or disease effects WM integrity, and whether it is reversible using cardiac rehabilitation (CR), remains unclear. PURPOSE To assess the effects of brain aging, cardiovascular disease, and CR on WM microstructure in brains of IHD patients following a cardiac event. STUDY TYPE Retrospective. POPULATION Thirty-five IHD patients (9 females; mean age = 59 ± 8 years), 21 age-matched healthy controls (10 females; mean age = 59 ± 8 years), and 25 younger controls (14 females; mean age = 26 ± 4 years). FIELD STRENGTH/SEQUENCE 3 T diffusion-weighted imaging with single-shot echo planar imaging acquired at 3 months and 9 months post-cardiac event. ASSESSMENT Tract-based spatial statistics (TBSS) and tractometry were used to compare fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in cerebral WM between: 1) older and younger controls to distinguish age-related from disease-related WM changes; 2) IHD patients at baseline (pre-CR) and age-matched controls to investigate if cardiovascular disease exacerbates age-related WM changes; and 3) IHD patients pre-CR and post-CR to investigate the neuroplastic effect of CR on WM microstructure. STATISTICAL TESTS Two-sample unpaired t-test (age: older vs. younger controls; IHD: IHD pre-CR vs. age-matched controls). One-sample paired t-test (CR: IHD pre- vs. post-CR). Statistical threshold: P < 0.05 (FWE-corrected). RESULTS TBSS and tractometry revealed widespread WM changes in older controls compared to younger controls while WM clusters of decreased FA in the fornix and increased MD in body of corpus callosum were observed in IHD patients pre-CR compared to age-matched controls. Robust WM improvements (increased FA, increased AD) were observed in IHD patients post-CR. DATA CONCLUSION In IHD, both brain aging and cardiovascular disease may contribute to WM disruptions. IHD-related WM disruptions may be favorably modified by CR. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Stefan E Poirier
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Neville G Suskin
- Division of Cardiology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Alexander V Khaw
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jonathan D Thiessen
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Joel K Shoemaker
- School of Kinesiology, Western University, London, Ontario, Canada
| | - Udunna C Anazodo
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Research Centre for Studies in Aging, McGill University, Montréal, Québec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
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Driver C, Boyes A, Mohamed AZ, Levenstein JM, Parker M, Hermens DF. Understanding Wellbeing Profiles According to White Matter Structural Connectivity Sub-types in Early Adolescents: The First Hundred Brains Cohort from the Longitudinal Adolescent Brain Study. J Youth Adolesc 2024; 53:1029-1046. [PMID: 38217837 PMCID: PMC10980632 DOI: 10.1007/s10964-024-01939-2] [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] [Received: 09/11/2023] [Accepted: 01/04/2024] [Indexed: 01/15/2024]
Abstract
Wellbeing is protective against the emergence of psychopathology. Neurobiological markers associated with mental wellbeing during adolescence are important to understand. Limited research has examined neural networks (white matter tracts) and mental wellbeing in early adolescence specifically. A cross-sectional diffusion tensor imaging analysis approach was conducted, from the Longitudinal Adolescent Brain study, First Hundred Brains cohort (N = 99; 46.5% female; Mage = 13.01, SD = 0.55). Participants completed self-report measures including wellbeing, quality-of-life, and psychological distress. Potential neurobiological profiles using fractional anisotropy, axial, and radial diffusivity were determined via a whole brain voxel-wise approach, and hierarchical cluster analysis of fractional anisotropy values, obtained from 21 major white matter tracts. Three cluster groups with significantly different neurobiological profiles were distinguished. No significant differences were found between the three cluster groups and measures of wellbeing, but two left lateralized significant associations between white matter tracts and wellbeing measures were found. These results provide preliminary evidence for potential neurobiological markers of mental health and wellbeing in early adolescence and should be tracked longitudinally to provide more detailed and robust findings.
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Affiliation(s)
- Christina Driver
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia.
| | - Amanda Boyes
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia
| | - Abdalla Z Mohamed
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia
| | - Jacob M Levenstein
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia
| | - Marcella Parker
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia
| | - Daniel F Hermens
- Thompson Institute, University of the Sunshine Coast, 12 Innovation Parkway, Birtinya, Sunshine Coast, QLD, Australia
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10
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Meisler SL, Gabrieli JDE, Christodoulou JA. White matter microstructural plasticity associated with educational intervention in reading disability. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2024; 2:10.1162/imag_a_00108. [PMID: 38974814 PMCID: PMC11225775 DOI: 10.1162/imag_a_00108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
Children's reading progress typically slows during extended breaks in formal education, such as summer vacations. This stagnation can be especially concerning for children with reading difficulties or disabilities, such as dyslexia, because of the potential to exacerbate the skills gap between them and their peers. Reading interventions can prevent skill loss and even lead to appreciable gains in reading ability during the summer. Longitudinal studies relating intervention response to brain changes can reveal educationally relevant insights into rapid learning-driven brain plasticity. The current work focused on reading outcomes and white matter connections, which enable communication among the brain regions required for proficient reading. We collected reading scores and diffusion-weighted images at the beginning and end of summer for 41 children with reading difficulties who had completed either 1st or 2nd grade. Children were randomly assigned to either receive an intensive reading intervention (n = 26; Seeing Stars from Lindamood-Bell which emphasizes orthographic fluency) or be deferred to a wait-list group (n = 15), enabling us to analyze how white matter properties varied across a wide spectrum of skill development and regression trajectories. On average, the intervention group had larger gains in reading compared to the non-intervention group, who declined in reading scores. Improvements on a proximal measure of orthographic processing (but not other more distal reading measures) were associated with decreases in mean diffusivity within core reading brain circuitry (left arcuate fasciculus and left inferior longitudinal fasciculus) and increases in fractional anisotropy in the left corticospinal tract. Our findings suggest that responses to intensive reading instruction are related predominantly to white matter plasticity in tracts most associated with reading.
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Affiliation(s)
- Steven L. Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - John D. E. Gabrieli
- Program in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- McGovern Institute for Brain Research, Cambridge, MA, United States
| | - Joanna A. Christodoulou
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- McGovern Institute for Brain Research, Cambridge, MA, United States
- Department of Communication Sciences and Disorders, MGH Institute of Health Professions, Charlestown, MA, United States
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11
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Feng Y, Chandio BQ, Villalon-Reina JE, Benavidez S, Chattopadhyay T, Chehrzadeh S, Laltoo E, Thomopoulos SI, Joshi H, Venkatasubramanian G, John JP, Jahanshad N, Thompson PM. Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.578943. [PMID: 38370817 PMCID: PMC10871218 DOI: 10.1101/2024.02.05.578943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro-and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT's capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer's disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.
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Affiliation(s)
- Yixue Feng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Bramsh Q Chandio
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Julio E Villalon-Reina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sebastian Benavidez
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Tamoghna Chattopadhyay
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sasha Chehrzadeh
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Himanshu Joshi
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Ganesan Venkatasubramanian
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - John P John
- Multimodal Brain Image Analysis Laboratory, Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
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12
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Young F, Aquilina K, Seunarine KK, Mancini L, Clark CA, Clayden JD. Fibre orientation atlas guided rapid segmentation of white matter tracts. Hum Brain Mapp 2024; 45:e26578. [PMID: 38339907 PMCID: PMC10826637 DOI: 10.1002/hbm.26578] [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: 05/25/2023] [Revised: 12/14/2023] [Accepted: 12/19/2023] [Indexed: 02/12/2024] Open
Abstract
Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.
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Affiliation(s)
- Fiona Young
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
- Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
| | - Kristian Aquilina
- Department of NeurosurgeryGreat Ormond Street Hospital for ChildrenLondonUK
| | - Kiran K. Seunarine
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
- Department of RadiologyGreat Ormond Street Hospital for ChildrenLondonUK
| | - Laura Mancini
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Chris A. Clark
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
| | - Jonathan D. Clayden
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
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13
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Guillevin R, Naudin M, Fayolle P, Giraud C, Le Guillou X, Thomas C, Herpe G, Miranville A, Fernandez-Maloigne C, Pellerin L, Guillevin C. Diagnostic and Therapeutic Issues in Glioma Using Imaging Data: The Challenge of Numerical Twinning. J Clin Med 2023; 12:7706. [PMID: 38137775 PMCID: PMC10744312 DOI: 10.3390/jcm12247706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 11/28/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
Glial tumors represent the leading etiology of primary brain tumors. Their particularities lie in (i) their location in a highly functional organ that is difficult to access surgically, including for biopsy, and (ii) their rapid, anisotropic mode of extension, notably via the fiber bundles of the white matter, which further limits the possibilities of resection. The use of mathematical tools enables the development of numerical models representative of the oncotype, genotype, evolution, and therapeutic response of lesions. The significant development of digital technologies linked to high-resolution NMR exploration, coupled with the possibilities offered by AI, means that we can envisage the creation of digital twins of tumors and their host organs, thus reducing the use of physical sampling.
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Affiliation(s)
- Rémy Guillevin
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Mathieu Naudin
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Pierre Fayolle
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Clément Giraud
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Xavier Le Guillou
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
- Department of Genetic, University Hospital Center of Poitiers, 86000 Poitiers, France
| | - Clément Thomas
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Guillaume Herpe
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | - Alain Miranville
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
| | | | - Luc Pellerin
- IRMETIST Laboratory, INSERM U1313, University of Poitiers and University Hospital Center of Poitiers, 86000 Poitiers, France
| | - Carole Guillevin
- Department of Imaging, University Hospital Center of Poitiers, 86000 Poitiers, France
- Labcom I3M, University of Poitiers, 86000 Poitiers, France
- DACTIM-MIS Team, Laboratoire de Mathématiques Appliquées LMA, CNRS UMR 7348, 86021 Poitiers, France
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14
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He J, Zhang F, Pan Y, Feng Y, Rushmore J, Torio E, Rathi Y, Makris N, Kikinis R, Golby AJ, O'Donnell LJ. Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods. Hum Brain Mapp 2023; 44:6055-6073. [PMID: 37792280 PMCID: PMC10619402 DOI: 10.1002/hbm.26497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/09/2023] [Accepted: 09/13/2023] [Indexed: 10/05/2023] Open
Abstract
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.
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Affiliation(s)
- Jianzhong He
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Fan Zhang
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- University of Electronic Science and Technology of ChinaChengduSichuanChina
| | - Yiang Pan
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Yuanjing Feng
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Jarrett Rushmore
- Departments of Psychiatry, Neurology and RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Erickson Torio
- Department of NeurosurgeryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Departments of Psychiatry, Neurology and RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Alexandra J. Golby
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurosurgeryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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15
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Schilling KG, Li M, Rheault F, Gao Y, Cai L, Zhao Y, Xu L, Ding Z, Anderson AW, Landman BA, Gore JC. Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis. Proc Natl Acad Sci U S A 2023; 120:e2219666120. [PMID: 37824529 PMCID: PMC10589709 DOI: 10.1073/pnas.2219666120] [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: 11/28/2022] [Accepted: 08/11/2023] [Indexed: 10/14/2023] Open
Abstract
Recent studies have revealed the production of time-locked blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. "Whole-brain" actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.
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Affiliation(s)
- Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
| | - Francois Rheault
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN37235
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
| | - Leon Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
| | - Bennett A. Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN37235
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
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16
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Zhu H, Li T, Zhao B. Statistical Learning Methods for Neuroimaging Data Analysis with Applications. Annu Rev Biomed Data Sci 2023; 6:73-104. [PMID: 37127052 DOI: 10.1146/annurev-biodatasci-020722-100353] [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] [Indexed: 05/03/2023]
Abstract
The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.
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Affiliation(s)
- Hongtu Zhu
- Department of Biostatistics, Department of Statistics, Department of Genetics, and Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA;
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Tengfei Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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17
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Valcourt Caron A, Shmuel A, Hao Z, Descoteaux M. versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database. Front Neuroinform 2023; 17:1191200. [PMID: 37637471 PMCID: PMC10449583 DOI: 10.3389/fninf.2023.1191200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/27/2023] [Indexed: 08/29/2023] Open
Abstract
The lack of "gold standards" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.
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Affiliation(s)
- Alex Valcourt Caron
- Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Amir Shmuel
- Brain Imaging Signals Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Ziqi Hao
- Brain Imaging Signals Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
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18
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Nebe S, Reutter M, Baker DH, Bölte J, Domes G, Gamer M, Gärtner A, Gießing C, Gurr C, Hilger K, Jawinski P, Kulke L, Lischke A, Markett S, Meier M, Merz CJ, Popov T, Puhlmann LMC, Quintana DS, Schäfer T, Schubert AL, Sperl MFJ, Vehlen A, Lonsdorf TB, Feld GB. Enhancing precision in human neuroscience. eLife 2023; 12:e85980. [PMID: 37555830 PMCID: PMC10411974 DOI: 10.7554/elife.85980] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/23/2023] [Indexed: 08/10/2023] Open
Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
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Affiliation(s)
- Stephan Nebe
- Zurich Center for Neuroeconomics, Department of Economics, University of ZurichZurichSwitzerland
| | - Mario Reutter
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Daniel H Baker
- Department of Psychology and York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Jens Bölte
- Institute for Psychology, University of Münster, Otto-Creuzfeldt Center for Cognitive and Behavioral NeuroscienceMünsterGermany
| | - Gregor Domes
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
- Institute for Cognitive and Affective NeuroscienceTrierGermany
| | - Matthias Gamer
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Anne Gärtner
- Faculty of Psychology, Technische Universität DresdenDresdenGermany
| | - Carsten Gießing
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University of OldenburgOldenburgGermany
| | - Caroline Gurr
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | - Kirsten Hilger
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
- Department of Psychology, Psychological Diagnostics and Intervention, Catholic University of Eichstätt-IngolstadtEichstättGermany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Louisa Kulke
- Department of Developmental with Educational Psychology, University of BremenBremenGermany
| | - Alexander Lischke
- Department of Psychology, Medical School HamburgHamburgGermany
- Institute of Clinical Psychology and Psychotherapy, Medical School HamburgHamburgGermany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Maria Meier
- Department of Psychology, University of KonstanzKonstanzGermany
- University Psychiatric Hospitals, Child and Adolescent Psychiatric Research Department (UPKKJ), University of BaselBaselSwitzerland
| | - Christian J Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University BochumBochumGermany
| | - Tzvetan Popov
- Department of Psychology, Methods of Plasticity Research, University of ZurichZurichSwitzerland
| | - Lara MC Puhlmann
- Leibniz Institute for Resilience ResearchMainzGermany
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Daniel S Quintana
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- NevSom, Department of Rare Disorders & Disabilities, Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of OsloOsloNorway
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | | | - Matthias FJ Sperl
- Department of Clinical Psychology and Psychotherapy, University of GiessenGiessenGermany
- Center for Mind, Brain and Behavior, Universities of Marburg and GiessenGiessenGermany
| | - Antonia Vehlen
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of BielefeldBielefeldGermany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychology, Heidelberg UniversityHeidelbergGermany
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
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19
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Driver C, Moore L, Mohamed A, Boyes A, Sacks DD, Mills L, McLoughlin LT, Lagopoulos J, Hermens DF. Structural connectivity and its association with social connectedness in early adolescence. Behav Brain Res 2023; 440:114259. [PMID: 36528168 DOI: 10.1016/j.bbr.2022.114259] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
Adolescence is a critical period of social and neural development. Brain regions which process social information develop throughout adolescence as young people learn to navigate social environments. Studies investigating brain structural connectivity (indexed by white matter (WM) integrity), and social connectedness in adolescents have been limited until recently, with literature stemming mostly from adult samples, broad age ranges within adolescence or based on social network characteristics as opposed to social connectedness. This cross-sectional study of 12-year-olds (N = 73) explored the relationship between social connectedness (SCS) and structural connectivity in early adolescence, to gauge how this snapshot of WM development is associated with social behaviour. Whole brain voxel-wise diffusion tensor imaging was undertaken to determine correlations between SCS and fractional anisotropy (FA), radial (RD) and axial (AD) diffusivity of clusters within WM tracts. Significant negative relationships between FA and SCS scores were found in clusters within 11 WM tracts, with significant positive correlations between SCS and both RD and AD across clusters within 13 and 8 clusters, respectively. Clusters within the genu of the corpus callosum (CCgn) showed strong correlations for all three metrics, and regression models that included gender, age, and psychological distress, revealed SCS to be the only significant predictor of CCgn FA, RD and AD values. Overall, these findings suggest that those with lower social connectedness had a WM profile suggestive of reduced axonal density and/or coherence. Longitudinal research is needed to track such WM profiles during adolescent development and determine the associations with mental health and well-being outcomes.
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Affiliation(s)
- Christina Driver
- Thompson Institute, University of the Sunshine Coast, Innovation Parkway, Birtinya, QLD, Australia.
| | - Lisa Moore
- Thompson Institute, University of the Sunshine Coast, Innovation Parkway, Birtinya, QLD, Australia
| | - Abdalla Mohamed
- Thompson Institute, University of the Sunshine Coast, Innovation Parkway, Birtinya, QLD, Australia
| | - Amanda Boyes
- Thompson Institute, University of the Sunshine Coast, Innovation Parkway, Birtinya, QLD, Australia
| | - Dashiell D Sacks
- Thompson Institute, University of the Sunshine Coast, Innovation Parkway, Birtinya, QLD, Australia
| | - Lia Mills
- Thompson Institute, University of the Sunshine Coast, Innovation Parkway, Birtinya, QLD, Australia
| | - Larisa T McLoughlin
- Behaviour-Brain-Body Research Centre, University of South Australia, Australia
| | - Jim Lagopoulos
- Thompson Institute, University of the Sunshine Coast, Innovation Parkway, Birtinya, QLD, Australia
| | - Daniel F Hermens
- Thompson Institute, University of the Sunshine Coast, Innovation Parkway, Birtinya, QLD, Australia
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20
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Labus JS, Wang C, Mayer EA, Gupta A, Oughourlian T, Kilpatrick L, Tillisch K, Chang L, Naliboff B, Ellingson BM. Sex-specific brain microstructural reorganization in irritable bowel syndrome. Pain 2023; 164:292-304. [PMID: 35639426 PMCID: PMC9691795 DOI: 10.1097/j.pain.0000000000002699] [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: 02/07/2022] [Accepted: 05/17/2022] [Indexed: 02/06/2023]
Abstract
ABSTRACT Preliminary evidence suggests that there are sex differences in microstructural brain organization among individuals with irritable bowel syndrome (IBS). The aim of this study was to further investigate sex-dependent differences in brain microstructure and organization in a large sample of well-phenotyped participants with IBS compared with healthy controls. We hypothesized that female patients with IBS would show evidence for increased axonal strength and myelination within and between brain regions concerned with pain and sensory processing, when compared with males with IBS. We also hypothesized that female compared with male IBS subjects show greater levels of somatic awareness and sensory sensitivity consistent with multisystem sensory sensitivity. Diffusion tensor images and clinical assessments were obtained in 100 healthy controls (61 females) and 152 IBS (107 females) on a 3T Siemens Trio. Whole brain voxel-wise differences in fractional anisotropy, mean, radial and axial diffusivity, and track density as differences in somatic awareness and sensory sensitivity were assessed using the general linear model. Female compared with male IBS participants showed extensive microstructural alterations in sensorimotor, corticothalamic, and basal ganglia circuits involved in pain processing and integration of sensorimotor information. Together with the observed increases in symptom severity, somatic awareness, and sensory sensitivity, the findings support the hypotheses that the etiology and maintenance of symptoms in females with IBS may be driven by greater central sensitivity for multiple sensory stimuli.
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Affiliation(s)
- Jennifer S. Labus
- Oppenheimer Center for the Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Emeran A Mayer
- Oppenheimer Center for the Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Arpana Gupta
- Oppenheimer Center for the Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Talia Oughourlian
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Lisa Kilpatrick
- Oppenheimer Center for the Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Kirsten Tillisch
- Oppenheimer Center for the Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Lin Chang
- Oppenheimer Center for the Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Bruce Naliboff
- Oppenheimer Center for the Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Vatche and Tamar Manoukian Division of Digestive Diseases, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
| | - Benjamin M. Ellingson
- Oppenheimer Center for the Neurobiology of Stress and Resilience, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA
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21
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Zhylka A, Leemans A, Pluim JPW, De Luca A. Anatomically informed multi-level fiber tractography for targeted virtual dissection. MAGMA (NEW YORK, N.Y.) 2023; 36:79-93. [PMID: 35904612 PMCID: PMC9992235 DOI: 10.1007/s10334-022-01033-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/30/2022] [Accepted: 07/15/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVES Diffusion-weighted MRI can assist preoperative planning by reconstructing the trajectory of eloquent fiber pathways, such as the corticospinal tract (CST). However, accurate reconstruction of the full extent of the CST remains challenging with existing tractography methods. We suggest a novel tractography algorithm exploiting unused fiber orientations to produce more complete and reliable results. METHODS Our novel approach, referred to as multi-level fiber tractography (MLFT), reconstructs fiber pathways by progressively considering previously unused fiber orientations at multiple levels of tract propagation. Anatomical priors are used to minimize the number of false-positive pathways. The MLFT method was evaluated on synthetic data and in vivo data by reconstructing the CST while compared to conventional tractography approaches. RESULTS The radial extent of MLFT reconstructions is comparable to that of probabilistic reconstruction: [Formula: see text] for the left and [Formula: see text] for the right hemisphere according to Wilcoxon test, while achieving significantly higher topography preservation compared to probabilistic tractography: [Formula: see text]. DISCUSSION MLFT provides a novel way to reconstruct fiber pathways by adding the capability of including branching pathways in fiber tractography. Thanks to its robustness, feasible reconstruction extent and topography preservation, our approach may assist in clinical practice as well as in virtual dissection studies.
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Affiliation(s)
- Andrey Zhylka
- Biomedical Engineering, Eindhoven University of Technology, Rondom 70, 5612 AP, Eindhoven, The Netherlands.
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Josien P W Pluim
- Biomedical Engineering, Eindhoven University of Technology, Rondom 70, 5612 AP, Eindhoven, The Netherlands
| | - Alberto De Luca
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.,Neurology Department, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
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22
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The structural connectivity of the human angular gyrus as revealed by microdissection and diffusion tractography. Brain Struct Funct 2023; 228:103-120. [PMID: 35995880 DOI: 10.1007/s00429-022-02551-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 08/03/2022] [Indexed: 01/07/2023]
Abstract
The angular gyrus (AG) has been described in numerous studies to be consistently activated in various functional tasks. The angular gyrus is a critical connector epicenter linking multiple functional networks due to its location in the posterior part of the inferior parietal cortex, namely at the junction between the parietal, temporal, and occipital lobes. It is thus crucial to identify the different pathways that anatomically connect this high-order association region to the rest of the brain. Our study revisits the three-dimensional architecture of the structural AG connectivity by combining state-of-the-art postmortem blunt microdissection with advanced in vivo diffusion tractography to comprehensively describe the association, projection, and commissural fibers that connect the human angular gyrus. AG appears as a posterior "angular stone" of associative connections belonging to mid- and long-range dorsal and ventral fibers of the superior and inferior longitudinal systems, respectively, to short-range parietal, occipital, and temporal fibers, including U-shaped fibers in the posterior transverse system. Thus, AG is at a pivotal dorso-ventral position reflecting its critical role in the different functional networks, particularly in language elaboration and spatial attention and awareness in the left and right hemispheres, respectively. We also reveal striatal, thalamic, and brainstem connections and a typical inter-hemispheric homotopic callosal connectivity supporting the suggested AG role in the integration of sensory input for modulating motor control and planning. The present description of AG's highly distributed wiring diagram may drastically improve intraoperative subcortical testing and post-operative neurologic outcomes related to surgery in and around the angular gyrus.
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23
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Meisler SL, Gabrieli JDE. Fiber-specific structural properties relate to reading skills in children and adolescents. eLife 2022; 11:e82088. [PMID: 36576253 PMCID: PMC9815823 DOI: 10.7554/elife.82088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022] Open
Abstract
Recent studies suggest that the cross-sectional relationship between reading skills and white matter microstructure, as indexed by fractional anisotropy, is not as robust as previously thought. Fixel-based analyses yield fiber-specific micro- and macrostructural measures, overcoming several shortcomings of the traditional diffusion tensor model. We ran a whole-brain analysis investigating whether the product of fiber density and cross-section (FDC) related to single-word reading skills in a large, open, quality-controlled dataset of 983 children and adolescents ages 6-18. We also compared FDC between participants with (n = 102) and without (n = 570) reading disabilities. We found that FDC positively related to reading skills throughout the brain, especially in left temporoparietal and cerebellar white matter, but did not differ between reading proficiency groups. Exploratory analyses revealed that among metrics from other diffusion models - diffusion tensor imaging, diffusion kurtosis imaging, and neurite orientation dispersion and density imaging - only the orientation dispersion and neurite density indexes from NODDI were associated (inversely) with reading skills. The present findings further support the importance of left-hemisphere dorsal temporoparietal white matter tracts in reading. Additionally, these results suggest that future DWI studies of reading and dyslexia should be designed to benefit from advanced diffusion models, include cerebellar coverage, and consider continuous analyses that account for individual differences in reading skill.
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Affiliation(s)
- Steven Lee Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard Medical SchoolBostonUnited States
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24
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Thomson P, Vijayakumar N, Fuelscher I, Malpas CB, Hazell P, Silk TJ. White matter and sustained attention in children with attention/deficit-hyperactivity disorder: A longitudinal fixel-based analysis. Cortex 2022; 157:129-141. [PMID: 36283135 DOI: 10.1016/j.cortex.2022.09.006] [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] [Received: 12/23/2021] [Revised: 05/29/2022] [Accepted: 09/20/2022] [Indexed: 12/15/2022]
Abstract
Sustained attention is a cognitive function with known links to academic success and mental health disorders such as attention/deficit-hyperactivity disorder (ADHD). Several functional networks are critical to sustained attention, however the association between white matter maturation in tracts linking functional nodes and sustained attention in typical and atypical development is unknown. 309 diffusion-weighted imaging scans were acquired from 161 children and adolescents (80 ADHD, 81 control) at up to three timepoints over ages 9-14. A fixel-based analysis approach was used to calculate mean fiber density and fiber-bundle cross section in tracts of interest. Sustained attention was measured using omission errors and response time variability on the out-of-scanner sustained attention to response task. Linear mixed effects models examined associations of age, group and white matter metrics with sustained attention. Greater fiber density in the bilateral superior longitudinal fasciculus (SLF) I and right SLF II was associated with fewer attention errors in the control group only. In ADHD and control groups, greater fiber density in the left ILF and right thalamo-premotor pathway, as well as greater fiber cross-section in the left SLF I and II and right SLF III, was associated with better sustained attention. Relationships were consistent across the age span. Results suggest that greater axon diameter or number in the dorsal and middle SLF may facilitate sustained attention in neurotypical children but does not assist those with ADHD potentially due to disorder-related alterations in this region. Greater capacity for information transfer across the SLF was associated with attention maintenance in 9-14-year-olds regardless of diagnostic status, suggesting white matter macrostructure may also be important for attention maintenance. White matter and sustained attention associations were consistent across the longitudinal study, according with the stability of structural organization over this time. Future studies can investigate modifiability of white matter properties through ADHD medications.
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Affiliation(s)
- Phoebe Thomson
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia; Murdoch Children's Research Institute, Melbourne, Australia.
| | | | - Ian Fuelscher
- School of Psychology, Deakin University, Melbourne, Australia
| | - Charles B Malpas
- Murdoch Children's Research Institute, Melbourne, Australia; Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia; Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - Philip Hazell
- Discipline of Psychiatry, The University of Sydney, Sydney, Australia
| | - Timothy J Silk
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia; Murdoch Children's Research Institute, Melbourne, Australia; School of Psychology, Deakin University, Melbourne, Australia
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25
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Siegbahn M, Engmér Berglin C, Moreno R. Automatic segmentation of the core of the acoustic radiation in humans. Front Neurol 2022; 13:934650. [PMID: 36212647 PMCID: PMC9539320 DOI: 10.3389/fneur.2022.934650] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 09/19/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Acoustic radiation is one of the most important white matter fiber bundles of the human auditory system. However, segmenting the acoustic radiation is challenging due to its small size and proximity to several larger fiber bundles. TractSeg is a method that uses a neural network to segment some of the major fiber bundles in the brain. This study aims to train TractSeg to segment the core of acoustic radiation. Methods We propose a methodology to automatically extract the acoustic radiation from human connectome data, which is both of high quality and high resolution. The segmentation masks generated by TractSeg of nearby fiber bundles are used to steer the generation of valid streamlines through tractography. Only streamlines connecting the Heschl's gyrus and the medial geniculate nucleus were considered. These streamlines are then used to create masks of the core of the acoustic radiation that is used to train the neural network of TractSeg. The trained network is used to automatically segment the acoustic radiation from unseen images. Results The trained neural network successfully extracted anatomically plausible masks of the core of the acoustic radiation in human connectome data. We also applied the method to a dataset of 17 patients with unilateral congenital ear canal atresia and 17 age- and gender-paired controls acquired in a clinical setting. The method was able to extract 53/68 acoustic radiation in the dataset acquired with clinical settings. In 14/68 cases, the method generated fragments of the acoustic radiation and completely failed in a single case. The performance of the method on patients and controls was similar. Discussion In most cases, it is possible to segment the core of the acoustic radiations even in images acquired with clinical settings in a few seconds using a pre-trained neural network.
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Affiliation(s)
- Malin Siegbahn
- Division of Ear, Nose and Throat Diseases, Department of Clinical Science, Intervention and Technology, Karolinska Institute, Stockholm, Sweden
- Medical Unit Ear, Nose, Throat and Hearing, Karolinska University Hospital, Stockholm, Sweden
| | - Cecilia Engmér Berglin
- Division of Ear, Nose and Throat Diseases, Department of Clinical Science, Intervention and Technology, Karolinska Institute, Stockholm, Sweden
- Medical Unit Ear, Nose, Throat and Hearing, Karolinska University Hospital, Stockholm, Sweden
| | - Rodrigo Moreno
- Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
- *Correspondence: Rodrigo Moreno
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Leveraging manifold learning techniques to explore white matter anomalies: An application of the TractLearn pipeline in epilepsy. Neuroimage Clin 2022; 36:103209. [PMID: 36162235 PMCID: PMC9668609 DOI: 10.1016/j.nicl.2022.103209] [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: 05/22/2022] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 12/14/2022]
Abstract
An accurate description of brain white matter anatomy in vivo remains a challenge. However, technical progress allows us to analyze structural variations in an increasingly sophisticated way. Current methods of processing diffusion MRI data now make it possible to correct some limiting biases. In addition, the development of statistical learning algorithms offers the opportunity to analyze the data from a new perspective. We applied newly developed tractography models to extract quantitative white matter parameters in a group of patients with chronic temporal lobe epilepsy. Furthermore, we implemented a statistical learning workflow optimized for the MRI diffusion data - the TractLearn pipeline - to model inter-individual variability and predict structural changes in patients. Finally, we interpreted white matter abnormalities in the context of several other parameters reflecting clinical status, as well as neuronal and cognitive functioning for these patients. Overall, we show the relevance of such a diffusion data processing pipeline for the evaluation of clinical populations. The "global to fine scale" funnel statistical approach proposed in this study also contributes to the understanding of neuroplasticity mechanisms involved in refractory epilepsy, thus enriching previous findings.
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Schilling KG, Li M, Rheault F, Ding Z, Anderson AW, Kang H, Landman BA, Gore JC. Anomalous and heterogeneous characteristics of the BOLD hemodynamic response function in white matter. Cereb Cortex Commun 2022; 3:tgac035. [PMID: 36196360 PMCID: PMC9519945 DOI: 10.1093/texcom/tgac035] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/09/2022] [Accepted: 08/12/2022] [Indexed: 01/12/2023] Open
Abstract
Detailed knowledge of the BOLD hemodynamic response function (HRF) is crucial for accurate analyses and interpretation of functional MRI data. Considerable efforts have been made to characterize the HRF in gray matter (GM), but much less attention has been paid to BOLD effects in white matter (WM). However, several recent reports have demonstrated reliable detection and analyses of WM BOLD signals both after stimulation and in a resting state. WM and GM differ in composition, energy requirements, and blood flow, so their neurovascular couplings also may well be different. We aimed to derive a comprehensive characterization of the HRF in WM across a population, including accurate measurements of its shape and its variation along and between WM pathways, using resting-state fMRI acquisitions. Our results show that the HRF is significantly different between WM and GM. Features of the HRF, such as a prominent initial dip, show strong relationships with features of the tissue microstructure derived from diffusion imaging, and these relationships differ between WM and GM, consistent with BOLD signal fluctuations reflecting different energy demands and neurovascular couplings in tissues of different composition and function. We also show that the HRF varies in shape significantly along WM pathways and is different between different WM pathways, suggesting the temporal evolution of BOLD signals after an event vary in different parts of the WM. These features of the HRF in WM are especially relevant for interpretation of the biophysical basis of BOLD effects in WM.
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Affiliation(s)
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
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