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Harper L, Strandberg O, Spotorno N, Nilsson M, Lindberg O, Hansson O, Santillo AF. Structural and functional connectivity associations with anterior cingulate sulcal variability. Brain Struct Funct 2024:10.1007/s00429-024-02812-5. [PMID: 38900167 DOI: 10.1007/s00429-024-02812-5] [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: 01/03/2024] [Accepted: 05/26/2024] [Indexed: 06/21/2024]
Abstract
Sulcation of the anterior cingulate may be defined by presence of a paracingulate sulcus, a tertiary sulcus developing during the third gestational trimester with implications on cognitive function and disease. In this cross-sectional study we examine task-free resting state functional connectivity and diffusion-weighted tract segmentation data from a cohort of healthy adults (< 60-year-old, n = 129), exploring the impact of ipsilateral paracingulate sulcal presence on structural and functional connectivity. Presence of a left paracingulate sulcus was associated with reduced fractional anisotropy in the left cingulum bundle and the left peri-genual and dorsal bundle segments, suggesting reduced structural organisational coherence in these tracts. This association was not observed in the offsite temporal cingulum bundle segment. Left paracingulate sulcal presence was associated with increased left peri-genual radial diffusivity and tract volume possibly suggesting increased U-fibre density in this region. Greater network dispersity was identified in individuals with an absent left paracingulate sulcus by presence of a significant, predominantly intraregional, frontal component of resting state functional connectivity which was not present in individuals with a present left paracingulate sulcus. Seed-based functional connectivity in pre-defined networks was not associated with paracingulate sulcal presence. These results identify a novel association between sulcation and structural connectivity in a healthy adult population with implications for conditions where this variation is of interest. Presence of a left paracingulate sulcus appears to alter local structural and functional connectivity, possibly as a result of the presence of a local network reliant on short association fibres.
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Affiliation(s)
- Luke Harper
- Clinical Memory Research Unit, Department of Clinical Sciences, Medical Sciences, Neuroscience, Lund University, Sölvegatan 19, 22100, Lund, Sweden.
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Medical Sciences, Neuroscience, Lund University, Sölvegatan 19, 22100, Lund, Sweden
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences, Medical Sciences, Neuroscience, Lund University, Sölvegatan 19, 22100, Lund, Sweden
| | - Markus Nilsson
- Diagnostic Radiology, Faculty of Medicine, Department of Clinical Sciences, Lund, Sweden
| | - Olof Lindberg
- Division of Clinical Geriatrics, Karolinska Institute, Stockholm, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Medical Sciences, Neuroscience, Lund University, Sölvegatan 19, 22100, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Lund, Sweden
| | - Alexander F Santillo
- Clinical Memory Research Unit, Department of Clinical Sciences, Medical Sciences, Neuroscience, Lund University, Sölvegatan 19, 22100, Lund, Sweden
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Wang Y, Cheng L, Li D, Lu Y, Wang C, Wang Y, Gao C, Wang H, Vanduffel W, Hopkins W, Sherwood C, Jiang T, Chu C, Fan L. Comparative Analysis of Human-Chimpanzee Divergence in Brain Connectivity and its Genetic Correlates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597252. [PMID: 38895242 PMCID: PMC11185649 DOI: 10.1101/2024.06.03.597252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Chimpanzees (Pan troglodytes) are humans' closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occured along the human lineage. However, conducting comparisons of brain connectivity between humans and chimpanzees remains challenging, as cross-species brain atlases established within the same framework are currently lacking. Without the availability of cross-species brain atlases, the region-wise connectivity patterns between humans and chimpanzees cannot be directly compared. To address this gap, we built the first Chimpanzee Brainnetome Atlas (ChimpBNA) by following a well-established connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex between the two species. Intriguingly, these patterns significantly deviate from the patterns of cortical expansion observed in humans compared to chimpanzees. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes associated with these divergent connectivities were found to be enriched in cell types crucial for cortical projection circuits and synapse formation. These genes exhibited more pronounced differences in expression patterns in regions with higher connectivity divergence, suggesting a potential foundation for brain connectivity evolution. Therefore, our study not only provides a fine-scale brain atlas of chimpanzees but also highlights the connectivity divergence between humans and chimpanzees in a more rigorous and comparative manner and suggests potential genetic correlates for the observed divergence in brain connectivity patterns between the two species. This can help us better understand the origins and development of uniquely human cognitive capabilities.
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Wang Y, Wang T, Yu Z, Wang J, Liu F, Ye M, Fang X, Liu Y, Liu J. Alterations in structural integrity of superior longitudinal fasciculus III associated with cognitive performance in cerebral small vessel disease. BMC Med Imaging 2024; 24:138. [PMID: 38858645 PMCID: PMC11165890 DOI: 10.1186/s12880-024-01324-2] [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: 12/01/2023] [Accepted: 06/05/2024] [Indexed: 06/12/2024] Open
Abstract
BACKGROUND This study aimed to investigate the alterations in structural integrity of superior longitudinal fasciculus subcomponents with increasing white matter hyperintensity severity as well as the relationship to cognitive performance in cerebral small vessel disease. METHODS 110 cerebral small vessel disease study participants with white matter hyperintensities were recruited. According to Fazekas grade scale, white matter hyperintensities of each subject were graded. All subjects were divided into two groups. The probabilistic fiber tracking method was used for analyzing microstructure characteristics of superior longitudinal fasciculus subcomponents. RESULTS Probabilistic fiber tracking results showed that mean diffusion, radial diffusion, and axial diffusion values of the left arcuate fasciculus as well as the mean diffusion value of the right arcuate fasciculus and left superior longitudinal fasciculus III in high white matter hyperintensities rating group were significantly higher than those in low white matter hyperintensities rating group (p < 0.05). The mean diffusion value of the left superior longitudinal fasciculus III was negatively related to the Montreal Cognitive Assessment score of study participants (p < 0.05). CONCLUSIONS The structural integrity injury of bilateral arcuate fasciculus and left superior longitudinal fasciculus III is more severe with the aggravation of white matter hyperintensities. The structural integrity injury of the left superior longitudinal fasciculus III correlates to cognitive impairment in cerebral small vessel disease.
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Affiliation(s)
- Yifan Wang
- Department of Radiology, Eye& ENT Hospital of Shanghai Medical School, Fudan University, Shanghai, China
| | - Tianyao Wang
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zekuan Yu
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China
| | - Junjie Wang
- Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Beijing, China
- Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Fang Liu
- Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Beijing, China
| | - Mengwen Ye
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China
| | - Xianjin Fang
- Anhui University of Science and Technology, Anhui, China
| | - Yinhong Liu
- Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Beijing, China.
| | - Jun Liu
- Department of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, 1111 XianXia Road, Shanghai, 200050, China.
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Sun W, Ahmed I, Dubrof ST, Park HJ, West FD, Zhao Q. Affinity of structural white matter tracts between infant and adult pig. J Neurosci Methods 2024; 406:110134. [PMID: 38588923 DOI: 10.1016/j.jneumeth.2024.110134] [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: 10/16/2023] [Revised: 03/13/2024] [Accepted: 04/03/2024] [Indexed: 04/10/2024]
Abstract
BACKGROUND The piglet brain has been increasingly used as an excellent surrogate for investigation of pediatric neurodevelopment, nutrition, and traumatic brain injuries. This study intends to establish a piglet brain's structural connectivity model and compare it with the adult pig, enhancing its application for structurally guided functional analysis. METHODS In this study, diffusion-weighted (DW)-MRI data from piglets (n=11, 3-week-old) was used to establish piglet model and compare with adult pigs. We employed a data-driven independent component analysis (ICA) method to derive piglet-specific tracts. Pearson correlations and Kullback-Leibler (KL) divergences was employed to identify common tracts and unique tracts for piglet. Common tracts were then used in a blueprint connectome study to highlight differences in regions of interest (ROI). RESULTS The data-driven approach applied to piglet brains revealed 17 common tracts, showing high similarity with adult pigs' white matter (WM) tracts, and identified 3 tracts unique to piglets and 10 negative marker tracts. Additionally, the study highlighted notable differences in 3 ROIs associated with blueprint connectome. COMPARING WITH EXISTING METHODS This study marks a significant shift from surface-based to voxel-based methodologies in analyzing pig brain structural connectivity and generating connectome blueprints. Additionally, it sheds light on the use of the piglet model for developmental studies, offering new perspectives in this area. CONCLUSION This study established a piglet brain tract model and conducts a comparative analysis of adult pig's and piglet's structural connectivity. These findings underscore the potential use of the piglet brain model in employing piglet model for developmental studies.
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Affiliation(s)
- Wenwu Sun
- Department of Physics and Astronomy, University of Georgia, Athens, GA, USA; Regenerative Bioscience Center, University of Georgia, Athens, GA, USA
| | - Ishfaque Ahmed
- Department of Physics and Astronomy, University of Georgia, Athens, GA, USA; Regenerative Bioscience Center, University of Georgia, Athens, GA, USA; Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - Stephanie T Dubrof
- Department of Nutritional Sciences, College of Family and Consumer Sciences, USA
| | - Hea Jin Park
- Department of Nutritional Sciences, College of Family and Consumer Sciences, USA
| | - Franklin D West
- Regenerative Bioscience Center, University of Georgia, Athens, GA, USA; Department of Animal and Diary Science, University of Georgia, Athens, GA, USA
| | - Qun Zhao
- Department of Physics and Astronomy, University of Georgia, Athens, GA, USA; Regenerative Bioscience Center, University of Georgia, Athens, GA, USA; Bio-Imaging Research Center, University of Georgia, Athens, GA, USA.
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Ramirez-Garcia G, Escutia-Macedo X, Cook DJ, Moreno-Andrade T, Villarreal-Garza E, Campos-Coy M, Elizondo-Riojas G, Gongora-Rivera F, Garza-Villarreal EA, Fernandez-Ruiz J. Consistent spatial lesion-symptom patterns: A comprehensive analysis using triangulation in lesion-symptom mapping in a cohort of stroke patients. Magn Reson Imaging 2024; 109:286-293. [PMID: 38531463 DOI: 10.1016/j.mri.2024.03.031] [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: 02/08/2024] [Revised: 02/29/2024] [Accepted: 03/19/2024] [Indexed: 03/28/2024]
Abstract
INTRODUCTION The relationship between brain lesions and stroke outcomes is crucial for advancing patient prognosis and developing effective therapies. Stroke is a leading cause of disability worldwide, and it is important to understand the neurological basis of its varied symptomatology. Lesion-symptom mapping (LSM) methods provide a means to identify brain areas that are strongly associated with specific symptoms. However, inner variations in LSM methods can yield different results. To address this, our study aimed to characterize the lesion-symptom mapping variability using three different LSM methods. Specifically, we sought to determine a lesion symptom core across LSM approaches enhancing the robustness of the analysis and removing potential spatial bias. MATERIAL & METHODS A cohort consisting of 35 patients with either right- or left-sided middle cerebral artery strokes were enrolled and evaluated using the NIHSS at 24 h post-stroke. Anatomical T1w MRI scans were also obtained 24 h post-stroke. Lesion masks were segmented manually and three distinctive LSM methods were implemented: ROI correlation-based, univariate, and multivariate approaches. RESULTS The results of the LSM analyses showed substantial spatial differences in the extension of each of the three lesion maps. However, upon overlaying all three lesion-symptom maps, a consistent lesion core emerged, corresponding to the territory associated with elevated NIHSS scores. This finding not only enhances the spatial accuracy of the lesion map but also underscores its clinical relevance. CONCLUSION This study underscores the significance of exploring complementary LSM approaches to investigate the association between brain lesions and stroke outcomes. By utilizing multiple methods, we can increase the robustness of our results, effectively addressing and neutralizing potential spatial bias introduced by each individual method. Such an approach holds promise for enhancing our understanding of stroke pathophysiology and optimizing patient care strategies.
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Affiliation(s)
- Gabriel Ramirez-Garcia
- Laboratorio de Neuropsicologia, Departamento de Fisiologia, Facultad de Medicina, Universidad Nacional Autonoma de Mexico, Ciudad de Mexico, Mexico; Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada
| | - Ximena Escutia-Macedo
- Laboratorio de Neuropsicologia, Departamento de Fisiologia, Facultad de Medicina, Universidad Nacional Autonoma de Mexico, Ciudad de Mexico, Mexico
| | - Douglas J Cook
- Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada; Translational Stroke Research Lab, Department of Surgery, Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
| | - Talia Moreno-Andrade
- Departamento de Neurologia, Hospital Universitario Dr. Jose Eleuterio Gonzalez Universidad Autonoma de Nuevo León, Monterrey, Nuevo Leon, Mexico; Unidad de Neuromodulacion y Plasticidad Cerebral, Centro de Investigacion y Desarrollo en Ciencias de la Salud, Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo Leon, Mexico
| | - Estefania Villarreal-Garza
- Departamento de Neurologia, Hospital Universitario Dr. Jose Eleuterio Gonzalez Universidad Autonoma de Nuevo León, Monterrey, Nuevo Leon, Mexico
| | - Mario Campos-Coy
- Unidad de Neuromodulacion y Plasticidad Cerebral, Centro de Investigacion y Desarrollo en Ciencias de la Salud, Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo Leon, Mexico; Departamento de Imagen Diagnostica, Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo Leon, Mexico
| | - Guillermo Elizondo-Riojas
- Unidad de Neuromodulacion y Plasticidad Cerebral, Centro de Investigacion y Desarrollo en Ciencias de la Salud, Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo Leon, Mexico; Departamento de Imagen Diagnostica, Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo Leon, Mexico
| | - Fernando Gongora-Rivera
- Departamento de Neurologia, Hospital Universitario Dr. Jose Eleuterio Gonzalez Universidad Autonoma de Nuevo León, Monterrey, Nuevo Leon, Mexico; Unidad de Neuromodulacion y Plasticidad Cerebral, Centro de Investigacion y Desarrollo en Ciencias de la Salud, Universidad Autonoma de Nuevo Leon, Monterrey, Nuevo Leon, Mexico
| | - Eduardo A Garza-Villarreal
- Instituto de Neurobiologia, Universidad Nacional Autonoma de Mexico, Juriquilla, Queretaro, Mexico; Departamento de Neurologia, Hospital Universitario Dr. Jose Eleuterio Gonzalez Universidad Autonoma de Nuevo León, Monterrey, Nuevo Leon, Mexico
| | - Juan Fernandez-Ruiz
- Laboratorio de Neuropsicologia, Departamento de Fisiologia, Facultad de Medicina, Universidad Nacional Autonoma de Mexico, Ciudad de Mexico, Mexico; Facultad de Psicologia, Universidad Veracruzana, Xalapa, Veracruz, Mexico.
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Zheng J, Cheng Y, Wu X, Li X, Fu Y, Yang Z. Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks. Front Neurosci 2024; 18:1405734. [PMID: 38855440 PMCID: PMC11157044 DOI: 10.3389/fnins.2024.1405734] [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: 03/23/2024] [Accepted: 05/07/2024] [Indexed: 06/11/2024] Open
Abstract
Objective In this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics. Methods Spatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male-female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis. Results The proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients. Conclusion The proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.
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Affiliation(s)
- Jianhui Zheng
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
| | - Yuhao Cheng
- Huaxi Molecular Imaging Research Laboratory, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xi Wu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Xiaojie Li
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Ying Fu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Zhipeng Yang
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
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Conte S, Zimmerman D, Richards JE. White matter trajectories over the lifespan. PLoS One 2024; 19:e0301520. [PMID: 38758830 PMCID: PMC11101104 DOI: 10.1371/journal.pone.0301520] [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: 03/28/2023] [Accepted: 03/14/2024] [Indexed: 05/19/2024] Open
Abstract
White matter (WM) changes occur throughout the lifespan at a different rate for each developmental period. We aggregated 10879 structural MRIs and 6186 diffusion-weighted MRIs from participants between 2 weeks to 100 years of age. Age-related changes in gray matter and WM partial volumes and microstructural WM properties, both brain-wide and on 29 reconstructed tracts, were investigated as a function of biological sex and hemisphere, when appropriate. We investigated the curve fit that would best explain age-related differences by fitting linear, cubic, quadratic, and exponential models to macro and microstructural WM properties. Following the first steep increase in WM volume during infancy and childhood, the rate of development slows down in adulthood and decreases with aging. Similarly, microstructural properties of WM, particularly fractional anisotropy (FA) and mean diffusivity (MD), follow independent rates of change across the lifespan. The overall increase in FA and decrease in MD are modulated by demographic factors, such as the participant's age, and show different hemispheric asymmetries in some association tracts reconstructed via probabilistic tractography. All changes in WM macro and microstructure seem to follow nonlinear trajectories, which also differ based on the considered metric. Exponential changes occurred for the WM volume and FA and MD values in the first five years of life. Collectively, these results provide novel insight into how changes in different metrics of WM occur when a lifespan approach is considered.
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Affiliation(s)
- Stefania Conte
- Department of Psychology, State University of New York at Binghamton, Vestal, NY, United States of America
| | - Dabriel Zimmerman
- Department of Biomedical Engineering, Boston University, Boston, MA, United States of America
| | - John E. Richards
- Department of Psychology, University of South Carolina, Columbia, SC, United States of America
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Schmid W, Danstrom IA, Crespo Echevarria M, Adkinson J, Mattar L, Banks GP, Sheth SA, Watrous AJ, Heilbronner SR, Bijanki KR, Alabastri A, Bartoli E. A biophysically constrained brain connectivity model based on stimulation-evoked potentials. J Neurosci Methods 2024; 405:110106. [PMID: 38453060 DOI: 10.1016/j.jneumeth.2024.110106] [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: 11/03/2023] [Revised: 01/24/2024] [Accepted: 03/04/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. NEW METHOD Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. RESULTS The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures. COMPARISON WITH EXISTING METHOD Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences. CONCLUSIONS These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA
| | - Isabel A Danstrom
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Maria Crespo Echevarria
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Layth Mattar
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Garrett P Banks
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Andrew J Watrous
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Sarah R Heilbronner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA
| | - Alessandro Alabastri
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA.
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Mochizuki M, Uchiyama Y, Domen K, Koyama T. Clinical applicability of automated tractography for stroke rehabilitation: Z-score conversion of fractional anisotropy. J Phys Ther Sci 2024; 36:319-324. [PMID: 38694010 PMCID: PMC11060757 DOI: 10.1589/jpts.36.319] [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: 01/05/2024] [Accepted: 02/08/2024] [Indexed: 05/03/2024] Open
Abstract
[Purpose] To expand the applicability of diffusion-tensor tractography fractional anisotropy for stroke rehabilitation, this study aimed to provide references for representative neural tracts from non-lesioned hemispheres. Therefore, we applied the assessment of neural integrity to representative stroke patients using Z-score conversion. [Participants and Methods] Fractional anisotropy values were assessed in neural tracts, including the corticospinal tract, inferior fronto-occipital fasciculus, uncinate fasciculus, and anterior thalamic radiation, of stroke patients receiving acute care. [Results] Data were collected from 60 patients for the non-lesioned right hemisphere and 68 patients for the non-lesioned left hemisphere. Mean fractional anisotropy values in the corticospinal tract and inferior fronto-occipital fasciculus were notably elevated, reaching approximately 0.6 and 0.5, respectively. The mean fractional anisotropy values for other neural tracts were approximately 0.4, and, the overall standard deviations were approximately 0.04. In two typical stroke patients assessed using Z-scores, the scores in the corticospinal tract corresponded to the severity of the hemiparesis. The scores in the anterior thalamic radiation and inferior fronto-occipital fasciculus were associated with more significant brain dysfunction, including inattention and aphasia. [Conclusion] In this study, the Z-score findings related to stroke symptoms align with those reported in the literature, indicating the appropriateness of the methodology used and its potential in future applications.
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Affiliation(s)
- Midori Mochizuki
- Department of Rehabilitation Medicine, Nishinomiya Kyoritsu
Neurosurgical Hospital: 11-1 Imazu-Yamanaka-cho, Nishinomiya, Hyogo 663-8211, Japan
| | - Yuki Uchiyama
- Department of Rehabilitation Medicine, School of Medicine,
Hyogo Medical University, Japan
| | - Kazuhisa Domen
- Department of Rehabilitation Medicine, School of Medicine,
Hyogo Medical University, Japan
| | - Tetsuo Koyama
- Department of Rehabilitation Medicine, Nishinomiya Kyoritsu
Neurosurgical Hospital: 11-1 Imazu-Yamanaka-cho, Nishinomiya, Hyogo 663-8211, Japan
- Department of Rehabilitation Medicine, School of Medicine,
Hyogo Medical University, Japan
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Krontira AC, Cruceanu C, Dony L, Kyrousi C, Link MH, Rek N, Pöhlchen D, Raimundo C, Penner-Goeke S, Schowe A, Czamara D, Lahti-Pulkkinen M, Sammallahti S, Wolford E, Heinonen K, Roeh S, Sportelli V, Wölfel B, Ködel M, Sauer S, Rex-Haffner M, Räikkönen K, Labeur M, Cappello S, Binder EB. Human cortical neurogenesis is altered via glucocorticoid-mediated regulation of ZBTB16 expression. Neuron 2024; 112:1426-1443.e11. [PMID: 38442714 DOI: 10.1016/j.neuron.2024.02.005] [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: 01/17/2023] [Revised: 08/15/2023] [Accepted: 02/06/2024] [Indexed: 03/07/2024]
Abstract
Glucocorticoids are important for proper organ maturation, and their levels are tightly regulated during development. Here, we use human cerebral organoids and mice to study the cell-type-specific effects of glucocorticoids on neurogenesis. We show that glucocorticoids increase a specific type of basal progenitors (co-expressing PAX6 and EOMES) that has been shown to contribute to cortical expansion in gyrified species. This effect is mediated via the transcription factor ZBTB16 and leads to increased production of neurons. A phenome-wide Mendelian randomization analysis of an enhancer variant that moderates glucocorticoid-induced ZBTB16 levels reveals causal relationships with higher educational attainment and altered brain structure. The relationship with postnatal cognition is also supported by data from a prospective pregnancy cohort study. This work provides a cellular and molecular pathway for the effects of glucocorticoids on human neurogenesis that relates to lasting postnatal phenotypes.
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Affiliation(s)
- Anthi C Krontira
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany; International Max Planck Research School for Translational Psychiatry, Munich 80804, Germany.
| | - Cristiana Cruceanu
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany; Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm 17177, Sweden
| | - Leander Dony
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany; International Max Planck Research School for Translational Psychiatry, Munich 80804, Germany; Department for Computational Health, Helmholtz Munich, Neuherberg 85764, Germany; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising 85354, Germany
| | - Christina Kyrousi
- Developmental Neurobiology, Max Planck Institute of Psychiatry, Munich 80804, Germany; First Department of Psychiatry, Medical School, National and Kapodistrian University of Athens, Eginition Hospital, Athens 15784, Greece; University Mental Health, Neurosciences and Precision Medicine Research Institute "Costas Stefanis", Athens 15601, Greece
| | - Marie-Helen Link
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Nils Rek
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany; International Max Planck Research School for Translational Psychiatry, Munich 80804, Germany
| | - Dorothee Pöhlchen
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany; International Max Planck Research School for Translational Psychiatry, Munich 80804, Germany
| | - Catarina Raimundo
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Signe Penner-Goeke
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Alicia Schowe
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany; Graduate School of Systemic Neurosciences, Ludwig-Maximilians-University, Munich 82152, Germany
| | - Darina Czamara
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Marius Lahti-Pulkkinen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki 00014, Finland; Finnish Institute for Health and Welfare, Helsinki 00271, Finland; Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Sara Sammallahti
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, Helsinki 00014, Finland
| | - Elina Wolford
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki 00014, Finland
| | - Kati Heinonen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki 00014, Finland; Psychology/Welfare, Faculty of Social Sciences, University of Tampere, Tampere 33014, Finland; Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON M5T 1P8, Canada
| | - Simone Roeh
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Vincenza Sportelli
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Barbara Wölfel
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Maik Ködel
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Susann Sauer
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Monika Rex-Haffner
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki 00014, Finland
| | - Marta Labeur
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany
| | - Silvia Cappello
- Developmental Neurobiology, Max Planck Institute of Psychiatry, Munich 80804, Germany; Physiological Genomics, Biomedical Center (BMC), Faculty of Medicine, Ludwig-Maximilians-University (LMU), Munich 82152, Germany
| | - Elisabeth B Binder
- Department Genes and Environment, Max Planck Institute of Psychiatry, Munich 80804, Germany.
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11
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Joshi A, Li H, Parikh NA, He L. A systematic review of automated methods to perform white matter tract segmentation. Front Neurosci 2024; 18:1376570. [PMID: 38567281 PMCID: PMC10985163 DOI: 10.3389/fnins.2024.1376570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.
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Affiliation(s)
- Ankita Joshi
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Hailong Li
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A. Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lili He
- Imaging Research Center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
- Computer Science, Biomedical Informatics, and Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States
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12
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Lu Y, Cui Y, Cao L, Dong Z, Cheng L, Wu W, Wang C, Liu X, Liu Y, Zhang B, Li D, Zhao B, Wang H, Li K, Ma L, Shi W, Li W, Ma Y, Du Z, Zhang J, Xiong H, Luo N, Liu Y, Hou X, Han J, Sun H, Cai T, Peng Q, Feng L, Wang J, Paxinos G, Yang Z, Fan L, Jiang T. Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology. Sci Bull (Beijing) 2024:S2095-9273(24)00187-7. [PMID: 38580551 DOI: 10.1016/j.scib.2024.03.031] [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: 10/12/2023] [Revised: 01/18/2024] [Accepted: 03/11/2024] [Indexed: 04/07/2024]
Abstract
The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.
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Affiliation(s)
- Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Long Cao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China; Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Wen Wu
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Xinyi Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Youtong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Deying Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bokai Zhao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Zongchang Du
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaqi Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Xiong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yanyan Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoxiao Hou
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jinglu Han
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Hongji Sun
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tao Cai
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Qiang Peng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Linqing Feng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - George Paxinos
- Neuroscience Research Australia and The University of New South Wales, Sydney NSW 2031, Australia
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
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13
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Deuter D, Hense K, Kunkel K, Vollmayr J, Schachinger S, Wendl C, Schicho A, Fellner C, Salzberger B, Hitzenbichler F, Zeller J, Vielsmeier V, Dodoo-Schittko F, Schmidt NO, Rosengarth K. SARS-CoV2 evokes structural brain changes resulting in declined executive function. PLoS One 2024; 19:e0298837. [PMID: 38470899 DOI: 10.1371/journal.pone.0298837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 01/30/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Several research has underlined the multi-system character of COVID-19. Though effects on the Central Nervous System are mainly discussed as disease-specific affections due to the virus' neurotropism, no comprehensive disease model of COVID-19 exists on a neurofunctional base by now. We aimed to investigate neuroplastic grey- and white matter changes related to COVID-19 and to link these changes to neurocognitive testings leading towards a multi-dimensional disease model. METHODS Groups of acutely ill COVID-19 patients (n = 16), recovered COVID-19 patients (n = 21) and healthy controls (n = 13) were prospectively included into this study. MR-imaging included T1-weighted sequences for analysis of grey matter using voxel-based morphometry and diffusion-weighted sequences to investigate white matter tracts using probabilistic tractography. Comprehensive neurocognitive testing for verbal and non-verbal domains was performed. RESULTS Alterations strongly focused on grey matter of the frontal-basal ganglia-thalamus network and temporal areas, as well as fiber tracts connecting these areas. In acute COVID-19 patients, a decline of grey matter volume was found with an accompanying diminution of white matter tracts. A decline in executive function and especially verbal fluency was found in acute patients, partially persisting in recovered. CONCLUSION Changes in gray matter volume and white matter tracts included mainly areas involved in networks of executive control and language. Deeper understanding of these alterations is necessary especially with respect to long-term impairments, often referred to as 'Post-COVID'.
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Affiliation(s)
- Daniel Deuter
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Katharina Hense
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Kevin Kunkel
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Johanna Vollmayr
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Sebastian Schachinger
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Christina Wendl
- Institut für Röntgendiagnostik, University Hospital Regensburg, Regensburg, Germany
- Institut für Neuroradiologie, Medbo Bezirksklinikum Regensburg, Regensburg, Germany
| | - Andreas Schicho
- Institut für Röntgendiagnostik, University Hospital Regensburg, Regensburg, Germany
| | - Claudia Fellner
- Institut für Röntgendiagnostik, University Hospital Regensburg, Regensburg, Germany
| | - Bernd Salzberger
- Abteilung für Krankenhaushygiene und Infektiologie, University Hospital Regensburg, Regensburg, Germany
| | - Florian Hitzenbichler
- Abteilung für Krankenhaushygiene und Infektiologie, University Hospital Regensburg, Regensburg, Germany
| | - Judith Zeller
- Klinik und Poliklinik für Innere Medizin II, University Hospital Regensburg, Regensburg, Germany
| | - Veronika Vielsmeier
- Klinik und Poliklinik für Hals-Nasen-Ohren-Heilkunde, University Hospital Regensburg, Regensburg, Germany
| | - Frank Dodoo-Schittko
- Institut für Sozialmedizin und Gesundheitsforschung, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Nils Ole Schmidt
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Katharina Rosengarth
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
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14
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Hur KH, Meisler SL, Yassin W, Frederick BB, Kohut SJ. Prefrontal-Limbic Circuitry Is Associated With Reward Sensitivity in Nonhuman Primates. Biol Psychiatry 2024:S0006-3223(24)01131-4. [PMID: 38432521 DOI: 10.1016/j.biopsych.2024.02.1011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 02/23/2024] [Accepted: 02/24/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND Abnormal reward sensitivity is a risk factor for psychiatric disorders, including eating disorders such as overeating and binge-eating disorder, but the brain structural mechanisms that underlie it are not completely understood. Here, we sought to investigate the relationship between multimodal whole-brain structural features and reward sensitivity in nonhuman primates. METHODS Reward sensitivity was evaluated through behavioral economic analysis in which monkeys (adult rhesus macaques; 7 female, 5 male) responded for sweetened condensed milk (10%, 30%, 56%), Gatorade, or water using an operant procedure in which the response requirement increased incrementally across sessions (i.e., fixed ratio 1, 3, 10). Animals were divided into high (n = 6) or low (n = 6) reward sensitivity groups based on essential value for 30% milk. Multimodal magnetic resonance imaging was used to measure gray matter volume and white matter microstructure. Brain structural features were compared between groups, and their correlations with reward sensitivity for various stimuli was investigated. RESULTS Animals in the high sensitivity group had greater dorsolateral prefrontal cortex, centromedial amygdaloid complex, and middle cingulate cortex volumes than animals in the low sensitivity group. Furthermore, compared with monkeys in the low sensitivity group, high sensitivity monkeys had lower fractional anisotropy in the left dorsal cingulate bundle connecting the centromedial amygdaloid complex and middle cingulate cortex to the dorsolateral prefrontal cortex, and in the left superior longitudinal fasciculus 1 connecting the middle cingulate cortex to the dorsolateral prefrontal cortex. CONCLUSIONS These results suggest that neuroanatomical variation in prefrontal-limbic circuitry is associated with reward sensitivity. These brain structural features may serve as predictive biomarkers for vulnerability to food-based and other reward-related disorders.
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Affiliation(s)
- Kwang-Hyun Hur
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, Massachusetts
| | - Walid Yassin
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Blaise B Frederick
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; McLean Imaging Center, McLean Hospital, Belmont, Massachusetts
| | - Stephen J Kohut
- Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts; McLean Imaging Center, McLean Hospital, Belmont, Massachusetts.
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15
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Assimopoulos S, Warrington S, Bryant KL, Pszczolkowski S, Jbabdi S, Mars RB, Sotiropoulos SN. Generalising XTRACT tractography protocols across common macaque brain templates. Brain Struct Funct 2024:10.1007/s00429-024-02760-0. [PMID: 38388696 DOI: 10.1007/s00429-024-02760-0] [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: 11/03/2023] [Accepted: 01/09/2024] [Indexed: 02/24/2024]
Abstract
Non-human primates are extensively used in neuroscience research as models of the human brain, with the rhesus macaque being a prominent example. We have previously introduced a set of tractography protocols (XTRACT) for reconstructing 42 corresponding white matter (WM) bundles in the human and the macaque brain and have shown cross-species comparisons using such bundles as WM landmarks. Our original XTRACT protocols were developed using the F99 macaque brain template. However, additional macaque template brains are becoming increasingly common. Here, we generalise the XTRACT tractography protocol definitions across five macaque brain templates, including the F99, D99, INIA, Yerkes and NMT. We demonstrate equivalence of such protocols in two ways: (a) Firstly by comparing the bodies of the tracts derived using protocols defined across the different templates considered, (b) Secondly by comparing the projection patterns of the reconstructed tracts across the different templates in two cross-species (human-macaque) comparison tasks. The results confirm similarity of all predictions regardless of the macaque brain template used, providing direct evidence for the generalisability of these tractography protocols across the five considered templates.
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Affiliation(s)
- Stephania Assimopoulos
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Katherine L Bryant
- Laboratoire de Psychologie Cognitive, Aix-Marseille Université, Marseille, France
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stefan Pszczolkowski
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK.
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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16
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Helmer M, Warrington S, Mohammadi-Nejad AR, Ji JL, Howell A, Rosand B, Anticevic A, Sotiropoulos SN, Murray JD. On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations. Commun Biol 2024; 7:217. [PMID: 38383808 DOI: 10.1038/s42003-024-05869-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 01/28/2024] [Indexed: 02/23/2024] Open
Abstract
Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS associations is stability of their feature patterns. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues systematically, we developed a generative modeling framework to simulate synthetic datasets. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the feature pattern underlying the association. We confirmed these trends across two neuroimaging modalities and in independent datasets with n ≈ 1000 and n = 20,000, and found that only the latter comprised sufficient observations for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses. Collectively, we characterize how to limit detrimental effects of overfitting on CCA/PLS stability, and provide recommendations for future studies.
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Affiliation(s)
- Markus Helmer
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Manifest Technologies, New Haven, CT, 06510, USA
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, United Kingdom
| | - Ali-Reza Mohammadi-Nejad
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, United Kingdom
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Ctr, Queens Medical Ctr, Nottingham, United Kingdom
| | - Jie Lisa Ji
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Manifest Technologies, New Haven, CT, 06510, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Amber Howell
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Benjamin Rosand
- Department of Physics, Yale University, New Haven, CT, 06511, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA
- Manifest Technologies, New Haven, CT, 06510, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, 06511, USA
- Department of Psychology, Yale University, New Haven, CT, 06511, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, United Kingdom.
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Ctr, Queens Medical Ctr, Nottingham, United Kingdom.
| | - John D Murray
- Department of Psychiatry, Yale School of of Medicine, New Haven, CT, 06511, USA.
- Manifest Technologies, New Haven, CT, 06510, USA.
- Department of Physics, Yale University, New Haven, CT, 06511, USA.
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, 03755, USA.
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Watson DM, Andrews TJ. Mapping the functional and structural connectivity of the scene network. Hum Brain Mapp 2024; 45:e26628. [PMID: 38376190 PMCID: PMC10878195 DOI: 10.1002/hbm.26628] [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: 10/26/2023] [Revised: 01/19/2024] [Accepted: 02/05/2024] [Indexed: 02/21/2024] Open
Abstract
The recognition and perception of places has been linked to a network of scene-selective regions in the human brain. While previous studies have focussed on functional connectivity between scene-selective regions themselves, less is known about their connectivity with other cortical and subcortical regions in the brain. Here, we determine the functional and structural connectivity profile of the scene network. We used fMRI to examine functional connectivity between scene regions and across the whole brain during rest and movie-watching. Connectivity within the scene network revealed a bias between posterior and anterior scene regions implicated in perceptual and mnemonic aspects of scene perception respectively. Differences between posterior and anterior scene regions were also evident in the connectivity with cortical and subcortical regions across the brain. For example, the Occipital Place Area (OPA) and posterior Parahippocampal Place Area (PPA) showed greater connectivity with visual and dorsal attention networks, while anterior PPA and Retrosplenial Complex showed preferential connectivity with default mode and frontoparietal control networks and the hippocampus. We further measured the structural connectivity of the scene network using diffusion tractography. This indicated both similarities and differences with the functional connectivity, highlighting biases between posterior and anterior regions, but also between ventral and dorsal scene regions. Finally, we quantified the structural connectivity between the scene network and major white matter tracts throughout the brain. These findings provide a map of the functional and structural connectivity of scene-selective regions to each other and the rest of the brain.
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Affiliation(s)
- David M. Watson
- Department of Psychology and York Neuroimaging CentreUniversity of YorkYorkUK
| | - Timothy J. Andrews
- Department of Psychology and York Neuroimaging CentreUniversity of YorkYorkUK
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18
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Oberman LM, Francis SM, Beynel L, Hynd M, Jaime M, Robins PL, Deng ZD, Stout J, van der Veen JW, Lisanby SH. Design and methodology for a proof of mechanism study of individualized neuronavigated continuous Theta burst stimulation for auditory processing in adolescents with autism spectrum disorder. Front Psychiatry 2024; 15:1304528. [PMID: 38389984 PMCID: PMC10881663 DOI: 10.3389/fpsyt.2024.1304528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
It has been suggested that aberrant excitation/inhibition (E/I) balance and dysfunctional structure and function of relevant brain networks may underlie the symptoms of autism spectrum disorder (ASD). However, the nomological network linking these constructs to quantifiable measures and mechanistically relating these constructs to behavioral symptoms of ASD is lacking. Herein we describe a within-subject, controlled, proof-of-mechanism study investigating the pathophysiology of auditory/language processing in adolescents with ASD. We utilize neurophysiological and neuroimaging techniques including magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG) metrics of language network structure and function. Additionally, we apply a single, individually targeted session of continuous theta burst stimulation (cTBS) as an experimental probe of the impact of perturbation of the system on these neurophysiological and neuroimaging outcomes. MRS, fMRI, and MEG measures are evaluated at baseline and immediately prior to and following cTBS over the posterior superior temporal cortex (pSTC), a region involved in auditory and language processing deficits in ASD. Also, behavioral measures of ASD and language processing and DWI measures of auditory/language network structures are obtained at baseline to characterize the relationship between the neuroimaging and neurophysiological measures and baseline symptom presentation. We hypothesize that local gamma-aminobutyric acid (GABA) and glutamate concentrations (measured with MRS), and structural and functional activity and network connectivity (measured with DWI and fMRI), will significantly predict MEG indices of auditory/language processing and behavioral deficits in ASD. Furthermore, a single session of cTBS over left pSTC is hypothesized to lead to significant, acute changes in local glutamate and GABA concentration, functional activity and network connectivity, and MEG indices of auditory/language processing. We have completed the pilot phase of the study (n=20 Healthy Volunteer adults) and have begun enrollment for the main phase with adolescents with ASD (n=86; age 14-17). If successful, this study will establish a nomological network linking local E/I balance measures to functional and structural connectivity within relevant brain networks, ultimately connecting them to ASD symptoms. Furthermore, this study will inform future therapeutic trials using cTBS to treat the symptoms of ASD.
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Affiliation(s)
- Lindsay M Oberman
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Sunday M Francis
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Lysianne Beynel
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Megan Hynd
- Clinical Affective Neuroscience Laboratory, Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, NC, United States
| | - Miguel Jaime
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Pei L Robins
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Zhi-De Deng
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Jeff Stout
- Magnetoencephalography Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Jan Willem van der Veen
- Magnetic Resonance Spectroscopy Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Sarah H Lisanby
- Noninvasive Neuromodulation Unit, Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
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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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Koyama T, Mochizuki M, Uchiyama Y, Domen K. Outcome Prediction by Combining Corticospinal Tract Lesion Load with Diffusion-tensor Fractional Anisotropy in Patients after Hemorrhagic Stroke. Prog Rehabil Med 2024; 9:20240001. [PMID: 38223334 PMCID: PMC10782178 DOI: 10.2490/prm.20240001] [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: 11/07/2023] [Accepted: 12/27/2023] [Indexed: 01/16/2024] Open
Abstract
Objectives The objective of this study was to evaluate the predictive precision of combining the corticospinal tract lesion load (CST-LL) with the diffusion-tensor fractional anisotropy of the corticospinal tract (CST-FA) in the lesioned hemispheres regarding motor outcomes. Methods Patients with putaminal and/or thalamic hemorrhage who had undergone computed tomography (CT) soon after onset in our hospital were retrospectively enrolled. The CST-LL was calculated after registration of the CT images to a standard brain. Diffusion-tensor imaging was performed during the second week after onset. Standardized automated tractography was employed to calculate the CST-FA. Outcomes were assessed at discharge from our affiliated rehabilitation facility using total scores of the motor component of the Stroke Impairment Assessment Set (SIAS-motor total; null to full, 0 to 25). Multivariate regression analysis was performed with CST-LL and CST-FA as explanatory variables and SIAS-motor total as a target value. Results Twenty-five patients participated in this study. SIAS-motor total ranged from 0 to 25 (median, 17). CST-LL ranged from 0.298 to 7.595 (median, 2.522) mL, and the lesion-side CST-FA ranged from 0.211 to 0.530 (median, 0.409). Analysis revealed that both explanatory variables were detected as statistically significant contributory factors. The estimated t values indicated that the contributions of these two variables were almost equal. The obtained regression model accounted for 63.9% of the variability of the target value. Conclusions Incorporation of the CST-LL with the lesion-side CST-FA enhances the precision of the stroke outcome prediction model.
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Affiliation(s)
- Tetsuo Koyama
- Department of Rehabilitation Medicine, Nishinomiya Kyoritsu
Neurosurgical Hospital, Nishinomiya, Japan
- Department of Rehabilitation Medicine, School of Medicine,
Hyogo Medical University, Nishinomiya, Japan
| | - Midori Mochizuki
- Department of Rehabilitation Medicine, Nishinomiya Kyoritsu
Neurosurgical Hospital, Nishinomiya, Japan
| | - Yuki Uchiyama
- Department of Rehabilitation Medicine, School of Medicine,
Hyogo Medical University, Nishinomiya, Japan
| | - Kazuhisa Domen
- Department of Rehabilitation Medicine, School of Medicine,
Hyogo Medical University, Nishinomiya, Japan
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21
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Harper L, Strandberg O, Spotorno N, Nilsson M, Lindberg O, Hansson O, Santillo AF. Structural and functional connectivity associations with anterior cingulate sulcal variability. RESEARCH SQUARE 2024:rs.3.rs-3831519. [PMID: 38260469 PMCID: PMC10802698 DOI: 10.21203/rs.3.rs-3831519/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background Sulcation of the anterior cingulate may be defined by presence of a paracingulate sulcus, a tertiary sulcus developing during the third gestational trimester with implications on cognitive function and disease. Methods In this retrospective analysis we examine task-free resting state functional connectivity and diffusion-weighted tract segmentation data from a cohort of healthy adults (< 60-year-old, n = 129), exploring the impact of ipsilateral paracingulate sulcal presence on structural and functional connectivity. Results Presence of a left paracingulate sulcus was associated with reduced fractional anisotropy in the left cingulum (P = 0.02) bundle and the peri-genual (P = 0.002) and dorsal (P = 0.03) but not the temporal cingulum bundle segments. Left paracingulate sulcal presence was associated with increased left peri-genual radial diffusivity (P = 0.003) and tract volume (P = 0.012). A significant, predominantly intraregional frontal component of altered resting state functional connectivity was identified in individuals possessing a left PCS (P = 0.01). Seed-based functional connectivity in pre-defined networks was not associated with paracingulate sulcal presence. Conclusion These results identify a novel association between neurodevelopmentally derived sulcation and altered structural connectivity in a healthy adult population with implications for conditions where this variation is of interest. Furthermore, they provide evidence of a link between the structural and functional connectivity of the brain in the presence of a paracingulate sulcus which may be mediated by a highly connected local functional network reliant on short association fibres.
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22
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Steiner L, Muri R, Wijesinghe D, Jann K, Maissen-Abgottspon S, Radojewski P, Pospieszny K, Kreis R, Kiefer C, Hochuli M, Trepp R, Everts R. Cerebral blood flow and white matter alterations in adults with phenylketonuria. Neuroimage Clin 2023; 41:103550. [PMID: 38091797 PMCID: PMC10716784 DOI: 10.1016/j.nicl.2023.103550] [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: 09/08/2023] [Revised: 11/15/2023] [Accepted: 12/08/2023] [Indexed: 03/16/2024]
Abstract
BACKGROUND Phenylketonuria (PKU) represents a congenital metabolic defect that disrupts the process of converting phenylalanine (Phe) into tyrosine. Earlier investigations have revealed diminished cognitive performance and changes in brain structure and function (including the presence of white matter lesions) among individuals affected by PKU. However, there exists limited understanding regarding cerebral blood flow (CBF) and its potential associations with cognition, white matter lesions, and metabolic parameters in patients with PKU, which we therefore aimed to investigate in this study. METHOD Arterial spin labeling perfusion MRI was performed to measure CBF in 30 adults with early-treated classical PKU (median age 35.5 years) and 59 healthy controls (median age 30.0 years). For all participants, brain Phe levels were measured with 1H spectroscopy, and white matter lesions were rated by two neuroradiologists on T2 weighted images. White matter integrity was examined with diffusion tensor imaging (DTI). For patients only, concurrent plasma Phe levels were assessed after an overnight fasting period. Furthermore, past Phe levels were collected to estimate historical metabolic control. On the day of the MRI, each participant underwent a cognitive assessment measuring IQ and performance in executive functions, attention, and processing speed. RESULTS No significant group difference was observed in global CBF between patients and controls (F (1, 87) = 3.81, p = 0.054). Investigating CBF on the level of cerebral arterial territories, reduced CBF was observed in the left middle and posterior cerebral artery (MCA and PCA), with the most prominent reduction of CBF in the anterior subdivision of the MCA (F (1, 87) = 6.15, p = 0.015, surviving FDR correction). White matter lesions in patients were associated with cerebral blood flow reduction in the affected structure. Particularly, patients with lesions in the occipital lobe showed significant CBF reductions in the left PCA (U = 352, p = 0.013, surviving FDR correction). Additionally, axial diffusivity measured with DTI was positively associated with CBF in the ACA and PCA (surviving FDR correction). Cerebral blood flow did not correlate with cognitive performance or metabolic parameters. CONCLUSION The relationship between cerebral blood flow and white matter indicates a complex interplay between vascular health and white matter alterations in patients with PKU. It highlights the importance of considering a multifactorial model when investigating the impact of PKU on the brain.
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Affiliation(s)
- Leonie Steiner
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Division of Neuropaediatrics, Development and Rehabilitation, Department of Paediatrics, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Raphaela Muri
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Dilmini Wijesinghe
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, USA
| | - Kay Jann
- Laboratory of Functional MRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, USA
| | - Stephanie Maissen-Abgottspon
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Katarzyna Pospieszny
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Roland Kreis
- Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Claus Kiefer
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland
| | - Michel Hochuli
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Roman Trepp
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland
| | - Regula Everts
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital and University of Bern, Switzerland; Division of Neuropaediatrics, Development and Rehabilitation, Department of Paediatrics, Inselspital, Bern University Hospital and University of Bern, Switzerland; Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.
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23
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Meesters S, Landers M, Rutten GJ, Florack L. Subject-Specific Automatic Reconstruction of White Matter Tracts. J Digit Imaging 2023; 36:2648-2661. [PMID: 37537513 PMCID: PMC10584769 DOI: 10.1007/s10278-023-00883-0] [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: 03/01/2023] [Revised: 07/05/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
MRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional-anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In this work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test-retest reproducibility) of tractography results of six clinically relevant white matter tracts by using healthy volunteer data (N = 136) from the Human Connectome Project consortium. A deep learning convolutional network-based approach was used for individualized segmentation of regions of interest, combined with an evidence-based tractography protocol and appropriate post-tractography filtering. Robustness was evaluated by estimating the consistency of tractography probability maps, i.e., averaged tractograms in normalized space, through the use of a hold-out cross-validation approach. No major outliers were found, indicating a high robustness of the tractography results. Reliability was evaluated at the individual level. First by examining the overlap of tractograms that resulted from repeatedly processed identical MRI scans (N = 10, 10 iterations) to establish an upper limit of reliability of the pipeline. Second, by examining the overlap for subjects that were scanned twice at different time points (N = 40). Both analyses indicated high reliability, with the second analysis showing a reliability near the upper limit. The robust and reliable subject-specific generation of white matter tracts in healthy subjects holds promise for future validation of our pipeline in a clinical population and subsequent implementation in brain tumor surgery.
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Affiliation(s)
- Stephan Meesters
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Maud Landers
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Geert-Jan Rutten
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands.
| | - Luc Florack
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
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Koyama T, Mochizuki M, Uchiyama Y, Domen K. Applicability of fractional anisotropy from standardized automated tractography for outcome prediction of patients after stroke. J Phys Ther Sci 2023; 35:838-844. [PMID: 38075519 PMCID: PMC10698312 DOI: 10.1589/jpts.35.838] [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: 07/27/2023] [Accepted: 09/14/2023] [Indexed: 03/22/2024] Open
Abstract
[Purpose] Diffusion-tensor fractional anisotropy has been used for outcome prediction in stroke patients. We assessed the clinical applicability of the two major fractional anisotropy methodologies-fractional anisotropy derived from segmentation maps in the standard brain (region of interest) and fractional anisotropy derived from standardized automated tractography-in relation to outcomes. [Participants and Methods] The study design was a retrospective survey of medical records collected from October 2021 to September 2022. Diffusion-tensor imaging was conducted in the second week after stroke onset. Outcomes were assessed using the total score of the motor component of the Stroke Impairment Assessment Set (null to full, 0 to 25). Correlations between fractional anisotropy and the outcomes were then assessed. [Results] Fourteen patients with hemorrhagic stroke were sampled. The fractional anisotropy from standardized automated tractography of the corticospinal tract on the lesion side (mean ± standard deviation, 0.403 ± 0.070) was significantly and tightly correlated (r=0.813) with the outcomes (13.4 ± 9.2), whereas the fractional anisotropy from a region of interest set in the cerebral peduncle on the lesion side (0.548 ± 0.064) was not significantly correlated with the outcomes (r=0.507). [Conclusion] The findings suggest that fractional anisotropy derived from standardized automated tractography can be more applicable to outcome prediction than that derived from a region of interest defined in the standard brain.
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Affiliation(s)
- Tetsuo Koyama
- Department of Rehabilitation Medicine, Nishinomiya Kyoritsu
Neurosurgical Hospital: 11-1 Imazu-Yamanaka-cho, Nishinomiya, Hyogo 663-8211, Japan
- Department of Rehabilitation Medicine, Hyogo Medical
University, Japan
| | - Midori Mochizuki
- Department of Rehabilitation Medicine, Nishinomiya Kyoritsu
Neurosurgical Hospital: 11-1 Imazu-Yamanaka-cho, Nishinomiya, Hyogo 663-8211, Japan
| | - Yuki Uchiyama
- Department of Rehabilitation Medicine, Hyogo Medical
University, Japan
| | - Kazuhisa Domen
- Department of Rehabilitation Medicine, Hyogo Medical
University, Japan
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25
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Mochizuki M, Uchiyama Y, Domen K, Koyama T. Automated Tractography for the Assessment of Aphasia in Acute Care Stroke Rehabilitation: A Case Series. Prog Rehabil Med 2023; 8:20230041. [PMID: 38024960 PMCID: PMC10661235 DOI: 10.2490/prm.20230041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Background Aphasia is a common disorder among stroke patients. Assessment of aphasia is essential for scheduling appropriate rehabilitative treatment. Although this is conventionally accomplished using neuropsychological test batteries, these tests are not always accessible because of attention and/or consciousness disturbances during acute care. To overcome this issue, we have introduced a newly developed automated tractography known as XTRACT. Cases Diffusion-tensor images were acquired from three patients on days 10-14. Brain images were processed by XTRACT, which automatically extracts neural tracts using standardized protocols. Fractional anisotropy (FA) values were then bilaterally evaluated in the following neural tracts associated with aphasia: arcuate fasciculus, inferior fronto-occipital fasciculus, middle longitudinal fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus. Case 1 had word-finding difficulty on admission. FA values in the lesioned left hemisphere were not decreased in all tracts and this patient fully recovered during acute care. Case 2 had reduced spontaneous speech and a low FA value in the left arcuate fasciculus. Rehabilitative treatment was scheduled to improve the verbal output of sentences and word recall. Case 3 could not complete the conventional aphasia test battery because of attention disturbance. He had low FA values in all tracts in the left hemisphere. Rehabilitative treatment was designed to focus on both speaking and auditory comprehension. Discussion Automated tractography enables quantitative assessment of the neural damage associated with aphasia, even in patients with attention and/or consciousness disturbances. This modality can aid in the assessment of aphasia and allows the planning of appropriate rehabilitative treatment.
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Affiliation(s)
- Midori Mochizuki
- Department of Rehabilitation Medicine, Nishinomiya Kyoritsu Neurosurgical Hospital, Nishinomiya, Japan
| | - Yuki Uchiyama
- Department of Rehabilitation Medicine, Hyogo Medical University, Nishinomiya, Japan
| | - Kazuhisa Domen
- Department of Rehabilitation Medicine, Hyogo Medical University, Nishinomiya, Japan
| | - Tetsuo Koyama
- Department of Rehabilitation Medicine, Nishinomiya Kyoritsu Neurosurgical Hospital, Nishinomiya, Japan
- Department of Rehabilitation Medicine, Hyogo Medical University, Nishinomiya, Japan
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26
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Schmid W, Danstrom IA, Echevarria MC, Adkinson J, Mattar L, Banks GP, Sheth SA, Watrous AJ, Heilbronner SR, Bijanki KR, Alabastri A, Bartoli E. A biophysically constrained brain connectivity model based on stimulation-evoked potentials. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.03.565525. [PMID: 37986830 PMCID: PMC10659345 DOI: 10.1101/2023.11.03.565525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Background Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New Method Using intracranial electrophysiology data recorded from a single patient undergoing sEEG evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D conductivity propagation from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. Results The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlates with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing methods Existing methods for estimating conductivity propagation are imaging-based and thus rely on anatomical inferences. Conclusions These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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Affiliation(s)
- William Schmid
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston 77005, Texas, USA
| | - Isabel A. Danstrom
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Maria Crespo Echevarria
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Joshua Adkinson
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Layth Mattar
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Garrett P. Banks
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Andrew J. Watrous
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Sarah R. Heilbronner
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Kelly R. Bijanki
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
| | - Alessandro Alabastri
- Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston 77005, Texas, USA
| | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston 77030, Texas, USA
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27
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Karadachka K, Assem M, Mitchell DJ, Duncan J, Medendorp WP, Mars RB. Structural connectivity of the multiple demand network in humans and comparison to the macaque brain. Cereb Cortex 2023; 33:10959-10971. [PMID: 37798142 PMCID: PMC10646692 DOI: 10.1093/cercor/bhad314] [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: 05/01/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 10/07/2023] Open
Abstract
Fluid intelligence encompasses a wide range of abilities such as working memory, problem-solving, and relational reasoning. In the human brain, these abilities are associated with the Multiple Demand Network, traditionally thought to involve combined activity of specific regions predominantly in the prefrontal and parietal cortices. However, the structural basis of the interactions between areas in the Multiple Demand Network, as well as their evolutionary basis among primates, remains largely unexplored. Here, we exploit diffusion MRI to elucidate the major white matter pathways connecting areas of the human core and extended Multiple Demand Network. We then investigate whether similar pathways can be identified in the putative homologous areas of the Multiple Demand Network in the macaque monkey. Finally, we contrast human and monkey networks using a recently proposed approach to compare different species' brains within a common organizational space. Our results indicate that the core Multiple Demand Network relies mostly on dorsal longitudinal connections and, although present in the macaque, these connections are more pronounced in the human brain. The extended Multiple Demand Network relies on distinct pathways and communicates with the core Multiple Demand Network through connections that also appear enhanced in the human compared with the macaque.
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Affiliation(s)
- Katrin Karadachka
- Donders Institute for Brain, Cognition and Behaviour, Faculty of Social Sciences, Radboud University Nijmegen, 6525HR Nijmegen, The Netherlands
| | - Moataz Assem
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - Daniel J Mitchell
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - W Pieter Medendorp
- Donders Institute for Brain, Cognition and Behaviour, Faculty of Social Sciences, Radboud University Nijmegen, 6525HR Nijmegen, The Netherlands
| | - Rogier B Mars
- Donders Institute for Brain, Cognition and Behaviour, Faculty of Social Sciences, Radboud University Nijmegen, 6525HR Nijmegen, The Netherlands
- Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford OX3 9DU, United Kingdom
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28
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Manzano-Patron JP, Moeller S, Andersson JLR, Ugurbil K, Yacoub E, Sotiropoulos SN. DENOISING DIFFUSION MRI: CONSIDERATIONS AND IMPLICATIONS FOR ANALYSIS. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.24.550348. [PMID: 37546835 PMCID: PMC10402048 DOI: 10.1101/2023.07.24.550348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Development of diffusion MRI (dMRI) denoising approaches has experienced considerable growth over the last years. As noise can inherently reduce accuracy and precision in measurements, its effects have been well characterised both in terms of uncertainty increase in dMRI-derived features and in terms of biases caused by the noise floor, the smallest measurable signal given the noise level. However, gaps in our knowledge still exist in objectively characterising dMRI denoising approaches in terms of both of these effects and assessing their efficacy. In this work, we reconsider what a denoising method should and should not do and we accordingly define criteria to characterise the performance. We propose a comprehensive set of evaluations, including i) benefits in improving signal quality and reducing noise variance, ii) gains in reducing biases and the noise floor and improving, iii) preservation of spatial resolution, iv) agreement of denoised data against a gold standard, v) gains in downstream parameter estimation (precision and accuracy), vi) efficacy in enabling noise-prone applications, such as ultra-high-resolution imaging. We further provide newly acquired complex datasets (magnitude and phase) with multiple repeats that sample different SNR regimes to highlight performance differences under different scenarios. Without loss of generality, we subsequently apply a number of exemplar patch-based denoising algorithms to these datasets, including Non-Local Means, Marchenko-Pastur PCA (MPPCA) in the magnitude and complex domain and NORDIC, and compare them with respect to the above criteria and against a gold standard complex average of multiple repeats. We demonstrate that all tested denoising approaches reduce noise-related variance, but not always biases from the elevated noise floor. They all induce a spatial resolution penalty, but its extent can vary depending on the method and the implementation. Some denoising approaches agree with the gold standard more than others and we demonstrate challenges in even defining such a standard. Overall, we show that dMRI denoising performed in the complex domain is advantageous to magnitude domain denoising with respect to all the above criteria.
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Affiliation(s)
| | - Steen Moeller
- Center for Magnetic Resonance Research, University of Minnesota, USA
| | | | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
- Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, UK
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29
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Bryant KL, Manger PR, Bertelsen MF, Khrapitchev AA, Sallet J, Benn RA, Mars RB. A map of white matter tracts in a lesser ape, the lar gibbon. Brain Struct Funct 2023:10.1007/s00429-023-02709-9. [PMID: 37904002 DOI: 10.1007/s00429-023-02709-9] [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: 02/22/2023] [Accepted: 09/01/2023] [Indexed: 11/01/2023]
Abstract
The recent development of methods for constructing directly comparable white matter atlases in primate brains from diffusion MRI allows us to probe specializations unique to humans, great apes, and other primate taxa. Here, we constructed the first white matter atlas of a lesser ape using an ex vivo diffusion-weighted scan of a brain from a young adult (5.5 years) male lar gibbon. We find that white matter architecture of the gibbon temporal lobe suggests specializations that are reminiscent of those previously reported for great apes, specifically, the expansion of the arcuate fasciculus and the inferior longitudinal fasciculus in the temporal lobe. Our findings suggest these white matter expansions into the temporal lobe were present in the last common ancestor to hominoids approximately 16 million years ago and were further modified in the great ape and human lineages. White matter atlases provide a useful resource for identifying neuroanatomical differences and similarities between humans and other primate species and provide insight into the evolutionary variation and stasis of brain organization.
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Affiliation(s)
- Katherine L Bryant
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
- Laboratoire de Psychologie Cognitive, Aix-Marseille Université, Marseille, France.
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
| | - Mads F Bertelsen
- Centre for Zoo and Wild Animal Health, Copenhagen Zoo, Frederiksberg, Denmark
| | | | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Stem Cell and Brain Research Institute, Université Lyon 1, Inserm, Bron, France
| | - R Austin Benn
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Integrative Neuroscience and Cognition Center, Université de Paris, CNRS, Paris, France
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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30
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Dumais F, Legarreta JH, Lemaire C, Poulin P, Rheault F, Petit L, Barakovic M, Magon S, Descoteaux M, Jodoin PM. FIESTA: Autoencoders for accurate fiber segmentation in tractography. Neuroimage 2023; 279:120288. [PMID: 37495198 DOI: 10.1016/j.neuroimage.2023.120288] [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: 06/02/2023] [Accepted: 07/20/2023] [Indexed: 07/28/2023] Open
Abstract
White matter bundle segmentation is a cornerstone of modern tractography to study the brain's structural connectivity in domains such as neurological disorders, neurosurgery, and aging. In this study, we present FIESTA (FIbEr Segmentation in Tractography using Autoencoders), a reliable and robust, fully automated, and easily semi-automatically calibrated pipeline based on deep autoencoders that can dissect and fully populate white matter bundles. This pipeline is built upon previous works that demonstrated how autoencoders can be used successfully for streamline filtering, bundle segmentation, and streamline generation in tractography. Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle. A latent space of streamlines is learned using autoencoder-based modeling combined with contrastive learning. Using an atlas of bundles in standard space (MNI), our proposed method segments new tractograms using the autoencoder latent distance between each tractogram streamline and its closest neighbor bundle in the atlas of bundles. Intra-subject bundle reliability is improved by recovering hard-to-track streamlines, using the autoencoder to generate new streamlines that increase the spatial coverage of each bundle while remaining anatomically correct. Results show that our method is more reliable than state-of-the-art automated virtual dissection methods such as RecoBundles, RecoBundlesX, TractSeg, White Matter Analysis and XTRACT. Our framework allows for the transition from one anatomical bundle definition to another with marginal calibration efforts. Overall, these results show that our framework improves the practicality and usability of current state-of-the-art bundle segmentation framework.
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Affiliation(s)
- Félix Dumais
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada.
| | - Jon Haitz Legarreta
- Department of Radiology, Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, USA
| | - Carl Lemaire
- Centre de Calcul Scientifique, Université de Sherbrooke, Canada
| | - Philippe Poulin
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada
| | - François Rheault
- Medical Imaging and Neuroinformatic (MINi) Lab, Department of Computer Science, Université de Sherbrooke, Canada
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle (GIN), CNRS, CEA, IMN, GIN, UMR 5293, F-33000 Bordeaux, Université de Bordeaux, France
| | - Muhamed Barakovic
- Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Stefano Magon
- Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Department of Computer Science, Université de Sherbrooke, Canada; Imeka Solutions inc, Sherbrooke, Canada
| | - Pierre-Marc Jodoin
- Videos & Images Theory and Analytics Lab (VITAL), Department of Computer Science, Université de Sherbrooke, Canada; Imeka Solutions inc, Sherbrooke, Canada
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Alagapan S, Choi KS, Heisig S, Riva-Posse P, Crowell A, Tiruvadi V, Obatusin M, Veerakumar A, Waters AC, Gross RE, Quinn S, Denison L, O'Shaughnessy M, Connor M, Canal G, Cha J, Hershenberg R, Nauvel T, Isbaine F, Afzal MF, Figee M, Kopell BH, Butera R, Mayberg HS, Rozell CJ. Cingulate dynamics track depression recovery with deep brain stimulation. Nature 2023; 622:130-138. [PMID: 37730990 PMCID: PMC10550829 DOI: 10.1038/s41586-023-06541-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 08/09/2023] [Indexed: 09/22/2023]
Abstract
Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD)1. However, achieving stable recovery is unpredictable2, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting3. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.
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Affiliation(s)
- Sankaraleengam Alagapan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stephen Heisig
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Andrea Crowell
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Vineet Tiruvadi
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Emory University School of Medicine, Atlanta, GA, USA
| | - Mosadoluwa Obatusin
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashan Veerakumar
- Department of Psychiatry, Schulich School of Medicine and Dentistry at Western University, London, Ontario, Canada
| | - Allison C Waters
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert E Gross
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Sinead Quinn
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Lydia Denison
- Emory University School of Medicine, Atlanta, GA, USA
| | - Matthew O'Shaughnessy
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Marissa Connor
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Gregory Canal
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jungho Cha
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rachel Hershenberg
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Tanya Nauvel
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Faical Isbaine
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Muhammad Furqan Afzal
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Martijn Figee
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian H Kopell
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Butera
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Helen S Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Christopher J Rozell
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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Crockett RA, Wilkins KB, Zeineh MM, McNab JA, Henderson JM, Buch VP, Brontë-Stewart HM. An Individualized Tractography Pipeline for the Nucleus Basalis of Meynert Lateral Tract. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.31.23294922. [PMID: 37693520 PMCID: PMC10491381 DOI: 10.1101/2023.08.31.23294922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background At the center of the cortical cholinergic network, the nucleus basalis of Meynert (NBM) is crucial for the cognitive domains most vulnerable in PD. Preclinical evidence has demonstrated the positive impact of NBM deep brain stimulation (DBS) on cognition but early human trials have had mixed results. It is possible that DBS of the lateral NBM efferent white matter fiber bundle may be more effective at improving cognitive-motor function. However, precise tractography modelling is required to identify the optimal target for neurosurgical planning. Individualized tractography approaches have been shown to be highly effective for accurately identifying DBS targets but have yet to be developed for the NBM. Methods Using structural and diffusion weighted imaging, we developed a tractography pipeline for precise individualized identification of the lateral NBM target tract. Using dice similarity coefficients, the reliability of the tractography outputs was assessed across three cohorts to investigate: 1) whether this manual pipeline is more reliable than an existing automated pipeline currently used in the literature; 2) the inter- and intra-rater reliability of our pipeline in research scans of patients with PD; and 3) the reliability and practicality of this pipeline in clinical scans of DBS patients. Results The individualized manual pipeline was found to be significantly more reliable than the existing automated pipeline for both the segmentation of the NBM region itself (p<0.001) and the reconstruction of the target lateral tract (p=0.002). There was also no significant difference between the reliability of two different raters in the PD cohort (p=0.25), which showed high inter- (mean Dice coefficient >0.6) and intra-rater (mean Dice coefficient >0.7) reliability across runs. Finally, the pipeline was shown to be highly reliable within the clinical scans (mean Dice coefficient = 0.77). However, accurate reconstruction was only evident in 7/10 tracts. Conclusion We have developed a reliable tractography pipeline for the identification and analysis of the NBM lateral tract in research and clinical grade imaging of healthy young adult and PD patient scans.
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Affiliation(s)
- Rachel A. Crockett
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, California, USA
| | - Kevin B. Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, California, USA
| | - Michael M. Zeineh
- Department of Radiology, Stanford University School of Medicine, California, USA
- Wu Tsai Neurosciences Institute, Stanford University, California, USA
- Bio-X, Stanford University, California, USA
| | - Jennifer A. McNab
- Department of Radiology, Stanford University School of Medicine, California, USA
- Wu Tsai Neurosciences Institute, Stanford University, California, USA
- Bio-X, Stanford University, California, USA
| | - Jaimie M. Henderson
- Wu Tsai Neurosciences Institute, Stanford University, California, USA
- Bio-X, Stanford University, California, USA
- Department of Neurosurgery, Stanford University School of Medicine, California, USA
| | - Vivek P. Buch
- Wu Tsai Neurosciences Institute, Stanford University, California, USA
- Bio-X, Stanford University, California, USA
- Department of Neurosurgery, Stanford University School of Medicine, California, USA
| | - Helen M. Brontë-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, California, USA
- Wu Tsai Neurosciences Institute, Stanford University, California, USA
- Bio-X, Stanford University, California, USA
- Department of Neurosurgery, Stanford University School of Medicine, California, USA
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33
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Crockett RA, Wilkins KB, Aditham S, Brontë-Stewart HM. No laughing white matter: Reduced integrity of the cortical cholinergic pathways in Parkinson's disease-related cognitive impairment. Neurobiol Dis 2023; 185:106243. [PMID: 37524210 PMCID: PMC10510752 DOI: 10.1016/j.nbd.2023.106243] [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: 06/29/2023] [Revised: 07/17/2023] [Accepted: 07/28/2023] [Indexed: 08/02/2023] Open
Abstract
BACKGROUND Approximately one third of recently diagnosed Parkinson's disease (PD) patients experience cognitive decline. The nucleus basalis of Meynert (NBM) degenerates early in PD and is crucial for cognitive function. The two main NBM white matter pathways include a lateral and medial trajectory. However, research is needed to determine which pathway, if any, are associated with PD-related cognitive decline. METHODS Thirty-seven PD patients with no mild cognitive impairment (MCI) were included in this study. Participants either developed MCI at 1-year follow up (PD MCI-Converters; n = 16) or did not (PD no-MCI; n = 21). Mean diffusivity (MD) and fractional anisotropy (FA) of the medial and lateral NBM tracts were extracted using probabilistic tractography. Between-group differences in MD and FA for each tract was compared using ANCOVA, controlling for age, sex, and disease duration. Control comparisons of the internal capsule MD and FA were also performed. Associations between baseline MD or FA and cognitive outcomes (working memory, psychomotor speed, delayed recall, and visuospatial function) were assessed using linear mixed models. RESULTS PD MCI-Converters had significantly greater MD and lower FA (p < .001) of both NBM tracts compared to PD no-MCI. No difference was found in the MD (p = .06) or FA (p = .31) of the control region. Trends were identified between: 1) lateral tract MD and FA with working memory decline; and 2) medial tract MD and reduced psychomotor speed. CONCLUSIONS Reduced integrity of the NBM tracts is evident in PD patients up to one year prior to the development of MCI. Thus, deterioration of the NBM tracts in PD may be an early marker of those at risk of cognitive decline.
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Affiliation(s)
- Rachel A Crockett
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin B Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Sudeep Aditham
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Helen M Brontë-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
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34
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Roediger DJ, Griffin C, Marin FV, Verdoorn H, Fiecas M, Mueller BA, Lim KO, Camchong J. Relating white matter microstructure in theoretically defined addiction networks to relapse in alcohol use disorder. Cereb Cortex 2023; 33:9756-9763. [PMID: 37415080 PMCID: PMC10472493 DOI: 10.1093/cercor/bhad241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 07/08/2023] Open
Abstract
Theoretical models group maladaptive behaviors in addiction into neurocognitive domains such as incentive salience (IS), negative emotionality (NE), and executive functioning (EF). Alterations in these domains lead to relapse in alcohol use disorder (AUD). We examine whether microstructural measures in the white matter pathways supporting these domains are associated with relapse in AUD. Diffusion kurtosis imaging data were collected from 53 individuals with AUD during early abstinence. We used probabilistic tractography to delineate the fornix (IS), uncinate fasciculus (NE), and anterior thalamic radiation (EF) in each participant and extracted mean fractional anisotropy (FA) and kurtosis fractional anisotropy (KFA) within each tract. Binary (abstained vs. relapsed) and continuous (number of days abstinent) relapse measures were collected over a 4-month period. Across tracts, anisotropy measures were typically (i) lower in those that relapsed during the follow-up period and (ii) positively associated with the duration of sustained abstinence during the follow-up period. However, only KFA in the right fornix reached significance in our sample. The association between microstructural measures in these fiber tracts and treatment outcome in a small sample highlights the potential utility of the three-factor model of addiction and the role of white matter alterations in AUD.
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Affiliation(s)
- Donovan J Roediger
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States
| | - Claire Griffin
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA 02215, United States
| | - Frances V Marin
- Center for Mindfulness and Compassion, Cambridge Health Alliance, Cambridge, MA 02141, United States
| | - Hannah Verdoorn
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States
| | - Mark Fiecas
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, United States
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States
| | - Kelvin O Lim
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jazmin Camchong
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States
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Tahedl M, Tan EL, Siah WF, Hengeveld JC, Doherty MA, McLaughlin RL, Hardiman O, Finegan E, Bede P. Radiological correlates of pseudobulbar affect: Corticobulbar and cerebellar components in primary lateral sclerosis. J Neurol Sci 2023; 451:120726. [PMID: 37421883 DOI: 10.1016/j.jns.2023.120726] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/02/2023] [Accepted: 06/28/2023] [Indexed: 07/10/2023]
Abstract
INTRODUCTION Pseudobulbar affect (PBA) is a distressing symptom of a multitude of neurological conditions affecting patients with a rage of neuroinflammatory, neurovascular and neurodegenerative conditions. It manifests in disproportionate emotional responses to minimal or no contextual stimulus. It has considerable quality of life implications and treatment can be challenging. METHODS A prospective multimodal neuroimaging study was conducted to explore the neuroanatomical underpinnings of PBA in patients with primary lateral sclerosis (PLS). All participants underwent whole genome sequencing and screening for C9orf72 hexanucleotide repeat expansions, a comprehensive neurological assessment, neuropsychological screening (ECAS, HADS, FrSBe) and PBA was evaluated by the emotional lability questionnaire. Structural, diffusivity and functional MRI data were systematically evaluated in whole-brain (WB) data-driven and region of interest (ROI) hypothesis-driven analyses. In ROI analyses, functional and structural corticobulbar connectivity and cerebello-medullary connectivity alterations were evaluated separately. RESULTS Our data-driven whole-brain analyses revealed associations between PBA and white matter degeneration in descending corticobulbar as well as in commissural tracts. In our hypothesis-driven analyses, PBA was associated with increased right corticobulbar tract RD (p = 0.006) and decreased FA (p = 0.026). The left-hemispheric corticobulbar tract, as well as functional connectivity, showed similar tendencies. While uncorrected p-maps revealed both voxelwise and ROI trends for associations between PBA and cerebellar measures, these did not reach significance to unequivocally support the "cerebellar hypothesis". CONCLUSIONS Our data confirm associations between cortex-brainstem disconnection and the clinical severity of PBA. While our findings may be disease-specific, they are consistent with the classical cortico-medullary model of pseudobulbar affect.
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Affiliation(s)
- Marlene Tahedl
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland
| | - Ee Ling Tan
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland
| | - We Fong Siah
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland
| | | | - Mark A Doherty
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | | | - Orla Hardiman
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland
| | - Eoin Finegan
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland
| | - Peter Bede
- Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Ireland; Department of Neurology, St James's Hospital, Dublin, Ireland.
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Howard AFD, Huszar IN, Smart A, Cottaar M, Daubney G, Hanayik T, Khrapitchev AA, Mars RB, Mollink J, Scott C, Sibson NR, Sallet J, Jbabdi S, Miller KL. An open resource combining multi-contrast MRI and microscopy in the macaque brain. Nat Commun 2023; 14:4320. [PMID: 37468455 PMCID: PMC10356772 DOI: 10.1038/s41467-023-39916-1] [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: 09/16/2022] [Accepted: 07/03/2023] [Indexed: 07/21/2023] Open
Abstract
Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. Here we introduce the BigMac dataset, a resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available.
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Affiliation(s)
- Amy F D Howard
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Istvan N Huszar
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Adele Smart
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Greg Daubney
- Wellcome Centre for Integrative Neuroimaging, Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Taylor Hanayik
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Connor Scott
- Division of Clinical Neurology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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Mahmoodi AL, Landers MJF, Rutten GJM, Brouwers HB. Characterization and Classification of Spatial White Matter Tract Alteration Patterns in Glioma Patients Using Magnetic Resonance Tractography: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:3631. [PMID: 37509291 PMCID: PMC10377290 DOI: 10.3390/cancers15143631] [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: 04/27/2023] [Revised: 07/03/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
INTRODUCTION Magnetic resonance (MR) tractography can be used to study the spatial relations between gliomas and white matter (WM) tracts. Various spatial patterns of WM tract alterations have been described in the literature. We reviewed classification systems of these patterns, and investigated whether low-grade gliomas (LGGs) and high-grade gliomas (HGGs) demonstrate distinct spatial WM tract alteration patterns. METHODS We conducted a systematic review and meta-analysis to summarize the evidence regarding MR tractography studies that investigated spatial WM tract alteration patterns in glioma patients. RESULTS Eleven studies were included. Overall, four spatial WM tract alteration patterns were reported in the current literature: displacement, infiltration, disruption/destruction and edematous. There was a considerable heterogeneity in the operational definitions of these terms. In a subset of studies, sufficient homogeneity in the classification systems was found to analyze pooled results for the displacement and infiltration patterns. Our meta-analyses suggested that LGGs displaced WM tracts significantly more often than HGGs (n = 259 patients, RR: 1.79, 95% CI [1.14, 2.79], I2 = 51%). No significant differences between LGGs and HGGs were found for WM tract infiltration (n = 196 patients, RR: 1.19, 95% CI [0.95, 1.50], I2 = 4%). CONCLUSIONS The low number of included studies and their considerable methodological heterogeneity emphasize the need for a more uniform classification system to study spatial WM tract alteration patterns using MR tractography. This review provides a first step towards such a classification system, by showing that the current literature is inconclusive and that the ability of fractional anisotropy (FA) to define spatial WM tract alteration patterns should be critically evaluated. We found variations in spatial WM tract alteration patterns between LGGs and HGGs, when specifically examining displacement and infiltration in a subset of the included studies.
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Affiliation(s)
- Arash L Mahmoodi
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands
| | - Maud J F Landers
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands
| | - Geert-Jan M Rutten
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands
| | - H Bart Brouwers
- Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Hilvarenbeekseweg 60, 5022 GC Tilburg, The Netherlands
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Asaridou SS, Cler GJ, Wiedemann A, Krishnan S, Smith HJ, Willis HE, Healy MP, Watkins KE. Microstructural Properties of the Cerebellar Peduncles in Children with Developmental Language Disorder. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548858. [PMID: 37503009 PMCID: PMC10370025 DOI: 10.1101/2023.07.13.548858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of cortico-striatal systems, little is known about the role of the cerebellum in DLD. Cortico-cerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination in this tract, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not cortico-cerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech acquisition and development.
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Affiliation(s)
- Salomi S. Asaridou
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Gabriel J. Cler
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Speech & Hearing Sciences, University of Washington, Seattle, USA
| | - Anna Wiedemann
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Saloni Krishnan
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, Royal Holloway, University of London, Egham Hill, Surrey, UK
| | - Harriet J. Smith
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Hanna E. Willis
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Máiréad P. Healy
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kate E. Watkins
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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Feng Y, Chandio BQ, Thomopoulos SI, Chattopadhyay T, Thompson PM. Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-6. [PMID: 38083771 DOI: 10.1109/embc40787.2023.10340009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model - a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings. We conducted a quantitative shape analysis by calculating bundle shape metrics, and found that our bundle templates better capture the shape distribution of the bundles than the atlas data used in the original segmentation derived from young healthy adults. We further demonstrated the use of our framework for direct bundle segmentation from whole-brain tractograms.
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40
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Xia X, Zeng X, Gao F, Yuan Z. Mapping cross-species connectome atlas of human and macaque striatum. Cereb Cortex 2023; 33:7518-7530. [PMID: 36928317 PMCID: PMC10267647 DOI: 10.1093/cercor/bhad057] [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: 12/23/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 03/18/2023] Open
Abstract
Cross-species connectome atlas (CCA) that can provide connectionally homogeneous and homologous brain nodes is essential and customized for cross-species neuroscience. However, existing CCAs were flawed in design and coarse-grained in results. In this study, a normative mapping framework of CCA was proposed and applied on human and macaque striatum. Specifically, all striatal voxels in the 2 species were mixed together and classified based on their represented and characterized feature of within-striatum resting-state functional connectivity, which was shared between the species. Six pairs of striatal parcels in these species were delineated in both hemispheres. Furthermore, this striatal parcellation was demonstrated by the best-matched whole-brain functional and structural connectivity between interspecies corresponding subregions. Besides, detailed interspecies differences in whole-brain multimodal connectivities and involved brain functions of these subregions were described to flesh out this CCA of striatum. In particular, this flexible and scalable mapping framework enables reliable construction of CCA of the whole brain, which would enable reliable findings in future cross-species research and advance our understandings into how the human brain works.
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Affiliation(s)
- Xiaoluan Xia
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau 999078, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
| | - Xinglin Zeng
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau 999078, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
| | - Fei Gao
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau 999078, China
| | - Zhen Yuan
- Centre for Cognitive and Brain Sciences, University of Macau, Taipa, Macau 999078, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
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Delinte N, Dricot L, Macq B, Gosse C, Van Reybroeck M, Rensonnet G. Unraveling multi-fixel microstructure with tractography and angular weighting. Front Neurosci 2023; 17:1199568. [PMID: 37351427 PMCID: PMC10282555 DOI: 10.3389/fnins.2023.1199568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/15/2023] [Indexed: 06/24/2023] Open
Abstract
Recent advances in MRI technology have enabled richer multi-shell sequences to be implemented in diffusion MRI, allowing the investigation of both the microscopic and macroscopic organization of the brain white matter and its complex network of neural fibers. The emergence of advanced diffusion models has enabled a more detailed analysis of brain microstructure by estimating the signal received from a voxel as the combination of responses from multiple fiber populations. However, disentangling the individual microstructural properties of different macroscopic white matter tracts where those pathways intersect remains a challenge. Several approaches have been developed to assign microstructural properties to macroscopic streamlines, but often present shortcomings. ROI-based heuristics rely on averages that are not tract-specific. Global methods solve a computationally-intensive global optimization but prevent the use of microstructural properties not included in the model and often require restrictive hypotheses. Other methods use atlases that might not be adequate in population studies where the shape of white matter tracts varies significantly between patients. We introduce UNRAVEL, a framework combining the microscopic and macroscopic scales to unravel multi-fixel microstructure by utilizing tractography. The framework includes commonly-used heuristics as well as a new algorithm, estimating the microstructure of a specific white matter tract with angular weighting. Our framework grants considerable freedom as the inputs required, a set of streamlines defining a tract and a multi-fixel diffusion model estimated in each voxel, can be defined by the user. We validate our approach on synthetic data and in vivo data, including a repeated scan of a subject and a population study of children with dyslexia. In each case, we compare the estimation of microstructural properties obtained with angular weighting to other commonly-used approaches. Our framework provides estimations of the microstructure at the streamline level, volumetric maps for visualization and mean microstructural values for the whole tract. The angular weighting algorithm shows increased accuracy, robustness to uncertainties in its inputs and maintains similar or better reproducibility compared to commonly-used analysis approaches. UNRAVEL will provide researchers with a flexible and open-source tool enabling them to study the microstructure of specific white matter pathways with their diffusion model of choice.
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Affiliation(s)
- Nicolas Delinte
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
| | - Laurence Dricot
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
| | - Benoit Macq
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Claire Gosse
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
- Psychological Sciences Research Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Marie Van Reybroeck
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
- Psychological Sciences Research Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Gaetan Rensonnet
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
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Zhang J, Wang J, Xu X, You Z, Huang Q, Huang Y, Guo Q, Guan Y, Zhao J, Liu J, Xu W, Deng Y, Xie F, Li B. In vivo synaptic density loss correlates with impaired functional and related structural connectivity in Alzheimer's disease. J Cereb Blood Flow Metab 2023; 43:977-988. [PMID: 36718002 PMCID: PMC10196742 DOI: 10.1177/0271678x231153730] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 12/29/2022] [Accepted: 01/02/2023] [Indexed: 02/01/2023]
Abstract
Synapse loss has been considered as a major pathological change in Alzheimer's disease (AD). It remains unclear about whether and how synapse loss relates to functional and structural connectivity dysfunction in AD. We measured synaptic vesicle glycoprotein 2 A (SV2A) binding using 18F-SynVesT-1 PET to evaluate synaptic alterations in 33 participants with AD, 31 with mild cognitive impairment (MCI), and 30 controls. We examined the correlation between synaptic density and cognitive function. Functional MRI was performed to analyze functional connectivity in lower synaptic density regions. We tracked the white matter tracts between impaired functional connectivity regions using Diffusion MRI. In AD group, lower synaptic density in bilateral cortex and hippocampus was found when compared with controls. The synaptic density changes in right insular cortex and bilateral caudal middle frontal gyrus (MFG) were correlated with cognitive decline. Among them, right MFG synaptic density was positively associated with right MFG - bilateral superior frontal gyrus (SFG) functional connectivity. AD had lower probability of tract (POT) between right MFG and SFG than controls, which was significantly associated with global cognition. These findings provide evidence supporting synapse loss contributes to functional and related structural connectivity alterations underlying cognitive impairment of AD.
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Affiliation(s)
- Junfang Zhang
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Jie Wang
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaomeng Xu
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Zhiwen You
- Department of Nuclear Medicine,
Shanghai East Hospital, Tongji University School of Medicine, Shanghai,
China
| | - Qi Huang
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yiyun Huang
- PET Center, Department of Radiology
and Biomedical Imaging, Yale University School of Medicine, New Haven,
Connecticut, USA
| | - Qihao Guo
- Department of Gerontology, Shanghai
Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yihui Guan
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun Zhao
- Department of Nuclear Medicine,
Shanghai East Hospital, Tongji University School of Medicine, Shanghai,
China
| | - Jun Liu
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
- Clinical Neuroscience Center,
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai,
China
| | - Wei Xu
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
| | - Yulei Deng
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
- Clinical Neuroscience Center,
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai,
China
- Department of Neurology, Ruijin
Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine,
Shanghai, China
| | - Fang Xie
- Department of Nuclear Medicine
& PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Binyin Li
- Department of Neurology and
Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai, China
- Clinical Neuroscience Center,
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai,
China
- Department of Neurology, Ruijin
Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine,
Shanghai, China
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Klingbeil J, Brandt ML, Stockert A, Baum P, Hoffmann KT, Saur D, Wawrzyniak M. Associations of lesion location, structural disconnection, and functional diaschisis with depressive symptoms post stroke. Front Neurol 2023; 14:1144228. [PMID: 37265471 PMCID: PMC10231644 DOI: 10.3389/fneur.2023.1144228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 04/20/2023] [Indexed: 06/03/2023] Open
Abstract
Introduction Post-stroke depressive symptoms (PSDS) are common and relevant for patient outcome, but their complex pathophysiology is ill understood. It likely involves social, psychological and biological factors. Lesion location is a readily available information in stroke patients, but it is unclear if the neurobiological substrates of PSDS are spatially localized. Building on previous analyses, we sought to determine if PSDS are associated with specific lesion locations, structural disconnection and/or localized functional diaschisis. Methods In a prospective observational study, we examined 270 patients with first-ever stroke with the Hospital Anxiety and Depression Scale (HADS) around 6 months post-stroke. Based on individual lesion locations and the depression subscale of the HADS we performed support vector regression lesion-symptom mapping, structural-disconnection-symptom mapping and functional lesion network-symptom-mapping, in a reanalysis of this previously published cohort to infer structure-function relationships. Results We found that depressive symptoms were associated with (i) lesions in the right insula, right putamen, inferior frontal gyrus and right amygdala and (ii) structural disconnection in the right temporal lobe. In contrast, we found no association with localized functional diaschisis. In addition, we were unable to confirm a previously described association between depressive symptom load and a network damage score derived from functional disconnection maps. Discussion Based on our results, and other recent lesion studies, we see growing evidence for a prominent role of right frontostriatal brain circuits in PSDS.
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Affiliation(s)
- Julian Klingbeil
- Neuroimaging Laboratory, Department of Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - Max-Lennart Brandt
- Neuroimaging Laboratory, Department of Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - Anika Stockert
- Neuroimaging Laboratory, Department of Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - Petra Baum
- Department of Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - Karl-Titus Hoffmann
- Department of Neuroradiology, University of Leipzig Medical Center, Leipzig, Germany
| | - Dorothee Saur
- Neuroimaging Laboratory, Department of Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - Max Wawrzyniak
- Neuroimaging Laboratory, Department of Neurology, University of Leipzig Medical Center, Leipzig, Germany
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Feng Y, Chandio BQ, Thomopoulos SI, Chattopadhyay T, Thompson PM. Variational Autoencoders for Generating Synthetic Tractography-Based Bundle Templates in a Low-Data Setting. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.24.529954. [PMID: 36909490 PMCID: PMC10002615 DOI: 10.1101/2023.02.24.529954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
White matter tracts generated from whole brain tractography are often processed using automatic segmentation methods with standard atlases. Atlases are generated from hundreds of subjects, which becomes time-consuming to create and difficult to apply to all populations. In this study, we extended our prior work on using a deep generative model a Convolutional Variational Autoencoder - to map complex and data-intensive streamlines to a low-dimensional latent space given a limited sample size of 50 subjects from the ADNI3 dataset, to generate synthetic population-specific bundle templates using Kernel Density Estimation (KDE) on streamline embeddings. We conducted a quantitative shape analysis by calculating bundle shape metrics, and found that our bundle templates better capture the shape distribution of the bundles than the atlas data used in the original segmentation derived from young healthy adults. We further demonstrated the use of our framework for direct bundle segmentation from whole-brain tractograms.
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Yan Y, Wu Y, Xiao G, Wang L, Zhou S, Wei L, Tian Y, Wu X, Hu P, Wang K. White Matter Changes as an Independent Predictor of Alzheimer's Disease. J Alzheimers Dis 2023:JAD221037. [PMID: 37182867 DOI: 10.3233/jad-221037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
BACKGROUND Abnormalities in white matter (WM) may be a crucial physiologic feature of Alzheimer's disease (AD). However, neuroimaging's ability to visualize the underlying functional degradation of the WM region in AD is unclear. OBJECTIVE This study aimed to explore the differences in amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) in the WM region of patients with AD and healthy controls (HC) and to investigate further whether these values can provide supplementary information for diagnosing AD. METHODS Forty-eight patients with AD and 46 age-matched HC were enrolled and underwent resting-state functional magnetic resonance imaging and a neuropsychological battery assessment. We analyzed the differences in WM activity between the two groups and further explored the correlation between WM activity in the different regions and cognitive function in the AD group. Finally, a machine learning algorithm was adopted to construct a classifier in detecting the clinical classification ability of the values of ALFF/ALFF in the WM. RESULTS Compared with HCs, patients with AD had lower WM activity in the right anterior thalamic radiation, left frontal aslant tract, and left forceps minor, which are all positively related to global cognitive function, memory, and attention function (all p < 0.05). Based on the combined WM ALFF and fALFF characteristics in the different regions, individuals not previously assessed were classified with moderate accuracy (75%), sensitivity (71%), specificity (79%), and area under the receiver operating characteristic curve (85%). CONCLUSION Our results suggest that WM activity is reduced in AD and can be used for disease classification.
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Affiliation(s)
- Yibing Yan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Yue Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Guixian Xiao
- Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Lu Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Shanshan Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
| | - Ling Wei
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China
- Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Xingqi Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, the School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China
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Crockett RA, Wilkins KB, Aditham S, Brontë-Stewart HM. No Laughing White Matter: Cortical Cholinergic Pathways and Cognitive Decline in Parkinson's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.01.23289348. [PMID: 37205443 PMCID: PMC10187344 DOI: 10.1101/2023.05.01.23289348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Background Approximately one third of recently diagnosed Parkinson's disease (PD) patients experience cognitive decline. The nucleus basalis of Meynert (NBM) degenerates early in PD and is crucial for cognitive function. The two main NBM white matter pathways include a lateral and medial trajectory. However, research is needed to determine which pathway, if any, are associated with PD-related cognitive decline. Methods Thirty-seven PD patients with no mild cognitive impairment (MCI) were included in this study. Participants either developed MCI at 1-year follow up (PD MCI-Converters; n=16) or did not (PD no-MCI; n=21). Mean diffusivity (MD) of the medial and lateral NBM tracts were extracted using probabilistic tractography. Between-group differences in MD for each tract was compared using ANCOVA, controlling for age, sex, and disease duration. Control comparisons of the internal capsule MD were also performed. Associations between baseline MD and cognitive outcomes (working memory, psychomotor speed, delayed recall, and visuospatial function) were assessed using linear mixed models. Results PD MCI-Converters had significantly greater MD of both NBM tracts compared to PD no-MCI (p<.001). No difference was found in the control region (p=.06). Trends were identified between: 1) lateral tract MD, poorer visuospatial performance (p=.05) and working memory decline (p=.04); and 2) medial tract MD and reduced psychomotor speed (p=.03). Conclusions Reduced integrity of the NBM tracts is evident in PD patients up to one year prior to the development of MCI. Thus, deterioration of the NBM tracts in PD may be an early marker of those at risk of cognitive decline.
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Affiliation(s)
- Rachel A. Crockett
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Kevin B. Wilkins
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Sudeep Aditham
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
| | - Helen M. Brontë-Stewart
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, USA
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California, USA
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47
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Matyi MA, Spielberg JM. Negative emotion differentiation and white matter microstructure. J Affect Disord 2023; 332:238-246. [PMID: 37059190 DOI: 10.1016/j.jad.2023.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/17/2023] [Accepted: 04/07/2023] [Indexed: 04/16/2023]
Abstract
BACKGROUND Deficits in the differentiation of negative emotions - the ability to specifically identify one's negative emotions - are associated with poorer mental health outcomes. However, the processes that lead to individual differences in negative emotion differentiation are not well understood, hampering our understanding of why this process is related to poor mental health outcomes. Given that disruptions in some affective processes are associated with white matter microstructure, identifying the circuitry associated with different affective processes can inform our understanding of how disturbances in these networks may lead to psychopathology. Thus, examination of how white matter microstructure relates to individual differences in negative emotion differentiation (NED) may provide insights into (i) its component processes and (ii) its relationship to brain structure. METHOD The relationship between white matter microstructure and NED was examined. RESULTS NED was related to white matter microstructure in right anterior thalamic radiation and inferior fronto-occipital fasciculus and left peri-genual cingulum. LIMITATIONS Although participants self-reported psychiatric diagnoses and previous psychological treatment, psychopathology was not directly targeted, and thus, the extent to which microstructure related to NED could be examined in relation to maladaptive outcomes is limited. CONCLUSIONS Results indicate that NED is related to white matter microstructure and suggest that pathways subserving processes that facilitate memory, semantics, and affective experience are important for NED. Our findings provide insights into the mechanisms by which individual differences in NED arise, suggesting intervention targets that may disrupt the relationship between poor differentiation and psychopathology.
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Affiliation(s)
- Melanie A Matyi
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE 19716, USA.
| | - Jeffrey M Spielberg
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE 19716, USA
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Ji JL, Demšar J, Fonteneau C, Tamayo Z, Pan L, Kraljič A, Matkovič A, Purg N, Helmer M, Warrington S, Winkler A, Zerbi V, Coalson TS, Glasser MF, Harms MP, Sotiropoulos SN, Murray JD, Anticevic A, Repovš G. QuNex-An integrative platform for reproducible neuroimaging analytics. Front Neuroinform 2023; 17:1104508. [PMID: 37090033 PMCID: PMC10113546 DOI: 10.3389/fninf.2023.1104508] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/21/2023] [Indexed: 04/08/2023] Open
Abstract
Introduction Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. Methods To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. Results The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Discussion Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.
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Affiliation(s)
- Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Manifest Technologies, North Haven, CT, United States
| | - Jure Demšar
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Zailyn Tamayo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Lining Pan
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Aleksij Kraljič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Nina Purg
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Markus Helmer
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Manifest Technologies, North Haven, CT, United States
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Anderson Winkler
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Valerio Zerbi
- Centre for Biomedical Imaging (CIBM), Lausanne, Switzerland
- Neuro-X Institute, School of Engineering (STI), EPFL, Lausanne, Switzerland
| | - Timothy S. Coalson
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
| | - Matthew F. Glasser
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Michael P. Harms
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham NIHR Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Department of Physics, Yale University, New Haven, CT, United States
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Department of Psychology, Yale University School of Medicine, New Haven, CT, United States
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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Li X, Rangelov D, Mattingley JB, Oestreich L, Lévy-Bencheton D, O'Sullivan MJ. White matter microstructure is associated with the precision of visual working memory. Neuroimage 2023; 272:120069. [PMID: 37003445 DOI: 10.1016/j.neuroimage.2023.120069] [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: 10/28/2022] [Revised: 03/02/2023] [Accepted: 03/29/2023] [Indexed: 04/03/2023] Open
Abstract
Visual working memory is critical for goal-directed behaviour as it maintains continuity between previous and current visual input. Functional neuroimaging studies have shown that visual working memory relies on communication between distributed brain regions, which implies an important role for long-range white matter connections in visual working memory performance. Here, we characterised the relationship between the microstructure of white matter association tracts and the precision of visual working memory representations. To that purpose, we devised a delayed estimation task which required participants to reproduce visual features along a continuous scale. A sample of 80 healthy adults performed the task and underwent diffusion-weighted MRI. We applied mixture distribution modelling to quantify the precision of working memory representations, swap errors, and guess rates, all of which contribute to observed responses. Latent components of microstructural properties in sets of anatomical tracts were identified by principal component analysis. We found an interdependency between fibre coherence in the bilateral SLF I, SLF II, and SLF III, on one hand, and the bilateral IFOF, on the other, in mediating the precision of visual working memory in a functionally specific manner. We also found that individual differences in axonal density in a network comprising the bilateral ILF and SLF III and right SLF II, in combination with a supporting network located elsewhere in the brain, form a common system for visual working memory to modulate response precision, swap errors, and random guess rates.
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Affiliation(s)
- Xuqian Li
- UQ Centre for Clinical Research, The University of Queensland, Brisbane, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
| | - Dragan Rangelov
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Jason B Mattingley
- Queensland Brain Institute, The University of Queensland, Brisbane, Australia; School of Psychology, The University of Queensland, Brisbane, Australia; Canadian Institute for Advanced Research, Toronto, Canada
| | - Lena Oestreich
- UQ Centre for Clinical Research, The University of Queensland, Brisbane, Australia; Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | | | - Michael J O'Sullivan
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia; Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Australia
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Brown HDH, Gale RP, Gouws AD, Vernon RJW, Airody A, Hanson RLW, Baseler HA, Morland AB. Assessing the structure of the posterior visual pathway in bilateral macular degeneration. Sci Rep 2023; 13:5008. [PMID: 36973337 PMCID: PMC10042846 DOI: 10.1038/s41598-023-31819-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/17/2023] [Indexed: 03/29/2023] Open
Abstract
Macular degeneration (MD) embodies a collection of disorders causing a progressive loss of central vision. Cross-sectional MRI studies have revealed structural changes in the grey and white matter in the posterior visual pathway in MD but there remains a need to understand how such changes progress over time. To that end we assessed the posterior pathway, characterising the visual cortex and optic radiations over a ~ 2-year period in MD patients and controls. We performed cross-sectional and longitudinal analysis of the former. Reduced cortical thickness and white matter integrity were observed in patients compared to controls, replicating previous findings. While faster, neither the rate of thinning in visual cortex nor the reduction in white matter integrity during the ~ 2-year period reached significance. We also measured cortical myelin density; cross-sectional data showed this was higher in patients than controls, likely as a result of greater thinning of non-myelinated tissue in patients. However, we also found evidence of a greater rate of loss of myelin density in the occipital pole in the patient group indicating that the posterior visual pathway is at risk in established MD. Taken together, our results revealed a broad decline in grey and white matter in the posterior visual pathway in bilateral MD; cortical thickness and fractional anisotropy show hints of an accelerated rate of loss also, with larger effects emerging in the occipital pole.
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Affiliation(s)
- Holly D H Brown
- Centre for Cognition and Neuroscience, Department of Psychology, University of Huddersfield, Huddersfield, UK.
- Department of Psychology, University of York, York, UK.
- York Neuroimaging Centre, University of York, York, UK.
- York Biomedical Research Institute, University of York, York, UK.
| | - Richard P Gale
- Hull York Medical School, University of York, York, UK
- Academic Unit of Ophthalmology, York and Scarborough Teaching Hospital NHS Foundation Trust, York, UK
| | - André D Gouws
- York Neuroimaging Centre, University of York, York, UK
| | - Richard J W Vernon
- Department of Psychology, University of York, York, UK
- York Neuroimaging Centre, University of York, York, UK
- York Biomedical Research Institute, University of York, York, UK
| | - Archana Airody
- Academic Unit of Ophthalmology, York and Scarborough Teaching Hospital NHS Foundation Trust, York, UK
| | - Rachel L W Hanson
- Department of Psychology, University of York, York, UK
- York Neuroimaging Centre, University of York, York, UK
- York Biomedical Research Institute, University of York, York, UK
- Academic Unit of Ophthalmology, York and Scarborough Teaching Hospital NHS Foundation Trust, York, UK
| | - Heidi A Baseler
- Department of Psychology, University of York, York, UK
- York Neuroimaging Centre, University of York, York, UK
- York Biomedical Research Institute, University of York, York, UK
- Hull York Medical School, University of York, York, UK
| | - Antony B Morland
- Department of Psychology, University of York, York, UK
- York Neuroimaging Centre, University of York, York, UK
- York Biomedical Research Institute, University of York, York, UK
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