1
|
Takemura H, Kaneko T, Sherwood CC, Johnson GA, Axer M, Hecht EE, Ye FQ, Leopold DA. A prominent vertical occipital white matter fasciculus unique to primate brains. Curr Biol 2024; 34:3632-3643.e4. [PMID: 38991613 PMCID: PMC11338705 DOI: 10.1016/j.cub.2024.06.034] [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: 02/18/2024] [Revised: 06/09/2024] [Accepted: 06/11/2024] [Indexed: 07/13/2024]
Abstract
Vision in humans and other primates enlists parallel processing streams in the dorsal and ventral visual cortex, known to support spatial and object processing, respectively. These streams are bridged, however, by a prominent white matter tract, the vertical occipital fasciculus (VOF), identified in both classical neuroanatomy and recent diffusion-weighted magnetic resonance imaging (dMRI) studies. Understanding the evolution of the VOF may shed light on its origin, function, and role in visually guided behaviors. To this end, we acquired high-resolution dMRI data from the brains of select mammalian species, including anthropoid and strepsirrhine primates, a tree shrew, rodents, and carnivores. In each species, we attempted to delineate the VOF after first locating the optic radiations in the occipital white matter. In all primate species examined, the optic radiation was flanked laterally by a prominent and coherent white matter fasciculus recognizable as the VOF. By contrast, the equivalent analysis applied to four non-primate species from the same superorder as primates (tree shrew, ground squirrel, paca, and rat) failed to reveal white matter tracts in the equivalent location. Clear evidence for a VOF was also absent in two larger carnivore species (ferret and fox). Although we cannot rule out the existence of minor or differently organized homologous fiber pathways in the non-primate species, the results suggest that the VOF has greatly expanded, or possibly emerged, in the primate lineage. This adaptation likely facilitated the evolution of unique visually guided behaviors in primates, with direct impacts on manual object manipulation, social interactions, and arboreal locomotion.
Collapse
Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, 38 Nishigonaka Myodaiji, Okazaki-shi, Aichi 444-8585, Japan; The Graduate Institute for Advanced Studies, SOKENDAI, Shonan Village, Hayama-cho, Kanagawa 240-0193, Japan; Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, 1-4 Yamadaoka, Suita-shi, Osaka 565-0871, Japan.
| | - Takaaki Kaneko
- Center for the Evolutionary Origins of Human Behavior, Kyoto University, 41-2 Kanrin, Inuyama-shi, Aichi 484-8506, Japan; Division of Behavioral Development, Department of System Neuroscience, National Institute for Physiological Sciences, 38 Nishigonaka Myodaiji, Okazaki-shi, Aichi, Japan
| | - Chet C Sherwood
- Department of Anthropology, The George Washington University, 800 22nd St. NW, Washington, DC 20052, USA
| | - G Allan Johnson
- Department of Radiology, Duke Center for In Vivo Microscopy, Duke Medical Center, 311 Research Drive, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University, 101 Science Dive., Durham, NC 27705, USA
| | - Markus Axer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich 52425, Germany; Department of Physics, School of Mathematics and Natural Sciences, University of Wuppertal, Gaußstraße 20 42119, Wuppertal, Germany
| | - Erin E Hecht
- Department of Human Evolutionary Biology, Harvard University, 11 Divinity Avenue, Cambridge, MA 02138, USA
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD 20814, USA
| | - David A Leopold
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD 20814, USA; Systems Neurodevelopment Laboratory, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20814, USA.
| |
Collapse
|
2
|
Takemura H, Kruper JA, Miyata T, Rokem A. Tractometry of Human Visual White Matter Pathways in Health and Disease. Magn Reson Med Sci 2024; 23:316-340. [PMID: 38866532 PMCID: PMC11234945 DOI: 10.2463/mrms.rev.2024-0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.
Collapse
Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Kanagawa, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - John A Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Toshikazu Miyata
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| |
Collapse
|
3
|
Crouse JJ, Park SH, Hermens DF, Lagopoulos J, Park M, Shin M, Carpenter JS, Scott EM, Hickie IB. Chronotype and subjective sleep quality predict white matter integrity in young people with emerging mental disorders. Eur J Neurosci 2024; 59:3322-3336. [PMID: 38650167 DOI: 10.1111/ejn.16351] [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: 05/18/2023] [Revised: 12/13/2023] [Accepted: 03/18/2024] [Indexed: 04/25/2024]
Abstract
Protecting brain health is a goal of early intervention. We explored whether sleep quality or chronotype could predict white matter (WM) integrity in emerging mental disorders. Young people (N = 364) accessing early-intervention clinics underwent assessments for chronotype, subjective sleep quality, and diffusion tensor imaging. Using machine learning, we examined whether chronotype or sleep quality (alongside diagnostic and demographic factors) could predict four measures of WM integrity: fractional anisotropy (FA), and radial, axial, and mean diffusivities (RD, AD and MD). We prioritised tracts that showed a univariate association with sleep quality or chronotype and considered predictors identified by ≥80% of machine learning (ML) models as 'important'. The most important predictors of WM integrity were demographics (age, sex and education) and diagnosis (depressive and bipolar disorders). Subjective sleep quality only predicted FA in the perihippocampal cingulum tract, whereas chronotype had limited predictive importance for WM integrity. To further examine links with mood disorders, we conducted a subgroup analysis. In youth with depressive and bipolar disorders, chronotype emerged as an important (often top-ranking) feature, predicting FA in the cingulum (cingulate gyrus), AD in the anterior corona radiata and genu of the corpus callosum, and RD in the corona radiata, anterior corona radiata, and genu of corpus callosum. Subjective quality was not important in this subgroup analysis. In summary, chronotype predicted altered WM integrity in the corona radiata and corpus callosum, whereas subjective sleep quality had a less significant role, suggesting that circadian factors may play a more prominent role in WM integrity in emerging mood disorders.
Collapse
Affiliation(s)
- Jacob J Crouse
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Shin Ho Park
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Daniel F Hermens
- Thompson Institute, University of the Sunshine Coast, Sunshine Coast, Queensland, Australia
| | - Jim Lagopoulos
- Thompson Institute, University of the Sunshine Coast, Sunshine Coast, Queensland, Australia
| | - Minji Park
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Mirim Shin
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Joanne S Carpenter
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Elizabeth M Scott
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - Ian B Hickie
- Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| |
Collapse
|
4
|
Kruper J, Richie-Halford A, Benson NC, Caffarra S, Owen J, Wu Y, Egan C, Lee AY, Lee CS, Yeatman JD, Rokem A. Convolutional neural network-based classification of glaucoma using optic radiation tissue properties. COMMUNICATIONS MEDICINE 2024; 4:72. [PMID: 38605245 PMCID: PMC11009254 DOI: 10.1038/s43856-024-00496-w] [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: 02/08/2023] [Accepted: 03/28/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. METHODS We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. RESULTS We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. CONCLUSIONS Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.
Collapse
Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Adam Richie-Halford
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Noah C Benson
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Sendy Caffarra
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
- University of Modena and Reggio Emilia, Modena, Italy
| | - Julia Owen
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | | | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Jason D Yeatman
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, USA.
- eScience Institute, University of Washington, Seattle, WA, USA.
| |
Collapse
|
5
|
Uesaki M, Furlan M, Smith AT, Takemura H. White matter tracts adjacent to the human cingulate sulcus visual area (CSv). PLoS One 2024; 19:e0300575. [PMID: 38578743 PMCID: PMC10997140 DOI: 10.1371/journal.pone.0300575] [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: 07/07/2023] [Accepted: 02/29/2024] [Indexed: 04/07/2024] Open
Abstract
Human cingulate sulcus visual area (CSv) was first identified as an area that responds selectively to visual stimulation indicative of self-motion. It was later shown that the area is also sensitive to vestibular stimulation as well as to bodily motion compatible with locomotion. Understanding the anatomical connections of CSv will shed light on how CSv interacts with other parts of the brain to perform information processing related to self-motion and navigation. A previous neuroimaging study (Smith et al. 2018, Cerebral Cortex, 28, 3685-3596) used diffusion-weighted magnetic resonance imaging (dMRI) to examine the structural connectivity of CSv, and demonstrated connections between CSv and the motor and sensorimotor areas in the anterior and posterior cingulate sulcus. The present study aimed to complement this work by investigating the relationship between CSv and adjacent major white matter tracts, and to map CSv's structural connectivity onto known white matter tracts. By re-analysing the dataset from Smith et al. (2018), we identified bundles of fibres (i.e. streamlines) from the whole-brain tractography that terminate near CSv. We then assessed to which white matter tracts those streamlines may belong based on previously established anatomical prescriptions. We found that a significant number of CSv streamlines can be categorised as part of the dorsalmost branch of the superior longitudinal fasciculus (SLF I) and the cingulum. Given current thinking about the functions of these white matter tracts, our results support the proposition that CSv provides an interface between sensory and motor systems in the context of self-motion.
Collapse
Affiliation(s)
- Maiko Uesaki
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan
- Open Innovation & Collaboration Research Organization, Ritsumeikan University, Ibaraki, Osaka, Japan
| | - Michele Furlan
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Trieste, Italy
| | - Andrew T. Smith
- Department of Psychology, Royal Holloway, University of London, Egham, Surrey, United Kingdom
| | - Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), Suita, Osaka, Japan
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Kanagawa, Japan
| |
Collapse
|
6
|
Michel LC, McCormick EM, Kievit RA. Grey and white matter metrics demonstrate distinct and complementary prediction of differences in cognitive performance in children: Findings from ABCD (N= 11 876). BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.06.529634. [PMID: 36945470 PMCID: PMC10028815 DOI: 10.1101/2023.03.06.529634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either grey or white matter metrics in humans, leaving open the key question as to whether grey or white matter microstructure play distinct or complementary roles supporting cognitive performance. To compare the role of grey and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with grey and white matter measures. Specifically, we compared how grey matter (volume, cortical thickness and surface area) and white matter measures (volume, fractional anisotropy and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study, 5680 female; 6196 male) at 10 years old. We found that grey and white matter metrics bring partly non-overlapping information to predict cognitive performance. The models with only grey or white matter explained respectively 15.4% and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in we additionally found that different metrics within grey and white matter had different predictive power, and that the tracts/regions that were most predictive of cognitive performance differed across metric. These results show that studies focusing on a single metric in either grey or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.
Collapse
Affiliation(s)
- Lea C Michel
- Cognitive Neuroscience Department, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Ethan M McCormick
- Cognitive Neuroscience Department, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, Netherlands
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, United States
| | - Rogier A Kievit
- Cognitive Neuroscience Department, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| |
Collapse
|
7
|
Wang D, Fan Q, Xiao X, He H, Yang Y, Li Y. Structural Fingerprinting of the Frontal Aslant Tract: Predicting Cognitive Control Capacity and Obsessive-Compulsive Symptoms. J Neurosci 2023; 43:7016-7027. [PMID: 37696666 PMCID: PMC10586535 DOI: 10.1523/jneurosci.0628-23.2023] [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: 04/04/2023] [Revised: 06/29/2023] [Accepted: 08/18/2023] [Indexed: 09/13/2023] Open
Abstract
White matter of the human brain is influenced by common genetic variations and shaped by neural activity-dependent experiences. Variations in microstructure of cerebral white matter across individuals and even across fiber tracts might underlie differences in cognitive capacity and vulnerabilities to mental disorders. The frontoparietal and cingulo-opercular networks of the brain constitute the central system supporting cognitive functions, and functional connectivity of these networks has been used to distinguish individuals known as "functional fingerprinting." The frontal aslant tract (FAT) that passes through the two networks has been implicated in executive functions. However, whether FAT can be used as a "structural fingerprint" to distinguish individuals and predict an individual's cognitive function and dysfunction is unknown. Here we investigated the fingerprinting property of FAT microstructural profiles using three independent diffusion MRI datasets with repeated scans on human participants including both females and males. We found that diffusion and geometric profiles of FAT can be used to distinguish individuals with a high accuracy. Next, we demonstrated that fractional anisotropy in different FAT segments predicted distinct cognitive functions, including working memory, inhibitory control, and relational reasoning. Finally, we assessed the contribution of altered FAT microstructural profiles to cognitive dysfunction in unmedicated patients with obsessive-compulsive disorders. We found that the altered microstructure in FAT was associated with the severity of obsessive-compulsive symptoms. Collectively, our findings suggest that the microstructural profiles of FAT can identify individuals with a high accuracy and may serve as an imaging marker for predicting an individual's cognitive capacity and disease severity.SIGNIFICANCE STATEMENT The frontoparietal network and cingulo-opercular network of the brain constitute a dual-network architecture for human cognitive functions, and functional connectivity of these two networks can be used as a "functional fingerprint" to distinguish individuals. However, the structural underpinnings of these networks subserving individual heterogeneities in their functional connectivity and cognitive ability remain unknown. We show here that the frontal aslant tract (FAT) that passes through the two networks distinguishes individuals with a high accuracy. Further, we demonstrate that the diffusion profiles of FAT predict distinct cognitive functions in healthy subjects and are associated with the clinical symptoms in patients with obsessive-compulsive disorders. Our findings suggest that the FAT may serve as a unique structural fingerprint underlying individual cognitive capability.
Collapse
Affiliation(s)
- Danni Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Qing Fan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, People's Republic of China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, People's Republic of China
| | - Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou 310027, People's Republic of China
- School of Physics, Zhejiang University, Hangzhou 310027, People's Republic of China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
| |
Collapse
|
8
|
Nie X, Shi Y. Flow-based Geometric Interpolation of Fiber Orientation Distribution Functions. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14227:46-55. [PMID: 38549783 PMCID: PMC10978007 DOI: 10.1007/978-3-031-43993-3_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
The fiber orientation distribution function (FOD) is an advanced model for high angular resolution diffusion MRI representing complex fiber geometry. However, the complicated mathematical structures of the FOD function pose challenges for FOD image processing tasks such as interpolation, which plays a critical role in the propagation of fiber tracts in tractography. In FOD-based tractography, linear interpolation is commonly used for numerical efficiency, but it is prone to generate false artificial information, leading to anatomically incorrect fiber tracts. To overcome this difficulty, we propose a flowbased and geometrically consistent interpolation framework that considers peak-wise rotations of FODs within the neighborhood of each location. Our method decomposes a FOD function into multiple components and uses a smooth vector field to model the flows of each peak in its neighborhood. To generate the interpolated result along the flow of each vector field, we develop a closed-form and efficient method to rotate FOD peaks in neighboring voxels and realize geometrically consistent interpolation of FOD components. By combining the interpolation results from each peak, we obtain the final interpolation of FODs. Experimental results on Human Connectome Project (HCP) data demonstrate that our method produces anatomically more meaningful FOD interpolations and significantly enhances tractography performance.
Collapse
Affiliation(s)
- Xinyu Nie
- USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Yonggang Shi
- USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA 90033, USA
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA
| |
Collapse
|
9
|
Kruper J, Benson NC, Caffarra S, Owen J, Wu Y, Lee AY, Lee CS, Yeatman JD, Rokem A. Optic radiations representing different eccentricities age differently. Hum Brain Mapp 2023; 44:3123-3135. [PMID: 36896869 PMCID: PMC10171550 DOI: 10.1002/hbm.26267] [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: 05/28/2022] [Revised: 01/10/2023] [Accepted: 02/16/2023] [Indexed: 03/11/2023] Open
Abstract
The neural pathways that carry information from the foveal, macular, and peripheral visual fields have distinct biological properties. The optic radiations (OR) carry foveal and peripheral information from the thalamus to the primary visual cortex (V1) through adjacent but separate pathways in the white matter. Here, we perform white matter tractometry using pyAFQ on a large sample of diffusion MRI (dMRI) data from subjects with healthy vision in the U.K. Biobank dataset (UKBB; N = 5382; age 45-81). We use pyAFQ to characterize white matter tissue properties in parts of the OR that transmit information about the foveal, macular, and peripheral visual fields, and to characterize the changes in these tissue properties with age. We find that (1) independent of age there is higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal and macular OR than in peripheral OR, consistent with denser, more organized nerve fiber populations in foveal/parafoveal pathways, and (2) age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in foveal OR decreases faster with age than in peripheral OR, while diffusivity increases faster in peripheral OR, suggesting foveal/peri-foveal OR and peripheral OR differ in how they age.
Collapse
Affiliation(s)
- John Kruper
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Noah C. Benson
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | - Sendy Caffarra
- Graduate School of Education, Stanford University and Division of Developmental‐Behavioral Pediatrics, Stanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Julia Owen
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Yue Wu
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Aaron Y. Lee
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Cecilia S. Lee
- Department of OphthalmologyUniversity of WashingtonSeattleWashingtonUSA
- Roger and Angie Karalis Johnson Retina CenterUniversity of WashingtonSeattleWashingtonUSA
| | - Jason D. Yeatman
- Graduate School of Education, Stanford University and Division of Developmental‐Behavioral Pediatrics, Stanford University School of MedicineStanford UniversityStanfordCaliforniaUSA
| | - Ariel Rokem
- Department of PsychologyUniversity of WashingtonSeattleWashingtonUSA
- eScience InstituteUniversity of WashingtonSeattleWashingtonUSA
| | | |
Collapse
|
10
|
Takemura H, Liu W, Kuribayashi H, Miyata T, Kida I. Evaluation of simultaneous multi-slice readout-segmented diffusion-weighted MRI acquisition in human optic nerve measurements. Magn Reson Imaging 2023; 102:103-114. [PMID: 37149064 DOI: 10.1016/j.mri.2023.05.001] [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: 03/31/2022] [Revised: 05/02/2023] [Accepted: 05/02/2023] [Indexed: 05/08/2023]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is the only available method to measure the tissue properties of white matter tracts in living human brains and has opened avenues for neuroscientific and clinical studies on human white matter. However, dMRI using conventional simultaneous multi-slice (SMS) single-shot echo planar imaging (ssEPI) still presents challenges in the analyses of some specific white matter tracts, such as the optic nerve, which are heavily affected by susceptibility-induced artifacts. In this study, we evaluated dMRI data acquired by using SMS readout-segmented EPI (rsEPI), which aims to reduce susceptibility-induced artifacts by dividing the acquisition space into multiple segments along the readout direction to reduce echo spacing. To this end, we acquired dMRI data from 11 healthy volunteers by using SMS ssEPI and SMS rsEPI, and then compared the dMRI data of the human optic nerve between the SMS ssEPI and SMS rsEPI datasets by visual inspection of the datasets and statistical comparisons of fractional anisotropy (FA) values. In comparison with the SMS ssEPI data, the SMS rsEPI data showed smaller susceptibility-induced distortion and exhibited a significantly higher FA along the optic nerve. In summary, this study demonstrates that despite its prolonged acquisition time, SMS rsEPI is a promising approach for measuring the tissue properties of the optic nerve in living humans and will be useful for future neuroscientific and clinical investigations of this pathway.
Collapse
Affiliation(s)
- Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Japan; Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Japan; Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Japan.
| | - Wei Liu
- Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | | | - Toshikazu Miyata
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Japan; Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita, Japan
| | - Ikuhiro Kida
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita, Japan
| |
Collapse
|
11
|
Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Satterthwaite TD, Rokem A. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2022; 9:616. [PMID: 36224186 PMCID: PMC9556519 DOI: 10.1038/s41597-022-01695-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022] Open
Abstract
We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.
Collapse
Affiliation(s)
- Adam Richie-Halford
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA.
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA.
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
| | - Lei Ai
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
| | - Sendy Caffarra
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- University of Modena and Reggio Emilia, Department of Biomedical, Metabolic and Neural Sciences, 41125, Modena, Italy
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Alexandre R Franco
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Iliana I Karipidis
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
- University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - John Kruper
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
| | - Michael Milham
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Bárbara Avelar-Pereira
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
| | - Ethan Roy
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Jason D Yeatman
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ariel Rokem
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
- University of Washington, eScience Institute, Seattle, Washington, 98195, USA
| |
Collapse
|
12
|
Laczkovics C, Nenning KH, Wittek T, Schmidbauer V, Schwarzenberg J, Maurer ES, Wagner G, Seidel S, Philipp J, Prayer D, Kasprian G, Karwautz A. White matter integrity is disrupted in adolescents with acute anorexia nervosa: A diffusion tensor imaging study. Psychiatry Res Neuroimaging 2022; 320:111427. [PMID: 34952446 DOI: 10.1016/j.pscychresns.2021.111427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 12/09/2021] [Accepted: 12/10/2021] [Indexed: 11/30/2022]
Abstract
Anorexia nervosa (AN) is a highly debilitating mental illness with multifactorial etiology. It oftentimes begins in adolescence, therefore understanding the pathophysiology in this period is important. Few studies investigated the possible impact of the acute state of illness on white matter (WM) tissue properties in the developing adolescent brain. The present study expands our understanding of the implications of AN and starvation on WM integrity. 67 acutely ill adolescent patients suffering from AN restricting type were compared with 32 healthy controls using diffusion tensor imaging assessing fractional anisotropy (FA) and mean diffusivity (MD). We found widespread alterations in the vast majority of the WM regions with significantly decreased FA and increased MD in the AN group. In this highly selective sample in the acute stage of AN, the alterations are likely to be the consequence of starvation. Still, we cannot rule out that some of the affected regions might play a key role in AN-specific psychopathology.
Collapse
Affiliation(s)
- Clarissa Laczkovics
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria.
| | - Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Tanja Wittek
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Victor Schmidbauer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Julia Schwarzenberg
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Elisabeth Sophie Maurer
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Gudrun Wagner
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Stefan Seidel
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Julia Philipp
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| | - Daniela Prayer
- Department of Neurology, Medical University of Vienna, Austria
| | - Gregor Kasprian
- Department of Neurology, Medical University of Vienna, Austria
| | - Andreas Karwautz
- Eating Disorders Unit, Department of Child and Adolescent Psychiatry, Medical University of Vienna, Austria
| |
Collapse
|
13
|
Gao Y, Meng X, Bai Z, Liu X, Zhang M, Li H, Ding G, Liu L, Booth JR. Left and Right Arcuate Fasciculi Are Uniquely Related to Word Reading Skills in Chinese-English Bilingual Children. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2022; 3:109-131. [PMID: 37215330 PMCID: PMC10158580 DOI: 10.1162/nol_a_00051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 07/10/2021] [Indexed: 05/24/2023]
Abstract
Whether reading in different writing systems recruits language-unique or language-universal neural processes is a long-standing debate. Many studies have shown the left arcuate fasciculus (AF) to be involved in phonological and reading processes. In contrast, little is known about the role of the right AF in reading, but some have suggested that it may play a role in visual spatial aspects of reading or the prosodic components of language. The right AF may be more important for reading in Chinese due to its logographic and tonal properties, but this hypothesis has yet to be tested. We recruited a group of Chinese-English bilingual children (8.2 to 12.0 years old) to explore the common and unique relation of reading skill in English and Chinese to fractional anisotropy (FA) in the bilateral AF. We found that both English and Chinese reading skills were positively correlated with FA in the rostral part of the left AF-direct segment. Additionally, English reading skill was positively correlated with FA in the caudal part of the left AF-direct segment, which was also positively correlated with phonological awareness. In contrast, Chinese reading skill was positively correlated with FA in certain segments of the right AF, which was positively correlated with visual spatial ability, but not tone discrimination ability. Our results suggest that there are language universal substrates of reading across languages, but that certain left AF nodes support phonological mechanisms important for reading in English, whereas certain right AF nodes support visual spatial mechanisms important for reading in Chinese.
Collapse
Affiliation(s)
- Yue Gao
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiangzhi Meng
- School of Psychological and Cognitive Sciences, Beijing Key Laboratory of Behavioral and Mental Health, Peking University, Beijing, China
- PekingU-PolyU Center for Child Development and Learning, Beijing, China
| | - Zilin Bai
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xin Liu
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Manli Zhang
- Department of Cognitive Neuroscience and Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Hehui Li
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Guosheng Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Li Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - James R. Booth
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
14
|
Caffarra S, Joo SJ, Bloom D, Kruper J, Rokem A, Yeatman JD. Development of the visual white matter pathways mediates development of electrophysiological responses in visual cortex. Hum Brain Mapp 2021; 42:5785-5797. [PMID: 34487405 PMCID: PMC8559498 DOI: 10.1002/hbm.25654] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/23/2021] [Accepted: 08/27/2021] [Indexed: 12/24/2022] Open
Abstract
The latency of neural responses in the visual cortex changes systematically across the lifespan. Here, we test the hypothesis that development of visual white matter pathways mediates maturational changes in the latency of visual signals. Thirty-eight children participated in a cross-sectional study including diffusion magnetic resonance imaging (MRI) and magnetoencephalography (MEG) sessions. During the MEG acquisition, participants performed a lexical decision and a fixation task on words presented at varying levels of contrast and noise. For all stimuli and tasks, early evoked fields were observed around 100 ms after stimulus onset (M100), with slower and lower amplitude responses for low as compared to high contrast stimuli. The optic radiations and optic tracts were identified in each individual's brain based on diffusion MRI tractography. The diffusion properties of the optic radiations predicted M100 responses, especially for high contrast stimuli. Higher optic radiation fractional anisotropy (FA) values were associated with faster and larger M100 responses. Over this developmental window, the M100 responses to high contrast stimuli became faster with age and the optic radiation FA mediated this effect. These findings suggest that the maturation of the optic radiations over childhood accounts for individual variations observed in the developmental trajectory of visual cortex responses.
Collapse
Affiliation(s)
- Sendy Caffarra
- Division of Developmental‐Behavioral PediatricsStanford University School of MedicineStanfordCalifornia
- Stanford University Graduate School of EducationStanfordCalifornia
- Basque Center on Cognition Brain and LanguageSan SebastianSpain
- Department of Biomedical, Metabolic and Neural SciencesUniversity of Modena and Reggio EmiliaModenaItaly
| | - Sung Jun Joo
- Department of PsychologyPusan National UniversityPusanRepublic of Korea
| | - David Bloom
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - John Kruper
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - Ariel Rokem
- Department of PsychologyUniversity of WashingtonSeattleWashington
- eScience InstituteUniversity of WashingtonSeattleWashington
| | - Jason D. Yeatman
- Division of Developmental‐Behavioral PediatricsStanford University School of MedicineStanfordCalifornia
- Stanford University Graduate School of EducationStanfordCalifornia
| |
Collapse
|
15
|
Bu X, Cao M, Huang X, He Y. The structural connectome in ADHD. PSYCHORADIOLOGY 2021; 1:257-271. [PMID: 38666220 PMCID: PMC10939332 DOI: 10.1093/psyrad/kkab021] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/12/2021] [Accepted: 12/13/2021] [Indexed: 02/05/2023]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) has been conceptualized as a brain dysconnectivity disorder. In the past decade, noninvasive diffusion magnetic resonance imaging (dMRI) studies have demonstrated that individuals with ADHD have alterations in the white matter structural connectome, and that these alterations are associated with core symptoms and cognitive deficits in patients. This review aims to summarize recent dMRI-based structural connectome studies in ADHD from voxel-, tractography-, and network-based perspectives. Voxel- and tractography-based studies have demonstrated disrupted microstructural properties predominantly located in the frontostriatal tracts, the corpus callosum, the corticospinal tracts, and the cingulum bundle in patients with ADHD. Network-based studies have suggested abnormal global and local efficiency as well as nodal properties in the prefrontal and parietal regions in the ADHD structural connectomes. The altered structural connectomes in those with ADHD provide significant signatures for prediction of symptoms and diagnostic classification. These studies suggest that abnormalities in the structural connectome may be one of the neural underpinnings of ADHD psychopathology and show potential for establishing imaging biomarkers in clinical evaluation. However, given that there are inconsistent findings across studies due to sample heterogeneity and analysis method variations, these ADHD-related white matter alterations are still far from informing clinical practice. Future studies with larger and more homogeneous samples are needed to validate the consistency of current results; advanced dMRI techniques can help to generate much more precise estimation of white matter pathways and assure specific fiber configurations; and finally, dimensional analysis frameworks can deepen our understanding of the neurobiology underlying ADHD.
Collapse
Affiliation(s)
- Xuan Bu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Miao Cao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai 200433, China
| | - Xiaoqi Huang
- Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
| |
Collapse
|
16
|
Caffarra S, Karipidis II, Yablonski M, Yeatman JD. Anatomy and physiology of word-selective visual cortex: from visual features to lexical processing. Brain Struct Funct 2021; 226:3051-3065. [PMID: 34636985 PMCID: PMC8639194 DOI: 10.1007/s00429-021-02384-8] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/07/2021] [Indexed: 12/20/2022]
Abstract
Over the past 2 decades, researchers have tried to uncover how the human brain can extract linguistic information from a sequence of visual symbols. The description of how the brain's visual system processes words and enables reading has improved with the progressive refinement of experimental methodologies and neuroimaging techniques. This review provides a brief overview of this research journey. We start by describing classical models of object recognition in non-human primates, which represent the foundation for many of the early models of visual word recognition in humans. We then review functional neuroimaging studies investigating the word-selective regions in visual cortex. This research led to the differentiation of highly specialized areas, which are involved in the analysis of different aspects of written language. We then consider the corresponding anatomical measurements and provide a description of the main white matter pathways carrying neural signals crucial to word recognition. Finally, in an attempt to integrate structural, functional, and electrophysiological findings, we propose a view of visual word recognition, accounting for spatial and temporal facets of word-selective neural processes. This multi-modal perspective on the neural circuitry of literacy highlights the relevance of a posterior-anterior differentiation in ventral occipitotemporal cortex for visual processing of written language and lexical features. It also highlights unanswered questions that can guide us towards future research directions. Bridging measures of brain structure and function will help us reach a more precise understanding of the transformation from vision to language.
Collapse
Affiliation(s)
- Sendy Caffarra
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA, 94305-5101, USA
- Stanford University Graduate School of Education, 485 Lasuen Mall, Stanford, CA, 94305, USA
- Basque Center on Cognition, Brain and Language, Mikeletegi 69, 20009, San Sebastian, Spain
- University of Modena and Reggio Emilia, Via Campi 287, 41125, Modena, Italy
| | - Iliana I Karipidis
- Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, School of Medicine, Stanford University, 401 Quarry Road, Stanford, CA, 94305-5717, USA.
| | - Maya Yablonski
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA, 94305-5101, USA
- Stanford University Graduate School of Education, 485 Lasuen Mall, Stanford, CA, 94305, USA
| | - Jason D Yeatman
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA, 94305-5101, USA
- Stanford University Graduate School of Education, 485 Lasuen Mall, Stanford, CA, 94305, USA
| |
Collapse
|
17
|
Abstract
We describe a collection of T1-, diffusion- and functional T2*-weighted magnetic resonance imaging data from human individuals with albinism and achiasma. This repository can be used as a test-bed to develop and validate tractography methods like diffusion-signal modeling and fiber tracking as well as to investigate the properties of the human visual system in individuals with congenital abnormalities. The MRI data is provided together with tools and files allowing for its preprocessing and analysis, along with the data derivatives such as manually curated masks and regions of interest for performing tractography.
Collapse
|
18
|
Schurr R, Mezer AA. The glial framework reveals white matter fiber architecture in human and primate brains. Science 2021; 374:762-767. [PMID: 34618596 DOI: 10.1126/science.abj7960] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
[Figure: see text].
Collapse
Affiliation(s)
- Roey Schurr
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aviv A Mezer
- Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
| |
Collapse
|
19
|
Sun H, Yang H, Wu Y, Bian H, Wang M, Huang Y, Jin J. iRhom1 rescues cognitive dysfunction in multiple sclerosis via preventing myelin injury. GENES BRAIN AND BEHAVIOR 2021; 20:e12771. [PMID: 34672089 DOI: 10.1111/gbb.12771] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 09/03/2021] [Accepted: 09/03/2021] [Indexed: 11/29/2022]
Abstract
Multiple sclerosis (MS) is characterized by myelin sheath injury. A disintegrin and metalloprotease-17 (ADAM17), a disintegrin and metalloproteinase, is essential in regulating oligodendrocyte (OL) regeneration and remyelination under demyelinating conditions. iRhom1, a highly conserved inactive protease that belongs to the rhomboid family, is one of key regulators for ADAM17 maturation. However, it is unknown whether iRhom1 also plays a role in central neuron system myelination under demyelinating conditions like MS. In this study, we investigated the function of iRhom1/ADAM17 in cognitive capability in MS by establishing the mice with iRhom1 overexpression in the hippocampus.
Collapse
Affiliation(s)
- Haolu Sun
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Hui Yang
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Yiwang Wu
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Hege Bian
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Menglin Wang
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| | - Yan Huang
- School of Pharmacy, Anhui Medical University, Hefei, China
| | - Juan Jin
- School of Basic Medical Sciences, Anhui Medical University, Hefei, China
| |
Collapse
|
20
|
He J, Zhang F, Xie G, Yao S, Feng Y, Bastos DCA, Rathi Y, Makris N, Kikinis R, Golby AJ, O'Donnell LJ. Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI. Hum Brain Mapp 2021; 42:3887-3904. [PMID: 33978265 PMCID: PMC8288095 DOI: 10.1002/hbm.25472] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/24/2021] [Accepted: 04/25/2021] [Indexed: 12/31/2022] Open
Abstract
The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points.
Collapse
Affiliation(s)
- Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of TechnologyHangzhouChina
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fan Zhang
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Guoqiang Xie
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurosurgeryNuclear Industry 215 Hospital of Shaanxi ProvinceXianyangChina
| | - Shun Yao
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Center for Pituitary Tumor Surgery, Department of NeurosurgeryThe First Affiliated Hospital, Sun Yat‐sen UniversityGuangzhouChina
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of TechnologyHangzhouChina
| | - Dhiego C. A. Bastos
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Departments of Psychiatry, Neurology and Radiology, Massachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Ron Kikinis
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Alexandra J. Golby
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Neurosurgery, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| |
Collapse
|
21
|
McPherson BC, Pestilli F. A single mode of population covariation associates brain networks structure and behavior and predicts individual subjects' age. Commun Biol 2021; 4:943. [PMID: 34354185 PMCID: PMC8342440 DOI: 10.1038/s42003-021-02451-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 06/15/2021] [Indexed: 02/07/2023] Open
Abstract
Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r = 0.5808). Individual variability within the single association axis well predicted the age of the subject (r = 0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach.
Collapse
Affiliation(s)
- Brent C McPherson
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA.
- Department of Psychology, The University of Texas at Austin, Austin, TX, USA.
| |
Collapse
|
22
|
Wang Y, Metoki A, Xia Y, Zang Y, He Y, Olson IR. A large-scale structural and functional connectome of social mentalizing. Neuroimage 2021; 236:118115. [PMID: 33933599 DOI: 10.1016/j.neuroimage.2021.118115] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 03/29/2021] [Accepted: 04/13/2021] [Indexed: 12/21/2022] Open
Abstract
Humans have a remarkable ability to infer the mind of others. This mentalizing skill relies on a distributed network of brain regions but how these regions connect and interact is not well understood. Here we leveraged large-scale multimodal neuroimaging data to elucidate the brain-wide organization and mechanisms of mentalizing processing. Key connectomic features of the mentalizing network (MTN) have been delineated in exquisite detail. We found the structural architecture of MTN is organized by two parallel subsystems and constructed redundantly by local and long-range white matter fibers. We uncovered an intrinsic functional architecture that is synchronized according to the degree of mentalizing, and its hierarchy reflects the inherent information integration order. We also examined the correspondence between the structural and functional connectivity in the network and revealed their differences in network topology, individual variance, spatial specificity, and functional specificity. Finally, we scrutinized the connectome resemblance between the default mode network and MTN and elaborated their inherent differences in dynamic patterns, laterality, and homogeneity. Overall, our study demonstrates that mentalizing processing unfolds across functionally heterogeneous regions with highly structured fiber tracts and unique hierarchical functional architecture, which make it distinguishable from the default mode network and other vicinity brain networks supporting autobiographical memory, semantic memory, self-referential, moral reasoning, and mental time travel.
Collapse
Affiliation(s)
- Yin Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Athanasia Metoki
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yinyin Zang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Ingrid R Olson
- Department of Psychology, Temple University, Philadelphia, PA, USA.
| |
Collapse
|
23
|
Simpson-Kent IL, Fried EI, Akarca D, Mareva S, Bullmore ET, Kievit RA. Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners. J Intell 2021; 9:32. [PMID: 34204009 PMCID: PMC8293355 DOI: 10.3390/jintelligence9020032] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/26/2021] [Accepted: 06/02/2021] [Indexed: 12/24/2022] Open
Abstract
Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role 'between' brain and behavior. We discuss implications and possible avenues for future studies.
Collapse
Affiliation(s)
- Ivan L. Simpson-Kent
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Eiko I. Fried
- Department of Clinical Psychology, Leiden University, 2300 RA Leiden, The Netherlands;
| | - Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Silvana Mareva
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, Cambridgeshire CB2 0SP, UK;
| | - the CALM Team
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Rogier A. Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
- Cognitive Neuroscience Department, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| |
Collapse
|
24
|
Richie-Halford A, Yeatman JD, Simon N, Rokem A. Multidimensional analysis and detection of informative features in human brain white matter. PLoS Comput Biol 2021; 17:e1009136. [PMID: 34181648 PMCID: PMC8270416 DOI: 10.1371/journal.pcbi.1009136] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/09/2021] [Accepted: 05/31/2021] [Indexed: 12/20/2022] Open
Abstract
The white matter contains long-range connections between different brain regions and the organization of these connections holds important implications for brain function in health and disease. Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to quantify tissue properties along the trajectories of these connections. Statistical inference from tractometry usually either averages these quantities along the length of each fiber bundle or computes regression models separately for each point along every one of the bundles. These approaches are limited in their sensitivity, in the former case, or in their statistical power, in the latter. We developed a method based on the sparse group lasso (SGL) that takes into account tissue properties along all of the bundles and selects informative features by enforcing both global and bundle-level sparsity. We demonstrate the performance of the method in two settings: i) in a classification setting, patients with amyotrophic lateral sclerosis (ALS) are accurately distinguished from matched controls. Furthermore, SGL identifies the corticospinal tract as important for this classification, correctly finding the parts of the white matter known to be affected by the disease. ii) In a regression setting, SGL accurately predicts "brain age." In this case, the weights are distributed throughout the white matter indicating that many different regions of the white matter change over the lifespan. Thus, SGL leverages the multivariate relationships between diffusion properties in multiple bundles to make accurate phenotypic predictions while simultaneously discovering the most relevant features of the white matter.
Collapse
Affiliation(s)
- Adam Richie-Halford
- eScience Institute, University of Washington, Seattle, Washington, United States of America
| | - Jason D. Yeatman
- Graduate School of Education and Division of Developmental and Behavioral Pediatrics, Stanford University, Stanford, California, United States of America
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Ariel Rokem
- eScience Institute, University of Washington, Seattle, Washington, United States of America
- Department of Psychology, University of Washington, Seattle, Washington, United States of America
| |
Collapse
|
25
|
Białecka-Dębek A, Granda D, Pietruszka B. The role of docosahexaenoic acid (DHA) in the prevention
of cognitive impairment in the elderly. POSTEP HIG MED DOSW 2021. [DOI: 10.5604/01.3001.0014.8986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Aging is an inevitable and progressive biological process that leads to irreversible physiological
and functional changes, also in the nervous system. Cognitive decline occurring with age can
significantly affect the quality of life of older people. Docosahexaenoic acid (DHA) is necessary
for the proper functioning of the nervous system; it can affect its action directly through its
impact on neurogenesis and neuroplasticity, but also indirectly by affecting the functioning
of the cardiovascular system or anti-inflammatory effect. Literature analysis shows that good
nutritional status of n-3 fatty acids, determined on the basis of their level in blood plasma or
erythrocytes, is associated with a lower risk of cognitive decline in selected cognitive domains,
as well as a lower risk of dementia or Alzheimer’s disease, although studies are also available
where the above relationship has not been confirmed. Apart from this, studies on DHA and
EPA diet intake, as well as in the form of dietary supplements, show their beneficial effects in
the context of cognitive functioning and the risk of dementia. Also, the results of intervention
studies, although not explicit, suggest that high doses of DHA and EPA in the form of dietary
supplements may slow down the process of deteriorating the cognitive functioning of the elderly within selected domains. Based on the review of the literature, it can be concluded
that DHA and EPA play an essential role in the prevention of cognitive impairment.
Collapse
Affiliation(s)
- Agata Białecka-Dębek
- Katedra Żywienia Człowieka, Instytut Nauk o Żywieniu Człowieka, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
| | - Dominika Granda
- Katedra Żywienia Człowieka, Instytut Nauk o Żywieniu Człowieka, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
| | - Barbara Pietruszka
- Katedra Żywienia Człowieka, Instytut Nauk o Żywieniu Człowieka, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
| |
Collapse
|
26
|
Abstract
Tractography is an important technique that allows the in vivo reconstruction of structural connections in the brain using diffusion MRI. Although tracking algorithms have improved during the last two decades, results of validation studies and international challenges warn about the reliability of tractography and point out the need for improved algorithms. In propagation-based tracking, connections have traditionally been modeled as piece-wise linear segments. In this work, we propose a novel propagation-based tracker that is capable of generating geometrically smooth ( C1 ) curves using parallel transport frames. Notably, our approach does not increase the complexity of the propagation problem that remains two-dimensional. Moreover, our tracker has a novel mechanism to reduce noise related propagation errors by incorporating topographic regularity of connections, a neuroanatomic property of many brain pathways. We ran extensive experiments and compared our approach against deterministic and other probabilistic algorithms. Our experiments on FiberCup and ISMRM 2015 challenge datasets as well as on 56 subjects of the Human Connectome Project show highly promising results both visually and quantitatively. Open-source implementations of the algorithm are shared publicly.
Collapse
|
27
|
Monin MY, Rahmouni L, Merlini A, Andriulli FP. A Hybrid Volume-Surface-Wire Integral Equation for the Anisotropic Forward Problem in Electroencephalography. ACTA ACUST UNITED AC 2020. [DOI: 10.1109/jerm.2020.2966121] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
28
|
Bertò G, Bullock D, Astolfi P, Hayashi S, Zigiotto L, Annicchiarico L, Corsini F, De Benedictis A, Sarubbo S, Pestilli F, Avesani P, Olivetti E. Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation. Neuroimage 2020; 224:117402. [PMID: 32979520 DOI: 10.1016/j.neuroimage.2020.117402] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 09/12/2020] [Accepted: 09/18/2020] [Indexed: 12/18/2022] Open
Abstract
Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.
Collapse
Affiliation(s)
- Giulia Bertò
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy
| | - Daniel Bullock
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA
| | - Pietro Astolfi
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy; PAVIS, Italian Institute of Technology (IIT), Genova, Italy
| | - Soichi Hayashi
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA
| | - Luca Zigiotto
- Division of Neurosurgery, Structural and Functional Connectivity Lab, S. Chiara Hospital, Trento, Italy
| | - Luciano Annicchiarico
- Division of Neurosurgery, Structural and Functional Connectivity Lab, S. Chiara Hospital, Trento, Italy
| | - Francesco Corsini
- Division of Neurosurgery, Structural and Functional Connectivity Lab, S. Chiara Hospital, Trento, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Department of Neuroscience, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | - Silvio Sarubbo
- Division of Neurosurgery, Structural and Functional Connectivity Lab, S. Chiara Hospital, Trento, Italy
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA
| | - Paolo Avesani
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy
| | - Emanuele Olivetti
- NeuroInformatics Laboratory (NILab), Bruno Kessler Foundation (FBK), Trento, Italy; Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy.
| |
Collapse
|
29
|
Min BK, Hämäläinen MS, Pantazis D. New Cognitive Neurotechnology Facilitates Studies of Cortical-Subcortical Interactions. Trends Biotechnol 2020; 38:952-962. [PMID: 32278504 PMCID: PMC7442676 DOI: 10.1016/j.tibtech.2020.03.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 03/06/2020] [Accepted: 03/06/2020] [Indexed: 11/26/2022]
Abstract
Most of the studies employing neuroimaging have focused on cortical and subcortical signals individually to obtain neurophysiological signatures of cognitive functions. However, understanding the dynamic communication between the cortex and subcortical structures is essential for unraveling the neural correlates of cognition. In this quest, magnetoencephalography (MEG) and electroencephalography (EEG) are the methods of choice because they are noninvasive electrophysiological recording techniques with high temporal resolution. Sophisticated MEG/EEG source estimation techniques and network analysis methods, developed recently, can provide a more comprehensive understanding of the neurophysiological mechanisms of fundamental cognitive processes. Used together with noninvasive modulation of cortical-subcortical communication, these approaches may open up new possibilities for expanding the repertoire of noninvasive cognitive neurotechnology.
Collapse
Affiliation(s)
- Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Korea; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| |
Collapse
|
30
|
Takemura H, Palomero-Gallagher N, Axer M, Gräßel D, Jorgensen MJ, Woods R, Zilles K. Anatomy of nerve fiber bundles at micrometer-resolution in the vervet monkey visual system. eLife 2020; 9:e55444. [PMID: 32844747 PMCID: PMC7532002 DOI: 10.7554/elife.55444] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 08/22/2020] [Indexed: 12/11/2022] Open
Abstract
Although the primate visual system has been extensively studied, detailed spatial organization of white matter fiber tracts carrying visual information between areas has not been fully established. This is mainly due to the large gap between tracer studies and diffusion-weighted MRI studies, which focus on specific axonal connections and macroscale organization of fiber tracts, respectively. Here we used 3D polarization light imaging (3D-PLI), which enables direct visualization of fiber tracts at micrometer resolution, to identify and visualize fiber tracts of the visual system, such as stratum sagittale, inferior longitudinal fascicle, vertical occipital fascicle, tapetum and dorsal occipital bundle in vervet monkey brains. Moreover, 3D-PLI data provide detailed information on cortical projections of these tracts, distinction between neighboring tracts, and novel short-range pathways. This work provides essential information for interpretation of functional and diffusion-weighted MRI data, as well as revision of wiring diagrams based upon observations in the vervet visual system.
Collapse
Affiliation(s)
- Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka UniversityOsakaJapan
- Graduate School of Frontier Biosciences, Osaka UniversityOsakaJapan
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH AachenAachenGermany
- C. & O. Vogt Institute for Brain Research, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Markus Axer
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - David Gräßel
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Matthew J Jorgensen
- Department of Pathology, Section on Comparative Medicine, Wake Forest School of MedicineWinston-SalemUnited States
| | - Roger Woods
- Ahmanson-Lovelace Brain Mapping Center, Departments of Neurology and of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLALos AngelesUnited States
| | - Karl Zilles
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- JARA - Translational Brain MedicineAachenGermany
| |
Collapse
|
31
|
Takeuchi H, Taki Y, Nouchi R, Yokoyama R, Nakagawa S, Iizuka K, Sakaki K, Araki T, Nozawa T, Ikeda S, Yokota S, Hanawa S, Magistro D, Kotozaki Y, Sasaki Y, Dos S Kawata KH, Kawashima R. The associations of BMI with mean diffusivity of basal ganglia among young adults with mild obesity and without obesity. Sci Rep 2020; 10:12566. [PMID: 32724120 PMCID: PMC7387490 DOI: 10.1038/s41598-020-69438-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 07/09/2020] [Indexed: 11/09/2022] Open
Abstract
Obesity causes a wide range of systemic diseases and is associated with mood and anxiety disorders. It is also associated with dopaminergic reward system function. However, the relationships between microstructural properties of the dopaminergic system and body mass index (BMI) have not been investigated. In this study, we investigated the associations of BMI with mean diffusivity (MD), diffusion tensor imaging measure in areas of the dopaminergic system (MDDS) in 435 healthy young adults with mild obesity and without obesity (BMI < 40). We detected the association between greater BMI and lower MD of the right globus pallidus and the right putamen. These results suggest that the property of the dopaminergic system is associated with BMI among young adults with mild obesity and without obesity.
Collapse
Affiliation(s)
- Hikarua Takeuchi
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer (IDAC), Tohoku University, 4-1 Seiryo-cho, Aoba-ku, Sendai, 980-8575, Japan.
| | - Yasuyuki Taki
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer (IDAC), Tohoku University, 4-1 Seiryo-cho, Aoba-ku, Sendai, 980-8575, Japan
- Division of Medical Neuroimaging Analysis, Department of Community Medical Supports, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Department of Radiology and Nuclear Medicine, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Rui Nouchi
- Creative Interdisciplinary Research Division, Frontier Research Institute for Interdisciplinary Science, Tohoku University, Sendai, Japan
- Human and Social Response Research Division, International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
- Department of Advanced Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | | | - Seishu Nakagawa
- Department of Human Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Division of Psychiatry, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Kunio Iizuka
- Division of Psychiatry, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Kohei Sakaki
- Department of Advanced Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | | | - Takayuki Nozawa
- Collaborative Research Center for Happiness Co-Creation Society Through Intelligent Communications, Tokyo Institute of Technology, Tokyo, Japan
| | - Shigeyuki Ikeda
- Department of Ubiquitous Sensing, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Susumu Yokota
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer (IDAC), Tohoku University, 4-1 Seiryo-cho, Aoba-ku, Sendai, 980-8575, Japan
| | - Sugiko Hanawa
- Department of Human Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Daniele Magistro
- National Centre for Sport and Exercise Medicine (NCSEM), The NIHR Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit, School of Sport, Exercise, and Health Sciences, Loughborough University, Loughborough, England
| | - Yuka Kotozaki
- Division of Clinical Research, Medical-Industry Translational Research Center, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Yukako Sasaki
- Department of Advanced Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | | | - Ryuta Kawashima
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer (IDAC), Tohoku University, 4-1 Seiryo-cho, Aoba-ku, Sendai, 980-8575, Japan
- Creative Interdisciplinary Research Division, Frontier Research Institute for Interdisciplinary Science, Tohoku University, Sendai, Japan
- Department of Advanced Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| |
Collapse
|
32
|
Kurzawski JW, Mikellidou K, Morrone MC, Pestilli F. The visual white matter connecting human area prostriata and the thalamus is retinotopically organized. Brain Struct Funct 2020; 225:1839-1853. [PMID: 32535840 PMCID: PMC7321903 DOI: 10.1007/s00429-020-02096-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Accepted: 06/05/2020] [Indexed: 11/30/2022]
Abstract
The human visual system is capable of processing visual information from fovea to the far peripheral visual field. Recent fMRI studies have shown a full and detailed retinotopic map in area prostriata, located ventro-dorsally and anterior to the calcarine sulcus along the parieto-occipital sulcus with strong preference for peripheral and wide-field stimulation. Here, we report the anatomical pattern of white matter connections between area prostriata and the thalamus encompassing the lateral geniculate nucleus (LGN). To this end, we developed and utilized an automated pipeline comprising a series of Apps that run openly on the cloud computing platform brainlife.io to analyse 139 subjects of the Human Connectome Project (HCP). We observe a continuous and extended bundle of white matter fibers from which two subcomponents can be extracted: one passing ventrally parallel to the optic radiations (OR) and another passing dorsally circumventing the lateral ventricle. Interestingly, the loop travelling dorsally connects the thalamus with the central visual field representation of prostriata located anteriorly, while the other loop travelling more ventrally connects the LGN with the more peripheral visual field representation located posteriorly. We then analyse an additional cohort of 10 HCP subjects using a manual plane extraction method outside brainlife.io to study the relationship between the two extracted white matter subcomponents and eccentricity, myelin and cortical thickness gradients within prostriata. Our results are consistent with a retinotopic segregation recently demonstrated in the OR, connecting the LGN and V1 in humans and reveal for the first time a retinotopic segregation regarding the trajectory of a fiber bundle between the thalamus and an associative visual area.
Collapse
Affiliation(s)
| | - Kyriaki Mikellidou
- Department of Psychology and Center for Applied Neuroscience, University of Cyprus, Nicosia, Cyprus
| | - Maria Concetta Morrone
- IRCCS Stella Maris, Viale del Tirreno, 331, Pisa, Italy.,Department of Translational Research On New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Program in Neuroscience and Program in Cognitive Science, Indiana University, 1101 E 10th Street, Bloomington, IN, 47401, USA.
| |
Collapse
|
33
|
Young PNE, Estarellas M, Coomans E, Srikrishna M, Beaumont H, Maass A, Venkataraman AV, Lissaman R, Jiménez D, Betts MJ, McGlinchey E, Berron D, O'Connor A, Fox NC, Pereira JB, Jagust W, Carter SF, Paterson RW, Schöll M. Imaging biomarkers in neurodegeneration: current and future practices. Alzheimers Res Ther 2020; 12:49. [PMID: 32340618 PMCID: PMC7187531 DOI: 10.1186/s13195-020-00612-7] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 04/01/2020] [Indexed: 12/12/2022]
Abstract
There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course "Biomarkers in neurodegenerative diseases". In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.
Collapse
Affiliation(s)
- Peter N E Young
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Mar Estarellas
- Centre for Medical Image Computing (CMIC), Department of Computer Science & Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Emma Coomans
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Meera Srikrishna
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Helen Beaumont
- Neuroscience and Aphasia Research Unit, Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Ashwin V Venkataraman
- Division of Brain Sciences, Imperial College London, London, UK
- United Kingdom Dementia Research Institute, Imperial College London, London, UK
| | - Rikki Lissaman
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, UK
| | - Daniel Jiménez
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
- Department of Neurological Sciences, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Matthew J Betts
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | | | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Antoinette O'Connor
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - William Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, USA
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Stephen F Carter
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Wolfson Molecular Imaging Centre, Division of Neuroscience and Experimental Psychology, MAHSC, University of Manchester, Manchester, UK
| | - Ross W Paterson
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Michael Schöll
- Wallenberg Centre for Molecular and Translational Medicine and the Department of Psychiatry and Neurochemistry, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
- Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK.
- Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
| |
Collapse
|
34
|
Association between diffusivity measures and language and cognitive-control abilities from early toddler’s age to childhood. Brain Struct Funct 2020; 225:1103-1122. [DOI: 10.1007/s00429-020-02062-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 03/20/2020] [Indexed: 12/20/2022]
|
35
|
Wang Y, Metoki A, Smith DV, Medaglia JD, Zang Y, Benear S, Popal H, Lin Y, Olson IR. Multimodal mapping of the face connectome. Nat Hum Behav 2020; 4:397-411. [PMID: 31988441 PMCID: PMC7167350 DOI: 10.1038/s41562-019-0811-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 12/09/2019] [Indexed: 01/13/2023]
Abstract
Face processing supports our ability to recognize friend from foe, form tribes and understand the emotional implications of changes in facial musculature. This skill relies on a distributed network of brain regions, but how these regions interact is poorly understood. Here we integrate anatomical and functional connectivity measurements with behavioural assays to create a global model of the face connectome. We dissect key features, such as the network topology and fibre composition. We propose a neurocognitive model with three core streams; face processing along these streams occurs in a parallel and reciprocal manner. Although long-range fibre paths are important, the face network is dominated by short-range fibres. Finally, we provide evidence that the well-known right lateralization of face processing arises from imbalanced intra- and interhemispheric connections. In summary, the face network relies on dynamic communication across highly structured fibre tracts, enabling coherent face processing that underpins behaviour and cognition.
Collapse
Affiliation(s)
- Yin Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Athanasia Metoki
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - David V Smith
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yinyin Zang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Susan Benear
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Haroon Popal
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Ying Lin
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Ingrid R Olson
- Department of Psychology, Temple University, Philadelphia, PA, USA.
| |
Collapse
|
36
|
Hodgetts CJ, Stefani M, Williams AN, Kolarik BS, Yonelinas AP, Ekstrom AD, Lawrence AD, Zhang J, Graham KS. The role of the fornix in human navigational learning. Cortex 2020; 124:97-110. [PMID: 31855730 PMCID: PMC7061322 DOI: 10.1016/j.cortex.2019.10.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 07/12/2019] [Accepted: 10/24/2019] [Indexed: 12/30/2022]
Abstract
Experiments on rodents have demonstrated that transecting the white matter fibre pathway linking the hippocampus with an array of cortical and subcortical structures - the fornix - impairs flexible navigational learning in the Morris Water Maze (MWM), as well as similar spatial learning tasks. While diffusion magnetic resonance imaging (dMRI) studies in humans have linked inter-individual differences in fornix microstructure to episodic memory abilities, its role in human spatial learning is currently unknown. We used high-angular resolution diffusion MRI combined with constrained spherical deconvolution-based tractography, to ask whether inter-individual differences in fornix microstructure in healthy young adults would be associated with spatial learning in a virtual reality navigation task. To efficiently capture individual learning across trials, we adopted a novel curve fitting approach to estimate a single index of learning rate. We found a statistically significant correlation between learning rate and the microstructure (mean diffusivity) of the fornix, but not that of a comparison tract linking occipital and anterior temporal cortices (the inferior longitudinal fasciculus, ILF). Further, this correlation remained significant when controlling for both hippocampal volume and participant gender. These findings extend previous animal studies by demonstrating the functional relevance of the fornix for human spatial learning in a virtual reality environment, and highlight the importance of a distributed neuroanatomical network, underpinned by key white matter pathways, such as the fornix, in complex spatial behaviour.
Collapse
Affiliation(s)
- Carl J Hodgetts
- Department of Psychology, Royal Holloway University of London, Egham, UK; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff Wales, UK.
| | - Martina Stefani
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff Wales, UK
| | - Angharad N Williams
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff Wales, UK
| | - Branden S Kolarik
- Center for the Neurobiology of Learning & Memory, University of California, Irvine, USA
| | - Andrew P Yonelinas
- Department of Psychology, University of California, Davis, CA, USA; Center for Neuroscience, University of California, Davis, CA, USA
| | - Arne D Ekstrom
- Department of Psychology, The University of Arizona, AZ USA
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff Wales, UK
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff Wales, UK
| | - Kim S Graham
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff Wales, UK
| |
Collapse
|
37
|
Simpson-Kent IL, Fuhrmann D, Bathelt J, Achterberg J, Borgeest GS, Kievit RA. Neurocognitive reorganization between crystallized intelligence, fluid intelligence and white matter microstructure in two age-heterogeneous developmental cohorts. Dev Cogn Neurosci 2020; 41:100743. [PMID: 31999564 PMCID: PMC6983934 DOI: 10.1016/j.dcn.2019.100743] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 11/03/2019] [Accepted: 11/29/2019] [Indexed: 12/01/2022] Open
Abstract
Despite the reliability of intelligence measures in predicting important life outcomes such as educational achievement and mortality, the exact configuration and neural correlates of cognitive abilities remain poorly understood, especially in childhood and adolescence. Therefore, we sought to elucidate the factorial structure and neural substrates of child and adolescent intelligence using two cross-sectional, developmental samples (CALM: N = 551 (N = 165 imaging), age range: 5-18 years, NKI-Rockland: N = 337 (N = 65 imaging), age range: 6-18 years). In a preregistered analysis, we used structural equation modelling (SEM) to examine the neurocognitive architecture of individual differences in childhood and adolescent cognitive ability. In both samples, we found that cognitive ability in lower and typical-ability cohorts is best understood as two separable constructs, crystallized and fluid intelligence, which became more distinct across development, in line with the age differentiation hypothesis. Further analyses revealed that white matter microstructure, most prominently the superior longitudinal fasciculus, was strongly associated with crystallized (gc) and fluid (gf) abilities. Finally, we used SEM trees to demonstrate evidence for developmental reorganization of gc and gf and their white matter substrates such that the relationships among these factors dropped between 7-8 years before increasing around age 10. Together, our results suggest that shortly before puberty marks a pivotal phase of change in the neurocognitive architecture of intelligence.
Collapse
Affiliation(s)
- Ivan L Simpson-Kent
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, CB2 7EF, UK.
| | - Delia Fuhrmann
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, CB2 7EF, UK
| | - Joe Bathelt
- Dutch Autism & ADHD Research Center, Brain & Cognition, University of Amsterdam, 1018 WS Amsterdam, Netherlands
| | - Jascha Achterberg
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, CB2 7EF, UK
| | - Gesa Sophia Borgeest
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, CB2 7EF, UK
| | - Rogier A Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, CB2 7EF, UK
| |
Collapse
|
38
|
Fuhrmann D, Simpson-Kent IL, Bathelt J, Kievit RA. A Hierarchical Watershed Model of Fluid Intelligence in Childhood and Adolescence. Cereb Cortex 2020; 30:339-352. [PMID: 31211362 PMCID: PMC7029679 DOI: 10.1093/cercor/bhz091] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 03/18/2019] [Accepted: 04/04/2019] [Indexed: 11/13/2022] Open
Abstract
Fluid intelligence is the capacity to solve novel problems in the absence of task-specific knowledge and is highly predictive of outcomes like educational attainment and psychopathology. Here, we modeled the neurocognitive architecture of fluid intelligence in two cohorts: the Centre for Attention, Leaning and Memory sample (CALM) (N = 551, aged 5-17 years) and the Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) (N = 335, aged 6-17 years). We used multivariate structural equation modeling to test a preregistered watershed model of fluid intelligence. This model predicts that white matter contributes to intermediate cognitive phenotypes, like working memory and processing speed, which, in turn, contribute to fluid intelligence. We found that this model performed well for both samples and explained large amounts of variance in fluid intelligence (R2CALM = 51.2%, R2NKI-RS = 78.3%). The relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7-12 years. This age effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that intelligence is part of a complex hierarchical system of partially independent effects.
Collapse
Affiliation(s)
- Delia Fuhrmann
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Ivan L Simpson-Kent
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Joe Bathelt
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Rogier A Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| |
Collapse
|
39
|
Schurr R, Zelman A, Mezer AA. Subdividing the superior longitudinal fasciculus using local quantitative MRI. Neuroimage 2019; 208:116439. [PMID: 31821870 DOI: 10.1016/j.neuroimage.2019.116439] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/06/2019] [Accepted: 12/03/2019] [Indexed: 01/17/2023] Open
Abstract
The association fibers of the superior longitudinal fasciculus (SLF) connect parietal and frontal cortical regions in the human brain. The SLF comprises of three distinct sub-bundles, each presenting a different anatomical trajectory, and specific functional roles. Nevertheless, in vivo studies of the SLF often consider the entire SLF complex as a single entity. In this work, we suggest a data-driven approach that relies on microstructure measurements for separating SLF-III from the rest of the SLF. We apply the SLF-III separation procedure in three independent datasets using parameters of diffusion MRI (fractional anisotropy), as well as relaxometry-based parameters (T1, T2, T2* and T2-weighted/T1-weighted). We show that the proposed procedure is reproducible across datasets and tractography algorithms. Finally, we suggest that differential crossing with different white-matter tracts is the source of the distinct MRI signatures of SLF-II and SLF-III.
Collapse
Affiliation(s)
- Roey Schurr
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Ady Zelman
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aviv A Mezer
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| |
Collapse
|
40
|
Ramanoël S, York E, Le Petit M, Lagrené K, Habas C, Arleo A. Age-Related Differences in Functional and Structural Connectivity in the Spatial Navigation Brain Network. Front Neural Circuits 2019; 13:69. [PMID: 31736716 PMCID: PMC6828843 DOI: 10.3389/fncir.2019.00069] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 10/09/2019] [Indexed: 12/13/2022] Open
Abstract
Spatial navigation involves multiple cognitive processes including multisensory integration, visuospatial coding, memory, and decision-making. These functions are mediated by the interplay of cerebral structures that can be broadly separated into a posterior network (subserving visual and spatial processing) and an anterior network (dedicated to memory and navigation planning). Within these networks, areas such as the hippocampus (HC) are known to be affected by aging and to be associated with cognitive decline and navigation impairments. However, age-related changes in brain connectivity within the spatial navigation network remain to be investigated. For this purpose, we performed a neuroimaging study combining functional and structural connectivity analyses between cerebral regions involved in spatial navigation. Nineteen young (μ = 27 years, σ = 4.3; 10 F) and 22 older (μ = 73 years, σ = 4.1; 10 F) participants were examined in this study. Our analyses focused on the parahippocampal place area (PPA), the retrosplenial cortex (RSC), the occipital place area (OPA), and the projections into the visual cortex of central and peripheral visual fields, delineated from independent functional localizers. In addition, we segmented the HC and the medial prefrontal cortex (mPFC) from anatomical images. Our results show an age-related decrease in functional connectivity between low-visual areas and the HC, associated with an increase in functional connectivity between OPA and PPA in older participants compared to young subjects. Concerning the structural connectivity, we found age-related differences in white matter integrity within the navigation brain network, with the exception of the OPA. The OPA is known to be involved in egocentric navigation, as opposed to allocentric strategies which are more related to the hippocampal region. The increase in functional connectivity between the OPA and PPA may thus reflect a compensatory mechanism for the age-related alterations around the HC, favoring the use of the preserved structural network mediating egocentric navigation. Overall, these findings on age-related differences of functional and structural connectivity may help to elucidate the cerebral bases of spatial navigation deficits in healthy and pathological aging.
Collapse
Affiliation(s)
- Stephen Ramanoël
- Sorbonne Universités, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Elizabeth York
- Sorbonne Universités, INSERM, CNRS, Institut de la Vision, Paris, France.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Marine Le Petit
- Sorbonne Universités, INSERM, CNRS, Institut de la Vision, Paris, France.,Normandie Université, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France
| | - Karine Lagrené
- Sorbonne Universités, INSERM, CNRS, Institut de la Vision, Paris, France
| | | | - Angelo Arleo
- Sorbonne Universités, INSERM, CNRS, Institut de la Vision, Paris, France
| |
Collapse
|
41
|
Takemura H, Pestilli F, Weiner KS. Comparative neuroanatomy: Integrating classic and modern methods to understand association fibers connecting dorsal and ventral visual cortex. Neurosci Res 2019; 146:1-12. [PMID: 30389574 PMCID: PMC6491271 DOI: 10.1016/j.neures.2018.10.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 10/19/2018] [Accepted: 10/25/2018] [Indexed: 12/13/2022]
Abstract
Comparative neuroanatomy studies improve understanding of brain structure and function and provide insight regarding brain development, evolution, and also what features of the brain are uniquely human. With modern methods such as diffusion MRI (dMRI) and quantitative MRI (qMRI), we are able to measure structural features of the brain with the same methods across human and non-human primates. In this review article, we discuss how recent dMRI measurements of vertical occipital connections in humans and macaques can be compared with previous findings from invasive anatomical studies that examined connectivity, including relatively forgotten classic strychnine neuronography studies. We then review recent progress in understanding the neuroanatomy of vertical connections within the occipitotemporal cortex by combining modern quantitative MRI and classical histological measurements in human and macaque. Finally, we a) discuss current limitations of dMRI and tractography and b) consider potential paths for future investigations using dMRI and tractography for comparative neuroanatomical studies of white matter tracts between species. While we focus on vertical association connections in visual cortex in the present paper, this same approach can be applied to other white matter tracts. Similar efforts are likely to continue to advance our understanding of the neuroanatomical features of the brain that are shared across species, as well as to distinguish the features that are uniquely human.
Collapse
Affiliation(s)
- Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita, Japan.
| | - Franco Pestilli
- Departments of Psychological and Brain Sciences, Computer Science and Intelligent Systems Engineering, Programs in Neuroscience and Cognitive Science, School of Optometry, Indiana University, Bloomington, IN, USA
| | - Kevin S Weiner
- Department of Psychology, University of California, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| |
Collapse
|
42
|
Associative white matter connecting the dorsal and ventral posterior human cortex. Brain Struct Funct 2019; 224:2631-2660. [DOI: 10.1007/s00429-019-01907-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 06/07/2019] [Indexed: 02/05/2023]
|
43
|
Avesani P, McPherson B, Hayashi S, Caiafa CF, Henschel R, Garyfallidis E, Kitchell L, Bullock D, Patterson A, Olivetti E, Sporns O, Saykin AJ, Wang L, Dinov I, Hancock D, Caron B, Qian Y, Pestilli F. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 2019; 6:69. [PMID: 31123325 PMCID: PMC6533280 DOI: 10.1038/s41597-019-0073-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 04/11/2019] [Indexed: 12/31/2022] Open
Abstract
We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.
Collapse
Affiliation(s)
- Paolo Avesani
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Brent McPherson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Soichi Hayashi
- Department of Psychological and Brain Sciences and Pervasive Technology Institute, University Information Technology Services, Indiana University, 1101 E 10th Street, Bloomington, IN, 47405, USA
| | - Cesar F Caiafa
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
- Instituto Argentino de Radioastronomía (CCT-La Plata, CONICET; CICPBA), CC5 V, Elisa, 1894, Argentina
- Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, C1063ACV, Argentina
| | - Robert Henschel
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Programs in Neuroscience and Cognitive Science, Indiana University Bloomington, 700N Woodlawn Ave, Bloomington, Indiana, 47408, USA
| | - Lindsey Kitchell
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Daniel Bullock
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew Patterson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Emanuele Olivetti
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Programs in Neuroscience and Cognitive Science, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew J Saykin
- Indiana University School of Medicine, Departments of Radiology and Imaging Sciences and Medical and Molecular Genetics, and the Indiana Alzheimer Disease Center, Indiana University, 355 W 16th St., Indianapolis, Indiana, 46202, USA
| | - Lei Wang
- Departments of Psychiatry and Behavioral Sciences and Radiology, Northwestern University Feinberg School of Medicine, 710N. Lake Shore Drive, Abbott Hall 1322, Chicago, IL, 60611, USA
| | - Ivo Dinov
- Statistics Online Computational Resource (SOCR), Center for Complexity of Self-Management in Chronic Disease (CSCD), Health Behavior and Biological Sciences, Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 49109, USA
| | - David Hancock
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Bradley Caron
- Pestilli Lab. Indiana University School of Optometry and Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, USA
| | - Yiming Qian
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Franco Pestilli
- Pestilli Lab. Department of Psychological and Brain Sciences, Engineering, Computer Science, Programs in Neuroscience and Cognitive Science, School of Optometry, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA.
| |
Collapse
|
44
|
Travis KE, Castro MRH, Berman S, Dodson CK, Mezer AA, Ben-Shachar M, Feldman HM. More than myelin: Probing white matter differences in prematurity with quantitative T1 and diffusion MRI. Neuroimage Clin 2019; 22:101756. [PMID: 30901711 PMCID: PMC6428958 DOI: 10.1016/j.nicl.2019.101756] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 03/03/2019] [Accepted: 03/09/2019] [Indexed: 01/07/2023]
Abstract
OBJECTIVE We combined diffusion MRI (dMRI) with quantitative T1 (qT1) relaxometry in a sample of school-aged children born preterm and full term to determine whether reduced fractional anisotropy (FA) within the corpus callosum of the preterm group could be explained by a reduction in myelin content, as indexed by R1 (1/T1) from qT1 scans. METHODS 8-year-old children born preterm (n = 29; GA 22-32 weeks) and full term (n = 24) underwent dMRI and qT1 scans. Four subdivisions of the corpus callosum were segmented in individual native space according to cortical projection zones (occipital, temporal, motor and anterior-frontal). Fractional anisotropy (FA) and R1 were quantified along the tract trajectory of each subdivision and compared across two birth groups. RESULTS Compared to controls, preterm children demonstrated significantly decreased FA in 3 of 4 analyzed corpus callosum subdivisions (temporal, motor, and anterior frontal segments) and decreased R1 in only 2 of 4 corpus callosum subdivisions (temporal and motor segments). FA and RD were significantly associated with R1 within temporal but not anterior frontal subdivisions of the corpus callosum in the term group; RD correlated with R1 in the anterior subdivision in the preterm group only. CONCLUSIONS Myelin content, as indexed by R1, drives some but not all of the differences in white matter between preterm and term born children. Other factors, such as axonal diameter and directional coherence, likely contributed to FA differences in the anterior frontal segment of the corpus callosum that were not well explained by R1.
Collapse
Affiliation(s)
- Katherine E Travis
- Division of Developmental and Behavioral Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Maria R H Castro
- Division of Developmental and Behavioral Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Shai Berman
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Cory K Dodson
- Division of Developmental and Behavioral Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Aviv A Mezer
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michal Ben-Shachar
- The Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan, Israel; Department of English Literature and Linguistics, Bar Ilan University, Ramat Gan, Israel
| | - Heidi M Feldman
- Division of Developmental and Behavioral Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
| |
Collapse
|
45
|
Ather S, Proudlock FA, Welton T, Morgan PS, Sheth V, Gottlob I, Dineen RA. Aberrant visual pathway development in albinism: From retina to cortex. Hum Brain Mapp 2019; 40:777-788. [PMID: 30511784 PMCID: PMC6865554 DOI: 10.1002/hbm.24411] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 09/08/2018] [Accepted: 09/18/2018] [Indexed: 12/27/2022] Open
Abstract
Albinism refers to a group of genetic abnormalities in melanogenesis that are associated neuronal misrouting through the optic chiasm. We perform quantitative assessment of visual pathway structure and function in 23 persons with albinism (PWA) and 20 matched controls using optical coherence tomography (OCT), volumetric magnetic resonance imaging (MRI), diffusion tensor imaging and visual evoked potentials (VEP). PWA had a higher streamline decussation index (percentage of total tractography streamlines decussating at the chiasm) compared with controls (Z = -2.24, p = .025), and streamline decussation index correlated weakly with inter-hemispheric asymmetry measured using VEP (r = .484, p = .042). For PWA, a significant correlation was found between foveal development index and total number of streamlines (r = .662, p < .001). Significant positive correlations were found between peri-papillary retinal nerve fibre layer thickness and optic nerve (r = .642, p < .001) and tract (r = .663, p < .001) width. Occipital pole cortical thickness was 6.88% higher (Z = -4.10, p < .001) in PWA and was related to anterior visual pathway structures including foveal retinal pigment epithelium complex thickness (r = -.579, p = .005), optic disc (r = .478, p = .021) and rim areas (r = .597, p = .003). We were unable to demonstrate a significant relationship between OCT-derived foveal or optic nerve measures and MRI-derived chiasm size or streamline decussation index. Our novel tractographic demonstration of altered chiasmatic decussation in PWA corresponds to VEP measured cortical asymmetry and is consistent with chiasmatic misrouting in albinism. We also demonstrate a significant relationship between retinal pigment epithelium and visual cortex thickness indicating that retinal pigmentation defects in albinism lead to downstream structural reorganisation of the visual cortex.
Collapse
Affiliation(s)
- Sarim Ather
- Nuffield Department of Surgical SciencesUniversity of OxfordOxfordUnited Kingdom
| | - Frank Anthony Proudlock
- University of Leicester Ulverscroft Eye UnitRobert Kilpatrick Clinical Sciences BuildingLeicesterUnited Kingdom
| | - Thomas Welton
- Radiological Sciences, Division of Clinical NeuroscienceUniversity of Nottingham, Queen's Medical CentreNottinghamUnited Kingdom
- Sir Peter Mansfield Imaging Centre, University of NottinghamQueen's Medical CentreNottinghamUnited Kingdom
| | - Paul S. Morgan
- Sir Peter Mansfield Imaging Centre, University of NottinghamQueen's Medical CentreNottinghamUnited Kingdom
- Medical Physics and Clinical Engineering, Nottingham University Hospitals NHS TrustQueen's Medical CentreNottinghamUnited Kingdom
| | - Viral Sheth
- University of Leicester Ulverscroft Eye UnitRobert Kilpatrick Clinical Sciences BuildingLeicesterUnited Kingdom
| | - Irene Gottlob
- University of Leicester Ulverscroft Eye UnitRobert Kilpatrick Clinical Sciences BuildingLeicesterUnited Kingdom
| | - Rob A. Dineen
- Radiological Sciences, Division of Clinical NeuroscienceUniversity of Nottingham, Queen's Medical CentreNottinghamUnited Kingdom
- Sir Peter Mansfield Imaging Centre, University of NottinghamQueen's Medical CentreNottinghamUnited Kingdom
| |
Collapse
|
46
|
Weiner KS. The Mid‐Fusiform Sulcus (
sulcus sagittalis gyri fusiformis
). Anat Rec (Hoboken) 2019; 302:1491-1503. [DOI: 10.1002/ar.24041] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 08/28/2018] [Accepted: 09/11/2018] [Indexed: 01/24/2023]
Affiliation(s)
- Kevin S. Weiner
- Department of PsychologyUC Berkeley Berkeley California
- Helen Wills Neuroscience Institute Berkeley California
| |
Collapse
|
47
|
Sani I, McPherson BC, Stemmann H, Pestilli F, Freiwald WA. Functionally defined white matter of the macaque monkey brain reveals a dorso-ventral attention network. eLife 2019; 8:e40520. [PMID: 30601116 PMCID: PMC6345568 DOI: 10.7554/elife.40520] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 12/20/2018] [Indexed: 12/18/2022] Open
Abstract
Classical studies of attention have identified areas of parietal and frontal cortex as sources of attentional control. Recently, a ventral region in the macaque temporal cortex, the posterior infero-temporal dorsal area PITd, has been suggested as a third attentional control area. This raises the question of whether and how spatially distant areas coordinate a joint focus of attention. Here we tested the hypothesis that parieto-frontal attention areas and PITd are directly interconnected. By combining functional MRI with ex-vivo high-resolution diffusion MRI, we found that PITd and dorsal attention areas are all directly connected through three specific fascicles. These results ascribe a new function, the communication of attention signals, to two known fiber-bundles, highlight the importance of vertical interactions across the two visual streams, and imply that the control of endogenous attention, hitherto thought to reside in macaque dorsal cortical areas, is exerted by a dorso-ventral network.
Collapse
Affiliation(s)
- Ilaria Sani
- Laboratory of Neural SystemsThe Rockefeller UniversityNew YorkUnited States
| | - Brent C McPherson
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUnited States
| | - Heiko Stemmann
- Institute for Brain Research and Center for Advanced ImagingUniversity of BremenBremenGermany
| | - Franco Pestilli
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUnited States
| | - Winrich A Freiwald
- Laboratory of Neural SystemsThe Rockefeller UniversityNew YorkUnited States
| |
Collapse
|
48
|
Aydogan DB, Shi Y. Tracking and validation techniques for topographically organized tractography. Neuroimage 2018; 181:64-84. [PMID: 29986834 PMCID: PMC6139055 DOI: 10.1016/j.neuroimage.2018.06.071] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 05/18/2018] [Accepted: 06/26/2018] [Indexed: 12/22/2022] Open
Abstract
Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.
Collapse
Affiliation(s)
- Dogu Baran Aydogan
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| |
Collapse
|
49
|
Schurr R, Duan Y, Norcia AM, Ogawa S, Yeatman JD, Mezer AA. Tractography optimization using quantitative T1 mapping in the human optic radiation. Neuroimage 2018; 181:645-658. [DOI: 10.1016/j.neuroimage.2018.06.060] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 06/03/2018] [Accepted: 06/20/2018] [Indexed: 12/31/2022] Open
|
50
|
Micheva KD, Chang EF, Nana AL, Seeley WW, Ting JT, Cobbs C, Lein E, Smith SJ, Weinberg RJ, Madison DV. Distinctive Structural and Molecular Features of Myelinated Inhibitory Axons in Human Neocortex. eNeuro 2018; 5:ENEURO.0297-18.2018. [PMID: 30406183 PMCID: PMC6220577 DOI: 10.1523/eneuro.0297-18.2018] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 09/02/2018] [Accepted: 09/10/2018] [Indexed: 11/21/2022] Open
Abstract
Numerous types of inhibitory neurons sculpt the performance of human neocortical circuits, with each type exhibiting a constellation of subcellular phenotypic features in support of its specialized functions. Axonal myelination has been absent among the characteristics used to distinguish inhibitory neuron types; in fact, very little is known about myelinated inhibitory axons in human neocortex. Here, using array tomography to analyze samples of neurosurgically excised human neocortex, we show that inhibitory myelinated axons originate predominantly from parvalbumin-containing interneurons. Compared to myelinated excitatory axons, they have higher neurofilament and lower microtubule content, shorter nodes of Ranvier, and more myelin basic protein (MBP) in their myelin sheath. Furthermore, these inhibitory axons have more mitochondria, likely to sustain the high energy demands of parvalbumin interneurons, as well as more 2',3'-cyclic nucleotide 3'-phosphodiesterase (CNP), a protein enriched in the myelin cytoplasmic channels that are thought to facilitate the delivery of nutrients from ensheathing oligodendrocytes. Our results demonstrate that myelinated axons of parvalbumin inhibitory interneurons exhibit distinctive features that may support the specialized functions of this neuron type in human neocortical circuits.
Collapse
Affiliation(s)
- Kristina D. Micheva
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, 94305
| | - Edward F. Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, 94143
| | - Alissa L. Nana
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, 94143
| | - William W. Seeley
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, 94143
- Department of Pathology, University of California San Francisco, San Francisco, CA, 94143
| | - Jonathan T. Ting
- Cell Types Program, Allen Institute for Brain Science, Seattle, WA, 98109
| | - Charles Cobbs
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA, 98122
| | - Ed Lein
- Cell Types Program, Allen Institute for Brain Science, Seattle, WA, 98109
| | - Stephen J Smith
- Cell Types Program, Allen Institute for Brain Science, Seattle, WA, 98109
| | - Richard J. Weinberg
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC 27599
| | - Daniel V. Madison
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, 94305
| |
Collapse
|