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Anbarasi J, Kumari R, Ganesh M, Agrawal R. Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights. BMC Neurol 2024; 24:364. [PMID: 39342171 PMCID: PMC11438080 DOI: 10.1186/s12883-024-03864-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 09/16/2024] [Indexed: 10/01/2024] Open
Abstract
Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimaging has led to extensive mapping of the brain functional and structural connectome on a macroscale level through modalities such as functional and diffusion MRI. In parallel, the healthcare field has witnessed a surge in the application of machine learning and artificial intelligence for diagnostics, especially in imaging. While reviews covering machine learn ing and macroscale connectomics exist for specific disorders, none provide an overview that captures their evolving role, especially through the lens of clinical application and translation. The applications include understanding disorders, classification, identifying neuroimaging biomarkers, assessing severity, predicting outcomes and intervention response, identifying potential targets for brain stimulation, and evaluating the effects of stimulation intervention on the brain and connectome mapping in patients before neurosurgery. The covered studies span neurodegenerative, neurodevelopmental, neuropsychiatric, and neurological disorders. Along with applications, the review provides a brief of common ML methods to set context. Conjointly, limitations in ML studies within connectomics and strategies to mitigate them have been covered.
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Affiliation(s)
- Janova Anbarasi
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India
| | - Radha Kumari
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India
| | - Malvika Ganesh
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India
| | - Rimjhim Agrawal
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India.
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Wang MB, Rahmani F, Benzinger TLS, Raji CA. Edge Density Imaging Identifies White Matter Biomarkers of Late-Life Obesity and Cognition. Aging Dis 2024; 15:1899-1912. [PMID: 37196133 PMCID: PMC11272213 DOI: 10.14336/ad.2022.1210] [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: 08/13/2022] [Accepted: 12/10/2022] [Indexed: 05/19/2023] Open
Abstract
Alzheimer disease (AD) and obesity are related to disruptions in the white matter (WM) connectome. We examined the link between the WM connectome and obesity and AD through edge-density imaging/index (EDI), a tractography-based method that characterizes the anatomical embedding of tractography connections. A total of 60 participants, 30 known to convert from normal cognition or mild-cognitive impairment to AD within a minimum of 24 months of follow up, were selected from the Alzheimer disease Neuroimaging Initiative (ADNI). Diffusion-weighted MR images from the baseline scans were used to extract fractional anisotropy (FA) and EDI maps that were subsequently averaged using deterministic WM tractography based on the Desikan-Killiany atlas. Multiple linear and logistic regression analysis were used to identify the weighted sum of tract-specific FA or EDI indices that maximized correlation to body-mass-index (BMI) or conversion to AD. Participants from the Open Access Series of Imaging Studies (OASIS) were used as an independent validation for the BMI findings. The edge-density rich, periventricular, commissural and projection fibers were among the most important WM tracts linking BMI to FA as well as to EDI. WM fibers that contributed significantly to the regression model related to BMI overlapped with those that predicted conversion; specifically in the frontopontine, corticostriatal, and optic radiation pathways. These results were replicated by testing the tract-specific coefficients found using ADNI in the OASIS-4 dataset. WM mapping with EDI enables identification of an abnormal connectome implicated in both obesity and conversion to AD.
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Affiliation(s)
- Maxwell Bond Wang
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
- Medical Scientist Training Program, University of Pittsburgh/Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Farzaneh Rahmani
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, Missouri, USA.
| | - Tammie L. S Benzinger
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, Missouri, USA.
| | - Cyrus A Raji
- Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA.
- Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC), Washington University, St. Louis, Missouri, USA.
- Department of Neurology, Washington University in Saint Louis, St. Louis, Missouri, USA
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Cai LT, Moon J, Camacho PB, Anderson AT, Chwa WJ, Sutton BP, Markowitz AJ, Palacios EM, Rodriguez A, Manley GT, Shankar S, Bremer PT, Mukherjee P, Madduri RK. MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline. Neuroinformatics 2024; 22:177-191. [PMID: 38446357 DOI: 10.1007/s12021-024-09650-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/29/2024] [Indexed: 03/07/2024]
Abstract
Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .
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Affiliation(s)
- Lanya T Cai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA
| | - Joseph Moon
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Paul B Camacho
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N Mathews Ave, Urbana, IL, 61801, USA
| | - Aaron T Anderson
- Beckman Institute, University of Illinois at Urbana-Champaign, 405 N Mathews Ave, Urbana, IL, 61801, USA
| | - Won Jong Chwa
- Department of Radiology, Washington University in St. Louis, 510 S Kingshighway Blvd, St. Louis, MO, 63110, USA
| | - Bradley P Sutton
- Bioengineering Department, University of Illinois at Urbana-Champaign, 506 S Mathews Ave, Urbana, IL, 61801, USA
| | - Amy J Markowitz
- Department of Neurosurgery, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Eva M Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA
| | - Alexis Rodriguez
- Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA
| | - Geoffrey T Manley
- Department of Neurosurgery, University of California, San Francisco, 400 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Shivsundaram Shankar
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Peer-Timo Bremer
- Lawrence Livermore National Laboratory, 7000 East Ave, Livermore, CA, 94550, USA
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA.
| | - Ravi K Madduri
- Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL, 60439, USA.
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Jang WH, Lee SH. Diffusion tensor imaging of the brain in children with sensory processing disorder: A review. J Neuroimaging 2024; 34:167-178. [PMID: 38183169 DOI: 10.1111/jon.13186] [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: 07/11/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/07/2024] Open
Abstract
Sensory processing disorder (SPD) is a clinical condition characterized by difficulties in the neurological processes of registering, discriminating, organizing, and responding to various sensory sensations. This study aimed to review the association between impaired white matter (WM) tract structure and neurofunctional deficits in children with SPD using diffusion tensor imaging (DTI). A comprehensive literature search was conducted using the online databases Google Scholar and PubMed (from 2010 to July 2023), resulting in the selection of nine relevant studies. Findings revealed that the splenium of the corpus callosum (SCC), superior longitudinal fasciculus (SLF), posterior corona radiata (PCR), and posterior thalamic radiation (PTR) exhibited reduced microstructural integrity, strongly associated with SPD. Specifically, auditory over-responsivity, a subtype of SPD, was linked to impaired integrity of the PCR, PTR, anterior corona radiata, and SLF. Tactile over-responsivity (TOR) was correlated with markers of decreased integrity in the SCC, superior corona radiata, and left PTR. Among the DTI parameters, decreased fractional anisotropy (FA) emerged as the most reliable factor for identifying SPD, followed by increased radial diffusivity (RD) and mean diffusivity (MD). Notably, significant correlations were observed between with auditory over-responsivity and TOR with the DTI parameters (positive for FA and negative for RD and MD). Overall, this review confirms the impaired integrity of specific WM tracts in children with SPD and establishes correlations between DTI parameters and neurobehavioral deficits associated with the disorder. The insights gained from this review contribute to a better understanding of SPD and hold clinical implications for its diagnosis and treatment.
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Affiliation(s)
- Woo-Hyuk Jang
- Department of Occupational Therapy, College of Health Science, Kangwon National University, Samcheok-si, Republic of Korea
| | - Seon-Hee Lee
- Department of Occupational Therapy, College of Health Science, Kangwon National University, Samcheok-si, Republic of Korea
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Weber CF, Lake EMR, Haider SP, Mozayan A, Bobba PS, Mukherjee P, Scheinost D, Constable RT, Ment L, Payabvash S. Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes. Front Neurosci 2023; 17:1285396. [PMID: 38075286 PMCID: PMC10702224 DOI: 10.3389/fnins.2023.1285396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 10/30/2023] [Indexed: 02/12/2024] Open
Abstract
Introduction Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan. Methods We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds. Results In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults. Discussion We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.
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Affiliation(s)
- Clara F Weber
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
- Social Neuroscience Lab, Department of Psychiatry and Psychotherapy, Lübeck University, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), Lübeck University, Lübeck, Germany
| | - Evelyn M R Lake
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
| | - Stefan P Haider
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
- Department of Otorhinolaryngology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Ali Mozayan
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
| | - Pratheek S Bobba
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Dustin Scheinost
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
| | - Robert T Constable
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
| | - Laura Ment
- Yale University School of Medicine, Department of Pediatrics and Neurology, New Haven, CT, United States
| | - Seyedmehdi Payabvash
- Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT, United States
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Weber CF, Lake EMR, Haider SP, Mozayan A, Mukherjee P, Scheinost D, Bamford NS, Ment L, Constable T, Payabvash S. Age-dependent white matter microstructural disintegrity in autism spectrum disorder. Front Neurosci 2022; 16:957018. [PMID: 36161157 PMCID: PMC9490315 DOI: 10.3389/fnins.2022.957018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
There has been increasing evidence of White Matter (WM) microstructural disintegrity and connectome disruption in Autism Spectrum Disorder (ASD). We evaluated the effects of age on WM microstructure by examining Diffusion Tensor Imaging (DTI) metrics and connectome Edge Density (ED) in a large dataset of ASD and control patients from different age cohorts. N = 583 subjects from four studies from the National Database of Autism Research were included, representing four different age groups: (1) A Longitudinal MRI Study of Infants at Risk of Autism [infants, median age: 7 (interquartile range 1) months, n = 155], (2) Biomarkers of Autism at 12 months [toddlers, 32 (11)m, n = 102], (3) Multimodal Developmental Neurogenetics of Females with ASD [adolescents, 13.1 (5.3) years, n = 230], (4) Atypical Late Neurodevelopment in Autism [young adults, 19.1 (10.7)y, n = 96]. For each subject, we created Fractional Anisotropy (FA), Mean- (MD), Radial- (RD), and Axial Diffusivity (AD) maps as well as ED maps. We performed voxel-wise and tract-based analyses to assess the effects of age, ASD diagnosis and sex on DTI metrics and connectome ED. We also optimized, trained, tested, and validated different combinations of machine learning classifiers and dimensionality reduction algorithms for prediction of ASD diagnoses based on tract-based DTI and ED metrics. There is an age-dependent increase in FA and a decline in MD and RD across WM tracts in all four age cohorts, as well as an ED increase in toddlers and adolescents. After correction for age and sex, we found an ASD-related decrease in FA and ED only in adolescents and young adults, but not in infants or toddlers. While DTI abnormalities were mostly limited to the corpus callosum, connectomes showed a more widespread ASD-related decrease in ED. Finally, the best performing machine-leaning classification model achieved an area under the receiver operating curve of 0.70 in an independent validation cohort. Our results suggest that ASD-related WM microstructural disintegrity becomes evident in adolescents and young adults-but not in infants and toddlers. The ASD-related decrease in ED demonstrates a more widespread involvement of the connectome than DTI metrics, with the most striking differences being localized in the corpus callosum.
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Affiliation(s)
- Clara F. Weber
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States,Social Neuroscience Lab, Department of Psychiatry and Psychotherapy, Lübeck University, Lübeck, Germany
| | - Evelyn M. R. Lake
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Stefan P. Haider
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States,Department of Otorhinolaryngology, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Ali Mozayan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Nigel S. Bamford
- Departments of Pediatrics, Neurology, Cellular and Molecular Physiology, Yale University, New Haven, CT, United States
| | - Laura Ment
- Departments of Pediatrics, Neurology, Cellular and Molecular Physiology, Yale University, New Haven, CT, United States
| | - Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States
| | - Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States,*Correspondence: Seyedmehdi Payabvash,
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Favaretto C, Allegra M, Deco G, Metcalf NV, Griffis JC, Shulman GL, Brovelli A, Corbetta M. Subcortical-cortical dynamical states of the human brain and their breakdown in stroke. Nat Commun 2022; 13:5069. [PMID: 36038566 PMCID: PMC9424299 DOI: 10.1038/s41467-022-32304-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 07/25/2022] [Indexed: 11/17/2022] Open
Abstract
The mechanisms controlling dynamical patterns in spontaneous brain activity are poorly understood. Here, we provide evidence that cortical dynamics in the ultra-slow frequency range (<0.01–0.1 Hz) requires intact cortical-subcortical communication. Using functional magnetic resonance imaging (fMRI) at rest, we identify Dynamic Functional States (DFSs), transient but recurrent clusters of cortical and subcortical regions synchronizing at ultra-slow frequencies. We observe that shifts in cortical clusters are temporally coincident with shifts in subcortical clusters, with cortical regions flexibly synchronizing with either limbic regions (hippocampus/amygdala), or subcortical nuclei (thalamus/basal ganglia). Focal lesions induced by stroke, especially those damaging white matter connections between basal ganglia/thalamus and cortex, provoke anomalies in the fraction times, dwell times, and transitions between DFSs, causing a bias toward abnormal network integration. Dynamical anomalies observed 2 weeks after stroke recover in time and contribute to explaining neurological impairment and long-term outcome. Favaretto et al. show that the brain rapidly alternates between transient connectivity patterns, with cortical regions flexibly synchronizing with two groups of subcortical regions, and that this dynamic is abnormal in stroke patients.
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Affiliation(s)
- Chiara Favaretto
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy. .,Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, 35128, Padova, Italy.
| | - Michele Allegra
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy.,Department of Physics and Astronomy "Galileo Galilei", University of Padova, via Marzolo 8, 35131, Padova, Italy.,Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain.,Institució Catalana de Recerca I Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010, Barcelona, Catalonia, Spain
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA.,Department of Radiology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padova, Italy. .,Department of Neuroscience (DNS), University of Padova, via Giustiniani 2, 35128, Padova, Italy. .,Department of Neurology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA. .,Department of Radiology, Washington University School of Medicine, 660S. Euclid Ave, St. Louis, MO, 63110, USA. .,VIMM, Venetian Institute of Molecular Medicine (VIMM), Biomedical Foundation, via Orus 2, 35129, Padova, Italy.
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Moon JY, Mukherjee P, Madduri RK, Markowitz AJ, Cai LT, Palacios EM, Manley GT, Bremer PT. The Case for Optimized Edge-Centric Tractography at Scale. Front Neuroinform 2022; 16:752471. [PMID: 35651721 PMCID: PMC9148990 DOI: 10.3389/fninf.2022.752471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.
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Affiliation(s)
- Joseph Y. Moon
- Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | | | - Amy J. Markowitz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Lanya T. Cai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Eva M. Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Geoffrey T. Manley
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Peer-Timo Bremer
- Lawrence Livermore National Laboratory, Livermore, CA, United States
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9
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Moody JF, Adluru N, Alexander AL, Field AS. The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI. Semin Ultrasound CT MR 2021; 42:507-522. [PMID: 34537118 DOI: 10.1053/j.sult.2021.07.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A comprehensive mapping of the structural and functional circuitry of the brain is a major unresolved problem in contemporary neuroimaging research. Diffusion-weighted and functional MRI have provided investigators with the capability to assess structural and functional connectivity in-vivo, driven primarily by methods of white matter tractography and resting-state fMRI, respectively. These techniques have paved the way for the construction of the functional and structural connectomes, which are quantitative representations of brain architecture as neural networks, comprised of nodes and edges. The connectomes, typically depicted as matrices or graphs, possess topological properties that inherently characterize the strength, efficiency, and organization of the connections between distinct brain regions. Graph theory, a general mathematical framework for analyzing networks, can be implemented to derive metrics from the connectomes that are sensitive to changes in brain connectivity associated with age, sex, cognitive function, and disease. These quantities can be assessed at either the global (whole brain) or local levels, allowing for the identification of distinct regional connectivity hubs and associated localized brain networks, which together serve crucial roles in establishing the structural and functional architecture of the brain. As a result, structural and functional connectomes have each been employed to study the brain circuitry underlying early brain development, neuroplasticity, developmental disorders, psychopathology, epilepsy, aging, neurodegenerative disorders, and traumatic brain injury. While these studies have yielded important insights into brain structure, function, and pathology, a precise description of the innate relationship between functional and structural networks across the brain remains unachieved. To date, connectome research has merely scratched the surface of potential clinical applications and related characterizations of brain-wide connectivity. Continued advances in diffusion and functional MRI acquisition, the delineation of functional and structural networks, and the quantification of neural network properties in specific brain regions, will be invaluable to future progress in neuroimaging science.
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Affiliation(s)
- Jason F Moody
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI; Department of Radiology, University of Wisconsin-Madison, Madison, WI
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Aaron S Field
- Department of Radiology, University of Wisconsin-Madison, Madison, WI.
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10
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Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs. Proc Natl Acad Sci U S A 2021; 118:2018784118. [PMID: 33941692 DOI: 10.1073/pnas.2018784118] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Hubs are highly connected brain regions important for coordinating processing in brain networks. It is unclear, however, which measures of network "hubness" are most useful in identifying brain regions critical to human cognition. We tested how closely two measures of hubness-edge density and participation coefficient, derived from white and gray matter, respectively-were associated with general cognitive impairment after brain damage in two large cohorts of patients with focal brain lesions (N = 402 and 102, respectively) using cognitive tests spanning multiple cognitive domains. Lesions disrupting white matter regions with high edge density were associated with cognitive impairment, whereas lesions damaging gray matter regions with high participation coefficient had a weaker, less consistent association with cognitive outcomes. Similar results were observed with six other gray matter hubness measures. This suggests that damage to densely connected white matter regions is more cognitively impairing than similar damage to gray matter hubs, helping to explain interindividual differences in cognitive outcomes after brain damage.
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Du J, Zhu H, Zhou J, Lu P, Qiu Y, Yu L, Cao W, Zhi N, Yang J, Xu Q, Sun J, Zhou Y. Structural Brain Network Disruption at Preclinical Stage of Cognitive Impairment Due to Cerebral Small Vessel Disease. Neuroscience 2020; 449:99-115. [PMID: 32896599 DOI: 10.1016/j.neuroscience.2020.08.037] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 08/26/2020] [Accepted: 08/27/2020] [Indexed: 11/25/2022]
Abstract
Cerebral small vessel disease (CSVD) is a common disease among elderly individuals and recognized as a major cause of vascular cognitive impairment. Recent studies demonstrated that CSVD is a disconnection syndrome. However, due to the covert neurological symptoms and subtle changes in clinical performance, the connection alterations during the stage of preclinical cognitive impairment (PCI) and mild cognitive impairment (MCI) are usually neglected and still largely unknown. Using diffusion tensor imaging (DTI), we investigated the early structural network changes in PCI and MCI patients. The PCI group demonstrated well preserved rich-club organization, less nodal strength loss, and disruption of connections centered in the feeder and local connections. Nevertheless, the MCI group manifested a disruption in the rich-club organization, a worse nodal strength loss especially in hub nodes, and an overall disturbance in rich-club, feeder and local connections. Moreover, in all CSVD patients, the strength of the rich-club, feeder and local connections was significantly correlated with multiple cognitive scores, especially in attention, executive, and memory domains; while in MCI patients, only the strength of the rich-club connections was significantly correlated with cognition. Furthermore, based on the network-based statistic analysis, we also identified distinct network component disruption pattern between the PCI group and the MCI group, validating the results described above. These results suggest a disruption pattern from peripheral to central connections with the change of cognitive status, shedding light on the early identification and the underlying disruption of CSVD.
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Affiliation(s)
- Jing Du
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Health Management Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Hong Zhu
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie Zhou
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Peiwen Lu
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Health Management Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Yage Qiu
- Department of Radiology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Ling Yu
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Wenwei Cao
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Nan Zhi
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Jie Yang
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Health Management Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China
| | - Qun Xu
- Renji-UNSW CHeBA Neurocognitive Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China; Department of Health Management Center, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Junfeng Sun
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Yan Zhou
- Department of Radiology, Renji Hospital, Medical School of Shanghai Jiao Tong University, Shanghai 200127, China.
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Qi X, Arfanakis K. Regionconnect: Rapidly extracting standardized brain connectivity information in voxel-wise neuroimaging studies. Neuroimage 2020; 225:117462. [PMID: 33075560 PMCID: PMC7811895 DOI: 10.1016/j.neuroimage.2020.117462] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 08/03/2020] [Accepted: 10/09/2020] [Indexed: 02/06/2023] Open
Abstract
Reporting white matter findings in voxel-wise neuroimaging studies typically lacks specificity in terms of brain connectivity. Therefore, the purpose of this work was to develop an approach for rapidly extracting standardized brain connectivity information for white matter regions with significant findings in voxel-wise neuroimaging studies. The new approach was named regionconnect and is based on precalculated average healthy adult brain connectivity information stored in standard space in a fashion that allows fast retrieval and integration. Towards this goal, the present work first generated and evaluated the white matter connectome of the IIT Human Brain Atlas v.5.0. It was demonstrated that the edges of the atlas connectome are representative of those of individual participants of the Human Connectome Project in terms of the spatial organization of streamlines and spatial patterns of track-density. Next, the new white matter connectome was used to develop multi-layer, connectivity-based labels for each white matter voxel of the atlas, consistent with the fact that each voxel may contain axons from multiple connections. The regionconnect algorithm was then developed to rapidly integrate information contained in the multi-layer labels across voxels of a white matter region and to generate a list of the most probable connections traversing that region. Usage of regionconnect does not require high angular resolution diffusion MRI or any MRI data. The regionconnect algorithm as well as the white matter tractogram and connectome, multi-layer, connectivity-based labels, and associated resources developed for the IIT Human Brain Atlas v.5.0 in this work are available at www.nitrc.org/projects/iit. An interactive, online version of regionconnect is also available at www.iit.edu/~mri.
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Affiliation(s)
- Xiaoxiao Qi
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States; Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, United States.
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Multivariate Lesion-Behavior Mapping of General Cognitive Ability and Its Psychometric Constituents. J Neurosci 2020; 40:8924-8937. [PMID: 33046547 DOI: 10.1523/jneurosci.1415-20.2020] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 09/15/2020] [Accepted: 10/01/2020] [Indexed: 01/09/2023] Open
Abstract
General cognitive ability, or general intelligence (g), is central to cognitive science, yet the processes that constitute it remain unknown, in good part because most prior work has relied on correlational methods. Large-scale behavioral and neuroanatomical data from neurologic patients with focal brain lesions can be leveraged to advance our understanding of the key mechanisms of g, as this approach allows inference on the independence of cognitive processes along with elucidation of their respective neuroanatomical substrates. We analyzed behavioral and neuroanatomical data from 402 humans (212 males; 190 females) with chronic, focal brain lesions. Structural equation models (SEMs) demonstrated a psychometric isomorphism between g and working memory in our sample (which we refer to as g/Gwm), but not between g and other cognitive abilities. Multivariate lesion-behavior mapping analyses indicated that g and working memory localize most critically to a site of converging white matter tracts deep to the left temporo-parietal junction. Tractography analyses demonstrated that the regions in the lesion-behavior map of g/Gwm were primarily associated with the arcuate fasciculus. The anatomic findings were validated in an independent cohort of acute stroke patients (n = 101) using model-based predictions of cognitive deficits generated from the Iowa cohort lesion-behavior maps. The neuroanatomical localization of g/Gwm provided the strongest prediction of observed g in the new cohort (r = 0.42, p < 0.001), supporting the anatomic specificity of our findings. These results provide converging behavioral and anatomic evidence that working memory is a key mechanism contributing to domain-general cognition.SIGNIFICANCE STATEMENT General cognitive ability (g) is thought to play an important role in individual differences in adaptive behavior, yet its core processes remain unknown, in large part because of difficulties in making causal inferences from correlated data. Using data from patients with focal brain damage, we demonstrate that there is a strong psychometric correspondence between g and working memory - the ability to maintain and control mental information, and that the critical neuroanatomical substrates of g and working memory include the arcuate fasciculus. This work provides converging behavioral and neuroanatomical evidence that working memory is a key mechanism contributing to domain-general cognition.
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Connectome mapping with edge density imaging differentiates pediatric mild traumatic brain injury from typically developing controls: proof of concept. Pediatr Radiol 2020; 50:1594-1601. [PMID: 32607611 PMCID: PMC7501221 DOI: 10.1007/s00247-020-04743-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 04/26/2020] [Accepted: 05/24/2020] [Indexed: 01/06/2023]
Abstract
BACKGROUND Although acute neurologic impairment might be transient, other long-term effects can be observed with mild traumatic brain injury. However, when pediatric patients with mild traumatic brain injury present for medical care, conventional imaging with CT and MR imaging often does not reveal abnormalities. OBJECTIVE To determine whether edge density imaging can separate pediatric mild traumatic brain injury from typically developing controls. MATERIALS AND METHODS Subjects were recruited as part of the "Therapeutic Resources for Attention Improvement using Neuroimaging in Traumatic Brain Injury" (TRAIN-TBI) study. We included 24 adolescents (χ=14.1 years of age, σ=1.6 years, range 10-16 years), 14 with mild traumatic brain injury (TBI) and 10 typically developing controls. Neurocognitive assessments included the pediatric version of the California Verbal Learning Test (CVLT) and the Attention Network Task (ANT). Diffusion MR imaging was acquired on a 3-tesla (T) scanner. Edge density images were computed utilizing fiber tractography. Principal component analysis (PCA) and support vector machines (SVM) were used in an exploratory analysis to separate mild TBI and control groups. The diagnostic accuracy of edge density imaging, neurocognitive tests, and fractional anisotropy (FA) from diffusion tensor imaging (DTI) was computed with two-sample t-tests and receiver operating characteristic (ROC) metrics. RESULTS Support vector machine-principal component analysis of edge density imaging maps identified three white matter regions distinguishing pediatric mild TBI from controls. The bilateral tapetum, sagittal stratum, and callosal splenium identified mild TBI subjects with sensitivity of 79% and specificity of 100%. Accuracy from the area under the ROC curve (AUC) was 94%. Neurocognitive testing provided an AUC of 61% (CVLT) and 71% (ANT). Fractional anisotropy yielded an AUC of 48%. CONCLUSION In this proof-of-concept study, we show that edge density imaging is a new form of connectome mapping that provides better diagnostic delineation between pediatric mild TBI and healthy controls than DTI or neurocognitive assessments of memory or attention.
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15
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Rane S, Owen J, Hippe DS, Cholerton B, Zabetian CP, Montine T, Grabowski TJ. White Matter Lesions in Mild Cognitive Impairment and Idiopathic Parkinson's Disease: Multimodal Advanced MRI and Cognitive Associations. J Neuroimaging 2020; 30:843-850. [PMID: 32937003 DOI: 10.1111/jon.12778] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 08/13/2020] [Indexed: 01/08/2023] Open
Abstract
Cerebrovascular disease is a common comorbidity in older adults, typically assessed in terms of white matter hyperintensities (WMHs) on MRI. While it is well known that WMHs exacerbate cognitive symptoms, the exact relation of WMHs with cognitive performance and other degenerative diseases is unknown. Furthermore, based on location, WMHs are often classified into periventricular and deep WMHs and are believed to have different pathological origins. Whether the two types of WMHs influence cognition differently is unclear. Using regression models, we assessed the independent association of these two types of WMHs with cognitive performance in two separate studies focused on distinct degenerative diseases, early Alzheimer's (mild cognitive impairment), and Parkinson's disease. We further tested if the two types of WMHs were differentially associated with reduced cortical cerebral blood flow (CBF) as measured by arterial spin labeling and increased mean diffusivity (MD, a marker of tissue injury) as measured by diffusion imaging. Our approach revealed that both deep and periventricular WMHs were associated with poor performance on tests of global cognition (Montreal cognitive Assessment, MoCA), task processing (Trail making test), and category fluency in the study of mild cognitive impairment. They were associated with poor performance in global cognition (MoCA) and category fluency in the Parkinson's disease study. Of note, more associations were detected between cognitive performance and deep WMHs than between cognitive performance and periventricular WMHs. Mechanistically, both deep and periventricular WMHs were associated with increased MD. Both deep and periventricular WMHs were also associated with reduced CBF in the gray matter.
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Affiliation(s)
- Swati Rane
- Department of Radiology, Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA
| | - Julia Owen
- Department of Radiology, Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA
| | - Daniel S Hippe
- Department of Radiology, Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA
| | | | - Cyrus P Zabetian
- Veterans Affairs Puget Sound Health Care System, Seattle, WA.,Department of Neurology, University of Washington Medical Center, Seattle, WA
| | - Tom Montine
- Department of Pathology, Stanford University, Stanford, CA
| | - Thomas J Grabowski
- Department of Radiology, Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA.,Department of Neurology, University of Washington Medical Center, Seattle, WA
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16
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Palacios EM, Owen JP, Yuh EL, Wang MB, Vassar MJ, Ferguson AR, Diaz-Arrastia R, Giacino JT, Okonkwo DO, Robertson CS, Stein MB, Temkin N, Jain S, McCrea M, MacDonald CL, Levin HS, Manley GT, Mukherjee P. The evolution of white matter microstructural changes after mild traumatic brain injury: A longitudinal DTI and NODDI study. SCIENCE ADVANCES 2020; 6:eaaz6892. [PMID: 32821816 PMCID: PMC7413733 DOI: 10.1126/sciadv.aaz6892] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 06/26/2020] [Indexed: 05/11/2023]
Abstract
Neuroimaging biomarkers that can detect white matter (WM) pathology after mild traumatic brain injury (mTBI) and predict long-term outcome are needed to improve care and develop therapies. We used diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to investigate WM microstructure cross-sectionally and longitudinally after mTBI and correlate these with neuropsychological performance. Cross-sectionally, early decreases of fractional anisotropy and increases of mean diffusivity corresponded to WM regions with elevated free water fraction on NODDI. This elevated free water was more extensive in the patient subgroup reporting more early postconcussive symptoms. The longer-term longitudinal WM changes consisted of declining neurite density on NODDI, suggesting axonal degeneration from diffuse axonal injury for which NODDI is more sensitive than DTI. Therefore, NODDI is a more sensitive and specific biomarker than DTI for WM microstructural changes due to mTBI that merits further study for mTBI diagnosis, prognosis, and treatment monitoring.
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Affiliation(s)
- E. M. Palacios
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - J. P. Owen
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - E. L. Yuh
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
| | - M. B. Wang
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - M. J. Vassar
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - A. R. Ferguson
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - R. Diaz-Arrastia
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - J. T. Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Charlestown, MA, USA
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, USA
| | - D. O. Okonkwo
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - C. S. Robertson
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - M. B. Stein
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Family Medicine & Public Health, University of California, San Diego, La Jolla, CA, USA
| | - N. Temkin
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - S. Jain
- Department of Family Medicine & Public Health, University of California, San Diego, La Jolla, CA, USA
| | - M. McCrea
- Departments of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - C. L. MacDonald
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - H. S. Levin
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - G. T. Manley
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - P. Mukherjee
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Corresponding author.
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17
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Zhang F, Wu Y, Norton I, Rathi Y, Golby AJ, O'Donnell LJ. Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering. Hum Brain Mapp 2019; 40:3041-3057. [PMID: 30875144 PMCID: PMC6548665 DOI: 10.1002/hbm.24579] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 02/28/2019] [Accepted: 03/07/2019] [Indexed: 01/22/2023] Open
Abstract
There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Ye Wu
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Isaiah Norton
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Yogesh Rathi
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
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Payabvash S, Palacios EM, Owen JP, Wang MB, Tavassoli T, Gerdes M, Brandes-Aitken A, Marco EJ, Mukherjee P. Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers. Neuroimage Clin 2019; 23:101831. [PMID: 31035231 PMCID: PMC6488562 DOI: 10.1016/j.nicl.2019.101831] [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: 11/11/2018] [Revised: 03/22/2019] [Accepted: 04/18/2019] [Indexed: 12/11/2022]
Abstract
The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), we applied probabilistic tractography to generate edge density (ED) and track density (TD) from DTI maps. For identification of children with SPD, accurate classification rates from a combination of DTI microstructural (FA, MD, AD, and RD), connectivity (TD) and connectomic (ED) metrics with different machine learning algorithms - including naïve Bayes, random forest, support vector machine, and neural networks - were determined. In voxel-wise analysis, children with SPD had lower FA, ED, and TD but higher MD and RD compared to TDC - predominantly in posterior white matter tracts including posterior corona radiata, posterior thalamic radiation, and posterior body and splenium of corpus callosum. In stepwise penalized logistic regression, the only independent variable distinguishing children with SPD from TDC was the average TD in the splenium (p < 0.001). Among different combinations of machine learning algorithms and DTI/connectivity metrics, random forest models using tract-based TD yielded the highest accuracy in classification of SPD - 77.5% accuracy, 73.8% sensitivity, and 81.6% specificity. Our findings demonstrate impaired microstructural and connectivity/connectomic integrity in children with SPD, predominantly in posterior white matter tracts, and with reduced TD of the splenium of corpus callosum as the most distinctive pattern. Applying machine learning algorithms, these connectivity metrics can be used to devise novel imaging biomarkers for neurodevelopmental disorders.
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Affiliation(s)
- Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States of America; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
| | - Eva M Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
| | - Julia P Owen
- Department of Radiology, University of Washington, Seattle, WA, United States of America
| | - Maxwell B Wang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America
| | - Teresa Tavassoli
- Department of Psychology and Clinical Sciences, University of Reading, Reading, United Kingdom
| | - Molly Gerdes
- Department of Neurology, University of California, San Francisco, CA, United States of America
| | - Anne Brandes-Aitken
- Department of Applied Psychology, New York University, New York, NY, United States of America
| | - Elysa J Marco
- Department of Neurology, University of California, San Francisco, CA, United States of America; Department of Pediatric Neurology, Cortica Healthcare, San Rafael, CA, United States of America
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States of America; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, United States of America.
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19
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Payabvash S, Palacios EM, Owen JP, Wang MB, Tavassoli T, Gerdes M, Brandes-Aitken A, Mukherjee P, Marco EJ. White Matter Connectome Correlates of Auditory Over-Responsivity: Edge Density Imaging and Machine-Learning Classifiers. Front Integr Neurosci 2019; 13:10. [PMID: 30983979 PMCID: PMC6450221 DOI: 10.3389/fnint.2019.00010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 03/13/2019] [Indexed: 01/25/2023] Open
Abstract
Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8–12 years. In addition to conventional diffusion tensor imaging (DTI) maps – including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps. The tract-based spatial statistics was used for voxel-wise comparison of diffusion and ED maps. Then, stepwise penalized logistic regression was applied to identify independent variable (s) predicting AOR, as potential imaging biomarker (s) for AOR. Finally, we compared different combinations of machine learning algorithms (i.e., naïve Bayes, random forest, and support vector machine (SVM) and tract-based DTI/connectome metrics for classification of children with AOR. In direct sensory phenotype assessment, 15 (out of 39) boys exhibited AOR (with or without neurodevelopmental concerns). Voxel-wise analysis demonstrates extensive impairment of white matter microstructural integrity in children with AOR on DTI maps – evidenced by lower FA and higher MD and RD; moreover, there was lower connectome ED in anterior-superior corona radiata, genu and body of corpus callosum. In stepwise logistic regression, the average FA of left superior longitudinal fasciculus (SLF) was the single independent variable distinguishing children with AOR (p = 0.007). Subsequently, the left SLF average FA yielded an area under the curve of 0.756 in receiver operating characteristic analysis for prediction of AOR (p = 0.008) as a region-of-interest (ROI)-based imaging biomarker. In comparative study of different combinations of machine-learning models and DTI/ED metrics, random forest algorithms using ED had higher accuracy for AOR classification. Our results demonstrate extensive white matter microstructural impairment in children with AOR, with specifically lower connectomic ED in anterior-superior tracts and associated commissural pathways. Also, average FA of left SLF can be applied as ROI-based imaging biomarker for prediction of SOR. Finally, machine-learning models can provide accurate and objective image-based classifiers for identification of children with AOR based on white matter tracts connectome ED.
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Affiliation(s)
- Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Eva M Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Julia P Owen
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Maxwell B Wang
- University of Pittsburg School of Medicine, Pittsburgh, PA, United States
| | - Teresa Tavassoli
- Department of Psychology and Clinical Sciences, University of Reading, Reading, United Kingdom
| | - Molly Gerdes
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Annie Brandes-Aitken
- Department of Applied Psychology, New York University, New York, NY, United States
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.,Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States
| | - Elysa J Marco
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.,Department of Pediatric Neurology, Cortica Healthcare, San Rafael, CA, United States
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Payabvash S, Palacios EM, Owen JP, Wang MB, Tavassoli T, Gerdes M, Brandes-Aitken A, Cuneo D, Marco EJ, Mukherjee P. White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models. Brain Connect 2019; 9:209-220. [PMID: 30661372 PMCID: PMC6444925 DOI: 10.1089/brain.2018.0658] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Prior neuroimaging studies have reported white matter network underconnectivity as a potential mechanism for autism spectrum disorder (ASD). In this study, we examined the structural connectome of children with ASD using edge density imaging (EDI), and then applied machine-learning algorithms to identify children with ASD based on tract-based connectivity metrics. Boys aged 8-12 years were included: 14 with ASD and 33 typically developing children. The edge density (ED) maps were computed from probabilistic streamline tractography applied to high angular resolution diffusion imaging. Tract-based spatial statistics was used for voxel-wise comparison and coregistration of ED maps in addition to conventional diffusion tensor imaging (DTI) metrics of fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Tract-based average DTI/connectome metrics were calculated and used as input for different machine-learning models: naïve Bayes, random forest, support vector machines (SVMs), and neural networks. For these models, cross-validation was performed with stratified random sampling ( × 1,000 permutations). The average accuracy among validation samples was calculated. In voxel-wise analysis, the body and splenium of corpus callosum, bilateral superior and posterior corona radiata, and left superior longitudinal fasciculus showed significantly lower ED in children with ASD; whereas, we could not find significant difference in FA, MD, and RD maps between the two study groups. Overall, machine-learning models using tract-based ED metrics had better performance in identification of children with ASD compared with those using FA, MD, and RD. The EDI-based random forest models had greater average accuracy (75.3%), specificity (97.0%), and positive predictive value (81.5%), whereas EDI-based polynomial SVM had greater sensitivity (51.4%) and negative predictive values (77.7%). In conclusion, we found reduced density of connectome edges in the posterior white matter tracts of children with ASD, and demonstrated the feasibility of connectome-based machine-learning algorithms in identification of children with ASD.
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Affiliation(s)
- Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
- Department of Radiology, University of Washington, Seattle, Washington
| | - Eva M. Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Julia P. Owen
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
- University of Pittsburg School of Medicine, Pittsburgh, Pennsylvania
| | - Maxwell B. Wang
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
- Department of Neurology, University of California, San Francisco, San Francisco, California
| | - Teresa Tavassoli
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
| | - Molly Gerdes
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
| | - Anne Brandes-Aitken
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
| | - Daniel Cuneo
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
| | - Elysa J. Marco
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
- Department of Pediatrics, University of California, San Francisco, San Francisco, California
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California
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21
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Greene C, Cieslak M, Grafton ST. Effect of different spatial normalization approaches on tractography and structural brain networks. Netw Neurosci 2018; 2:362-380. [PMID: 30294704 PMCID: PMC6145854 DOI: 10.1162/netn_a_00035] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2017] [Accepted: 10/30/2017] [Indexed: 11/29/2022] Open
Abstract
To facilitate the comparison of white matter morphologic connectivity across target populations, it is invaluable to map the data to a standardized neuroanatomical space. Here, we evaluated direct streamline normalization (DSN), where the warping was applied directly to the streamlines, with two publically available approaches that spatially normalize the diffusion data and then reconstruct the streamlines. Prior work has shown that streamlines generated after normalization from reoriented diffusion data do not reliably match the streamlines generated in native space. To test the impact of these different normalization methods on quantitative tractography measures, we compared the reproducibility of the resulting normalized connectivity matrices and network metrics with those originally obtained in native space. The two methods that reconstruct streamlines after normalization led to significant differences in network metrics with large to huge standardized effect sizes, reflecting a dramatic alteration of the same subject’s native connectivity. In contrast, after normalizing with DSN we found no significant difference in network metrics compared with native space with only very small-to-small standardized effect sizes. DSN readily outperformed the other methods at preserving native space connectivity and introduced novel opportunities to define connectome networks without relying on gray matter parcellations. Direct streamline normalization (DSN) directly warps the streamlines into any template space by using the transformations output from Advanced Normalization Tools (ANTs). DSN overcomes the limitations of diffusion weighted images (DWI) spatial normalization. It allows DWIs to be acquired with any desired sampling scheme. Fiber orientation distributions (FODs) or orientation distribution functions (ODFs) can also be reconstructed using any desired method and streamlines generated using any algorithm. Most importantly, it avoids the problem of generating tracts from FODs or ODFs that have become distorted because of spatial normalization. Our results show that DSN has minimal influence on tractography measures such as tract count and structure and does not significantly alter structural networks with only very small to small effect sizes. We have developed a framework in Python that works with most diffusion software platforms. It is available at http://github.com/clintg6/DSN.
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Affiliation(s)
- Clint Greene
- Signal Compression Lab, Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA
| | - Matt Cieslak
- Action Lab, Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Scott T Grafton
- Action Lab, Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
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22
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Ouyang M, Dubois J, Yu Q, Mukherjee P, Huang H. Delineation of early brain development from fetuses to infants with diffusion MRI and beyond. Neuroimage 2018; 185:836-850. [PMID: 29655938 DOI: 10.1016/j.neuroimage.2018.04.017] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 04/01/2018] [Accepted: 04/08/2018] [Indexed: 02/08/2023] Open
Abstract
Dynamic macrostructural and microstructural changes take place from the mid-fetal stage to 2 years after birth. Delineating structural changes of the brain during early development provides new insights into the complicated processes of both typical development and the pathological mechanisms underlying various psychiatric and neurological disorders including autism, attention deficit hyperactivity disorder and schizophrenia. Decades of histological studies have identified strong spatial and functional maturation gradients in human brain gray and white matter. The recent improvements in magnetic resonance imaging (MRI) techniques, especially diffusion MRI (dMRI), relaxometry imaging, and magnetization transfer imaging (MTI) have provided unprecedented opportunities to non-invasively quantify and map the early developmental changes at whole brain and regional levels. Here, we review the recent advances in understanding early brain structural development during the second half of gestation and the first two postnatal years using modern MR techniques. Specifically, we review studies that delineate the emergence and microstructural maturation of white matter tracts, as well as dynamic mapping of inhomogeneous cortical microstructural organization unique to fetuses and infants. These imaging studies converge into maturational curves of MRI measurements that are distinctive across different white matter tracts and cortical regions. Furthermore, contemporary models offering biophysical interpretations of the dMRI-derived measurements are illustrated to infer the underlying microstructural changes. Collectively, this review summarizes findings that contribute to charting spatiotemporally heterogeneous gray and white matter structural development, offering MRI-based biomarkers of typical brain development and setting the stage for understanding aberrant brain development in neurodevelopmental disorders.
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Affiliation(s)
- Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States
| | - Jessica Dubois
- INSERM, UMR992, CEA, NeuroSpin Center, University Paris Saclay, Gif-sur-Yvette, France
| | - Qinlin Yu
- Radiology Research, Children's Hospital of Philadelphia, PA, United States
| | - Pratik Mukherjee
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, United States
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, United States.
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23
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White matter microstructure predicts cognitive training-induced improvements in attention and executive functioning in schizophrenia. Schizophr Res 2018; 193:276-283. [PMID: 28689758 PMCID: PMC5999406 DOI: 10.1016/j.schres.2017.06.062] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 06/29/2017] [Accepted: 06/30/2017] [Indexed: 12/11/2022]
Abstract
We examined the relationship between white matter microstructure in schizophrenia using diffusion tensor imaging (DTI) and cognitive improvements induced by 70h (~16weeks) of cognitive training. We measured anatomical connectivity in 48 patients with schizophrenia (SZ) and 28 healthy control participants (HC) at baseline, and then examined the relationship between anatomical connectivity at baseline and training-induced cognitive gains in 30 SZ who performed diffusion imaging after completing 70h of training. Compared with healthy control participants, individuals with schizophrenia showed reduced white matter integrity at baseline, as indexed by fractional anisotropy metrics, in bilateral posterior corona radiata, bilateral retrolenticular internal capsules, bilateral posterior thalamic radiation, left anterior corona radiata, left superior longitudinal fasciculus, left sagittal stratum, right cerebral peduncle and the genu and splenium of the corpus callosum. After training, schizophrenia participants showed significant gains in attention/vigilance, speed of processing, verbal learning, visual learning and executive functioning. White matter integrity within the right fronto-occipital fasciculus predicted training-induced improvements in attention/vigilance, while white matter integrity within the right corticospinal tract and bilateral medial lemnisci predicted cognitive training-induced improvements in executive functioning, areas that did not show white matter tract deficits at baseline. These findings suggest that preserved white matter integrity connecting long-range prefrontal-thalamic-sensorimotor areas may be an important determinant for training-induced neurocognitive plasticity.
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24
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Probing the reproducibility of quantitative estimates of structural connectivity derived from global tractography. Neuroimage 2018; 175:215-229. [PMID: 29438843 DOI: 10.1016/j.neuroimage.2018.01.086] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2017] [Revised: 01/12/2018] [Accepted: 01/30/2018] [Indexed: 11/20/2022] Open
Abstract
As quantitative measures derived from fiber tractography are increasingly being used to characterize the structural connectivity of the brain, it is important to establish their reproducibility. However, no such information is as yet available for global tractography. Here we provide the first comprehensive analysis of the reproducibility of streamline counts derived from global tractography as quantitative estimates of structural connectivity. In a sample of healthy young adults scanned twice within one week, within-session and between-session test-retest reproducibility was estimated for streamline counts of connections based on regions of the AAL atlas using the intraclass correlation coefficient (ICC) for absolute agreement. We further evaluated the influence of the type of head-coil (12 versus 32 channels) and the number of reconstruction repetitions (reconstructing streamlines once or aggregated over ten repetitions). Factorial analyses demonstrated that reproducibility was significantly greater for within- than between-session reproducibility and significantly increased by aggregating streamline counts over ten reconstruction repetitions. Using a high-resolution head-coil incurred only small beneficial effects. Overall, ICC values were positively correlated with the streamline count of a connection. Additional analyses assessed the influence of different selection variants (defining fuzzy versus no fuzzy borders of the seed mask; selecting streamlines that end in versus pass through a seed) showing that an endpoint-based variant using fuzzy selection provides the best compromise between reproducibility and anatomical specificity. In sum, aggregating quantitative indices over repeated estimations and higher numbers of streamlines are important determinants of test-retest reproducibility. If these factors are taken into account, streamline counts derived from global tractography provide an adequately reproducible quantitative measure that can be used to gauge the structural connectivity of the brain in health and disease.
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25
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Wang MB, Owen JP, Mukherjee P, Raj A. Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease. PLoS Comput Biol 2017. [PMID: 28640803 PMCID: PMC5480812 DOI: 10.1371/journal.pcbi.1005550] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Recent research has demonstrated the use of the structural connectome as a powerful tool to characterize the network architecture of the brain and potentially generate biomarkers for neurologic and psychiatric disorders. In particular, the anatomic embedding of the edges of the cerebral graph have been postulated to elucidate the relative importance of white matter tracts to the overall network connectivity, explaining the varying effects of localized white matter pathology on cognition and behavior. Here, we demonstrate the use of a linear diffusion model to quantify the impact of these perturbations on brain connectivity. We show that the eigenmodes governing the dynamics of this model are strongly conserved between healthy subjects regardless of cortical and sub-cortical parcellations, but show significant, interpretable deviations in improperly developed brains. More specifically, we investigated the effect of agenesis of the corpus callosum (AgCC), one of the most common brain malformations to identify differences in the effect of virtual corpus callosotomies and the neurodevelopmental disorder itself. These findings, including the strong correspondence between regions of highest importance from graph eigenmodes of network diffusion and nexus regions of white matter from edge density imaging, show converging evidence toward understanding the relationship between white matter anatomy and the structural connectome. While the structural connectome of the brain has emerged as a powerful tool towards understanding the progression of neurologic and psychiatric disorders, links between the anatomy of connections within the brain and the effects of localized white matter pathology on cognition are still an active area of investigation. Here, we propose the use of the diffusion process towards understanding perturbations of brain connectivity. We find that while the dynamics of this process are strongly conserved in healthy subjects, they display significant, interpretable deviations in agenesis of the corpus callosum, one of the most common brain malformations. These findings, including the strong similarity between regions identified to be crucial towards diffusion and nexus regions of white matter from edge density imaging, show converging evidence towards understanding the relationship between white matter anatomy and the structural connectome.
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Affiliation(s)
- Maxwell B. Wang
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, United States of America
| | - Julia P. Owen
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, United States of America
| | - Pratik Mukherjee
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, United States of America
- Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, California, United States of America
| | - Ashish Raj
- Department of Radiology, Weill Cornell Medical College, New York, New York, United States of America
- * E-mail:
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26
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Liu K, Zhang T, Chu WCW, Mok VCT, Wang D, Shi L. Group comparison of cortical fiber connectivity map: An application between post-stroke patients and healthy subjects. Neuroscience 2017; 344:15-24. [PMID: 28039039 DOI: 10.1016/j.neuroscience.2016.12.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2016] [Revised: 12/04/2016] [Accepted: 12/18/2016] [Indexed: 10/20/2022]
Abstract
Structural connectome measurement combined with diffusion magnetic resonance imaging (MRI) and tractography allows generation of a whole-brain connectome. However, current cortical structural connectivity (SC) measurements have not been well combined with the vertex-wise multi-subjects statistical analysis. The aim of this study was to examine the feasibility of using group comparison vertex-wise analysis for cortical SC measurement. A fiber connectivity density (FiCD) method based on a combination of a diffusion fiber tracking technique and cortical surface-based analysis was used to measure the whole-brain cortical SC map (FiCD map). A public MRI dataset (GigaDB) was employed to evaluate the reproducibility of the FiCD method. For group comparison, 14 post-stroke patients (mean age, 68.36±7.33y) and 19 healthy participants (mean age, 66.84±8.58y) had FiCD measurement. The intergroup comparison of the FiCD map was performed using vertex-wise multi-subject statistical analysis. Reliability testing showed the mean intra- and inter-subject FiCD variability was 3.51±2.12% and 19.44±4.79%, respectively. The group comparison of the whole-brain FiCD identified cortical regions with altered FiCD values, and there was a spatial consistency between the cortical clusters with low FiCD values and the subcortical lesions of patients. This study demonstrated the feasibility of vertex-wise group comparison for evaluating cortical fiber connectivity density. The FiCD method has good intra- and inter-individual reproducibility, and accurately reflects the affected cortical regions in post-stroke patients. This method may be helpful for neuroscience research.
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Affiliation(s)
- Kai Liu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Teng Zhang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Vincent C T Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Shatin, New Territories, Hong Kong, China; Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
| | - Defeng Wang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China; Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China; Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.
| | - Lin Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Shatin, New Territories, Hong Kong, China; Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China; Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
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27
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Tuladhar AM, Lawrence A, Norris DG, Barrick TR, Markus HS, de Leeuw F. Disruption of rich club organisation in cerebral small vessel disease. Hum Brain Mapp 2016; 38:1751-1766. [PMID: 27935154 PMCID: PMC6866838 DOI: 10.1002/hbm.23479] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 11/13/2016] [Accepted: 11/16/2016] [Indexed: 11/07/2022] Open
Abstract
Cerebral small vessel disease (SVD) is an important cause of vascular cognitive impairment. Recent studies have demonstrated that structural connectivity of brain networks in SVD is disrupted. However, little is known about the extent and location of the reduced connectivity in SVD. Here they investigate the rich club organisation-a set of highly connected and interconnected regions-and investigate whether there is preferential rich club disruption in SVD. Diffusion tensor imaging (DTI) and cognitive assessment were performed in a discovery sample of SVD patients (n = 115) and healthy control subjects (n = 50). Results were replicated in an independent dataset (49 SVD with confluent WMH cases and 108 SVD controls) with SVD patients having a similar SVD phenotype to that of the discovery cases. Rich club organisation was examined in structural networks derived from DTI followed by deterministic tractography. Structural networks in SVD patients were less dense with lower network strength and efficiency. Reduced connectivity was found in SVD, which was preferentially located in the connectivity between the rich club nodes rather than in the feeder and peripheral connections, a finding confirmed in both datasets. In discovery dataset, lower rich club connectivity was associated with lower scores on psychomotor speed (β = 0.29, P < 0.001) and executive functions (β = 0.20, P = 0.009). These results suggest that SVD is characterized by abnormal connectivity between rich club hubs in SVD and provide evidence that abnormal rich club organisation might contribute to the development of cognitive impairment in SVD. Hum Brain Mapp 38:1751-1766, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Anil M. Tuladhar
- Department of NeurologyRadboud University Medical Center, Donders Institute for Brain, Cognition and BehaviourNijmegenThe Netherlands
- Centre for Cognitive NeuroimagingRadboud University, Donders Institute for Brain, Cognition and BehaviourNijmegenThe Netherlands
| | - Andrew Lawrence
- Department of Clinical Neurosciences, Neurology UnitUniversity of CambridgeCambridgeUnited Kingdom
| | - David. G. Norris
- Centre for Cognitive NeuroimagingRadboud University, Donders Institute for Brain, Cognition and BehaviourNijmegenThe Netherlands
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg‐EssenArendahls Wiese 199, Tor 3EssenD‐45141Germany
- MIRA Institute for Biomedical Technology and Technical Medicine, University of TwenteEnschedeThe Netherlands
| | - Thomas R. Barrick
- St. George's University of London, Neuroscience Research Centre, Cardiovascular and Cell Sciences Research InstituteLondonUnited Kingdom
| | - Hugh S. Markus
- Department of Clinical Neurosciences, Neurology UnitUniversity of CambridgeCambridgeUnited Kingdom
| | - Frank‐Erik de Leeuw
- Department of NeurologyRadboud University Medical Center, Donders Institute for Brain, Cognition and BehaviourNijmegenThe Netherlands
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28
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Bell PT, Shine JM. Subcortical contributions to large-scale network communication. Neurosci Biobehav Rev 2016; 71:313-322. [DOI: 10.1016/j.neubiorev.2016.08.036] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 08/29/2016] [Indexed: 01/20/2023]
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29
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Lang S. Cognitive eloquence in neurosurgery: Insight from graph theoretical analysis of complex brain networks. Med Hypotheses 2016; 98:49-56. [PMID: 28012604 DOI: 10.1016/j.mehy.2016.11.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Accepted: 11/22/2016] [Indexed: 12/19/2022]
Abstract
The structure and function of the brain can be described by complex network models, and the topological properties of these models can be quantified by graph theoretical analysis. This has given insight into brain regions, known as hubs, which are critical for integrative functioning and information transfer, both fundamental aspects of cognition. In this manuscript a hypothesis is put forward for the concept of cognitive eloquence in neurosurgery; that is regions (cortical, subcortical and white matter) of the brain which may not necessarily have readily identifiable neurological function, but if injured may result in disproportionate cognitive morbidity. To this end, the effects of neurosurgical resection on cognition is reviewed and an overview of the role of complex network analysis in the understanding of brain structure and function is provided. The literature describing network, behavioral, and cognitive effects resulting from lesions to, and disconnections of, centralized hub regions will be emphasized as evidence for the espousal of the concept of cognitive eloquence.
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Affiliation(s)
- Stefan Lang
- University of Calgary, Department of Clinical Neuroscience, Canada.
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30
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Owen JP, Wang MB, Mukherjee P. Periventricular White Matter Is a Nexus for Network Connectivity in the Human Brain. Brain Connect 2016; 6:548-57. [DOI: 10.1089/brain.2016.0431] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Julia P. Owen
- Department of Radiology, University of California, San Francisco, San Francisco, California
| | - Maxwell B. Wang
- Department of Radiology, University of California, San Francisco, San Francisco, California
| | - Pratik Mukherjee
- Department of Radiology, University of California, San Francisco, San Francisco, California
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31
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Xia M, Lin Q, Bi Y, He Y. Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain. Front Hum Neurosci 2016; 10:158. [PMID: 27148015 PMCID: PMC4835491 DOI: 10.3389/fnhum.2016.00158] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Accepted: 03/30/2016] [Indexed: 11/20/2022] Open
Abstract
White matter (WM) tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption) and topological contributions to the brain's network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus) and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity) and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain's hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.
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Affiliation(s)
| | | | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China
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