1
|
Levitt JJ, Zhang F, Vangel M, Nestor PG, Rathi Y, Cetin-Karayumak S, Kubicki M, Coleman MJ, Lewandowski KE, Holt DJ, Keshavan M, Bouix S, Öngür D, Breier A, Shenton ME, O'Donnell LJ. The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis. Mol Psychiatry 2023; 28:2301-2311. [PMID: 37173451 DOI: 10.1038/s41380-023-02031-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/13/2023] [Accepted: 03/08/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography. METHODS Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively. RESULTS We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right (p = 0.03), but not left (p = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p = .00023; p = .00023) originating in selective PFC subregions. CONCLUSIONS In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.
Collapse
Affiliation(s)
- J J Levitt
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - F Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Vangel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - P G Nestor
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychology, University of Massachusetts, Boston, MA, 02125, USA
| | - Y Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - S Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M J Coleman
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - K E Lewandowski
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - D J Holt
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M Keshavan
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - S Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Software Engineering and Information Technology, École de technologie supérieure, Université du Québec, Montréal, QC, H3C 1K3, Canada
| | - D Öngür
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - A Breier
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - M E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - L J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| |
Collapse
|
2
|
Chen Y, Zhang C, Xue T, Song Y, Makris N, Rathi Y, Cai W, Zhang F, O'Donnell LJ. Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation. Neuroimage 2023; 273:120086. [PMID: 37019346 PMCID: PMC10958986 DOI: 10.1016/j.neuroimage.2023.120086] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/02/2023] [Indexed: 04/05/2023] Open
Abstract
White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.
Collapse
Affiliation(s)
- Yuqian Chen
- Harvard Medical School, MA, USA; The University of Sydney, NSW, Australia
| | | | - Tengfei Xue
- Harvard Medical School, MA, USA; The University of Sydney, NSW, Australia
| | - Yang Song
- The University of New South Wales, NSW, Australia
| | | | | | | | | | | |
Collapse
|
3
|
Retzler C, Hallam G, Johnson S, Retzler J. Person-centred Approaches to Psychopathology in the ABCD Study: Phenotypes and Neurocognitive Correlates. Res Child Adolesc Psychopathol 2023:10.1007/s10802-023-01065-w. [PMID: 37119331 PMCID: PMC10368562 DOI: 10.1007/s10802-023-01065-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 05/01/2023]
Abstract
Issues with classifying psychopathology using narrow diagnostic categories have prompted calls for the use of dimensional approaches. Yet questions remain about how closely dimensional approaches reflect the way symptoms cluster in individuals, whether known risk factors (e.g. preterm birth) produce distinct symptom phenotypes, and whether profiles reflecting symptom clusters are associated with neurocognitive factors. To identify distinct profiles of psychopathology, latent class analysis was applied to the syndrome scales of the parent-reported Child Behaviour Checklist for 11,381 9- and 10- year-olds from the Adolescent Brain Cognitive Development study. Four classes were identified, reflecting different profiles, to which children were assigned probabilistically; Class 1 (88.6%) reflected optimal functioning; Class 2 (7.1%), predominantly internalising; Class 3 (2.4%), predominantly externalising; and Class 4 (1.9%), universal difficulties. To investigate the presence of a possible preterm behavioural phenotype, the proportion of participants allocated to each class was cross-tabulated with gestational age category. No profile was specific to preterm birth. Finally, to assess the neurocognitive factors associated with class membership, elastic net regressions were conducted revealing a relatively distinct set of neurocognitive factors associated with each class. Findings support the use of large datasets to identify psychopathological profiles, explore phenotypes, and identify associated neurocognitive factors.
Collapse
Affiliation(s)
- Chris Retzler
- Department of Psychology, School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK.
| | - Glyn Hallam
- Department of Psychology, School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK
| | - Samantha Johnson
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Jenny Retzler
- Department of Psychology, School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK
| |
Collapse
|
4
|
Umminger LF, Rojczyk P, Seitz-Holland J, Sollmann N, Kaufmann E, Kinzel P, Zhang F, Kochsiek J, Langhein M, Kim CL, Wiegand TLT, Kilts JD, Naylor JC, Grant GA, Rathi Y, Coleman MJ, Bouix S, Tripodis Y, Pasternak O, George MS, McAllister TW, Zafonte R, Stein MB, O'Donnell LJ, Marx CE, Shenton ME, Koerte IK. White Matter Microstructure Is Associated with Serum Neuroactive Steroids and Psychological Functioning. J Neurotrauma 2023; 40:649-664. [PMID: 36324218 PMCID: PMC10061338 DOI: 10.1089/neu.2022.0111] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Military service members are at increased risk for mental health issues, and comorbidity with mild traumatic brain injury (mTBI) is common. Largely overlapping symptoms between conditions suggest a shared pathophysiology. The present work investigates the associations among white matter microstructure, psychological functioning, and serum neuroactive steroids that are part of the stress-response system. Diffusion-weighted brain imaging was acquired from 163 participants (with and without military affiliation) and free-water-corrected fractional anisotropy (FAT) was extracted. Associations between serum neurosteroid levels of allopregnanolone (ALLO) and pregnenolone (PREGNE), psychological functioning, and whole-brain white matter microstructure were assessed using regression models. Moderation models tested the effect of mTBI and comorbid post-traumatic stress disorder (PTSD) and mTBI on these associations. ALLO is associated with whole-brain white matter FAT (β = 0.24, t = 3.05, p = 0.006). This association is significantly modulated by PTSD+mTBI comorbidity (β = 0.00, t = 2.50, p = 0.027), although an mTBI diagnosis alone did not significantly impact this association (p = 0.088). There was no significant association between PREGNE and FAT (p = 0.380). Importantly, lower FAT is associated with poor psychological functioning (β = -0.19, t = -2.35, p = 0.020). This study provides novel insight into a potential common pathophysiological mechanism of neurosteroid dysregulation underlying the high risk for mental health issues in military service members. Further, comorbidity of PTSD and mTBI may bring the compensatory effects of the brain's stress response to their limit. Future research is needed to investigate whether neurosteroid regulation may be a promising tool for restoring brain health and improving psychological functioning.
Collapse
Affiliation(s)
- Lisa F. Umminger
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Philine Rojczyk
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nico Sollmann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Elisabeth Kaufmann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Neurology, Epilepsy Center, Ludwig-Maximilians-Universität, Munich, Germany
| | - Philipp Kinzel
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Fan Zhang
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Janna Kochsiek
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Mina Langhein
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Cara L. Kim
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Tim L. T. Wiegand
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Jason D. Kilts
- VA Mid-Atlantic Mental Illness Research and Clinical Center (MIRECC) and Durham VA Medical Center, Durham, NorthCarolina, USA
- Department of Psychiatry and Behavior Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jennifer C. Naylor
- VA Mid-Atlantic Mental Illness Research and Clinical Center (MIRECC) and Durham VA Medical Center, Durham, NorthCarolina, USA
- Department of Psychiatry and Behavior Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Gerald A. Grant
- Department of Neurosurgery, Duke University School of Medicine, Durham, North Carolina, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Michael J. Coleman
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yorghos Tripodis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mark S. George
- Psychiatry Department, Medical University of South Carolina, Charleston, South Carolina, USA
- Ralph H. Johnson VA Medical Center, Charleston, South Carolina, USA
| | - Thomas W. McAllister
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ross Zafonte
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Murray B. Stein
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
- School of Public Health, University of California San Diego, La Jolla, California, USA
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
| | - Lauren J. O'Donnell
- Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Christine E. Marx
- VA Mid-Atlantic Mental Illness Research and Clinical Center (MIRECC) and Durham VA Medical Center, Durham, NorthCarolina, USA
- Department of Psychiatry and Behavior Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Inga K. Koerte
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatic and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Graduate School of Systemic Neuroscience, Ludwig-Maximilians-Universität, Munich, Germany
| |
Collapse
|
5
|
Rojczyk P, Seitz-Holland J, Kaufmann E, Sydnor VJ, Kim CL, Umminger LF, Wiegand TLT, Guenette JP, Zhang F, Rathi Y, Bouix S, Pasternak O, Fortier CB, Salat D, Hinds SR, Heinen F, O’Donnell LJ, Milberg WP, McGlinchey RE, Shenton ME, Koerte IK. Sleep Quality Disturbances Are Associated with White Matter Alterations in Veterans with Post-Traumatic Stress Disorder and Mild Traumatic Brain Injury. J Clin Med 2023; 12:2079. [PMID: 36902865 PMCID: PMC10004675 DOI: 10.3390/jcm12052079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/09/2023] Open
Abstract
Sleep disturbances are strongly associated with mild traumatic brain injury (mTBI) and post-traumatic stress disorder (PTSD). PTSD and mTBI have been linked to alterations in white matter (WM) microstructure, but whether poor sleep quality has a compounding effect on WM remains largely unknown. We evaluated sleep and diffusion magnetic resonance imaging (dMRI) data from 180 male post-9/11 veterans diagnosed with (1) PTSD (n = 38), (2) mTBI (n = 25), (3) comorbid PTSD+mTBI (n = 94), and (4) a control group with neither PTSD nor mTBI (n = 23). We compared sleep quality (Pittsburgh Sleep Quality Index, PSQI) between groups using ANCOVAs and calculated regression and mediation models to assess associations between PTSD, mTBI, sleep quality, and WM. Veterans with PTSD and comorbid PTSD+mTBI reported poorer sleep quality than those with mTBI or no history of PTSD or mTBI (p = 0.012 to <0.001). Poor sleep quality was associated with abnormal WM microstructure in veterans with comorbid PTSD+mTBI (p < 0.001). Most importantly, poor sleep quality fully mediated the association between greater PTSD symptom severity and impaired WM microstructure (p < 0.001). Our findings highlight the significant impact of sleep disturbances on brain health in veterans with PTSD+mTBI, calling for sleep-targeted interventions.
Collapse
Affiliation(s)
- Philine Rojczyk
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Elisabeth Kaufmann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
- Department of Neurology, Ludwig-Maximilians-University, 81377 Munich, Germany
| | - Valerie J. Sydnor
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
| | - Cara L. Kim
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Lisa F. Umminger
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Tim L. T. Wiegand
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Jeffrey P. Guenette
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Software Engineering and IT, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Catherine B. Fortier
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
| | - David Salat
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, 02115 MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Department of Radiology, Boston, MA 02129, USA
| | - Sidney R. Hinds
- Department of Neurology, Uniformed Services University, Bethesda, MD 20814, USA
| | - Florian Heinen
- Department of Pediatric Neurology and Developmental Medicine and LMU Center for Children with Medical Complexity, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians-University, 80337 Munich, Germany
| | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - William P. Milberg
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, 02115 MA, USA
| | - Regina E. McGlinchey
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, 02115 MA, USA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Inga K. Koerte
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-University, 82152 Munich, Germany
| |
Collapse
|
6
|
Yuan Z, Wang W, Zhang X, Bai X, Tang H, Mei Y, Zhang P, Qiu D, Zhang X, Zhang Y, Yu X, Sui B, Wang Y. Altered functional connectivity of the right caudate nucleus in chronic migraine: a resting-state fMRI study. J Headache Pain 2022; 23:154. [PMID: 36460958 PMCID: PMC9717534 DOI: 10.1186/s10194-022-01506-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/06/2022] [Accepted: 10/06/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND The definitive pathogenic mechanisms underlying chronic migraine (CM) remain unclear. Mounting evidence from functional and structural magnetic resonance imaging (MRI) studies suggests that the caudate nucleus (CN) plays a role in the cognitive, sensory, and emotional integration of pain information in patients with migraine. However, evidence concerning the role played by CN in CM patients is limited. Here, we used the CN as the seed to explore patterns of functional connectivity (FC) among healthy controls (HCs), patients with episodic migraine (EM), and patients with CM. METHODS We included 25 HCs, 23 EM patients, and 46 CM patients in this study. All participants underwent resting-state functional MRI scans on a GE 3.0T MRI system. We performed seed-based FC analyses among the three groups using the bilateral CNs as seeds. We also compared the subgroups of CM (with and without medication overuse headache, males and females) and performed Pearson's correlation analyses between FC values and the clinical features of CM patients. RESULTS FC values between the right CN and five clusters (mainly involved in emotion, cognition, and sensory-related brain regions) were higher in CM patients than in HCs. Compared to EM patients, enhanced FC values between the bilateral precuneus, left anterior cingulate gyrus, right middle cingulate cortex, right lingual gyrus, and right CN were shown in the CM patients. There were no significant differences between CM patients with and without MOH, males and females. FC values between the bilateral calcarine cortex, lingual gyrus, and right CN were positively correlated with body mass index. Moreover, right CN-related FC values in the left calcarine cortex and right lingual gyrus were inversely correlated with visual analogue scale scores for headaches. CONCLUSION Our results revealed abnormal right CN-based FC values in CM patients, suggesting dysfunction of brain networks associated with pain perception and multi-regulation (emotion, cognition, and sensory). Aberrant FC of the CN can provide potential neuroimaging markers for the diagnosis and treatment of CM.
Collapse
Affiliation(s)
- Ziyu Yuan
- grid.24696.3f0000 0004 0369 153XHeadache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Wei Wang
- grid.24696.3f0000 0004 0369 153XHeadache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Xueyan Zhang
- grid.412633.10000 0004 1799 0733Department of Neurology, The First Affiliated Hospital of Zhengzhou University, No.1, Jianshe East Road, 450000 Zhengzhou, China
| | - Xiaoyan Bai
- Tiantan Neuroimaging Center of Excellence, National Clinical Research Center for Neurological Diseases, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China ,grid.24696.3f0000 0004 0369 153XDepartment of Radiology, Beijing Tiantan Hospital, Beijing Neurosurgical Institute, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Hefei Tang
- grid.24696.3f0000 0004 0369 153XHeadache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Yanliang Mei
- grid.24696.3f0000 0004 0369 153XHeadache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Peng Zhang
- grid.24696.3f0000 0004 0369 153XHeadache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Dong Qiu
- grid.24696.3f0000 0004 0369 153XHeadache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Xue Zhang
- Tiantan Neuroimaging Center of Excellence, National Clinical Research Center for Neurological Diseases, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China ,grid.24696.3f0000 0004 0369 153XDepartment of Radiology, Beijing Tiantan Hospital, Beijing Neurosurgical Institute, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Yaqing Zhang
- grid.24696.3f0000 0004 0369 153XHeadache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Xueying Yu
- grid.24696.3f0000 0004 0369 153XHeadache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Binbin Sui
- Tiantan Neuroimaging Center of Excellence, National Clinical Research Center for Neurological Diseases, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| | - Yonggang Wang
- grid.24696.3f0000 0004 0369 153XHeadache Center, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, 100070 Beijing, China
| |
Collapse
|
7
|
Calderón-Garcidueñas L, Hernández-Luna J, Mukherjee PS, Styner M, Chávez-Franco DA, Luévano-Castro SC, Crespo-Cortés CN, Stommel EW, Torres-Jardón R. Hemispheric Cortical, Cerebellar and Caudate Atrophy Associated to Cognitive Impairment in Metropolitan Mexico City Young Adults Exposed to Fine Particulate Matter Air Pollution. TOXICS 2022; 10:toxics10040156. [PMID: 35448417 PMCID: PMC9028857 DOI: 10.3390/toxics10040156] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/14/2022] [Accepted: 03/22/2022] [Indexed: 12/16/2022]
Abstract
Exposures to fine particulate matter PM2.5 are associated with Alzheimer's, Parkinson's (AD, PD) and TDP-43 pathology in young Metropolitan Mexico City (MMC) residents. High-resolution structural T1-weighted brain MRI and/or Montreal Cognitive Assessment (MoCA) data were examined in 302 volunteers age 32.7 ± 6.0 years old. We used multivariate linear regressions to examine cortical surface area and thickness, subcortical and cerebellar volumes and MoCA in ≤30 vs. ≥31 years old. MMC residents were exposed to PM2.5 ~ 30.9 µg/m3. Robust hemispheric differences in frontal and temporal lobes, caudate and cerebellar gray and white matter and strong associations between MoCA total and index scores and caudate bilateral volumes, frontotemporal and cerebellar volumetric changes were documented. MoCA LIS scores are affected early and low pollution controls ≥ 31 years old have higher MoCA vs. MMC counterparts (p ≤ 0.0001). Residency in MMC is associated with cognitive impairment and overlapping targeted patterns of brain atrophy described for AD, PD and Fronto-Temporal Dementia (FTD). MMC children and young adult longitudinal studies are urgently needed to define brain development impact, cognitive impairment and brain atrophy related to air pollution. Identification of early AD, PD and FTD biomarkers and reductions on PM2.5 emissions, including poorly regulated heavy-duty diesel vehicles, should be prioritized to protect 21.8 million highly exposed MMC urbanites.
Collapse
Affiliation(s)
- Lilian Calderón-Garcidueñas
- College of Health, The University of Montana, Missoula, MT 59812, USA
- Escuela de Ciencias de la Salud, Universidad del Valle de México, Mexico City 14370, Mexico; (D.A.C.-F.); (S.C.L.-C.); (C.N.C.-C.)
- Correspondence: ; Tel.: +1-406-243-4785
| | | | - Partha S. Mukherjee
- Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata 700108, India;
| | - Martin Styner
- Neuro Image Research and Analysis Lab, University of North Carolina, Chapel Hill, NC 27599, USA;
| | - Diana A. Chávez-Franco
- Escuela de Ciencias de la Salud, Universidad del Valle de México, Mexico City 14370, Mexico; (D.A.C.-F.); (S.C.L.-C.); (C.N.C.-C.)
| | - Samuel C. Luévano-Castro
- Escuela de Ciencias de la Salud, Universidad del Valle de México, Mexico City 14370, Mexico; (D.A.C.-F.); (S.C.L.-C.); (C.N.C.-C.)
| | - Celia Nohemí Crespo-Cortés
- Escuela de Ciencias de la Salud, Universidad del Valle de México, Mexico City 14370, Mexico; (D.A.C.-F.); (S.C.L.-C.); (C.N.C.-C.)
| | - Elijah W. Stommel
- Department of Neurology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA;
| | - Ricardo Torres-Jardón
- Instituto de Ciencias de la Atmósfera y Cambio Climático, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
| |
Collapse
|
8
|
Zekelman LR, Zhang F, Makris N, He J, Chen Y, Xue T, Liera D, Drane DL, Rathi Y, Golby AJ, O'Donnell LJ. White matter association tracts underlying language and theory of mind: An investigation of 809 brains from the Human Connectome Project. Neuroimage 2021; 246:118739. [PMID: 34856375 PMCID: PMC8862285 DOI: 10.1016/j.neuroimage.2021.118739] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 10/20/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
Language and theory of mind (ToM) are the cognitive capacities that allow for the successful interpretation and expression of meaning. While functional MRI investigations are able to consistently localize language and ToM to specific cortical regions, diffusion MRI investigations point to an inconsistent and sometimes overlapping set of white matter tracts associated with these two cognitive domains. To further examine the white matter tracts that may underlie these domains, we use a two-tensor tractography method to investigate the white matter microstructure of 809 participants from the Human Connectome Project. 20 association white matter tracts (10 in each hemisphere) are uniquely identified by leveraging a neuroanatomist-curated automated white matter tract atlas. The fractional anisotropy (FA), mean diffusivity (MD), and number of streamlines (NoS) are measured for each white matter tract. Performance on neuropsychological assessments of semantic memory (NIH Toolbox Picture Vocabulary Test, TPVT) and emotion perception (Penn Emotion Recognition Test, PERT) are used to measure critical subcomponents of the language and ToM networks, respectively. Regression models are constructed to examine how structural measurements of left and right white matter tracts influence performance across these two assessments. We find that semantic memory performance is influenced by the number of streamlines of the left superior longitudinal fasciculus III (SLF-III), and emotion perception performance is influenced by the number of streamlines of the right SLF-III. Additionally, we find that performance on both semantic memory & emotion perception is influenced by the FA of the left arcuate fasciculus (AF). The results point to multiple, overlapping white matter tracts that underlie the cognitive domains of language and ToM. Results are discussed in terms of hemispheric dominance and concordance with prior investigations.
Collapse
Affiliation(s)
- Leo R Zekelman
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, USA.
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, USA; Center for Morphometric Analysis, Department of Psychiatry and Neurology, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Psychiatric Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Jianzhong He
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Yuqian Chen
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; School of Computer Science, University of Sydney, NSW, Australia
| | - Tengfei Xue
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; School of Computer Science, University of Sydney, NSW, Australia
| | | | - Daniel L Drane
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA; Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, University of Washington School of Medicine, Seattle, WA, US
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| |
Collapse
|