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Seitz-Holland J, Alemán-Gómez Y, Cho KIK, Pasternak O, Cleusix M, Jenni R, Baumann PS, Klauser P, Conus P, Hagmann P, Do KQ, Kubicki M, Dwir D. Matrix metalloproteinase 9 (MMP-9) activity, hippocampal extracellular free water, and cognitive deficits are associated with each other in early phase psychosis. Neuropsychopharmacology 2024; 49:1140-1150. [PMID: 38431757 PMCID: PMC11109110 DOI: 10.1038/s41386-024-01814-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 12/18/2023] [Accepted: 01/29/2024] [Indexed: 03/05/2024]
Abstract
Increasing evidence points toward the role of the extracellular matrix, specifically matrix metalloproteinase 9 (MMP-9), in the pathophysiology of psychosis. MMP-9 is a critical regulator of the crosstalk between peripheral and central inflammation, extracellular matrix remodeling, hippocampal development, synaptic pruning, and neuroplasticity. Here, we aim to characterize the relationship between plasma MMP-9 activity, hippocampal microstructure, and cognition in healthy individuals and individuals with early phase psychosis. We collected clinical, blood, and structural and diffusion-weighted magnetic resonance imaging data from 39 individuals with early phase psychosis and 44 age and sex-matched healthy individuals. We measured MMP-9 plasma activity, hippocampal extracellular free water (FW) levels, and hippocampal volumes. We used regression analyses to compare MMP-9 activity, hippocampal FW, and volumes between groups. We then examined associations between MMP-9 activity, FW levels, hippocampal volumes, and cognitive performance assessed with the MATRICS battery. All analyses were controlled for age, sex, body mass index, cigarette smoking, and years of education. Individuals with early phase psychosis demonstrated higher MMP-9 activity (p < 0.0002), higher left (p < 0.05) and right (p < 0.05) hippocampal FW levels, and lower left (p < 0.05) and right (p < 0.05) hippocampal volume than healthy individuals. MMP-9 activity correlated positively with hippocampal FW levels (all participants and individuals with early phase psychosis) and negatively with hippocampal volumes (all participants and healthy individuals). Higher MMP-9 activity and higher hippocampal FW levels were associated with slower processing speed and worse working memory performance in all participants. Our findings show an association between MMP-9 activity and hippocampal microstructural alterations in psychosis and an association between MMP-9 activity and cognitive performance. Further, more extensive longitudinal studies should examine the therapeutic potential of MMP-9 modulators in psychosis.
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Affiliation(s)
- Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Yasser Alemán-Gómez
- Connectomics Lab, Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Medical Image Analysis Laboratory, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Martine Cleusix
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Raoul Jenni
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Philipp S Baumann
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Paul Klauser
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Philippe Conus
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Patric Hagmann
- Connectomics Lab, Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Kim Q Do
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniella Dwir
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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Zhang F, Cho KIK, Seitz-Holland J, Ning L, Legarreta JH, Rathi Y, Westin CF, O'Donnell LJ, Pasternak O. DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1191-1202. [PMID: 37943635 PMCID: PMC10994696 DOI: 10.1109/tmi.2023.3331691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Parcellation of anatomically segregated cortical and subcortical brain regions is required in diffusion MRI (dMRI) analysis for region-specific quantification and better anatomical specificity of tractography. Most current dMRI parcellation approaches compute the parcellation from anatomical MRI (T1- or T2-weighted) data, using tools such as FreeSurfer or CAT12, and then register it to the diffusion space. However, the registration is challenging due to image distortions and low resolution of dMRI data, often resulting in mislabeling in the derived brain parcellation. Furthermore, these approaches are not applicable when anatomical MRI data is unavailable. As an alternative we developed the Deep Diffusion Parcellation (DDParcel), a deep learning method for fast and accurate parcellation of brain anatomical regions directly from dMRI data. The input to DDParcel are dMRI parameter maps and the output are labels for 101 anatomical regions corresponding to the FreeSurfer Desikan-Killiany (DK) parcellation. A multi-level fusion network leverages complementary information in the different input maps, at three network levels: input, intermediate layer, and output. DDParcel learns the registration of diffusion features to anatomical MRI from the high-quality Human Connectome Project data. Then, to predict brain parcellation for a new subject, the DDParcel network no longer requires anatomical MRI data but only the dMRI data. Comparing DDParcel's parcellation with T1w-based parcellation shows higher test-retest reproducibility and a higher regional homogeneity, while requiring much less computational time. Generalizability is demonstrated on a range of populations and dMRI acquisition protocols. Utility of DDParcel's parcellation is demonstrated on tractography analysis for fiber tract identification.
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Mak E, Dounavi ME, Operto G, Ziukelis ET, Jones PS, Low A, Swann P, Newton C, Muniz Terrera G, Malhotra P, Koychev I, Falcon C, Mackay C, Lawlor B, Naci L, Wells K, Ritchie C, Ritchie K, Su L, Gispert JD, O’Brien JT. APOE ɛ4 exacerbates age-dependent deficits in cortical microstructure. Brain Commun 2024; 6:fcad351. [PMID: 38384997 PMCID: PMC10881196 DOI: 10.1093/braincomms/fcad351] [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: 05/30/2023] [Revised: 10/20/2023] [Accepted: 02/09/2024] [Indexed: 02/23/2024] Open
Abstract
The apolipoprotein E ɛ4 allele is the primary genetic risk factor for the sporadic type of Alzheimer's disease. However, the mechanisms by which apolipoprotein E ɛ4 are associated with neurodegeneration are still poorly understood. We applied the Neurite Orientation Dispersion Model to characterize the effects of apolipoprotein ɛ4 and its interactions with age and education on cortical microstructure in cognitively normal individuals. Data from 1954 participants were included from the PREVENT-Dementia and ALFA (ALzheimer and FAmilies) studies (mean age = 57, 1197 non-carriers and 757 apolipoprotein E ɛ4 carriers). Structural MRI datasets were processed with FreeSurfer v7.2. The Microstructure Diffusion Toolbox was used to derive Orientation Dispersion Index maps from diffusion MRI datasets. Primary analyses were focused on (i) the main effects of apolipoprotein E ɛ4, and (ii) the interactions of apolipoprotein E ɛ4 with age and education on lobar and vertex-wise Orientation Dispersion Index and implemented using Permutation Analysis of Linear Models. There were apolipoprotein E ɛ4 × age interactions in the temporo-parietal and frontal lobes, indicating steeper age-dependent Orientation Dispersion Index changes in apolipoprotein E ɛ4 carriers. Steeper age-related Orientation Dispersion Index declines were observed among apolipoprotein E ɛ4 carriers with lower years of education. We demonstrated that apolipoprotein E ɛ4 worsened age-related Orientation Dispersion Index decreases in brain regions typically associated with atrophy patterns of Alzheimer's disease. This finding also suggests that apolipoprotein E ɛ4 may hasten the onset age of dementia by accelerating age-dependent reductions in cortical Orientation Dispersion Index.
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Affiliation(s)
- Elijah Mak
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Maria-Eleni Dounavi
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Grégory Operto
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona 08005, Spain
| | - Elina T Ziukelis
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Peter Simon Jones
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Audrey Low
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Peter Swann
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Coco Newton
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | | | - Paresh Malhotra
- Department of Brain Sciences, Imperial College, London W12 0NN, UK
| | - Ivan Koychev
- Department of Psychiatry, Oxford University, Oxford OX3 7JX, UK
| | - Carles Falcon
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona 08005, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona 08003, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid 28029, Spain
| | - Clare Mackay
- Department of Psychiatry, Oxford University, Oxford OX3 7JX, UK
| | - Brian Lawlor
- Institute of Neuroscience, Trinity College Dublin, University of Dublin, Dublin D02 PX31, Ireland
| | - Lorina Naci
- Institute of Neuroscience, Trinity College Dublin, University of Dublin, Dublin D02 PX31, Ireland
| | - Katie Wells
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Craig Ritchie
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Karen Ritchie
- Institut National de la Santé et de la Recherche Médicale, U1061 Neuropsychiatrie, Montpellier 34093, France
- Faculty of Medicine, University of Montpellier, Montpellier 34093, France
| | - Li Su
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Juan Domingo Gispert
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona 08005, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona 08003, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid 28029, Spain
| | - John T O’Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
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Ni MH, Yu Y, Yang Y, Li ZY, Ma T, Xie H, Li SN, Dai P, Cao XY, Cui YY, Zhu JL, Cui GB, Yan LF. Functional-structural decoupling in visual network is associated with cognitive decline in patients with type 2 diabetes mellitus: evidence from a multimodal MRI analysis. Brain Imaging Behav 2024; 18:73-82. [PMID: 37874444 DOI: 10.1007/s11682-023-00801-6] [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: 09/07/2023] [Indexed: 10/25/2023]
Abstract
Type 2 diabetes mellitus (T2DM) and cognitive dysfunction are highly prevalent disorders worldwide. Although visual network (VN) alteration and functional-structural coupling are potential warning factors for mild cognitive impairment (MCI) in T2DM patients, the relationship between the three in T2DM without MCI is unclear. Thirty T2DM patients without MCI and twenty-nine healthy controls (HC) were prospectively enrolled. Visual components (VC) were estimated by independent component analysis (ICA). Degree centrality (DC), amplitude of low frequency fluctuation (ALFF) and fractional anisotropy (FA) were established to reflect functional and structural characteristics in these VCs respectively. Functional-structural coupling coefficients were further evaluated using combined FA and DC or ALFF. Partial correlations were performed among neuroimaging indicators and neuropsychological scores and clinical variables. Three VCs were selected using group ICA. Deteriorated DC, ALFF and DC-FA coefficients in the VC1 were observed in the T2DM group compared with the HC group, while FA and ALFF-FA coefficients in these three VCs showed no significant differences. In the T2DM group, DC in the VC1 positively correlated with 2 dimensions in the California Verbal Learning Test, including Trial 4 and Total trial 1-5. The impaired DC-FA coefficients in the VC1 markedly affected the Total perseverative responses % of the Wisconsin Card Sorting Test. These findings indicate that DC and DC-FA coefficients in VN may be potential imaging biomarkers revealing early cognitive deficits in T2DM.
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Affiliation(s)
- Min-Hua Ni
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
- Faculty of Medical Technology, Shaanxi University of Chinese Medicine, 1 Middle Section of Shiji Road, Xianyang, 712046, Shaanxi, China
| | - Ying Yu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Yang Yang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Ze-Yang Li
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Teng Ma
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Hao Xie
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Si-Ning Li
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
- Faculty of Medical Technology, Xi`an Medical University, 1 Xinwang Road, Xi'an, 710016, Shaanxi, China
| | - Pan Dai
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
- Faculty of Medical Technology, Xi`an Medical University, 1 Xinwang Road, Xi'an, 710016, Shaanxi, China
| | - Xin-Yu Cao
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
- Faculty of Medical Technology, Yan'an University, 580 Shengdi Road, Yan'an, 716000, Shaanxi, China
| | - Yan-Yan Cui
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
- Faculty of Medical Technology, Shaanxi University of Chinese Medicine, 1 Middle Section of Shiji Road, Xianyang, 712046, Shaanxi, China
| | - Jun-Ling Zhu
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China
| | - Guang-Bin Cui
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
| | - Lin-Feng Yan
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, 569 Xinsi Road, Xi'an, 710038, Shaanxi, China.
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5
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Geenjaar EP, Lewis NL, Fedorov A, Wu L, Ford JM, Preda A, Plis SM, Calhoun VD. Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia. Hum Brain Mapp 2023; 44:5828-5845. [PMID: 37753705 PMCID: PMC10619380 DOI: 10.1002/hbm.26479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 08/04/2023] [Accepted: 08/24/2023] [Indexed: 09/28/2023] Open
Abstract
This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. We apply our proposed framework, which disentangles multimodal data into private and shared sets of features from pairs of structural (sMRI), functional (sFNC and ICA), and diffusion MRI data (FA maps). With our approach, we find that heterogeneity in schizophrenia is potentially a function of modality pairs. Results show (1) schizophrenia is highly multimodal and includes changes in specific networks, (2) non-linear relationships with schizophrenia are observed when interpolating among shared latent dimensions, and (3) we observe a decrease in the modularity of functional connectivity and decreased visual-sensorimotor connectivity for schizophrenia patients for the FA-sFNC and sMRI-sFNC modality pairs, respectively. Additionally, our results generally indicate decreased fractional corpus callosum anisotropy, and decreased spatial ICA map and voxel-based morphometry strength in the superior frontal lobe as found in the FA-sFNC, sMRI-FA, and sMRI-ICA modality pair clusters. In sum, we introduce a powerful new multimodal neuroimaging framework designed to provide a rich and intuitive understanding of the data which we hope challenges the reader to think differently about how modalities interact.
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Affiliation(s)
- Eloy P.T. Geenjaar
- School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, EmoryAtlantaGeorgiaUSA
| | - Noah L. Lewis
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, EmoryAtlantaGeorgiaUSA
- School of Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Alex Fedorov
- School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, EmoryAtlantaGeorgiaUSA
| | - Lei Wu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, EmoryAtlantaGeorgiaUSA
| | - Judith M. Ford
- San Francisco Veterans Affairs Medical CenterSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Adrian Preda
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Sergey M. Plis
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, EmoryAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
| | - Vince D. Calhoun
- School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, EmoryAtlantaGeorgiaUSA
- School of Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- Department of Computer ScienceGeorgia State UniversityAtlantaGeorgiaUSA
- Department of PsychologyGeorgia State UniversityAtlantaGeorgiaUSA
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6
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Geenjaar EPT, Lewis NL, Fedorov A, Wu L, Ford JM, Preda A, Plis SM, Calhoun VD. Chromatic fusion: generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.18.23290184. [PMID: 37292973 PMCID: PMC10246163 DOI: 10.1101/2023.05.18.23290184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. By linking colors to private and shared information from modalities, we introduce chromatic fusion, a framework that allows for intuitively interpreting multimodal data. We test our framework on structural, functional, and diffusion modality pairs. In this framework, we use a multimodal variational autoencoder to learn separate latent subspaces; a private space for each modality, and a shared space between both modalities. These subspaces are then used to cluster subjects, and colored based on their distance from the variational prior, to obtain meta-chromatic patterns (MCPs). Each subspace corresponds to a different color, red is the private space of the first modality, green is the shared space, and blue is the private space of the second modality. We further analyze the most schizophrenia-enriched MCPs for each modality pair and find that distinct schizophrenia subgroups are captured by schizophrenia-enriched MCPs for different modality pairs, emphasizing schizophrenia's heterogeneity. For the FA-sFNC, sMRI-ICA, and sMRI-ICA MCPs, we generally find decreased fractional corpus callosum anisotropy and decreased spatial ICA map and voxel-based morphometry strength in the superior frontal lobe for schizophrenia patients. To additionally highlight the importance of the shared space between modalities, we perform a robustness analysis of the latent dimensions in the shared space across folds. These robust latent dimensions are subsequently correlated with schizophrenia to reveal that for each modality pair, multiple shared latent dimensions strongly correlate with schizophrenia. In particular, for FA-sFNC and sMRI-sFNC shared latent dimensions, we respectively observe a reduction in the modularity of the functional connectivity and a decrease in visual-sensorimotor connectivity for schizophrenia patients. The reduction in modularity couples with increased fractional anisotropy in the left part of the cerebellum dorsally. The reduction in the visual-sensorimotor connectivity couples with a reduction in the voxel-based morphometry generally but increased dorsal cerebellum voxel-based morphometry. Since the modalities are trained jointly, we can also use the shared space to try and reconstruct one modality from the other. We show that cross-reconstruction is possible with our network and is generally much better than depending on the variational prior. In sum, we introduce a powerful new multimodal neuroimaging framework designed to provide a rich and intuitive understanding of the data that we hope challenges the reader to think differently about how modalities interact.
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Affiliation(s)
- Eloy P T Geenjaar
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, 30303, USA
| | - Noah L Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, 30303, USA
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Alex Fedorov
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, 30303, USA
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, 30303, USA
| | - Judith M Ford
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Sergey M Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, 30303, USA
- Dept. of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D Calhoun
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, 30303, USA
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- Dept. of Computer Science, Georgia State University, Atlanta, GA, USA
- Dept. of Psychology, Georgia State University, Atlanta, GA, USA
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7
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Heller C, Kimmig ACS, Kubicki MR, Derntl B, Kikinis Z. Imaging the human brain on oral contraceptives: A review of structural imaging methods and implications for future research goals. Front Neuroendocrinol 2022; 67:101031. [PMID: 35998859 DOI: 10.1016/j.yfrne.2022.101031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/30/2022] [Accepted: 08/15/2022] [Indexed: 12/21/2022]
Abstract
Worldwide over 150 million women use oral contraceptives (OCs), which are the most prescribed form of contraception in both the United States and in European countries. Sex hormones, such as estradiol and progesterone, are important endogenous hormones known for shaping the brain across the life span. Synthetic hormones, which are present in OCs, interfere with the natural hormonal balance by reducing the endogenous hormone levels. Little is known how this affects the brain, especially during the most vulnerable times of brain maturation. Here, we review studies that investigate differences in brain gray and white matter in women using OCs in comparison to naturally cycling women. We focus on two neuroimaging methods used to quantify structural gray and white matter changes, namely structural MRI and diffusion MRI. Finally, we discuss the potential of these imaging techniques to advance knowledge about the effects of OCs on the brain and wellbeing in women.
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Affiliation(s)
- Carina Heller
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry and Psychotherapy, Jena University Hospital, Germany; Department of Clinical Psychology, Friedrich Schiller University Jena, Germany.
| | - Ann-Christin S Kimmig
- Department of Psychiatry and Psychotherapy, Innovative Neuroimaging, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany; Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen, Germany
| | - Marek R Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Birgit Derntl
- Department of Psychiatry and Psychotherapy, Innovative Neuroimaging, Tübingen Center for Mental Health (TüCMH), University of Tübingen, Tübingen, Germany; Lead Graduate School, University of Tübingen, Tübingen, Germany
| | - Zora Kikinis
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Baxi M, Cetin-Karayumak S, Papadimitriou G, Makris N, van der Kouwe A, Jenkins B, Moore TL, Rosene DL, Kubicki M, Rathi Y. Investigating the contribution of cytoarchitecture to diffusion MRI measures in gray matter using histology. FRONTIERS IN NEUROIMAGING 2022; 1:947526. [PMID: 37555179 PMCID: PMC10406256 DOI: 10.3389/fnimg.2022.947526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 07/19/2022] [Indexed: 08/10/2023]
Abstract
Postmortem studies are currently considered a gold standard for investigating brain structure at the cellular level. To investigate cellular changes in the context of human development, aging, or disease treatment, non-invasive in-vivo imaging methods such as diffusion MRI (dMRI) are needed. However, dMRI measures are only indirect measures and require validation in gray matter (GM) in the context of their sensitivity to the underlying cytoarchitecture, which has been lacking. Therefore, in this study we conducted direct comparisons between in-vivo dMRI measures and histology acquired from the same four rhesus monkeys. Average and heterogeneity of fractional anisotropy and trace from diffusion tensor imaging and mean squared displacement (MSD) and return-to-origin-probability from biexponential model were calculated in nine cytoarchitectonically different GM regions using dMRI data. DMRI measures were compared with corresponding histology measures of regional average and heterogeneity in cell area density. Results show that both average and heterogeneity in trace and MSD measures are sensitive to the underlying cytoarchitecture (cell area density) and capture different aspects of cell composition and organization. Trace and MSD thus would prove valuable as non-invasive imaging biomarkers in future studies investigating GM cytoarchitectural changes related to development and aging as well as abnormal cellular pathologies in clinical studies.
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Affiliation(s)
- Madhura Baxi
- Graduate Program for Neuroscience, Boston University, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - George Papadimitriou
- Center for Morphometric Analysis, Massachusetts General Hospital, Charlestown, MA, United States
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Andre van der Kouwe
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Bruce Jenkins
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Tara L. Moore
- Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Center for Systems Neuroscience, Boston, MA, United States
| | - Douglas L. Rosene
- Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Center for Systems Neuroscience, Boston, MA, United States
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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9
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Mahoney SO, Chowdhury NF, Ngo V, Imms P, Irimia A. Mild Traumatic Brain Injury Results in Significant and Lasting Cortical Demyelination. Front Neurol 2022; 13:854396. [PMID: 35812106 PMCID: PMC9262516 DOI: 10.3389/fneur.2022.854396] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
Despite contributing to neurocognitive deficits, intracortical demyelination after traumatic brain injury (TBI) is understudied. This study uses magnetic resonance imaging (MRI) to map intracortical myelin and its change in healthy controls and after mild TBI (mTBI). Acute mTBI involves reductions in relative myelin content primarily in lateral occipital regions. Demyelination mapped ~6 months post-injury is significantly more severe than that observed in typical aging (p < 0.05), with temporal, cingulate, and insular regions losing more myelin (30%, 20%, and 16%, respectively) than most other areas, although occipital regions experience 22% less demyelination. Thus, occipital regions may be more susceptible to primary injury, whereas temporal, cingulate and insular regions may be more susceptible to later manifestations of injury sequelae. The spatial profiles of aging- and mTBI-related chronic demyelination overlap substantially; exceptions include primary motor and somatosensory cortices, where myelin is relatively spared post-mTBI. These features resemble those of white matter demyelination and cortical thinning during Alzheimer's disease, whose risk increases after mTBI.
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Affiliation(s)
- Sean O. Mahoney
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Nahian F. Chowdhury
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Van Ngo
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Phoebe Imms
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, United States
- Corwin D. Denney Research Center, Department of Biomedical Engineering, Andrew and Edna Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
- *Correspondence: Andrei Irimia
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10
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Zhou S, Huang Y, Kuang Q, Yan S, Li H, Wu K, Wu F, Huang X. Kynurenine pathway metabolites are associated with gray matter volume in subjects with schizophrenia. Front Psychiatry 2022; 13:941479. [PMID: 36016974 PMCID: PMC9395706 DOI: 10.3389/fpsyt.2022.941479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/29/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND There has been growing evidence of the existence of abnormalities in the kynurenine pathway (KP) and structural gray matter volume (GMV) in schizophrenia (SCZ). Numerous studies have suggested that abnormal kynurenine metabolism (KM) in the brain is clearly associated with the pathogenesis of schizophrenia and may be one of the pathological mechanisms of SCZ. In this pilot study, we investigated whether there was a correlation between KP and GMV in schizophrenia patients. METHODS The plasma levels of KM were measured in 41 patients who met the Structured Clinical Interview of the Diagnostic IV criteria for schizophrenia and 60 healthy controls by using liquid chromatography-tandem mass spectrometry, and cortical thickness (as measured via magnetic resonance imaging) was obtained. RESULTS Our study showed no statistically significant differences in the concentrations of kynurenine (KYN), tryptophan (TRP), and KYNA/TRP (all p > 0.05), but kynurenic acid (KYNA) and the KYNA/KYN ratio were significantly higher in the schizophrenia subjects than in the healthy controls (F = 4.750, p = 0.032; F = 6.153, p = 0.015, respectively) after controlling for age and sex. Spearman's tests showed that KYN concentrations in SCZ patients were negatively correlated with GMV in the left front cingulate belt (r = -0.325, p = 0.046) and that KYN/TRP was negatively correlated with GMV in the left island (r = -0.396, p = 0.014) and right island (r = -0.385, p = 0.017). CONCLUSION Our findings appear to provide new insights into the predisposition of an imbalance in the relative metabolism of KYN/TRP and KYN to GMV in schizophrenia.
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Affiliation(s)
- Sumiao Zhou
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuanyuan Huang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qijie Kuang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Su Yan
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Hehua Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.,School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Xingbing Huang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
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11
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Mapping Sex-Specific Neurodevelopmental Alterations in Neurite Density and Morphology in a Rat Genetic Model of Psychiatric Illness. eNeuro 2021; 8:ENEURO.0426-20.2020. [PMID: 33441401 PMCID: PMC7986540 DOI: 10.1523/eneuro.0426-20.2020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/19/2020] [Accepted: 12/28/2020] [Indexed: 11/21/2022] Open
Abstract
Neurite orientation dispersion and density imaging (NODDI) is an emerging magnetic resonance (MR) diffusion-weighted imaging (DWI) technique that permits non-invasive quantitative assessment of neurite density and morphology. NODDI has improved our ability to image neuronal microstructure over conventional techniques such as diffusion tensor imaging (DTI) and is particularly suited for studies of the developing brain as it can measure and characterize the dynamic changes occurring in dendrite cytoarchitecture that are critical to early brain development. Neurodevelopmental alterations to the diffusion tensor have been reported in psychiatric illness, but it remains unknown whether advanced DWI techniques such as NODDI are able to sensitively and specifically detect neurodevelopmental changes in brain microstructure beyond those provided by DTI. We show, in an extension of our previous work with a Disc1 svΔ2 rat genetic model of psychiatric illness, the enhanced sensitivity and specificity of NODDI to identify neurodevelopmental and sex-specific changes in brain microstructure that are otherwise difficult to observe with DTI and further corroborate observed changes in brain microstructure to differences in sex-specific systems-level animal behavior. Together, these findings inform the potential application and clinical translational utility of NODDI in studies of brain microstructure in psychiatric illness throughout neurodevelopment and further, the ability of advanced DWI methods such as NODDI to examine the role of biological sex and its influence on brain microstructure in psychiatric illness.
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12
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Baxi M, Di Biase MA, Lyall AE, Cetin-Karayumak S, Seitz J, Ning L, Makris N, Rosene D, Kubicki M, Rathi Y. Quantifying Genetic and Environmental Influence on Gray Matter Microstructure Using Diffusion MRI. Cereb Cortex 2020; 30:6191-6205. [PMID: 32676671 DOI: 10.1093/cercor/bhaa174] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 05/28/2020] [Accepted: 05/28/2020] [Indexed: 01/10/2023] Open
Abstract
Early neuroimaging work in twin studies focused on studying genetic and environmental influence on gray matter macrostructure. However, it is also important to understand how gray matter microstructure is influenced by genes and environment to facilitate future investigations of their influence in mental disorders. Advanced diffusion MRI (dMRI) measures allow more accurate assessment of gray matter microstructure compared with conventional diffusion tensor measures. To understand genetic and environmental influence on gray matter, we used diffusion and structural MRI data from a large twin and sibling study (N = 840) and computed advanced dMRI measures including return to origin probability (RTOP), which is heavily weighted toward intracellular and intra-axonal restricted spaces, and mean squared displacement (MSD), more heavily weighted to diffusion in extracellular space and large cell bodies in gray matter. We show that while macrostructural features like brain volume are mainly genetically influenced, RTOP and MSD can together tap into both genetic and environmental influence on microstructure.
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Affiliation(s)
- Madhura Baxi
- Graduate Program of Neuroscience, Boston University, Boston, MA 02118, USA.,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Maria A Di Biase
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Amanda E Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Johanna Seitz
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Lipeng Ning
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA
| | - Douglas Rosene
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA.,Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
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13
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Sydnor VJ, Bouix S, Pasternak O, Hartl E, Levin-Gleba L, Reid B, Tripodis Y, Guenette JP, Kaufmann D, Makris N, Fortier C, Salat DH, Rathi Y, Milberg WP, McGlinchey RE, Shenton ME, Koerte IK. Mild traumatic brain injury impacts associations between limbic system microstructure and post-traumatic stress disorder symptomatology. Neuroimage Clin 2020; 26:102190. [PMID: 32070813 PMCID: PMC7026283 DOI: 10.1016/j.nicl.2020.102190] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 01/16/2020] [Accepted: 01/19/2020] [Indexed: 12/30/2022]
Abstract
BACKGROUND Post-traumatic stress disorder (PTSD) is a psychiatric disorder that afflicts many individuals, yet the neuropathological mechanisms that contribute to this disorder remain to be fully determined. Moreover, it is unclear how exposure to mild traumatic brain injury (mTBI), a condition that is often comorbid with PTSD, particularly among military personnel, affects the clinical and neurological presentation of PTSD. To address these issues, the present study explores relationships between PTSD symptom severity and the microstructure of limbic and paralimbic gray matter brain regions, as well as the impact of mTBI comorbidity on these relationships. METHODS Structural and diffusion MRI data were acquired from 102 male veterans who were diagnosed with current PTSD. Diffusion data were analyzed with free-water imaging to quantify average CSF-corrected fractional anisotropy (FA) and mean diffusivity (MD) in 18 limbic and paralimbic gray matter regions. Associations between PTSD symptom severity and regional average dMRI measures were examined with repeated measures linear mixed models. Associations were studied separately in veterans with PTSD only, and in veterans with PTSD and a history of military mTBI. RESULTS Analyses revealed that in the PTSD only cohort, more severe symptoms were associated with higher FA in the right amygdala-hippocampus complex, lower FA in the right cingulate cortex, and lower MD in the left medial orbitofrontal cortex. In the PTSD and mTBI cohort, more severe PTSD symptoms were associated with higher FA bilaterally in the amygdala-hippocampus complex, with higher FA bilaterally in the nucleus accumbens, with lower FA bilaterally in the cingulate cortex, and with higher MD in the right amygdala-hippocampus complex. CONCLUSIONS These findings suggest that the microstructure of limbic and paralimbic brain regions may influence PTSD symptomatology. Further, given the additional associations observed between microstructure and symptom severity in veterans with head trauma, we speculate that mTBI may exacerbate the impact of brain microstructure on PTSD symptoms, especially within regions of the brain known to be vulnerable to chronic stress. A heightened sensitivity to the microstructural environment of the brain could partially explain why individuals with PTSD and mTBI comorbidity experience more severe symptoms and poorer illness prognoses than those without a history of brain injury. The relevance of these microstructural findings to the conceptualization of PTSD as being a disorder of stress-induced neuronal connectivity loss is discussed.
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Affiliation(s)
- Valerie J Sydnor
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Elisabeth Hartl
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Neurology, University Hospital, LMU Munich, Munich, Germany
| | - Laura Levin-Gleba
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, United States
| | - Benjamin Reid
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Yorghos Tripodis
- Boston University School of Public Health, Boston University, Boston, MA, United States
| | - Jeffrey P Guenette
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - David Kaufmann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Child and Adolescent Psychiatry, Psychosomatic, and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Center for Morphometric Analysis, Departments of Psychiatry and Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Catherine Fortier
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, United States; Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - David H Salat
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, United States; Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, MA, United States
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - William P Milberg
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, United States; Department of Psychiatry, Harvard Medical School, Boston, MA, United States; Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA, United States
| | - Regina E McGlinchey
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, United States; Department of Psychiatry, Harvard Medical School, Boston, MA, United States; Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA, United States
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; VA Boston Healthcare System, Brockton Division, Brockton, MA, United States
| | - Inga K Koerte
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Child and Adolescent Psychiatry, Psychosomatic, and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
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14
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McKenna FF, Miles L, Babb JS, Goff DC, Lazar M. Diffusion kurtosis imaging of gray matter in schizophrenia. Cortex 2019; 121:201-224. [PMID: 31629198 DOI: 10.1016/j.cortex.2019.08.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 07/18/2019] [Accepted: 08/09/2019] [Indexed: 01/08/2023]
Abstract
Prior postmortem studies have shown gray matter (GM) microstructural abnormalities in schizophrenia. However, few studies to date have examined GM microstructural integrity in schizophrenia in vivo. Here, we employed diffusion kurtosis imaging (DKI) to test for differences in GM microstructure in eighteen schizophrenia (SZ) patients versus nineteen healthy controls (HC). GM microstructure was characterized in each participant using DKI-derived metrics of mean kurtosis (MK) and mean diffusivity (MD). Individual T1-weighted images were used to create subject-specific cortically-labelled regions of interest (ROIs) of the four cortical lobes and sixty-eight cortical GM regions delineated by the Desikan-Killiany atlas, and to derive the associated cortical thickness and area measures. The derived ROIs were also registered to the diffusion space of each subject and used to generate region-specific mean MK and MD values. We additionally administered the Wisconsin Card Sorting Test (WCST), Stroop test, and Trail Making Test part B (Trails-B) to test the relationship between GM metrics and executive function in SZ. We found significantly increased MK and MD in SZ compared to HC participants in the temporal lobe, sub-lobar temporal cortical regions (fusiform, inferior temporal, middle temporal and temporal pole), and posterior cingulate cortex after correcting for multiple comparisons. Correlational analyses revealed significant associations of MK and MD with executive function scores derived from the WCST, Stroop, and Trails-B tests, along with an inverse relationship between MK and MD and cortical thickness and area. A hierarchical multiple linear regression analysis showed that up to 85% of the inter-subject variability in cognitive function in schizophrenia measured by the WCST could be explained by MK in combination with either GM thickness or area. MK and MD appear to be sensitive to GM microstructural pathology in schizophrenia and may provide useful biomarkers of abnormal cortical microstructure in this disorder.
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Affiliation(s)
- Faye F McKenna
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA.
| | - Laura Miles
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - James S Babb
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Donald C Goff
- Department of Psychiatry, New York University School of Medicine, New York, NY, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Mariana Lazar
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA; Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
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15
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Lyall AE, Rathi Y, Kubicki M, Shenton ME. Diffusion Magnetic Resonance Imaging Advances the Study of Nuclei-Specific Thalamocortical Connectivity in Early Stage Psychosis. Biol Psychiatry 2019; 85:10-12. [PMID: 30527208 DOI: 10.1016/j.biopsych.2018.10.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 10/17/2018] [Indexed: 12/15/2022]
Affiliation(s)
- Amanda E Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Havard Medical School, Boston, Massachusetts; Department of Psychiatry, Massachussetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Havard Medical School, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Havard Medical School, Boston, Massachusetts; Department of Psychiatry, Massachussetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Havard Medical School, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Havard Medical School, Boston, Massachusetts; Department of Psychiatry, Massachussetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Havard Medical School, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Havard Medical School, Boston, Massachusetts; Department of Psychiatry, Massachussetts General Hospital, Harvard Medical School, Boston, Massachusetts; Veterans Affairs Boston Healthcare System, Boston, Massachusetts.
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