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Sghirripa S, Bhalerao G, Griffanti L, Gillis G, Mackay C, Voets N, Wong S, Jenkinson M, For the Alzheimer's Disease Neuroimaging Initiative. Evaluating Traditional, Deep Learning and Subfield Methods for Automatically Segmenting the Hippocampus From MRI. Hum Brain Mapp 2025; 46:e70200. [PMID: 40143669 PMCID: PMC11947432 DOI: 10.1002/hbm.70200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2024] [Revised: 03/10/2025] [Accepted: 03/16/2025] [Indexed: 03/28/2025] Open
Abstract
Given the relationship between hippocampal atrophy and cognitive impairment in various pathological conditions, hippocampus segmentation from MRI is an important task in neuroimaging. Manual segmentation, though considered the gold standard, is time-consuming and error-prone, leading to the development of numerous automatic segmentation methods. However, no study has yet independently compared the performance of traditional, deep learning-based and hippocampal subfield segmentation methods within a single investigation. We evaluated 10 automatic hippocampal segmentation methods (FreeSurfer, SynthSeg, FastSurfer, FIRST, e2dhipseg, Hippmapper, Hippodeep, FreeSurfer-Subfields, HippUnfold and HSF) across 3 datasets with manually segmented hippocampus labels. Performance metrics included overlap with manual labels, correlations between manual and automatic volumes, volume similarity, diagnostic group differentiation and systematically located false positives and negatives. Most methods, especially deep learning-based ones that were trained on manual labels, performed well on public datasets but showed more error and variability on clinical data. Many methods tended to over-segment, particularly at the anterior hippocampus border, but were able to distinguish between healthy controls, MCI, and dementia patients based on hippocampal volume. Our findings highlight the challenges in hippocampal segmentation from MRI and the need for more publicly accessible datasets with manual labels across diverse ages and pathological conditions.
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Affiliation(s)
- Sabrina Sghirripa
- Australian Institute for Machine Learning, School of Computer and Mathematical SciencesThe University of AdelaideAdelaideSouth AustraliaAustralia
- Hopwood Centre of Neurobiology, Lifelong Health ThemeSouth Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
| | - Gaurav Bhalerao
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of PsychiatryUniversity of OxfordOxfordUK
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of PsychiatryUniversity of OxfordOxfordUK
| | - Grace Gillis
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of PsychiatryUniversity of OxfordOxfordUK
| | - Clare Mackay
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of PsychiatryUniversity of OxfordOxfordUK
| | - Natalie Voets
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Stephanie Wong
- College of Education, Psychology and Social WorkFlinders UniversityAdelaideAustralia
| | - Mark Jenkinson
- Australian Institute for Machine Learning, School of Computer and Mathematical SciencesThe University of AdelaideAdelaideSouth AustraliaAustralia
- Hopwood Centre of Neurobiology, Lifelong Health ThemeSouth Australian Health and Medical Research InstituteAdelaideSouth AustraliaAustralia
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
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Tayebi M, Kwon E, McGeown J, Potter L, Taylor D, Condron P, Qiao M, McHugh P, Maller J, Nielsen P, Wang A, Fernandez J, Scadeng M, Shim V, Holdsworth S. Characterizing the Effect of Repetitive Head Impact Exposure and mTBI on Adolescent Collision Sports Players' Brain with Diffusion Magnetic Resonance Imaging. J Neurotrauma 2025; 42:349-366. [PMID: 39714998 DOI: 10.1089/neu.2024.0064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2024] Open
Abstract
Athletes in collision sports frequently sustain repetitive head impacts (RHI), which, while not individually severe enough for a clinical mild traumatic brain injury (mTBI) diagnosis, can compromise neuronal organization by transferring mechanical energy to the brain. Although numerous studies target athletes with mTBI, there is a lack of longitudinal research on young collision sport participants, highlighting an unaddressed concern regarding cumulative RHI effects on brain microstructures. Therefore, this study aimed to investigate the microstructural changes in the brains' of high school rugby players due to repeated head impacts and to establish a correlation between clinical symptoms, cumulative effects of RHI exposure, and changes in the brain's microstructure. We conducted a longitudinal magnetic resonance imaging (MRI) study on 36 male high school rugby players across a season using 3D T1-weighted and multi-shell diffusion MRI sequences, comparing them with 20 matched controls. Players with concussions were separately tracked up to 6 weeks post-injury with three-times scans within this period. The Sport Concussion Assessment Tool (SCAT5) symptom scale assessed mTBI symptoms, and mouthguard-embedded kinematic sensors recorded head impacts. No significant volumetric changes in subcortical structures were found post-rugby season. However, there were substantial differences in mean diffusivity (MD) and axial diffusivity (AD) between the rugby players and controls across widespread brain regions. Diffusion metrics, especially AD, MD, and radial diffusivity of certain brain tracts, displayed strong correlations with SCAT5 symptom severity. Repeated head impacts during a rugby season may adversely affect the structural organization of the brain's white matter. The observed diffusion changes, closely tied to SCAT5 symptom burden, stress the profound effects of seasonal head impacts and highlight individual variability in response to repetitive head impact exposure. To better manage sports-related mTBI and guide return-to-play decisions, comprehensive studies on brain injury mechanisms and recovery post-mTBI/RHI exposure are required.
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Affiliation(s)
- Maryam Tayebi
- Auckland Bioengineering Institute, The University of Auckland, Gisborne, New Zealand
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Eryn Kwon
- Auckland Bioengineering Institute, The University of Auckland, Gisborne, New Zealand
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Josh McGeown
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Leigh Potter
- Mātai Medical Research Institute, Gisborne, New Zealand
| | | | - Paul Condron
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Miao Qiao
- Department of Computer Science, The University of Auckland, Auckland, New Zealand
| | | | | | - Poul Nielsen
- Auckland Bioengineering Institute, The University of Auckland, Gisborne, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Gisborne, New Zealand
- Mātai Medical Research Institute, Gisborne, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, The University of Auckland, Gisborne, New Zealand
- Mātai Medical Research Institute, Gisborne, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
| | - Miriam Scadeng
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Gisborne, New Zealand
- Mātai Medical Research Institute, Gisborne, New Zealand
| | - Samantha Holdsworth
- Mātai Medical Research Institute, Gisborne, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
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Hosseini E, Sepehrinezhad A, Momeni J, Ascenzi BM, Gorji A, Sahab-Negah S. The Telencephalon. FROM ANATOMY TO FUNCTION OF THE CENTRAL NERVOUS SYSTEM 2025:401-427. [DOI: 10.1016/b978-0-12-822404-5.00014-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2025]
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Lieberman JA, Mendelsohn A, Goldberg TE, Emsley R. Preventing disease progression in schizophrenia: What are we waiting for. J Psychiatr Res 2025; 181:716-727. [PMID: 39754992 DOI: 10.1016/j.jpsychires.2024.12.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/09/2024] [Accepted: 12/26/2024] [Indexed: 01/06/2025]
Abstract
Despite research advances and progress in health care, schizophrenia remains a debilitating and costly disease. Onset occurs typically during youth and can lead to a relapsing and ultimately chronic course with persistent symptoms and functional impairment if not promptly and properly treated. Consequently, over time, schizophrenia causes substantial distress and disability for patients, their families and accrues to a collective burden to society. Recent research has revealed much about the pathophysiology that underlies the progressive nature of schizophrenia. Additionally, treatment strategies for disease management have been developed that have the potential to not just control psychotic symptoms but limit the cumulative morbidity of the illness. Given the evidence for their effectiveness and feasibility for their application, it is perplexing that this model of care has not yet become the standard of care and widely implemented to reduce the burden of illness on patients and society. This begs the question of whether the failure of implementation of a potentially disease-modifying strategy is due to the lack of evidence of efficacy (or belief in it) and readiness for implementation, or whether it's the lack of motivation and political will to support their utilization. To address this question, we reviewed and summarized the literature describing the natural history, pathophysiology and therapeutic strategies that can alleviate symptoms, prevent relapse, and potentially modify the course of schizophrenia. We conclude that, while we await further advances in mental health care from research, we must fully appreciate and take advantage of the effectiveness of existing treatments and overcome the attitudinal, policy, and infrastructural barriers to providing optimal mental health care capable of providing a disease-modifying treatment to patients with schizophrenia.
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Affiliation(s)
- Jeffrey A Lieberman
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
| | - Alana Mendelsohn
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Terry E Goldberg
- Division of Geriatric Psychiatry, Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
| | - Robin Emsley
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
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Li X, Wang L, Liu H, Ma B, Chu L, Dong X, Zeng D, Che T, Jiang X, Wang W, Hu J, Li S. Syn_SegNet: A Joint Deep Neural Network for Ultrahigh-Field 7T MRI Synthesis and Hippocampal Subfield Segmentation in Routine 3T MRI. IEEE J Biomed Health Inform 2023; 27:4866-4877. [PMID: 37581964 DOI: 10.1109/jbhi.2023.3305377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
Precise delineation of hippocampus subfields is crucial for the identification and management of various neurological and psychiatric disorders. However, segmenting these subfields automatically in routine 3T MRI is challenging due to their complex morphology and small size, as well as the limited signal contrast and resolution of the 3T images. This research proposes Syn_SegNet, an end-to-end, multitask joint deep neural network that leverages ultrahigh-field 7T MRI synthesis to improve hippocampal subfield segmentation in 3T MRI. Our approach involves two key components. First, we employ a modified Pix2PixGAN as the synthesis model, incorporating self-attention modules, image and feature matching loss, and ROI loss to generate high-quality 7T-like MRI around the hippocampal region. Second, we utilize a variant of 3D-U-Net with multiscale deep supervision as the segmentation subnetwork, incorporating an anatomic weighted cross-entropy loss that capitalizes on prior anatomical knowledge. We evaluate our method on hippocampal subfield segmentation in paired 3T MRI and 7T MRI with seven different anatomical structures. The experimental findings demonstrate that Syn_SegNet's segmentation performance benefits from integrating synthetic 7T data in an online manner and is superior to competing methods. Furthermore, we assess the generalizability of the proposed approach using a publicly accessible 3T MRI dataset. The developed method would be an efficient tool for segmenting hippocampal subfields in routine clinical 3T MRI.
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Valizadeh G, Babapour Mofrad F. A Comprehensive Survey on Two and Three-Dimensional Fourier Shape Descriptors: Biomedical Applications. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING 2022; 29:4643-4681. [DOI: 10.1007/s11831-022-09750-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 04/11/2022] [Indexed: 10/12/2024]
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Roeske MJ, Lyu I, McHugo M, Blackford JU, Woodward ND, Heckers S. Incomplete Hippocampal Inversion: A Neurodevelopmental Mechanism for Hippocampal Shape Deformation in Schizophrenia. Biol Psychiatry 2022; 92:314-322. [PMID: 35487783 PMCID: PMC9339515 DOI: 10.1016/j.biopsych.2022.02.954] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/09/2022] [Accepted: 02/16/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Shape analyses of patients with schizophrenia have revealed bilateral deformations of the anterolateral hippocampus, primarily localized to the CA1 subfield. Incomplete hippocampal inversion (IHI), an anatomical variant of the human hippocampus resulting from an arrest during neurodevelopment, is more prevalent and severe in patients with schizophrenia. We hypothesized that IHI would affect the shape of the hippocampus and contribute to hippocampal shape differences in schizophrenia. METHODS We studied 199 patients with schizophrenia and 161 healthy control participants with structural magnetic resonance imaging to measure the prevalence and severity of IHI. High-fidelity hippocampal surface reconstructions were generated with the SPHARM-PDM toolkit. We used general linear models in SurfStat to test for group shape differences, the impact of IHI on hippocampal shape variation, and whether IHI contributes to hippocampal shape abnormalities in schizophrenia. RESULTS Not including IHI as a main effect in our between-group comparison replicated well-established hippocampal shape differences in patients with schizophrenia localized to the CA1 subfield in the anterolateral hippocampus. Shape differences were also observed near the uncus and hippocampal tail. IHI was associated with outward displacements of the dorsal and ventral surfaces of the hippocampus and inward displacements of the medial and lateral surfaces. Including IHI as a main effect in our between-group comparison eliminated the bilateral shape differences in the CA1 subfield. Shape differences in the uncus persisted after including IHI. CONCLUSIONS IHI impacts hippocampal shape. Our results suggest IHI as a neurodevelopmental mechanism for the well-known shape differences, particularly in the CA1 subfield, in schizophrenia.
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Affiliation(s)
- Maxwell J Roeske
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
| | - Ilwoo Lyu
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - Maureen McHugo
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jennifer Urbano Blackford
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee; Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, Nebraska
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
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Menjivar Quijano SA, Ryczek CA, Horne MR. The effect of schizotypy on spatial learning in an environment with a distinctive shape. Front Psychol 2022; 13:929653. [PMID: 35967704 PMCID: PMC9373985 DOI: 10.3389/fpsyg.2022.929653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
In two experiments, participants completed the Oxford-Liverpool Inventory of Feelings and Experiences measuring schizotypal traits across four dimensions (unusual experiences, cognitive disorganization, introvertive anhedonia, and impulsive non-conformity). They then took part in a virtual navigation task where they were required to learn about the position of a hidden goal with reference to geometric cues of a rectangular arena or rely on colored wall panels to find the hidden goal in a square-shaped arena. Unusual experience and cognitive disorganization were significant predictors of the use of geometric cues, but no significant predictors were found for the use of wall panels. Implications to hippocampal function and the clinical domain are considered.
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Affiliation(s)
| | - Cameron A. Ryczek
- Department of Psychology, California State University, San Bernardino, San Bernardino, CA, United States
| | - Murray R. Horne
- Department of Psychology, California State University, East Bay, Hayward, CA, United States
- *Correspondence: Murray R. Horne,
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A statistical shape model of soleus muscle morphology in spastic cerebral palsy. Sci Rep 2022; 12:7711. [PMID: 35546597 PMCID: PMC9095689 DOI: 10.1038/s41598-022-11611-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 04/12/2022] [Indexed: 11/16/2022] Open
Abstract
This study investigated morphological characteristics of the soleus muscle in cerebral palsy (CP) and typically developing (TD) cohorts using a statistical shape model and differentiated dominant features between the two cohorts. We generated shape models of CP and TD cohorts to characterize dominant features within each. We then generated a combined shape model of both CP and TD to assess deviations of the cohorts’ soleuses from a common mean shape, and statistically analysed differences between the cohorts. The shape models revealed similar principal components (PCs) with different variance between groups. The CP shape model yielded a distinct feature (superior–inferior shift of the broad central region) accounting for 8.1% of the model’s cumulative variance. The combined shape model presented two PCs where differences arose between CP and TD cohorts: size and aspect ratio of length–width–thickness. The distinct appearance characteristic in the CP model—described above—may implicate impaired muscle function in children with CP. Overall, children with CP had smaller muscles that also tended to be long, thin, and narrow. Shape modelling captures dominant morphological features of structures, which was used here to quantitatively describe CP muscles and further probe our understanding of the disease’s impact on the muscular system.
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Gutman BA, van Erp TG, Alpert K, Ching CRK, Isaev D, Ragothaman A, Jahanshad N, Saremi A, Zavaliangos‐Petropulu A, Glahn DC, Shen L, Cong S, Alnæs D, Andreassen OA, Doan NT, Westlye LT, Kochunov P, Satterthwaite TD, Wolf DH, Huang AJ, Kessler C, Weideman A, Nguyen D, Mueller BA, Faziola L, Potkin SG, Preda A, Mathalon DH, Bustillo J, Calhoun V, Ford JM, Walton E, Ehrlich S, Ducci G, Banaj N, Piras F, Piras F, Spalletta G, Canales‐Rodríguez EJ, Fuentes‐Claramonte P, Pomarol‐Clotet E, Radua J, Salvador R, Sarró S, Dickie EW, Voineskos A, Tordesillas‐Gutiérrez D, Crespo‐Facorro B, Setién‐Suero E, van Son JM, Borgwardt S, Schönborn‐Harrisberger F, Morris D, Donohoe G, Holleran L, Cannon D, McDonald C, Corvin A, Gill M, Filho GB, Rosa PGP, Serpa MH, Zanetti MV, Lebedeva I, Kaleda V, Tomyshev A, Crow T, James A, Cervenka S, Sellgren CM, Fatouros‐Bergman H, Agartz I, Howells F, Stein DJ, Temmingh H, Uhlmann A, de Zubicaray GI, McMahon KL, Wright M, Cobia D, Csernansky JG, Thompson PM, Turner JA, Wang L. A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium. Hum Brain Mapp 2022; 43:352-372. [PMID: 34498337 PMCID: PMC8675416 DOI: 10.1002/hbm.25625] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 01/06/2023] Open
Abstract
Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.
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Affiliation(s)
- Boris A. Gutman
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Institute for Information Transmission Problems (Kharkevich Institute)MoscowRussia
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
| | - Kathryn Alpert
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Dmitry Isaev
- Department of Biomedical EngineeringDuke UniversityDurhamNorth CarolinaUSA
| | - Anjani Ragothaman
- Department of biomedical engineeringOregon Health and Science universityPortlandOregonUSA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Arvin Saremi
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Artemis Zavaliangos‐Petropulu
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - David C. Glahn
- Department of PsychiatryBoston Children's Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Li Shen
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Shan Cong
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Dag Alnæs
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Ole Andreas Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Nhat Trung Doan
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Peter Kochunov
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Theodore D. Satterthwaite
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Daniel H. Wolf
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Alexander J. Huang
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Charles Kessler
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Andrea Weideman
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Dana Nguyen
- Department of PediatricsUniversity of California IrvineIrvineCaliforniaUSA
| | - Bryon A. Mueller
- Department of Psychiatry and Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Lawrence Faziola
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Steven G. Potkin
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Adrian Preda
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Daniel H. Mathalon
- Department of Psychiatry and Weill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Judith Ford Mental HealthVA San Francisco Healthcare SystemSan FranciscoCaliforniaUSA
| | - Juan Bustillo
- Departments of Psychiatry & NeuroscienceUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Vince Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology]Emory UniversityAtlantaGeorgiaUSA
- Department of Electrical and Computer EngineeringThe University of New MexicoAlbuquerqueNew MexicoUSA
| | - Judith M. Ford
- Judith Ford Mental HealthVA San Francisco Healthcare SystemSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | | | - Stefan Ehrlich
- Division of Psychological & Social Medicine and Developmental NeurosciencesFaculty of Medicine, TU‐DresdenDresdenGermany
| | | | - Nerisa Banaj
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Fabrizio Piras
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Federica Piras
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Gianfranco Spalletta
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | | | | | | | - Joaquim Radua
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
- Institut d'Investigacions Biomdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
| | - Erin W. Dickie
- Centre for Addiction and Mental Health (CAMH)TorontoCanada
| | | | | | | | | | | | - Stefan Borgwardt
- Department of PsychiatryUniversity of BaselBaselSwitzerland
- Department of Psychiatry and PsychotherapyUniversity of LübeckLübeckGermany
| | | | - Derek Morris
- Centre for Neuroimaging and Cognitive Genomics, Discipline of BiochemistryNational University of Ireland GalwayGalwayIreland
| | - Gary Donohoe
- Centre for Neuroimaging and Cognitive Genomics, School of PsychologyNational University of Ireland GalwayGalwayIreland
| | - Laurena Holleran
- Centre for Neuroimaging and Cognitive Genomics, School of PsychologyNational University of Ireland GalwayGalwayIreland
| | - Dara Cannon
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive GenomicsNational University of Ireland GalwayGalwayIreland
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive GenomicsNational University of Ireland GalwayGalwayIreland
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of PsychiatryTrinity College DublinDublinIreland
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of PsychiatryTrinity College DublinDublinIreland
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Geraldo Busatto Filho
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Mauricio H. Serpa
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
- Hospital Sirio‐LibanesSao PauloSPBrazil
| | - Irina Lebedeva
- Laboratory of Neuroimaging and Multimodal AnalysisMental Health Research CenterMoscowRussia
| | - Vasily Kaleda
- Department of Endogenous Mental DisordersMental Health Research CenterMoscowRussia
| | - Alexander Tomyshev
- Laboratory of Neuroimaging and Multimodal AnalysisMental Health Research CenterMoscowRussia
| | - Tim Crow
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Anthony James
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Simon Cervenka
- Centre for Psychiatry Reserach, Department of Clinical NeuroscienceKarolinska Institutet, & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Carl M Sellgren
- Department of Physiology and PharmacologyKarolinska InstitutetStockholmSweden
| | - Helena Fatouros‐Bergman
- Centre for Psychiatry Reserach, Department of Clinical NeuroscienceKarolinska Institutet, & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Ingrid Agartz
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Fleur Howells
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Neuroscience InstituteUniversity of Cape Town, Cape TownWCSouth Africa
| | - Dan J. Stein
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Neuroscience InstituteUniversity of Cape Town, Cape TownWCSouth Africa
- SA MRC Unit on Risk & Resilience in Mental DisordersUniversity of Cape TownCape TownWCSouth Africa
| | - Henk Temmingh
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
| | - Anne Uhlmann
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Department of Child and Adolescent PsychiatryTU DresdenGermany
| | - Greig I. de Zubicaray
- School of Psychology, Faculty of HealthQueensland University of Technology (QUT)BrisbaneQLDAustralia
| | - Katie L. McMahon
- School of Clinical SciencesQueensland University of Technology (QUT)BrisbaneQLDAustralia
| | - Margie Wright
- Queensland Brain InstituteUniversity of QueenslandBrisbaneQLDAustralia
| | - Derin Cobia
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Psychology and Neuroscience CenterBrigham Young UniversityProvoUtahUSA
| | - John G. Csernansky
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - Lei Wang
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Psychiatry and Behavioral HealthOhio State University Wexner Medical CenterColumbusOhioUSA
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11
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Roeske MJ, McHugo M, Vandekar S, Blackford JU, Woodward ND, Heckers S. Incomplete hippocampal inversion in schizophrenia: prevalence, severity, and impact on hippocampal structure. Mol Psychiatry 2021; 26:5407-5416. [PMID: 33437006 PMCID: PMC8589684 DOI: 10.1038/s41380-020-01010-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 12/16/2020] [Accepted: 12/17/2020] [Indexed: 11/09/2022]
Abstract
Incomplete hippocampal inversion (IHI) is an anatomical variant of the human brain resulting from an arrest in brain development, especially prevalent in the left hemisphere. We hypothesized that IHI is more common in schizophrenia and contributes to the well-known hippocampal structural differences. We studied 199 schizophrenia patients and 161 healthy control participants with 3 T MRI to establish IHI prevalence and the relationship of IHI with hippocampal volume and asymmetry. IHI was more prevalent (left hemisphere: 15% of healthy control participants, 27% of schizophrenia patients; right hemisphere: 4% of healthy control participants, 10% of schizophrenia patients) and more severe in schizophrenia patients compared to healthy control participants. Severe IHI cases were associated with a higher rate of automated segmentation failure. IHI contributed to smaller hippocampal volume and increased R > L volume asymmetry in schizophrenia. The increased prevalence and severity of IHI supports the neurodevelopmental model of schizophrenia. The impact of this developmental variant deserves further exploration in studies of the hippocampus in schizophrenia.
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Affiliation(s)
- Maxwell J Roeske
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Maureen McHugo
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jennifer Urbano Blackford
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Research Health Scientist, Research and Development, Veterans Affairs Medical Center, Nashville, TN, USA
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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12
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Gong M, Liu P, Sciurba FC, Stojanov P, Tao D, Tseng GC, Zhang K, Batmanghelich K. Unpaired data empowers association tests. Bioinformatics 2021; 37:785-792. [PMID: 33070196 PMCID: PMC8098021 DOI: 10.1093/bioinformatics/btaa886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 09/07/2020] [Accepted: 10/05/2020] [Indexed: 11/25/2022] Open
Abstract
Motivation There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test. Results We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system. Availability and implementation Code is available on https://github.com/batmanlab/Semi-paired-Association-Test. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mingming Gong
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA.,Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA.,School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Peng Liu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Frank C Sciurba
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Petar Stojanov
- Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Dacheng Tao
- Australia School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia
| | - George C Tseng
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Kun Zhang
- Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Kayhan Batmanghelich
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
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13
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Adiponectin receptor2 and HCLS1 associated proteinX-1 levels are altered in postmortem schizophrenic brain. Meta Gene 2021. [DOI: 10.1016/j.mgene.2020.100834] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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14
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Ma B, Zhao Y, Yang Y, Zhang X, Dong X, Zeng D, Ma S, Li S. MRI image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hippocampal subfields segmentation. Comput Med Imaging Graph 2020; 86:101800. [PMID: 33130416 DOI: 10.1016/j.compmedimag.2020.101800] [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: 04/18/2020] [Revised: 07/27/2020] [Accepted: 09/24/2020] [Indexed: 12/01/2022]
Abstract
BACKGROUND AND OBJECTIVE Hippocampal subfields (HS) segmentation accuracy on high resolution (HR) MRI images is higher than that on low resolution (LR) MRI images. However, HR MRI data collection is more expensive and time-consuming. Thus, we intend to generate HR MRI images from the corresponding LR MRI images for HS segmentation. METHODS AND RESULTS To generate high-quality HR MRI hippocampus region images, we use a dual discriminator adversarial learning model with difficulty-aware attention mechanism in hippocampus regions (da-GAN). A local discriminator is applied in da-GAN to evaluate the visual quality of hippocampus region voxels of the synthetic images. And the difficulty-aware attention mechanism based on the local discriminator can better model the generation of hard-to-synthesis voxels in hippocampus regions. Additionally, we design a SemiDenseNet model with 3D Dense CRF postprocessing and an Unet-based model to perform HS segmentation. The experiments are implemented on Kulaga-Yoskovitz dataset. Compared with conditional generative adversarial network (c-GAN), the PSNR of generated HR T2w images acquired by our da-GAN achieves 0.406 and 0.347 improvement in left and right hippocampus regions. When using two segmentation models to segment HS, the DSC values achieved on the generated HR T1w and T2w images are both improved than that on LR T1w images. CONCLUSION Experimental results show that da-GAN model can generate higher-quality MRI images, especially in hippocampus regions, and the generated MRI images can improve HS segmentation accuracy.
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Affiliation(s)
- Baoqiang Ma
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Yan Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Yujing Yang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Xiaohui Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Xiaoxi Dong
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Debin Zeng
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Siyu Ma
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Shuyu Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.
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15
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Volumetric and morphological characteristics of the hippocampus are associated with progression to schizophrenia in patients with first-episode psychosis. Eur Psychiatry 2020; 45:1-5. [DOI: 10.1016/j.eurpsy.2017.06.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Revised: 06/19/2017] [Accepted: 06/20/2017] [Indexed: 01/06/2023] Open
Abstract
AbstractBackground:Abnormalities in the hippocampus have been implicated in the pathophysiology of psychosis. However, it is still unclear whether certain abnormalities are a pre-existing vulnerability factor, a sign of disease progression or a consequence of environmental factors. We hypothesized that first-episode psychosis patients who progress to schizophrenia after one year of follow up will display greater volumetric and morphological changes from the very beginning of the disorder.Methods:We studied the hippocampus of 41 patients with a first-episode psychosis and 41 matched healthy controls. MRI was performed at the time of the inclusion in the study. After one year, the whole sample was reevaluated and divided in two groups depending on the diagnoses (schizophrenia vs. non-schizophrenia).Results:Patients who progressed to schizophrenia showed a significantly smaller left hippocampus volume than control group and no-schizophrenia group (F = 3.54; df = 2, 77; P = 0.03). We also found significant differences in the morphology of the anterior hippocampus (CA1) of patients with first-episode psychosis who developed schizophrenia compared with patients who did not.Conclusions:These results are consistent with the assumption of hyperfunctioning dopaminergic cortico-subcortical circuits in schizophrenia, which might be related with an alteration of subcortical structures, such as the hippocampus, along the course of the disease. According with these results, hippocampus abnormalities may serve as a prognostic marker of clinical outcome in patients with a first-episode psychosis.
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16
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McLachlan E, Rai S, Al-Shihabi A, Huntley J, Burgess N, Howard R, Reeves S. Neuroimaging correlates of false memory in 'Alzheimer's disease: A preliminary systematic review. Psychiatry Res Neuroimaging 2020; 296:111021. [PMID: 31887712 DOI: 10.1016/j.pscychresns.2019.111021] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 10/28/2019] [Accepted: 12/06/2019] [Indexed: 12/21/2022]
Abstract
Alzheimer's disease (AD) is characterised by episodic memory impairment, but people also experience memory distortions, including false memories, which can impact on safety and reduce functioning. Understanding the neural networks that underpin false memories could help to predict the need for intervention and guide development of cognitive strategies to reduce memory errors. However, there is a relative absence of research into how the neuropathology of AD contributes to false memory generation. This paper systematically reviews the methodology and outcomes of studies investigating the neuroimaging correlates of false memory in AD. Four studies using structural imaging and three studies using functional imaging were identified. Studies were heterogenous in methodology and received mostly 'weak' quality assessment ratings. Combined, and consistent with neuroimaging findings in non-AD populations, results from identified studies provide preliminary support for the hypothesis that medial temporal lobe and prefrontal cortex dysfunction may lead to generation of false memories in AD. However, the small number of studies and significant heterogeneity within them means further study is necessary to assess replicability of results.
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Affiliation(s)
- Emma McLachlan
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London, United Kingdom, W1T 7NF.
| | - Salina Rai
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London, United Kingdom, W1T 7NF
| | - Ahmed Al-Shihabi
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London, United Kingdom, W1T 7NF
| | - Jonathan Huntley
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London, United Kingdom, W1T 7NF
| | - Neil Burgess
- Institute of Cognitive Neuroscience, University College London, Alexandra House, 17-19 Queen Square, Bloomsbury, London, United Kingdom, WC1N 3AZ
| | - Robert Howard
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London, United Kingdom, W1T 7NF
| | - Suzanne Reeves
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London, United Kingdom, W1T 7NF
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17
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Tang X, Lyu G, Chen M, Huang W, Lin Y. Amygdalar and Hippocampal Morphometry Abnormalities in First-Episode Schizophrenia Using Deformation-Based Shape Analysis. Front Psychiatry 2020; 11:677. [PMID: 32765318 PMCID: PMC7379331 DOI: 10.3389/fpsyt.2020.00677] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 06/29/2020] [Indexed: 11/14/2022] Open
Abstract
In this study, we investigated and quantified the amygdalar and hippocampal morphometry abnormalities exerted by first-episode schizophrenia using a total of 92 patients and 106 healthy control participants. Magnetic resonance imaging (MRI) based automated segmentation was conducted to obtain the amygdalar and hippocampal segmentations. Disease-versus-control volume differences of the bilateral amygdalas and hippocampi were quantified. In addition, deformation-based statistical shape analysis was employed to quantify the region-specific shape abnormalities of each structure of interest. To better identify the key relevant areas in the pathology of first-episode schizophrenia, each structure was divided into four subregions; CA1, CA2, CA3 combined with dentate gyrus for the hippocampus in each hemisphere and basolateral, basomedial, centromedial, and lateral nucleus for the amygdala in each hemisphere. We observed significant global volume reduction and localized shape atrophy in each of the four structures of interest. The amygdalar shape abnormalities mainly occurred at the basolateral and centromedial subregions, whereas the hippocampal shape abnormalities mainly concentrated on the CA1 and CA2 subregions. For the same structure, the one on the right hemisphere was affected more by the disease pathology than that on the left hemisphere. To conclude, we have successfully quantified the global and local morphometric abnormalities of the bilateral amygdalas and hippocampi using a sophisticated statistical analysis pipeline and high-field subregion segmentations, with MRI data of a considerable sample size. This study is one of the very first of such kind in first-episode schizophrenia analyses.
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Affiliation(s)
- Xiaoying Tang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Guiwen Lyu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Minhua Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China.,Department of Electrical and Electronic Engineering, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
| | - Weikai Huang
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yin Lin
- Department of Psychology, Shenzhen Children's Hospital, Shenzhen, China
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18
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Safavian N, Batouli SAH, Oghabian MA. An automatic level set method for hippocampus segmentation in MR images. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2019. [DOI: 10.1080/21681163.2019.1706054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Nazanin Safavian
- Neuroimaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Seyed Amir Hossein Batouli
- Neuroimaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Neuroimaging and Analysis Group (NIAG), Tehran University of Medical Sciences, Tehran, Iran
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran
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19
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Do cognitive and neuropsychological functioning deficits coincide with hippocampal alteration during first-psychotic episode? CNS Spectr 2019; 24:472-478. [PMID: 30507369 DOI: 10.1017/s1092852918001293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Numerous studies shown that structural hippocampal alterations are present in subjects at high risk of developing psychosis or schizophrenia. These findings indicate that in a subset of patients undergoing first-psychosis episode (FPE), hippocampal volume alterations are accompanied by associated cognitive and neuropsychological deficits. The combination of psychological deficits and neuroanatomical alterations, in turn, appears to increase treatment complexity and worsen clinical outcomes. OBJECTIVE We aim to determine whether cognitive and neuropsychological functioning deficits precede or follow hippocampal alterations during early onset psychosis. METHODS This cross-sectional study describes 3 case-studies of adolescent subjects, ages 16-17, admitted at the child and adolescent inpatient psychiatric unit in lieu of first psychotic episode. We conducted detailed structured clinical psychiatric interviews, anatomical-structural magnetic resonance imaging (MRI), sleep-deprived electroencephalogram (EEG) recordings, laboratory testing, and a comprehensive battery of psychological testing to better understand their clinical pictures. RESULTS Psychological testing in each patient demonstrated the presence of low to borderline intellectual functioning coupled with neuropsychological deficits in different psychiatric domains. Interestingly, these changes coincided with structural MRI alterations in the hippocampal area. CONCLUSIONS Our case report adds to the armamentarium of literature signifying that radiologically detectable alterations of the hippocampus may occur either concomitantly or closely following the development of early cognitive deficits in patients with FPE.
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20
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Cerrolaza JJ, Picazo ML, Humbert L, Sato Y, Rueckert D, Ballester MÁG, Linguraru MG. Computational anatomy for multi-organ analysis in medical imaging: A review. Med Image Anal 2019; 56:44-67. [DOI: 10.1016/j.media.2019.04.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 02/05/2019] [Accepted: 04/13/2019] [Indexed: 12/19/2022]
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21
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Younes L, Albert M, Moghekar A, Soldan A, Pettigrew C, Miller MI. Identifying Changepoints in Biomarkers During the Preclinical Phase of Alzheimer's Disease. Front Aging Neurosci 2019; 11:74. [PMID: 31001108 PMCID: PMC6454004 DOI: 10.3389/fnagi.2019.00074] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 03/14/2019] [Indexed: 01/29/2023] Open
Abstract
Objective: Several models have been proposed for the evolution of Alzheimer's disease (AD) biomarkers. The aim of this study was to identify changepoints in a range of biomarkers during the preclinical phase of AD. Methods: We examined nine measures based on cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) and cognitive testing, obtained from 306 cognitively normal individuals, a subset of whom subsequently progressed to the symptomatic phase of AD. A changepoint model was used to determine which of the measures had a significant change in slope in relation to clinical symptom onset. Results: All nine measures had significant changepoints, all of which preceded symptom onset, however, the timing of these changepoints varied considerably. A single measure, CSF t-tau, had an early changepoint (34 years prior to symptom onset). A group of measures, including the remaining CSF measures (CSF Abeta and phosphorylated tau) and all cognitive tests had changepoints 10-15 years prior to symptom onset. A second group is formed by medial temporal lobe shape composite measures, with a 6-year time difference between the right and left side (respectively nine and 3 years prior to symptom onset). Conclusion: These findings highlight the long period of time prior to symptom onset during which AD pathology is accumulating in the brain. There are several significant findings, including the early changes in cognition and the laterality of the MRI findings. Additional work is needed to clarify their significance.
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Affiliation(s)
- Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Anja Soldan
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University, Baltimore, MD, United States
| | - Michael I Miller
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
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22
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Kuo SS, Pogue-Geile MF. Variation in fourteen brain structure volumes in schizophrenia: A comprehensive meta-analysis of 246 studies. Neurosci Biobehav Rev 2019; 98:85-94. [PMID: 30615934 PMCID: PMC6401304 DOI: 10.1016/j.neubiorev.2018.12.030] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 11/21/2018] [Accepted: 12/31/2018] [Indexed: 12/24/2022]
Abstract
Despite hundreds of structural MRI studies documenting smaller brain volumes on average in schizophrenia compared to controls, little attention has been paid to group differences in the variability of brain volumes. Examination of variability may help interpret mean group differences in brain volumes and aid in better understanding the heterogeneity of schizophrenia. Variability in 246 MRI studies was meta-analyzed for 13 structures that have shown medium to large mean effect sizes (Cohen's d≥0.4): intracranial volume, total brain volume, lateral ventricles, third ventricle, total gray matter, frontal gray matter, prefrontal gray matter, temporal gray matter, superior temporal gyrus gray matter, planum temporale, hippocampus, fusiform gyrus, insula; and a control structure, caudate nucleus. No significant differences in variability in cortical/subcortical volumes were detected in schizophrenia relative to controls. In contrast, increased variability was found in schizophrenia compared to controls for intracranial and especially lateral and third ventricle volumes. These findings highlight the need for more attention to ventricles and detailed analyses of brain volume distributions to better elucidate the pathophysiology of schizophrenia.
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Affiliation(s)
- Susan S Kuo
- Department of Psychology, University of Pittsburgh, 4209 Sennott Square, 210 South Bouquet St., Pittsburgh PA 15260, USA.
| | - Michael F Pogue-Geile
- Department of Psychology, University of Pittsburgh, 4209 Sennott Square, 210 South Bouquet St., Pittsburgh PA 15260, USA; Department of Psychology and Department of Psychiatry, University of Pittsburgh, 4207 Sennott Square, 210 South Bouquet St., Pittsburgh PA 15260, USA.
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23
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Kulason S, Tward DJ, Brown T, Sicat CS, Liu CF, Ratnanather JT, Younes L, Bakker A, Gallagher M, Albert M, Miller MI. Cortical thickness atrophy in the transentorhinal cortex in mild cognitive impairment. Neuroimage Clin 2018; 21:101617. [PMID: 30552075 PMCID: PMC6412863 DOI: 10.1016/j.nicl.2018.101617] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 11/19/2018] [Accepted: 11/24/2018] [Indexed: 11/24/2022]
Abstract
This study examines the atrophy rates of subjects with mild cognitive impairment (MCI) compared to controls in four regions within the medial temporal lobe: the transentorhinal cortex (TEC), entorhinal cortex (ERC), hippocampus, and amygdala. These regions were manually segmented and then corrected for undesirable longitudinal variability via Large Deformation Diffeomorphic Metric Mapping (LDDMM) based longitudinal diffeomorphometry. Diffeomorphometry techniques were used to compare thickness measurements in the TEC with the ERC. There were more significant changes in thickness atrophy rate in the TEC than medial regions of the entorhinal cortex. Volume measures were also calculated for all four regions. Classifiers were constructed using linear discriminant analysis to demonstrate that average thickness and atrophy rate of TEC together was the most discriminating measure compared to the thickness and volume measures in the areas examined, in differentiating MCI from controls. These findings are consistent with autopsy findings demonstrating that initial neuronal changes are found in TEC before spreading more medially in the ERC and to other regions in the medial temporal lobe. These findings suggest that the TEC thickness could serve as a biomarker for Alzheimer's disease in the prodromal phase of the disease.
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Affiliation(s)
- Sue Kulason
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
| | - Daniel J Tward
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Timothy Brown
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Chelsea S Sicat
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Chin-Fu Liu
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - J Tilak Ratnanather
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Laurent Younes
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Michela Gallagher
- Department of Psychological and Brain Sciences, Johns Hopkins School of Arts and Sciences, Baltimore, MD 21218, USA
| | - Marilyn Albert
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Michael I Miller
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD 21218, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA; Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA
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Lieberman JA, Girgis RR, Brucato G, Moore H, Provenzano F, Kegeles L, Javitt D, Kantrowitz J, Wall MM, Corcoran CM, Schobel SA, Small SA. Hippocampal dysfunction in the pathophysiology of schizophrenia: a selective review and hypothesis for early detection and intervention. Mol Psychiatry 2018; 23:1764-1772. [PMID: 29311665 PMCID: PMC6037569 DOI: 10.1038/mp.2017.249] [Citation(s) in RCA: 266] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 09/18/2017] [Accepted: 09/27/2017] [Indexed: 02/07/2023]
Abstract
Scientists have long sought to characterize the pathophysiologic basis of schizophrenia and develop biomarkers that could identify the illness. Extensive postmortem and in vivo neuroimaging research has described the early involvement of the hippocampus in the pathophysiology of schizophrenia. In this context, we have developed a hypothesis that describes the evolution of schizophrenia-from the premorbid through the prodromal stages to syndromal psychosis-and posits dysregulation of glutamate neurotransmission beginning in the CA1 region of the hippocampus as inducing attenuated psychotic symptoms and initiating the transition to syndromal psychosis. As the illness progresses, this pathological process expands to other regions of the hippocampal circuit and projection fields in other anatomic areas including the frontal cortex, and induces an atrophic process in which hippocampal neuropil is reduced and interneurons are lost. This paper will describe the studies of our group and other investigators supporting this pathophysiological hypothesis, as well as its implications for early detection and therapeutic intervention.
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Affiliation(s)
- JA Lieberman
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - RR Girgis
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - G Brucato
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - H Moore
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - F Provenzano
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - L Kegeles
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - D Javitt
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - J Kantrowitz
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA
| | - MM Wall
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA,New York State Psychiatric Institute, New York, NY, USA,Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - CM Corcoran
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - SA Schobel
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - SA Small
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University, New York, NY, USA,Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA,Department of Radiology, College of Physicians & Surgeons, Columbia University, New York, NY, USA
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Dill V, Klein PC, Franco AR, Pinho MS. Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters. Comput Biol Med 2018; 95:90-98. [DOI: 10.1016/j.compbiomed.2018.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 02/07/2018] [Accepted: 02/08/2018] [Indexed: 10/18/2022]
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26
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Woolley J, McGuire P. Neuroimaging in schizophrenia: what does it tell the clinician? ACTA ACUST UNITED AC 2018. [DOI: 10.1192/apt.11.3.195] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Neuroimaging has been used in clinical practice for over 30 years, but it is still perceived as rarely offering the psychiatrist much help in direct patient management. As newer imaging modalities are introduced (from computed tomography and positron and single photon emission tomography to magnetic and functional magnetic resonance imaging), the promise of imminent clinical utility is reawakened, only to fade as the innovation is shown to be another, albeit useful, research tool. The aim of this article is to update readers on some recent advances that are starting to align the research and clinical functions of neuroimaging. As imaging becomes more accessible and affordable there is real promise that both clinicians and patients will begin to benefit more directly.
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Mueller SG, Yushkevich PA, Das S, Wang L, Van Leemput K, Iglesias JE, Alpert K, Mezher A, Ng P, Paz K, Weiner MW. Systematic comparison of different techniques to measure hippocampal subfield volumes in ADNI2. Neuroimage Clin 2017; 17:1006-1018. [PMID: 29527502 PMCID: PMC5842756 DOI: 10.1016/j.nicl.2017.12.036] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 12/18/2017] [Accepted: 12/23/2017] [Indexed: 12/25/2022]
Abstract
Objective Subfield-specific measurements provide superior information in the early stages of neurodegenerative diseases compared to global hippocampal measurements. The overall goal was to systematically compare the performance of five representative manual and automated T1 and T2 based subfield labeling techniques in a sub-set of the ADNI2 population. Methods The high resolution T2 weighted hippocampal images (T2-HighRes) and the corresponding T1 images from 106 ADNI2 subjects (41 controls, 57 MCI, 8 AD) were processed as follows. A. T1-based: 1. Freesurfer + Large-Diffeomorphic-Metric-Mapping in combination with shape analysis. 2. FreeSurfer 5.1 subfields using in-vivo atlas. B. T2-HighRes: 1. Model-based subfield segmentation using ex-vivo atlas (FreeSurfer 6.0). 2. T2-based automated multi-atlas segmentation combined with similarity-weighted voting (ASHS). 3. Manual subfield parcellation. Multiple regression analyses were used to calculate effect sizes (ES) for group, amyloid positivity in controls, and associations with cognitive/memory performance for each approach. Results Subfield volumetry was better than whole hippocampal volumetry for the detection of the mild atrophy differences between controls and MCI (ES: 0.27 vs 0.11). T2-HighRes approaches outperformed T1 approaches for the detection of early stage atrophy (ES: 0.27 vs.0.10), amyloid positivity (ES: 0.11 vs 0.04), and cognitive associations (ES: 0.22 vs 0.19). Conclusions T2-HighRes subfield approaches outperformed whole hippocampus and T1 subfield approaches. None of the different T2-HghRes methods tested had a clear advantage over the other methods. Each has strengths and weaknesses that need to be taken into account when deciding which one to use to get the best results from subfield volumetry.
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Affiliation(s)
- Susanne G Mueller
- Dept. of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory, Dept. of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu Das
- Penn Image Computing and Science Laboratory, Dept. of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lei Wang
- Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Koen Van Leemput
- Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA; Dept. of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA; Translational Imaging Group, University College London, London, UK
| | - Kate Alpert
- Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Adam Mezher
- Center for Imaging of Neurodegenerative Diseases (CIND), VAMC San Francisco, San Francisco, CA, USA
| | - Peter Ng
- Center for Imaging of Neurodegenerative Diseases (CIND), VAMC San Francisco, San Francisco, CA, USA
| | - Katrina Paz
- Center for Imaging of Neurodegenerative Diseases (CIND), VAMC San Francisco, San Francisco, CA, USA
| | - Michael W Weiner
- Dept. of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
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Chang C, Huang C, Zhou N, Li SX, Ver Hoef L, Gao Y. The bumps under the hippocampus. Hum Brain Mapp 2017; 39:472-490. [PMID: 29058349 DOI: 10.1002/hbm.23856] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 10/09/2017] [Accepted: 10/11/2017] [Indexed: 12/27/2022] Open
Abstract
Shown in every neuroanatomy textbook, a key morphological feature is the bumpy ridges, which we refer to as hippocampal dentation, on the inferior aspect of the hippocampus. Like the folding of the cerebral cortex, hippocampal dentation allows for greater surface area in a confined space. However, examining numerous approaches to hippocampal segmentation and morphology analysis, virtually all published 3D renderings of the hippocampus show the inferior surface to be quite smooth or mildly irregular; we have rarely seen the characteristic bumpy structure on reconstructed 3D surfaces. The only exception is a 9.4T postmortem study (Yushkevich et al. [2009]: NeuroImage 44:385-398). An apparent question is, does this indicate that this specific morphological signature can only be captured using ultra high-resolution techniques? Or, is such information buried in the data we commonly acquire, awaiting a computation technique that can extract and render it clearly? In this study, we propose an automatic and robust super-resolution technique that captures the fine scale morphometric features of the hippocampus based on common 3T MR images. The method is validated on 9.4T ultra-high field images and then applied on 3T data sets. This method opens possibilities of future research on the hippocampus and other sub-cortical structural morphometry correlating the degree of dentation with a range of diseases including epilepsy, Alzheimer's disease, and schizophrenia. Hum Brain Mapp 39:472-490, 2018. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Cheng Chang
- Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, New York, 11794
| | - Chuan Huang
- Department of Radiology, Stony Brook University, Stony Brook, New York, 11794.,Department of Psychiatry, Stony Brook University, Stony Brook, New York, 11794
| | - Naiyun Zhou
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, 11794
| | - Shawn Xiang Li
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China
| | - Lawrence Ver Hoef
- Department of Neurology, The University of Alabama at Birmingham, CIRC 312, Birmingham, Alabama, 35294.,Epilepsy center, The University of Alabama at Birmingham, CIRC 312, Birmingham, Alabama, 35294
| | - Yi Gao
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060, China.,Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, 518060, China.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, 11794
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Segmenting hippocampal subfields from 3T MRI with multi-modality images. Med Image Anal 2017; 43:10-22. [PMID: 28961451 DOI: 10.1016/j.media.2017.09.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 08/14/2017] [Accepted: 09/18/2017] [Indexed: 11/23/2022]
Abstract
Hippocampal subfields play important roles in many brain activities. However, due to the small structural size, low signal contrast, and insufficient image resolution of 3T MR, automatic hippocampal subfields segmentation is less explored. In this paper, we propose an automatic learning-based hippocampal subfields segmentation method using 3T multi-modality MR images, including structural MRI (T1, T2) and resting state fMRI (rs-fMRI). The appearance features and relationship features are both extracted to capture the appearance patterns in structural MR images and also the connectivity patterns in rs-fMRI, respectively. In the training stage, these extracted features are adopted to train a structured random forest classifier, which is further iteratively refined in an auto-context model by adopting the context features and the updated relationship features. In the testing stage, the extracted features are fed into the trained classifiers to predict the segmentation for each hippocampal subfield, and the predicted segmentation is iteratively refined by the trained auto-context model. To our best knowledge, this is the first work that addresses the challenging automatic hippocampal subfields segmentation using relationship features from rs-fMRI, which is designed to capture the connectivity patterns of different hippocampal subfields. The proposed method is validated on two datasets and the segmentation results are quantitatively compared with manual labels using the leave-one-out strategy, which shows the effectiveness of our method. From experiments, we find a) multi-modality features can significantly increase subfields segmentation performance compared to those only using one modality; b) automatic segmentation results using 3T multi-modality MR images could be partially comparable to those using 7T T1 MRI.
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Kalmady SV, Shivakumar V, Arasappa R, Subramaniam A, Gautham S, Venkatasubramanian G, Gangadhar BN. Clinical correlates of hippocampus volume and shape in antipsychotic-naïve schizophrenia. Psychiatry Res Neuroimaging 2017; 263:93-102. [PMID: 28371658 DOI: 10.1016/j.pscychresns.2017.03.014] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 03/09/2017] [Accepted: 03/20/2017] [Indexed: 01/25/2023]
Abstract
While volume deficit of hippocampus is an established finding in schizophrenia, very few studies have examined large sample of patients without the confounding effect of antipsychotic treatment. Concurrent evaluation of hippocampus shape will offer additional information on the hippocampal aberrations in schizophrenia. In this study, we analyzed the volume and shape of hippocampus in antipsychotic-naïve schizophrenia patients (N=71) in comparison to healthy controls (N=82). Using 3-T MRI data, gray matter (GM) volume (anterior and posterior sub-divisions) and shape of the hippocampus were analyzed. Schizophrenia patients had significant hippocampal GM volume deficits (specifically the anterior sub-division) in comparison to healthy controls. There were significant positive correlations between anterior hippocampus volume and psychopathology scores of positive syndrome. Shape analyses revealed significant inward deformation of bilateral hippocampal surface in patients. In conclusion, our study findings add robust support for volume deficit in hippocampus in antipsychotic-naïve schizophrenia. Hippocampal shape deficits in schizophrenia observed in this study map to anterior CA1 sub-region. The differential relationship of anterior hippocampus (but not posterior hippocampus) with clinical symptoms is in tune with the findings in animal models. Further systematic studies are needed to evaluate the relationship between these hippocampal gray matter deficits with white matter and functional connectivity to facilitate understanding the hippocampal network abnormalities in schizophrenia.
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Affiliation(s)
- Sunil Vasu Kalmady
- The Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India; Translational Psychiatry Laboratory, Cognitive Neurobiology Division, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - Venkataram Shivakumar
- The Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India; Translational Psychiatry Laboratory, Cognitive Neurobiology Division, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - Rashmi Arasappa
- The Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - Aditi Subramaniam
- The Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - S Gautham
- The Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - Ganesan Venkatasubramanian
- The Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India; Translational Psychiatry Laboratory, Cognitive Neurobiology Division, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, India.
| | - Bangalore N Gangadhar
- The Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India
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Fleming Beattie J, Martin RC, Kana RK, Deshpande H, Lee S, Curé J, Ver Hoef L. Hippocampal dentation: Structural variation and its association with episodic memory in healthy adults. Neuropsychologia 2017; 101:65-75. [PMID: 28472628 DOI: 10.1016/j.neuropsychologia.2017.04.036] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Revised: 04/26/2017] [Accepted: 04/29/2017] [Indexed: 11/29/2022]
Abstract
While the hippocampus has long been identified as a structure integral to memory, the relationship between morphology and function has yet to be fully explained. We present an analysis of hippocampal dentation, a morphological feature previously unexplored in regard to its relationship with episodic memory. "Hippocampal dentation" in this case refers to surface convolutions, primarily present in the CA1/subiculum on the inferior aspect of the hippocampus. Hippocampal dentation was visualized using ultra-high resolution structural MRI and evaluated using a novel visual rating scale. The degree of hippocampal dentation was found to vary considerably across individuals, and was positively associated with verbal memory recall and visual memory recognition in a sample of 22 healthy adults. This study is the first to characterize the variation in hippocampal dentation in a healthy cohort and to demonstrate its association with aspects of episodic memory.
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Affiliation(s)
| | - Roy C Martin
- University of Alabama at Birmingham, Department of Neurology, USA
| | - Rajesh K Kana
- University of Alabama at Birmingham, Department of Psychology, USA
| | | | - Seongtaek Lee
- University of Alabama at Birmingham, Department of Biomedical Engineering, USA
| | - Joel Curé
- University of Alabama at Birmingham, Department of Radiology, USA
| | - Lawrence Ver Hoef
- University of Alabama at Birmingham, Department of Neurology, USA; Birmingham VA Medical Center, Department of Neurology, USA.
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Stratton J, Brook M, Hanlon RE. Murder and psychosis: Neuropsychological profiles of homicide offenders with schizophrenia. CRIMINAL BEHAVIOUR AND MENTAL HEALTH : CBMH 2017; 27:146-161. [PMID: 26864713 DOI: 10.1002/cbm.1990] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Revised: 02/27/2015] [Accepted: 11/11/2015] [Indexed: 06/05/2023]
Abstract
BACKGROUND Neurocognitive dysfunction, a core feature of schizophrenia, is thought to contribute to the impulsive violent aggression manifested by some individuals with schizophrenia, but not enough is known about how homicidal individuals with schizophrenia perform on neuropsychological measures. AIMS The primary aim of our study was to describe the neuropsychological profiles of homicide offenders with schizophrenia. Supplementary analyses compared the criminal, psychiatric and neuropsychological features of schizophrenic homicide offenders with and without God/Satan/demon-themed psychotic symptoms. METHODS Twenty-five men and women diagnosed with schizophrenia who had killed another person - 21 convicted of first-degree murder and 4 found not guilty by reason of insanity - completed neuropsychological testing during forensic evaluations. RESULTS The sample was characterised by extensive neurocognitive impairments, involving executive dysfunction (60%), memory dysfunction (68%) and attentional dysfunction (50%), although those with God/Satan/demon-themed psychotic symptoms performed better than those with nonreligious psychotic content. CONCLUSIONS Our findings indicate that impaired cognition may play an important role in the commission of homicide by individuals with schizophrenia. A subgroup with God/Satan/demon delusions seem sufficiently less impaired that they might be able to engage in metacognitive treatment approaches, aimed at changing their relationship to their psychotic symptoms, thus reducing the perception of power and omnipotence of hallucinated voices and increasing their safety. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- John Stratton
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Michael Brook
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
| | - Robert E Hanlon
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
- Neuropsychological Associates of Chicago, Chicago, IL, USA
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Apple AC, Ryals AJ, Alpert KI, Wagner LI, Shih PA, Dokucu M, Cella D, Penedo FJ, Voss JL, Wang L. Subtle hippocampal deformities in breast cancer survivors with reduced episodic memory and self-reported cognitive concerns. NEUROIMAGE-CLINICAL 2017; 14:685-691. [PMID: 28377882 PMCID: PMC5369871 DOI: 10.1016/j.nicl.2017.03.004] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Revised: 03/02/2017] [Accepted: 03/10/2017] [Indexed: 12/13/2022]
Abstract
Cancer survivors have lingering cognitive problems, however the anatomical basis for these problems has yet to be fully elucidated. Clinical studies as well as animal models of chemotherapy have pinpointed cell and volume loss to the hippocampus, however, few studies have performed shape analysis of the hippocampus on cancer survivors. This study used high-dimensional deformation mapping analysis to test whether localized hippocampal deformation differs in breast cancer survivors who received adjuvant chemotherapy coupled with hormone blockade therapy, and if deformation was related to subjective self-reported concerns and cognitive performance. 3 T MRI images were acquired from 16 pre-menopausal breast cancer survivors and 18 healthy controls without a history of cancer. Breast cancer survivors had undergone chemotherapy within the eighteen months prior to the study, and were receiving estrogen-blockade therapy at the time of the study. Automated high-dimensional deformation mapping was used to compare localized hippocampal deformation differences between groups. Self-reported subjective concerns were assessed using Neuro-QOL Cognitive Function assessment, whereas cognitive performance was evaluated using the NIH Toolbox Cognition Battery. Relative to healthy controls, cancer survivors showed significantly more inward hippocampal deformation, worse self-reported cognitive functioning, and inferior episodic memory test score. This study is the first of its kind to examine the relationship between hippocampal deformity and cognitive impairment in cancer survivors. Cancer survivors demonstrated significant inward hippocampal deformation. Survivors self-reported worse cognitive functioning. Survivors performed worse than controls on a test of episodic memory.
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Affiliation(s)
- Alexandra C. Apple
- Division of Clinical Psychology, Northwestern University Feinberg School of Medicine, United States
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, United States
- Corresponding author at: Northwestern University Feinberg School of Medicine, Abbott Hall Suite 1306, 710 N Lake Shore Drive, Chicago, IL 60611, United States.Northwestern University Feinberg School of MedicineAbbott Hall Suite 1306710 N Lake Shore DriveChicagoIL60611United States
| | - Anthony J. Ryals
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, United States
| | - Kathryn I. Alpert
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, United States
| | - Lynne I. Wagner
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, United States
| | - Pei-An Shih
- Department of Psychiatry, University of California, San Diego, United States
| | - Mehmet Dokucu
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, United States
| | - David Cella
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, United States
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, United States
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, United States
| | - Frank J. Penedo
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, United States
| | - Joel L. Voss
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, United States
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, United States
- Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, United States
| | - Lei Wang
- Division of Clinical Psychology, Northwestern University Feinberg School of Medicine, United States
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, United States
- Department of Radiology, Northwestern University Feinberg School of Medicine, United States
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Anticevic A, Schleifer C, Youngsun TC. Emotional and cognitive dysregulation in schizophrenia and depression: understanding common and distinct behavioral and neural mechanisms. DIALOGUES IN CLINICAL NEUROSCIENCE 2016. [PMID: 26869843 PMCID: PMC4734880 DOI: 10.31887/dcns.2015.17.4/aanticevic] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Emerging behavioral and neuroimaging studies in schizophrenia (SCZ) and major depressive disorder (MD) are mapping mechanisms of co-occurring and distinct affective disturbances across these disorders. This constitutes a critical goal towards developing rationally guided therapies for upstream neural pathways that contribute to comorbid symptoms across disorders. We highlight the current state of the art in our understanding of emotional dysregulation in SCZ versus MD by focusing on broad domains of behavioral function that can map onto underlying neural systems, namely deficits in hedonics, anticipatory behaviors, computations underlying value and effort, and effortful goal-directed behaviors needed to pursue rewarding outcomes. We highlight unique disturbances in each disorder that may involve dissociable neural systems, but also possible interactions between affect and cognition in MD versus SCZ. Finally, we review computational and translational approaches that offer mechanistic insight into how cellular-level disruptions can lead to complex affective disturbances, informing development of therapies across MD and SCZ.
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Affiliation(s)
- Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine; Interdepartmental Neuroscience Program, Yale University; NIAAA Center for the Translational Neuroscience of Alcoholism; Department of Psychology, Yale University; Division of Neurocognition, Neurogenetics & Neurocomputation, Yale University School of Medicine (Alan Anticevic) - New Haven, Connecticut, USA
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Multidimensional heritability analysis of neuroanatomical shape. Nat Commun 2016; 7:13291. [PMID: 27845344 PMCID: PMC5116071 DOI: 10.1038/ncomms13291] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 09/21/2016] [Indexed: 12/11/2022] Open
Abstract
In the dawning era of large-scale biomedical data, multidimensional phenotype vectors will play an increasing role in examining the genetic underpinnings of brain features, behaviour and disease. For example, shape measurements derived from brain MRI scans are multidimensional geometric descriptions of brain structure and provide an alternate class of phenotypes that remains largely unexplored in genetic studies. Here we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide SNP and MRI data from 1,320 unrelated, young and healthy individuals. We replicate our findings in an extended twin sample from the Human Connectome Project (HCP). Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and can serve as a complementary phenotype to study the genetic determinants and clinical relevance of brain structure.
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Crum WR, Danckaers F, Huysmans T, Cotel MC, Natesan S, Modo MM, Sijbers J, Williams SCR, Kapur S, Vernon AC. Chronic exposure to haloperidol and olanzapine leads to common and divergent shape changes in the rat hippocampus in the absence of grey-matter volume loss. Psychol Med 2016; 46:3081-3093. [PMID: 27516217 PMCID: PMC5108303 DOI: 10.1017/s0033291716001768] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2015] [Revised: 06/23/2016] [Accepted: 06/23/2016] [Indexed: 12/15/2022]
Abstract
BACKGROUND One of the most consistently reported brain abnormalities in schizophrenia (SCZ) is decreased volume and shape deformation of the hippocampus. However, the potential contribution of chronic antipsychotic medication exposure to these phenomena remains unclear. METHOD We examined the effect of chronic exposure (8 weeks) to clinically relevant doses of either haloperidol (HAL) or olanzapine (OLZ) on adult rat hippocampal volume and shape using ex vivo structural MRI with the brain retained inside the cranium to prevent distortions due to dissection, followed by tensor-based morphometry (TBM) and elastic surface-based shape deformation analysis. The volume of the hippocampus was also measured post-mortem from brain tissue sections in each group. RESULTS Chronic exposure to either HAL or OLZ had no effect on the volume of the hippocampus, even at exploratory thresholds, which was confirmed post-mortem. In contrast, shape deformation analysis revealed that chronic HAL and OLZ exposure lead to both common and divergent shape deformations (q = 0.05, FDR-corrected) in the rat hippocampus. In particular, in the dorsal hippocampus, HAL exposure led to inward shape deformation, whereas OLZ exposure led to outward shape deformation. Interestingly, outward shape deformations that were common to both drugs occurred in the ventral hippocampus. These effects remained significant after controlling for hippocampal volume suggesting true shape changes. CONCLUSIONS Chronic exposure to either HAL or OLZ leads to both common and divergent effects on rat hippocampal shape in the absence of volume change. The implications of these findings for the clinic are discussed.
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Affiliation(s)
- W. R. Crum
- Department of Neuroimaging,
King's College London, Institute of
Psychiatry, Psychology and Neuroscience,
Centre for Neuroimaging Sciences, De Crespigny
Park, London, UK
| | - F. Danckaers
- Department of Physics,
iMinds-Vision Laboratory, University of
Antwerp, Antwerp, Belgium
| | - T. Huysmans
- Department of Physics,
iMinds-Vision Laboratory, University of
Antwerp, Antwerp, Belgium
| | - M.-C. Cotel
- Department of Psychosis Studies,
King's College London, Institute of
Psychiatry, Psychology and Neuroscience, De
Crespigny Park, London, UK
| | - S. Natesan
- Department of Psychosis Studies,
King's College London, Institute of
Psychiatry, Psychology and Neuroscience, De
Crespigny Park, London, UK
| | - M. M. Modo
- Department of Basic and Clinical
Neuroscience, King's College London,
Institute of Psychiatry, Psychology and
Neuroscience, Maurice Wohl Institute for Clinical
Neuroscience, London, UK
| | - J. Sijbers
- Department of Physics,
iMinds-Vision Laboratory, University of
Antwerp, Antwerp, Belgium
| | - S. C. R. Williams
- Department of Neuroimaging,
King's College London, Institute of
Psychiatry, Psychology and Neuroscience,
Centre for Neuroimaging Sciences, De Crespigny
Park, London, UK
| | - S. Kapur
- Department of Psychosis Studies,
King's College London, Institute of
Psychiatry, Psychology and Neuroscience, De
Crespigny Park, London, UK
| | - A. C. Vernon
- Department of Psychosis Studies,
King's College London, Institute of
Psychiatry, Psychology and Neuroscience, De
Crespigny Park, London, UK
- Department of Basic and Clinical
Neuroscience, King's College London,
Institute of Psychiatry, Psychology and
Neuroscience, Maurice Wohl Institute for Clinical
Neuroscience, London, UK
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Kang E, Wen Z, Song H, Christian KM, Ming GL. Adult Neurogenesis and Psychiatric Disorders. Cold Spring Harb Perspect Biol 2016; 8:cshperspect.a019026. [PMID: 26801682 DOI: 10.1101/cshperspect.a019026] [Citation(s) in RCA: 137] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Psychiatric disorders continue to be among the most challenging disorders to diagnose and treat because there is no single genetic or anatomical locus that is causative for the disease. Current treatments are often blunt tools used to ameliorate the most severe symptoms, at the risk of disrupting functional neural systems. There is a critical need to develop new therapeutic strategies that can target circumscribed functional or anatomical domains of pathology. Adult hippocampal neurogenesis may be one such domain. Here, we review the evidence suggesting that adult hippocampal neurogenesis plays a role in emotional regulation and forms of learning and memory that include temporal and spatial memory encoding and context discrimination, and that its dysregulation is associated with psychiatric disorders, such as affective disorders, schizophrenia, and drug addiction. Further, adult neurogenesis has proven to be an effective model to investigate basic processes of neuronal development and converging evidence suggests that aberrant neural development may be an etiological factor, even in late-onset diseases. Constitutive neurogenesis in the hippocampus of the mature brain reflects large-scale plasticity unique to this region and could be a potential hub for modulation of a subset of cognitive and affective behaviors that are affected by multiple psychiatric disorders.
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Affiliation(s)
- Eunchai Kang
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205
| | - Zhexing Wen
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205
| | - Hongjun Song
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 Graduate Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205
| | - Kimberly M Christian
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205
| | - Guo-Li Ming
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 Graduate Program in Cellular and Molecular Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205
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39
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Faria AV, Ratnanather JT, Tward DJ, Lee DS, van den Noort F, Wu D, Brown T, Johnson H, Paulsen JS, Ross CA, Younes L, Miller MI. Linking white matter and deep gray matter alterations in premanifest Huntington disease. Neuroimage Clin 2016; 11:450-460. [PMID: 27104139 PMCID: PMC4827723 DOI: 10.1016/j.nicl.2016.02.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 02/17/2016] [Accepted: 02/22/2016] [Indexed: 01/07/2023]
Abstract
Huntington disease (HD) is a fatal progressive neurodegenerative disorder for which only symptomatic treatment is available. A better understanding of the pathology, and identification of biomarkers will facilitate the development of disease-modifying treatments. HD is potentially a good model of a neurodegenerative disease for development of biomarkers because it is an autosomal-dominant disease with complete penetrance, caused by a single gene mutation, in which the neurodegenerative process can be assessed many years before onset of signs and symptoms of manifest disease. Previous MRI studies have detected abnormalities in gray and white matter starting in premanifest stages. However, the understanding of how these abnormalities are related, both in time and space, is still incomplete. In this study, we combined deep gray matter shape diffeomorphometry and white matter DTI analysis in order to provide a better mapping of pathology in the deep gray matter and subcortical white matter in premanifest HD. We used 296 MRI scans from the PREDICT-HD database. Atrophy in the deep gray matter, thalamus, hippocampus, and nucleus accumbens was analyzed by surface based morphometry, and while white matter abnormalities were analyzed in (i) regions of interest surrounding these structures, using (ii) tractography-based analysis, and using (iii) whole brain atlas-based analysis. We detected atrophy in the deep gray matter, particularly in putamen, from early premanifest stages. The atrophy was greater both in extent and effect size in cases with longer exposure to the effects of the CAG expansion mutation (as assessed by greater CAP-scores), and preceded detectible abnormalities in the white matter. Near the predicted onset of manifest HD, the MD increase was widespread, with highest indices in the deep and posterior white matter. This type of in-vivo macroscopic mapping of HD brain abnormalities can potentially indicate when and where therapeutics could be targeted to delay the onset or slow the disease progression.
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Affiliation(s)
- Andreia V Faria
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - J Tilak Ratnanather
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Daniel J Tward
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - David Soobin Lee
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Frieda van den Noort
- MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - Dan Wu
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Timothy Brown
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA
| | - Hans Johnson
- Department of Psychiatry, The University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Jane S Paulsen
- Department of Psychiatry, The University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Christopher A Ross
- Division of Neurobiology, Department of Psychiatry, and Departments of Neurology, Neuroscience and Pharmacology, Johns Hopkins University, Baltimore, MD, USA
| | - Laurent Younes
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, The Johns Hopkins University, Baltimore, MD, USA
| | - Michael I Miller
- Center for Imaging Science, The Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA
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40
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Dean DJ, Orr JM, Bernard JA, Gupta T, Pelletier-Baldelli A, Carol EE, Mittal VA. Hippocampal Shape Abnormalities Predict Symptom Progression in Neuroleptic-Free Youth at Ultrahigh Risk for Psychosis. Schizophr Bull 2016; 42:161-9. [PMID: 26113620 PMCID: PMC4681548 DOI: 10.1093/schbul/sbv086] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Hippocampal abnormalities have been widely studied in schizophrenia spectrum populations including those at ultrahigh risk (UHR) for psychosis. There have been inconsistent findings concerning hippocampal morphology prior to and during the transition to psychosis, and little is known about how specific subregions are related to the symptom progression. METHODS A total of 80 participants (38 UHR and 42 healthy controls) underwent a 3T MRI scan, as well as structured clinical interviews. Shape analysis of hippocampi was conducted with FSL/FIRST vertex analysis to yield a localized measure of shape differences between groups. A subgroup of the sample (24 UHR and 24 controls) also returned for a 12-month clinical follow-up assessment. RESULTS The UHR group exhibited smaller hippocampal volumes bilaterally, and shape analysis revealed significant inversion in the left ventral posterior hippocampus in the UHR group. Greater inversion in this subregion was related to elevated symptomatology at baseline and increased positive symptoms, negative symptoms, and impaired tolerance to normal stress 12 months later. These results did not hold when left hippocampal volume was used as a predictor instead. DISCUSSION This represents the first study to use vertex analysis in a UHR sample and results suggest that abnormalities in hippocampal shape appear to reflect underlying pathogenic processes driving the progression of illness. These findings suggest that examining shape and volume may provide an important new perspective for our conception of brain alterations in the UHR period.
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Affiliation(s)
- Derek J Dean
- Department of Psychology and Neuroscience, Center for Neuroscience, and
| | - Joseph M Orr
- Institute for Cognitive Science, University of Colorado Boulder, Boulder, CO
| | | | - Tina Gupta
- Department of Psychology and Neuroscience
| | | | | | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL
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41
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Miller MI, Trouvé A, Younes L. Hamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson. Annu Rev Biomed Eng 2015; 17:447-509. [PMID: 26643025 DOI: 10.1146/annurev-bioeng-071114-040601] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The Computational Anatomy project is the morphome-scale study of shape and form, which we model as an orbit under diffeomorphic group action. Metric comparison calculates the geodesic length of the diffeomorphic flow connecting one form to another. Geodesic connection provides a positioning system for coordinatizing the forms and positioning their associated functional information. This article reviews progress since the Euler-Lagrange characterization of the geodesics a decade ago. Geodesic positioning is posed as a series of problems in Hamiltonian control, which emphasize the key reduction from the Eulerian momentum with dimension of the flow of the group, to the parametric coordinates appropriate to the dimension of the submanifolds being positioned. The Hamiltonian viewpoint provides important extensions of the core setting to new, object-informed positioning systems. Several submanifold mapping problems are discussed as they apply to metamorphosis, multiple shape spaces, and longitudinal time series studies of growth and atrophy via shape splines.
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Affiliation(s)
- Michael I Miller
- Center of Imaging Science.,Department of Biomedical Engineering.,Kavli Neuroscience Discovery Institute, and
| | - Alain Trouvé
- CMLA, ENS Cachan, CNRS, Université Paris-Saclay, 94235 Cachan, France;
| | - Laurent Younes
- Center of Imaging Science.,Department of Applied Mathematics, The John Hopkins University, Baltimore, Maryland 21218; ,
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42
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Kalmady SV, Shivakumar V, Gautham S, Arasappa R, Jose DA, Venkatasubramanian G, Gangadhar BN. Dermatoglyphic correlates of hippocampus volume: Evaluation of aberrant neurodevelopmental markers in antipsychotic-naïve schizophrenia. Psychiatry Res 2015; 234:113-20. [PMID: 26385539 DOI: 10.1016/j.pscychresns.2015.09.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Revised: 07/23/2015] [Accepted: 09/02/2015] [Indexed: 12/31/2022]
Abstract
Schizophrenia is a disorder of aberrant neurodevelopment is marked by abnormalities in brain structure and dermatoglyphic traits. However, the link between these two (i.e. dermatoglyphic parameters and brain structure) which share ectodermal origin and common developmental window has not been explored extensively. The current study examined dermatoglyphic correlates of hippocampal volume in antipsychotic-naïve schizophrenia patients in comparison with matched healthy controls. Ridge counts and asymmetry measures for palmar inter-digital areas (a-b, b-c, c-d) were obtained using high resolution digital scans of palms from 89 schizophrenia patients [M:F=48:41] and 48 healthy controls [M:F=30:18]. Brain scans were obtained for subset of subjects including 26 antipsychotic-naïve patients [M:F=13:13] and 29 healthy controls [M:F=19:10] using 3 T-MRI. Hippocampal volume and palmar ridge counts were measured by blinded raters with good inter-rater reliability using valid methods. Directional asymmetry (DA) of b-c and bilateral hippocampal volume were significantly lower in patients than controls. Significant positive correlation was found between DA and ridge count of b-c with bilateral anterior hippocampal volume. Study demonstrates the utility of dermatoglyphic markers in identifying structural changes in the brain which may form the basis for neurodevelopmental pathogenesis in schizophrenia.
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Affiliation(s)
- Sunil V Kalmady
- InSTAR Program, Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India; Translational Psychiatry Laboratory, Neurobiology Research Centre, NIMHANS, Bangalore, India
| | - Venkataram Shivakumar
- InSTAR Program, Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India; Translational Psychiatry Laboratory, Neurobiology Research Centre, NIMHANS, Bangalore, India
| | - S Gautham
- Translational Psychiatry Laboratory, Neurobiology Research Centre, NIMHANS, Bangalore, India
| | - Rashmi Arasappa
- InSTAR Program, Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India; Translational Psychiatry Laboratory, Neurobiology Research Centre, NIMHANS, Bangalore, India
| | - Dania A Jose
- Translational Psychiatry Laboratory, Neurobiology Research Centre, NIMHANS, Bangalore, India
| | - Ganesan Venkatasubramanian
- InSTAR Program, Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India; Translational Psychiatry Laboratory, Neurobiology Research Centre, NIMHANS, Bangalore, India.
| | - B N Gangadhar
- InSTAR Program, Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health & Neurosciences (NIMHANS), Bangalore, India; Translational Psychiatry Laboratory, Neurobiology Research Centre, NIMHANS, Bangalore, India
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43
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Guo T, Winterburn JL, Pipitone J, Duerden EG, Park MTM, Chau V, Poskitt KJ, Grunau RE, Synnes A, Miller SP, Mallar Chakravarty M. Automatic segmentation of the hippocampus for preterm neonates from early-in-life to term-equivalent age. NEUROIMAGE-CLINICAL 2015; 9:176-93. [PMID: 26740912 PMCID: PMC4561668 DOI: 10.1016/j.nicl.2015.07.019] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Revised: 07/15/2015] [Accepted: 07/16/2015] [Indexed: 11/26/2022]
Abstract
Introduction The hippocampus, a medial temporal lobe structure central to learning and memory, is particularly vulnerable in preterm-born neonates. To date, segmentation of the hippocampus for preterm-born neonates has not yet been performed early-in-life (shortly after birth when clinically stable). The present study focuses on the development and validation of an automatic segmentation protocol that is based on the MAGeT-Brain (Multiple Automatically Generated Templates) algorithm to delineate the hippocampi of preterm neonates on their brain MRIs acquired at not only term-equivalent age but also early-in-life. Methods First, we present a three-step manual segmentation protocol to delineate the hippocampus for preterm neonates and apply this protocol on 22 early-in-life and 22 term images. These manual segmentations are considered the gold standard in assessing the automatic segmentations. MAGeT-Brain, automatic hippocampal segmentation pipeline, requires only a small number of input atlases and reduces the registration and resampling errors by employing an intermediate template library. We assess the segmentation accuracy of MAGeT-Brain in three validation studies, evaluate the hippocampal growth from early-in-life to term-equivalent age, and study the effect of preterm birth on the hippocampal volume. The first experiment thoroughly validates MAGeT-Brain segmentation in three sets of 10-fold Monte Carlo cross-validation (MCCV) analyses with 187 different groups of input atlases and templates. The second experiment segments the neonatal hippocampi on 168 early-in-life and 154 term images and evaluates the hippocampal growth rate of 125 infants from early-in-life to term-equivalent age. The third experiment analyzes the effect of gestational age (GA) at birth on the average hippocampal volume at early-in-life and term-equivalent age using linear regression. Results The final segmentations demonstrate that MAGeT-Brain consistently provides accurate segmentations in comparison to manually derived gold standards (mean Dice's Kappa > 0.79 and Euclidean distance <1.3 mm between centroids). Using this method, we demonstrate that the average volume of the hippocampus is significantly different (p < 0.0001) in early-in-life (621.8 mm3) and term-equivalent age (958.8 mm3). Using these differences, we generalize the hippocampal growth rate to 38.3 ± 11.7 mm3/week and 40.5 ± 12.9 mm3/week for the left and right hippocampi respectively. Not surprisingly, younger gestational age at birth is associated with smaller volumes of the hippocampi (p = 0.001). Conclusions MAGeT-Brain is capable of segmenting hippocampi accurately in preterm neonates, even at early-in-life. Hippocampal asymmetry with a larger right side is demonstrated on early-in-life images, suggesting that this phenomenon has its onset in the 3rd trimester of gestation. Hippocampal volume assessed at the time of early-in-life and term-equivalent age is linearly associated with GA at birth, whereby smaller volumes are associated with earlier birth. We develop a MAGeT-Brain based automatic protocol to segment hippocampus in preterm neonates. MAGeT-Brain can accurately segment hippocampus in preterm neonates, even at early-in-life. Hippocampal asymmetry with a larger right side is demonstrated on early-in-life images. Smaller hippocampal volumes are associated with earlier birth in preterm neonates.
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Affiliation(s)
- Ting Guo
- Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, ON, Canada
| | - Julie L Winterburn
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Kimel Family Translational Imaging, Genetics Research Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Canada
| | - Jon Pipitone
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Kimel Family Translational Imaging, Genetics Research Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Canada
| | - Emma G Duerden
- Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, ON, Canada
| | - Min Tae M Park
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health Research Institute, Verdun, QC, Canada
| | - Vann Chau
- Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, ON, Canada
| | - Kenneth J Poskitt
- Department of Pediatrics, University of British Columbia and Child and Family Research Institute, Vancouver, BC, Canada
| | - Ruth E Grunau
- Department of Pediatrics, University of British Columbia and Child and Family Research Institute, Vancouver, BC, Canada
| | - Anne Synnes
- Department of Pediatrics, University of British Columbia and Child and Family Research Institute, Vancouver, BC, Canada
| | - Steven P Miller
- Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, ON, Canada; Department of Paediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, ON, Canada
| | - M Mallar Chakravarty
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Cerebral Imaging Centre, Douglas Mental Health Research Institute, Verdun, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada
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Wolff AR, Bilkey DK. Prenatal immune activation alters hippocampal place cell firing characteristics in adult animals. Brain Behav Immun 2015; 48:232-43. [PMID: 25843370 DOI: 10.1016/j.bbi.2015.03.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2014] [Revised: 03/12/2015] [Accepted: 03/24/2015] [Indexed: 12/27/2022] Open
Abstract
Prenatal maternal immune activation (MIA) is a risk factor for several developmental neuropsychiatric disorders, including autism, bipolar disorder and schizophrenia. Adults with these disorders display alterations in memory function that may result from changes in the structure and function of the hippocampus. In the present study we use an animal model to investigate the effect that a transient prenatal maternal immune activation episode has on the spatially-modulated firing activity of hippocampal neurons in adult animals. MIA was induced in pregnant rat dams with a single injection of the synthetic cytokine inducer polyinosinic:polycytidylic acid (poly I:C) on gestational day 15. Control dams were given a saline equivalent. Firing activity and local field potentials (LFPs) were recorded from the CA1 region of the adult male offspring of these dams as they moved freely in an open arena. Most neurons displayed characteristic spatially-modulated 'place cell' firing activity and while there was no between-group difference in mean firing rate between groups, place cells had smaller place fields in MIA-exposed animals when compared to control-group cells. Cells recorded in MIA-group animals also displayed an altered firing-phase synchrony relationship to simultaneously recorded LFPs. When the floor of the arena was rotated, the place fields of MIA-group cells were more likely to shift in the same direction as the floor rotation, suggesting that local cues may have been more salient for these animals. In contrast, place fields in control group cells were more likely to shift firing position to novel spatial locations suggesting an altered response to contextual cues. These findings show that a single MIA intervention is sufficient to change several important characteristics of hippocampal place cell activity in adult offspring. These changes could contribute to the memory dysfunction that is associated with MIA, by altering the encoding of spatial context and by disrupting plasticity mechanisms that are dependent on spike timing synchrony.
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Affiliation(s)
- Amy R Wolff
- Department of Psychology, Brain Health Research Centre, University of Otago, Dunedin, New Zealand
| | - David K Bilkey
- Department of Psychology, Brain Health Research Centre, University of Otago, Dunedin, New Zealand.
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45
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Onofrey JA, Papademetris X, Staib LH. Low-Dimensional Non-Rigid Image Registration Using Statistical Deformation Models From Semi-Supervised Training Data. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1522-1532. [PMID: 25720017 PMCID: PMC8802338 DOI: 10.1109/tmi.2015.2404572] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Accurate and robust image registration is a fundamental task in medical image analysis applications, and requires non-rigid transformations with a large number of degrees of freedom. Statistical deformation models (SDMs) attempt to learn the distribution of non-rigid deformations, and can be used both to reduce the transformation dimensionality and to constrain the registration process. However, high-dimensional SDMs are difficult to train given orders of magnitude fewer training samples. In this paper, we utilize both a small set of annotated imaging data and a large set of unlabeled data to effectively learn an SDM of non-rigid transformations in a semi-supervised training (SST) framework. We demonstrate results applying this framework towards inter-subject registration of skull-stripped, magnetic resonance (MR) brain images. Our approach makes use of 39 labeled MR datasets to create a set of supervised registrations, which we augment with a set of over 1200 unsupervised registrations using unlabeled MRIs. Through leave-one-out cross validation, we show that SST of a non-rigid SDM results in a robust registration algorithm with significantly improved accuracy compared to standard, intensity-based registration, and does so with a 99% reduction in transformation dimensionality.
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Affiliation(s)
- John A. Onofrey
- Department of Diagnostic Radiology, Yale University, New Haven, CT 06520 USA
| | - Xenophon Papademetris
- Departments of Diagnostic Radiology and Biomedical Engineering, Yale University, New Haven, CT 06520 USA
| | - Lawrence H. Staib
- Departments of Diagnostic Radiology, Electrical Engineering, and Biomedical Engineering, Yale University, New Haven, CT 06520 USA
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46
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Cole JH, Filippetti ML, Allin MPG, Walshe M, Nam KW, Gutman BA, Murray RM, Rifkin L, Thompson PM, Nosarti C. Subregional Hippocampal Morphology and Psychiatric Outcome in Adolescents Who Were Born Very Preterm and at Term. PLoS One 2015; 10:e0130094. [PMID: 26091104 PMCID: PMC4474892 DOI: 10.1371/journal.pone.0130094] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 05/15/2015] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The hippocampus has been reported to be structurally and functionally altered as a sequel of very preterm birth (<33 weeks gestation), possibly due its vulnerability to hypoxic-ischemic damage in the neonatal period. We examined hippocampal volumes and subregional morphology in very preterm born individuals in mid- and late adolescence and their association with psychiatric outcome. METHODS Structural brain magnetic resonance images were acquired at two time points (baseline and follow-up) from 65 ex-preterm adolescents (mean age = 15.5 and 19.6 years) and 36 term-born controls (mean age=15.0 and 19.0 years). Hippocampal volumes and subregional morphometric differences were measured from manual tracings and with three-dimensional shape analysis. Psychiatric outcome was assessed with the Rutter Parents' Scale at baseline, the General Health Questionnaire at follow-up and the Peters Delusional Inventory at both time points. RESULTS In contrast to previous studies we did not find significant difference in the cross-sectional or longitudinal hippocampal volumes between individuals born preterm and controls, despite preterm individual having significantly smaller whole brain volumes. Shape analysis at baseline revealed subregional deformations in 28% of total bilateral hippocampal surface, reflecting atrophy, in ex-preterm individuals compared to controls, and in 22% at follow-up. In ex-preterm individuals, longitudinal changes in hippocampal shape accounted for 11% of the total surface, while in controls they reached 20%. In the whole sample (both groups) larger right hippocampal volume and bilateral anterior surface deformations at baseline were associated with delusional ideation scores at follow-up. CONCLUSIONS This study suggests a dynamic association between cross-sectional hippocampal volumes, longitudinal changes and surface deformations and psychosis proneness.
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Affiliation(s)
- James H. Cole
- The Computational, Cognitive & Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, Burlington Danes Building, Du Cane Road, London, United Kingdom
| | - Maria Laura Filippetti
- Department of Psychosis Studies, Institute of Psychiatry, King’s Health Partners, King’s College London, De Crespigny Park, London, United Kingdom
| | - Matthew P. G. Allin
- Department of Psychosis Studies, Institute of Psychiatry, King’s Health Partners, King’s College London, De Crespigny Park, London, United Kingdom
| | - Muriel Walshe
- Department of Psychosis Studies, Institute of Psychiatry, King’s Health Partners, King’s College London, De Crespigny Park, London, United Kingdom
| | - Kie Woo Nam
- Department of Psychosis Studies, Institute of Psychiatry, King’s Health Partners, King’s College London, De Crespigny Park, London, United Kingdom
| | - Boris A. Gutman
- Imaging Genetics Center, University of Southern California, 4676 Admiralty Way, Marina del Rey, California, United States of America
| | - Robin M. Murray
- Department of Psychosis Studies, Institute of Psychiatry, King’s Health Partners, King’s College London, De Crespigny Park, London, United Kingdom
| | - Larry Rifkin
- Department of Psychosis Studies, Institute of Psychiatry, King’s Health Partners, King’s College London, De Crespigny Park, London, United Kingdom
| | - Paul M. Thompson
- Imaging Genetics Center, University of Southern California, 4676 Admiralty Way, Marina del Rey, California, United States of America
| | - Chiara Nosarti
- Department of Psychosis Studies, Institute of Psychiatry, King’s Health Partners, King’s College London, De Crespigny Park, London, United Kingdom
- * E-mail:
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47
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Anvari AA, Friedman LA, Greenstein D, Gochman P, Gogtay N, Rapoport JL. Hippocampal volume change relates to clinical outcome in childhood-onset schizophrenia. Psychol Med 2015; 45:2667-2674. [PMID: 25936396 DOI: 10.1017/s0033291715000677] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Fixed hippocampal volume reductions and shape abnormalities are established findings in schizophrenia, but the relationship between hippocampal volume change and clinical outcome has been relatively unexplored in schizophrenia and other psychotic disorders. In light of recent findings correlating hippocampal volume change and clinical outcome in first-episode psychotic adults, we hypothesized that fewer decreases in hippocampal volume would be associated with better functional outcome and fewer psychotic symptoms in our rare and chronically ill population of childhood-onset schizophrenia (COS) patients. METHOD We prospectively obtained 114 structural brain magnetic resonance images (MRIs) from 27 COS subjects, each with three or more scans between the ages of 10 and 30 years. Change in hippocampal volume, measured by fit slope and percentage change, was regressed against clinical ratings (Children's Global Assessment Scale, Scale for the Assessment of Positive Symptoms, Scale for the Assessment of Negative Symptoms) at last scan (controlling for sex, time between scans and total intracranial volume). RESULTS Fewer negative symptoms were associated with less hippocampal volume decrease (fit slope: p = 0.0003, and percentage change: p = 0.005) while positive symptoms were not related to hippocampal change. There was also a relationship between improved clinical global functioning and maintained hippocampal volumes (fit slope: p = 0.025, and percentage change: p = 0.043). CONCLUSIONS These results suggest that abnormal hippocampal development in schizophrenia can be linked to global functioning and negative symptoms. The hippocampus can be considered a potential treatment target for future therapies.
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Affiliation(s)
- A A Anvari
- Child Psychiatry Branch,National Institute of Mental Health, National Institutes of Health,Bethesda,MD,USA
| | - L A Friedman
- Child Psychiatry Branch,National Institute of Mental Health, National Institutes of Health,Bethesda,MD,USA
| | - D Greenstein
- Child Psychiatry Branch,National Institute of Mental Health, National Institutes of Health,Bethesda,MD,USA
| | - P Gochman
- Child Psychiatry Branch,National Institute of Mental Health, National Institutes of Health,Bethesda,MD,USA
| | - N Gogtay
- Child Psychiatry Branch,National Institute of Mental Health, National Institutes of Health,Bethesda,MD,USA
| | - J L Rapoport
- Child Psychiatry Branch,National Institute of Mental Health, National Institutes of Health,Bethesda,MD,USA
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48
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Soldan A, Pettigrew C, Lu Y, Wang MC, Selnes O, Albert M, Brown T, Ratnanather JT, Younes L, Miller MI. Relationship of medial temporal lobe atrophy, APOE genotype, and cognitive reserve in preclinical Alzheimer's disease. Hum Brain Mapp 2015; 36:2826-41. [PMID: 25879865 DOI: 10.1002/hbm.22810] [Citation(s) in RCA: 83] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Accepted: 03/31/2015] [Indexed: 12/14/2022] Open
Abstract
This study evaluated the utility of baseline and longitudinal magnetic resonance imaging (MRI) measures of medial temporal lobe brain regions collected when participants were cognitively normal and largely in middle age (mean age 57 years) to predict the time to onset of clinical symptoms associated with mild cognitive impairment (MCI). Furthermore, we examined whether the relationship between MRI measures and clinical symptom onset was modified by apolipoprotein E (ApoE) genotype and level of cognitive reserve (CR). MRI scans and measures of CR were obtained at baseline from 245 participants who had been followed for up to 18 years (mean follow-up 11 years). A composite score based on reading, vocabulary, and years of education was used as an index of CR. Cox regression models showed that lower baseline volume of the right hippocampus and smaller baseline thickness of the right entorhinal cortex predicted the time to symptom onset independently of CR and ApoE-ɛ4 genotype, which also predicted the onset of symptoms. The atrophy rates of bilateral entorhinal cortex and amygdala volumes were also associated with time to symptom onset, independent of CR, ApoE genotype, and baseline volume. Only one measure, the left entorhinal cortex baseline volume, interacted with CR, such that smaller volumes predicted symptom onset only in individuals with lower CR. These results suggest that MRI measures of medial temporal atrophy, ApoE-ɛ4 genotype, and the protective effects of higher CR all predict the time to onset of symptoms associated with MCI in a largely independent, additive manner during the preclinical phase of Alzheimer's disease.
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Affiliation(s)
- Anja Soldan
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Corinne Pettigrew
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yi Lu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Mei-Cheng Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Ola Selnes
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Marilyn Albert
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Timothy Brown
- Center for Imaging Science and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - J Tilak Ratnanather
- Center for Imaging Science and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Laurent Younes
- Center for Imaging Science and Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Michael I Miller
- Center for Imaging Science and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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Zhang Z, van Praag H. Maternal immune activation differentially impacts mature and adult-born hippocampal neurons in male mice. Brain Behav Immun 2015; 45:60-70. [PMID: 25449671 DOI: 10.1016/j.bbi.2014.10.010] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Revised: 10/14/2014] [Accepted: 10/16/2014] [Indexed: 12/30/2022] Open
Abstract
Schizophrenia is associated with deficits in the hippocampus, a brain area important for learning and memory. The dentate gyrus (DG) of the hippocampus develops both before and after birth. To study the relative contribution of mature and adult-born DG granule cells to disease etiology, we compared both cell populations in a mouse model of psychiatric illness resulting from maternal immune activation. Polyriboinosinic-polyribocytidilic acid (PolyIC, 5mg/kg) or saline was given on gestation day 15 to pregnant female C57Bl/6 mice. Male offspring (n=105), was administered systemic bromodeoxyuridine (BrdU, 50mg/kg) (n=52) or intracerebral retroviral injection into the DG (n=53), to label dividing cells at one month of age. Two months later behavioral tests were performed to evaluate disease phenotype. Immunohistochemistry and whole-cell patch clamping were used to assess morphological and physiological characteristics of DG cells. Three-month-old PolyIC exposed male offspring exhibited deficient pre-pulse inhibition, spatial maze performance and motor coordination, as well as increased depression-like behavior. Histological analysis showed reduced DG volume and parvalbumin positive interneuron number. Both mature and new hippocampal neurons showed modifications in intrinsic properties such as increased input resistance and lower current threshold, and decreased action potential number. Reduced GABAergic inhibitory transmission was observed only in mature DG neurons. Differential impairments in mature DG cells and adult-born new neurons may have implications for behavioral deficits associated with maternal immune activation.
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Affiliation(s)
- Zhi Zhang
- Neuroplasticity and Behavior Unit, Laboratory of Neurosciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Henriette van Praag
- Neuroplasticity and Behavior Unit, Laboratory of Neurosciences, Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
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50
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Kawano M, Sawada K, Shimodera S, Ogawa Y, Kariya S, Lang DJ, Inoue S, Honer WG. Hippocampal subfield volumes in first episode and chronic schizophrenia. PLoS One 2015; 10:e0117785. [PMID: 25658118 PMCID: PMC4319836 DOI: 10.1371/journal.pone.0117785] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Accepted: 12/30/2014] [Indexed: 02/07/2023] Open
Abstract
Background Reduced hippocampal volume in schizophrenia is a well-replicated finding. New imaging techniques allow delineation of hippocampal subfield volumes. Studies including predominantly chronic patients demonstrate differences between subfields in sensitivity to illness, and in associations with clinical features. We carried out a cross-sectional and longitudinal study of first episode, sub-chronic, and chronic patients, using an imaging strategy that allows for the assessment of multiple hippocampal subfields. Methods Hippocampal subfield volumes were measured in 34 patients with schizophrenia (19 first episode, 6 sub-chronic, 9 chronic) and 15 healthy comparison participants. A subset of 10 first episode and 12 healthy participants were rescanned after six months. Results Total left hippocampal volume was smaller in sub-chronic (p = 0.04, effect size 1.12) and chronic (p = 0.009, effect size 1.42) patients compared with healthy volunteers. The CA2-3 subfield volume of chronic patients was significantly decreased (p = 0.009, effect size 1.42) compared to healthy volunteers. The CA4-DG volume was significantly reduced in all three patient groups compared to healthy group (all p < 0.005). The two affected subfield volumes were inversely correlated with severity of negative symptoms (p < 0.05). There was a small, but statistically significant decline in left CA4-DG volume over the first six months of illness (p = 0.01). Conclusions Imaging strategies defining the subfields of the hippocampus may be informative in linking symptoms and structural abnormalities, and in understanding more about progression during the early phases of illness in schizophrenia.
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Affiliation(s)
- Mitsuhiko Kawano
- Department of Neuropsychiatry, Kochi Medical School, Kochi, Japan
| | - Ken Sawada
- Department of Neuropsychiatry, Kochi Medical School, Kochi, Japan
- Department of Psychiatry, Aki General Hospital, Kochi, Japan
- * E-mail:
| | - Shinji Shimodera
- Department of Neuropsychiatry, Kochi Medical School, Kochi, Japan
| | - Yasuhiro Ogawa
- Department of Radiology, Hyogo Prefectural Kakogawa Hospital, Hyogo, Japan
| | - Shinji Kariya
- Departments of Diagnostic Radiology and Radiation Oncology, Kochi Medical School, Kochi, Japan
| | - Donna J. Lang
- Department of Radiology, University of British Columbia, Vancouver, Canada
| | - Shimpei Inoue
- Department of Neuropsychiatry, Aizu Medical Center, Fukushima Medical University, Fukushima, Japan
| | - William G. Honer
- Department of Psychiatry, University of British Columbia, Vancouver, Canada
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