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Del Giovane M, David MCB, Kolanko MA, Gontsarova A, Parker T, Hampshire A, Sharp DJ, Malhotra PA, Carswell C. Methodological challenges of measuring brain volumes and cortical thickness in idiopathic normal pressure hydrocephalus with a surface-based approach. Front Neurosci 2024; 18:1366029. [PMID: 39099637 PMCID: PMC11295655 DOI: 10.3389/fnins.2024.1366029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 06/28/2024] [Indexed: 08/06/2024] Open
Abstract
Identifying disease-specific imaging features of idiopathic Normal Pressure Hydrocephalus (iNPH) is crucial to develop accurate diagnoses, although the abnormal brain anatomy of patients with iNPH creates challenges in neuroimaging analysis. We quantified cortical thickness and volume using FreeSurfer 7.3.2 in 19 patients with iNPH, 28 patients with Alzheimer's disease (AD), and 30 healthy controls (HC). We noted the frequent need for manual correction of the automated segmentation in iNPH and examined the effect of correction on the results. We identified statistically significant higher proportion of volume changes associated with manual edits in individuals with iNPH compared to both HC and patients with AD. Changes in cortical thickness and volume related to manual correction were also partly correlated with the severity of radiological features of iNPH. We highlight the challenges posed by the abnormal anatomy in iNPH when conducting neuroimaging analysis and emphasise the importance of quality checking and correction in this clinical population.
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Affiliation(s)
- Martina Del Giovane
- UK Dementia Research Institute, Care Research & Technology Centre, Imperial College and the University of Surrey, London, United Kingdom
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Michael C. B. David
- UK Dementia Research Institute, Care Research & Technology Centre, Imperial College and the University of Surrey, London, United Kingdom
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Magdalena A. Kolanko
- UK Dementia Research Institute, Care Research & Technology Centre, Imperial College and the University of Surrey, London, United Kingdom
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | | | - Thomas Parker
- UK Dementia Research Institute, Care Research & Technology Centre, Imperial College and the University of Surrey, London, United Kingdom
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Adam Hampshire
- Department of Brain Sciences, Imperial College London, London, United Kingdom
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - David J. Sharp
- UK Dementia Research Institute, Care Research & Technology Centre, Imperial College and the University of Surrey, London, United Kingdom
- Department of Brain Sciences, Imperial College London, London, United Kingdom
| | - Paresh A. Malhotra
- UK Dementia Research Institute, Care Research & Technology Centre, Imperial College and the University of Surrey, London, United Kingdom
- Department of Brain Sciences, Imperial College London, London, United Kingdom
- Department of Neurology, Imperial College Healthcare NHS Trust, London, United Kingdom
| | - Christopher Carswell
- Department of Brain Sciences, Imperial College London, London, United Kingdom
- Department of Neurology, Imperial College Healthcare NHS Trust, London, United Kingdom
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2
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Turesky TK, Escalante E, Loh M, Gaab N. Longitudinal trajectories of brain development from infancy to school age and their relationship to literacy development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.29.601366. [PMID: 39005343 PMCID: PMC11244924 DOI: 10.1101/2024.06.29.601366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Reading is one of the most complex skills that we utilize daily, and it involves the early development and interaction of various lower-level subskills, including phonological processing and oral language. These subskills recruit brain structures, which begin to develop long before the skill manifests and exhibit rapid development during infancy. However, how longitudinal trajectories of early brain development in these structures supports long-term acquisition of literacy subskills and subsequent reading is unclear. Children underwent structural and diffusion MRI scanning at multiple timepoints between infancy and second grade and were tested for literacy subskills in preschool and decoding and word reading in early elementary school. We developed and implemented a reproducible pipeline to generate longitudinal trajectories of early brain development to examine associations between these trajectories and literacy (sub)skills. Furthermore, we examined whether familial risk of reading difficulty and a child's home literacy environment, two common literacy-related covariates, influenced those trajectories. Results showed that individual differences in curve features (e.g., intercepts and slopes) for longitudinal trajectories of volumetric, surface-based, and white matter organization measures in left-hemispheric reading-related regions and tracts were linked directly to phonological processing and indirectly to second-grade decoding and word reading skills via phonological processing. Altogether, these findings suggest that the brain bases of phonological processing, previously identified as the strongest behavioral predictor of reading and decoding skills, may already begin to develop early in infancy but undergo further refinement between birth and preschool. The present study underscores the importance of considering academic skill acquisition from the very beginning of life.
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de Moraes FHP, Sudo F, Carneiro Monteiro M, de Melo BRP, Mattos P, Mota B, Tovar-Moll F. Cortical folding correlates to aging and Alzheimer's Disease's cognitive and CSF biomarkers. Sci Rep 2024; 14:3222. [PMID: 38332140 PMCID: PMC10853184 DOI: 10.1038/s41598-023-50780-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/25/2023] [Indexed: 02/10/2024] Open
Abstract
This manuscript presents the quantification and correlation of three aspects of Alzheimer's Disease evolution, including structural, biochemical, and cognitive assessments. We aimed to test a novel structural biomarker for neurodegeneration based on a cortical folding model for mammals. Our central hypothesis is that the cortical folding variable, representative of axonal tension in white matter, is an optimal discriminator of pathological aging and correlates with altered loadings in Cerebrospinal Fluid samples and a decline in cognition and memory. We extracted morphological features from T1w 3T MRI acquisitions using FreeSurfer from 77 Healthy Controls (age = 66 ± 8.4, 69% females), 31 Mild Cognitive Impairment (age = 72 ± 4.8, 61% females), and 13 Alzheimer's Disease patients (age = 77 ± 6.1, 62% females) of recruited volunteers in Brazil to test its discriminative power using optimal cut-point analysis. Cortical folding distinguishes the groups with reasonable accuracy (Healthy Control-Alzheimer's Disease, accuracy = 0.82; Healthy Control-Mild Cognitive Impairment, accuracy = 0.56). Moreover, Cerebrospinal Fluid biomarkers (total Tau, A[Formula: see text]1-40, A[Formula: see text]1-42, and Lipoxin) and cognitive scores (Cognitive Index, Rey's Auditory Verbal Learning Test, Trail Making Test, Digit Span Backward) were correlated with the global neurodegeneration in MRI aiming to describe health, disease, and the transition between the two states using morphology.
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Affiliation(s)
- Fernanda Hansen P de Moraes
- Brain Connectivity Unit, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil
- Instituto de Física, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, 21941-909, Brazil
| | - Felipe Sudo
- Memory Clinic, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil
| | - Marina Carneiro Monteiro
- Brain Connectivity Unit, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil
| | - Bruno R P de Melo
- Brain Connectivity Unit, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil
| | - Paulo Mattos
- Memory Clinic, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil
| | - Bruno Mota
- Instituto de Física, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, 21941-909, Brazil
| | - Fernanda Tovar-Moll
- Brain Connectivity Unit, D'Or Institute of Research and Education (IDOR), Rio de Janeiro, 225281-100, Brazil.
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4
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Barth C, Nerland S, Jørgensen KN, Haatveit B, Wortinger LA, Melle I, Haukvik UK, Ueland T, Andreassen OA, Agartz I. Altered Sex Differences in Hippocampal Subfield Volumes in Schizophrenia. Schizophr Bull 2024; 50:107-119. [PMID: 37354490 PMCID: PMC10754184 DOI: 10.1093/schbul/sbad091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
BACKGROUND AND HYPOTHESIS The hippocampus is a heterogenous brain structure that differs between the sexes and has been implicated in the pathophysiology of psychiatric illnesses. Here, we explored sex and diagnostic group differences in hippocampal subfield volumes, in individuals with schizophrenia spectrum disorder (SZ), bipolar disorders (BD), and healthy controls (CTL). STUDY DESIGN One thousand and five hundred and twenty-one participants underwent T1-weighted magnetic resonance imaging (SZ, n = 452, mean age 30.7 ± 9.2 [SD] years, males 59.1%; BD, n = 316, 33.7 ± 11.4, 41.5%; CTL, n = 753, 34.1 ± 9.1, 55.6%). Total hippocampal, subfield, and intracranial volumes were estimated with Freesurfer (v6.0.0). Analysis of covariance and multiple regression models were fitted to examine sex-by-diagnostic (sub)group interactions in volume. In SZ and BD, separately, associations between volumes and clinical as well as cognitive measures were examined between the sexes using regression models. STUDY RESULTS Significant sex-by-group interactions were found for the total hippocampus, dentate gyrus, molecular layer, presubiculum, fimbria, hippocampal-amygdaloid transition area, and CA4, indicating a larger volumetric deficit in male patients relative to female patients when compared with same-sex CTL. Subgroup analyses revealed that this interaction was driven by males with schizophrenia. Effect sizes were overall small (partial η < 0.02). We found no significant sex differences in the associations between hippocampal volumes and clinical or cognitive measures in SZ and BD. CONCLUSIONS Using a well-powered sample, our findings indicate that the pattern of morphological sex differences in hippocampal subfields is altered in individuals with schizophrenia relative to CTL, due to higher volumetric deficits in males.
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Affiliation(s)
- Claudia Barth
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, NORMENT, Oslo, Norway
| | - Stener Nerland
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, NORMENT, Oslo, Norway
| | - Kjetil N Jørgensen
- Institute of Clinical Medicine, University of Oslo, NORMENT, Oslo, Norway
- Department of Psychiatry, Telemark Hospital, Skien, Norway
| | - Beathe Haatveit
- Institute of Clinical Medicine, University of Oslo, NORMENT, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, NORMENT, Oslo, Norway
| | - Laura A Wortinger
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, NORMENT, Oslo, Norway
| | - Ingrid Melle
- Institute of Clinical Medicine, University of Oslo, NORMENT, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, NORMENT, Oslo, Norway
| | - Unn K Haukvik
- Division of Mental Health and Addiction, Oslo University Hospital, NORMENT, Oslo, Norway
- Department of Adult Mental Health, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Torill Ueland
- Division of Mental Health and Addiction, Oslo University Hospital, NORMENT, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Institute of Clinical Medicine, University of Oslo, NORMENT, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, NORMENT, Oslo, Norway
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, NORMENT, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet and Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
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Debiasi G, Mazzonetto I, Bertoldo A. The effect of processing pipelines, input images and age on automatic cortical morphology estimates. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107825. [PMID: 37806120 DOI: 10.1016/j.cmpb.2023.107825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 09/01/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Magnetic resonance imaging of the brain allows to enrich the study of the relationship between cortical morphology, healthy ageing, diseases and cognition. Since manual segmentation of the cerebral cortex is time consuming and subjective, many software packages have been developed. FreeSurfer (FS) and Advanced Normalization Tools (ANTs) are the most used and allow as inputs a T1-weighted (T1w) image or its combination with a T2-weighted (T2w) image. In this study we evaluated the impact of different software and input images on cortical estimates. Additionally, we investigated whether the variation of the results depending on software and inputs is also influenced by age. METHODS For 240 healthy subjects, cortical thickness was computed with ANTs and FreeSurfer. Estimates were derived using both the T1w image and adding the T2w image. Significant effects due to software, input images and age range were investigated with ANOVA statistical analysis. Moreover, the accuracy of the cortical thickness estimates was assessed based on their age-prediction precision. RESULTS Using FreeSurfer and ANTs with T1w or T1w-T2w images resulted in significant differences in the cortical thickness estimates. These differences change with the age range of the subjects. Regardless of the images used, the more recent FS version tested exhibited the best performances in terms of age prediction. CONCLUSIONS Our study points out the importance of i) consistently processing data using the same tool; ii) considering the software, input images and the age range of the subjects when comparing multiple studies.
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Affiliation(s)
- Giulia Debiasi
- Department of Information Engineering, University of Padova, via Gradenigo 6/b, Padova 35131, Italy
| | - Ilaria Mazzonetto
- Department of Information Engineering, University of Padova, via Gradenigo 6/b, Padova 35131, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, via Gradenigo 6/b, Padova 35131, Italy; Padova Neuroscience Center (PNC), University of Padova, via Orus 2/b, Padova 35131, Italy.
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6
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Wu Y, Ridwan AR, Niaz MR, Bennett DA, Arfanakis K. High resolution 0.5mm isotropic T 1-weighted and diffusion tensor templates of the brain of non-demented older adults in a common space for the MIITRA atlas. Neuroimage 2023; 282:120387. [PMID: 37783362 PMCID: PMC10625170 DOI: 10.1016/j.neuroimage.2023.120387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 09/22/2023] [Indexed: 10/04/2023] Open
Abstract
High quality, high resolution T1-weighted (T1w) and diffusion tensor imaging (DTI) brain templates located in a common space can enhance the sensitivity and precision of template-based neuroimaging studies. However, such multimodal templates have not been constructed for the older adult brain. The purpose of this work which is part of the MIITRA atlas project was twofold: (A) to develop 0.5 mm isotropic resolution T1w and DTI templates that are representative of the brain of non-demented older adults and are located in the same space, using advanced multimodal template construction techniques and principles of super resolution on data from a large, diverse, community cohort of 400 non-demented older adults, and (B) to systematically compare the new templates to other standardized templates. It was demonstrated that the new MIITRA-0.5mm T1w and DTI templates are well-matched in space, exhibit good definition of brain structures, including fine structures, exhibit higher image sharpness than other standardized templates, and are free of artifacts. The MIITRA-0.5mm T1w and DTI templates allowed higher intra-modality inter-subject spatial normalization precision as well as higher inter-modality intra-subject spatial matching of older adult T1w and DTI data compared to other available templates. Consequently, MIITRA-0.5mm templates allowed detection of smaller inter-group differences for older adult data compared to other templates. The MIITRA-0.5mm templates were also shown to be most representative of the brain of non-demented older adults compared to other templates with submillimeter resolution. The new templates constructed in this work constitute two of the final products of the MIITRA atlas project and are anticipated to have important implications for the sensitivity and precision of studies on older adults.
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Affiliation(s)
- Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, United States; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States.
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7
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Savitz J, Goeckner BD, Ford BN, Kent Teague T, Zheng H, Harezlak J, Mannix R, Tugan Muftuler L, Brett BL, McCrea MA, Meier TB. The effects of cytomegalovirus on brain structure following sport-related concussion. Brain 2023; 146:4262-4273. [PMID: 37070698 PMCID: PMC10545519 DOI: 10.1093/brain/awad126] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 03/06/2023] [Accepted: 03/27/2023] [Indexed: 04/19/2023] Open
Abstract
The neurotrophic herpes virus cytomegalovirus is a known cause of neuropathology in utero and in immunocompromised populations. Cytomegalovirus is reactivated by stress and inflammation, possibly explaining the emerging evidence linking it to subtle brain changes in the context of more minor disturbances of immune function. Even mild forms of traumatic brain injury, including sport-related concussion, are major physiological stressors that produce neuroinflammation. In theory, concussion could predispose to the reactivation of cytomegalovirus and amplify the effects of physical injury on brain structure. However, to our knowledge this hypothesis remains untested. This study evaluated the effect of cytomegalovirus serostatus on white and grey matter structure in a prospective study of athletes with concussion and matched contact-sport controls. Athletes who sustained concussion (n = 88) completed MRI at 1, 8, 15 and 45 days post-injury; matched uninjured athletes (n = 73) completed similar visits. Cytomegalovirus serostatus was determined by measuring serum IgG antibodies (n = 30 concussed athletes and n = 21 controls were seropositive). Inverse probability of treatment weighting was used to adjust for confounding factors between athletes with and without cytomegalovirus. White matter microstructure was assessed using diffusion kurtosis imaging metrics in regions previously shown to be sensitive to concussion. T1-weighted images were used to quantify mean cortical thickness and total surface area. Concussion-related symptoms, psychological distress, and serum concentration of C-reactive protein at 1 day post-injury were included as exploratory outcomes. Planned contrasts compared the effects of cytomegalovirus seropositivity in athletes with concussion and controls, separately. There was a significant effect of cytomegalovirus on axial and radial kurtosis in athletes with concussion but not controls. Cytomegalovirus positive athletes with concussion showed greater axial (P = 0.007, d = 0.44) and radial (P = 0.010, d = 0.41) kurtosis than cytomegalovirus negative athletes with concussion. Similarly, there was a significant association of cytomegalovirus with cortical thickness in athletes with concussion but not controls. Cytomegalovirus positive athletes with concussion had reduced mean cortical thickness of the right hemisphere (P = 0.009, d = 0.42) compared with cytomegalovirus negative athletes with concussion and showed a similar trend for the left hemisphere (P = 0.036, d = 0.33). There was no significant effect of cytomegalovirus on kurtosis fractional anisotropy, surface area, symptoms and C-reactive protein. The results raise the possibility that cytomegalovirus infection contributes to structural brain abnormalities in the aftermath of concussion perhaps via an amplification of concussion-associated neuroinflammation. More work is needed to identify the biological pathways underlying this process and to clarify the clinical relevance of this putative viral effect.
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Affiliation(s)
- Jonathan Savitz
- Laureate Institute for Brain Research, Tulsa, OK 74136, USA
- Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK 74119, USA
| | - Bryna D Goeckner
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Bart N Ford
- Department of Pharmacology and Physiology, Oklahoma State University Center for Health Sciences, Tulsa, OK 74107, USA
| | - T Kent Teague
- Department of Psychiatry, The University of Oklahoma School of Community Medicine, Tulsa, OK 74135, USA
- Department of Surgery, The University of Oklahoma School of Community Medicine, Tulsa, OK 74135, USA
- Department of Pharmaceutical Sciences, University of Oklahoma College of Pharmacy, Tulsa, OK 74135, USA
| | - Haixia Zheng
- Laureate Institute for Brain Research, Tulsa, OK 74136, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, School of Public Health-Bloomington, Indiana University, Bloomington, IN 47405, USA
| | - Rebekah Mannix
- Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA 02115, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - L Tugan Muftuler
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Benjamin L Brett
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Michael A McCrea
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Timothy B Meier
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- Department of Cell Biology, Neurobiology and Anatomy, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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8
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Bedford SA, Ortiz-Rosa A, Schabdach JM, Costantino M, Tullo S, Piercy T, Lai MC, Lombardo MV, Di Martino A, Devenyi GA, Chakravarty MM, Alexander-Bloch AF, Seidlitz J, Baron-Cohen S, Bethlehem RA. The impact of quality control on cortical morphometry comparisons in autism. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-21. [PMID: 38495338 PMCID: PMC10938341 DOI: 10.1162/imag_a_00022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 08/11/2023] [Accepted: 09/13/2023] [Indexed: 03/19/2024]
Abstract
Structural magnetic resonance imaging (MRI) quality is known to impact and bias neuroanatomical estimates and downstream analysis, including case-control comparisons, and a growing body of work has demonstrated the importance of careful quality control (QC) and evaluated the impact of image and image-processing quality. However, the growing size of typical neuroimaging datasets presents an additional challenge to QC, which is typically extremely time and labour intensive. One of the most important aspects of MRI quality is the accuracy of processed outputs, which have been shown to impact estimated neurodevelopmental trajectories. Here, we evaluate whether the quality of surface reconstructions by FreeSurfer (one of the most widely used MRI processing pipelines) interacts with clinical and demographic factors. We present a tool, FSQC, that enables quick and efficient yet thorough assessment of outputs of the FreeSurfer processing pipeline. We validate our method against other existing QC metrics, including the automated FreeSurfer Euler number, two other manual ratings of raw image quality, and two popular automated QC methods. We show strikingly similar spatial patterns in the relationship between each QC measure and cortical thickness; relationships for cortical volume and surface area are largely consistent across metrics, though with some notable differences. We next demonstrate that thresholding by QC score attenuates but does not eliminate the impact of quality on cortical estimates. Finally, we explore different ways of controlling for quality when examining differences between autistic individuals and neurotypical controls in the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrating that inadequate control for quality can alter results of case-control comparisons.
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Affiliation(s)
- Saashi A. Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Alfredo Ortiz-Rosa
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
| | - Jenna M. Schabdach
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Manuela Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
| | - Tom Piercy
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | | | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry and Autism Research Unit, The Hospital for Sick Children, Toronto, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Michael V. Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | | | - Gabriel A. Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
- Department of Psychiatry, McGill University, Montreal, Canada
| | - M. Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, McGill University, Montreal, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Aaron F. Alexander-Bloch
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Jakob Seidlitz
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough, United Kingdom
| | - Richard A.I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
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9
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Marzi C, Scheda R, Salvadori E, Giorgio A, De Stefano N, Poggesi A, Inzitari D, Pantoni L, Mascalchi M, Diciotti S. Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis. Front Hum Neurosci 2023; 17:1231513. [PMID: 37822707 PMCID: PMC10562576 DOI: 10.3389/fnhum.2023.1231513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/31/2023] [Indexed: 10/13/2023] Open
Abstract
Background The relative contribution of changes in the cerebral white matter (WM) and cortical gray matter (GM) to the transition to dementia in patients with mild cognitive impairment (MCI) is not yet established. In this longitudinal study, we aimed to analyze MRI features that may predict the transition to dementia in patients with MCI and T2 hyperintensities in the cerebral WM, also known as leukoaraiosis. Methods Sixty-four participants with MCI and moderate to severe leukoaraiosis underwent baseline MRI examinations and annual neuropsychological testing over a 2 year period. The diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological, and several MRI features at baseline as predictors of the clinical transition. The MRI features included visually assessed MRI features, such as the number of lacunes, microbleeds, and dilated perivascular spaces, and quantitative MRI features, such as volumes of the cortical GM, hippocampus, T2 hyperintensities, and diffusion indices of the cerebral WM. Additionally, we examined advanced quantitative features such as the fractal dimension (FD) of cortical GM and WM, which represents an index of tissue structural complexity derived from 3D-T1 weighted images. To assess the prediction of transition to dementia, we employed an XGBoost-based machine learning system using SHapley Additive exPlanations (SHAP) values to provide explainability to the machine learning model. Results After 2 years, 18 (28.1%) participants had transitioned from MCI to dementia. The area under the receiving operator characteristic curve was 0.69 (0.53, 0.85) [mean (90% confidence interval)]. The cortical GM-FD emerged as the top-ranking predictive feature of transition. Furthermore, aggregated quantitative neuroimaging features outperformed visually assessed MRI features in predicting conversion to dementia. Discussion Our findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more sensitive than other brain features.
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Affiliation(s)
- Chiara Marzi
- Department of Statistics, Computer Science, Applications "Giuseppe Parenti, " University of Florence, Florence, Italy
| | - Riccardo Scheda
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi, " University of Bologna, Cesena, Italy
| | - Emilia Salvadori
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Antonio Giorgio
- Department of Medicine, Surgery, and Neuroscience, University of Siena, Siena, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery, and Neuroscience, University of Siena, Siena, Italy
| | - Anna Poggesi
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Domenico Inzitari
- NEUROFARBA Department, Neuroscience Section, University of Florence, Florence, Italy
| | - Leonardo Pantoni
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences "Mario Serio, " University of Florence, Florence, Italy
- Division of Epidemiology and Clinical Governance, Institute for Study, Prevention and Network in Oncology (ISPRO), Florence, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi, " University of Bologna, Cesena, Italy
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
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10
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de Zoete RMJ, McMahon KL, Coombes JS, Sterling M. The effects of physical exercise on structural, functional, and biochemical brain characteristics in individuals with chronic whiplash-associated disorder: A pilot randomized clinical trial. Pain Pract 2023; 23:759-775. [PMID: 37157897 DOI: 10.1111/papr.13240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 02/01/2023] [Accepted: 04/24/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Exercise for people with whiplash associated disorder (WAD) induces hypoalgesic effects in some, but hyperalgesic effects in others. We investigated the exercise-induced neurobiological effects of aerobic and strengthening exercise in individuals with chronic WAD. METHODS Sixteen participants (8 WAD, 8 pain-free [CON]) were randomised to either aerobic or strengthening exercise. MRI for brain morphometry, functional MRI for brain connectivity, and magnetic resonance spectroscopy for brain biochemistry, were used at baseline and after the 8-week intervention. RESULTS There were no differences in brain changes between exercise groups in either the WAD or CON group, therefore aerobic and strengthening data were combined to optimise sample size. After the exercise intervention, the CON group demonstrated increased cortical thickness (left parahippocampus: mean difference = 0.04, 95% CI = 0.07-0.00, p = 0.032; and left lateral orbital frontal cortex: mean difference = 0.03, 95% CI = 0.00-0.06, p = 0.048). The WAD group demonstrated an increase in prefrontal cortex (right medial orbital frontal) volume (mean difference = 95.57, 95% CI = 2.30-192.84, p = 0.046). Functional changes from baseline to follow-up between the default mode network and the insula, cingulate cortex, temporal lobe, and somatosensory and motor cortices, were found in the CON group, but not in the WAD group. There were no changes post-exercise in brain biochemistry. CONCLUSION Aerobic and strengthening exercises did not exert differential effects on brain characteristics, however differences in structural and functional changes were found between WAD and CON groups. This suggests that an altered central pain modulatory response may be responsible for differential effects of exercise in individuals with chronic WAD.
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Affiliation(s)
- Rutger M J de Zoete
- Recover Injury Research Centre, NHMRC Centre of Research Excellence in Recovery Following Road Traffic Injuries, The University of Queensland, Brisbane, Queensland, Australia
- School of Allied Health Science and Practice, The University of Adelaide, Adelaide, South Australia, Australia
| | - Katie L McMahon
- Herston Imaging Research Facility, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia
- School of Clinical Sciences, Faculty of Health, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jeff S Coombes
- School of Human Movement and Nutrition Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Michele Sterling
- Recover Injury Research Centre, NHMRC Centre of Research Excellence in Recovery Following Road Traffic Injuries, The University of Queensland, Brisbane, Queensland, Australia
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11
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Fislage M, Feinkohl I, Borchers F, Pischon T, Spies CD, Winterer G, Zacharias N. Preoperative thalamus volume is not associated with preoperative cognitive impairment (preCI) or postoperative cognitive dysfunction (POCD). Sci Rep 2023; 13:11732. [PMID: 37474784 PMCID: PMC10359451 DOI: 10.1038/s41598-023-38673-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 07/12/2023] [Indexed: 07/22/2023] Open
Abstract
A growing body of literature suggests the important role of the thalamus in cognition and neurodegenerative diseases. This study aims to elucidate whether the preoperative thalamic volume is associated with preoperative cognitive impairment (preCI) and whether it is predictive for postoperative cognitive dysfunction at 3 months (POCD). We enrolled 301 patients aged 65 or older and without signs of dementia who were undergoing elective surgery. Magnetic resonance imaging was conducted prior to surgery. Freesurfer (version 5.3.) was used to automatically segment the thalamus volume. A neuropsychological test battery was administered before surgery and at a 3 month follow-up. It included the computerized tests Paired Associate Learning (PAL), Verbal Recognition Memory (VRM), Spatial Span Length (SSP), Simple Reaction Time (SRT), the pen-and-paper Trail-Making-Test (TMT) and the manual Grooved Pegboard Test (GPT). Using a reliable change index, preCI and POCD were defined as total Z-score > 1.96 (sum score over all tests) and/or Z-scores > 1.96 in ≥ 2 individual cognitive test parameters. For statistical analyses, multivariable logistic regression models were applied. Age, sex and intracranial volume were covariates in the models. Of 301 patients who received a presurgical neuropsychological testing and MRI, 34 (11.3%) had preCI. 89 patients (29.5%) were lost to follow-up. The remaining 212 patients received a follow-up cognitive test after 3 months, of whom 25 (8.3%) presented with POCD. Independently of age, sex and intracranial volume, neither preCI (OR per cm3 increment 0.81 [95% CI 0.60-1.07] p = 0.14) nor POCD (OR 1.02 per cm3 increment [95% CI 0.75-1.40] p = 0.87) were statistically significantly associated with patients' preoperative thalamus volume. In this cohort we could not show an association of presurgical thalamus volume with preCI or POCD.Clinical Trial Number: NCT02265263 ( https://clinicaltrials.gov/ct2/show/results/NCT02265263 ).
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Affiliation(s)
- Marinus Fislage
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Insa Feinkohl
- Faculty of Health/School of Medicine, Witten/Herdecke University, Witten, Germany
- Molecular Epidemiology Research Group, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Friedrich Borchers
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Biobank Technology Platform, Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Core Facility Biobank, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Claudia D Spies
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Georg Winterer
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Pharmaimage Biomarker Solutions GmbH, Berlin, Germany
| | - Norman Zacharias
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Pharmaimage Biomarker Solutions GmbH, Berlin, Germany
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12
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Slapø NB, Jørgensen KN, Elvsåshagen T, Nerland S, Roelfs D, Valstad M, Timpe CMF, Richard G, Beck D, Sæther LS, Frogner Werner MC, Lagerberg TV, Andreassen OA, Melle I, Agartz I, Westlye LT, Moberget T, Jönsson EG. Relationship between function and structure in the visual cortex in healthy individuals and in patients with severe mental disorders. Psychiatry Res Neuroimaging 2023; 332:111633. [PMID: 37028226 DOI: 10.1016/j.pscychresns.2023.111633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/12/2023] [Accepted: 03/17/2023] [Indexed: 04/09/2023]
Abstract
Patients with schizophrenia spectrum disorders (SCZspect) and bipolar disorders (BD) show impaired function in the primary visual cortex (V1), indicated by altered visual evoked potential (VEP). While the neural substrate for altered VEP in these patients remains elusive, altered V1 structure may play a role. One previous study found a positive relationship between the amplitude of the P100 component of the VEP and V1 surface area, but not V1 thickness, in a small sample of healthy individuals. Here, we aimed to replicate these findings in a larger healthy control (HC) sample (n = 307) and to examine the same relationship in patients with SCZspect (n = 30) or BD (n = 45). We also compared the mean P100 amplitude, V1 surface area and V1 thickness between controls and patients and found no significant group differences. In HC only, we found a significant positive P100-V1 surface area association, while there were no significant P100-V1 thickness relationships in HC, SCZspect or BD. Together, our results confirm previous findings of a positive P100-V1 surface area association in HC, whereas larger patient samples are needed to further clarify the function-structure relationship in V1 in SCZspect and BD.
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Affiliation(s)
- Nora Berz Slapø
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway.
| | - Kjetil Nordbø Jørgensen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry, Telemark Hospital, Skien, Norway
| | - Torbjørn Elvsåshagen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Neurology, Oslo University Hospital, Norway
| | - Stener Nerland
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Daniel Roelfs
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway
| | - Mathias Valstad
- Department of Mental Disorders, Norwegian Institute of Public Health, Norway
| | - Clara M F Timpe
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | | | - Dani Beck
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | | | | | - Trine Vik Lagerberg
- NORMENT, Division of Mental Health and Addiction, Oslo University hospital, Norway
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University hospital, Norway
| | - Ingrid Melle
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University hospital, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Sciences, Stockholm Region, Stockholm, Sweden
| | - Lars T Westlye
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Torgeir Moberget
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Behavioral Sciences, Faculty of Health Sciences, Oslo Metropolitan University, OsloMet, Oslo, Norway
| | - Erik G Jönsson
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Sciences, Stockholm Region, Stockholm, Sweden
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13
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de Zoete RMJ, Berryman CF, Nijs J, Walls A, Jenkinson M. Differential Structural Brain Changes Between Responders and Nonresponders After Physical Exercise Therapy for Chronic Nonspecific Neck Pain. Clin J Pain 2023; 39:270-277. [PMID: 37220328 DOI: 10.1097/ajp.0000000000001115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 03/23/2023] [Indexed: 05/25/2023]
Abstract
OBJECTIVES Physical exercise therapy is effective for some people with chronic nonspecific neck pain but not for others. Differences in exercise-induced pain-modulatory responses are likely driven by brain changes. We investigated structural brain differences at baseline and changes after an exercise intervention. The primary aim was to investigate changes in structural brain characteristics after physical exercise therapy for people with chronic nonspecific neck pain. The secondary aims were to investigate (1) baseline differences in structural brain characteristics between responders and nonresponders to exercise therapy, and (2) differential brain changes after exercise therapy between responders and nonresponders. MATERIALS AND METHODS This was a prospective longitudinal cohort study. Twenty-four participants (18 females, mean age 39.7 y) with chronic nonspecific neck pain were included. Responders were selected as those with ≥20% improvement in Neck Disability Index. Structural magnetic resonance imaging was obtained before and after an 8-week physical exercise intervention delivered by a physiotherapist. Freesurfer cluster-wise analyses were performed and supplemented with an analysis of pain-specific brain regions of interest. RESULTS Various changes in grey matter volume and thickness were found after the intervention, for example, frontal cortex volume decreased (cluster-weighted P value = 0.0002, 95% CI: 0.0000-0.0004). We found numerous differences between responders and nonresponders, most notably, after the exercise intervention bilateral insular volume decreased in responders, but increased in nonresponders (cluster-weighted P value ≤ 0.0002). DISCUSSION The brain changes found in this study may underpin clinically observed differential effects between responders and nonresponders to exercise therapy for people with chronic neck pain. Identification of these changes is an important step toward personalized treatment approaches.
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Affiliation(s)
| | - Carolyn F Berryman
- Brain Stimulation, Imaging and Cognition Group, School of Medicine
- IIMPACT in Health, The University of South Australia
| | - Jo Nijs
- Pain in Motion Research Group (PAIN), Department of Physiotherapy, Human Physiology and Anatomy, Vrije Universiteit Brussel
- Chronic pain rehabilitation, Department of Physical Medicine and Physiotherapy, University Hospital Brussels, Belgium
- Department of Health and Rehabilitation, Unit of Physiotherapy, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Angela Walls
- Clinical and Research Imaging Centre, South Australian Health and Medical Research Institute
| | - Mark Jenkinson
- Australian Institute for Machine Learning (AIML), School of Computer Science, University of Adelaide
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide, Australia
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
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14
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Raffin J, Rolland Y, Fischer C, Mangin JF, Gabelle A, Vellas B, de Souto Barreto P. Cross-sectional associations between cortical thickness and physical activity in older adults with spontaneous memory complaints: The MAPT Study. JOURNAL OF SPORT AND HEALTH SCIENCE 2023; 12:324-332. [PMID: 33545345 DOI: 10.1016/j.jshs.2021.01.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 10/03/2020] [Accepted: 11/30/2020] [Indexed: 05/17/2023]
Abstract
BACKGROUND Age-related changes in brain structure may constitute the starting point for cerebral function alteration. Physical activity (PA) demonstrated favorable associations with total brain volume, but its relationship with cortical thickness (CT) remains unclear. We investigated the cross-sectional associations between PA level and CT in community-dwelling people aged 70 years and older. METHODS A total of 403 older adults aged 74.8 ± 4.0 years (mean ± SD) who underwent a baseline magnetic resonance imaging examination and who had data on PA and confounders were included. PA was assessed with a questionnaire. Participants were categorized according to PA levels. Multiple linear regressions were used to compare the brain CT (mm) of the inactive group (no PA at all) with 6 active groups (growing PA levels) in 34 regions of interest. RESULTS Compared with inactive persons, people who achieved PA at a level of 1500-1999 metabolic equivalent task-min/week (i.e., about 6-7 h of brisk walking for exercise and those who achieved it at 2000-2999 metabolic equivalent task-min/week (i.e., 8-11 h of brisk walking for exercise) had higher CT in the fusiform gyrus and the temporal pole. Additionally, dose-response associations between PA and CT were found in the fusiform gyrus (B = 0.011, SE = 0.004, adj. p = 0.035), the temporal pole (B = 0.026, SE = 0.009, adj. p = 0.048), and the caudal middle frontal gyrus, the entorhinal, medial orbitofrontal, lateral occipital, and insular cortices. CONCLUSION This study demonstrates a positive association between PA level and CT in temporal areas such as the fusiform gyrus, a brain region often associated to Alzheimer's disease in people aged 70 years and older. Future investigations focusing on PA type may help to fulfil remaining knowledge gaps in this field.
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Affiliation(s)
- Jérémy Raffin
- Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, Toulouse 31000, France.
| | - Yves Rolland
- Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, Toulouse 31000, France; Université Paul-Sabatier/Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche 1027, Faculté de médecine, University of Toulouse III, Toulouse 31000, France
| | - Clara Fischer
- Centre pour l'Acquisition et le Traitement des Images Multicenter Neuroimaging Platform, Neurospin, Université Paris-Saclay, Gif sur Yvette 91191, France
| | - Jean-François Mangin
- Centre pour l'Acquisition et le Traitement des Images Multicenter Neuroimaging Platform, Neurospin, Université Paris-Saclay, Gif sur Yvette 91191, France
| | - Audrey Gabelle
- Memory Resources and Research Center, Montpellier University Hospital, Montpellier 34295, France; Institut National de la Santé et de la Recherche Médicale Unité 1061 i-site Montpellier Université d'Excellence, University of Montpellier, Montpellier 34090, France
| | - Bruno Vellas
- Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, Toulouse 31000, France; Université Paul-Sabatier/Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche 1027, Faculté de médecine, University of Toulouse III, Toulouse 31000, France
| | - Philipe de Souto Barreto
- Gérontopôle de Toulouse, Institut du Vieillissement, Centre Hospitalo-Universitaire de Toulouse, Toulouse 31000, France; Université Paul-Sabatier/Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche 1027, Faculté de médecine, University of Toulouse III, Toulouse 31000, France
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15
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Ji JL, Demšar J, Fonteneau C, Tamayo Z, Pan L, Kraljič A, Matkovič A, Purg N, Helmer M, Warrington S, Winkler A, Zerbi V, Coalson TS, Glasser MF, Harms MP, Sotiropoulos SN, Murray JD, Anticevic A, Repovš G. QuNex-An integrative platform for reproducible neuroimaging analytics. Front Neuroinform 2023; 17:1104508. [PMID: 37090033 PMCID: PMC10113546 DOI: 10.3389/fninf.2023.1104508] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/21/2023] [Indexed: 04/08/2023] Open
Abstract
Introduction Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. Methods To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a "turnkey" command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. Results The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Discussion Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.
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Affiliation(s)
- Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Manifest Technologies, North Haven, CT, United States
| | - Jure Demšar
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Zailyn Tamayo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Lining Pan
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Aleksij Kraljič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Nina Purg
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Markus Helmer
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Manifest Technologies, North Haven, CT, United States
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Anderson Winkler
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Valerio Zerbi
- Centre for Biomedical Imaging (CIBM), Lausanne, Switzerland
- Neuro-X Institute, School of Engineering (STI), EPFL, Lausanne, Switzerland
| | - Timothy S. Coalson
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
| | - Matthew F. Glasser
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Michael P. Harms
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, United States
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham NIHR Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Department of Physics, Yale University, New Haven, CT, United States
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
- Department of Psychology, Yale University School of Medicine, New Haven, CT, United States
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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16
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Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3062. [PMID: 36991773 PMCID: PMC10053494 DOI: 10.3390/s23063062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
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Affiliation(s)
| | | | | | - Edmond Prakash
- Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK
| | - Chaminda Hewage
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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17
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Andreou D, Steen NE, Jørgensen KN, Smelror RE, Wedervang-Resell K, Nerland S, Westlye LT, Nærland T, Myhre AM, Joa I, Reitan SMK, Vaaler A, Morken G, Bøen E, Elvsåshagen T, Boye B, Malt UF, Aukrust P, Skrede S, Kroken RA, Johnsen E, Djurovic S, Andreassen OA, Ueland T, Agartz I. Lower circulating neuron-specific enolase concentrations in adults and adolescents with severe mental illness. Psychol Med 2023; 53:1479-1488. [PMID: 35387700 PMCID: PMC10009386 DOI: 10.1017/s0033291721003056] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/05/2021] [Accepted: 07/13/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND Both neurodegenerative and neurodevelopmental abnormalities have been suggested to be part of the etiopathology of severe mental illness (SMI). Neuron-specific enolase (NSE), mainly located in the neuronal cytoplasm, may indicate the process as it is upregulated after neuronal injury while a switch from non-neuronal enolase to NSE occurs during neuronal maturation. METHODS We included 1132 adult patients with SMI [schizophrenia (SZ) or bipolar spectrum disorders], 903 adult healthy controls (HC), 32 adolescent patients with SMI and 67 adolescent HC. Plasma NSE concentrations were measured by enzyme immunoassay. For 842 adults and 85 adolescents, we used total grey matter volume (TGMV) based on T1-weighted magnetic resonance images processed in FreeSurfer v6.0. We explored NSE case-control differences in adults and adolescents separately. To investigate whether putative case-control differences in NSE were TGMV-dependent we controlled for TGMV. RESULTS We found significantly lower NSE concentrations in both adult (p < 0.001) and adolescent patients with SMI (p = 0.007) compared to HC. The results remained significant after controlling for TGMV. Among adults, both patients with SZ spectrum (p < 0.001) and bipolar spectrum disorders (p = 0.005) had lower NSE than HC. In both patient subgroups, lower NSE levels were associated with increased symptom severity. Among adults (p < 0.001) and adolescents (p = 0.040), females had lower NSE concentrations than males. CONCLUSION We found lower NSE concentrations in adult and adolescent patients with SMI compared to HC. The results suggest the lack of progressive neuronal injury, and may reflect abnormal neuronal maturation. This provides further support of a neurodevelopmental rather than a neurodegenerative mechanism in SMI.
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Affiliation(s)
- Dimitrios Andreou
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Nils Eiel Steen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
| | - Kjetil Nordbø Jørgensen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Runar Elle Smelror
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Kirsten Wedervang-Resell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Child and Adolescent Mental Health Research Unit, Division of Mental Health and Addiction, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| | - Stener Nerland
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Lars T. Westlye
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Terje Nærland
- K.G. Jebsen Center for Neurodevelopmental Disorders, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NevSom, Department of Rare Disorders, Oslo University Hospital, Oslo, Norway
| | - Anne Margrethe Myhre
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Department of Research and Innovation, Oslo University Hospital, Oslo, Norway
| | - Inge Joa
- TIPS – Network for Clinical Research in Psychosis, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health, Network for Medical Sciences, University of Stavanger, Stavanger, Norway
| | - Solveig Merete Klæbo Reitan
- Faculty of Medicine and Health Sciences, Department of Mental Health, NTNU, Trondheim, Norway
- St Olavs Hospital, Department of Mental Health, Trondheim, Norway
| | - Arne Vaaler
- Faculty of Medicine and Health Sciences, Department of Mental Health, NTNU, Trondheim, Norway
- St Olavs Hospital, Department of Mental Health, Trondheim, Norway
| | - Gunnar Morken
- Faculty of Medicine and Health Sciences, Department of Mental Health, NTNU, Trondheim, Norway
- St Olavs Hospital, Department of Mental Health, Trondheim, Norway
| | - Erlend Bøen
- Psychosomatic and C-L Psychiatry, Adult, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Torbjørn Elvsåshagen
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
- Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Birgitte Boye
- Psychosomatic and C-L Psychiatry, Adult, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Behavioural Medicine, University of Oslo, Oslo, Norway
| | - Ulrik Fredrik Malt
- Department of Neurology, Division of Clinical Neuroscience, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Pål Aukrust
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Research Institute of Internal Medicine, Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Silje Skrede
- Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital, Bergen, Norway
| | - Rune Andreas Kroken
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Haukeland University Hospital, Bergen, Norway
| | - Erik Johnsen
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Haukeland University Hospital, Bergen, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- Department of Clinical Science, Norwegian Centre for Mental Disorders Research (NORMENT), University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Norwegian Centre for Mental Disorders Research (NORMENT), Oslo University Hospital, Oslo, Norway
| | - Thor Ueland
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- K.G. Jebsen Thrombosis Research and Expertise Center, University of Tromsø, Tromsø, Norway
- Research Institute of Internal Medicine, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
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Elyounssi S, Kunitoki K, Clauss JA, Laurent E, Kane K, Hughes DE, Hopkinson CE, Bazer O, Sussman RF, Doyle AE, Lee H, Tervo-Clemmens B, Eryilmaz H, Gollub RL, Barch DM, Satterthwaite TD, Dowling KF, Roffman JL. Uncovering and mitigating bias in large, automated MRI analyses of brain development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.530498. [PMID: 36909456 PMCID: PMC10002762 DOI: 10.1101/2023.02.28.530498] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Large, population-based MRI studies of adolescents promise transformational insights into neurodevelopment and mental illness risk 1,2. However, MRI studies of youth are especially susceptible to motion and other artifacts 3,4. These artifacts may go undetected by automated quality control (QC) methods that are preferred in high-throughput imaging studies, 5 and can potentially introduce non-random noise into clinical association analyses. Here we demonstrate bias in structural MRI analyses of children due to inclusion of lower quality images, as identified through rigorous visual quality control of 11,263 T1 MRI scans obtained at age 9-10 through the Adolescent Brain Cognitive Development (ABCD) Study6. Compared to the best-rated images (44.9% of the sample), lower-quality images generally associated with decreased cortical thickness and increased cortical surface area measures (Cohen's d 0.14-2.84). Variable image quality led to counterintuitive patterns in analyses that associated structural MRI and clinical measures, as inclusion of lower-quality scans altered apparent effect sizes in ways that increased risk for both false positives and negatives. Quality-related biases were partially mitigated by controlling for surface hole number, an automated index of topological complexity that differentiated lower-quality scans with good specificity at Baseline (0.81-0.93) and in 1,000 Year 2 scans (0.88-1.00). However, even among the highest-rated images, subtle topological errors occurred during image preprocessing, and their correction through manual edits significantly and reproducibly changed thickness measurements across much of the cortex (d 0.15-0.92). These findings demonstrate that inadequate QC of youth structural MRI scans can undermine advantages of large sample size to detect meaningful associations.
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Affiliation(s)
- Safia Elyounssi
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Keiko Kunitoki
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Jacqueline A. Clauss
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Eline Laurent
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Kristina Kane
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Dylan E. Hughes
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Departments of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles
| | - Casey E. Hopkinson
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Oren Bazer
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Rachel Freed Sussman
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Alysa E. Doyle
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Center for Genomic Medicine, Massachusetts General Hospital
| | - Hang Lee
- Biostatistics Center, Massachusetts General Hospital and Harvard Medical School
| | | | - Hamdi Eryilmaz
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Randy L. Gollub
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Deanna M. Barch
- Department of Psychological and Brain Sciences, Washington University in St. Louis
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine
- Penn Lifespan and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine
- Penn-CHOP Lifespan Brain Institute
| | - Kevin F. Dowling
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Department of Psychiatry, University of Pittsburgh
| | - Joshua L. Roffman
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
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19
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Wu Y, Ridwan AR, Niaz MR, Qi X, Zhang S, Alzheimer's Disease Neuroimaging Initiative, Bennett DA, Arfanakis K. Development of high quality T 1-weighted and diffusion tensor templates of the older adult brain in a common space. Neuroimage 2022; 260:119417. [PMID: 35793748 PMCID: PMC9437946 DOI: 10.1016/j.neuroimage.2022.119417] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 05/27/2022] [Accepted: 06/27/2022] [Indexed: 01/23/2023] Open
Abstract
High-quality T1-weighted (T1w) and diffusion tensor imaging (DTI) brain templates that are representative of the individuals under study enhance the accuracy of template-based neuroimaging investigations, and when they are also located in a common space they facilitate optimal integration of information on brain morphometry and diffusion characteristics. However, such multimodal templates have not been constructed for the brain of older adults. The purpose of this work was threefold: (A) to introduce an iterative method for construction of multimodal T1w and DTI templates that aims at maximizing the quality of each template separately as well as the spatial matching between templates, (B) to use this method to develop T1w and DTI templates of the older adult brain in a common space, and (C) to evaluate the performance of the method across iterations and compare it to the performance of state-of-the-art approaches based on multichannel registration. It was demonstrated that more iterations of the proposed method enhanced the characteristics and spatial matching of the resulting T1w and DTI templates. The templates of the older adult brain generated by the final iteration of the proposed method provided better delineation of brain structures, higher discriminability between tissues, and higher image sharpness near the cortex compared to templates generated with approaches employing multichannel registration. In addition, the spatial matching between the T1w and DTI templates constructed by the proposed method approximated the template alignment achieved with methods employing multichannel registration. Finally, when using the templates generated by the proposed method as references for spatial normalization of older adult T1w and DTI data, both the intra-modality inter-subject normalization precision and the inter-modality spatial matching were higher in most metrics than those achieved with templates constructed with other methods. Overall, the present work brought new insights into multimodal template construction, generated much-needed high quality T1w and DTI templates of the older adult brain in a common space, and conducted a thorough, quantitative evaluation of available multimodal template construction methods.
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Affiliation(s)
- Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL USA
| | - Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL USA
| | - Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL USA
| | - Xiaoxiao Qi
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL USA
| | - Shengwei Zhang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois USA
| | - Alzheimer's Disease Neuroimaging Initiative
- A portion of the data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL USA; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois USA.
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20
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de Moraes FHP, Mello VBB, Tovar-Moll F, Mota B. Establishing a Baseline for Human Cortical Folding Morphological Variables: A Multisite Study. Front Neurosci 2022; 16:897226. [PMID: 35924225 PMCID: PMC9340792 DOI: 10.3389/fnins.2022.897226] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 06/20/2022] [Indexed: 11/28/2022] Open
Abstract
Differences in the way human cerebral cortices fold have been correlated to health, disease, development, and aging. However, to obtain a deeper understanding of the mechanisms that generate such differences, it is useful to derive one's morphometric variables from the first principles. This study explores one such set of variables that arise naturally from a model for universal self-similar cortical folding that was validated on comparative neuroanatomical data. We aim to establish a baseline for these variables across the human lifespan using a heterogeneous compilation of cross-sectional datasets as the first step to extending the model to incorporate the time evolution of brain morphology. We extracted the morphological features from structural MRI of 3,650 subjects: 3,095 healthy controls (CTL) and 555 patients with Alzheimer's Disease (AD) from 9 datasets, which were harmonized with a straightforward procedure to reduce the uncertainty due to heterogeneous acquisition and processing. The unprecedented possibility of analyzing such a large number of subjects in this framework allowed us to compare CTL and AD subjects' lifespan trajectories, testing if AD is a form of accelerated aging at the brain structural level. After validating this baseline from development to aging, we estimate the variables' uncertainties and show that Alzheimer's Disease is similar to premature aging when measuring global and local degeneration. This new methodology may allow future studies to explore the structural transition between healthy and pathological aging and may be essential to generate data for the cortical folding process simulations.
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Affiliation(s)
- Fernanda H. P. de Moraes
- Brain Connectivity Unit, Instituto D'Or de Pesquisa e Ensino, Rio de Janeiro, Brazil
- *Correspondence: Fernanda Tovar-Moll
| | - Victor B. B. Mello
- metaBIO, Instituto de Física, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Fernanda Tovar-Moll
- Brain Connectivity Unit, Instituto D'Or de Pesquisa e Ensino, Rio de Janeiro, Brazil
- Fernanda H. P. de Moraes
| | - Bruno Mota
- metaBIO, Instituto de Física, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
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21
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Andreou D, Jørgensen KN, Nerland S, Ueland T, Vaskinn A, Haukvik UK, Yolken RH, Andreassen OA, Agartz I. Herpes simplex virus 1 infection on grey matter and general intelligence in severe mental illness. Transl Psychiatry 2022; 12:276. [PMID: 35821107 PMCID: PMC9276804 DOI: 10.1038/s41398-022-02044-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 06/22/2022] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
Schizophrenia and bipolar disorder are severe mental illnesses (SMI) linked to both genetic and environmental factors. Herpes simplex virus 1 (HSV1) is a common neurotropic pathogen which after the primary infection establishes latency with periodic reactivations. We hypothesized that the latent HSV1 infection is associated with brain structural abnormalities and cognitive impairment, especially in SMI. We included 420 adult patients with SMI (schizophrenia or bipolar spectrum) and 481 healthy controls. Circulating HSV1 immunoglobulin G concentrations were measured with immunoassays. We measured the total grey matter volume (TGMV), cortical, subcortical, cerebellar and regional cortical volumes based on T1-weighted MRI scans processed in FreeSurfer v6.0.0. Intelligence quotient (IQ) was assessed with the Wechsler Abbreviated Scale of Intelligence. Seropositive patients had significantly smaller TGMV than seronegative patients (642 cm3 and 654 cm3, respectively; p = 0.019) and lower IQ (104 and 107, respectively; p = 0.018). No TGMV or IQ differences were found between seropositive and seronegative healthy controls. Post-hoc analysis showed that (a) in both schizophrenia and bipolar spectrum, seropositive patients had similarly smaller TGMV than seronegative patients, whereas the HSV1-IQ association was driven by the schizophrenia spectrum group, and (b) among all patients, seropositivity was associated with smaller total cortical (p = 0.016), but not subcortical or cerebellar grey matter volumes, and with smaller left caudal middle frontal, precentral, lingual, middle temporal and banks of superior temporal sulcus regional cortical grey matter volumes. The results of this cross-sectional study indicate that HSV1 may be an environmental factor associated with brain structural abnormalities and cognitive impairment in SMI.
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Affiliation(s)
- Dimitrios Andreou
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway. .,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway. .,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden.
| | - Kjetil Nordbø Jørgensen
- grid.5510.10000 0004 1936 8921Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway ,grid.413684.c0000 0004 0512 8628Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Stener Nerland
- grid.5510.10000 0004 1936 8921Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway ,grid.413684.c0000 0004 0512 8628Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Torill Ueland
- grid.55325.340000 0004 0389 8485Psychosis Research Section, Oslo University Hospital, Oslo, Norway ,grid.5510.10000 0004 1936 8921Department of Psychology, University of Oslo, Oslo, Norway ,grid.55325.340000 0004 0389 8485Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Anja Vaskinn
- grid.5510.10000 0004 1936 8921Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway ,grid.55325.340000 0004 0389 8485Centre for Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Unn K. Haukvik
- grid.5510.10000 0004 1936 8921Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway ,grid.55325.340000 0004 0389 8485Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway ,grid.55325.340000 0004 0389 8485Centre for Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Robert H. Yolken
- grid.21107.350000 0001 2171 9311Stanley Division of Developmental Neurovirology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD USA
| | - Ole A. Andreassen
- grid.5510.10000 0004 1936 8921Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway ,grid.55325.340000 0004 0389 8485Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- grid.5510.10000 0004 1936 8921Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway ,grid.413684.c0000 0004 0512 8628Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway ,grid.425979.40000 0001 2326 2191Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
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22
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Kuula J, Martola J, Hakkarainen A, Räikkönen K, Savolainen S, Salli E, Hovi P, Björkqvist J, Kajantie E, Lundbom N. Brain Volumes and Abnormalities in Adults Born Preterm at Very Low Birth Weight. J Pediatr 2022; 246:48-55.e7. [PMID: 35301016 DOI: 10.1016/j.jpeds.2022.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 02/03/2022] [Accepted: 03/09/2022] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To assess radiographic brain abnormalities and investigate volumetric differences in adults born preterm at very low birth weight (<1500 g), using siblings as controls. STUDY DESIGN We recruited 79 adult same-sex sibling pairs with one born preterm at very low birth weight and the sibling at term. We acquired 3-T brain magnetic resonance imaging from 78 preterm participants and 72 siblings. A neuroradiologist, masked to participants' prematurity status, reviewed the images for parenchymal and structural abnormalities, and FreeSurfer software 6.0 was used to conduct volumetric analyses. Data were analyzed by linear mixed models. RESULTS We found more structural abnormalities in very low birth weight participants than in siblings (37% vs 13%). The most common finding was periventricular leukomalacia, present in 15% of very low birth weight participants and in 3% of siblings. The very low birth weight group had smaller absolute brain volumes (-0.4 SD) and, after adjusting for estimated intracranial volume, less gray matter (-0.2 SD), larger ventricles (1.5 SD), smaller thalami (-0.6 SD), caudate nuclei (-0.4 SD), right hippocampus (-0.4 SD), and left pallidum (-0.3 SD). We saw no volume differences in total white matter (-0.04 SD; 95% CI, -0.13 to 0.09). CONCLUSIONS Preterm very low birth weight adults had a higher prevalence of brain abnormalities than their term-born siblings. They also had smaller absolute brain volumes, less gray but not white matter, and smaller volumes in several gray matter structures.
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Affiliation(s)
- Juho Kuula
- HUS Medical Imaging Center, Department of Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland.
| | - Juha Martola
- HUS Medical Imaging Center, Department of Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Antti Hakkarainen
- HUS Medical Imaging Center, Department of Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Katri Räikkönen
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland
| | - Sauli Savolainen
- HUS Medical Imaging Center, Department of Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physics, University of Helsinki, Helsinki, Finland
| | - Eero Salli
- HUS Medical Imaging Center, Department of Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Petteri Hovi
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland
| | - Johan Björkqvist
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland
| | - Eero Kajantie
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki and Oulu, Finland; PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland; Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway; Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Nina Lundbom
- HUS Medical Imaging Center, Department of Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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Pulli EP, Silver E, Kumpulainen V, Copeland A, Merisaari H, Saunavaara J, Parkkola R, Lähdesmäki T, Saukko E, Nolvi S, Kataja EL, Korja R, Karlsson L, Karlsson H, Tuulari JJ. Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab. Front Neurosci 2022; 16:874062. [PMID: 35585923 PMCID: PMC9108497 DOI: 10.3389/fnins.2022.874062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/12/2022] [Indexed: 02/03/2023] Open
Abstract
Pediatric neuroimaging is a quickly developing field that still faces important methodological challenges. Pediatric images usually have more motion artifact than adult images. The artifact can cause visible errors in brain segmentation, and one way to address it is to manually edit the segmented images. Variability in editing and quality control protocols may complicate comparisons between studies. In this article, we describe in detail the semiautomated segmentation and quality control protocol of structural brain images that was used in FinnBrain Birth Cohort Study and relies on the well-established FreeSurfer v6.0 and ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium tools. The participants were typically developing 5-year-olds [n = 134, 5.34 (SD 0.06) years, 62 girls]. Following a dichotomous quality rating scale for inclusion and exclusion of images, we explored the quality on a region of interest level to exclude all regions with major segmentation errors. The effects of manual edits on cortical thickness values were relatively minor: less than 2% in all regions. Supplementary Material cover registration and additional edit options in FreeSurfer and comparison to the computational anatomy toolbox (CAT12). Overall, we conclude that despite minor imperfections FreeSurfer can be reliably used to segment cortical metrics from T1-weighted images of 5-year-old children with appropriate quality assessment in place. However, custom templates may be needed to optimize the results for the subcortical areas. Through visual assessment on a level of individual regions of interest, our semiautomated segmentation protocol is hopefully helpful for investigators working with similar data sets, and for ensuring high quality pediatric neuroimaging data.
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Affiliation(s)
- Elmo P. Pulli
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- *Correspondence: Elmo P. Pulli, ; orcid.org/0000-0003-3871-8563
| | - Eero Silver
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Venla Kumpulainen
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Anni Copeland
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Harri Merisaari
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Radiology, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, Turku, Finland
| | - Tuire Lähdesmäki
- Department of Pediatrics and Adolescent Medicine, Turku University Hospital, University of Turku, Turku, Finland
| | - Ekaterina Saukko
- Department of Radiology, Turku University Hospital, Turku, Finland
| | - Saara Nolvi
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Turku Institute for Advanced Studies, University of Turku, Turku, Finland
- Department of Psychology, University of Turku, Turku, Finland
| | - Eeva-Leena Kataja
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Riikka Korja
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychology, University of Turku, Turku, Finland
| | - Linnea Karlsson
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Hasse Karlsson
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Jetro J. Tuulari
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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24
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Gómez-Ramírez J, Fernández-Blázquez MA, González-Rosa JJ. Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation. Brain Sci 2022; 12:brainsci12050579. [PMID: 35624966 PMCID: PMC9139275 DOI: 10.3390/brainsci12050579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/19/2022] [Accepted: 04/23/2022] [Indexed: 01/11/2023] Open
Abstract
Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.
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Affiliation(s)
- Jaime Gómez-Ramírez
- Institute of Biomedical Research Cadiz (INiBICA), Universidad de Cádiz, 11003 Cádiz, Spain;
- Correspondence:
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Andreou D, Jørgensen KN, Nerland S, Yolken RH, Haukvik UK, Andreassen OA, Agartz I. Cytomegalovirus Infection Associated with Smaller Total Cortical Surface Area in Schizophrenia Spectrum Disorders. Schizophr Bull 2022; 48:1164-1173. [PMID: 35388401 PMCID: PMC9434442 DOI: 10.1093/schbul/sbac036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
OBJECTIVES Cytomegalovirus (CMV) congenital infection and in immunodeficiency can have deleterious effects on human cortex. In immunocompetent adults, the putative association between CMV infection and cortical measures has not been explored. We hypothesized that CMV exposure is associated with smaller cortical surface area or cortical thinning mainly in patients with schizophrenia spectrum disorders. STUDY DESIGN We included 67 adult patients with schizophrenia spectrum disorders and 262 adult healthy controls. We measured circulating CMV IgG antibody concentrations with solid-phase immunoassay techniques. We measured the total cortical surface area, regional cortical surface areas and the overall mean cortical thickness based on T1-weighted MRI scans processed in FreeSurfer v6.0. STUDY RESULTS In the whole sample analysis, we found a significant diagnostic group-by-CMV status interaction on the total surface area (P = .020). Among patients, CMV antibody positivity was significantly associated with smaller total surface area (P = .002, partial eta2 = 0.138) whereas no such association was found in healthy controls (P = .059). Post hoc analysis among patients showed that higher CMV antibody concentrations were also significantly associated with smaller total surface area (P = .038), and that CMV antibody positivity was significantly inversely associated with 14 left and 16 right regional surface areas mainly in the frontal and temporal lobes. CMV infection was not associated with the overall mean cortical thickness. CONCLUSIONS The results are indicative of a cortical surface area vulnerability to CMV infection in patients with schizophrenia spectrum disorders but not in healthy controls. CMV infection may contribute to the established cortical surface area aberrations in schizophrenia.
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Affiliation(s)
- Dimitrios Andreou
- To whom correspondence should be addressed; Diakonhjemmet Hospital, Department of Psychiatric Research, Forskningsveien 7, 0373, Oslo, Norway; tel: +46737678848, fax: +4722029901, e-mail:
| | - Kjetil Nordbø Jørgensen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Stener Nerland
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Robert H Yolken
- Department of Pediatrics, Stanley Division of Developmental Neurovirology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Unn K Haukvik
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Forensic Research and Education, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway,Department of Clinical Neuroscience, Centre for Psychiatry Research, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
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26
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Wortinger LA, Engen K, Barth C, Andreassen OA, Nordbø Jørgensen K, Agartz I. Asphyxia at birth affects brain structure in patients on the schizophrenia-bipolar disorder spectrum and healthy participants. Psychol Med 2022; 52:1050-1059. [PMID: 32772969 PMCID: PMC9069351 DOI: 10.1017/s0033291720002779] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 06/05/2020] [Accepted: 07/16/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND Uncertainty exists about what causes brain structure alterations associated with schizophrenia (SZ) and bipolar disorder (BD). Whether a history of asphyxia-related obstetric complication (ASP) - a common but harmful condition for neural tissue - contributes to variations in adult brain structure is unclear. We investigated ASP and its relationship to intracranial (ICV), global brain volumes and regional cortical and subcortical structures. METHODS A total of 311 patients on the SZ - BD spectrum and 218 healthy control (HC) participants underwent structural magnetic resonance imaging. They were evaluated for ASP using prospective information obtained from the Medical Birth Registry of Norway. RESULTS In all groups, ASP was related to smaller ICV, total brain, white and gray matter volumes and total surface area, but not to cortical thickness. Smaller cortical surface areas were found across frontal, parietal, occipital, temporal and insular regions. Smaller hippocampal, amygdala, thalamus, caudate and putamen volumes were reported for all ASP subgroups. ASP effects did not survive ICV correction, except in the caudate, which remained significantly smaller in both patient ASP subgroups, but not in the HC. CONCLUSIONS Since ASP was associated with smaller brain volumes in all groups, the genetic risk of developing a severe mental illness, alone, cannot easily explain the smaller ICV. Only the smaller caudate volumes of ASP patients specifically suggest that injury from ASP can be related to disease development. Our findings give support for the ICV as a marker of aberrant neurodevelopment and ASP in the etiology of brain development in BD and SZ.
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Affiliation(s)
- Laura Anne Wortinger
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kristine Engen
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Claudia Barth
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, NORMENT, Oslo University Hospital, Oslo, Norway
| | - Kjetil Nordbø Jørgensen
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institute, Stockholm, Sweden
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27
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Niaz MR, Ridwan AR, Wu Y, Bennett DA, Arfanakis K. Development and evaluation of a high resolution 0.5mm isotropic T1-weighted template of the older adult brain. Neuroimage 2022; 248:118869. [PMID: 34986396 PMCID: PMC8855670 DOI: 10.1016/j.neuroimage.2021.118869] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 12/08/2021] [Accepted: 12/29/2021] [Indexed: 10/28/2022] Open
Abstract
Investigating the structure of the older adult brain at high spatial resolution is of high significance, and a dedicated older adult structural brain template with sub-millimeter resolution is currently lacking. Therefore, the purpose of this work was twofold: (A) to develop a 0.5mm isotropic resolution standardized T1-weighted template of the older adult brain by applying principles of super resolution to high quality MRI data from 222 older adults (65-95 years of age), and (B) to systematically compare the new template to other standardized and study-specific templates in terms of image quality and performance when used as a reference for alignment of older adult data. The new template exhibited higher spatial resolution and improved visualization of fine structural details of the older adult brain compared to a template constructed using a conventional template building approach and the same data. In addition, the new template exhibited higher image sharpness and did not contain image artifacts observed in some of the other templates considered in this work. Due to the above enhancements, the new template provided higher inter-subject spatial normalization precision for older adult data compared to the other templates, and consequently enabled detection of smaller inter-group morphometric differences in older adult data. Finally, the new template was among those that were most representative of older adult brain data. Overall, the new template constructed here is an important resource for studies of aging, and the findings of the present work have important implications in template selection for investigations on older adults.
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Affiliation(s)
- Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, 3440 S Dearborn St, M-100, Chicago, IL 60616, United States
| | - Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, 3440 S Dearborn St, M-100, Chicago, IL 60616, United States
| | - Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, 3440 S Dearborn St, M-100, Chicago, IL 60616, United States
| | | | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, 3440 S Dearborn St, M-100, Chicago, IL 60616, United States; Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, United States; Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, United States.
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28
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Ye C, Huang J, Liang L, Yan Z, Qi Z, Kang X, Liu Z, Dong H, Lv H, Ma T, Lu J. Coupling of brain activity and structural network in multiple sclerosis: A graph frequency analysis study. J Neurosci Res 2022; 100:1226-1238. [PMID: 35184336 DOI: 10.1002/jnr.25028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 12/06/2021] [Accepted: 01/27/2022] [Indexed: 11/10/2022]
Affiliation(s)
| | - Jing Huang
- Department of Radiology and Nuclear Medicine Xuanwu Hospital, Capital Medical University Beijing China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Capital Medical University Beijing China
| | - Li Liang
- Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen Shenzhen China
| | - Zehong Yan
- Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen Shenzhen China
| | - Zhigang Qi
- Department of Radiology and Nuclear Medicine Xuanwu Hospital, Capital Medical University Beijing China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Capital Medical University Beijing China
| | - Xiong Kang
- Department of Radiology and Nuclear Medicine Xuanwu Hospital, Capital Medical University Beijing China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Capital Medical University Beijing China
| | - Zheng Liu
- Department of Neurology Xuanwu Hospital, Capital Medical University Beijing China
| | - Huiqing Dong
- Department of Neurology Xuanwu Hospital, Capital Medical University Beijing China
| | - Haiyan Lv
- Mindsgo Life Science Shenzhen Co. Ltd Shenzhen China
| | - Ting Ma
- Peng Cheng Laboratory Shenzhen China
- Department of Electronic and Information Engineering Harbin Institute of Technology at Shenzhen Shenzhen China
- Advanced Innovation Center for Human Brain Protection Capital Medical University Beijing China
- National Clinical Research Center for Geriatric Disorders Xuanwu Hospital Capital Medical University Beijing China
| | - Jie Lu
- Department of Radiology and Nuclear Medicine Xuanwu Hospital, Capital Medical University Beijing China
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Capital Medical University Beijing China
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29
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de Zoete RMJ, Stanwell P, Weber KA, Snodgrass SJ. Differences in Structural Brain Characteristics Between Individuals with Chronic Nonspecific Neck Pain and Asymptomatic Controls: A Case–Control Study. J Pain Res 2022; 15:521-531. [PMID: 35210851 PMCID: PMC8863323 DOI: 10.2147/jpr.s345365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 12/18/2021] [Indexed: 11/23/2022] Open
Abstract
Background Neck pain is a prevalent and costly problem, but its underlying mechanisms are poorly understood. Neuroimaging studies show alterations in brain morphometry in chronic musculoskeletal pain, but reports on neck pain are scarce. Objective This study investigates (1) differences in brain morphometry between individuals with chronic nonspecific neck pain and asymptomatic individuals and (2) associations between brain morphometry and patient-reported outcomes. Methods Sixty-three participants (33 pain, 11 female, mean [SD] age 35 [10] years; 30 control, 12 female, age 35 [11] years) underwent magnetic resonance imaging. Brain regions of interest (ROIs) were determined a priori, outcomes included cortical thickness and volume. Between-group differences were determined using cluster-wise correction for multiple comparisons and analyses of pain-related ROIs. Results Between-group differences in volume were identified in the precentral, frontal, occipital, parietal, temporal, and paracentral cortices. ROI analyses showed that parahippocampal cortical thickness was larger in the neck pain group (p=0.015, 95% CI: −0.27 to −0.03). Moderate to strong associations between volume and thickness of the cingulate cortex, prefrontal cortex, and temporal lobe and neck pain duration, pain intensity, and neck disability were identified (p-values 0.006 to 0.048). Conclusion Alterations in brain morphology that are associated with clinical characteristics inform the mechanisms underlying chronic nonspecific neck pain and may guide the development of more effective treatment approaches.
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Affiliation(s)
- Rutger M J de Zoete
- School of Allied Health Science and Practice, The University of Adelaide, Adelaide, SA, Australia
- School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Newcastle, NSW, Australia
- Correspondence: Rutger MJ de Zoete, School of Allied Health Science and Practice, The University of Adelaide, North Terrace, Adelaide, SA, 5005, Australia, Email
| | - Peter Stanwell
- School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Newcastle, NSW, Australia
| | - Kenneth A Weber
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, Palo Alto, CA, USA
| | - Suzanne J Snodgrass
- School of Health Sciences, Faculty of Health and Medicine, The University of Newcastle, Newcastle, NSW, Australia
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Hedges EP, Dimitrov M, Zahid U, Brito Vega B, Si S, Dickson H, McGuire P, Williams S, Barker GJ, Kempton MJ. Reliability of structural MRI measurements: The effects of scan session, head tilt, inter-scan interval, acquisition sequence, FreeSurfer version and processing stream. Neuroimage 2022; 246:118751. [PMID: 34848299 PMCID: PMC8784825 DOI: 10.1016/j.neuroimage.2021.118751] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 11/18/2021] [Accepted: 11/20/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Large-scale longitudinal and multi-centre studies are used to explore neuroimaging markers of normal ageing, and neurodegenerative and mental health disorders. Longitudinal changes in brain structure are typically small, therefore the reliability of automated techniques is crucial. Determining the effects of different factors on reliability allows investigators to control those adversely affecting reliability, calculate statistical power, or even avoid particular brain measures with low reliability. This study examined the impact of several image acquisition and processing factors and documented the test-retest reliability of structural MRI measurements. METHODS In Phase I, 20 healthy adults (11 females; aged 20-30 years) were scanned on two occasions three weeks apart on the same scanner using the ADNI-3 protocol. On each occasion, individuals were scanned twice (repetition), after re-entering the scanner (reposition) and after tilting their head forward. At one year follow-up, nine returning individuals and 11 new volunteers were recruited for Phase II (11 females; aged 22-31 years). Scans were acquired on two different scanners using the ADNI-2 and ADNI-3 protocols. Structural images were processed using FreeSurfer (v5.3.0, 6.0.0 and 7.1.0) to provide subcortical and cortical volume, cortical surface area and thickness measurements. Intra-class correlation coefficients (ICC) were calculated to estimate test-retest reliability. We examined the effect of repetition, reposition, head tilt, time between scans, MRI sequence and scanner on reliability of structural brain measurements. Mean percentage differences were also calculated in supplementary analyses. RESULTS Using the FreeSurfer v7.1.0 longitudinal pipeline, we observed high reliability for subcortical and cortical volumes, and cortical surface areas at repetition, reposition, three weeks and one year (mean ICCs>0.97). Cortical thickness reliability was lower (mean ICCs>0.82). Head tilt had the greatest adverse impact on ICC estimates, for example reducing mean right cortical thickness to ICC=0.74. In contrast, changes in ADNI sequence or MRI scanner had a minimal effect. We observed an increase in reliability for updated FreeSurfer versions, with the longitudinal pipeline consistently having a higher reliability than the cross-sectional pipeline. DISCUSSION Longitudinal studies should monitor or control head tilt to maximise reliability. We provided the ICC estimates and mean percentage differences for all FreeSurfer brain regions, which may inform power analyses for clinical studies and have implications for the design of future longitudinal studies.
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Affiliation(s)
- Emily P Hedges
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom.
| | - Mihail Dimitrov
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Uzma Zahid
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom
| | - Barbara Brito Vega
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom
| | - Shuqing Si
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom
| | - Hannah Dickson
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom
| | - Steven Williams
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Gareth J Barker
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, United Kingdom
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London SE5 8AF, United Kingdom
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The effect of gadolinium-based contrast-agents on automated brain atrophy measurements by FreeSurfer in patients with multiple sclerosis. Eur Radiol 2022; 32:3576-3587. [PMID: 34978580 PMCID: PMC9038813 DOI: 10.1007/s00330-021-08405-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 09/07/2021] [Accepted: 10/12/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To determine whether reliable brain atrophy measures can be obtained from post-contrast 3D T1-weighted images in patients with multiple sclerosis (MS) using FreeSurfer. METHODS Twenty-two patients with MS were included, in which 3D T1-weighted MR images were obtained during the same scanner visit, with the same acquisition protocol, before and after administration of gadolinium-based contrast agents (GBCAs). Two FreeSurfer versions (v.6.0.1 and v.7.1.1.) were applied to calculate grey matter (GM) and white matter (WM) volumes and global and regional cortical thickness. The consistency between measures obtained in pre- and post-contrast images was assessed by intra-class correlation coefficient (ICC), the difference was investigated by paired t-tests, and the mean percentage increase or decrease was calculated for total WM and GM matter volume, total deep GM and thalamus volume, and mean cortical thickness. RESULTS Good to excellent reliability was found between all investigated measures, with ICC ranging from 0.926 to 0.996, all p values < 0.001. GM volumes and cortical thickness measurements were significantly higher in post-contrast images by 3.1 to 17.4%, while total WM volume decreased significantly by 1.7% (all p values < 0.001). CONCLUSION The consistency between values obtained from pre- and post-contrast images was excellent, suggesting it may be possible to extract reliable brain atrophy measurements from T1-weighted images acquired after administration of GBCAs, using FreeSurfer. However, absolute values were systematically different between pre- and post-contrast images, meaning that such images should not be compared directly. Potential systematic effects, possibly dependent on GBCA dose or the delay time after contrast injection, should be investigated. TRIAL REGISTRATION Clinical trials.gov. identifier: NCT00360906. KEY POINTS • The influence of gadolinium-based contrast agents (GBCAs) on atrophy measurements is still largely unknown and challenges the use of a considerable source of historical and prospective real-world data. • In 22 patients with multiple sclerosis, the consistency between brain atrophy measurements obtained from pre- and post-contrast images was excellent, suggesting it may be possible to extract reliable atrophy measurements in T1-weighted images acquired after administration of GBCAs, using FreeSurfer. • Absolute values were systematically different between pre- and post-contrast images, meaning that such images should not be compared directly, and measurements extracted from certain regions (e.g., the temporal pole) should be interpreted with caution.
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Frazier-Logue N, Wang J, Wang Z, Sodums D, Khosla A, Samson AD, McIntosh AR, Shen K. A Robust Modular Automated Neuroimaging Pipeline for Model Inputs to TheVirtualBrain. Front Neuroinform 2022; 16:883223. [PMID: 35784190 PMCID: PMC9239912 DOI: 10.3389/fninf.2022.883223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/26/2022] [Indexed: 11/29/2022] Open
Abstract
TheVirtualBrain, an open-source platform for large-scale network modeling, can be personalized to an individual using a wide range of neuroimaging modalities. With the growing number and scale of neuroimaging data sharing initiatives of both healthy and clinical populations comes an opportunity to create large and heterogeneous sets of dynamic network models to better understand individual differences in network dynamics and their impact on brain health. Here we present TheVirtualBrain-UK Biobank pipeline, a robust, automated and open-source brain image processing solution to address the expanding scope of TheVirtualBrain project. Our pipeline generates connectome-based modeling inputs compatible for use with TheVirtualBrain. We leverage the existing multimodal MRI processing pipeline from the UK Biobank made for use with a variety of brain imaging modalities. We add various features and changes to the original UK Biobank implementation specifically for informing large-scale network models, including user-defined parcellations for the construction of matching whole-brain functional and structural connectomes. Changes also include detailed reports for quality control of all modalities, a streamlined installation process, modular software packaging, updated software versions, and support for various publicly available datasets. The pipeline has been tested on various datasets from both healthy and clinical populations and is robust to the morphological changes observed in aging and dementia. In this paper, we describe these and other pipeline additions and modifications in detail, as well as how this pipeline fits into the TheVirtualBrain ecosystem.
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Affiliation(s)
- Noah Frazier-Logue
- Rotman Research Institute, Baycrest, Toronto, ON, Canada.,Institute for Neuroscience and Neurotechnology, Simon Fraser University, Burnaby, BC, Canada
| | - Justin Wang
- Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | - Zheng Wang
- Rotman Research Institute, Baycrest, Toronto, ON, Canada
| | - Devin Sodums
- Rotman Research Institute, Baycrest, Toronto, ON, Canada.,Kunin-Lunenfeld Centre for Applied Research and Innovation, Baycrest, Toronto, ON, Canada
| | - Anisha Khosla
- Rotman Research Institute, Baycrest, Toronto, ON, Canada.,Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Alexandria D Samson
- Rotman Research Institute, Baycrest, Toronto, ON, Canada.,Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Anthony R McIntosh
- Rotman Research Institute, Baycrest, Toronto, ON, Canada.,Institute for Neuroscience and Neurotechnology, Simon Fraser University, Burnaby, BC, Canada.,Department of Psychology, University of Toronto, Toronto, ON, Canada.,Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, Canada
| | - Kelly Shen
- Rotman Research Institute, Baycrest, Toronto, ON, Canada.,Institute for Neuroscience and Neurotechnology, Simon Fraser University, Burnaby, BC, Canada
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Andreou D, Jørgensen KN, Nerland S, Smelror RE, Wedervang-Resell K, Johannessen CH, Myhre AM, Andreassen OA, Blennow K, Zetterberg H, Agartz I. Lower plasma total tau in adolescent psychosis: Involvement of the orbitofrontal cortex. J Psychiatr Res 2021; 144:255-261. [PMID: 34700214 DOI: 10.1016/j.jpsychires.2021.10.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 10/16/2021] [Accepted: 10/19/2021] [Indexed: 10/20/2022]
Abstract
Schizophrenia is thought to be a neurodevelopmental disorder with neuronal migration, differentiation and maturation disturbances. Tau is a microtubule-associated protein with a crucial role in these processes. Lower circulating tau levels have been reported in adults with schizophrenia, but this association has not been investigated in adolescent psychosis. We aimed to test the hypotheses that a) adolescents with early-onset psychosis (EOP; age of onset <18 years) display lower plasma tau concentrations compared to healthy controls, and b) among patients with psychosis, tau levels are linked to structural brain measures associated with the microtubule-associated tau (MAPT) gene and psychosis. We included 37 adolescent patients with EOP (mean age 16.4 years) and 59 adolescent healthy controls (mean age 16.2 years). We investigated putative patient-control differences in plasma total tau concentrations measured by a Single molecule array (Simoa) immunoassay. We explored the correlations between tau and selected structural brain measures based on T1-weighted MRI scans processed in FreeSurfer v6.0. We found significantly lower plasma tau concentrations in patients compared to healthy controls (p = 0.017, partial eta-squared = 0.061). Tau was not associated with antipsychotic use or the antipsychotic dosage. Among patients but not healthy controls, tau levels were positively correlated with the cortical orbitofrontal surface area (p = 0.013, R-squared = 0.24). The results are suggestive of a tau-related neurodevelopmental disturbance in adolescent psychosis.
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Affiliation(s)
- Dimitrios Andreou
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden.
| | - Kjetil Nordbø Jørgensen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Stener Nerland
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Runar Elle Smelror
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Kirsten Wedervang-Resell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Child and Adolescent Mental Health Research Unit, Department of Research and Innovation, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Cecilie Haggag Johannessen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anne Margrethe Myhre
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Research and Innovation, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK; UK Dementia Research Institute at UCL, London, UK
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
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Ledesma S, Ibarra-Manzano MA, Almanza-Ojeda DL, Fallavollita P, Steffener J. Artificial Intelligence to Analyze the Cortical Thickness Through Age. Front Artif Intell 2021; 4:549255. [PMID: 34723171 PMCID: PMC8548778 DOI: 10.3389/frai.2021.549255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 08/30/2021] [Indexed: 11/30/2022] Open
Abstract
In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. One advantage of using artificial neural networks is that they can learn and model non-linear and complex relationships. Also, artificial neural networks are immune to noise in the samples and can handle unseen data. That is, the models based on artificial neural networks can predict the behavior of samples that were not used for training. Furthermore, several studies have shown that artificial neural networks are capable of deriving information from imprecise data. Because of these advantages, the results obtained in this study by the artificial neural networks provide valuable information to analyze and model the cortical thickness.
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Affiliation(s)
- Sergio Ledesma
- Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.,School of Engineering, University of Guanajuato, Guanajuato, Mexico
| | | | | | | | - Jason Steffener
- Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada
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Haines S, Butler E, Stuckey S, Hester R, Grech LB. Relationship Between Interpersonal Depressive Symptoms and Reduced Amygdala Volume in People with Multiple Sclerosis: Considerations for Clinical Practice. Int J MS Care 2021; 23:178-185. [PMID: 34483757 DOI: 10.7224/1537-2073.2020-015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Background The lifetime prevalence of depression in people with multiple sclerosis (MS) is approximately 50% compared with around 15% in the general population. There is a relationship between depression and quality of life in people with MS and evidence that depression may contribute to disease progression. Methods This cross-sectional pilot study assessed the association between depression and regional brain atrophy, including amygdala and hippocampal volume. Forty-nine participants with MS recruited through a hospital MS clinic were administered the Center for Epidemiological Studies Depression Scale Revised (CESD-R) to investigate whether higher endorsements on the items depressive affect and interpersonal symptoms were associated with volumetric magnetic resonance imaging measurements of hippocampal and amygdala atrophy. Results Regression analysis revealed an association between depression-related interpersonal symptoms and right amygdala volume. No association was found between depression and hippocampal volume. Conclusions These results provide preliminary support for a unilateral, biologically based relationship between the right amygdala and characteristic interpersonal depressive symptoms expressed by people with MS and add to the growing body of literature implicating regional brain atrophy in MS-associated depression. Given that the interpersonal subcomponent of the CESD-R measures social functioning, and the neural networks in the amygdala are known to be implicated in processing social stimuli, this research suggests that targeted diagnosis and treatments for depression in people with MS may be particularly beneficial. Further confirmatory research of this relationship is required.
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Blumen HM, Schwartz E, Allali G, Beauchet O, Callisaya M, Doi T, Shimada H, Srikanth V, Verghese J. Cortical Thickness, Volume, and Surface Area in the Motoric Cognitive Risk Syndrome. J Alzheimers Dis 2021; 81:651-665. [PMID: 33867359 DOI: 10.3233/jad-201576] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The motoric cognitive risk (MCR) syndrome is a pre-clinical stage of dementia characterized by slow gait and cognitive complaint. Yet, the brain substrates of MCR are not well established. OBJECTIVE To examine cortical thickness, volume, and surface area associated with MCR in the MCR-Neuroimaging Consortium, which harmonizes image processing/analysis of multiple cohorts. METHODS Two-hundred MRIs (M age 72.62 years; 47.74%female; 33.17%MCR) from four different cohorts (50 each) were first processed with FreeSurfer 6.0, and then analyzed using multivariate and univariate general linear models with 1,000 bootstrapped samples (n-1; with resampling). All models adjusted for age, sex, education, white matter lesions, total intracranial volume, and study site. RESULTS Overall, cortical thickness was lower in individuals with MCR than in those without MCR. There was a trend in the same direction for cortical volume (p = 0.051). Regional cortical thickness was also lower among individuals with MCR than individuals without MCR in prefrontal, insular, temporal, and parietal regions. CONCLUSION Cortical atrophy in MCR is pervasive, and include regions previously associated with human locomotion, but also social, cognitive, affective, and motor functions. Cortical atrophy in MCR is easier to detect in cortical thickness than volume and surface area because thickness is more affected by healthy and pathological aging.
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Affiliation(s)
- Helena M Blumen
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Emily Schwartz
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Gilles Allali
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Clinical Neurosciences, Geneva University Hospitals and University of Geneva, Switzerland
| | - Olivier Beauchet
- Division of Geriatric Medicine, Sir Mortimer B. Davis Jewish General Hospital & Dr. Joseph Kaufmann Chair in Geriatric Medicine, Faculty of Medicine McGill University, Montreal, Quebec, Canada
| | - Michele Callisaya
- Peninsula Clinical School, Central Clinical School, Monash University, Victoria, Australia.,Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia
| | - Takehiko Doi
- Section for Health Promotion, Department of Preventive Gerontology
| | - Hiroyuki Shimada
- National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Velandai Srikanth
- Peninsula Clinical School, Central Clinical School, Monash University, Victoria, Australia.,Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia
| | - Joe Verghese
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
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Walden LM, Hu S, Madabhushi A, Prescott JW. Amyloid Deposition Is Greater in Cerebral Gyri than in Cerebral Sulci with Worsening Clinical Diagnosis Across the Alzheimer's Disease Spectrum. J Alzheimers Dis 2021; 83:423-433. [PMID: 34334397 DOI: 10.3233/jad-210308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Histopathologic studies have demonstrated differential amyloid-β (Aβ) burden between cortical sulci and gyri in Alzheimer's disease (AD), with sulci having a greater Aβ burden. OBJECTIVE To characterize Aβ deposition in the sulci and gyri of the cerebral cortex in vivo among subjects with normal cognition (NC), mild cognitive impairment (MCI), and AD, and to evaluate if these differences could improve discrimination between diagnostic groups. METHODS T1-weighted 3T MR and florbetapir (amyloid) positron emission tomography (PET) data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). T1 images were segmented and the cortex was separated into sulci/gyri based on pial surface curvature measurements. T1 images were registered to PET images and regional standardized uptake value ratios (SUVr) were calculated. A linear mixed effects model was used to analyze the relationship between clinical variables and amyloid PET SUVr measurements in the sulci/gyri. Receiver operating characteristic (ROC) analysis was performed to define amyloid positivity. Logistic models were used to evaluate predictive performance of clinical diagnosis using amyloid PET SUVr measurements in sulci/gyri. RESULTS 719 subjects were included: 272 NC, 315 MCI, and 132 AD. Gyral and sulcal Aβ increased with worsening cognition, however there was a greater increase in gyral Aβ. Females had a greater gyral and sulcal Aβ burden. Focusing on sulcal and gyral Aβ did not improve predictive power for diagnostic groups. CONCLUSION While there were significant differences in Aβ deposition in cerebral sulci and gyri across the AD spectrum, these differences did not translate into improved prediction of diagnosis. Females were found to have greater gyral and sulcal Aβ burden.
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Affiliation(s)
- Lucas M Walden
- MetroHealth, Department of Radiology, Cleveland, OH, USA
| | - Song Hu
- MetroHealth, Department of Radiology, Cleveland, OH, USA
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Center for Computational Imaging & Personalized Diagnostics, Cleveland, OH, USA.,Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Jeffrey W Prescott
- MetroHealth, Department of Radiology, Cleveland, OH, USA.,Case Western Reserve University, School of Medicine, Cleveland, OH, USA
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Weis CN, Webb EK, Huggins AA, Kallenbach M, Miskovich TA, Fitzgerald JM, Bennett KP, Krukowski JL, deRoon-Cassini TA, Larson CL. Stability of hippocampal subfield volumes after trauma and relationship to development of PTSD symptoms. Neuroimage 2021; 236:118076. [PMID: 33878374 PMCID: PMC8284190 DOI: 10.1016/j.neuroimage.2021.118076] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 03/01/2021] [Accepted: 04/08/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The hippocampus plays a central role in post-traumatic stress disorder (PTSD) pathogenesis, and the majority of neuroimaging research on PTSD has studied the hippocampus in its entirety. Although extensive literature demonstrates changes in hippocampal volume are associated with PTSD, fewer studies have probed the relationship between symptoms and the hippocampus' functionally and structurally distinct subfields. We utilized data from a longitudinal study examining post-trauma outcomes to determine whether hippocampal subfield volumes change post-trauma and whether specific subfields are significantly associated with, or prospectively related to, PTSD symptom severity. As a secondary aim, we leveraged our unique study design sample to also investigate reliability of hippocampal subfield volumes using both cross-sectional and longitudinal pipelines available in FreeSurfer v6.0. METHODS Two-hundred and fifteen traumatically injured individuals were recruited from an urban Emergency Department. Two-weeks post-injury, participants underwent two consecutive days of neuroimaging (time 1: T1, and time 2: T2) with magnetic resonance imaging (MRI) and completed self-report assessments. Six-months later (time 3: T3), participants underwent an additional scan and were administered a structured interview assessing PTSD symptoms. First, we calculated reliability of hippocampal measurements at T1 and T2 (automatically segmented with FreeSurfer v6.0). We then examined the prospective (T1 subfields) and cross-sectional (T3 subfields) relationship between volumes and PTSD. Finally, we tested whether change in subfield volumes between T1 and T3 explained PTSD symptom variability. RESULTS After controlling for sex, age, and total brain volume, none of the subfield volumes (T1) were prospectively related to T3 PTSD symptoms nor were subfield volumes (T3) associated with current PTSD symptoms (T3). Tl - T2 reliability of all hippocampal subfields ranged from good to excellent (intraclass correlation coefficient (ICC) values > 0.83), with poorer reliability in the hippocampal fissure. CONCLUSION Our study was a novel examination of the prospective relationship between hippocampal subfield volumes in relation to PTSD in a large trauma-exposed urban sample. There was no significant relationship between subfield volumes and PTSD symptoms, however, we confirmed FreeSurfer v6.0 hippocampal subfield segmentation is reliable when applied to a traumatically-injured sample, using both cross-sectional and longitudinal analysis pipelines. Although hippocampal subfield volumes may be an important marker of individual variability in PTSD, findings are likely conditional on the timing of the measurements (e.g. acute or chronic post-trauma periods) and analysis strategy (e.g. cross-sectional or prospective).
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Affiliation(s)
- C N Weis
- University of Wisconsin Milwaukee, Psychology, Department of Psychology, 334 Garland Hall, 2441 E. Hartford Ave, Milwaukee, WI 53211, United States.
| | - E K Webb
- University of Wisconsin Milwaukee, Psychology, Department of Psychology, 334 Garland Hall, 2441 E. Hartford Ave, Milwaukee, WI 53211, United States
| | - A A Huggins
- University of Wisconsin Milwaukee, Psychology, Department of Psychology, 334 Garland Hall, 2441 E. Hartford Ave, Milwaukee, WI 53211, United States
| | - M Kallenbach
- University of Wisconsin Milwaukee, Psychology, Department of Psychology, 334 Garland Hall, 2441 E. Hartford Ave, Milwaukee, WI 53211, United States
| | - T A Miskovich
- University of Wisconsin Milwaukee, Psychology, Department of Psychology, 334 Garland Hall, 2441 E. Hartford Ave, Milwaukee, WI 53211, United States
| | - J M Fitzgerald
- University of Wisconsin Milwaukee, Psychology, Department of Psychology, 334 Garland Hall, 2441 E. Hartford Ave, Milwaukee, WI 53211, United States
| | - K P Bennett
- University of Wisconsin Milwaukee, Psychology, Department of Psychology, 334 Garland Hall, 2441 E. Hartford Ave, Milwaukee, WI 53211, United States
| | - J L Krukowski
- University of Wisconsin Milwaukee, Psychology, Department of Psychology, 334 Garland Hall, 2441 E. Hartford Ave, Milwaukee, WI 53211, United States
| | - T A deRoon-Cassini
- University of Wisconsin Milwaukee, Psychology, Department of Psychology, 334 Garland Hall, 2441 E. Hartford Ave, Milwaukee, WI 53211, United States
| | - C L Larson
- University of Wisconsin Milwaukee, Psychology, Department of Psychology, 334 Garland Hall, 2441 E. Hartford Ave, Milwaukee, WI 53211, United States
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Andreou D, Jørgensen KN, Nerland S, Engen K, Yolken RH, Andreassen OA, Agartz I. Cytomegalovirus infection associated with smaller dentate gyrus in men with severe mental illness. Brain Behav Immun 2021; 96:54-62. [PMID: 34010712 DOI: 10.1016/j.bbi.2021.05.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 04/05/2021] [Accepted: 05/14/2021] [Indexed: 10/21/2022] Open
Abstract
Cytomegalovirus (CMV) infection is usually inapparent in healthy adults but persists for life. Neural progenitor/stem cells are main CMV targets, and dentate gyrus (DG) a major neurogenic niche. Smaller DG volume has been repeatedly reported in severe mental illness (SMI). Considering the suggested immune system, blood-brain barrier and DG disturbances in SMI, we hypothesized that CMV exposure is associated with smaller DG volume in patients, but not healthy controls (HC). Due to the differential male and female immune response to CMV, we hypothesized sex-dependent associations. 381 adult patients with SMI (schizophrenia spectrum or bipolar spectrum disorders) and 396 HC were included. MRI scans were obtained with 1.5T Siemens MAGNETOM Sonata scanner or 3T General Electric Signa HDxt scanner, and processed with FreeSurfer v6.0. CMV immunoglobulin G antibody concentrations were measured by solid phase immunoassay. We investigated main and interaction effects of CMV status (antibody positivity/CMV + vs. negativity/CMV-) and sex on DG in patients and HC. Among patients, there was a significant CMV-by-sex interaction on DG (p = 0.009); CMV + male patients had significantly smaller DG volume than CMV- male patients (p = 0.001, 39 mm3 volume difference) whereas no CMV-DG association was found in female patients. Post-hoc analysis among male patients showed that the CMV-DG association was present in both hemispheres and in both patients with schizophrenia spectrum and bipolar spectrum disorders, and further, that higher CMV antibody titers were associated with smaller DG. No CMV-DG association was found in HC. The results indicate a DG vulnerability to CMV infection in men with SMI.
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Affiliation(s)
- Dimitrios Andreou
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden.
| | - Kjetil Nordbø Jørgensen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Stener Nerland
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Kristine Engen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Robert H Yolken
- Stanley Division of Developmental Neurovirology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
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Ross MC, Dvorak D, Sartin-Tarm A, Botsford C, Cogswell I, Hoffstetter A, Putnam O, Schomaker C, Smith P, Stalsberg A, Wang Y, Xiong M, Cisler JM. Gray matter volume correlates of adolescent posttraumatic stress disorder: A comparison of manual intervention and automated segmentation in FreeSurfer. Psychiatry Res Neuroimaging 2021; 313:111297. [PMID: 33962164 PMCID: PMC8205994 DOI: 10.1016/j.pscychresns.2021.111297] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 03/24/2021] [Accepted: 04/22/2021] [Indexed: 01/08/2023]
Abstract
Exposure to early life trauma is common and confers risk for psychological disorders in adolescence, including posttraumatic stress disorder (PTSD). Trauma exposure and PTSD are also consistently linked to alterations in gray matter volume (GMV). Despite the quantity of structural neuroimaging research in trauma-exposed populations, little consensus exists amongst research groups on best practices for image processing method and manual editing procedures. The purpose of this report is to evaluate the utility of manual editing of magnetic resonance (MR) images for detecting PTSD-related group differences in GMV. Here, T1-weighted MR images from adolescent girls aged 11-17 were obtained and analyzed. Two datasets were created from the FreeSurfer reconall pipeline, one of which was manually edited by trained research assistants. Gray matter regions of interest were selected and total volume estimates were entered into linear mixed effects models with method (manual edits or automated) as a within-subjects factor and group dummy-coded with PTSD as the reference group. Consistent with prior literature, individuals with PTSD demonstrated reduced GMV of the amygdala compared to trauma-exposed and non-trauma exposed controls, independent of editing method. Our results demonstrate that amygdala GMV reductions in PTSD are robust to certain methodological choices and do not suggest a benefit to the time-intensive manual editing pipeline in FreeSurfer for quantifying PTSD-related GMV.
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Affiliation(s)
- Marisa C Ross
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI United States; Neuroscience and Public Policy Program, University of Wisconsin-Madison, Madison, WI United States.
| | - Delaney Dvorak
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI United States
| | - Anneliis Sartin-Tarm
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE United States
| | - Chloe Botsford
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI United States
| | - Ian Cogswell
- Institute for Health Metrics and Evaluation, Seattle, WA United States
| | - Ashley Hoffstetter
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI United States
| | - Olivia Putnam
- Department of Psychology, Northwestern University, Evanston, IL United States
| | - Chloe Schomaker
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI United States
| | - Penda Smith
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI United States
| | - Anna Stalsberg
- Department of Sociology, University of Minnesota- Twin Cities, Minneapolis, MN United States
| | - Yunling Wang
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI United States
| | - Megan Xiong
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI United States
| | - Josh M Cisler
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI United States
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Leung IHK, Broadhouse KM, Mowszowski L, LaMonica HM, Palmer JR, Hickie IB, Naismith SL, Duffy SL. Association between lifetime depression history, hippocampal volume and memory in non-amnestic mild cognitive impairment. Eur J Neurosci 2021; 54:4953-4970. [PMID: 33765347 DOI: 10.1111/ejn.15207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/01/2021] [Accepted: 03/14/2021] [Indexed: 11/28/2022]
Abstract
Hippocampal subfield volume loss in older adults with amnestic mild cognitive impairment (aMCI) and depression history are associated with amyloid beta and tau pathology, thereby increasing the risk for Alzheimer's disease (AD). However, no studies have exclusively examined distinct alterations in hippocampal subfields in non-amnestic MCI (naMCI) in relation to depression history. Here, we used both longitudinal and transverse hippocampal segmentation methods using the automated FreeSurfer software to examine whether a lifetime depression history is associated with differences in hippocampal head/body/tail (H/B/T) and key subfield volumes (CA1, subiculum, dentate gyrus) in older adults with naMCI. Further, we explored whether differences in hippocampal H/B/T and subfield volumes were associated with structured and unstructured verbal encoding and retention, comparing those with and without a depression history. The naMCI with a depression history group demonstrated larger or relatively preserved right CA1 volumes, which were associated with better unstructured verbal encoding and as well as structured verbal memory retention. This association between memory encoding and hippocampal CA1 and total head volume was significantly different to those with no depression history. The relationship between right CA1 volume and memory retention was also moderated by depression history status F (5,143) = 7.84, p < 0.001, R2 = 0.22. Those participants taking antidepressants had significantly larger hippocampal subiculum (p = 0.008), and right hippocampal body (p = 0.004) and better performance on structured encoding (p = 0.011) and unstructured memory retention (p = 0.009). These findings highlight the importance of lifetime depression history and antidepressant use on the hippocampus and encoding and memory retention in naMCI.
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Affiliation(s)
- Isabella Hoi Kei Leung
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia.,Faculty of Medicine and Health, Central Clinical School, University of Sydney, Sydney, NSW, Australia
| | - Kathryn Mary Broadhouse
- Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia.,School of Science and Engineering, University of the Sunshine Coast, Sunshine Coast, QLD, Australia
| | - Loren Mowszowski
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Faculty of Science, School of Psychology, University of Sydney, Sydney, NSW, Australia
| | - Haley M LaMonica
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Faculty of Medicine and Health, Central Clinical School, University of Sydney, Sydney, NSW, Australia
| | - Jake Robert Palmer
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Department of Psychology, Macquarie University, Sydney, NSW, Australia
| | - Ian B Hickie
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia
| | - Sharon L Naismith
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia.,Faculty of Science, School of Psychology, University of Sydney, Sydney, NSW, Australia
| | - Shantel Leigh Duffy
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia.,Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia.,Charles Perkins Centre, Discipline of Exercise and Sport Science, Faculty of Health Sciences, University of Sydney, Sydney, NSW, Australia
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42
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Addiego FM, Zajur K, Knack S, Jamieson J, Rayhan RU, Baraniuk JN. Subcortical brain segment volumes in Gulf War Illness and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Life Sci 2021; 282:119749. [PMID: 34214570 DOI: 10.1016/j.lfs.2021.119749] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/20/2021] [Accepted: 06/11/2021] [Indexed: 01/29/2023]
Abstract
AIMS There is controversy about brain volumes in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (CFS) and Gulf War Illness (GWI). Subcortical regions were assessed because of significant differences in blood oxygenation level dependent signals in the midbrain between these diseases. MATERIALS AND METHOD Magnetization-prepared rapid acquisition with gradient echo (MPRAGE) images from 3 Tesla structural magnetic resonance imaging scans from sedentary control (n = 34), CFS (n = 38) and GWI (n = 90) subjects were segmented in FreeSurfer. Segmented subcortical volumes were regressed against intracranial volume and age, then iteratively analyzed by multivariate general linear modeling with disease status, gender and demographics as independent co-variates. KEY FINDINGS The optimal model for all subjects used disease status and gender as fixed factors with independent variables eliminated after iteration. Volumes of anterior and midanterior corpus callosum were significantly larger in GWI than CFS. Gender was a significant variable for many segment volumes, and so female and male subjects were analyzed separately. CFS females had smaller left putamen, right caudate and left cerebellum white matter than control women. CFS males had larger left hippocampus than GWI males. Orthostatic status and posttraumatic distress syndrome were not significant covariates. SIGNIFICANCE CFS and GWI were appropriate "illness controls" for each other. The different patterns of adjusted segment volumes suggested that sexual dimorphisms contributed to pathological changes. Previous volumetric studies may need to be reevaluated to account for gender differences. The findings are framed by comparison to the spectrum of magnetic resonance imaging outcomes in the literature.
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Affiliation(s)
| | - Kristina Zajur
- Pain Fatigue Research Alliance, Georgetown University, Washington, DC 20007-2197, USA
| | - Sarah Knack
- Pain Fatigue Research Alliance, Georgetown University, Washington, DC 20007-2197, USA
| | - Jessie Jamieson
- Pain Fatigue Research Alliance, Georgetown University, Washington, DC 20007-2197, USA
| | - Rakib U Rayhan
- Pain Fatigue Research Alliance, Georgetown University, Washington, DC 20007-2197, USA
| | - James N Baraniuk
- Pain Fatigue Research Alliance, Georgetown University, Washington, DC 20007-2197, USA.
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43
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Quality control strategies for brain MRI segmentation and parcellation: Practical approaches and recommendations - insights from the Maastricht study. Neuroimage 2021; 237:118174. [PMID: 34000406 DOI: 10.1016/j.neuroimage.2021.118174] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/03/2021] [Accepted: 05/13/2021] [Indexed: 12/19/2022] Open
Abstract
Quality control of brain segmentation is a fundamental step to ensure data quality. Manual quality control strategies are the current gold standard, although these may be unfeasible for large neuroimaging samples. Several options for automated quality control have been proposed, providing potential time efficient and reproducible alternatives. However, those have never been compared side to side, which prevents consensus in the appropriate quality control strategy to use. This study aimed to elucidate the changes manual editing of brain segmentations produce in morphological estimates, and to analyze and compare the effects of different quality control strategies on the reduction of the measurement error. Structural brain MRI from 259 participants of The Maastricht Study were used. Morphological estimates were automatically extracted using FreeSurfer 6.0. Segmentations with inaccuracies were manually edited, and morphological estimates were compared before and after editing. In parallel, 12 quality control strategies were applied to the full sample. Those included: two manual strategies, in which images were visually inspected and either excluded or manually edited; five automated strategies, where outliers were excluded based on the tools "MRIQC" and "Qoala-T", and the metrics "morphological global measures", "Euler numbers" and "Contrast-to-Noise ratio"; and five semi-automated strategies, where the outliers detected through the mentioned tools and metrics were not excluded, but visually inspected and manually edited. In order to quantify the effects of each quality control strategy, the proportion of unexplained variance relative to the total variance was extracted after the application of each strategy, and the resulting differences compared. Manually editing brain surfaces produced particularly large changes in subcortical brain volumes and moderate changes in cortical surface area, thickness and hippocampal volumes. The performance of the quality control strategies depended on the morphological measure of interest. Overall, manual quality control strategies yielded the largest reduction in relative unexplained variance. The best performing automated alternatives were those based on Euler numbers and MRIQC scores. The exclusion of outliers based on global morphological measures produced an increase of relative unexplained variance. Manual quality control strategies are the most reliable solution for quality control of brain segmentation and parcellation. However, measures must be taken to prevent the subjectivity associated with these strategies. The detection of inaccurate segmentations based on Euler numbers or MRIQC provides a time efficient and reproducible alternative. The exclusion of outliers based on global morphological estimates must be avoided.
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Thomas T, Perdue MV, Khalaf S, Landi N, Hoeft F, Pugh K, Grigorenko EL. Neuroimaging genetic associations between SEMA6D, brain structure, and reading skills. J Clin Exp Neuropsychol 2021; 43:276-289. [PMID: 33960276 PMCID: PMC8225580 DOI: 10.1080/13803395.2021.1912300] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 03/30/2021] [Indexed: 01/15/2023]
Abstract
Specific reading disability (SRD) is defined by genetic and neural risk factors that are not fully understood. The current study used imaging genetics methodology to investigate relationships between SEMA6D, brain structure, and reading. SEMA6D, located on SRD risk locus DYX1, is involved in axon guidance, synapse formation, and dendrite development. SEMA6D's associations with brain structure in reading-related regions of interest (ROIs) were investigated in a sample of children with a range of reading performance, from sites in Connecticut, CT (n = 67, 6-13 years, mean age = 9.07) and San Francisco, SF (n = 28, 5-8 years, mean age = 6.5). Multiple regression analyses revealed significant associations between SEMA6D's rs16959669 and cortical thickness in the fusiform gyrus and rs4270119 and gyrification in the supramarginal gyrus in the CT sample, but this was not replicated in the SF sample. Significant clusters were not associated with reading. For white matter volume, combined analyses across both samples revealed associations between reading and the left transverse temporal gyrus, left pars triangularis, left cerebellum, and right cerebellum. White matter volume in the left transverse temporal gyrus was nominally related to rs1817178, rs12050859, and rs1898110 in SEMA6D, and rs1817178 was significantly related to reading. Haplotype analyses revealed significant associations between the whole gene and brain phenotypes. Results suggest SEMA6D likely has an impact on multiple reading-related neural structures, but only white matter volume in the transverse temporal gyrus was significantly related to reading in the current sample. As the sample was young, the transverse temporal gyrus, involved in auditory perception, may be more strongly involved in reading because phonological processing is still being learned. The relationship between SEMA6D and reading may change as different brain regions are involved during reading development. Future research should examine mediating effects, use additional brain measures, and use an older sample to better understand effects.
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Affiliation(s)
- Tina Thomas
- Department of Psychology, University of Houston, Houston, TX, USA
| | - Meaghan V. Perdue
- University of Connecticut Dept. of Psychological Sciences, Storrs, CT, USA
- Haskins Laboratories, University of Connecticut, New Haven, CT, USA
| | - Shiva Khalaf
- Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, TX, USA
| | - Nicole Landi
- University of Connecticut Dept. of Psychological Sciences, Storrs, CT, USA
- Haskins Laboratories, University of Connecticut, New Haven, CT, USA
| | - Fumiko Hoeft
- University of Connecticut Dept. of Psychological Sciences, Storrs, CT, USA
- Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Kenneth Pugh
- University of Connecticut Dept. of Psychological Sciences, Storrs, CT, USA
- Haskins Laboratories, University of Connecticut, New Haven, CT, USA
| | - Elena L. Grigorenko
- Department of Psychology, University of Houston, Houston, TX, USA
- Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, TX, USA
- Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
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45
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Sarkinaite M, Gleizniene R, Adomaitiene V, Dambrauskiene K, Raskauskiene N, Steibliene V. Volumetric MRI Analysis of Brain Structures in Patients with History of First and Repeated Suicide Attempts: A Cross Sectional Study. Diagnostics (Basel) 2021; 11:diagnostics11030488. [PMID: 33801896 PMCID: PMC8000590 DOI: 10.3390/diagnostics11030488] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 02/27/2021] [Accepted: 03/06/2021] [Indexed: 12/03/2022] Open
Abstract
Structural brain changes are found in suicide attempters and in patients with mental disorders. It remains unclear whether the suicidal behaviors are related to atrophy of brain regions and how the morphology of specific brain areas is changing with each suicide attempt. The sample consisted of 56 patients hospitalized after first suicide attempt (first SA) (n = 29), more than one suicide attempt (SA > 1) (n = 27) and 54 healthy controls (HC). Brain volume was measured using FreeSurfer 6.0 automatic segmentation technique. In comparison to HC, patients with first SA had significantly lower cortical thickness of the superior and rostral middle frontal areas, the inferior, middle and superior temporal areas of the left hemisphere and superior frontal area of the right hemisphere. In comparison to HC, patients after SA > 1 had a significantly lower cortical thickness in ten areas of frontal cortex of the left hemisphere and seven areas of the right hemisphere. The comparison of hippocampus volume showed a significantly lower mean volume of left and right parts in patients with SA > 1, but not in patients with first SA. The atrophy of frontal, temporal cortex and hippocampus parts was significantly higher in repeated suicide attempters than in patients with first suicide attempt.
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Affiliation(s)
- Milda Sarkinaite
- Department of Radiology, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania;
- Correspondence: ; Tel.: +370-67876580
| | - Rymante Gleizniene
- Department of Radiology, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania;
| | - Virginija Adomaitiene
- Psychiatry Clinic of Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania; (V.A.); (K.D.); (V.S.)
| | - Kristina Dambrauskiene
- Psychiatry Clinic of Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania; (V.A.); (K.D.); (V.S.)
| | - Nijole Raskauskiene
- Laboratory of Behavioural Medicine, Neuroscience Institute, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania;
| | - Vesta Steibliene
- Psychiatry Clinic of Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania; (V.A.); (K.D.); (V.S.)
- Laboratory of Behavioural Medicine, Neuroscience Institute, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania;
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46
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Carmo D, Silva B, Yasuda C, Rittner L, Lotufo R. Hippocampus segmentation on epilepsy and Alzheimer's disease studies with multiple convolutional neural networks. Heliyon 2021; 7:e06226. [PMID: 33659748 PMCID: PMC7892928 DOI: 10.1016/j.heliyon.2021.e06226] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 12/06/2020] [Accepted: 02/03/2021] [Indexed: 12/26/2022] Open
Abstract
Background: Hippocampus segmentation on magnetic resonance imaging is of key importance for the diagnosis, treatment decision and investigation of neuropsychiatric disorders. Automatic segmentation is an active research field, with many recent models using deep learning. Most current state-of-the art hippocampus segmentation methods train their methods on healthy or Alzheimer's disease patients from public datasets. This raises the question whether these methods are capable of recognizing the hippocampus on a different domain, that of epilepsy patients with hippocampus resection. New Method: In this paper we present a state-of-the-art, open source, ready-to-use, deep learning based hippocampus segmentation method. It uses an extended 2D multi-orientation approach, with automatic pre-processing and orientation alignment. The methodology was developed and validated using HarP, a public Alzheimer's disease hippocampus segmentation dataset. Results and Comparisons: We test this methodology alongside other recent deep learning methods, in two domains: The HarP test set and an in-house epilepsy dataset, containing hippocampus resections, named HCUnicamp. We show that our method, while trained only in HarP, surpasses others from the literature in both the HarP test set and HCUnicamp in Dice. Additionally, Results from training and testing in HCUnicamp volumes are also reported separately, alongside comparisons between training and testing in epilepsy and Alzheimer's data and vice versa. Conclusion: Although current state-of-the-art methods, including our own, achieve upwards of 0.9 Dice in HarP, all tested methods, including our own, produced false positives in HCUnicamp resection regions, showing that there is still room for improvement for hippocampus segmentation methods when resection is involved.
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Affiliation(s)
- Diedre Carmo
- School of Electrical and Computer Engineering, UNICAMP, Campinas, São Paulo, Brazil
| | - Bruna Silva
- Faculty of Medical Sciences, UNICAMP, Campinas, São Paulo, Brazil
| | | | - Clarissa Yasuda
- Faculty of Medical Sciences, UNICAMP, Campinas, São Paulo, Brazil
| | - Letícia Rittner
- School of Electrical and Computer Engineering, UNICAMP, Campinas, São Paulo, Brazil
| | - Roberto Lotufo
- School of Electrical and Computer Engineering, UNICAMP, Campinas, São Paulo, Brazil
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47
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Ridwan AR, Niaz MR, Wu Y, Qi X, Zhang S, Kontzialis M, Javierre-Petit C, Tazwar M, Bennett DA, Yang Y, Arfanakis K. Development and evaluation of a high performance T1-weighted brain template for use in studies on older adults. Hum Brain Mapp 2021; 42:1758-1776. [PMID: 33449398 PMCID: PMC7978143 DOI: 10.1002/hbm.25327] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 11/16/2020] [Accepted: 12/13/2020] [Indexed: 01/03/2023] Open
Abstract
Τhe accuracy of template-based neuroimaging investigations depends on the template's image quality and representativeness of the individuals under study. Yet a thorough, quantitative investigation of how available standardized and study-specific T1-weighted templates perform in studies on older adults has not been conducted. The purpose of this work was to construct a high-quality standardized T1-weighted template specifically designed for the older adult brain, and systematically compare the new template to several other standardized and study-specific templates in terms of image quality, performance in spatial normalization of older adult data and detection of small inter-group morphometric differences, and representativeness of the older adult brain. The new template was constructed with state-of-the-art spatial normalization of high-quality data from 222 older adults. It was shown that the new template (a) exhibited high image sharpness, (b) provided higher inter-subject spatial normalization accuracy and (c) allowed detection of smaller inter-group morphometric differences compared to other standardized templates, (d) had similar performance to that of study-specific templates constructed with the same methodology, and (e) was highly representative of the older adult brain.
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Affiliation(s)
- Abdur Raquib Ridwan
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Mohammad Rakeen Niaz
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Yingjuan Wu
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Xiaoxiao Qi
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Shengwei Zhang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Marinos Kontzialis
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Carles Javierre-Petit
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Mahir Tazwar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | | | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Yongyi Yang
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, Illinois, USA.,Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA.,Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois, USA
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48
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Radwan AM, Emsell L, Blommaert J, Zhylka A, Kovacs S, Theys T, Sollmann N, Dupont P, Sunaert S. Virtual brain grafting: Enabling whole brain parcellation in the presence of large lesions. Neuroimage 2021; 229:117731. [PMID: 33454411 DOI: 10.1016/j.neuroimage.2021.117731] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 01/07/2021] [Accepted: 01/08/2021] [Indexed: 12/16/2022] Open
Abstract
Brain atlases and templates are at the heart of neuroimaging analyses, for which they facilitate multimodal registration, enable group comparisons and provide anatomical reference. However, as atlas-based approaches rely on correspondence mapping between images they perform poorly in the presence of structural pathology. Whilst several strategies exist to overcome this problem, their performance is often dependent on the type, size and homogeneity of any lesions present. We therefore propose a new solution, referred to as Virtual Brain Grafting (VBG), which is a fully-automated, open-source workflow to reliably parcellate magnetic resonance imaging (MRI) datasets in the presence of a broad spectrum of focal brain pathologies, including large, bilateral, intra- and extra-axial, heterogeneous lesions with and without mass effect. The core of the VBG approach is the generation of a lesion-free T1-weighted image, which enables further image processing operations that would otherwise fail. Here we validated our solution based on Freesurfer recon-all parcellation in a group of 10 patients with heterogeneous gliomatous lesions, and a realistic synthetic cohort of glioma patients (n = 100) derived from healthy control data and patient data. We demonstrate that VBG outperforms a non-VBG approach assessed qualitatively by expert neuroradiologists and Mann-Whitney U tests to compare corresponding parcellations (real patients U(6,6) = 33, z = 2.738, P < .010, synthetic-patients U(48,48) = 2076, z = 7.336, P < .001). Results were also quantitatively evaluated by comparing mean dice scores from the synthetic-patients using one-way ANOVA (unilateral VBG = 0.894, bilateral VBG = 0.903, and non-VBG = 0.617, P < .001). Additionally, we used linear regression to show the influence of lesion volume, lesion overlap with, and distance from the Freesurfer volumes of interest, on labeling accuracy. VBG may benefit the neuroimaging community by enabling automated state-of-the-art MRI analyses in clinical populations using methods such as FreeSurfer, CAT12, SPM, Connectome Workbench, as well as structural and functional connectomics. To fully maximize its availability, VBG is provided as open software under a Mozilla 2.0 license (https://github.com/KUL-Radneuron/KUL_VBG).
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Affiliation(s)
- Ahmed M Radwan
- KU Leuven, Department of Imaging and Pathology, Translational MRI, Leuven, Belgium.
| | - Louise Emsell
- KU Leuven, Department of Imaging and Pathology, Translational MRI, Leuven, Belgium; KU Leuven, Department of Geriatric Psychiatry, University Psychiatric Center, Leuven, Belgium; KU Leuven, Leuven Brain Institute (LBI), Department of Neurosciences, Leuven, Belgium
| | | | - Andrey Zhylka
- Department of Biomedical Engineering, Eindhoven University of Technology, Netherlands
| | | | - Tom Theys
- KU Leuven, Leuven Brain Institute (LBI), Department of Neurosciences, Leuven, Belgium; KU Leuven, Department of Neurosciences, Research Group Experimental Neurosurgery and Neuroanatomy, Leuven, Belgium
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany; TUM-Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Patrick Dupont
- KU Leuven, Leuven Brain Institute (LBI), Department of Neurosciences, Leuven, Belgium; KU Leuven, Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven, Belgium
| | - Stefan Sunaert
- KU Leuven, Department of Imaging and Pathology, Translational MRI, Leuven, Belgium; KU Leuven, Leuven Brain Institute (LBI), Department of Neurosciences, Leuven, Belgium; UZ Leuven, Department of Radiology, Leuven, Belgium
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49
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Bachman SL, Dahl MJ, Werkle-Bergner M, Düzel S, Forlim CG, Lindenberger U, Kühn S, Mather M. Locus coeruleus MRI contrast is associated with cortical thickness in older adults. Neurobiol Aging 2020; 100:72-82. [PMID: 33508564 PMCID: PMC7920995 DOI: 10.1016/j.neurobiolaging.2020.12.019] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 11/20/2020] [Accepted: 12/21/2020] [Indexed: 02/06/2023]
Abstract
There is growing evidence that neuronal integrity of the noradrenergic locus coeruleus (LC) is important for later-life cognition. Less understood is how LC integrity relates to brain correlates of cognition, such as brain structure. Here, we examined the relationship between cortical thickness and a measure reflecting LC integrity in older (n = 229) and younger adults (n = 67). Using a magnetic resonance imaging sequence which yields high signal intensity in the LC, we assessed the contrast between signal intensity of the LC and that of neighboring pontine reference tissue. The Freesurfer software suite was used to quantify cortical thickness. LC contrast was positively related to cortical thickness in older adults, and this association was prominent in parietal, frontal, and occipital regions. Brain regions where LC contrast was related to cortical thickness include portions of the frontoparietal network which have been implicated in noradrenergically modulated cognitive functions. These findings provide novel evidence for a link between LC structure and cortical brain structure in later adulthood.
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Affiliation(s)
- Shelby L Bachman
- Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Martin J Dahl
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Markus Werkle-Bergner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Caroline Garcia Forlim
- Department of Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Hamburg, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, UK
| | - Simone Kühn
- Department of Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Hamburg, Germany; Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
| | - Mara Mather
- Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
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50
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Schäfer A, Mormino EC, Kuhl E. Network Diffusion Modeling Explains Longitudinal Tau PET Data. Front Neurosci 2020; 14:566876. [PMID: 33424532 PMCID: PMC7785976 DOI: 10.3389/fnins.2020.566876] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 12/02/2020] [Indexed: 12/27/2022] Open
Abstract
Alzheimer's disease is associated with the cerebral accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The progressive occurrence of tau aggregates in different brain regions is closely related to neurodegeneration and cognitive impairment. However, our current understanding of tau propagation relies almost exclusively on postmortem histopathology, and the precise propagation dynamics of misfolded tau in the living brain remain poorly understood. Here we combine longitudinal positron emission tomography and dynamic network modeling to test the hypothesis that misfolded tau propagates preferably along neuronal connections. We follow 46 subjects for three or four annual positron emission tomography scans and compare their pathological tau profiles against brain network models of intracellular and extracellular spreading. For each subject, we identify a personalized set of model parameters that characterizes the individual progression of pathological tau. Across all subjects, the mean protein production rate was 0.21 ± 0.15 and the intracellular diffusion coefficient was 0.34 ± 0.43. Our network diffusion model can serve as a tool to detect non-clinical symptoms at an earlier stage and make informed predictions about the timeline of neurodegeneration on an individual personalized basis.
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Affiliation(s)
- Amelie Schäfer
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Elizabeth C Mormino
- Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, United States
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
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