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Zhao Q, Gao Z, Yu W, Xiao Y, Hu N, Wei X, Tao B, Zhu F, Li S, Lui S. Multivariate associations between neuroanatomy and cognition in unmedicated and medicated individuals with schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:62. [PMID: 39004627 PMCID: PMC11247086 DOI: 10.1038/s41537-024-00482-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 06/28/2024] [Indexed: 07/16/2024]
Abstract
Previous studies that focused on univariate correlations between neuroanatomy and cognition in schizophrenia identified some inconsistent findings. Moreover, antipsychotic medication may impact the brain-behavior profiles in affected individuals. It remains unclear whether unmedicated and medicated individuals with schizophrenia would share common neuroanatomy-cognition associations. Therefore, we aimed to investigate multivariate neuroanatomy-cognition relationships in both groups. A sample of 59 drug-naïve individuals with first-episode schizophrenia (FES) and a sample of 115 antipsychotic-treated individuals with schizophrenia were finally included. Multivariate modeling was conducted in the two patient samples between multiple cognitive domains and neuroanatomic features, such as cortical thickness (CT), cortical surface area (CSA), and subcortical volume (SV). We observed distinct multivariate correlational patterns between the two samples of individuals with schizophrenia. In the FES sample, better performance in token motor, symbol coding, and verbal fluency tests was associated with greater thalamic volumes but lower CT in the prefrontal and anterior cingulate cortices. Two significant multivariate correlations were identified in antipsychotic-treated individuals: 1) worse verbal memory performance was related to smaller volumes for the most subcortical structures and smaller CSA mainly in the temporal regions and inferior parietal lobule; 2) a lower symbol coding test score was correlated with smaller CSA in the right parahippocampal gyrus but greater volume in the right caudate. These multivariate patterns were sample-specific and not confounded by imaging quality, illness duration, antipsychotic dose, or psychopathological symptoms. Our findings may help to understand the neurobiological basis of cognitive impairments and the development of cognition-targeted interventions.
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Affiliation(s)
- Qiannan Zhao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ziyang Gao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Wei Yu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Yuan Xiao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Na Hu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Xia Wei
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Bo Tao
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Fei Zhu
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Siyi Li
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Su Lui
- Department of Radiology, and Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, China.
- Huaxi MR Research Center (HMRRC), West China Hospital of Sichuan University, Chengdu, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
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Ysbæk-Nielsen AT. Exploring volumetric abnormalities in subcortical L-HPA axis structures in pediatric generalized anxiety disorder. Nord J Psychiatry 2024; 78:402-410. [PMID: 38573199 DOI: 10.1080/08039488.2024.2335980] [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: 05/15/2023] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND Pediatric generalized anxiety disorder (GAD) is debilitating and increasingly prevalent, yet its etiology remains unclear. Some believe the disorder to be propagated by chronic dysregulation of the limbic-hypothalamic-pituitary-adrenal (L-HPA) axis, but morphometric studies of implicated subcortical areas have been largely inconclusive. Recognizing that certain subcortical subdivisions are more directly involved in L-HPA axis functioning, this study aims to detect specific abnormalities in these critical areas. METHODS Thirty-eight MRI scans of preschool children with (n = 15) and without (n = 23) GAD underwent segmentation and between-group volumetric comparisons of the basolateral amygdala (BLA), ventral hippocampal subiculum (vSC), and mediodorsal medial magnocellular (MDm) area of the thalamus. RESULTS Children with GAD displayed significantly larger vSC compared to healthy peers, F(1, 31) = 6.50, pFDR = .048. On average, children with GAD presented with larger BLA and MDm, Fs(1, 31) ≥ 4.86, psFDR ≤ .054. Exploratory analyses revealed right-hemispheric lateralization of all measures, most notably the MDm, F(1, 31) = 8.13, pFDR = .024, the size of which scaled with symptom severity, r = .83, pFDR = .033. CONCLUSION The BLA, vSC, and MDm are believed to be involved in the regulation of anxiety and stress, both individually and collectively through the excitation and inhibition of the L-HPA axis. All were found to be enlarged in children with GAD, perhaps reflecting hypertrophy related to hyperexcitability, or early neuronal overgrowth. Longitudinal studies should investigate the relationship between these early morphological differences and the long-term subcortical atrophy previously observed.
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Sadil P, Lindquist MA. Comparing Automated Subcortical Volume Estimation Methods; Amygdala Volumes Estimated by FSL and FreeSurfer Have Poor Consistency. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.07.583900. [PMID: 39005462 PMCID: PMC11244866 DOI: 10.1101/2024.03.07.583900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Subcortical volumes are a promising source of biomarkers and features in biosignatures, and automated methods facilitate extracting them in large, phenotypically rich datasets. However, while extensive research has verified that the automated methods produce volumes that are similar to those generated by expert annotation, the consistency of methods with each other is understudied. Using data from the UK Biobank, we compare the estimates of subcortical volumes produced by two popular software suites: FSL and FreeSurfer. Although most subcortical volumes exhibit good to excellent consistency across the methods, the tools produce diverging estimates of amygdalar volume. Through simulation, we show that this poor consistency can lead to conflicting results, where one but not the other tool suggests statistical significance, or where both tools suggest a significant relationship but in opposite directions. Considering these issues, we discuss several ways in which care should be taken when reporting on relationships involving amygdalar volume.
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Quek Y, Fung YL, Bourgeat P, Vogrin SJ, Collins SJ, Bowden SC. Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort. Brain Behav 2024; 14:e3505. [PMID: 38688879 PMCID: PMC11061200 DOI: 10.1002/brb3.3505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 05/02/2024] Open
Abstract
INTRODUCTION The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. METHODS Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. RESULTS Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. CONCLUSION Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.
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Affiliation(s)
- Yi‐En Quek
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Yi Leng Fung
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Pierrick Bourgeat
- The Australian e‐Health Research CentreCSIRO Health and BiosecurityHerstonQueenslandAustralia
| | - Simon J. Vogrin
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
| | - Steven J. Collins
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
- Department of MedicineThe Royal Melbourne HospitalThe University of MelbourneParkvilleVictoriaAustralia
| | - Stephen C. Bowden
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
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Loizillon S, Bottani S, Maire A, Ströer S, Dormont D, Colliot O, Burgos N. Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data. Med Image Anal 2024; 93:103073. [PMID: 38176355 DOI: 10.1016/j.media.2023.103073] [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: 03/29/2023] [Revised: 12/12/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024]
Abstract
Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted by these artefacts and may be unusable. Since their manual detection is impossible due to the large number of scans, it is necessary to develop tools to automatically exclude (or at least identify) images with motion in order to fully exploit CDWs. In this paper, we propose a novel transfer learning method from research to clinical data for the automatic detection of motion in 3D T1-weighted brain MRI. The method consists of two steps: a pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model to clinical data, relying on the labelling of 4045 images. The objectives were both (1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced accuracy>80 %). However, for the second objective, the performance was weaker and substantially lower than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical imaging and highlight the importance of a clinical validation of models trained on research data.
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Affiliation(s)
- Sophie Loizillon
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France
| | - Simona Bottani
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France
| | - Aurélien Maire
- AP-HP, Innovation & Données - Département des Services Numériques, Paris 75012, France
| | - Sebastian Ströer
- AP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris 75013, France
| | - Didier Dormont
- AP-HP, Hôpital de la Pitié Salpêtrière, Department of Neuroradiology, Paris 75013, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, DMU DIAMENT, Paris 75013, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France
| | - Ninon Burgos
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris 75013, France.
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Triana AM, Saramäki J, Glerean E, Hayward NMEA. Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping. Hum Brain Mapp 2024; 45:e26620. [PMID: 38436603 PMCID: PMC10911114 DOI: 10.1002/hbm.26620] [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: 05/17/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 03/05/2024] Open
Abstract
A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.
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Affiliation(s)
- Ana María Triana
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Jari Saramäki
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of ScienceAalto UniversityEspooFinland
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Rebsamen M, Capiglioni M, Hoepner R, Salmen A, Wiest R, Radojewski P, Rummel C. Growing importance of brain morphometry analysis in the clinical routine: The hidden impact of MR sequence parameters. J Neuroradiol 2024; 51:5-9. [PMID: 37116782 DOI: 10.1016/j.neurad.2023.04.003] [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: 03/02/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 04/30/2023]
Abstract
Volumetric assessment based on structural MRI is increasingly recognized as an auxiliary tool to visual reading, also in examinations acquired in the clinical routine. However, MRI acquisition parameters can significantly influence these measures, which must be considered when interpreting the results on an individual patient level. This Technical Note shall demonstrate the problem. Using data from a dedicated experiment, we show the influence of two crucial sequence parameters on the GM/WM contrast and their impact on the measured volumes. A simulated contrast derived from acquisition parameters TI/TR may serve as surrogate and is highly correlated (r=0.96) with the measured contrast.
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Affiliation(s)
- Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Milena Capiglioni
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Robert Hoepner
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Anke Salmen
- Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland; Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland; Swiss Institute for Translational and Entrepreneurial Medicine, sitem-insel, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.
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Zugman A, Ringlein GV, Finn ES, Lewis KM, Berman E, Silverman WK, Lebowitz ER, Pine DS, Winkler AM. Brain Functional Connectivity and Anatomical Features as Predictors of Cognitive Behavioral Therapy Outcome for Anxiety in Youths. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.29.24301959. [PMID: 38352528 PMCID: PMC10862993 DOI: 10.1101/2024.01.29.24301959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Background Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have major impact. However, existing clinical models are weakly predictive. The current study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms. Methods Two datasets were studied: (A) one consisted of n=54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of n=15 subjects treated for 8 weeks. Connectome Predictive Modeling (CPM) was used to predict treatment response, as assessed with the PARS; additionally we investigated models using anatomical features, instead of functional connectivity. The main analysis included network edges positively correlated with treatment outcome, and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses also are presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for R2, r and mean absolute error (MAE). Outcomes The main model showed a mean absolute error of approximately 3.5 (95%CI: [3.1-3.8]) points a R2 of 0.08 [-0.14 - 0.26] and r of 0.38 [0.24 - 0.511]. When testing this model in the left-out sample (B) the results were similar, with a MAE of 3.4 [2.8 - 4.7], R2-0.65 [-2.29 - 0.16] and r of 0.4 [0.24 - 0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low R2. Interpretation The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, the current study does not support extensive use of CPM to predict outcome in pediatric anxiety.
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Affiliation(s)
- Andre Zugman
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Grace V. Ringlein
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Emily S. Finn
- Psychological and Brain Sciences, Dartmouth College, 3 Maynard St, Hanover, NH, 03755, USA
| | - Krystal M. Lewis
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Erin Berman
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Wendy K. Silverman
- Child Study Center, Yale University, 230 South Frontage Rd., New Haven, CT 06520, USA
| | - Eli R. Lebowitz
- Child Study Center, Yale University, 230 South Frontage Rd., New Haven, CT 06520, USA
| | - Daniel S. Pine
- Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Anderson M. Winkler
- Division of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, 1 West University Blvd, Brownsville, TX 78520, USA
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Fu CHY, Antoniades M, Erus G, Garcia JA, Fan Y, Arnone D, Arnott SR, Chen T, Choi KS, Fatt CC, Frey BN, Frokjaer VG, Ganz M, Godlewska BR, Hassel S, Ho K, McIntosh AM, Qin K, Rotzinger S, Sacchet MD, Savitz J, Shou H, Singh A, Stolicyn A, Strigo I, Strother SC, Tosun D, Victor TA, Wei D, Wise T, Zahn R, Anderson IM, Craighead WE, Deakin JFW, Dunlop BW, Elliott R, Gong Q, Gotlib IH, Harmer CJ, Kennedy SH, Knudsen GM, Mayberg HS, Paulus MP, Qiu J, Trivedi MH, Whalley HC, Yan CG, Young AH, Davatzikos C. Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo. NATURE. MENTAL HEALTH 2024; 2:164-176. [PMID: 38948238 PMCID: PMC11211072 DOI: 10.1038/s44220-023-00187-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 11/17/2023] [Indexed: 07/02/2024]
Abstract
Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (β = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.
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Affiliation(s)
- Cynthia H. Y. Fu
- School of Psychology, University of East London, London, UK
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Mathilde Antoniades
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Jose A. Garcia
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Danilo Arnone
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | | | - Taolin Chen
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ki Sueng Choi
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Cherise Chin Fatt
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX USA
| | - Benicio N. Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario Canada
- Mood Disorders Treatment and Research Centre and Women’s Health Concerns Clinic, St Joseph’s Healthcare Hamilton, Hamilton, Ontario Canada
| | - Vibe G. Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- School of Psychology, University of East London, London, UK
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Beata R. Godlewska
- Department of Psychiatry, University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, UK
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, Alberta Canada
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta Canada
| | - Keith Ho
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
| | - Andrew M. McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Kun Qin
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Department of Radiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Ontario Canada
| | - Matthew D. Sacchet
- Meditation Research Program, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | | | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA USA
| | - Ashish Singh
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Aleks Stolicyn
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Irina Strigo
- Department of Psychiatry, University of California San Francisco, San Francisco, USA
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Centre, Toronto, Ontario Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario Canada
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA USA
| | | | - Dongtao Wei
- School of Psychology, Southwest University, Chongqing, China
| | - Toby Wise
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Roland Zahn
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
| | - Ian M. Anderson
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - W. Edward Craighead
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA USA
- Department of Psychology, Emory University, Atlanta, GA USA
| | - J. F. William Deakin
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Boadie W. Dunlop
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA USA
| | - Rebecca Elliott
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
| | - Ian H. Gotlib
- Department of Psychology, Stanford University, Stanford, CA USA
| | | | - Sidney H. Kennedy
- Department of Psychiatry, University Health Network, Toronto, Ontario Canada
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Ontario Canada
| | - Gitte M. Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helen S. Mayberg
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Madhukar H. Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas, TX USA
| | - Heather C. Whalley
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Chao-Gan Yan
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Allan H. Young
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK
- South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, London, UK
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
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10
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Mikolas P, Marxen M, Riedel P, Bröckel K, Martini J, Huth F, Berndt C, Vogelbacher C, Jansen A, Kircher T, Falkenberg I, Lambert M, Kraft V, Leicht G, Mulert C, Fallgatter AJ, Ethofer T, Rau A, Leopold K, Bechdolf A, Reif A, Matura S, Bermpohl F, Fiebig J, Stamm T, Correll CU, Juckel G, Flasbeck V, Ritter P, Bauer M, Pfennig A. Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features. Psychol Med 2024; 54:278-288. [PMID: 37212052 DOI: 10.1017/s0033291723001319] [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] [Indexed: 05/23/2023]
Abstract
BACKGROUND Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features. METHODS Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar). RESULTS For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11-0.361) and a balanced accuracy of 63.1% (95% CI 55.9-70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI -0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6-67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance. CONCLUSIONS Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.
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Affiliation(s)
- Pavol Mikolas
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Michael Marxen
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Philipp Riedel
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Kyra Bröckel
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Julia Martini
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Fabian Huth
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Christina Berndt
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Christoph Vogelbacher
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
| | - Andreas Jansen
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
| | - Tilo Kircher
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
| | - Irina Falkenberg
- Core-Facility Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany
- Department of Psychiatry, University of Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
| | - Martin Lambert
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Vivien Kraft
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gregor Leicht
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Mulert
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Centre for Psychiatry, Justus-Liebig University Giessen, Giessen, Germany
| | - Andreas J Fallgatter
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, Tuebingen, Germany
| | - Thomas Ethofer
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, Tuebingen, Germany
| | - Anne Rau
- Department of Psychiatry, Tuebingen Center for Mental Health, University of Tuebingen, Tuebingen, Germany
| | - Karolina Leopold
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Bechdolf
- Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, Vivantes Hospital Am Urban and Vivantes Hospital Im Friedrichshain, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt - Goethe University, Frankfurt am Main, Germany
| | - Silke Matura
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt - Goethe University, Frankfurt am Main, Germany
| | - Felix Bermpohl
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, Berlin, Germany
| | - Jana Fiebig
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, Berlin, Germany
| | - Thomas Stamm
- Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Charité University Medicine, Berlin, Germany
- Department of Clinical Psychiatry and Psychotherapy, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Christoph U Correll
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry, Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, USA
- Department of Psychiatry and Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Georg Juckel
- Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, Bochum, Germany
| | - Vera Flasbeck
- Department of Psychiatry, Psychotherapy and Preventive Medicine, LWL University Hospital, Ruhr-University, Bochum, Germany
| | - Philipp Ritter
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
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11
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Yao G, Luo J, Zou T, Li J, Hu S, Yang L, Li X, Tian Y, Zhang Y, Feng K, Xu Y, Liu P. Transcriptional patterns of the cortical Morphometric Inverse Divergence in first-episode, treatment-naïve early-onset schizophrenia. Neuroimage 2024; 285:120493. [PMID: 38086496 DOI: 10.1016/j.neuroimage.2023.120493] [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: 09/15/2023] [Revised: 12/06/2023] [Accepted: 12/08/2023] [Indexed: 12/18/2023] Open
Abstract
Early-onset Schizophrenia (EOS) is a profoundly progressive psychiatric disorder characterized by both positive and negative symptoms, whose pathogenesis is influenced by genes, environment and brain structure development. In this study, the MIND (Morphometric Inverse Divergence) network was employed to explore the relationship between morphological similarity and specific transcriptional expression patterns in EOS patients. This study involved a cohort of 187 participants aged between 7 and 17 years, consisting of 97 EOS patients and 90 healthy controls (HC). Multiple morphological features were used to construct the MIND network for all participants. Furthermore, we explored the associations between MIND network and brain-wide gene expression in EOS patients through partial least squares (PLS) regression, shared genetic predispositions with other psychiatric disorders, functional enrichment of PLS weighted genes, as well as transcriptional signature assessment of cell types, cortical layers, and developmental stages. The MIND showed similarity differences in the orbitofrontal cortex, pericalcarine cortex, lingual gyrus, and multiple networks in EOS patients compared to HC. Moreover, our exploration revealed a significant overlap of PLS2 weighted genes linking to EOS-related MIND differences and the dysregulated genes reported in other psychiatric diseases. Interestingly, genes correlated with MIND changes (PLS2-) exhibited a significant enrichment not only in metabolism-related pathways, but also in specific astrocytes, cortical layers (specifically layer I and III), and posterior developmental stages (late infancy to young adulthood stages). However, PLS2+ genes were primarily enriched in synapses signaling-related pathways and early developmental stages (from early-mid fetal to neonatal early infancy) but not in special cell types or layers. These findings provide a novel perspective on the intricate relationship between macroscopic morphometric structural abnormalities and microscopic transcriptional patterns during the onset and progression of EOS.
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Affiliation(s)
- Guanqun Yao
- School of Medicine, Tsinghua University, Beijing 100084, China; Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing 100040, China; Institute for Precision Medicine, Tsinghua University, Beijing 100084, China
| | - Jing Luo
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Ting Zou
- School of Life Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jing Li
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China; School of Mental Health, Shanxi Medical University, Taiyuan 030001, China
| | - Shuang Hu
- Shanghai Mental Health Center, Shanghai 200030, China
| | - Langxiong Yang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xinrong Li
- Department of Psychiatry, the First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Yu Tian
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Yuqi Zhang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Kun Feng
- School of Medicine, Tsinghua University, Beijing 100084, China; Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing 100040, China; Institute for Precision Medicine, Tsinghua University, Beijing 100084, China.
| | - Yong Xu
- Department of Clinical Psychology, The Eighth Affiliated Hospital, Sun Yat-Sen University, No. 3025, Shennan Middle Road, Futian Street, Futian District, Shenzhen 518031, China.
| | - Pozi Liu
- School of Medicine, Tsinghua University, Beijing 100084, China; Department of Psychiatry, Yuquan Hospital, Tsinghua University, Beijing 100040, China.
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12
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Hobbs NZ, Papoutsi M, Delva A, Kinnunen KM, Nakajima M, Van Laere K, Vandenberghe W, Herath P, Scahill RI. Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group. J Huntingtons Dis 2024; 13:163-199. [PMID: 38788082 DOI: 10.3233/jhd-240016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.
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Affiliation(s)
- Nicola Z Hobbs
- HD Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Marina Papoutsi
- HD Research Centre, UCL Institute of Neurology, UCL, London, UK
- IXICO plc, London, UK
| | - Aline Delva
- Department of Neurosciences, KU Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Belgium
| | | | | | - Koen Van Laere
- Department of Imaging and Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals Leuven, Belgium
| | - Wim Vandenberghe
- Department of Neurosciences, KU Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Belgium
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13
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Dimanova P, Borbás R, Raschle NM. From mother to child: How intergenerational transfer is reflected in similarity of corticolimbic brain structure and mental health. Dev Cogn Neurosci 2023; 64:101324. [PMID: 37979300 PMCID: PMC10692656 DOI: 10.1016/j.dcn.2023.101324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 10/31/2023] [Accepted: 11/10/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Intergenerational transfer effects include traits transmission from parent to child. While behaviorally well documented, studies on intergenerational transfer effects for brain structure or functioning are scarce, especially those examining relations of behavioral and neurobiological endophenotypes. This study aims to investigate behavioral and neural intergenerational transfer effects associated with the corticolimbic circuitry, relevant for socioemotional functioning and mental well-being. METHODS T1-neuroimaging and behavioral data was obtained from 72 participants (39 mother-child dyads/ 39 children; 7-13 years; 16 girls/ 33 mothers; 26-52 years). Gray matter volume (GMV) was extracted from corticolimbic regions (subcortical: amygdala, hippocampus, nucleus accumbens; neocortical: anterior cingulate, medial orbitofrontal areas). Mother-child similarity was quantified by correlation coefficients and comparisons to random adult-child pairs. RESULTS We identified significant corticolimbic mother-child similarity (r = 0.663) stronger for subcortical over neocortical regions. Mother-child similarity in mental well-being was significant (r = 0.409) and the degree of dyadic similarity in mental well-being was predicted by similarity in neocortical, but not subcortical GMV. CONCLUSION Intergenerational neuroimaging reveals significant mother-child transfer for corticolimbic GMV, most strongly for subcortical regions. However, variations in neocortical similarity predicted similarity in mother-child well-being. Ultimately, such techniques may enhance our knowledge of behavioral and neural familial transfer effects relevant for health and disease.
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Affiliation(s)
- Plamina Dimanova
- Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland.
| | - Réka Borbás
- Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland
| | - Nora Maria Raschle
- Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland.
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14
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Warrier V, Stauffer EM, Huang QQ, Wigdor EM, Slob EAW, Seidlitz J, Ronan L, Valk SL, Mallard TT, Grotzinger AD, Romero-Garcia R, Baron-Cohen S, Geschwind DH, Lancaster MA, Murray GK, Gandal MJ, Alexander-Bloch A, Won H, Martin HC, Bullmore ET, Bethlehem RAI. Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes. Nat Genet 2023; 55:1483-1493. [PMID: 37592024 PMCID: PMC10600728 DOI: 10.1038/s41588-023-01475-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 07/13/2023] [Indexed: 08/19/2023]
Abstract
Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.
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Affiliation(s)
- Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychology, University of Cambridge, Cambridge, UK.
| | | | | | | | - Eric A W Slob
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sofie L Valk
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Andrew D Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Rafael Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - Simon Baron-Cohen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Jane and TerrySemel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Institute of Precision Health, University of California, Los Angeles, CA, USA
| | - Madeline A Lancaster
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge, UK
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Cambridge, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Michael J Gandal
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Hyejung Won
- Department of Genetics and the Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Cambridge, UK
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychology, University of Cambridge, Cambridge, UK.
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15
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Delva A, Van Laere K, Vandenberghe W. Longitudinal Imaging of Regional Brain Volumes, SV2A, and Glucose Metabolism In Huntington's Disease. Mov Disord 2023; 38:1515-1526. [PMID: 37382295 DOI: 10.1002/mds.29501] [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: 03/03/2023] [Revised: 05/03/2023] [Accepted: 05/18/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND Development of disease-modifying treatments for Huntington's disease (HD) could be aided by the use of imaging biomarkers of disease progression. Positron emission tomography (PET) with 11 C-UCB-J, a radioligand for the brain-wide presynaptic marker synaptic vesicle protein 2A (SV2A), detects more widespread brain changes in early HD than volumetric magnetic resonance imaging (MRI) and 18 F-fludeoxyglucose (18 F-FDG) PET, but longitudinal 11 C-UCB-J PET data have not been reported. The aim of this study was to compare the sensitivity of 11 C-UCB-J PET, 18 F-FDG PET, and volumetric MRI for detection of longitudinal changes in early HD. METHODS Seventeen HD mutation carriers (six premanifest and 11 early manifest) and 13 healthy controls underwent 11 C-UCB-J PET, 18 F-FDG PET, and volumetric MRI at baseline (BL) and after 21.4 ± 2.7 months (Y2). Within-group and between-group longitudinal clinical and imaging changes were assessed. RESULTS The HD group showed significant 2-year worsening of Unified Huntington's Disease Rating Scale motor scores. There was significant longitudinal volume loss within the HD group in caudate (-4.5% ± 3.8%), putamen (-3.6% ± 3.5%), pallidum (-3.0% ± 2.7%), and frontal cortex (-2.0% ± 2.1%) (all P < 0.001). Within the HD group there was longitudinal loss of putaminal SV2A binding (6.4% ± 8.8%, P = 0.01) and putaminal glucose metabolism (-2.8% ± 4.4%, P = 0.008), but these changes were not significant after correction for multiple comparisons. Premanifest subjects at BL only had significantly lower SV2A binding than controls in basal ganglia structures, but at Y2 additionally had significant SV2A loss in frontal and parietal cortex, indicating spread of SV2A loss from subcortical to cortical regions. CONCLUSIONS Volumetric MRI may be more sensitive than 11 C-UCB-J PET and 18 F-FDG PET for detection of 2-year brain changes in early HD. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Aline Delva
- Department of Neurosciences, KU Leuven, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Koen Van Laere
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Wim Vandenberghe
- Department of Neurosciences, KU Leuven, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
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16
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Roe JM, Vidal-Pineiro D, Amlien IK, Pan M, Sneve MH, Thiebaut de Schotten M, Friedrich P, Sha Z, Francks C, Eilertsen EM, Wang Y, Walhovd KB, Fjell AM, Westerhausen R. Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex. eLife 2023; 12:e84685. [PMID: 37335613 PMCID: PMC10368427 DOI: 10.7554/elife.84685] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 06/16/2023] [Indexed: 06/21/2023] Open
Abstract
Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the developmental timing of asymmetry and the extent to which it arises through genetic and later influences in childhood. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in seven datasets and chart asymmetry trajectories longitudinally across life (4-89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large-scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry is low-moderately heritable (max h2SNP ~19%) and correlates phenotypically and genetically in specific regions, indicating coordinated development of asymmetries partly through genes. In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises early in life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.
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Affiliation(s)
- James M Roe
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Didac Vidal-Pineiro
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Inge K Amlien
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Mengyu Pan
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Markus H Sneve
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Michel Thiebaut de Schotten
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of BordeauxBordeauxFrance
- Brian Connectivity and Behaviour Laboratory, Sorbonne UniversityParisFrance
| | - Patrick Friedrich
- Institute of Neuroscience and Medicine, Research Centre JülichJülichGermany
| | - Zhiqiang Sha
- Language and Genetics Department, Max Planck Institute for PsycholinguisticsNijmegenNetherlands
| | - Clyde Francks
- Language and Genetics Department, Max Planck Institute for PsycholinguisticsNijmegenNetherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud UniversityNijmegenNetherlands
- Department of Human Genetics, Radboud University Medical CenterNijmegenNetherlands
| | - Espen M Eilertsen
- PROMENTA Research Center, Department of Psychology, University of OsloOsloNorway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
| | - Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
- Department of Radiology and Nuclear Medicine, Oslo University HospitalOsloNorway
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of OsloOsloNorway
- Department of Radiology and Nuclear Medicine, Oslo University HospitalOsloNorway
| | - René Westerhausen
- Section for Cognitive and Clinical Neuroscience, Department of Psychology, University of OsloOsloNorway
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17
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Verdi S, Rutherford S, Fraza C, Tosun D, Altmann A, Raket LL, Schott JM, Marquand AF, Cole JH. Personalising Alzheimer's Disease progression using brain atrophy markers. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.15.23291418. [PMID: 37398392 PMCID: PMC10312850 DOI: 10.1101/2023.06.15.23291418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
INTRODUCTION Neuroanatomical normative modelling can capture individual variability in Alzheimer's Disease (AD). We used neuroanatomical normative modelling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD. METHODS Cortical thickness and subcortical volume neuroanatomical normative models were generated using healthy controls (n~58k). These models were used to calculate regional Z-scores in 4361 T1-weighted MRI time-series scans. Regions with Z-scores <-1.96 were classified as outliers and mapped on the brain, and also summarised by total outlier count (tOC). RESULTS Rate of change in tOC increased in AD and in people with MCI who converted to AD and correlated with multiple non-imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of MCI progression to AD. Brain Z-score maps showed that the hippocampus had the highest rate of atrophy change. CONCLUSIONS Individual-level atrophy rates can be tracked by using regional outlier maps and tOC.
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Affiliation(s)
- Serena Verdi
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Saige Rutherford
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Charlotte Fraza
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Andre Altmann
- Centre for Medical Image Computing, University College London, London, UK
| | - Lars Lau Raket
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - James H Cole
- Centre for Medical Image Computing, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
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18
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Yin G, Li T, Jin S, Wang N, Li J, Wu C, He H, Wang J. A comprehensive evaluation of multicentric reliability of single-subject cortical morphological networks on traveling subjects. Cereb Cortex 2023:7169131. [PMID: 37197789 DOI: 10.1093/cercor/bhad178] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/29/2023] [Accepted: 04/30/2023] [Indexed: 05/19/2023] Open
Abstract
Despite the prevalence of research on single-subject cerebral morphological networks in recent years, whether they can offer a reliable way for multicentric studies remains largely unknown. Using two multicentric datasets of traveling subjects, this work systematically examined the inter-site test-retest (TRT) reliabilities of single-subject cerebral morphological networks, and further evaluated the effects of several key factors. We found that most graph-based network measures exhibited fair to excellent reliabilities regardless of different analytical pipelines. Nevertheless, the reliabilities were affected by choices of morphological index (fractal dimension > sulcal depth > gyrification index > cortical thickness), brain parcellation (high-resolution > low-resolution), thresholding method (proportional > absolute), and network type (binarized > weighted). For the factor of similarity measure, its effects depended on the thresholding method used (absolute: Kullback-Leibler divergence > Jensen-Shannon divergence; proportional: Jensen-Shannon divergence > Kullback-Leibler divergence). Furthermore, longer data acquisition intervals and different scanner software versions significantly reduced the reliabilities. Finally, we showed that inter-site reliabilities were significantly lower than intra-site reliabilities for single-subject cerebral morphological networks. Altogether, our findings propose single-subject cerebral morphological networks as a promising approach for multicentric human connectome studies, and offer recommendations on how to determine analytical pipelines and scanning protocols for obtaining reliable results.
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Affiliation(s)
- Guole Yin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Ting Li
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Suhui Jin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Changwen Wu
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou 310058, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
- Key Laboratory of Cognition and Education Sciences, Ministry of Education, Beijing 100816, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou 510000, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510000, China
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19
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Gebre RK, Senjem ML, Raghavan S, Schwarz CG, Gunter JL, Hofrenning EI, Reid RI, Kantarci K, Graff-Radford J, Knopman DS, Petersen RC, Jack CR, Vemuri P. Cross-scanner harmonization methods for structural MRI may need further work: A comparison study. Neuroimage 2023; 269:119912. [PMID: 36731814 PMCID: PMC10170652 DOI: 10.1016/j.neuroimage.2023.119912] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/26/2023] [Accepted: 01/28/2023] [Indexed: 02/01/2023] Open
Abstract
The clinical usefulness MRI biomarkers for aging and dementia studies relies on precise brain morphological measurements; however, scanner and/or protocol variations may introduce noise or bias. One approach to address this is post-acquisition scan harmonization. In this work, we evaluate deep learning (neural style transfer, CycleGAN and CGAN), histogram matching, and statistical (ComBat and LongComBat) methods. Participants who had been scanned on both GE and Siemens scanners (cross-sectional participants, known as Crossover (n = 113), and longitudinally scanned participants on both scanners (n = 454)) were used. The goal was to match GE MPRAGE (T1-weighted) scans to Siemens improved resolution MPRAGE scans. Harmonization was performed on raw native and preprocessed (resampled, affine transformed to template space) scans. Cortical thicknesses were measured using FreeSurfer (v.7.1.1). Distributions were checked using Kolmogorov-Smirnov tests. Intra-class correlation (ICC) was used to assess the degree of agreement in the Crossover datasets and annualized percent change in cortical thickness was calculated to evaluate the Longitudinal datasets. Prior to harmonization, the least agreement was found at the frontal pole (ICC = 0.72) for the raw native scans, and at caudal anterior cingulate (0.76) and frontal pole (0.54) for the preprocessed scans. Harmonization with NST, CycleGAN, and HM improved the ICCs of the preprocessed scans at the caudal anterior cingulate (>0.81) and frontal poles (>0.67). In the Longitudinal raw native scans, over- and under-estimations of cortical thickness were observed due to the changing of the scanners. ComBat matched the cortical thickness distributions throughout but was not able to increase the ICCs or remove the effects of scanner changeover in the Longitudinal datasets. CycleGAN and NST performed slightly better to address the cortical thickness variations between scanner change. However, none of the methods succeeded in harmonizing the Longitudinal dataset. CGAN was the worst performer for both datasets. In conclusion, the performance of the methods was overall similar and region dependent. Future research is needed to improve the existing approaches since none of them outperformed each other in terms of harmonizing the datasets at all ROIs. The findings of this study establish framework for future research into the scan harmonization problem.
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Affiliation(s)
- Robel K Gebre
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
| | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA; Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | | | - Robert I Reid
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
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20
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Vik A, Kociński M, Rye I, Lundervold AJ, Lundervold AS. Functional activity level reported by an informant is an early predictor of Alzheimer's disease. BMC Geriatr 2023; 23:205. [PMID: 37003981 PMCID: PMC10067216 DOI: 10.1186/s12877-023-03849-7] [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: 04/27/2022] [Accepted: 02/24/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND Loss of autonomy in day-to-day functioning is one of the feared outcomes of Alzheimer's disease (AD), and relatives may have been worried by subtle behavioral changes in ordinary life situations long before these changes are given medical attention. In the present study, we ask if such subtle changes should be given weight as an early predictor of a future AD diagnosis. METHODS Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to define a group of adults with a mild cognitive impairment (MCI) diagnosis remaining stable across several visits (sMCI, n=360; 55-91 years at baseline), and a group of adults who over time converted from having an MCI diagnosis to an AD diagnosis (cAD, n=320; 55-88 years at baseline). Eleven features were used as input in a Random Forest (RF) binary classifier (sMCI vs. cAD) model. This model was tested on an unseen holdout part of the dataset, and further explored by three different permutation-driven importance estimates and a comprehensive post hoc machine learning exploration. RESULTS The results consistently showed that measures of daily life functioning, verbal memory function, and a volume measure of hippocampus were the most important predictors of conversion from an MCI to an AD diagnosis. Results from the RF classification model showed a prediction accuracy of around 70% in the test set. Importantly, the post hoc analyses showed that even subtle changes in everyday functioning noticed by a close informant put MCI patients at increased risk for being on a path toward the major cognitive impairment of an AD diagnosis. CONCLUSION The results showed that even subtle changes in everyday functioning should be noticed when reported by relatives in a clinical evaluation of patients with MCI. Information of these changes should also be included in future longitudinal studies to investigate different pathways from normal cognitive aging to the cognitive decline characterizing different stages of AD and other neurodegenerative disorders.
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Affiliation(s)
- Alexandra Vik
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Marek Kociński
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ingrid Rye
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Astri J Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Alexander S Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway.
- Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway.
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21
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Maheux E, Koval I, Ortholand J, Birkenbihl C, Archetti D, Bouteloup V, Epelbaum S, Dufouil C, Hofmann-Apitius M, Durrleman S. Forecasting individual progression trajectories in Alzheimer's disease. Nat Commun 2023; 14:761. [PMID: 36765056 PMCID: PMC9918533 DOI: 10.1038/s41467-022-35712-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 12/19/2022] [Indexed: 02/12/2023] Open
Abstract
The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.
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Affiliation(s)
- Etienne Maheux
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Igor Koval
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Juliette Ortholand
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Colin Birkenbihl
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Damiano Archetti
- IRCCS Instituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Vincent Bouteloup
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Stéphane Epelbaum
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Institut de la mémoire et de la maladie d'Alzheimer (IM2A), center of excellence of neurodegenerative diseases (CoEN), department of Neurology, DMU Neurosciences, Paris, France
| | - Carole Dufouil
- Université de Bordeaux, CNRS UMR 5293, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre Hospitalier Universitaire (CHU) de Bordeaux, pôle de neurosciences cliniques, centre mémoire de ressources et de recherche, Bordeaux, France
| | - Martin Hofmann-Apitius
- Department of bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, 53115, Germany
| | - Stanley Durrleman
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France.
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22
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Atrophy asymmetry in hippocampal subfields in patients with Alzheimer's disease and mild cognitive impairment. Exp Brain Res 2023; 241:495-504. [PMID: 36593344 DOI: 10.1007/s00221-022-06543-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023]
Abstract
Volumetric analysis of hippocampal subfields and their asymmetry assessment recently has been useful biomarkers in neuroscience. In this study, hippocampal subfields atrophy and pattern of their asymmetry in the patient with Alzheimer's disease (AD) and mild cognitive impairment (MCI) were evaluated. MRI images of 20 AD patients, 20 MCI patients, and 20 healthy control (HC) were selected. The volumes of hippocampal subfields were extracted automatically using Freesurfer toolkit. The subfields asymmetry index (AI) and laterality ([Formula: see text]) were also evaluated. Analysis of covariance was used to compare the subfields volume between three patient groups (age and gender as covariates). We used ANOVA (P < 0.05) test for multiple comparisons with Bonferroni's post hoc correction method. Hippocampal subfields volume in AD patients were significantly lower than HC and MCI groups (P < 0.02); however, no significant difference was observed between MCI and HC groups. The asymmetry index (AI) in some subfields was significantly different between AD and MCI, as well as between AD and HC, while there was not any significant difference between MCI groups with HC. In all three patient groups, rightward laterality ([Formula: see text]) was seen in several subfields except subiculum, presubiculum, and parasubiculum, while in AD patient, rightward lateralization slightly decrease. Hippocampal subfields asymmetry can be used as a quantitative biomarker in neurocognitive disorders. In this study, it was observed that the asymmetry index of some subfields in AD is significantly different from MCI. In AD, patient rightward laterality was less MCI an HC group.
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23
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Vizcarra JC, Teich AF, Dugger BN, Gutman DA. Survey of Neuroanatomic Sampling and Staining Procedures in Alzheimer Disease Research Center Brain Banks. FREE NEUROPATHOLOGY 2023; 4:4-6. [PMID: 37347036 PMCID: PMC10280272 DOI: 10.17879/freeneuropathology-2023-4696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 03/28/2023] [Indexed: 06/23/2023]
Abstract
The collection of post-mortem brain tissue has been a core function of the Alzheimer Disease Research Center's (ADRCs) network located within the United States since its inception. Individual brain banks and centers follow detailed protocols to record, store, and manage complex datasets that include clinical data, demographics, and when post-mortem tissue is available, a detailed neuropathological assessment. Since each institution often has specific research foci, there can be variability in tissue collection and processing workflows. While published guidelines exist for select diseases, such as those put forth by the National Institute on Aging and Alzheimer Association (NIA-AA), it is of importance to denote the current practices across institutions. To this end a survey was developed and sent to United States based brain bank leaders, collecting data on brain region sampling, including anatomic landmarks used, staining (including antibodies used), as well as whole-slide-image scanning hardware. We distributed this survey to 40 brain banks and obtained a response rate of 95% (38 / 40). Most brain banks followed guidelines defined by the NIA-AA, having H&E staining in all recommended regions and targeted region-based amyloid beta, tau, and alpha-synuclein immunohistochemical staining. However, sampling consistency varied related to key anatomic landmarks/locations in select regions, such as the striatum, periventricular white matter, and parietal cortex. This study highlights the diversity and similarities amongst brain banks and discusses considerations when amalgamating data/samples across multiple centers. This survey aids in establishing benchmarks to enhance dialogues on divergent workflows in a feasible way.
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Affiliation(s)
- Juan C. Vizcarra
- Department of Biomedical Engineering, Emory University & Georgia Institute of Technology, Atlanta, USA
| | - Andrew F. Teich
- Department of Pathology and Cell Biology, Department of Neurology, The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, New York, USA
| | - Brittany N. Dugger
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - David A. Gutman
- Department of Neuropathology, Emory University, Atlanta, Georgia, USA
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24
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Devika K, Mahapatra D, Subramanian R, Ramana Murthy Oruganti V. Dense Attentive GAN-based One-Class Model for Detection of Autism and ADHD. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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25
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Rye I, Vik A, Kocinski M, Lundervold AS, Lundervold AJ. Predicting conversion to Alzheimer's disease in individuals with Mild Cognitive Impairment using clinically transferable features. Sci Rep 2022; 12:15566. [PMID: 36114257 PMCID: PMC9481567 DOI: 10.1038/s41598-022-18805-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 08/19/2022] [Indexed: 11/19/2022] Open
Abstract
Patients with Mild Cognitive Impairment (MCI) have an increased risk of Alzheimer's disease (AD). Early identification of underlying neurodegenerative processes is essential to provide treatment before the disease is well established in the brain. Here we used longitudinal data from the ADNI database to investigate prediction of a trajectory towards AD in a group of patients defined as MCI at a baseline examination. One group remained stable over time (sMCI, n = 357) and one converted to AD (cAD, n = 321). By running two independent classification methods within a machine learning framework, with cognitive function, hippocampal volume and genetic APOE status as features, we obtained a cross-validation classification accuracy of about 70%. This level of accuracy was confirmed across different classification methods and validation procedures. Moreover, the sets of misclassified subjects had a large overlap between the two models. Impaired memory function was consistently found to be one of the core symptoms of MCI patients on a trajectory towards AD. The prediction above chance level shown in the present study should inspire further work to develop tools that can aid clinicians in making prognostic decisions.
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Affiliation(s)
- Ingrid Rye
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Alexandra Vik
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Marek Kocinski
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
- Institute of Electronics, Lodz University of Technology, Lodz, Poland
| | - Alexander S Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
- Department of Computer Science, Electrical Engineering, and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway
| | - Astri J Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.
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26
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Choice of Voxel-based Morphometry processing pipeline drives variability in the location of neuroanatomical brain markers. Commun Biol 2022; 5:913. [PMID: 36068295 PMCID: PMC9448776 DOI: 10.1038/s42003-022-03880-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 08/23/2022] [Indexed: 11/12/2022] Open
Abstract
Fundamental and clinical neuroscience has benefited tremendously from the development of automated computational analyses. In excess of 600 human neuroimaging papers using Voxel-based Morphometry (VBM) are now published every year and a number of different automated processing pipelines are used, although it remains to be systematically assessed whether they come up with the same answers. Here we examined variability between four commonly used VBM pipelines in two large brain structural datasets. Spatial similarity and between-pipeline reproducibility of the processed gray matter brain maps were generally low between pipelines. Examination of sex-differences and age-related changes revealed considerable differences between the pipelines in terms of the specific regions identified. Machine learning-based multivariate analyses allowed accurate predictions of sex and age, however accuracy differed between pipelines. Our findings suggest that the choice of pipeline alone leads to considerable variability in brain structural markers which poses a serious challenge for reproducibility and interpretation. Four common processing pipelines tested on two Voxel-based Morphometry (VBM) datasets yield considerable variations in results, raising issues on the interpretability and robustness of VBM results.
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Natural history of MRI brain volumes in patients with neuronal ceroid lipofuscinosis 3: a sensitive imaging biomarker. Neuroradiology 2022; 64:1911-1912. [PMID: 36040516 PMCID: PMC9474367 DOI: 10.1007/s00234-022-03039-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Morgan CA, Roberts RP, Chaffey T, Tahara-Eckl L, van der Meer M, Günther M, Anderson TJ, Cutfield NJ, Dalrymple-Alford JC, Kirk IJ, Rose Addis D, Tippett LJ, Melzer TR. Reproducibility and repeatability of magnetic resonance imaging in dementia. Phys Med 2022; 101:8-17. [PMID: 35849909 DOI: 10.1016/j.ejmp.2022.06.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/09/2022] [Accepted: 06/27/2022] [Indexed: 01/01/2023] Open
Abstract
PURPOSE Individualised predictive models of cognitive decline require disease-monitoring markers that are repeatable. For wide-spread adoption, such markers also need to be reproducible at different locations. This study assessed the repeatability and reproducibility of MRI markers derived from a dementia protocol. METHODS Six participants were scanned at three different sites with a 3T MRI scanner. The protocol employed: T1-weighted (T1w) imaging, resting state functional MRI (rsfMRI), arterial spin labelling (ASL), diffusion-weighted imaging (DWI), T2-weighted fluid attenuation inversion recovery (FLAIR), T2-weighted (T2w) imaging, and susceptibility weighted imaging (SWI). Participants were scanned repeatedly, up to six times over a maximum period of five years. One participant was also scanned a further three times on sequential days on one scanner. Fifteen derived metrics were computed from the seven different modalities. RESULTS Reproducibility (coefficient of variation; CoV, across sites) was best for T1w derived grey matter, white matter and hippocampal volume (CoV < 1.5%), compared to rsfMRI and SWI derived metrics (CoV, 19% and 21%). For a given metric, long-term repeatability (CoV across time) was comparable to reproducibility, with short-term repeatability considerably better. CONCLUSIONS Reproducibility and repeatability were assessed for a suite of markers calculated from a dementia MRI protocol. In general, structural markers were less variable than functional MRI markers. Variability over time on the same scanner was comparable to variability measured across different scanners. Overall, the results support the viability of multi-site longitudinal studies for monitoring cognitive decline.
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Affiliation(s)
- Catherine A Morgan
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Centre for Advanced MRI, Auckland UniServices Limited, Auckland, New Zealand.
| | - Reece P Roberts
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Tessa Chaffey
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Lenore Tahara-Eckl
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Meghan van der Meer
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Matthias Günther
- Fraunhofer Institute for Digital Medicine and University of Bremen, Bremen, Germany
| | - Timothy J Anderson
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand; NZ Brain Research Institute, Christchurch, New Zealand
| | - Nicholas J Cutfield
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Dunedin, New Zealand
| | - John C Dalrymple-Alford
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand; NZ Brain Research Institute, Christchurch, New Zealand; School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Ian J Kirk
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Donna Rose Addis
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada; Department of Psychology, University of Toronto, Toronto, Canada
| | - Lynette J Tippett
- School of Psychology and Centre for Brain Research, The University of Auckland, Auckland, New Zealand; Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand
| | - Tracy R Melzer
- Brain Research New Zealand - Rangahau Roro Aotearoa, Centre of Research Excellence, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand; NZ Brain Research Institute, Christchurch, New Zealand; School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
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Jaster JH, Ottaviani G. Acute Heart Failure, 90-Day Mortality, and Gravitational Ischemia in the Brain. Diagnostics (Basel) 2022; 12:diagnostics12061473. [PMID: 35741284 PMCID: PMC9221596 DOI: 10.3390/diagnostics12061473] [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: 05/19/2022] [Revised: 06/04/2022] [Accepted: 06/14/2022] [Indexed: 12/20/2022] Open
Abstract
During the 90 days following hospitalization for acute heart failure, the ejection fraction and type of discharge medications have been shown in clinical trials to have little effect on mortality. We examined the recent literature addressing brain-related etiologies of sudden death following heart failure. Two mechanisms of sudden unexpected death have been suggested to possibly result from four significant influences on pathophysiology in the brain. The two causes of sudden death are (1) severe cardiac arrhythmia and (2) neurogenic pulmonary edema. They are both mediated through the brainstem autonomic nuclei generally and executed specifically through the dorsal motor nucleus of the vagus nerve. The four significant influences on pathophysiology, all contributing to ischemia in the brainstem autonomic nuclei, are: (1) Hyper-stimulation of neurons in the solitary tract nucleus, increasing their metabolic requirements; (2) Inadequate blood flow at a vascular watershed terminus, perfusing the autonomic nuclei; (3) Additionally decreased blood flow, globally throughout the brain, following vasoconstriction related to relative hyperventilation and decreased pCO2 levels; (4) Gravitational ischemia in the brainstem caused by the weight of the cerebral hemispheres sitting above the brainstem. Changes in head tilt release gravitational ischemia in the brain. There is no specific head position relative to gravity that is considered favorable or unfavorable for an extended period of time, longer than 24 h. Even a small degree of head elevation, used in managing pulmonary congestion, may increase gravitational ischemia in the posterior fossa and brainstem. In this paper, we suggest a new and important research avenue for intervening in cardiac arrhythmias and preventing their occurrence through the non-invasive use of head-tilting and other head repositioning maneuvers. This could potentially help many geriatric patients with heart failure, who have decreased mobility in bed, and who tend to stay in one position longer, thereby increasing gravitational ischemia in the brain.
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Affiliation(s)
| | - Giulia Ottaviani
- Anatomic Pathology, Lino Rossi Research Center, Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, 20122 Milan, Italy
- Correspondence: ; Tel.: +39-02-50320819; Fax: +39-02-50320823
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Zahid U, Hedges EP, Dimitrov M, Murray RM, Barker GJ, Kempton MJ. Impact of physiological factors on longitudinal structural MRI measures of the brain. Psychiatry Res 2022; 321:111446. [PMID: 35131573 PMCID: PMC8924876 DOI: 10.1016/j.pscychresns.2022.111446] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 01/14/2022] [Accepted: 01/24/2022] [Indexed: 11/24/2022]
Abstract
Longitudinal MRI is used in clinical research studies to examine illness progression, neurodevelopment, and the effect of medical interventions. Such studies typically report changes in brain volume of less than 5%. However, there is a concern that these findings could be obscured or confounded by small changes in brain volume estimates caused by physiological factors such as, dehydration, blood pressure, caffeine levels, and circadian rhythm. In this study, MRI scans using the ADNI-III protocol were acquired from 20 participants (11 female) at two time points (mean interval = 20.3 days). Hydration, systolic and diastolic blood pressure, caffeine intake, and time of day were recorded at both visits. Images were processed using FreeSurfer. Three a priori hypothesised brain regions (hippocampus, lateral ventricles, and total brain) were selected, and an exploratory analysis was conducted on FreeSurfer's auto-segmented brain regions. There was no significant effect of the physiological factors on changes in the hypothesised brain regions. We provide estimates for the maximum percentage change in regional brain volumes that could be expected to occur from normal variation in each of the physiological measures. In this study, normal variations in physiological parameters did not have a detectable effect on longitudinal changes in brain volume.
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Affiliation(s)
- Uzma Zahid
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom.
| | - Emily P Hedges
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Mihail Dimitrov
- Department of Forensic & Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Matthew J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
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Ren W, Ji B, Guan Y, Cao L, Ni R. Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics. Front Med (Lausanne) 2022; 9:771982. [PMID: 35402436 PMCID: PMC8987112 DOI: 10.3389/fmed.2022.771982] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 02/16/2022] [Indexed: 12/26/2022] Open
Abstract
Small animal models play a fundamental role in brain research by deepening the understanding of the physiological functions and mechanisms underlying brain disorders and are thus essential in the development of therapeutic and diagnostic imaging tracers targeting the central nervous system. Advances in structural, functional, and molecular imaging using MRI, PET, fluorescence imaging, and optoacoustic imaging have enabled the interrogation of the rodent brain across a large temporal and spatial resolution scale in a non-invasively manner. However, there are still several major gaps in translating from preclinical brain imaging to the clinical setting. The hindering factors include the following: (1) intrinsic differences between biological species regarding brain size, cell type, protein expression level, and metabolism level and (2) imaging technical barriers regarding the interpretation of image contrast and limited spatiotemporal resolution. To mitigate these factors, single-cell transcriptomics and measures to identify the cellular source of PET tracers have been developed. Meanwhile, hybrid imaging techniques that provide highly complementary anatomical and molecular information are emerging. Furthermore, deep learning-based image analysis has been developed to enhance the quantification and optimization of the imaging protocol. In this mini-review, we summarize the recent developments in small animal neuroimaging toward improved translational power, with a focus on technical improvement including hybrid imaging, data processing, transcriptomics, awake animal imaging, and on-chip pharmacokinetics. We also discuss outstanding challenges in standardization and considerations toward increasing translational power and propose future outlooks.
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Affiliation(s)
- Wuwei Ren
- School of Information Science and Technology, ShanghaiTech University, Shanghai, China
- Shanghai Engineering Research Center of Energy Efficient and Custom AI IC, Shanghai, China
| | - Bin Ji
- Department of Radiopharmacy and Molecular Imaging, School of Pharmacy, Fudan University, Shanghai, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Lei Cao
- Shanghai Changes Tech, Ltd., Shanghai, China
| | - Ruiqing Ni
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Institute for Biomedical Engineering, ETH Zürich and University of Zurich, Zurich, Switzerland
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Fujita S, Cencini M, Buonincontri G, Takei N, Schulte RF, Fukunaga I, Uchida W, Hagiwara A, Kamagata K, Hagiwara Y, Matsuyama Y, Abe O, Tosetti M, Aoki S. Simultaneous relaxometry and morphometry of human brain structures with 3D magnetic resonance fingerprinting: a multicenter, multiplatform, multifield-strength study. Cereb Cortex 2022; 33:729-739. [PMID: 35271703 PMCID: PMC9890456 DOI: 10.1093/cercor/bhac096] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/10/2022] [Accepted: 02/12/2022] [Indexed: 02/04/2023] Open
Abstract
Relaxation times and morphological information are fundamental magnetic resonance imaging-derived metrics of the human brain that reflect the status of the underlying tissue. Magnetic resonance fingerprinting (MRF) enables simultaneous acquisition of T1 and T2 maps inherently aligned to the anatomy, allowing whole-brain relaxometry and morphometry in a single scan. In this study, we revealed the feasibility of 3D MRF for simultaneous brain structure-wise morphometry and relaxometry. Comprehensive test-retest scan analyses using five 1.5-T and three 3.0-T systems from a single vendor including different scanner types across 3 institutions demonstrated that 3D MRF-derived morphological information and relaxation times are highly repeatable at both 1.5 T and 3.0 T. Regional cortical thickness and subcortical volume values showed high agreement and low bias across different field strengths. The ability to acquire a set of regional T1, T2, thickness, and volume measurements of neuroanatomical structures with high repeatability and reproducibility facilitates the ability of longitudinal multicenter imaging studies to quantitatively monitor changes associated with underlying pathologies, disease progression, and treatments.
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Affiliation(s)
- Shohei Fujita
- Corresponding author: Department of Radiology, Juntendo University School of Medicine, 12-1 Hongo, Bunkyo, Tokyo 113-8421, Japan.
| | - Matteo Cencini
- Imago7 Foundation, Pisa, Italy,IRCCS Stella Maris, Pisa, Italy
| | | | | | | | - Issei Fukunaga
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University, Tokyo, Japan
| | | | - Koji Kamagata
- Department of Radiology, Juntendo University, Tokyo, Japan
| | - Yasuhiro Hagiwara
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Yutaka Matsuyama
- Department of Biostatistics, School of Public Health, The University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Michela Tosetti
- Imago7 Foundation, Pisa, Italy,IRCCS Stella Maris, Pisa, Italy
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
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